STREAMLINED ANALYSIS WITH ADePT SOFTWARE 87812 Analyzing Food Security Using H0 use hoI d AnaMoltedo at a Nathalie Troubat 5Urvey D Michael~o~shin Zurab SaJala ~ THE WORLD BANK ~ iW ~ Analyzing Food Security Using Household Survey Data STREAMLINED ANALYSIS WITH ADePT SOFTWARE Analyzing Food Security Using Household Survey Data Ana Moltedo Nathalie Troubat Michael Lokshin Zurab Sajaia © 2014 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington, DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org Some rights reserved 1 2 3 4 17 16 15 14 This work is a product of the staff of The World Bank with external contributions. Note that The World Bank does not necessarily own each component of the content included in the work. The World Bank therefore does not warrant that the use of the content contained in the work will not infringe on the rights of third parties. The risk of claims resulting from such infringement rests solely with you. 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Contents Preface ........................................................................................................ xi Abbreviations........................................................................................... xiii Chapter 1 Food Security .............................................................................................. 1 Introduction ........................................................................................................1 Background ..........................................................................................................1 Sources of Food Consumption Data ...................................................................4 Summary ..............................................................................................................9 ADePT-Food Security Module ........................................................................10 Notes..................................................................................................................10 References ........................................................................................................12 Bibliography ......................................................................................................13 Chapter 2 Theoretical Concepts ............................................................................... 15 Introduction ......................................................................................................15 Food Data Collected in Household Surveys .....................................................15 Standardization Procedures ...............................................................................20 Indicators on Food Security ..............................................................................32 Annexes .............................................................................................................61 Notes..................................................................................................................68 v Contents References..........................................................................................................70 Bibliography ......................................................................................................71 Chapter 3 Guide to Output Tables ........................................................................... 73 Introduction ......................................................................................................73 Output Tables....................................................................................................74 Glossary of Indicators .....................................................................................151 Notes................................................................................................................176 References........................................................................................................178 Bibliography ....................................................................................................178 Chapter 4 Datasets................................................................................................... 181 Introduction ....................................................................................................181 Datasets Description........................................................................................182 Exogenous Parameters .....................................................................................205 Notes................................................................................................................207 References ......................................................................................................208 Chapter 5 Guide to Using ADePT-FSM .................................................................. 211 Introduction ....................................................................................................211 System Requirements ......................................................................................211 Installing ADePT ............................................................................................212 Registering ADePT .........................................................................................213 Launching ADePT ..........................................................................................214 Using the ADePT-FSM Main Window .........................................................215 Using ADePT-FSM .........................................................................................217 Examining the Tables ....................................................................................235 Viewing Basic Information about a Dataset’s Variables ...............................236 Working with Projects ....................................................................................240 Exiting ADePT................................................................................................242 Using ADePT in a Batch Mode .....................................................................242 Debug Mode ....................................................................................................244 Reference .........................................................................................................245 Index ........................................................................................................ 247 vi Contents Figures 2.1: Example of Food Consumption Demand as Function of Income ........44 2.2: Graphical Representation of the Model ................................................56 Screenshots 4.1: Example of Dataset 1 in SPSS Format.................................................186 4.2: Cross-Tabulation of Gender and Education of the Household Head .... 188 4.3: Example of Dataset 2 in SPSS Format.................................................191 4.4: Example of Dataset 3 in SPSS Format.................................................194 Tables 1.1: Comparison of Nutritional Dietary Surveys, National Household Surveys, and Food Balance Sheets ...........................................................9 2.1: Most Common Availability of Data by Source of Food Acquisition and Possible Limitations in Processing Data .....................20 2.2: Atwater System ......................................................................................21 2.3: Data Availability ....................................................................................23 2.4: Summary Table on Procedures of Standardization in ADePT-FSM ....32 2.5: Population Groups ..................................................................................33 2.6: FAO Food Commodity Groups’ Classification to Process Household Surveys ....................................................................34 2.7: Food Security Statistics Produced for Each Category of Population Groups ..................................................................................35 2.8: Food Security Statistics Produced for Each Food Commodity Group..... 36 2.9: Food Security Statistics Produced for Each Food Commodity ..............36 2A.1: Example of Different Units of Measurement in Which Food Data Are Collected and Respective Conversion into Metric Units..............61 2B.1: Procedure 1: Steps 3 to 4 ....................................................................... 62 2B.2: Procedure 1: Steps 5 to 6 ....................................................................... 63 2C.1: Procedure 2: Steps 1 to 2 ....................................................................... 64 2C.2: Procedure 2: Steps 3 to 5........................................................................65 2D.1: Example of Calculation of Food and Total Price Temporal Deflators ............................................................................................ 65 2E.1: Estimation of the Coefficient of Variation of Dietary Energy Consumption Due to Other Factors ...................................................... 66 2F.1: Estimation of the Minimum Dietary Energy Requirement ..................67 vii Contents 1.1: Prevalence of Undernourishment Using Mainly Survey Data ..............74 1.2: Prevalence of Undernourishment Using Mainly External Sources ......75 1.3: Selected Food Consumption Statistics by Population Groups ..............78 1.4: Selected Food Consumption Statistics of Population Groups by Income Deciles .......................................................................................79 1.5: Shares of Food Consumption by Food Sources (in Dietary Energy) ...... 80 1.6: Shares of Food Consumption by Food Sources (in Dietary Energy) by Income Deciles ..................................................................................82 1.7: Shares of Food Consumption by Food Sources (in Monetary Value) ..... 83 1.8: Shares of Food Consumption by Food Sources (in Monetary Value) by Income Deciles ..................................................................................84 1.9: Food Consumption in Dietary Energy, Monetary, and Nutrient Content by Population Groups...............................................85 1.10: Nutrient Contribution to Dietary Energy Consumption ......................87 1.11: Nutrient Contribution to Dietary Energy Consumption at Income Quintile Levels .......................................................................................88 1.12: Nutrient Density per 1,000 Kcal ............................................................89 1.13: Share of Animal Protein in Total Protein Consumption .....................90 1.14: Within-Region Differences in Nutrient Consumption, by Regional Income Quintiles ....................................................................................91 2.1: Food Consumption by Food Commodity Groups ..................................92 2.2: Contribution of Food Commodity Groups to Total Nutrient Consumption ..........................................................................................93 2.3: Food Consumption by Food Commodity Group and Income Quintile ....93 2.4: Food Consumption by Food Commodity Group and Area ...................94 2.5: Contribution of Food Commodity Groups to Total Nutrient Consumption by Area ............................................................................96 2.6: Food Consumption by Food Commodity Group and Region ...............97 2.7: Food Consumption by Food Commodity Group and Region in the First Quintile ..........................................................................................97 2.8: Nutrient Costs by Food Commodity Group ..........................................98 2.9: Food Consumption by Food Commodity Group and Food Sources (in Dietary Energy).................................................................................99 3.1: Consumption Statistics for Each Food Item at National Level ..........101 3.2: Food Item Protein Consumption at National Level............................ 102 3.3: Consumption Statistics for Each Food Item by Area ..........................102 3.4: Food Item Protein Consumption by Area ...........................................103 3.5: Consumption Statistics for Each Food Item by Region ......................104 viii Contents 3.6: Food Item Protein Consumption by Region ........................................105 3.7: Food Item Quantities by Food Source .................................................105 3.8: Food Item Quantities by Food Source and Area .................................108 3.9: Food Item Quantities by Food Source and Region..............................109 4.1: Dispersion Ratio of Food Consumption by Income Quintile within Population Groups ................................................................................111 4.2: Dispersion Ratios of Share of Food Consumption (in Dietary Energy) by Food Source, Income Quintile, and Population Groups .................. 112 4.3: Dispersion Ratios of Share of Food Consumption (in Monetary Values) by Food Source and Income Quintile within Population Groups...................................................................................................113 4.4: Dispersion Ratios of Food Dietary Energy Unit Values, Total Income, and Engel Ratio by Income Quintile within Population Groups ................................................................................114 4.5: Income Demand Elasticities by Income Decile within Population Groups ................................................................................115 5.1: Availability of Vitamin A ....................................................................116 5.2: Availability of B Vitamins ...................................................................118 5.3: Availability of Vitamin C and Calcium ..............................................119 5.4: Availability of Iron ...............................................................................120 5.5: Density of Calcium per 1,000 Kcal ......................................................121 5.6: Density of Vitamin A and Vitamin C per 1,000 Kcal ........................123 5.7: Density of B Vitamins per 1,000 Kcal .................................................124 6.1: Micronutrient Availability by Food Group .........................................126 6.2: Micronutrient Availability by Food Group and Income Quintile ......127 6.3: Micronutrient Availability by Food Group and Area .........................128 6.4: Micronutrient Availability by Food Group and Region...................... 129 6.5: Contribution of Food Groups to Micronutrient Availability..............131 6.6: Contribution of Food Groups to Micronutrient Availability by Area ............................................................................................. 132 6.7: Micronutrient Availability by Food Item ............................................133 6.8: Micronutrient Availability by Food Item and Area ............................134 6.9: Micronutrient Availability by Food Item and Region ........................135 7.1: Protein Consumption and Amino Acid Availability ..........................137 7.2: Amino Acid Availability per Gram of Protein ...................................138 8.1: Availability of Amino Acids by Food Group ......................................139 8.2: Availability of Amino Acids by Food Group and Income Quintile ......141 8.3: Availability of Amino Acids by Food Group and Area ......................142 ix Contents 8.4: Availability of Amino Acids by Food Group and Region ..................143 8.5: Contribution of Food Groups to Amino Acid Availability ................144 8.6: Contribution of Food Groups to Amino Acid Availability by Area .... 145 8.7: Contribution of Food Groups to Amino Acid Availability by Region ...................................................................................................147 8.8: Availability of Amino Acid by Food Item ..........................................148 8.9: Availability of Amino Acid by Food Item and Area ..........................149 8.10: Availability of Amino Acid by Food Item and Region.......................150 4.1: Dataset 1 (HOUSEHOLD) ..................................................................183 4.2: Review of the Number of Observations within the Population Groups ................................................................................188 4.3: Dataset 2 (INDIVIDUAL)...................................................................189 4.4: Treatment of Food Acquired but Not Consumed by the Household ... 193 4.5: Dataset 3 (FOOD) ................................................................................193 4.6: Dataset 4 (COUNTRY_NCT): Minimum Information Required .....196 4.7: Dataset 4 (COUNTRY_NCT): Micronutrient Analysis ....................198 4.8: Dataset 4 (COUNTRY_NCT): Amino Acids Analysis .....................199 4.9: Content of Protein in Rice Applying Equal Weights ........................202 4.10: Content of Protein in Rice Applying Different Weights ...................202 5.1: System Requirements ...........................................................................211 5.2: Description of the Commands Displayed in the Menu .......................220 5.3: Variables to Map According to the Type of Analysis .........................221 5.4: Description of the Commands Displayed in the Pop-Up Menu .........237 5.5: Operators That Can Be Used in Expressions ......................................238 5.6: Examples of Expressions .......................................................................238 x Preface This book and the development of the ADePT-Food Security Module (ADePT-FSM) were made possible by the financial support from the European Union under the “Improved Global Governance for Hunger Reduction” program. Both outputs are from part 2.1 of the program, man- aged by the Statistics Division of FAO, and are aimed at improving meth- odologies, tools, and guidance materials for generating food security and hunger-related statistics. ADePT-FSM is the adapted version of the Food Security Statistics Module (FSSM), which began development a decade ago by Jorge Mernies and Ricardo Sibrián, former director and senior statistician, respectively, of the Food Security Statistics Unit of the FAO Statistics Division. Their work and determination were essential in creating ADePT-FSM and this book. Without their involvement and guidance, it is certain that these products would not exist, and for this we offer our deepest gratitude. FSSM was designed to derive a comprehensive set of indicators on various aspects of food security at national and subnational levels, which greatly contributed to its attractiveness. It has been used in many coun- tries by national statistical offices or institutions involved in food security analysis. However, since it was not simple to use, in December 2011, the FAO Statistics Division became involved in a joint collaboration with the World Bank to adapt FSSM into ADePT-FSM. This collaboration aimed xi Preface to provide stand-alone software with a user-friendly interface to derive food security statistics from survey data. The authors wish to thank all the users of FSSM, as it was through their interest in the tool and their shared experiences that enabled FSSM to improve over time and to evolve into ADePT-FSM. This book is a compilation of 20 years of experience in processing food consumption data from national household surveys, and it has greatly benefited from the expertise of many actors in the field of food security from nutritionists to analysts. To list all of them would be an impossible endeavor, but the authors wish to acknowledge at least a few and apologize for all those who are not listed here although they contributed directly or indirectly to the manual. The authors are grateful to Pietro Gennari, director of the FAO Statistics Division, for his support and confidence in the project; to Ruth Charrondiere, nutrition officer at FAO, for her invaluable advice on build- ing nutrient conversion tables; to Carlo Cafiero, senior statistician at FAO, for the tremendous work he did in revising the methodology and for sharing his knowledge; to Piero Conforti, senior statistician and leader of the Food Security Analysis team, who recently joined as a new member and facilitated the publication of the manual; to Sergiy Radyakin and Stanislav Kolenikov, who were involved in the development of the software; to Michele Rocca for providing technical support; and to Ellen Wielezynski, who edited the entire manual. All members of the FAO Food Security Statistics team have contributed either by writing part of this manual or by providing essential comments. Also, very special thanks goes to Seevalingum Ramasawmy, statistician at FAO, who has been deeply involved since the beginning in developing the FSSM and whose vision and commitment planted the seed for the collaboration with the World Bank. Finally, the authors express their gratitude to the European Union for the financial support needed for this book, the development of the software, and the funding of many capacity development activities on deriving food security indicators using food consumption data from national household surveys. xii Abbreviations ADER average dietary energy requirement BMI body mass index BMR basal metabolic rate CPI consumer price index CV coefficient of variation DEC dietary energy consumption DER dietary energy requirement DES dietary energy supply DHS demographic and health survey EAR estimated average requirement EP edible portion FAO Food and Agriculture Organization FBDG food-based dietary guidelines FBS food balance sheet FCDB food composition database FCT food composition table FPI food price index FSSM Food Security Statistics Module ILO International Labour Organization INFOODS International Network of Food Data Systems KCAL kilocalorie xiii Abbreviations Lcu local currency MDER minimum dietary energy requirement MDGs Millennium Development Goals NAS National Academy of Sciences NDS nutritional dietary survey NHS national household survey PAL physical activity level PoU prevalence of undernourishment RAE retinol activity equivalent RI recommended intake RNI recommended nutrient intake SOFI The State of Food Insecurity in the World U5MR under-five mortality rate USDA U.S. Department of Agriculture WHO World Health Organization xiv Chapter 1 Food Security Ana Moltedo, Carlo Cafiero, Nathan Wanner Introduction In 2012, thanks to the collaboration of the World Bank Computational Tools Team,1 and under the umbrella of the European Union program “Improved Global Governance for Hunger Reduction,” the Food and Agriculture Organization (FAO) methodology was integrated into a user- friendly software named ADePT-Food Security Module (ADePT-FSM). This book aims to provide the essential guidelines of the use of ADePT- FSM and of its background methodology. It is organized into five chapters: • Chapter 1 introduces the background concepts of food security and food consumption data. • Chapter 2 describes the methodology used to derive different food security indicators. • Chapter 3 discusses the analysis of the derived food security statistics. • Chapter 4 provides guidelines on how to prepare the input datasets. • Chapter 5 explains how to install and use ADePT-FSM. Background Food and nutrition security has emerged as a primary development goal at the top of the global agenda. During the 1996 World Food Summit hosted by FAO, the participating heads of state and government2 committed to reduce the number of 1 Analyzing Food Security Using Household Survey Data undernourished people to half their present level by 2015. Four years later the United Nations General Assembly adopted the UN Millennium Declaration in which it was resolved to halve, by the year 2015, the propor- tion of people who suffer from hunger. In order to achieve these goals, the development of both a statistical methodology and software for obtaining reliable estimates of undernourish- ment was an essential step. Other initiatives like the Poverty Reduction Strategy Papers3 and the Rural Development Strategies also increased the need for reliable food security statistics at national and subnational levels. Food security statistics play a fundamental role in assessing the magnitude of food deprivation, estimating the level of food and nutrient consumption, forecasting the long- term food consumption demand, and evaluating the impact of food security programs over time. Food Security When international attention became increasingly focused on the problem of hunger following World War II, the term food security typically referred to the “incidence of famine” and the resulting deaths from starvation. The immediate cause of starvation was identified as a lack of sufficient food; hence “ensuring food security” was identified with providing an adequate supply of food to those in need. The limitations of such an interpretation became immediately evident: a disconnection emerged between the success in increasing food supplies through improved agricultural production and the persistence of hunger and malnutrition around the world. A scenario could result in which there is adequate food supply for the population at the aggregate level, but with some households receiving an inadequate supply while others have more than is needed. These high levels of disparity revealed the limits of a concept based only on the availability of food. Since then, attention has shifted toward food access as a key dimension of food security: ensuring enough food is not a sufficient condition for food security unless equal access to food by individual households is guaranteed. Over time, newfound impetus has been placed on some nonfood factors important for food security, such as access to clean water, sanitation, and health care. These factors are all involved in how effectively food is utilized to reach a state of nutritional wellbeing. The definition of food security 2 Chapter 1: Food Security has therefore further broadened to include a new dimension of nutritional concerns, utilization, which captures the elements important for the best use of food by the body to improve nutritional status. The three dimensions of food security (availability, access, and utili- zation) are crucial at any point in time, though it is important to ensure that food security conditions are continuously met. The fourth and final dimension of food security is therefore stability. To be food secure, a popu- lation, household, or individual must have access to adequate food at all times, and should not risk losing access to food as a consequence of sudden economic, climatic, or political shocks. The stability dimension also aims to monitor the robustness of the food security situation to cyclical, predictable variations connected with annual weather patterns. The definition of food security adopted by the 1996 World Food Summit4 includes all four described dimensions: “Food security exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.” Starting from the mid-1990s the nutritional aspects of food security gained increasingly more importance: the terms food security and nutrition and food and nutrition security have been increasingly used by the interna- tional community. The former term has been used to distinguish between actions needed at the global, national, and local levels from actions needed at the household and individual levels. Food and nutrition security, instead, highlights nutrition considerations throughout the food chain (CFS 2012). In 2012, the Committee on Food Security recommended using the fol- lowing definition of food and nutrition security: “Food and nutrition security exists when all people at all times have physical, social and economic access to food, which is safe and consumed in sufficient quantity and quality to meet their dietary needs and food preferences, and is supported by an envi- ronment of adequate sanitation, health services and care, allowing for a healthy and active life.” With the broadening of the definition of food security over time, it became clear that no single indicator would likely suffice in providing a comprehensive picture of the food security and nutrition situation.5 Rather, a carefully chosen suite of indicators6 is likely necessary to describe food insecurity in all its dimensions in order to reliably inform the international community and decision makers on how to design appropri- ate responses. 3 Analyzing Food Security Using Household Survey Data Analyzing food consumption data collected in national household surveys (NHS) is one way of looking at food security. ADePT-FSM allows us to derive indicators at national and subnational levels that encompass some of the dimensions of food security. Sources of Food Consumption Data Food consumption can be captured at the national, household, or individual level. Food balance sheets estimate food consumption through a national account of food available for consumption in a given country. The differ- ence between national household surveys and nutritional dietary surveys (NDS) is that the former capture food consumption at the household level and the latter at the individual level. National household surveys are multipurpose surveys not specifically designed for food security purposes but allow analysts to assess the distribution of food consumption in the observed population. NDS are specifically focused on food intake yet have some major limitations, mainly related with operational costs. Food Balance Sheets (FBS) Food balance sheets provide a national account of the food available for consumption in a given country, both in terms of calories7 and nutrients over a reference period of one year, drawing on information on production, trade, and stocks. They are useful in monitoring many aspects of the food economy in a country, including efficiency of production, quality of the available food supply, and effectiveness of food policies in increasing food supply. The FAO Statistics Division has been producing FBS for about 180 countries since 1980 to monitor food availability across countries and over time. The derived dietary energy supply (DES) is a fundamental parameter for the estimation of the FAO prevalence of undernourish- ment (PoU) indicator in a country, the other two being the variability and asymmetry of the distribution of food consumption.8 Historically, the dietary energy supply did not take into account food waste and losses at the retail level. However, share of food losses at the regional level and for broad food categories have recently been estimated by FAO (FAO 2011). In applying these shares to the DES of the country, it is now possible to account for food losses in the whole food chain, except for the food lost 4 Chapter 1: Food Security within households in the form of leftovers or spoilage due to improper food storage. Although FBS are an important tool for characterizing the overall availability of food, their limitations in terms of potentialities should also be stated. First and most important, FBS data are not meant to be used to study the dietary diversity of a population because they do not provide information on how food is distributed within the population. Indeed, while national household surveys are able to capture information at the household level, the food balance sheets provide information only at the aggregate national level. Another consequence of this is that the estimates derived from FBS do not allow for statistics at the subnational level, or for assess- ments of seasonal variations. Secondly, FBS measure food availability from a supply, rather than demand, perspective because the data refer to food availability at the level of commodities, without providing any piece of information on how these commodities are accessed. Another important drawback of FBS is that they do not capture food produced by private households for their own consumption. Finally, the reliability of data on stock fluctuations is often questionable, leading to some uncertainty in the estimates. National Household Surveys The general term national household survey encompasses different types of surveys, such as household income and expenditure surveys, household expenditure surveys, household budget surveys, and Living Standard Measurement Studies. They are designed for a number of different purposes, including updating the weighting basis of the consumer price index, studying household living conditions, and studying poverty and income distribution. Although these surveys are not specifically designed for food security analysis, they collect data on food consumption as an integral part of their broader inquiry on household consumption expenditures. Usually, food consumption data are collected as food consumed or acquired by households from different sources, in terms of both quantities and monetary values. In addition, NHS provide data on household income and expenditure and other socioeconomic and demographic characteristics useful for classifica- tion purposes. National household surveys usually cover the entirety of a country’s territory, with samples distributed throughout the year, thus taking into account the issue of seasonality. Moreover, they allow for the analysis 5 Analyzing Food Security Using Household Survey Data of variations over time when the survey is repeated in different years or is conducted on a continuous basis. Since launching surveys of this magnitude to specifically capture food security data is very costly, the ADePT-FSM attempts to efficiently utilize the information contained in these multipurpose surveys to obtain reliable food security statistics. Despite the described positive attributes, national household surveys are rarely designed to capture the level of the households’ habitual food consumption for a number of reasons. The first (and most important) issue is that NHS may collect information on food acquisition rather than consumption. In this case, it can be very difficult, if not impossible, to dis- tinguish the food acquired for actual household consumption over the data collection period from the food acquired for storage purposes. This issue could not affect food security statistics of poor populations (those most vulnerable to food insecurity) because they often cannot afford food storage. An additional drawback is that NHS generally collect food data with short reference periods (one week to one month), leading to increased vari- ability in the estimate of habitual consumption because of the inherently greater variability within a short reference period. This inherent variability is due to unusual events that may occur (such as a wedding), which call for increased food acquisition compared to what would normally be con- sumed. In addition, with short recall reference periods “telescoping errors” can occur whenever the respondents mistakenly recall events taking place more recently than they actually did. Contrarily, collecting data on longer reference periods has its own drawbacks because “recall loss” errors can take place when the respondent is unable to remember events that took place long before. Considering these two types of errors, food diaries are some- times considered the gold standard for the collection of food consumption data since they minimize errors due to recall; nonetheless, they are more burdensome to the respondent and may therefore not be completely and accurately filled out due to respondent fatigue, causing a different type of survey error. Another potential drawback of NHS is that the information on the size of the household may differ from the number of people who actually consumed the food (partakers) over the reference period; this may be due to the absence of some household members during the reference period, or to the consumption of food by guests or workers. Although this problem 6 Chapter 1: Food Security can be addressed in surveys that collect information on the number of food partakers, this piece of information is very often lacking. In addition, food consumed outside may not be well captured by the survey questionnaire due to the use of generic categories (e.g., meal consumed in restaurant), or to the inability of the questionnaire design to capture some aspects of the food consumed away (such as school lunches for children). Even when information on household food consumption is accurately captured, data on intrahousehold food distribution are very seldom avail- able, and hence there is no choice but to assume that each person within the household has equal access to food. Furthermore, although food waste is generally considered to be more of a problem for households with higher incomes, low-income households can also have food waste when food spoils due to inadequate food storage technology. Lastly, NHS do not always consider food acquired for purposes other than consumption (such as food given to other households or to charity, or used for resale). To further illustrate the difficulty in characterizing habitual food con- sumption from NHS, consider that even if the overall average calorie consumption in the sample may still be a good estimate of the mean, since “households in a large population group are equally likely to be draw- ing down on food stocks as they are to be accumulating them” (Smith, Alderman, and Aduayom 2006), the values calculated for each individual household would likely be biased whenever household-level storage of food is relevant. This shall have consequences on the estimated distribution of food consumption across households, as the variability within households will be confounded with the variability between households. Secondly, and perhaps more worrying, if the individual household sta- tus of being undernourished is going to be used to conduct disaggregated analysis by population subgroups (the possibility of which constitutes one of the most attractive aspects of household survey data), the risk exists that the analysis would yield inconsistent results if the difference between acquisition and consumption happens to be correlated with the grouping variable.9 It is hoped that in the near future more nationally representative house- hold surveys explicitly collecting average quantities of food consumed over the year will be available to improve the precision of the estimates at the population level. This could also allow the analysis of households’ food 7 Analyzing Food Security Using Household Survey Data consumption in relation to other socioeconomic characteristics. A minimal set of requirements for such a survey should include features that would allow the following: • A complete assessment of the type of food consumed by all household members, including food consumed away from home • Differentiation of actual food consumption of household from that of food acquisition over the surveyed period, recognizing that the lat- ter may include food acquired for other uses (partakers, storage, food given to guests, etc.) or for other periods of time • Control for possible seasonal variation in food consumption (ideally by conducting repeated observation on the same household in differ- ent points in time) One must remember, however, that even with all of the potential draw- backs, national household surveys are virtually the only source of available data to assess the distribution of food consumption, and can provide invalu- able information for food security analysts and policy makers. Nutritional Dietary Surveys Nutritional dietary surveys focus on food consumption data, conducted in a few countries with small sample sizes on an ad hoc basis. They measure indi- vidual food intake by collecting both qualitative descriptions and quantities consumed of each food item during the last 1 to 15 days10 by individuals. Nutritional surveys have a number of limitations for estimating food security statistics. Firstly, the survey period11 is normally shorter than three months, and hence it does not account for seasonal variations in individual food intake. Although seasonal intake is generally less variable in developed countries where some food products are globally imported and where there is a greater capacity for food storage, there can be huge variability in popula- tions that eat locally produced food or that lack the resources necessary for proper storage. A second problem with NDS is that they usually do not collect informa- tion on food intake occurred away from home. In countries where lunches at school or work, street food, meals at restaurants, etc., form a large part of the diet, these surveys can substantially underestimate the total dietary energy intake. Another potential problem is that nutritional dietary surveys 8 Chapter 1: Food Security do not collect information on household or individual food and nonfood expenditure, nor on their income. Perhaps the biggest drawback of NDS, however, is that they are very complex, labor intensive, and expensive to implement. They require highly trained enumerators and costly measuring equipment to collect food intake data. Monetary costs and difficulty of implementation for these surveys can be a major drawback, and for this reason these surveys are usually more use- ful for studies of limited coverage, targeting selected socioeconomic or other specific population groups such as children and pregnant women. Summary The ideal source of information to assess food security in a country is rep- resented by nutritional dietary surveys. However, as these surveys are costly and difficult to implement, national household surveys are often used as a readily available source of data on the distribution of food consumption. This piece of information is augmented with the parameter obtained from food balance sheets, the dietary energy requirement, to obtain the FAO esti- mate of the prevalence of undernourishment. The main differences between nutritional dietary surveys, national household surveys, and food balance sheets are shown in table 1.1 below. Table 1.1: Comparison of Nutritional Dietary Surveys, National Household Surveys, and Food Balance Sheets Nutritional dietary surveys National household surveys Food balance sheets Estimate food consumption Estimate food consumption from Estimate food consumption from food intake the demand perspective from the supply perspective Cover individuals Cover private households Cover private households and public establishments (hotels, residences, hospitals, military barracks, and prisons) Estimates are at the individual Estimates are at national and Estimates are at national level level subnational levels Do not capture seasonal Capture seasonal variation in food Do not capture seasonal variation in food consumption consumption variation in food consumption Conducted for specific Conducted yearly in some countries Compiled each year purposes and infrequently in others Not conducted in many Since the 1990s, increasing Cover almost all countries countries numbers of countries are conducting them 9 Analyzing Food Security Using Household Survey Data ADePT-Food Security Module Over the past years, increasing attention has been paid to national household surveys by the international community in order to collect reliable and timely information on food consumption for the purpose of food security assessment. National household surveys are in fact the only available source of informa- tion to assess the distribution of food consumption within a country. ADePT-FSM aims to derive consistent and readily available food security statistics from food consumption data collected in NHS. The software also provides a transparent platform in which the user can reproduce the FAO offi- cial estimates of the percentage of undernourished people within a country. Countries conduct their NHS according to international recommenda- tions and guidelines11 and collect three levels of information related to (1) the household, (2) the household members, and (3) the household income and expenditures in goods and services, including food. In order to execute ADePT-FSM, the preparation of three datasets12 from the original microdata is therefore required. ADePT-FSM also requires a fourth dataset including exogenous data on the nutrient content (proteins, carbohydrates, etc.) of the food commodities listed in the survey. Such data are found in food composition tables available for many countries all over the world. Lastly, ADePT-FSM does not limit its outcome to statistics belonging to the “access” dimension of food security, namely caloric intake and mac- ronutrients consumption. A balanced intake of macronutrients,13 in fact, is not in itself a sufficient condition for conducting a healthy life, as human beings also need to consume adequate amounts of minerals and vitamins (micronutrients) and indispensable amino acids. ADePT-FSM therefore allows for the analysis of some micronutrients14 and indispensable amino acids15 available for consumption.16 The statistics produced are presented in standard Excel tables ready to be included in national food insecurity assessment reports. Notes 1. This team belongs to the Development Research Group. 2. When government is used, it also refers to the European community within its areas of competence. 10 Chapter 1: Food Security 3. Prepared by countries every three years, the Poverty Reduction Strategy Papers describe a country’s macroeconomic, structural and social poli- cies, and programs over a three-year or longer horizon. 4. This definition was reaffirmed officially in the Declaration of the World Summit on Food Security, 2009 (CFS 2012). 5. For an example see De Haen (2002). 6. Several key indicators are published by the FAO Statistics Division on its website: http://www.fao.org/economic/ess/ess-home/en/. 7. This is the dietary energy supply. 8. These are both derived from national household survey data. 9. For example, consider the case in which most households build up food stocks in the period after the harvest. If this is not taken into account when defining the sampling plan, and that specific area of the country is surveyed in that period, the result will be biased in the data correlated with the location of the household. 10. Depending on the method used by the interviewer (often a nutrition- ist) to record food intake: (1) 24-hour weighted method; (2) 24-hour recall method; or (3) food frequency method. While for the former two methods the reference period (period of time over which the individual data are collected) is one day, for the food frequency method it is either 7 or 15 days. 11. The recommendations and guidelines include the UN National Household Survey Capability Programme (1989) and the UN manual Designing Household Survey Samples: Practical Guidelines (2005). 12. These datasets are prepared either in STATA® or SPSS® format. 13. Diet could be defined as balanced when all the following conditions are met (WHO/FAO 2003): • Proportion of dietary energy provided by protein is in the range of 10–15 percent • Proportion of dietary energy provided by fats is in the range of 15–30 percent • Proportion of total dietary energy provided by carbohydrates is in the range of 55–75 percent 14. These include vitamin A, ascorbic acid, thiamine, riboflavin, vitamin B6, cobalamin, and the minerals calcium and iron. 15. These include isoleucine, leucine, lysine, threonine, tryptophan, valine, histidine, methionine, cystine, phenylalanine, and tyrosine. 11 Analyzing Food Security Using Household Survey Data 16. The analysis of micronutrients is made in terms of availability, rather than consumption, because different food processing methods have different impacts on the nutrient profile. For example high tempera- ture processing can affect the vitamin content (e.g., vitamin C) and discarding of water used in cooking will lead to the loss of water soluble food components (e.g., B vitamins, vitamin C, and certain bioactive components) as seen in FAO/INFOODS (2012). References CFS (Committee on World Food Security). 2012. “Coming to Terms with Terminology.” Final report 2012/39, Rome, October 15–20. http://www.fao.org/fileadmin/user_upload/bodies/CFS_sessions/39th _Session/39emerg/MF027_CFS_39_FINAL_REPORT_compiled_E .pdf. De Haen, H. 2002. “Lessons Learned.” Paper presented at the Food and Agriculture Organization conference “Measurement and Assessment of Food Deprivation and Undernutrition,” Rome, June 26–28. FAO (Food and Agriculture Organization). 2011. “Global Food Losses and Food Waste.” Study conducted for the Messe Düsseldorf and FAO International Congress “SAVE FOOD” initiative at Interpack2011, Düsseldorf, May 12–18. FAO, and INFOODS (International Network of Food Data Systems). 2012. Guidelines for Food Matching: Version 1.2. Rome: FAO. http://www.fao .org/infoods/infoods/standards-guidelines/en/. Smith, L. C., H. Alderman, and D. Aduayom. 2006. Food Insecurity in Sub-Saharan Africa: New Estimates from Household Expenditure Surveys. Research Report 146. Washington, DC: International Food Policy Research Institute. UN Department of Technical Cooperation for Development, and Statistical Office. 1989. Household Income and Expenditure Surveys: A Technical Study. New York: United Nations. http://unstats.un.org/unsd/publication /unint/DP_UN_INT_88_X01_6E.pdf. UN DESA (United Nations Department of Economic and Social Affairs). 2005. Designing Household Survey Samples: Practical Guidelines. New York: United Nations. http://unstats.un.org/unsd/demographic/sources /surveys/Handbook23June05.pdf. 12 Chapter 1: Food Security WHO (World Health Organization), and FAO. 2003. Diet, Nutrition and the Prevention of Chronic Diseases. Report of a Joint WHO/FAO Expert Consultation, Geneva, January 28–February 1, WHO Technical Report Series 961, Geneva: WHO. Bibliography Cafiero, C. 2011. “Measuring Food Insecurity: Meaningful Concepts and Indicators for Evidence-Based Policy Making.” Paper presented at the Food and Agriculture Organization conference “Round Table on Monitoring Food Security,” Rome, September 12–13. ———. 2013. “What Do We Really Know About Food Security?” Working Paper 18861, National Bureau of Economic Research, Cambridge, MA. http://www.nber.org/papers/w18861. FAO (Food and Agriculture Organization). 2006. “Food Security.” Policy Brief 2, Agriculture Development Economics Division, FAO, Rome. 13 Chapter 2 Theoretical Concepts Carlo Cafiero, Ana Moltedo, Seevalingum Ramasawmy, Nathalie Troubat, Nathan Wanner Introduction This chapter presents various food security indicators that can be derived from food data collected in national household surveys (NHS). It also introduces procedures to estimate the indicators as well as to standardize food consumption and expenditures data into dietary or monetary values.1 Some of these proce- dures are done manually during the preparation of the datasets before execut- ing ADePT-FSM, and others are automatically implemented in the software. Food security indicators range from the prevalence of undernourishment to average consumption of various nutrients by source of food acquisition. These indicators are produced for different analytical groups based on the household and household’s head characteristics collected in the NHS. Section 1 of this chapter presents the food data collected in NHS, the procedures of standardization are further explained in section 2, and finally, indicators on food security and their related methodologies are introduced in the last section. Some practical examples related to the procedures of aggregation and standardization are provided in the annexes. Food Data Collected in Household Surveys Food data collected in household surveys are not standardized and strongly depend on survey design.2 The way surveys are processed depends therefore on the unique characteristics of the food data collected. Specifically, this includes the sources of food acquisition, units of measurement of quantities 15 Analyzing Food Security Using Household Survey Data collected, and whether food data were collected in quantity and/or mon- etary values. Sources of Food Consumption Household food consumption ideally refers to the habitual consumption of food commodities, including nonalcoholic and alcoholic beverages.3 However, only a few surveys (such as yearly panel surveys) collecting infor- mation on food partakers are designed to capture household habitual food consumption. Usually the collection of food data in NHS refers to food acquired or consumed in or outside the sampled household during a given reference period. Most food items acquired by the households are intended to be con- sumed by household members. Exceptions exist when acquired food is given to employees, guests, relatives, or pets. It may also be used to feed livestock, for small food businesses, or for resale. Therefore, to estimate the habitual household food consumption, the food not consumed by household mem- bers should be excluded in the food security analysis through proper identi- fication at the collection stage. Ideally, any food losses and waste produced by the household should also be collected. Households acquire food from various sources. Food can be purchased in markets, shops, food courts, restaurants, work canteens, from hawkers, etc. Food can also come from own production (farming, fishing, gathering, or hunting), or it can be withdrawn from private or business-owned stocks or received as payment or free from friends, relatives, and charity institutions. During the data collection reference period, households may consume food items withdrawn from their own stocks, make bulk purchases, or accumulate stocks from their own production. For these reasons, espe- cially for acquisition surveys, information on levels of initial and ending stocks should be properly reported during the food data collection period to avoid under- or overestimation of the habitual food consumption at the household level. Summarizing, households usually consume food that is acquired from the following main sources: • Purchased food. Food bought to be consumed inside the household, or food bought and consumed away from home, such as in restaurants, food courts, canteens, or from street vendors. 16 Chapter 2: Theoretical Concepts • Nonpurchased food. From own production (backyard gardens or farms), received free as gifts, donations, or transfers (including long- term food loan), received as payment in kind (including prepared food at workplaces), received as institutional food aid, or other (fishing, hunting, gathering, etc.). • Food stocks. Composed of purchased or nonpurchased food items acquired prior to the starting date or during the reference period of food data collection. Purchased Food Purchased food items involve a payment by either cash or credit. Food pur- chases may be on a daily, weekly, or monthly basis depending on the type of food and payment of wages. Food can be purchased for consumption either within or outside the household. As the type of information (food quantities or monetary values) and the approach used to estimate dietary energy are different depending on whether the food is consumed inside or outside the home, it is important that the NHS captures food purchased for consump- tion at home and away from home separately. In-House Consumption Perishable food commodities such as bread, milk, fresh fruits, and vegetables are usually purchased at shorter intervals (daily or weekly), while nonperishable commodities such as rice, flour, and sugar are usually acquired for consumption over a longer period of time (weekly or monthly). The payment of wages may be daily, weekly, fortnightly, monthly, or sometimes in relation to crop harvests (households usually pur- chase bulk quantities of specific food items in relation to the harvest cycles). Consumed Away from Home Food purchases include food consumed away from home, such as drinks and ready-prepared meals from vendors, restaurants, food courts, school, work canteens, etc. Nonpurchased Food Own Consumption Households acquire some food commodities such as cereals, roots, tubers, vegetables, fruits, milk, and meat from their own pro- duction (from backyard gardens or farms). Some households consume all their food production, while others consume only part of it, selling the rest 17 Analyzing Food Security Using Household Survey Data for income. This type of food acquisition is commonly referred to as own production or own consumption or self-production and does not involve any monetary transactions. However, it is important to have proper estimates of the food quantity and monetary values acquired from this source. Own pro- duction may constitute an important source of food for particular household groups involved in agricultural livelihoods, especially in rural areas. In Kind Households may also acquire and consume food items obtained free of charge, such as gifts, donations, or transfers from relatives and friends. In some countries, fishing, hunting and/or gathering provide a substantial amount of food to certain groups of the population. Various international or national institutions give some basic and essential food items to individuals or households as food aid on a regular or ad-hoc basis. Household members may also receive food from employers as part of payment (income in kind), especially those working in food activities such as vegetable cultivating, farming, or livestock food processing, or those working as food vendors. Food acquired from these sources may constitute an important part of the total household food consumption. Stocks Food stocks are usually comprised of nonperishable food such as cereals and preserved food. However, in some developed countries, it is also common that people stock perishable food by freezing it. Stocks are mainly accumu- lated by people in rural areas from own production during the harvest period or by urban rich households that can acquire bulk quantities at lower prices. Food Consumption Data Collected in Quantities and/or Monetary Values National household surveys collect data on food acquisition or consumption from purchases in monetary and quantitative terms. The data are collected at the food commodity level. Food purchased for consumption inside of the home refers to food items available in the market and usually expressed in well-specified standard units of weight or volume. Therefore, it is expected that the data collected in the survey have details related to quantity, unit of measurement, and cost (in monetary value). However, the information collected may vary depending on the source of food acquisition. 18 Chapter 2: Theoretical Concepts In some surveys, food purchased is collected only in monetary value. When quantities are not available, they need to be estimated using market retail prices. These prices correspond to local or regional markets or local food shops, or are derived from surveyed households for the reference period. It is recommended that those estimates are worked out at the data collection stage by the field interviewer in collaboration with the respondent. Food acquired for household members’ consumption from sources such as own production, gifts, and aid does not involve monetary value transactions. In this case the monetary values could be estimated using market retail prices or from surveyed households in the region. In most recent surveys, households are asked to report the monetary value of food from these sources as if the item were bought at the market. Finally, household members may purchase and consume food and drinks outside the home. The type of food can vary from a well-defined “takeaway” commodity such as beer, carbonated beverage, hamburgers, corn on the cob, roasted chicken, and fried rice to a more general, “ordered” description such as dinner, meal, or breakfast. While the takeaway food is purchased in standard local units such as a portion or plate, the ordered meal is consumed in bars, restaurants, or work canteens, and it mixes food items according to a recipe that may differ among the food outlets. For takeaway food, it is possible to have information on food quantities in standard units along with monetary values; however, for ordered food, usually only monetary values are available. For these reasons, nutrients and calories of these two food groups are estimated following different procedures. Table 2.1 summarizes the most common availability of data by source of food acquisition and the limitations that may appear when processing the data. Unit of Measurement On the one hand, the unit of measurement of quantities acquired can be standard, such as gram, kilogram, liter, or milliliter, or a local unit, such as bag, basket, cup, or heap. On the other hand, all factors to convert quan- tities into nutrient values are expressed in terms of nutrient content per 100 grams of the food product. To ensure a proper conversion of food quan- tities into nutrient values, it is important to have factors to convert local units into standard ones. For instance, if a household declared the acquisi- tion of a heap of parsley, the interviewer has to inquire how many standard 19 Analyzing Food Security Using Household Survey Data Table 2.1: Most Common Availability of Data by Source of Food Acquisition and Possible Limitations in Processing Data Quantity Value Food source details details Limitations Purchased for inside yes yes Availability of grams or milliliters household food conversion factors related to the local consumption. quantity units of measurement. Food purchased and (1) Prepared standard yes yes Availability of grams or milliliters consumed away from takeaway meals (Standard conversion factors related to the standard home. acquired outside home. portions portions. The calorie and nutrient collected from densities of the food products should be food providers) available in food composition tables. (2) Prepared meals acquired no yes Frequently only food monetary values are and eaten away from home available. (Ordered meals consumed in bars, restaurants, hotels, schools, workplaces, etc.). Food from own production yes yes/no Retail prices have to be estimated at the Food received free local market or obtained from surveyed Food received as payment households in the region. Food from stock Food obtained from institutional aid units (for instance grams) of parsley are usually in a heap in this specific region. In some countries, the unit of the National Statistical Office that is in charge of collecting prices also records gram weight equivalences of the local units of measurement. Standardization Procedures Indicators are expressed in terms of quantities, dietary energy, and monetary values per person per day. This means that all data collected in the survey and needed for the food security analysis should be standardized before being aggregated over time and space. Procedures of standardization start with the conversion of food monetary values or quantities collected in the NHS into dietary energy. These procedures are complex and strongly rely on the qual- ity of the food data collected, the food composition data, and the quality of the food matching.4 Estimation of Dietary Energy The human body requires energy for different purposes, including metabolic process, muscular activity, growth, and synthesis of new tissues. Humans 20 Chapter 2: Theoretical Concepts can access the required energy through the intake of energy-yielding macro- nutrients from foods that are protein, fats, carbohydrates (including fibers), and alcohol. Each contributes to the total calories but in a different propor- tion. Food energy is usually calculated on the macronutrients’ content of the food product to which energy conversion factors are applied. In this way grams of nutrients are transformed into energy. There are two units for energy: calories (expressed in kcal) en joules (expressed in kJ). See table 2.2. Joules is the recommended unit for energy due to historical reasons. However, the authors prefer to use calories as the unit of measurement for energy. Polyols and organic acids usually play a minor role and will therefore be omitted here. The total amount of calories using the Atwater formula is calculated in ADePT-FSM as follows: Calories(Kcal) = Protein(g) ∗ 4 + Fats(g) ∗ 9 + Av. Carbohydrates(g) ∗ 4 + Fiber(g) ∗ 2 + Alcohol(g) ∗ 7 Note: In the above equation: Available carbohydrates = total carbohydrates − fibers. Macro- and micronutrient consumption are estimated by multiplying food quantities (collected in the survey or calculated based on prices) by nutrient values from national or regional food composition tables (FCT) or databases (FCDB). These nutrient values are usually expressed as grams (g), milligrams (mg), or micrograms (μg) of nutrients per 100 grams edible portion (EP) on a fresh basis. The food reported in the survey must be matched to food in FCT/FCDB. This can be done only if the consumed foods are expressed in grams EP.5 Table 2.2: Atwater System kJ/g Kcal/g Protein 17 4 Fat 37 9 Available/total carbohydrate 17 4 Available carbohydrate in monosaccharide equivalents 16 3.75 (Dietary) fibera 8 2 Alcohol (i.e., ethanol) 29 7 Organic acidsb 13 3 Polyolsb 10 24 Source: FAO 2002. a. In case only a total carbohydrate value is available, no energy is attributed to the fiber value. b. There are also specific conversion factors for individual polyols and organic acids. 21 Analyzing Food Security Using Household Survey Data When food quantities are given in milliliter or liter, they need to be converted in grams EP using density values.6 Unfortunately, the process to convert food quantities into nutrient con- tent is not straightforward, mainly due to some limitations in the available data: • Food quantities expressed in local units of measurement without a conversion factor into standard units • Nonedible portions (e.g., bones, seeds, peels, etc.) are included in the reported food quantities but their proportion is not known to convert to EP • Undefined food items such as dinner, lunch, meal, etc. • No national or regional FCT/FCDB available • The impossibility of getting nutrient values for local food items or food with a broad definition such as other cereals, other meat, etc. Because of these limitations, the way to estimate the calories and nutri- ents consumed should be split into two procedures: • Procedure 1. Used when food quantities can be expressed in grams EP and nutrient values of quantities are available for the food item • Procedure 2. Used when food quantities do not exist or they cannot be converted to grams EP, but food expenditures are available As households consume/acquire food for inside and outside household consumption, the estimated energy consumed/acquired is calculated using a combination of the two procedures. Table 2.3 shows the various cases where procedure 1 or 2 should be applied. The steps to follow in both procedures are described below. A numeric example built on 19 households belonging to Region = 1, Area = Urban, and Income quintile = 2 is presented in annexes 2B and 2C. Procedure 1: Estimation of Nutrients and Calories from Food Quantities As was mentioned before, this procedure applies only when food quanti- ties can be expressed in standard units (grams EP) and nutrient values are available. 22 Chapter 2: Theoretical Concepts Table 2.3: Data Availability Conversion factors Calories and to convert local unit nutrients of measurement into Food conversion Food standard unit (grams or monetary factors from Procedure to follow in the estimation quantity milliliters) value FCT of calorie and nutrients consumption YES YES YES YES PROCEDURE 1 YES YES YES NO PROCEDURE 2 YES NO YES YES PROCEDURE 1: If the quantities can be expressed in a standard unit using the food commodity price and the monetary value. PROCEDURE 2: If it is not possible to apply the PROCEDURE 1 YES NO YES NO PROCEDURE 2 NO YES/NO YES YES PROCEDURE 1: If the quantities can be expressed in a standard unit using the food commodity price and the monetary value. PROCEDURE 2: If it is not possible to apply the PROCEDURE 1 NO NO YES NO PROCEDURE 2 Six main steps are involved: the first two should be done manually, while the remaining are implemented in ADePT-FSM: 1. Standardization of the food quantities into grams or milliliters equivalent 2. Conversion of milliliters into grams 3. Adjustment of food quantities for nonedible portions 4. Estimation of grams of nutrients per household 5. Estimation of calories provided by each nutrient 6. Estimation of total calories per household Step 1: Standardization of the Food Quantities into Grams or Milliliters Equivalent Food composition tables always refer to nutrient values per 100 grams edible food product. Therefore, the first important step is to ensure that all food quantities are converted into grams. When food quantities are expressed in a unit of measurement such as kilogram, gram, milliliter, liter, a can of 200 grams, or a bottle of 750 milliliters, conversion into gram or milliliter is straightforward. In these cases food quantities can be converted into grams or milliliter just multiplying the amount of quantity per 1,000, 1, 1, 1,000, 200, and 750, respectively. However, food quantities may be expressed in local units such as bag, basket, cup, glass, heap, plate, tin, 23 Analyzing Food Security Using Household Survey Data unit, etc. These local units are commonly used in many countries, and their gram equivalent differs by food product and country (in some cases, also by region within a country). If the local unit of measurement cannot be con- verted into standard units the analysis cannot be conducted; it is therefore important to estimate the gram equivalent for the local units by food item at the community level, based on a sample of food items. It is always recom- mended that households report the quantities of food consumed/acquired in a standard unit of measurement. When not possible the enumerator should convert local units into standard ones in the field at the time of data collection. If after the data collection cases remain where food quantities are missing or cannot be expressed in grams, an indirect method can be applied. It has to be kept in mind however that this estimation is lowering the quality of the estimated energy and other nutrients, which should be taken into account in the interpretation of the results. This method estimates food quantities using food unit values ($/gram) at the most representative level. As unit values change across time, the monetary values should be adjusted before computing the unit values. These are the steps: 1. Estimate deflators as the food price index of the month in which the household was interviewed divided by the average of the food price index over the survey period. 2. Deflate all food expenditures using the correspondent deflator. 3. Convert all food quantities into the same standard unit. 4. For each household compute the unit value per food item using the quantities in standard units and the associated deflated expenditures. 5. Compute the median unit values grouping households by region, area, and income quintile.7 6. Estimate the food quantities using the correspondent expenditure and the median unit value associated with the households. Step 2: Conversion of Milliliters into Grams Nutrient values in FCT are usually expressed per 100 grams EP, so the food quantities expressed in milliliters should be converted into grams. To do so, it is necessary to have information on the food product’s density coefficient (food product mass per unit of volume). Water at 4 degrees Celsius has a density of 1, whereas for the rest of the food items, the value can be less than 1 (e.g., oils) or more 24 Chapter 2: Theoretical Concepts than 1 (e.g., milk). Using density values,8 quantities in grams are estimated as below: ⎛ g ⎞ Food Quantity jh (g) = Food Quantity jh (ml) ∗ density j ⎜ ⎟ ⎝ ml ⎠ where j stands for food product for which a valid density coefficient exists and h stands for household. An example on how to transform food quantities expressed in units of volume into standard units is presented in annex 2A. Step 3: Adjustment of Food Quantities for Nonedible Portions While food quantities acquired include nonedible portions such as peels, bones, seeds, etc., nutrient values in the FCT are usually expressed per 100 grams EP. For this reason, there is the need to transform “as purchased” quantities into edible ones.9 This transformation is done for each food commodity by applying the appropriate refuse factor.10 Some food commodities, such as rice, milk, or sugar, are 100 percent edible, but this is not the case for other food items such as bananas, meat with bones, peaches, and walnuts in a shell. Therefore, the refuse factor depends on the food product, and when it is expressed as a percentage varies from 0 (all edible) to 76 (walnuts in a shell) or to even more for some food items. In many FCT/FCDB, the refuse factor is not reported, but instead the edible coefficient (ranging between 0 and 1), which is defined as the edible portion of the food, i.e., the portion of the food without refuse. For example, an edible coefficient of 0.70 means that 70 percent of the food is edible while 30 percent is inedible. Summarizing the food quantities obtained in grams after applying steps 1 and 2 are adjusted for nonedible portions with the formula: ⎛ Refuse j ⎞ Edible Food Quantity jh = Food Quantity jh ∗ ⎜ 1 − ⎟ ⎝ 100 ⎠ Or Edible Food Quantity jh = Food Quantity jh ∗ (Edible Coefficient j ) where Food Quantityjh refers to the grams of food product j consumed/ acquired by household h; including the refuse (nonedible portion); Edible 25 Analyzing Food Security Using Household Survey Data Food Quantityjh refers to the edible grams (excluding bones, peels, seeds, etc.) of the food product j consumed/acquired by household h; Refusej refers to the percentage of nonedible grams in the food product j; and Edible Coefficientj refers to the proportion of the food item being eligible. Step 4: Estimation of Grams of Nutrients per Household The estimation of the nutrient content by food product is done by applying the nutrient val- ues of the macronutrients (fat, protein, carbohydrates, fiber, and alcohol) as published in FCT/FCDB to the edible quantities expressed in grams. The total grams of nutrient, at household level, is the sum of the nutrients provided by each food item: g Qih = ∑((Edible Food Quantity j =1 jh ∗ Nutrient Valueij )/100) where i represents the nutrient (fat, available carbohydrate, protein, alco- hol, or fiber) in household h; j represents the food items for which there are valid nutrient values, and the food quantities are expressed in edible grams following steps 1, 2, and 3; and g is the total number of food items for which valid nutrient factors exist. The value of g is lower (or equal) to the total number of food items consumed/acquired by household h. Qih refers to the total grams of nutrient i in household h, and Nutrient Valueij is the total grams of nutrient i per 100 grams edible portion of the food product j. Step 5: Estimation of Calories Provided by Each Nutrient The macronutrient consumption contributes to the estimated energy available for the human body. The amount of calories provided by protein, fats, available carbohy- drates, fiber, and alcohol can be estimated using the Atwater system coef- ficients (see table 2.2 above): Nih = Ai * Qih where Ni stands for dietary energy (kcal) provided by nutrient i in household h; Ai represents the Atwater coefficient (kcal/gram) associated with the nutri- ent i; and Qih refers to the total grams of nutrient i consumed in household h. Step 6: Estimation of Total Calories per Household The total estimated energy from these g food items consumed/acquired per household is finally 26 Chapter 2: Theoretical Concepts derived summing from all foods the calories provided by protein, fats, available carbohydrates, fiber, and alcohol. DECh = ∑Ni ih where DECh represents estimated energy (kcal) acquired/consumed by household h from the g food items for which a valid nutrient factor exists; and Nih represents the dietary energy (kcal) provided by nutrient i in house- hold h from the g food items for which a valid nutrient factor exists. A numerical example following steps 3 to 6 of procedure 1 is presented in annex 2B. Procedure 2: Estimation of Nutrients and Calories from Food Expenditure Procedure 2 applies when (1) food quantities are not available, (2) the unit of measurement cannot be expressed in standard units, or (3) nutrient values for the food product are not available. For instance, this procedure is usually applied to food consumed away from home, prepared food, or food not well defined. Since procedure 2 generates estimations of lower quality, it is best to explore other possibilities to obtain the necessary data. For example, if nutri- ent values are not found in the national or regional FCT/FCDB, other FCT/ FCDB should be consulted, or they should be calculated using recipes, etc. Procedure 2 involves monetary values and follows four main steps, implemented in ADePT-FSM, to estimate the following: • Proportion of calories provided by protein and fats • Total missing calories • Corresponding calories from protein, fats, carbohydrates (including fiber), and alcohol • Missing grams of protein, fats, carbohydrates (including fiber), and alcohol The steps are described below, and annex 2C provides a numerical example based on 20 households. Step 1: Estimation of the Proportion of Calories Provided by Protein and Fats When procedure 1 was applied in a household to one or more food 27 Analyzing Food Security Using Household Survey Data items, the proportion of calories provided by these products from protein and fats are estimated with the formulas: ∑g j =1 PROTjh ∑g j =1 FATjh SPORTh = and SFATh = ∑g j =1 DEC jh ∑ j =1 DEC jh g where for household h, SPORTh and SFATh stand, respectively, for the proportion of dietary energy (kcal) derived from the content of protein and fat in the g food items to which procedure 1 was applied; for house- hold h, PROTjh and FATjh calories stand for the dietary energy (kcal) derived from the content of protein and fat in the food item j; and for household h, DECjh stands for total dietary energy (kcal) provided by the food item j. Step 2: Estimation of Total Missing Calories Missing calories are those cor- responding to food items for which it is not possible to apply procedure 1. The estimation of these calories is done using the median household calorie unit value ($/kcal) at region/area/income quintile level. This median calorie unit value is calculated with data corresponding to the food items for which procedure 1 was applied, and is equal to the ratio between the expenditures (adjusted for temporal price fluctuations) and the corresponding dietary energy values. ∑g j =1 FDEXPjh UVal h = DECh where UValh represents the calorie unit value of household h in local cur- rency per kcal; FDEXPjh represents the total food expenditures occurred by household h to acquire the g food items to which procedure 1 was applied; and DECh represents the total dietary energy of household h brought by the n food items. The final calorie unit value corresponds to the median household calorie unit value at region/area/income quintile level. This median calorie unit value is applied at the household level to the food expenditure of all the k food items for which corresponding calories were not estimated using procedure 1. FDEXPkh DECkh = UVal 28 Chapter 2: Theoretical Concepts where k represents all the food products acquired by household h and for which no quantity or nutrient value exists (e.g., food consumed away from home); the sum of g and k corresponds to the total number of food items consumed/acquired by the household h; DECkh represents the dietary energy of household h from the k food items; FDEXPkh represents the expenditures occurred by household h to acquire the k food items; and UVal represents the median calorie unit value. Step 3: Estimation of the Corresponding Calories from Protein, Fats, Carbohydrates (Including Fiber), and Alcohol The amount of calories provided by protein and fats are calculated applying the proportion of calories provided by them (computed in procedure 2, step 1) to the estimated missing calories (computed in procedure 2, step 2). Using the same notations as introduced in step 1 and step 2 of procedure 2, it becomes: PROTkh = DECkh * SPORTh and FATkh = DECkh * SFATh The calories provided by total carbohydrates (including fiber) and alcohol are calculated as the difference between the total estimated missing calories (computed in procedure 2, step 2) and the sum of calories provided by pro- tein and fats (computed in procedure 2, step 3): CARkh = DECkh − (PROTkh + FATkh) where PROTkh, FATkh and CARkh represent, respectively, the calories from protein, fats, carbohydrates (including fiber), and alcohol provided by the k food items acquired by household h. Step 4: Estimation of the Missing Grams of Protein, Fats, Carbohydrates (Including Fiber), and Alcohol To obtain the missing grams of protein, fats, and carbohydrates the respective Atwater coefficients are applied to the estimated missing calories provided by each of these nutrients (computed in procedure 2, step 3). Quality Consideration The quality of the calculated energy content of food products will heavily influence the quality of the energy consumed/acquired and therefore the 29 Analyzing Food Security Using Household Survey Data food security statistics. Therefore, when interpreting the results several issues need to be taken into consideration: • Food consumed outside the household is not always well collected. Therefore the value of food consumed/acquired over the reference period may be wrongly estimated. • Habitual food consumption may not be captured well. There could be shortcomings in the survey or questionnaire design (e.g., food consumption is not collected over the year; list of food products is not exhaustive enough to fully reflect habitual consumption of the household; food consumption may be collected through interview with long recall periods; use of many units of measurement when collecting quantities of food consumed/acquired). • The quality of the food matching between reported foods and those of the FCT. On the one hand, the higher the percentage of exact matches means the higher the quality of the dietary energy estima- tion. On the other hand, the higher the proportion of foods which cannot be converted to grams EP means the lower the quality of the estimate of dietary energy consumed/acquired. • The quality of the food composition data. The FCT used should be adequate for the country, of high quality, and complete. There should be no missing data because they would lead to an underestimation of the energy values. In addition, the higher the proportion of foods for which no specific nutrient values can be attributed means the lower the quality of the energy and nutrient estimations. • Treatment of outliers (i.e., implausible under- and overconsump- tion). Procedures to treat for outliers are not included in this book. Estimation of Food and Total Consumption Expenditures and Income If the data are reliable, total income is calculated as the sum of income of all household members. Otherwise, it can be approximated by the total household expenditure, which is equal to the sum of total consumption expenditure plus nonconsumption expenditures. Total consumption expenditure includes the following: • Food • Clothing and footwear 30 Chapter 2: Theoretical Concepts • Gross rent, fuel, and power • Furniture • Medical care and health expenses • Transport and communications • Recreation, entertainment, education, and cultural services • Miscellaneous (personal care, package tours, etc.) Nonconsumption expenditures include direct and indirect taxes, insur- ance premiums, charity donations, social security contributions, and remit- tances or gifts to other households. Adjustment to Account for Temporal Variability of Prices As already discussed, the period usually covered by NHS is one year to account for the seasonal variations of food consumption and income. In this way, households report the acquired food over different months within a year. The monetary value of food commodities may vary not only among regions within a country due to extra costs as part of the regional trade chain, but also over the survey period due to price fluctuations or economic factors. Variations in the monetary values because of the geographical distri- bution of households are not removed for food security analysis because they are indicative of price differentials on an item within the country. However, in the estimation of food expenditure, total consumption expenditure, and income, it is important to consider inflation and deflation. If food expenditures, consumption expenditures, and income have not been deflated before executing the ADePT-FSM, it can be done within the program by adjusting monetary values using monthly deflators. The defla- tors are calculated based on monthly food and consumer price indexes (FPI and CPI, respectively) associated with each household according to the month and year in which the household was surveyed. The deflators used to adjust food expenditure values are obtained as the ratio of the monthly FPI and the survey midperiod FPI, which is estimated as the average of all the monthly FPI during the survey period. The deflators used to adjust total consumption expenditure and income are obtained as the ratio of the monthly CPI and the survey midperiod CPI, which is estimated as the average of all the monthly CPI during the survey period. Annex 2D shows an example of the calculation of food and total price deflators. 31 Analyzing Food Security Using Household Survey Data Table 2.4: Summary Table on Procedures of Standardization in ADePT-FSM Conversion into dietary energy Procedure 1 Steps 1 to 2 Manual Steps 3 to 6 Automatic Procedure 2 Steps 1 to 4 Automatic Calculation of total expenditures Automatic Temporal adjustment Automatic Conversion in per person Automatic National and subnational inference Automatic Conversion in per Person per Day The indicators are standardized and expressed in terms of per person per day, to remove variations due to household size and time period of data collec- tion. Most of the data from household surveys do not allow for an analysis of the intrahousehold distribution of food consumption and expenditure. Therefore, it is assumed that both of them are equally distributed among household members. Some surveys collect information on the number of people who participated in the meal (partakers). When data on partakers are available food consumption statistics (excluding food expenditure) are calculated using the number of food partakers instead of the household size. Inference at National and Subnational Levels The users of food security statistics are interested in having the information disaggregated by population groups within the country. Therefore, the sta- tistics estimated for the sampled households are adjusted to infer statistics at national and subnational levels; to do so, population statistical weights are applied to the data. The population weight is the household weight multiplied by the number of household members. The household weight is corrected for nonresponses, and it is calculated as the inverse of the prob- ability of the household to be selected multiplied by the expansion factor. Table 2.4 summarizes all the procedures that are automatic in ADePT- FSM and those that need to be undertaken manually during the preparation of the datasets prior to executing the software. Indicators on Food Security This section presents the food security indicators generated by ADePT-FSM and their respective methods of estimation. As discussed in the section on standardization, the indicators produced by ADePT-FSM are standardized 32 Chapter 2: Theoretical Concepts in per person per day and are representative at the national and subnational levels according to the survey sampling design. Food Insecurity Indicators Produced by ADePT-FSM Groups of Analysis The food security indicators are derived at national and subnational levels. The subnational levels are subsamples of households in terms of geographic, demographic, or socioeconomic factors. Statistics are provided not only for groups of population but also for food groups or food commodities. The statistics derived using a parametric approach, such as prevalence of under- nourishment, can be produced only for the population groups for which the survey sample is representative. Category of Population Groups ADePT-FSM allows for the analysis of food security indicators derived by population groups. These population groups include households’ geographical location and household heads’ socioeco- nomic or demographic characteristics. Table 2.5 presents all the population groups that can be analyzed using ADePT-FSM. Food Commodity Groups The food commodity groups are defined by the user. Table 2.6 is an example of the classification used by the Statistics Table 2.5: Population Groups National Regional Urban and rural areas Quintile of income Household size Gender of the household’s head Age of the household’s head Economic activity of the household’s head Education level of the household’s head Occupation of the household’s head Population group 1 defined by the user (e.g., marital status of household’s head) Population group 2 defined by the user (e.g., type of access to drinkable water) Population group 3 defined by the user (e.g., does or does not receive institutional food aid) Population group 4 defined by the user (e.g., refugee or not) Population group 5 defined by the user (e.g., ethnicity) 33 Analyzing Food Security Using Household Survey Data Table 2.6: FAO Food Commodity Groups’ Classification to Process Household Surveys 1 Cereals and derived products 2 Roots and tubers, and derived products 3 Sugar crops and sweeteners and derived products 4 Pulses and derived products 5 Nuts and derived products 6 Oil-bearing crops and derived products 7 Vegetables and derived products 8 Fruits and derived products 9 Stimulant crops and derived products 10 Spices 11 Alcoholic beverages 12 Meat (including poultry and pork) and derived products 13 Eggs 14 Seafood and derived products 15 Milk, cheese, and derived products 16 Vegetable oils and fats 17 Animal oils and fats 18 Nonalcoholic beverages 19 Miscellaneous and prepared food Division of the FAO, which reflects the main food groups used for the food balance sheets. Food Commodity Items The food commodity items analyzed are those listed in the NHS and are well-defined, such as fresh milk; long-grained rice; tomatoes; boneless, frozen mutton, etc. The list and number of food commodities are country- and survey-specific. Indicators Produced by ADePT-FSM The indicators listed in table 2.7 are derived for each category of population group (LCU refers to local currency). Indicators produced only at national, urban/rural areas, or regional levels are indicated with a asterisk (*). The indicators listed in table 2.8 are derived for each food commodity group. The indicators listed in table 2.9 are derived for each food item of the NHS. Indicators and Methods of Estimation The various indicators and related methods of estimation are presented below. 34 Chapter 2: Theoretical Concepts Table 2.7: Food Security Statistics Produced for Each Category of Population Groups General Number of sampled households Average household size Estimated population Access to diet and quality of diet Dietary energy Average dietary energy consumption (kcal/person/day) and macro- Average protein consumption (g/person/day) nutrients Average carbohydrates consumption (g/person/day) Average fats consumption (g/person/day) Average availability of vitamin A, retinol, and beta-carotene (mcg RAE/person/day) Average availability of vitamins B1, B2, B6, and C, calcium (mg/person/day), and vitamin B12 (mcg/person/day) Micronutrients Average availability of animal, nonanimal, heme, and nonheme iron (mg/person/day) Ratio of vitamins A and B12 available to required (%) Ratio of vitamins A, B1, B2, B6, B12, and C, and calcium available to recommended safe intake (%) Amino acids Average availability of essential amino acids: isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, histidine, cystine, and tyrosine (g/person/day) Quality of diet Share of dietary energy consumption from protein (%) Share of dietary energy consumption from carbohydrates (%) Share of dietary energy consumption from fats (%) Share of animal protein in total protein consumption (%) Economic access to food Monetary Average food consumption (LCU/person/day) value Average total consumption (LCU/person/day) Average income (LCU/person/day) Price Average dietary energy unit value (LCU/1,000 kcal) Sources of Share of purchased food in total food consumption (in dietary energy) (%) acquisition Share of own produced food in total food consumption (in dietary energy) (%) Share of food consumed away from home in total food consumption (in dietary energy) (%) Share of food from other sources in total food consumption (in dietary energy) (%) Share of purchased food in total food consumption (in monetary value) (%) Share of own produced food in total food consumption (in monetary value) (%) Share of food consumed away from home in total food consumption (in monetary value) (%) Share of food from other sources in total food consumption (in monetary value) (%) Share of food consumption in total income (%) (Engel ratio) Responsiveness Income demand elasticity of dietary energy consumption of demand to Income demand elasticity of food consumption in monetary value income Income demand elasticity of Engel ratio inequality Dispersion ratio of food consumption in dietary energy (80/20) Dispersion ratio of food consumption in monetary value (80/20) Dispersion ratio of total consumption expenditure (80/20) Food Minimum and average dietary energy requirements (kcal/person/day) inadequacy Prevalence of undernourishment (%) (*) Depth of food deficit (kcal/person/day) (*) Dietary Energy and Macronutrient Consumption The average daily calories consumed by a representative individual in a population group of analysis are estimated as follows: ∑ H (hh_wgt h ∗ DECh) h =1 Calories per person per day = ∑ H (hh_sizeh ∗ hh_wgt h ∗ num_daysh) h =1 35 Analyzing Food Security Using Household Survey Data Table 2.8: Food Security Statistics Produced for Each Food Commodity Group Access to diet and quality of diet Dietary energy and Average dietary energy consumption (kcal/person/day) macronutrients Average protein consumption (g/person/day) Average carbohydrates consumption (g/person/day) Average fats consumption (g/person/day) Quality of diet Contribution of food groups to total dietary energy consumption (%) Contribution of food groups to total protein consumption (%) Contribution of food groups to total carbohydrates consumption (%) Contribution of food groups to total fats consumption (%) Average protein consumption per kcal (g/1,000 kcal) Average carbohydrates consumption per kcal (g/1,000 kcal) Average fats consumption per kcal (g/1,000 kcal) Micronutrients Average availability of vitamin A, retinol, and beta-carotene (mcg RAE/person/day) Average availability of vitamins B1, B2, B6, and C, and calcium (mg/person/day), and vitamin B12 (mcg/person/day) Average availability of animal, nonanimal, heme, and nonheme iron (mg/person/day) Contribution of food groups to micronutrient availability (%) Amino acids Average availability of essential amino acids: isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, histidine, cystine, and tyrosine (g/person/day) Contribution of food groups to amino acid availability (%) Economic access to food Monetary value Average food consumption (LCU/person/day) Price Average dietary energy unit value (LCU/1,000 kcal) Average protein unit value (LCU/100 g) Average carbohydrates unit value (LCU/100 g) Average fats unit value (LCU/100 g) Table 2.9: Food Security Statistics Produced for Each Food Commodity Access to diet and quality of diet Dietary energy and Average edible food quantity (g/person/day) macronutrients Average dietary energy consumption (kcal/person/day) Average protein consumption (g/person/day) Micronutrients Average availability of vitamin A, retinol, and beta-carotene (mcg RAE/person/day) Average availability of vitamins B1, B2, B6, and C, and calcium (mg/person/day), and vitamin B12 (mcg/person/day) Average availability of animal, nonanimal, heme, and nonheme iron (mg/person/day) Amino acids Average availability of essential amino acids: isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, histidine, cystine, and tyrosine (g/person/day) Economic access to food Monetary value Average food consumption (LCU/person/day) Price Dietary energy unit value (LCU/1000 kcal) ag k DECh = ∑ DEC + ∑ DEC j =1 hj j =1 hj The average daily macronutrients (protein, carbohydrates, and fat) con- sumed by a representative individual in a population group is estimated as follows: 36 Chapter 2: Theoretical Concepts ∑ H (hh_wgt h ∗ Nutrient h) h =1 Macronutrients per person per day = ∑ H (hh_sizeh ∗ hh_wgt h ∗ num_daysh) h =1 g k Nutrient h = ∑ Nutrient + ∑ Nutrient j =1 hj j =1 hj ⎛ ⎛ fq * lg j ⎞ ⎛ refuse j ⎞ ⎞ Nutrient jh = ⎜ ⎜ jh ⎟ ∗ ⎜1 − ⎟⎟ ⎝ ⎝ 100 ⎠ ⎝ 100 ⎠ ⎠ where H is the total number of sampled households belonging to the popu- lation group of analysis; hh_wgth is the household weight (expansion factor divided by the probability of the household to be sampled) of household h; hh_sizeh is the total number of members (household size) in household h; num_daysh is the number of days of the food data reference period for house- hold h; DEChj refers to the calories consumed of food item j by household h; g is the number of food items in household h, for which the nutrient content is estimated applying procedure 1; k is the number of food items in house- hold h, for which the nutrient content is estimated applying procedure 2; Nutrienth is the total amount of macronutrients in household h; Nutrienthj is the amount of macronutrients in food item j in household h; fqjh is the quan- tity of the food item j consumed/acquired by household h and expressed in grams “as purchased” (includes the nonedible part); lgj refers to the grams of micronutrients per 100 grams edible portion of the food item j (as in FCT); and refusej is the refuse factor (nonedible part) of the food item j expressed in percentage. Micronutrient Availability A proper intake of macronutrients in terms of a balanced diet is not enough for human beings to conduct a healthy life if they do not consume adequate amounts of minerals, vitamins (micronutrients), and indispensable amino acids. The micronutrients analyzed by the ADePT-Food Security Module are the A vitamins, ascorbic acid, thiamine, riboflavin, B6, cobalamin, and the minerals calcium and iron. The indispensable amino acids analyzed are isoleucine, leucine, lysine, threonine, tryptophan, valine, histidine, methio- nine and cystine, and phenylalanine and tyrosine. 37 Analyzing Food Security Using Household Survey Data Most NHS collect data on food acquisition rather than consumption. Further, the content of micronutrients in food may vary from the moment of its acquisition to its consumption because of several reasons includ- ing storage conditions and the way the food is processed. Moreover, the presence of other substances in the food may inhibit or enhance nutrient absorption.11 Therefore, the derived estimates are indicative, and they should not be interpreted as a result of the evaluation of intake by individu- als in the population groups. That is why the term availability is used instead of consumption. In the micronutrients assessment, neither supplementation nor fortifi- cation is taken into consideration because such information usually is not collected in the surveys. The equation applied to estimate the micronutrients available for con- sumption is given below: ∑ H (hh_wgt h ∗ Nutrient h) h =1 Micronutrients per person per day = ∑ H (hh_sizeh ∗ hh_wgt h ∗ num_daysh) h =1 n Nutrient h = ∑ Nutrient j =1 hj ⎛ ⎛ fq ∗ lg j ⎞ ⎛ refuse j ⎞ ⎞ Nutrient j = ⎜ ⎜ jh ⎟ ∗ ⎜1 − ⎟⎟ ⎝ ⎝ 100 ⎠ ⎝ 100 ⎠ ⎠ where H is the total number of sampled households belonging to the popu- lation group of analysis; hh_wgth is the household weight (expansion factor divided by the probability of the household to be sampled) of household h; hh_sizeh is the total number of members (household size) in household h; num_daysh is the number of days of the food data reference period for household h; n is the total number of food items (excluding those consumed away from home12) in household h; Nutrienth is the amount of micronutri- ents available in household h (excluding those consumed away from home); Nutrienthj is the amount of micronutrients available in food item j in house- hold h; fqjh is the quantity of the food item j consumed/acquired by household h and expressed in grams “as purchased” (includes the nonedible part); lgj refers to the grams of micronutrients in 100 edible grams of the food item j 38 Chapter 2: Theoretical Concepts (as in FCT); and refusej is the refuse factor (nonedible part) of the food item j expressed in percentage. For the micronutrients’ ADePT-FSM analysis, it is worth clarifying some concepts on vitamin A and iron. More than one unit of measurement can be found in the literature when referring to vitamin A. The ADePT-FSM was developed to express the availability of vitamin A in terms of retinol activity equivalent (RAE). The use of μg RAE rather than μg retinol equivalent (RE) or international units (IU) is preferred when calculating and reporting the amount of the total vitamin A in mixed foods or assessing the amount of dietary and supplemen- tal vitamin A consumed (see National Academy of Sciences [NAS] 2001). According to NAS the conversion of retinol and pro-vitamin A carotenoids into vitamin A is as follows: μg βcarotene Vitamin A in μg RAE = μg retinol + 12 μg αcarotene or βcryptoxanthin + 24 Regarding iron, it can be distinguished as animal or nonanimal. The first refers to meat, fish, eggs, milk, and cheese, and their derived products. Another type of iron classification is with respect to the mechanism of its absorption: heme and nonheme. The latter is present in food of both animal and nonanimal origin, whereas the former can be found only in meat and fish (as it is derived from hemoglobin and myoglobin).13 Amino Acids Amino acids are the building blocks of proteins. Some of their functions are building cells, protecting the body from viruses or bacteria, repairing damaged tissue, providing nitrogen, and carrying oxygen throughout the body. They can be classified as dispensable or indispensable. The latter are also called essential amino acids (EAA) and cannot be synthesized by the human body. Therefore, they should be supplied to the body through the consumption of protein in food.14 The indispensable amino acids analyzed in ADePT-FSM are isoleucine, leucine, lysine, threonine, tryptophan, valine, histidine, methionine and 39 Analyzing Food Security Using Household Survey Data cystine, and phenylalanine and tyrosine. ADePT-FSM estimates the aver- age daily per person grams of indispensable amino acids available for con- sumption and the equations used are: ∑ H (hh_wgt h ∗ AAh) h =1 Amino acid per person per day = ∑ H (hh_sizeh ∗ hh_wgt h ∗ num_daysh) h =1 n AAh = ∑ AA j =1 jh ⎛ ⎛ fq ∗ lg j ⎞ ⎛ ref ⎞ ⎛ pd ⎞ ⎞ AA jh = ⎜ ⎜ jh ⎟ ∗ ⎜1 − ⎟ ∗⎜ ⎟ ⎝ ⎝ 100 ⎠ ⎝ 100 ⎠ ⎝ 100 ⎠ ⎟ ⎠ where H is the total number of sampled households belonging to the population group of analysis; hh_wgth is the household weight (expansion factor divided by the probability of the household to be sampled) of house- hold h; hh_sizeh is the total number of members (household size) in house- hold h; num_daysh is the number of days of the food data reference period for household h; n is the total number of food items (excluding those consumed away from home15) in household h; AAh is the amount of amino acid avail- able in household h (excluding those consumed away from home); AAjh is the amount of amino acid available in food item j in household h; fqjh is the quantity of the food item j consumed/acquired by household h and expressed in grams “as purchased” (includes the nonedible part); lgj refers to the grams of amino acid in 100 edible grams of the food item j (as in FCT); ref is the refuse factor (nonedible part) of the food item j expressed in percentage; and pd is the protein digestibility of the food item j expressed in percentage. Balanced Diet A balanced diet is a diet that provides energy and all essential nutrients for growth and a healthy and active life. Since few foods contain all the nutri- ents required to permit the normal growth, maintenance, and functioning of the human body, a variety of food is needed to cover a person’s macro- and micronutrient needs. Any combination of foods that provides the correct amount of dietary energy and all essential nutrients in optimal amounts and proportions is a balanced diet (CFS 2012). 40 Chapter 2: Theoretical Concepts A joint WHO/FAO expert group established guidelines for a balanced diet (WHO 2003). These guidelines are related to effects on the chronic nondeficiency diseases. According to expert opinion, a balanced diet exists when the following conditions are met: • The proportion of dietary energy provided by protein is in the range of 10–15 percent. • The proportion of dietary energy provided by fats is in the range of 15–30 percent. • The proportion of total dietary energy available derived from carbo- hydrates is in the range of 55–75 percent. From surveys collecting food consumption or acquisition, it is not pos- sible to assess if a population group consumes a balanced diet, because there is no information about how people combine the food they consume or about intrahousehold differences in food consumption. However, from the data collected it is possible to infer whether or not households have access to a balanced diet. Monetary Values The average daily food expenditure of a representative individual in a popu- lation group is estimated as follows: ∑ H (hh_wgt h ∗ FMVh) Food monetary value per person per day = h =1 ∑ H (hh_sizeh ∗ hh_wgt h ∗ num_daysh) h =1 n FMVh = ∑ FMV j =1 jh where H is the total number of sampled households belonging to the population group of analysis; hh_wgth is the household weight (expan- sion factor divided by the probability of the household to be sampled) of household h; hh_sizeh is the total number of members (household size) in household h; num_daysh is the number of days of the food data reference period for household h; n is the total number of food items (including those consumed away from home) in household h; FMVh is the food monetary 41 Analyzing Food Security Using Household Survey Data value of household h; and FMVjh is the food monetary value of food item j in household h. A similar formula is applied to estimate the average daily total consump- tion expenditure and income of a representative individual in a population group. Price Food prices are important determinants of food security. ADePT-FSM cal- culates the calorie unit value of dietary energy expressed in monetary value per 1,000 kcal. At the population group level, the software computes the calorie unit value at household level and then estimates the correspondent mean. At food item and food group levels, because some food items or food groups may present only a few cases, the median calorie unit value is used instead of the mean. Finally, the calorie unit values do not include the cost of the energy required to cook food. In addition to the monetary value of 1,000 kcal, macronutrient unit monetary values are also provided, and they are expressed in monetary terms per 100 grams of nutrients. Responsiveness of Food Demand to Income The income elasticity of the demand of food is measured through the responsiveness of dietary energy, food expenditure, or the Engel ratio to a variation in income. In other words, this relative responsiveness depicts the relationship between acquired food and income, described by the Engel curve. The estimates assume that substitution among food commodities occurs for different income levels and that food commodity prices are constant. For a given country, it is assumed that household dietary energy con- sumption per person can be linked to household income per person by the following regression equation (FAO 1996): xh= b0 + b1 * ln(Vh) + uh where b0 and b1 are parameters of the equation; xh is the demand of food expressed in terms of dietary energy, food expenditure, or Engel ratio of household h on a per person basis; Vh is the income per person in house- hold h; and uh is the random variation of food demand across households. 42 Chapter 2: Theoretical Concepts In ADePT-FSM the individual household data are grouped by income decile classes, and the average food consumption and income per person in each income decile class is inferred and used to estimate the parameters of the equation. Therefore the equation becomes: x J = α 0 + α 1 * ln(Vj ) + u j where a0 and a1 are parameters of the equation; and x J is the inferred demand of food expressed in terms of dietary energy, food expenditure, or Engel ratio of the income class j on a per person basis. ∑ Hj ( fh * x h ) h =1 xJ = pop j VJ is the inferred per person income of the income class j. ∑ Hj ( fh * Vh ) h =1 V = j pop j Hh refers to the total number of sampled households in income decile j; xh is the demand of food expressed in terms of dietary energy, food expendi- ture, or Engel ratio of household h; and popj is the inferred total population in the income decile j. Hj pop j = ∑(hh_size * hh_wgt ) h =1 h fh is the inferred total number of people represented by household h. fh = (hh_size * hh_wgth) hh_sizeh is the total number of members (household size) in household h; hh_wgth is the household weight (expansion factor divided by the probability of the household to be sampled) of household h; and uj is the random varia- tion of the average food demand across income decile classes. Figure 2.1 shows an example of the demand of food consumption as a function of income. This example is from real country data and represents the average values of food demand (in terms of dietary energy and food expenditure) by average values of income (estimated for income decile groups of the population). 43 Analyzing Food Security Using Household Survey Data Figure 2.1: Example of Food Consumption Demand as Function of Income 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 Income per person (lcu/person/day) Demand of dietary energy consumption in kcal/person/day Demand of food expenditure (lcu/person/day) The elasticity of food consumption with respect to income is: ⎛ ∂x ⎞ V α 1 V α 1 η=⎜ ∗ = ∗ = ⎝ ∂V ⎟ ⎠ x V x x Therefore, the income elasticity (h) of food demand (represented by the mean per person μ) can be estimated as: α1 η= μ Reviewing: This expression of elasticity allows the estimation of the elasticity by income deciles. ⎛αˆ ⎞ ⎛ α ˆ1 ⎞ ηj = ⎜ 1 ⎟ = ⎜ ˆj ⎠ ⎝α ⎝x ˆ 1 * ln(VJ ) ⎟ ˆ0 +α ⎠ where α ˆ 1 is the estimated slope of the Engel function; and x ˆ j corresponds to the estimated fitted mean of dietary energy, food expenditure, or Engel ratio of the jth income decile. 44 Chapter 2: Theoretical Concepts Food Consumption Statistics by Sources of Acquisition As discussed previously, households acquire food from different sources, including purchases, own production, aid, and as payment for labor. The ADePT-FSM analyzes four main sources of food acquisition: purchases (excluding food consumed away from home), own production, consumed away from home, and all other sources. The latter includes food received as aid, gift or payment for labor, hunting, and wild harvesting. The contribution of each food source to total food consumption in both monetary and dietary energy terms varies depending on the population group of analysis. For instance, it is expected that the share of food con- sumption from own production in rural households is higher than that in urban households. The share of food expenditure in total income is also called the Engel ratio. Low-income households spend a large percentage of their total consumption expenditure on food. With higher income, the food ratio declines following Engel’s law, which states that the proportion of income spent on food decreases with increasing income, because food is a basic primary need. Measures of Inequality Dispersion Ratios The dispersion ratios measure inequality between the two extreme income quintile groups. They are calculated using as reference the average values corresponding to the first quintile. For instance, one ratio is defined as the average food consumption (in terms of dietary energy or mon- etary values) in the highest income quintile divided by the correspondent average food consumption in the lowest one. Coefficient of Variation The coefficient of variation (CV) is a relative measure of inequality in a given distribution. In principle, a direct estimate of the variability in the distribution of dietary energy consumption (DEC) could be obtained through a measure of the empirical dispersion of individu- als’ consumption from a survey. There are, however, several reasons why this may be problematic. Household food consumption data (collected in surveys) on a per person basis are very likely to be more dispersed than the actual per person yearly average of food consumption in the population. This is because of the presence of “spurious” variability (introduced both 45 Analyzing Food Security Using Household Survey Data systematically through features of survey design and accidentally due to nonsampling errors) related to the following: • Survey rounds of data collections usually spread over the year. This is done to avoid introducing biases in the estimation of mean consump- tion, when consumption of food is known to be varying over the seasons. Unfortunately, spreading data collection over the seasons means that seasonal variability in consumption (which should not be considered in estimating the variability of the average year consump- tion in the population) is still present. • Missing data and outliers. For example, nonsampling errors that are associated with errors in recall, under- or overreporting, incomplete- ness of data collection forms, especially with reference to food con- sumed away from home, interview effects, etc. • Surveys collecting food acquisition instead of food consumption. Food acquisition surveys may overestimate the distribution of calories across households because the variability within households will be confounded with the variability between households. Calories can be acquired through durable foods such as cereals to be stocked and consumed over a long period of time and not during the reference food collection period. • Expected variability. During the year, there is an expected variability in adequately nourished households, for instance, the result of a party given by the households. This variability is considered as an excess variability, since we are interested in capturing the habitual food consumption. All these factors might induce a systematic positive bias in the estimate of the variability parameter of the distribution. This bias is difficult to reduce once survey data have been collected. Cleaning the data to identify outliers and missing values can help reduce the potential bias, but this may intro- duce a certain degree of subjectivity in the analysis that should not go unno- ticed. In addition, when the distribution in the population is skewed, as seen in two-stage sampling (commonly used in household income expenditure surveys), a systemic bias in the estimate of variability indicators can result. All these considerations raise reservations about the possibility of obtaining a reliable estimate of DEC variability through the observed empirical variance of individual household data in a survey. Therefore, 46 Chapter 2: Theoretical Concepts the estimation of the CV of DEC can be derived as the combination of two sources of variability of DEC: one due to income and the other due to other factors. Indeed, while the role of income in explaining DEC and its variability within a population is at the heart of all theories of poverty and economic development, there are many other factors inducing variability in DEC. These factors have to be considered physiological in a population, and should therefore be tolerated. The overall value of the CV is then obtained following the steps described below: Step 1: Estimation of the CV of DEC Tabulated by Income Individual house- holds are grouped by classes of income decile, and the average per person food consumption in each class of income is inferred. By averaging within an income class, most of the variations in the level of DEC because of fac- tors that are not strongly correlated with income are clearly netted out. The resulting measure of CV should thus properly be interpreted as an estimate of the component of the total variability of DEC in the population tabulated by income, which we term CVx/v(x). The coefficient of variation of DEC tabulated by income is defined as: σ (x / v) CV(x / v) = μx where s(x /v) is the standard deviation of the distribution of the average per person dietary energy consumption of income decile groups and is derived from the formula: ⎡⎡ 2 ⎤⎤ ⎛ ∑ ⎞ 10 ⎢⎢ ⎜ fj ∗ x j ⎟ ⎥⎥ ⎝ ⎠ ∑ 10 j =1 ⎢⎢ (f j ∗ x2 j)− ⎥⎥ ⎢ ⎣⎢⎣ j =1 pop ⎦⎥ ⎥ ⎦ σ (x / v) = pop − 1 mx is the average per person dietary energy consumption at the income decile level and is derived from the formula: ∑ (f ∗x ) ∑ 10 H j j ( fh ∗ x h ) j =1 h =1 μx = = pop pop where j refers to income decile group; h refers to household; H is the total number of sampled households in the survey; Hj is the total number of 47 Analyzing Food Security Using Household Survey Data sampled households in income decile j; x j is the average per person dietary energy consumption of income decile j; xh is the average per person dietary energy consumption of household h; and pop is the inferred total population. H pop = ∑(hh_size ∗ hh_wgt ) h =1 h h fj is the inference total number of people in income decile j. Hj fi = ∑(hh_size * hh_wgt) h =1 h fh is the inference total number of people represented by household h. fh = (hh_size * hh_wgt)h hh_sizeh is the total number of members (household size) in household h; and hh_wgth is the household weight (expansion factor divided by the probability of the household to be sampled) of household h. Step 2: Estimation of CV of DEC Because of Other Factors If it is true that people tend to consume according to their respective dietary energy requirements (DER), and as long as there is an interindividual variation in DER, there will be variation in DEC due to physiological factors. For this reason, a component reflecting the variability of DEC induced by the fac- tors determining the variability of DER, CVx/r(x) = CV(r) is also considered to estimate the total CV. This variation of dietary energy due to require- ments is estimated taking into account the coefficient of variation of three components: body weight, physical activity level (PAL), and measurement error. The coefficient of variations due to body weight and PAL are esti- mated under the assumption of the lognormality. The regression equations used for estimating the basal metabolic rate (BMR) given a body weight are subject to a prediction error corresponding to a CV of about 0.08. Since this variation is of a random nature, it is not considered in deriving the dietary energy requirements. However, in this context, where the variation in energy requirement is used for estimating the variation in energy intake, the variation owing to error in estimating the BMR is taken into account (FAO 2002). For more details on the estimation of the CV of DEC due to physiological factors refer to annex 2E. 48 Chapter 2: Theoretical Concepts Step 3: Aggregation Finally, the CV of DEC is derived as the sum of the square of the two CVs as estimated in steps 1 and 2. CV(x) = (CVx / v (x))2 + (CVx / r (x))2 Step 4: Selection of the CV In ADePT-FSM, the coefficient of variation of dietary energy consumption corresponds to the CV whose value is the low- est between the CV from the empirical distribution and the CV obtained as a combination of the two sources of variability. Measures of Asymmetry The skewness measures the asymmetry of a distribution. As opposed to income that can increase infinitely when the mean increases (corresponding to a long tail to the right), the dietary energy of food consumed is limited by biological constraints. The method to estimate the value of the skewness depends on how the coefficient of variation of calories is derived. On one hand, when the final CV is obtained from the empirical distribution of calories across individuals, the value of the skewness is the one obtained from the empirical distribution, and a flexible distributional form is used for the calculation of the prevalance of undernourishment (PoU), known as the skewed-normal distribution. On the other hand, when the CV is derived as the sum of two components, the distribution is assumed to be lognormal and the skewness is given by:16 Skewness = (CV2 + 3) * CV Note that prior to the methodological improvements resulting from the choice of the CV, the addition of the skewness and the functional form of the log-normal distribution was always used for the calculation of the PoU. For this reason, the user has been left with the option of calculating the dis- tribution according to the old or improved methodology by selecting either the log-normal or skewed-normal distribution, respectively. Dietary Energy Requirements The most common levels of dietary energy requirements found in the literature are the minimum and the average. They are derived from the 49 Analyzing Food Security Using Household Survey Data consideration that food energy requirements can be safely defined only in terms of a distribution within a given class or population group, not at the individual level (FAO/WHO/UNU 2001). Nevertheless, a minimum or average level of dietary energy intake that is compatible with a healthy and productive life can be meaningfully defined statistically with reference to the representative individual in a group or class. As for the estimation of both requirements, the Food and Agriculture Organization (FAO) has devised an indirect procedure based on expert recommendations on what the acceptable ranges of DER would be in groups of individuals of the same sex and age, and on the observed sex/age composition of the countries. Minimum Dietary Energy Requirement The minimum dietary energy requirement (MDER) is estimated for each sex/age class of individuals based on the energy requirement (based on the basal metabolic rate) for the lowest acceptable body weight for that sex/age combination, adjusted for a minimal physical activity level compatible with a healthy life. Then a weighted average (the weights used are the proportions of the population in the corresponding sex/age groups) of the minimum DERs of each sex/age class is computed. Finally, the extra energy required by pregnant women is added to the weighted average to derive the mini- mum dietary energy requirement of a representative individual of the population. Therefore, the information needed to estimate the MDER through the equations suggested by the joint FAO/WHO/UNU expert consultation held in 2001 is the following: • Country birth ratio in the year of the survey (exogenous parameter) • Structure of the population in the country by specific sex/age groups (from the survey) Children Less Than 10 Years Old • Body mass index (BMI) (50th percentile) (exogenous parameter)17 • Height of people in the country for specific sex/age groups (cm) (from demographic and health surveys [DHS] or literature) • Weight gain per age (grams per day) (50th percentile) (exogenous parameter)18 • Energy per gram of weight gain (kcals) (exogenous parameter)19 50 Chapter 2: Theoretical Concepts • Country under-five mortality rate (U5MR) in the survey year (per 1,000 live births) (exogenous parameter) Adolescents and Adults • Body mass index (fifth percentile) (exogenous parameter) • Height of people in the country for specific sex/age groups (cm) (from DHS or literature) • The parameter used for adjusting the requirements due to the level of activity is the PAL. A PAL of 1.55 corresponds to sedentary physical activity (exogenous parameter).20 The BMI is used to estimate the weight in kilograms for the attained height, while the U5MR value defines which equations should be applied to estimate the energy requirements of children less than two years old. The birth ratio is used to estimate the extra energy requirement for pregnant women. For more details refer to annex 2F. Average Dietary Energy Requirement The formulas to estimate the average dietary energy requirement (ADER) are equal to those used in the estima- tion of MDER; however, some parameters are different. The ADER refers to the amount of energy considered adequate to meet the energy needs for normative average acceptable weight for an attained height while perform- ing moderate physical activity in good health. Therefore, only the 50th percentile of the BMI is applied to all the equations. The PAL parameter to estimate the average energy requirement is 1.85 and corresponds to a moderate level of physical activity. While no large variation is expected to exist between the metabolic rate of people in different countries within the same sex/age group (though differences across latitude could be important), the sex/age composition of the population changes over time, and so the estimated dietary energy requirements have to be adjusted to reflect this change in demographic structure. Micronutrients Availability versus Recommended or Required Intakes The so-called “hidden hunger” refers to a deficiency of micronutrients; it is a health threat, particularly for children and pregnant women. For an indi- vidual, the amount of micronutrients supplied in the diet should be in line 51 Analyzing Food Security Using Household Survey Data with his or her required levels of mineral and vitamins. To minimize the risk of nutrient deficit or excess, a joint FAO/WHO expert group defined the dietary requirement for a micronutrient as an intake level that meets speci- fied criteria for adequacy. This dietary requirement is expressed in terms of an estimated average requirement (EAR) and a recommended nutrient intake (RNI). EAR is the average daily nutrient intake level that meets the needs of 50 percent of the “healthy” individuals in a particular age and sex group. RNI is the daily intake, set at the EAR plus two standard deviations, which meets the nutrient requirements of almost all apparently healthy individuals in an age and sex specific population group (FAO/WHO 2004). Therefore, to express nutrient requirements and recommended intakes for population groups, the requirements applied separately to each individual belonging to the population of analysis are summed. The individual require- ments were defined for sex/age population groups by a FAO/WHO group of experts in 1998 (FAO/WHO 2004). Despite having the micronutrient content of food acquired or consumed by households, it is not possible to talk about micronutrient consumption but availability at the household level. The reason for this is that from the moment households acquire the food to the time they eat it the content of nutrients in the food has changed. The nutrient content varies with food storage practices and processing and preparation methods (NAS 2000). For example, (1) high temperature processing can affect the vitamin content, e.g., vitamin C; and (2) discarding of water used in cooking will lead to the loss of water-soluble food components (e.g., B vitamins, vitamin C, and certain bioactive components) (FAO/INFOODS 2012). According to the National Academy of Sciences (NAS) (2000), the household nutrient requirement estimated as the sum of the needs21 of the household members cannot be used as an EAR because intake and require- ment are not correlated for most nutrients. When a diet provides the amount of nutrient needed by household members, it is likely that food (and so the nutrient) will be distributed in proportion to energy needs of the individu- als, not to nutrient requirement needs. Therefore, it is suggested to estimate the required nutrient density of a household diet, such that when the diet is shared in proportion to calories, it is likely that the nutrient requirements of all the individuals will be met. The required nutrient density of the household is the highest nutrient density among household members (FAO/ WHO 1970). Note that if the dietary energy consumed is not enough for the total household, it cannot be assured that food (and nutrient consumption) 52 Chapter 2: Theoretical Concepts is distributed in proportion to the calories required by household members. Therefore, this approach is meaningful when households have a consump- tion of calories at least equal to their requirements. The NAS also stated that the calculation of required nutrient density is not as simple as computing the ratio of the estimated average requirement for the nutrient to the average energy requirement.22 On the one hand, the calculations must take into account variability of the nutrient requirement, expected variability of the nutrient density in ingested diets, and assurance of adequacy for the targeted individual. On the other hand, the recom- mended nutrient intake, which meets the nutrient requirements of almost all individuals (when requirement in the group has a normal distribution), should not be used as a cutoff point for assessing nutrient intakes of a popu- lation group because it would result in an overestimation of the proportion of people at risk of inadequacy. Currently, ADePT-FSM generates statistics that compare levels of mean nutrient availability with both mean nutrient requirements and recom- mended intakes. These statistics, which are described below, will be recon- sidered in light of the NAS report for future ADePT-FSM releases. Ratio of Micronutrient Available to Required For a given population group, the average amount of available micronutrients (to be consumed by a repre- sentative individual of the population) is divided by the estimated average requirement. The available amount for consumption is the numerator, and the EAR is the denominator of the ratio. In comparing the average avail- ability with a measure of average requirement, micronutrient distribution across the population is unaccounted.23 Ratio of Micronutrient Available to Recommended For a given population group, the average amount of micronutrients available (to be consumed by a representative individual of the population) is divided by the recommended nutrient intake. The available amount for consumption is the numerator, and the RNI is the denominator of the ratio. For most micronutrients, if the mean intake equals the RNI, a substantial proportion of the population will have intakes less than their own requirements. Other Ratios The development of Food-Based Dietary Guidelines (FBDG) focuses on how a combination of foods can meet nutrient requirements rather than on how each specific nutrient is provided in adequate amounts. 53 Analyzing Food Security Using Household Survey Data In contrast to recommended intakes (RI), FBDG are based on the fact that people eat food, not nutrients. Defining nutrient intakes alone is only part of the task of dealing with nutritional adequacy. The notion of nutrient density is helpful for defining FBDG and evaluating the adequacy of diets. Unlike RI, FBDG can be used to educate the public through the mass media and provide a practical guide to selecting foods by defining dietary adequacy (FAO/WHO 2004). Food items can have a high or low nutrient density. Nutrient dense foods are those providing substantial amounts of vitamins and minerals and relatively few calories. For instance, fruits and vegetables are nutri- ent dense commodities. On the other hand, food items with low nutrient density supply calories but a relatively small or null amount of micro- nutrients. This is the case of added sugars, fats, and alcohol (USHHS/ USDA 2005). For some vitamins and minerals, ADePT-FSM estimates nutrient densi- ties in the diet as the amount of micronutrients per 1,000 kcal provided by the food; it also estimates required/recommended nutrient densities that are estimated as the EAR/RNI of the nutrients per 1,000 required calories. The 1,000 required calories are based on the average dietary energy requirements for a representative individual of the population. Then, for these vitamins and minerals, the software computes ratios of nutrient densities in the diet to required/recommended nutrient densities. Prevalence of Undernourishment Since the beginning of its history FAO put emphasis on the problem of undernourishment and studied the problem in depth to reach a good estimation of the number of undernourished people in the world. The first publication on this subject was the “World Food Survey.” Starting in 1946, every five or seven years it depicted the situation of global under- nourishment. Subsequently, there has been substantial improvement in the methodology used to produce this estimation and in the many experts working on that issue (especially P. Sukhatme and L. Naiken). In 1996 the first FAO World Food Summit was held, and it was decided to have a yearly publication, The State of Food Insecurity in the World (SOFI), which provides the latest estimates of the number of chronically hungry people in the world and introduces the first comparable estimates ever made of the number of people who go hungry. This and subsequent editions 54 Chapter 2: Theoretical Concepts of SOFI serve as regular progress reports on global and national efforts to reach the goal set by the World Food Summit in 1996: to reduce the number of undernourished people in the world by half by the year 2015 (FAO 1999). The term undernourishment indicates the condition of not consuming, on average over an extended period of time (usually a year), an amount of dietary energy sufficient to cover the minimum require- ments for a healthy life. The calculation is an exercise in model-based statistical inference. A probability distribution model is assumed for the annual average dietary energy intake of a representative individual in the population, and its parameters are estimated on the basis of the best available data. Required data include (1) the average food consumption, (2) information on the dis- tribution of food access within the population (variability and asymmetry), (3) the demographic structure of the population (by age and sex population groups), and (4) anthropometric data. Once the probability distribution is characterized and the threshold is set, the proportion of the population that is likely suffering from chronic food deprivation, PoU, is estimated as the probability mass that falls below the threshold. Formally, the PoU expresses the probability that, by randomly select- ing one individual from the population, a person will be found to consume (on average and over a period of time) a level of food energy below the minimum required to maintain a healthy life. The operational definition of food insecurity that is embedded in this indicator is best labeled as “chronic undernourishment in a population.” The probability distribution framework is: PoU = P(x < rL ) = ∫ f (x) dx = F (x ) x < rL x x rL where PoU represents the probability that an individual randomly selected within a population is found to be undernourished; x represents the daily habitual dietary energy consumption within a year of a “representa- tive individual” in the population; rL is the daily minimum dietary energy requirements of a “representative individual” in the population; and f(x) represents the distribution of yearly habitual dietary energy consumption across individuals, or, equivalently, the probability distribution of the habitual food intake levels for the population’s “representative” individual. See figure 2.2. 55 Analyzing Food Security Using Household Survey Data Figure 2.2: Graphical Representation of the Model of the dietary energy consumption Probability density function Distribution of dietary energy consumption (lognormal or skewed normal distribution, both typically asymmetric) Prevalence of undernourishment Minimum dietary energy requirement (depends on the population’s structure by sex, age, and height) 0 2,000 4,000 6,000 8,000 10,000 Kcal/person/day The major limitations of this methodology are the following: • Though the concept relates to an individual condition, the indicator is designed to measure hunger at the level of a population. Its calculation neither depends on the possibility of collecting data on individuals, nor is the indicator intended to be used to assess the undernourishment condition of any specific individual or group of individuals in a reference population. Therefore, it does not capture possible idiosyncratic, individual problems in accessing food. However, the method can be applied to subnational popu- lations, provided subgroups are representative of the population and data pertaining to such subpopulations are available (Sibrián 2008). • By focusing on food access, it does not reflect cases of malnutrition associated with factors related to the efficient utilization of food. • Similarly, it misses the “quality” dimension of food security, for example, micronutrient deficiency and related morbidity. • Finally, by focusing on a determined period, the indicator misses the dimension of risk and vulnerability associated with the (in)stability in the access to food. Implementing the statistical concept just described to NHS data requires a set of ancillary assumptions, mostly driven by feasibility and 56 Chapter 2: Theoretical Concepts data availability constraints. The current practice at FAO is based on the following: 1. Food intake is approximated by quantities available for consumption at the household level, with no consideration of household level food waste. 2. The distribution of food available for consumption is analyzed between households. Therefore, possible unequal distribution of food within the household is ignored. 3. The minimum dietary energy requirement is defined at the popula- tion and not at the individual level. 4. An assumption needs to be made regarding the distribution of the usual intake of a typical person from the population. The first two assumptions are conditioned by data availability. Though measures of actual intake could be obtained from nutrition surveys, the vast majority of available datasets on food consumption do not allow for a precise estimation of household food waste. Similarly, data from very few and recent surveys could allow for an analysis of the intrahousehold distribution of food consumption. Assumption 3 is more substantial. It is derived from the consideration that food energy requirements can be safely defined only in terms of a dis- tribution within a given class or population group, not at the individual level (FAO/WHO/UNU 2001). This implies that classification of single individ- uals as undernourished based on a comparison of the level of habitual food intake with their individual requirements is problematic, because the latter cannot usually be estimated with sufficient precision. A minimum level of dietary energy intake that is compatible with a healthy and productive life can nevertheless be meaningfully defined in a statistical sense with reference to the representative individual in a group or class. Assumption 4 regards the statistical model used to conduct the inference at the population level. Originally, the log-normal distribution was chosen due to some desirable characteristics. For example, it is positive valued and with an elongated right tail, and the number of parameters needed for its characterization (only two: one for location and one for dispersion). Concerns were raised that the log-normal model may be not flexible enough to capture changes in the distribution of food access, especially if such changes affect the two “tails” of the distribution in opposite ways. For this 57 Analyzing Food Security Using Household Survey Data reason, in 2011, FAO’s Statistics Division explored alternative models that afford greater flexibility in representing the distribution of food consumption. Therefore, a more flexible model (the skew-normal introduced by Azzalini in 1980) has been deemed more appropriate to represent the distri- bution of habitual food consumption in the population; a major advantage compared to the log-normal distribution is that this model can now capture changes in the asymmetry of the distribution of food consumption. Estimation of PoU in ADePT ADePT-FSM provides estimates of undernourishment (derived from NHS data) at the national level, for urban and rural areas, and regions provided that these population groups have representativeness in the survey sample. MDG 1.9 Indicator of Prevalence of Food Deprivation In addition to the estimation of the PoU using NHS data, with ADePT- FSM it is possible to estimate, at the year of the survey, the value of the prevalence of undernourishment used to compute the MDG 1.9 indicator published yearly by FAO in SOFI. Indeed, one of FAO’s mandates is to provide a global estimate of the prevalence of undernourishment to monitor progress toward reduction of global hunger by half by 2015 compared to the level of 1990–92. To publish estimates of the prevalence of undernourish- ment for about 180 countries back to 1990–92, FAO is using the dietary energy supply (DES) as derived from the food balance sheets and corrected for losses at the retail level as the mean of the distribution. The MDG 1.9 indicator is computed as a three-year weighted average of the number of people undernourished using total population as weights. The PoU for a specific year used in the three-year average can be reproduced using ADePT-FSM if data24 on the DES, adjusted by losses at the retail level, CV, skewness, and MDER, are used as exogenous parameters in ADePT. Besides the MDG 1.9 indicator, ADePT-FSM also computes the PoU by region, urban, and rural areas. In these cases the values of CV and skewness remain the same while the DES adjusted by losses, and MDER are calculated by the program applying the formulas: DECSubHS DESSub = DESNat ∗ DECNatHS 58 Chapter 2: Theoretical Concepts MDERSubHS MDERSub = MDERSOFI ∗ MDERNatHS where DESSub is the individual daily dietary energy supply (corrected for losses25) at the regional or area level; DESNat is the individual daily dietary energy supply (corrected for losses26) at the national level; DECNatHS is the daily dietary energy consumption per person at the national level obtained from the NHS data; DECSubHS is the daily dietary energy consumption per person at the regional or area level obtained from the NHS data; MDERSub is the daily minimum dietary energy requirement per person at the regional or area level; MDERSOFI is the daily minimum dietary energy requirement per person at the national level as used in SOFI; MDERNatHS is the daily minimum dietary energy requirement per person at the national level obtained from the NHS data; and MDERSubHS is the daily minimum dietary energy requirement per person at the regional or area level obtained from the NHS data. Depth of the Food Deficit The depth of the food deficit indicates how many calories would be needed to lift the undernourished from their status, everything else being constant. The average intensity of food deprivation of the undernourished, estimated as the difference between the average dietary energy requirement and the average dietary energy consumption of the undernourished population, is multiplied by the number of undernourished to provide an estimate of the total food deficit in the country, which is then normalized by the total population. For each category of the population groups, the depth of the food deficit has to be estimated as the absolute difference between the average calo- ries consumed by the deprived population and the average dietary energy requirements multiplied by the prevalence of undernourishment. The average consumption of the undernourished population can be computed by taking the average consumption corresponding to the part of the distribution of dietary energy consumption below the minimum dietary energy requirement, as follows: MDER μu = ∫ 0 MDER xf (x) dx ∫ 0 f (x) dx 59 Analyzing Food Security Using Household Survey Data where mu is the average dietary energy consumption of the food-deprived population; f(x) is the density function of dietary energy consumption; and MDER is the minimum dietary energy requirement. Absolute food deficit from average dietary energy requirements (ADER) in food-deprived population ⎛ Kcal ⎞ ⎜ person ⎟ Food Deficit ⎜ ⎟ = ADER − μu ⎝ day ⎠ Depth of food deficit is then estimated as: ⎛ Kcal ⎞ ⎜ person ⎟ Depth of food deficit ⎜ ⎟ = (ADER − μu ) ∗ PoU ⎝ day ⎠ where PoU refers to the prevalence of undernourishment. 60 Chapter 2: Theoretical Concepts Annexes Annex 2A Table 2A.1: Example of Different Units of Measurement in Which Food Data Are Collected and Respective Conversion into Metric Units Unit of Unit equivalent Food item Density Food item measurement of to convert unit quantity coefficient quantity Food item in the food quantity of measurement collected in (gram/milliliter) Extra standardized in survey in survey into (A) survey (B) (C) factor (D) (E = A*B*C*D) Kilogram or liter Kilogram Flour of wheat Kilogram 1 2.5 1 1 2.5 Flour of wheat Gram 0.001 500 1 1 0.5 Beer Liter 1 3 1.007 1 3 Beer Milliliter 0.001 1,500 1.007 1 1.5 Egg Unit of 50 grams 0.05 12 1 1 0.6 Rice Bag of 5 kilograms 5 1 1 1 5 Crackers Packet of 500 grams 0.5 2 1 1 1 Tuna Pound 0.45359 0.5 1 1 0.23 Gram or milliliter Gram Flour of wheat Kilogram 1,000 2.5 1 1 2,500 Flour of wheat Gram 1 500 1 1 500 Beer Liter 1,000 3 1.007 1 3,021 Beer Milliliter 1 1,500 1.007 1 1,500 Egg Unit of 50 grams 50 12 1 1 600 Rice Bag of 5 kilograms 5,000 1 1 1 5,000 Crackers Packet of 500 grams 500 2 1 1 1,000 Tuna Pound 453.59 0.5 1 1 226.8 Gram or milliliter Pound Flour of wheat Kilogram 1,000 2.5 1 0.0022046 5.51 Flour of wheat Gram 1 500 1 0.0022046 1.1 Beer Liter 1,000 3 1.007 0.0022046 6.66 Beer Milliliter 1 1,500 1.007 0.0022046 3.31 Egg Unit of 50 grams 50 12 1 0.0022046 1.32 Rice Bag of 5 kilograms 5,000 1 1 0.0022046 11.02 Crackers Packet of 500 grams 500 2 1 0.0022046 2.2 Tuna Pound 453.59 0.5 1 0.0022046 0.5 61 62 Annex 2B Procedure 1: Estimation of Nutrients and Calories from Food Quantities Table 2B.1: Procedure 1: Steps 3 to 4 Conversion factors from food After applying conversion factors from composition table food composition table (g/person/day) Food Reference Edible quantity as Refuse House- period quantity Available Available acquired factor hold (number (g/person/ carbo- carbo- Item (grams) (%) size of days) day) Protein Fats hydrates Fibers Alcohol Protein Fats hydrates Fibers Alcohol Rice 9,660 0 3 14 230.0 6.50 0.52 76.35 2.80 0.00 14.95 1.20 175.61 6.44 0.00 Macaroni 1,764 0 3 14 42.0 14.63 1.40 75.03 8.30 0.00 6.14 0.59 31.51 3.49 0.00 Eggs 477 12 3 14 10.0 12.56 9.51 0.72 0.00 0.00 1.26 0.95 0.07 0.00 0.00 Milk 3,738 0 3 14 89.0 3.15 3.25 4.80 0.00 0.00 2.80 2.89 4.27 0.00 0.00 Yogurt 504 0 3 14 12.0 3.47 3.25 4.66 0.00 0.00 0.42 0.39 0.56 0.00 0.00 Potatoes 2,240 25 3 14 40.0 1.68 0.10 13.31 2.40 0.00 0.67 0.04 5.32 0.96 0.00 Onions 560 10 3 14 12.0 1.10 0.10 7.64 1.70 0.00 0.13 0.01 0.92 0.20 0.00 Garlic 29 13 3 14 0.6 6.36 0.50 30.96 2.10 0.00 0.04 0.00 0.19 0.01 0.00 Tomatoes 1,615 9 3 14 35.0 0.98 0.26 2.91 0.70 0.00 0.34 0.09 1.02 0.25 0.00 Oranges 1,323 27 3 14 23.0 0.94 0.12 9.35 2.40 0.00 0.22 0.03 2.15 0.55 0.00 Fresh Fish 4,141 29 3 14 70.0 18.60 13.89 0.00 0.00 0.00 13.02 9.72 0.00 0.00 0.00 Chicken 5,904 31 3 14 97.0 18.33 14.83 0.13 0.00 0.00 17.78 14.39 0.13 0.00 0.00 Minced 1,470 0 3 14 35.0 19.82 17.88 0.00 0.00 0.00 6.94 6.26 0.00 0.00 0.00 meat Salt 25 0 3 14 0.6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sugar 210 0 3 14 5.0 0.00 0.00 99.98 0.00 0.00 0.00 0.00 5.00 0.00 0.00 Oil 840 0 3 14 20.0 0.00 100.00 0.00 0.00 0.00 0.00 20.00 0.00 0.00 0.00 Tea 42 0 3 14 1.0 0.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 beverage Beer 1,260 0 3 14 30.0 0.46 0.00 3.55 0.00 3.90 0.14 0.00 1.07 0.00 1.17 outside home Restaurant .. .. 3 14 .. .. .. .. .. .. .. .. .. .. .. meal Table 2B.2: Procedure 1: Steps 5 to 6 From food items having food quantities expressed Calories from food in standard units and valid nutrient conversion items having valid factors (g/person/day) Calories (kcal/person/day) from the following: quantities and nutrient Available Available conversion factors HH ID Protein Fats carbohydrates Fiber Alcohol Protein Fats carbohydrates Fiber Alcohol (kcal/person/day) B*4 C*9 D*4 E*2 F*7 Kcal = G + H + I + J + K B C D E F G H I J K L 1 74 63 530 28 0 296 566 2,119 57 0 3,038 2 28 29 304 51 0 110 258 1,215 103 0 1,686 3 72 83 646 100 0 288 751 2,584 199 0 3,822 4 29 45 332 38 0 115 403 1,328 76 0 1,922 5 15 52 157 22 0 60 466 629 43 0 1,199 6 19 50 245 17 0 77 447 981 34 0 1,539 7 68 58 274 38 0 270 518 1,095 75 0 1,959 8 35 62 479 42 0 138 560 1,917 84 0 2,699 9 41 37 203 38 0 164 329 813 76 0 1,382 10 67 103 448 25 0 267 924 1,790 51 0 3,032 11 33 84 248 44 0 134 757 993 87 0 1,971 12 42 62 354 28 0 168 556 1,415 56 0 2,194 13 21 39 329 72 0 83 349 1,317 144 0 1,893 14 31 57 462 32 0 124 509 1,848 63 0 2,543 15 30 87 537 31 0 122 785 2,148 62 0 3,117 16 39 57 534 56 0 154 515 2,137 111 0 2,918 17 44 49 276 21 0 176 438 1,104 42 0 1,759 18 35 46 277 84 0 140 411 1,106 168 0 1,825 19 25 57 381 34 0 99 513 1,522 67 0 2,202 63 64 Annex 2C Procedure 2: Estimation of Nutrients and Calories from Food Expenditure Table 2C.1: Procedure 2: Steps 1 to 2 Food items having food quantities expressed in standard units and valid nutrient conversion factors Average calorie Proportion of Proportion of Calories Food Household price ($/Kcal) of Food expenditure Estimated calories from calories from (kcal/ expenditure calorie Region 1, urban associated to missing protein fats person/ (lcu/person/ price Income area and income missing calories calories (Kcal/ HH ID day) 100*G/L 100*H/L day) (lc/kcal) Region Area quintile quintile 2 (lcu/person/day) person/day) L M N O 1 3,038 10 19 2.4 0.0008 1 Urban 2 0.00083 1.8 2,147 2 1,686 7 15 1.2 0.0007 1 Urban 2 0.00083 1.3 1,604 3 4,947 8 20 3.3 0.0009 1 Urban 2 0.00083 1.2 1,448 4 1,922 6 21 1.2 0.0006 1 Urban 2 0.00083 0.8 1,001 5 1,199 5 39 1.2 0.0010 1 Urban 2 0.00083 1.1 1,339 6 1,539 5 29 0.7 0.0005 1 Urban 2 0.00083 0.9 1,122 7 1,959 14 26 2.6 0.0013 1 Urban 2 0.00083 0.4 531 8 2,699 5 21 6.6 0.0024 1 Urban 2 0.00083 0.9 1,074 9 1,382 12 24 2.3 0.0016 1 Urban 2 0.00083 0.5 639 10 3,032 9 30 2.1 0.0007 1 Urban 2 0.00083 0.7 808 11 1,971 7 38 1.4 0.0007 1 Urban 2 0.00083 1.3 1,604 12 2,194 8 25 1.4 0.0006 1 Urban 2 0.00083 0.7 808 13 1,893 4 18 1.4 0.0008 1 Urban 2 0.00083 0.5 639 14 2,543 5 20 1.2 0.0005 1 Urban 2 0.00083 0.6 663 15 3,117 4 25 1.6 0.0005 1 Urban 2 0.00083 0.5 639 16 2,918 5 18 1.5 0.0005 1 Urban 2 0.00083 0.4 434 17 1,759 10 25 1.7 0.0010 1 Urban 2 0.00083 0.5 627 18 1,825 8 23 1.6 0.0009 1 Urban 2 0.00083 0.5 603 19 2,202 4 23 1.0 0.0005 1 Urban 2 0.00083 0.5 603 Chapter 2: Theoretical Concepts Table 2C.2: Procedure 2: Steps 3 to 5 Estimated missing calories Estimated missing calories (kcal/person/day) from Estimated missing grams (person/day) (kcal/person/day) from carbohydrates (including Carbohydrates (including HH ID Protein Fats fiber and alcohol) Protein Fats fiber and alcohol) P Q R S T U O*M/100 O*N/100 O−P−Q P/4 Q/9 R/4 1 209 400 1,538 52 44 384 2 105 246 1,254 26 27 313 3 109 284 1,054 27 32 264 4 60 210 731 15 23 183 5 67 521 751 17 58 188 6 56 326 740 14 36 185 7 73 140 317 18 16 79 8 55 223 796 14 25 199 9 76 152 411 19 17 103 10 71 246 491 18 27 123 11 109 617 879 27 69 220 12 62 205 542 15 23 135 13 28 118 494 7 13 123 14 32 133 499 8 15 125 15 25 161 453 6 18 113 16 23 77 335 6 9 84 17 63 156 409 16 17 102 18 46 136 421 12 15 105 19 27 141 435 7 16 109 Annex 2D Table 2D.1: Example of Calculation of Food and Total Price Temporal Deflators Price indexes Deflators Consumer Food monetary Total consumption Food price price index values deflator and income deflator Month Year index (FPI) (CPI) (FPI/average FPI) (CPI/average CPI) 9 2004 121.91 122.44 0.962 0.958 10 2004 123.04 123.80 0.971 0.968 11 2004 123.74 124.73 0.977 0.976 12 2004 123.85 124.62 0.978 0.975 1 2005 124.61 125.30 0.984 0.980 2 2005 125.54 126.35 0.991 0.988 3 2005 126.61 127.69 0.999 0.999 4 2005 127.77 129.14 1.009 1.010 5 2005 129.79 131.33 1.024 1.027 6 2005 131.75 134.01 1.040 1.048 7 2005 131.56 133.66 1.038 1.046 8 2005 130.10 131.02 1.027 1.025 Average 126.69 127.84 65 Analyzing Food Security Using Household Survey Data Annex 2E Table 2E.1: Estimation of the Coefficient of Variation of Dietary Energy Consumption Due to Other Factors CV2x/r = CV2PAL + CV2wh + CV2err Where Ln refers to Neperian Logarithm, and normsinv(p) is the function which returns the value Z such that, with probability p, a standard normal random variable takes on a value that is less than or equal to Z. (CVPAL) Coefficient of variation of dietary energy requirement due to physical activity level (PAL) (Ln(2.4)−Ln(1.4))/(normsinv(0.95) − Standard deviation of PAL − Maximum (MXSDP) normsinv(0.05)) (Ln(2.4)−Ln(1.4))/(normsinv(0.975) − Standard deviation of PAL − Minimum (MNSDP) normsinv(0.025)) 2 CV of PAL − Maximum = (e MXSDP ) − 1 2 CV of PAL − Minimum = (e MNSDP ) − 1 If the proportion of labor force in the primary sector is more than 49% CV of dietary energy requirement due to PAL Maximum CV of PAL * proportion of labor force in the primary sector If the proportion of labor force in the primary sector is less than 50% CV of dietary energy requirement due to PAL Minimum CV of PAL * proportion of labor force in the primary sector (CVwh) Coefficient of variation of dietary energy requirement due to body weight 2 Weight in Kg using the 5th ⎛ height ⎞ BMI5th * ⎜ ⎝ 100 ⎟ percentile of the BMI and the median height in cm (W5th) ⎠ For each sex/age 2 population group Weight in Kg using the 95th ⎛ height ⎞ BMI95th * ⎜ ⎝ 100 ⎟ percentile of the BMI and the median height in cm (W95th) ⎠ Variance of the distribution of (Ln(W95th)−Ln(W5th))/(normsinv(0.95)− body weight normsinv(0.05))2 Where i refers to the sex/age group; f is the proportion of 31 ∑(f * δ ) the population belonging to Population weighted average value of the 2 i i the ith group; and d is the variance (VARBW) standard deviation of the i −1 distribution of body weight of the ith group. CV of dietary energy requirement due to body weight 2 (e VARBW ) − 1 (CVerr) Coefficient of cariation due to error CV of dietary energy requirement due to error 0.08 66 Chapter 2: Theoretical Concepts Annex 2F Table 2F.1: Estimation of the Minimum Dietary Energy Requirement Where TEE refers to total energy expenditure (kcal); U5MR refers to under-five mortality rate; the probability per 1,000 that a newborn baby will die before reaching age 5, if subject to current age-specific mortality rates; KG refers to BMI * (height/100)^2; height is in cm; WG refers to weight gain for age (g/day); ERwg is the energy required per gram of weight gain (kcal); and PAL refers to 1.55 for sedentary physical activity. Years: Less than 1/Class group: 1 Note Country with high children undernutrition and infection (U5MR proxy high) Male and if U5MR > 10‰ TEE = (−99.4 + 88.6 * KG) + 2 * WG * ERwg 50th percentile for female Country with low children undernutrition and infection (U5MR low) BMI and WG if U5MR <= 10‰ TEE = (−99.4 + 88.6 * KG) + WG * ERwg Years: From 1 to 1.9/Class group: 2 Note Country with high children undernutrition and infection (U5MR proxy high) Male if U5MR > 10‰ TEE = 0.93 * (310.2 + 63.3 * KG − 0.263 * KG2) + 2 * WG * ERwg Female if U5MR > 10‰ TEE = 0.93 * (263.4 + 65.3 * KG − 0.454 * KG2) + 2 * WG * ERwg 50th percentile for Country with low children undernutrition and infection (U5MR low) BMI and WG Male if U5MR <= 10‰ TEE = 0.93 * (310.2 + 63.3 * KG − 0.263 * KG2) + WG * ERwg Female if U5MR <= 10‰ TEE = 0.93 * (263.4 + 65.3 * KG − 0.454 * KG2) + WG * ERwg Years: From 2 to 9.9/Class group: From 3 to 10 Note Male TEE = (310.2 + 63.3 * KG − 0.263 * KG2) + WG * ERwg 50th percentile for BMI and WG Female TEE = (263.4 + 65.3 * KG − 0.454 * KG2) + WG * ERwg Years: From 10 to 17.9/Class group: From 11 to 18 Note Male TEE = 0.85 * (310.2 + 63.3 KG − 0.263 KG2) + WG * ERwg 5th percentile for BMI and WG Female TEE = 0.85 * (263.4 + 65.3 KG − 0.454 KG2) + WG * ERwg Years: From 18 to 29.9/Class group: From 19 to 22 Note Male TEE = PAL * (692.2 + 15.057 KG) 5th percentile for BMI Female TEE = PAL * (486.6 + 14.818 KG) Years: From 30 to 59.9/Class group: From 23 to 28 Note Male TEE = PAL * (873.1 + 11.472 KG) 5th percentile for BMI Female TEE = PAL * (845.6 + 8.126 KG) Years: More than 59.9/Class group: From 29 to 31 Note Male TEE = PAL * (587.7 + 11.711 KG) 5th percentile for BMI Female TEE = PAL * (658.5 + 9.082 KG) Pregnancy allowance Energy extra = Birth ratio * 210 67 Analyzing Food Security Using Household Survey Data Notes 1. Expressed as per person per day. 2. Further information in forthcoming Assessment of the Reliability and Relevance of the Food Data Collected in National Household Consumption and Expenditure Surveys. 3. Note that tobacco and narcotics are not considered as food and are excluded from the analysis. 4. For more information on food matching, see the FAO/INFOODS Guidelines for Food Matching, Version 1.2 (2012). The Guidelines for Food Matching include a quality scheme for the food matches. They should be recorded and used in the assessment of the quality of the estimated energy and nutrient intake and food security estimations. Food compo- sition data are not just values that can be used without previous knowl- edge on food composition. If done so, there is a high risk of applying the data wrongly. Therefore, it is highly recommended to complete the FAO/INFOODS e-Learning Course on Food Composition Data (avail- able free-of-charge at the INFOODS website). 5. For converting amounts of total foods (including inedible part) to EP, see the FAO/INFOODS Guidelines for Converting Units, Denominators and Expressions Version 1.0 (2012). 6. Density values for liquids or semiliquids can be found in the FAO/ INFOODS Density Database Version 2.0: http://www.fao.org/infoods /infoods/tables-and-databases/faoinfoods-databases/en/. 7. If the data were corrected for outliers the median should be close to the mean. 8. Density values of solids to be applied in this analysis are equal to 1. 9. Guidelines for Converting Units, Denominators and Expressions is available at http://www.fao.org/infoods/infoods/standards-guidelines/en/. 10. Unless it is a nutritional dietary survey that measures direct individual food intake. 11. For instance, ascorbic acid enhances iron absorption. 12. When estimating the available micronutrients for consumption, ADePT-FSM excludes from the analysis those provided by food con- sumed away from home. 13. Heme iron accounts for a minor part of total iron intake, especially in developing countries where the consumption of meat and fish is usu- ally low. Thus, nonheme iron is the main source of dietary iron intake 68 Chapter 2: Theoretical Concepts (Hallberg 1981). Still, not all the nonheme iron consumed is absorbed by the human body because this process is influenced either positively or negatively by many factors, such as the presence of certain substances in the diet. 14. The indispensable amino acids are not present in all food items. For instance, lysine, threonine, and tryptophan are marginal in cereals. Whereas the former (lysine) is lacking especially in wheat, the latter (tryptophan) is lacking in maize. Methionine and cysteine, which are equally abundant in cereal and animal proteins, are marginal in legume proteins (WHO 2007). 15. When estimating the available amino acids for consumption, ADePT- FSM excludes from the analysis those provided by food consumed away from home. 16. As the log-normal distribution is fully characterized by only two param- eters (μ and σ), the skewness coefficient is a simple monotonic transfor- 2 2 mation of the standard deviation SK = (e σ + 2) e σ − 1 , and it can also be conveniently expressed as a function of the coefficient of variation, according to the formula skewness = (CV2 + 3) × CV. 17. See WHO Child Growth Standards: BMI for age tables. WHO. Geneva. http://www.who.int/childgrowth/standards/bmi_for_age/en /index.html. In addition, see WHO 2007, WHO Growth reference 5–19 years: BMI for age tables. WHO. Geneva. http://www.who.int /growthref/who2007_bmi_for_age/en/index.html. 18. For further information see WHO (1983). 19. For further information see FAO/WHO/UNU (2001). 20. For further information see FAO/WHO/UNU (2001). 21. The sum of the needs is determined by using the average of needs of similar individuals. 22. Based on a publication FAO/WHO/UNU (1985). 23. The prevalence of inadequacy depends on the shape and variation of the usual intake distribution, not on mean intake. See NAS (2000). 24. See the FAO statistics website: http://www.fao.org/economic/ess/ess-fs /fs-methods/adept-fsn/en/. 25. The losses are at the retail level and exclude losses within house- holds. 26. Based on a publication FAO/WHO/UNU (1985). 69 Analyzing Food Security Using Household Survey Data References CFS (Committee on World Food Security). 2012. “Coming to Terms with Terminology.” Final report 2012/39, Rome, October 15–20. FAO (Food and Agriculture Organization). 1999. The State of Food Insecurity in the World. Rome: FAO. http://www.fao.org/docrep/007 /x3114e/x3114e00.htm. ———. 2002. “Food Energy: Methods of Analysis and Conversion Factors.” FAO Food and Nutrition Paper 77. FAO, Rome. http://www.fao.org /docrep/006/Y5022E/Y5022E00.HTM. ———. 2002. “Measurement and Assessment of Food Deprivation and Undernutrition.” Proceedings of the Food and Agriculture Organization symposium “International Scientific Symposium on Measurement and Assessment of Food Deprivation and Undernutrition,” Rome, June 28–30. http://www.fao.org/docrep/005/Y4249E/y4249e00.htm. FAO, and INFOODS (International Network of Food Data Systems). 2011. E-Learning Course on Food Composition Data. Rome: FAO. http:// www.fao.org/infoods/infoods/training/en/. ———. 2012. Guidelines for Converting Units, Denominators and Expressions Version 1.0. Rome: FAO. http://www.fao.org/infoods/infoods/standards -guidelines/en/. ———. 2012. Guidelines for Food Matching Version 1.2. Rome: FAO. http:// www.fao.org/infoods/infoods/standards-guidelines/en/. FAO, and WHO (World Health Organization). 1970. Requirements of Ascorbic Acid, Vitamin D, Vitamin B12, Folate, and Iron. WHO Technical Report Series No. 452 and FAO Nutrition Meetings Report Series No. 47. Geneva: WHO. ———. 2004. Vitamin and Mineral Requirements in Human Nutrition, 2nd ed. Rome: FAO. FAO, WHO, and UNU (United Nations University). 1985. Energy and Protein Requirements. Technical Report Series 724. Geneva: WHO. http://www.fao.org/docrep/003/aa040e/aa040e00.HTM. ———. 2001. Human Energy Requirements. Report of a joint FAO/WHO/ UNU Expert Consultation, Rome, October 17–24. http://www.fao.org /docrep/007/y5686e/y5686e00.htm. Hallberg, L. 1981. “Bioavailability of Dietary Iron in Man.” Annual Review of Nutrition (1): 123–47. 70 Chapter 2: Theoretical Concepts NAS (National Academy of Sciences). 2000. Dietary Reference Intakes: Applications in Dietary Assessment. Washington, DC: National Academy Press. http://www.nap.edu/catalog/9956.html. ———. 2001. Dietary Reference Intake. Food and Nutrition Board, Institute of Medicine. Washington, DC: National Academy Press. Sibrián, R. L., ed. 2008. Deriving Food Security Information from National Household Budget Surveys: Experiences, Achievements, Challenges. Rome: FAO. http://www.fao.org/docrep/011/i0430e/i0430e00.htm. USHHS (U.S. Department of Health and Human Services), and USDA (U.S. Department of Agriculture). 2005. Dietary Guidelines for Americans 2005, 6th ed. Washington, DC: U.S. Government Printing Office. WHO. 1983. Measuring Change in Nutritional Status. Geneva: WHO. ———. 2003. Diet, Nutrition and the Prevention of Chronic Diseases. Report of a Joint WHO and FAO Expert Consultation, Geneva, January 28– February 1, WHO Technical Report Series 961, Geneva: WHO. ———. 2007. Protein and Amino Acid Requirements in Human Nutrition. Report of a Joint WHO, FAO, and UNU Expert Consultation, WHO Technical Report Series 935, Geneva: WHO. Bibliography Cafiero, C. 2011. “Measuring Food Insecurity: Meaningful Concepts and Indicators for Evidence-Based Policy Making.” Paper prepared for the Committee on World Food Security (CFS) conference “Round Table on Monitoring Food Security,” FAO, Rome, September 12–13. FAO (Food and Agriculture Organization). 1996. The Sixth World Food Survey. Rome: FAO. Naken, L. 2002. “FAO Methodology for Estimating the Prevalence of Undernourishment.” Paper presented at the International Scientific Symposium “Measurement and Assessment of Food Deprivation and Undernutrition,” Rome, June 26–28. http://www.fao.org/docrep/005 /Y4249E/y4249e00.htm. ———. 2007. “The Probability Distribution Framework for Estimating the Prevalence of Undernourishment: Exploding the Myth of the Bivariate Distribution.” FAO Statistics Division Working Paper Series 71 Analyzing Food Security Using Household Survey Data ESS/ESSG/009e, FAO, Rome. http://www.fao.org/fileadmin/templates /ess/documents/food_security_statistics/working_paper_series/WP009e .pdf. Sibrián, R. 2007. “Indicators on Food Deprivation and Income Deprivation at National and Sub-national Levels: Methodological Issues.” Paper pre- sented at the FAO Statistics Division, “Fourth International Conference on Agriculture Statistics (ICAS-4),” Beijing, October 25. Sibrián, R., S. Ramasawmy, and J. Mernies. 2008. “Measuring Hunger at Subnational Levels from Household Surveys Using the FAO Approach.” FAO Statistics Division Working Paper Series ESS/ESSA/005e. FAO, Rome. http://www.fao.org/fileadmin/templates/ess/documents/food _security_statistics/working_paper_series/WP005e.pdf. Sukhatme, P. V. 1961. “The World’s Hunger and Future Needs in Food Supplies.” Journal of the Royal Statistical Society. Series A (124): 463–525. 72 Chapter 3 Guide to Output Tables Ana Moltedo, Chiara Brunelli, Nathalie Troubat Introduction Food security as a multidimensional phenomenon covers four dimensions: food availability, food access, food utilization, and vulnerability to food inse- curity. The ADePT-Food Security Module produces a suite of food security indicators that encompasses some of these dimensions. To better understand food consumption patterns and better target groups of a population in a situ- ation of food insecurity, indicators are disaggregated by the following: • Socioeconomic, demographic, and geographical characteristics of the household such as region, household size, gender, level of education, and occupation of the head of the household (28 Excel tables) • Groups of food commodities1 (22 Excel tables) • Food commodity (15 Excel tables) These indicators include statistics on dietary energy, value of food expenditures, and cost of the diet, as well as statistics related to the compo- sition of the food available in terms of macro- and micronutrients, amino acids, and vitamins. The purpose of this chapter is to present each output table produced by ADePT-FSM and to provide a brief interpretation of the indicators that are displayed in the tables. The same indicator may appear in different tables depending at which level of disaggregation (by population group, food commodity group, or food commodity) it is shown. To avoid repeating the interpretation of the indicator after each table, all indicators are described in the glossary of indicators that follows the section presenting 73 Analyzing Food Security Using Household Survey Data the output tables. Indicators are presented in alphabetical order to assist the reader when using the glossary. Output Tables Food Consumption (Dietary Energy, Macronutrients, and Monetary Values) Disaggregated by Population Group: Tables 1.1 to 1.14 Table 1.1: Prevalence of Undernourishment Using Mainly Survey Data This table shows estimates of the prevalence of undernourishment2 (PoU), using the methodology of the Food and Agriculture Organization (FAO), along with all the parameters used for its computation (i.e., dietary energy con- sumption, the minimum dietary energy requirement, coefficient of varia- tion, and skewness). The PoU is computed at national, regional, and urban/ rural levels under the assumption that the survey sample is representative for such geographic domains. If this is the case, the total number of people undernourished calculated as the sum from each region or from urban/rural is expected to be close to the one at the national level. The minimum and average dietary energy requirements at the national3 level are those used to estimate the Millennium Development Goal (MDG) Table 1.1: Prevalence of Undernourishment Using Mainly Survey Data Average Coefficient Minimum Average Depth dietary of variation Skewness dietary Preva- dietary of food energy of dietary of dietary energy lence of energy deficit consumption energy con- energy requirement under- requirement (kcal/ Population (kcal/person/ sumption consump- (kcal/person/ nourish- (kcal/person/ person/ (‘000s) day) (%) tion day) ment (%) day) day) Total 31,906.9 2,199.5 29.07 0.90 1,694.0 23.8 2,108.0 146.1 Area Capital city 1,845.3 2,063.6 39.13 1.23 1,784.6 N/A 2,258.2 N/A Other urban 4,406.6 2,179.3 32.18 1.00 1,728.5 N/A 2,168.3 N/A areas Rural areas 25,655.0 2,212.7 29.64 0.92 1,681.6 23.1 2,086.8 138.8 Region Region1 1,778.4 2,401.1 23.94 0.73 1,665.9 6.9 2,061.6 37.4 Region2 2,031.7 1,947.9 28.59 0.88 1,697.4 38.8 2,108.8 261.3 Region3 1,178.8 2,053.4 31.86 0.99 1,712.5 37.1 2,136.2 256.5 Region4 1,647.1 2,162.7 29.20 0.90 1,723.7 28.2 2,148.5 182.3 Region5 1,652.8 2,269.9 30.21 0.93 1,718.8 23.7 2,151.1 149.5 Region6 761.4 1,997.6 33.75 1.05 1,712.3 N/A 2,143.4 N/A 74 Chapter 3: Guide to Output Tables 1.9 indicator and depth of food deficit, respectively, published with The State of Food Insecurity in the world (SOFI). The statistics of dietary energy consumption, coefficient of variation, and skewness are derived from the survey data. When a N/A value appears, this means the value of skewness is higher than 1. A skewness higher than 1 indicates that there is a large number of food quantity outliers (or an excessive variability in the consumption/ acquisition distribution), and so a more careful food consumption data analysis has to be done. Table 1.2: Prevalence of Undernourishment Using Mainly External Sources This table shows estimates of the prevalence of undernourishment4, using the FAO methodology, along with all the parameters used for its compu- tation. The PoU is computed at the national, regional, and urban/rural levels under the assumption that the survey sample is representative for such geographic domains. If this is the case, the total number of people undernourished calculated as the sum from each region or from urban/rural areas is expected to be close to the one at the national level. When a N/A value appears, this means the value of skewness is higher than 1. A skewness higher than 1 indicates that there is a large number Table 1.2: Prevalence of Undernourishment Using Mainly External Sources Minimum Dietary Coefficient dietary Average Depth energy of variation Skewness energy Preva- dietary of food supply of dietary of dietary require- lence of energy deficit adjusted for energy energy ment (kcal/ under- requirement (kcal/ Population losses (kcal/ consump- consump- person/ nourish- (kcal/ person/ (‘000s) person/day) tion (%) tion day) ment (%) person/day) day) MDG 1.9 1,970.5 32.17 0.99 1,694.0 40.7 2,108.0 285.7 indicator (SOFI) Total 31,906.9 1,970.5 29.07 0.90 1,694.0 37.6 2,108.0 252.6 Area Capital city 1,845.3 1,848.7 39.13 1.23 1,784.6 N/A 2,258.2 N/A Other urban areas 4,406.6 1,952.4 32.18 1.00 1,728.5 N/A 2,168.3 N/A Rural areas 25,655.0 1,982.4 29.64 0.92 1,681.6 36.6 2,086.8 240.9 Region Region1 1,778.4 2,151.1 23.94 0.73 1,665.9 16.8 2,061.6 95.2 Region2 2,031.7 1,745.1 28.59 0.88 1,697.4 53.3 2,108.8 391.7 Region3 1,178.8 1,839.6 31.86 0.99 1,712.5 49.5 2,136.2 376.1 Region4 1,647.1 1,937.5 29.20 0.90 1,723.7 42.1 2,148.5 298.3 Region5 1,652.8 2,033.5 30.21 0.93 1,718.8 36.8 2,151.1 255.5 Region6 761.4 1,789.6 33.75 1.05 1,712.3 N/A 2,143.4 N/A 75 Analyzing Food Security Using Household Survey Data of food quantity outliers (or an excessive variability in the consumption/ acquisition distribution), and so a more careful food consumption data analysis has to be done. The first row of the table shows the MDG 1.9 indicator as published in the latest edition of SOFI corresponding to the same year of the survey and the parameters used (published in the Statistics Division of the FAO web- site5). These are the parameters: • Average dietary energy supply • Losses that occurred at the retail level • Coefficient of variation of dietary energy consumption • Skewness of dietary energy consumption • Average dietary energy requirement • Minimum dietary energy requirement To estimate the statistics at the subnational level (regions and urban/ rural areas): • The coefficient of variation and skewness are derived from the survey data. • The region- and area-specific dietary energy supplies adjusted for losses are estimated following the relationship of the dietary energy consump- tion in the subpopulation group with respect to the national calories. • The region- and area-specific minimum and average dietary energy requirements are estimated following the relationship of the require- ments of the subpopulation group with respect to the calorie require- ments as from SOFI. Example for area of residence: First, the software computes the dietary energy consumption (DEC) at the national, urban, and rural levels using national household survey (NHS) data. Then it calculates the ratio between (1) urban and national DEC and (2) rural and national DEC; the ratios are applied to the national dietary energy supply (DES), adjusted for losses to compute the DES at the urban and rural levels. The software estimates the national DES adjusted for losses from the two exogenous parameters, which are the DES as from the food balance sheets (FBS) and the share of losses at the retail level. Second, the software computes the minimum dietary energy requirement (MDER) at the national, urban, and rural levels using the NHS data. Then 76 Chapter 3: Guide to Output Tables it calculates the ratio between (1) urban and national MDER and (2) rural and national MDER. The ratios are applied to the minimum national dietary energy requirement (as in SOFI and introduced as an exogenous parameter) to compute the MDER at the urban and rural levels. The differ- ence in value between the MDER as in SOFI and the one computed from the NHS data is due to a different structure of the population used by sex and age groups. Also, because the reference height values by sex and age classes for the calculation of the MDER are taken from other sources (e.g., demographic health surveys), differences between heights recorded from the survey and the reference height values can lead to differences in the MDER. The table below shows an example of how SOFI parameters are calcu- lated at the subnational level using information from the survey. Dietary energy Minimum consumption dietary energy Minimum (DEC) derived Ratio of Dietary energy requirement Ratio of dietary energy from the survey subnational supply adjusted (MDER) derived subnational requirement (kcal/person/ DEC/national for losses (kcal/ from the survey MDER/national (kcal/person/ day) DEC person/day) (kcal/person/day) MDER day) National 2084 1.000 2360 (*) 1824 1.000 1830 (*) Urban 2130 1.022 2412 1805 0.992 1815 Rural 2035 0.976 2305 1834 1.008 1844 Note: (*) used to estimate MDG 1.9. Table 1.3: Selected Food Consumption Statistics by Population Groups This table shows some food consumption statistics expressed in dietary energy and monetary values, as well as the average unit value cost of 1,000 kcal and the minimum dietary energy required of a representative individual in the population group. The minimum energy requirement is computed using the age/sex structure of the population as derived from the survey. See glossary and chapter 2 for more details on the calculation of the minimum dietary energy consumption. Table 1.4: Selected Food Consumption Statistics of Population Groups by Income Deciles This table presents average values for food and total consumption as well as income disaggregated by income deciles. The first decile refers to the poorest group of the population while the tenth refers to the wealthiest one. As poor populations have lower values of income and consumption as compared to rich ones, the values of the statistics shown in this table are expected to increase from the first to the last income group. 77 78 Table 1.3: Selected Food Consumption Statistics by Population Groups Average Minimum Average food Average Average total Number of Average dietary energy dietary energy consumption in dietary energy consumption in sampled household consumption requirement monetary value unit value monetary value households size (kcal/person/day) (kcal/person/day) (LCU/person/day) (LCU/1000 kcals) (LCU/person/day) Total 22175 4.9 2199 1691 220.97 100.46 352.71 Quintiles of income Lowest quintile 2714 6.6 1596 1640 101.64 63.68 142.49 2 3236 5.6 2043 1676 166.18 81.33 241.69 3 4110 5.0 2250 1690 218.23 97.00 333.62 4 5002 4.2 2604 1719 299.82 115.12 480.80 Highest quintile 7113 3.4 3051 1782 448.89 147.13 812.53 Area Capital city 1225 4.3 2064 1782 395.73 191.77 726.83 Other urban areas 13382 4.5 2179 1726 291.85 133.92 494.62 Rural areas 7568 5.1 2213 1679 196.22 88.68 301.42 Household size One person 2503 1.0 3667 2022 529.04 144.28 924.86 Between 2 and 3 people 5676 2.6 2667 1784 307.82 115.40 501.16 Between 4 and 5 people 6226 4.5 2253 1672 237.00 105.19 380.89 Between 6 and 7 people 4141 6.4 2097 1656 198.68 94.76 309.84 More than 7 3629 10.3 1951 1677 170.34 87.30 266.79 Gender of the household head Male 16751 5.2 2207 1700 219.40 99.41 351.14 Female 5424 4.0 2167 1655 227.75 105.10 359.49 Age of the household head Less than 35 7133 3.9 2310 1635 240.19 103.99 388.01 Between 35 and 45 6728 5.3 2155 1681 222.74 103.37 360.31 Between 46 and 60 5433 5.8 2206 1745 212.94 96.55 340.93 More than 60 2881 5.0 2089 1708 198.75 95.13 296.83 Table 1.4: Selected Food Consumption Statistics of Population Groups by Income Deciles Average Average Average food Average total Average Number of Average dietary energy dietary energy consumption in consumption in income sampled household Estimated consumption unit value monetary value monetary value (LCU/ households size population (kcal/person/day) (LCU/1000kcals) (LCU/person/day) (LCU/person/day) person/day) Total 1 1344 7.2 4624421 1439 59.03 84.96 115.49 122.00 2 1370 5.9 3851732 1784 68.18 121.66 174.91 188.26 3 1634 5.9 3825344 2050 75.83 155.46 223.84 240.31 4 1602 5.2 3384098 2035 87.59 178.29 261.86 294.60 5 1858 5.1 3293406 2235 90.56 202.39 306.42 355.73 6 2252 4.9 3130354 2265 103.69 234.89 362.24 436.89 7 2311 4.4 2858068 2557 107.09 273.83 437.51 539.38 8 2691 4.0 2569153 2657 123.72 328.73 528.95 698.55 9 3064 3.6 2335255 2905 134.94 391.95 674.51 1001.65 10 4049 3.2 2035056 3219 159.74 514.23 970.90 2927.19 Area Capital city 1 7 3.7 18622 778 151.03 117.54 126.09 127.09 2 29 6.5 67083 983 121.82 119.69 174.57 185.05 3 36 5.9 79524 1109 140.47 155.79 236.00 243.00 4 37 7.3 78240 1345 146.93 197.66 290.41 300.12 5 65 6.0 198849 1316 152.92 201.27 344.05 357.19 6 86 5.6 184519 1560 162.38 253.34 414.75 439.84 7 132 4.7 201667 1744 167.35 291.78 499.91 540.86 8 174 4.4 279204 2050 185.18 379.69 659.03 720.97 9 277 4.0 361336 2422 196.12 474.99 859.09 1030.48 10 382 3.1 376271 3150 226.89 714.69 1449.77 2999.53 Other urban areas 1 471 6.7 243508 1244 70.35 87.52 119.72 126.87 2 592 6.4 282113 1413 92.55 130.81 180.39 189.23 3 792 5.8 325095 1569 96.73 151.80 221.52 239.95 4 825 5.7 328223 1699 104.03 176.79 267.95 296.18 5 1033 5.3 377364 1829 110.06 201.27 319.24 356.85 6 1394 5.2 550613 1879 124.83 234.53 371.24 436.92 7 1460 4.6 538197 2290 119.43 273.46 459.15 544.31 8 1773 4.4 636163 2528 133.78 338.14 567.13 708.65 9 2119 3.5 515264 2679 157.24 421.25 724.13 1000.35 10 2923 3.1 610035 3095 177.98 550.86 1038.77 3228.57 79 Analyzing Food Security Using Household Survey Data Particularly in this table, the number of sampled households used to produce the estimates by income deciles has to be analyzed to assess the reliability of the estimates. For instance, in table 1.4 the estimates of the first income decile group in the capital city are considered unreliable due to the low number of households used to derive the estimates (7 households). In general, a statistic obtained with data from fewer than 30 households is considered unreliable. Table 1.5: Shares of Food Consumption by Food Sources (in Dietary Energy) Households acquire food in different ways, the main ones being through purchases, own production, gifts/aid, bartering, and in-kind Table 1.5: Shares of Food Consumption by Food Sources (in Dietary Energy) Share of food Share of consumed Share of food purchased Share of own away from from other food in produced food home in sources in Number of total food in total food total food total food sampled consumption consumption consumption consumption households (%) (%) (%) (%) Total 22175 52.56 40.01 3.38 4.06 Quintiles of income Lowest quintile 2714 40.31 53.34 1.82 4.54 2 3236 42.01 51.83 2.11 4.06 3 4110 51.34 41.80 2.62 4.25 4 5002 62.10 30.64 3.37 3.88 Highest quintile 7113 67.84 21.44 7.19 3.54 Area Capital city 1225 84.15 0.45 13.27 2.13 Other urban areas 13382 79.69 12.58 4.69 3.04 Rural areas 7568 45.85 47.31 2.49 4.36 Household size One person 2503 56.62 19.60 18.00 5.78 Between 2 and 3 people 5676 55.02 36.36 3.52 5.10 Between 4 and 5 people 6226 53.90 39.13 3.16 3.81 Between 6 and 7 people 4141 52.26 40.60 2.83 4.31 More than 7 3629 49.63 44.72 2.39 3.26 Gender of the household head Male 16751 52.66 39.88 3.56 3.89 Female 5424 52.10 40.59 2.55 4.76 Age of the household head Less than 35 7133 53.61 37.69 4.59 4.11 Between 35 and 45 6728 55.88 37.31 3.23 3.58 Between 46 and 60 5433 50.43 42.97 2.69 3.91 More than 60 2881 47.48 44.51 2.72 5.29 80 Chapter 3: Guide to Output Tables payment. In addition, household members consume food at sit-down and fast food restaurants and from street vendors. For the purpose of the analysis, food sources are classified in four main categories according to the type of acquisition: (1) purchase (excluding food consumed away from home), (2) own production, (3) consumed away from home, and (4) others (including gifts/aid, in-kind payment, etc.). This table shows the proportion of total dietary energy provided by each of the four food sources. This information is useful, for instance, to assess how much households rely on the following: • Food purchases (illustrating potential vulnerability to food price shocks) • Own production (illustrating potential vulnerability to natural shocks such as drought or flood) Table 1.6: Shares of Food Consumption by Food Sources (in Dietary Energy) by Income Deciles This table shows the proportion of total dietary energy pro- vided by each of the four food sources: purchases to be consumed inside the home, own production, consumption away from home, and other sources combined. The data are disaggregated by income decile groups. Particularly in this table, the number of sampled households used to produce the estimates at income decile levels has to be analyzed to assess the reliability of the estimates. For instance, in table 1.6 the estimates of the first income decile group in the capital city are considered unreliable due to the low number of households used to derive the estimates (7 households). In general, a statistic obtained with data from fewer than 30 households is considered unreliable. Table 1.7: Shares of Food Consumption by Food Sources (in Monetary Value) This table shows the proportion of total food expenditure that each of the four food sources (purchases to be consumed inside the home, own production, consumption away from home, and other sources combined) represents. The share of money spent to purchase food in total household food expenditure is an indirect measure of a household’s vulnerability to market food crises. In general, the higher the propor- tion of food consumed from purchases, the higher the risk of households being affected by food price shocks. As well, the percentage of food from own production gives an idea of the dependency a household has on its own agricultural outcome. Households that practice farming are highly 81 Analyzing Food Security Using Household Survey Data Table 1.6: Shares of Food Consumption by Food Sources (in Dietary Energy) by Income Deciles Share of food Share of food Average Share of Share of own consumed away from other Number of income purchased food produced food from home sources in sampled (LCU/person/ in total food in total food in total food total food households day) consumption (%) consumption (%) consumption (%) consumption (%) Total 1 1344 122.00 40.44 52.96 1.69 4.91 2 1370 188.26 40.17 53.71 1.93 4.19 3 1634 240.31 40.30 53.70 1.89 4.11 4 1602 294.60 43.96 49.69 2.35 3.99 5 1858 355.73 50.55 43.32 2.26 3.88 6 2252 436.89 52.16 40.22 3.00 4.63 7 2311 539.38 60.39 33.26 2.48 3.88 8 2691 698.55 63.94 27.84 4.33 3.89 9 3064 1001.65 66.28 25.27 4.71 3.74 10 4049 2927.19 69.45 17.47 9.75 3.33 Area Capital city 1 7 127.09 86.64 0.57 4.41 8.37 2 29 185.05 87.37 4.62 3.34 4.67 3 36 243.00 87.23 1.06 8.65 3.06 4 37 300.12 88.49 0.91 5.63 4.97 5 65 357.19 93.55 0.30 4.83 1.33 6 86 439.84 90.83 0.36 7.13 1.68 7 132 540.86 91.20 0.06 7.77 0.97 8 174 720.97 85.37 0.06 12.36 2.20 9 277 1030.48 85.82 0.14 12.51 1.53 10 382 2999.53 75.71 0.72 20.93 2.64 Other urban areas 1 471 126.87 76.95 17.30 1.87 3.88 2 592 189.23 76.75 16.94 2.05 4.26 3 792 239.95 72.18 21.67 2.16 4.00 4 825 296.18 74.26 17.58 2.95 5.21 5 1033 356.85 76.54 18.56 2.09 2.81 6 1394 436.92 80.10 13.72 2.48 3.69 7 1460 544.31 81.90 11.99 3.26 2.85 8 1773 708.65 80.51 12.52 4.86 2.11 9 2119 1000.35 82.08 10.42 4.55 2.95 10 2923 3228.57 81.40 6.19 9.96 2.45 Rural areas 1 866 121.71 38.59 54.78 1.68 4.95 2 749 188.25 37.41 56.50 1.91 4.18 3 806 240.28 37.48 56.60 1.79 4.13 4 740 294.28 40.49 53.39 2.25 3.87 5 760 355.47 45.99 47.74 2.17 4.10 6 772 436.65 45.22 46.96 2.88 4.94 7 719 537.98 53.87 39.88 1.98 4.28 8 744 690.88 55.56 36.57 3.16 4.72 9 668 994.97 57.68 34.66 3.25 4.40 10 744 2725.93 60.83 29.31 5.81 4.04 82 Chapter 3: Guide to Output Tables Table 1.7: Shares of Food Consumption by Food Sources (in Monetary Value) Share of food Share of Share of own Share of food Share of food consumption purchased food produced food consumed away from other Number of in total in total food in total food from home sources in sampled income (%) consumption consumption in total food total food households (Engel ratio) (%) (%) consumption (%) consumption (%) Total 22175 40.57 65.38 26.38 4.24 4.01 Quintiles of income Lowest quintile 2714 66.82 50.15 42.89 1.84 5.12 2 3236 62.52 53.50 40.11 2.19 4.20 3 4110 55.21 62.04 30.88 2.76 4.33 4 5002 48.77 71.56 20.92 3.79 3.73 Highest quintile 7113 23.65 76.58 12.06 7.96 3.40 Area Capital city 1225 36.13 83.70 0.45 13.25 2.59 Other urban 13382 33.29 85.16 6.96 5.00 2.88 areas Rural areas 7568 43.80 57.67 35.10 2.73 4.50 Household size One person 2503 30.01 62.30 12.56 20.58 4.56 Between 2 and 5676 38.97 68.19 23.30 4.15 4.36 3 people Between 4 and 6226 41.27 67.47 25.13 3.71 3.69 5 people Between 6 and 4141 41.35 64.87 27.19 3.39 4.55 7 people More than 7 3629 43.07 61.79 31.93 2.79 3.49 Gender of the household head Male 16751 39.46 65.27 26.48 4.51 3.74 Female 5424 46.00 65.85 25.94 3.08 5.13 Age of the household head Less than 35 7133 35.90 66.61 23.04 6.12 4.22 Between 35 6728 40.20 68.76 23.79 3.95 3.49 and 45 Between 46 5433 43.09 63.00 30.05 3.18 3.77 and 60 More than 60 2881 49.40 59.53 32.10 3.09 5.28 exposed to natural shocks, so they are particularly vulnerable in disaster- prone countries where recurrent natural shocks damage household agri- cultural production. Table 1.7 also shows the Engel ratio defined as the percentage of total income dedicated to acquire food. Engel’s law states that the proportion of income spent on food decreases when income increases. This does not mean that food expenditure decreases as income increases; on the contrary, while food intake has an upper limit due to biological factors, food expenditure 83 Analyzing Food Security Using Household Survey Data does not. However, the relative importance of food expenditure tends to be greater among the poor households since they focus their acquisition on pri- mary need goods (thus limiting the expenses on the other items). The share of food expenditure tends to be lower among the wealthier households because they spend a greater proportion of their income on nonfood items. However, it should be noted that “while the share of food expenditure in total expenditure may be a good starting-point for assessing vulnerability, it is not sufficient within a given economic environment and the same food expenditure share would not necessarily represent the same level of vulner- ability across different economic environments” (Schmidhuber 2003). Table 1.8: Shares of Food Consumption by Food Sources (in Monetary Value) by Income Deciles This table shows the proportion of total food expendi- ture that each of the four food sources represents (purchases to be consumed Table 1.8: Shares of Food Consumption by Food Sources (in Monetary Value) by Income Deciles Share of food Share Share of Share of own consumed Share of food Average of food purchased produced away from from other income consumption food in food in home in sources in Number of (LCU/ in total total food total food total food total food sampled person/ income (%) consumption consumption consumption consumption households day) (Engel ratio) (%) (%) (%) (%) Total 1 1344 122.00 69.63 49.85 42.61 1.91 5.63 2 1370 188.26 64.62 50.41 43.12 1.79 4.69 3 1634 240.31 64.69 51.68 41.89 2.08 4.34 4 1602 294.60 60.52 55.29 38.35 2.30 4.07 5 1858 355.73 56.89 60.70 32.58 2.51 4.21 6 2252 436.89 53.77 63.26 29.33 2.98 4.43 7 2311 539.38 50.77 70.26 23.02 2.83 3.90 8 2691 698.55 47.06 72.77 18.97 4.69 3.57 9 3064 1001.65 39.13 75.65 15.17 5.50 3.68 10 4049 2927.19 17.57 77.40 9.35 10.11 3.15 Area Capital city 1 7 127.09 92.48 88.09 1.23 3.63 7.05 2 29 185.05 64.68 89.21 3.61 3.41 3.77 3 36 243.00 64.11 86.64 1.20 9.26 2.90 4 37 300.12 65.86 89.66 1.71 5.77 2.86 5 65 357.19 56.35 93.24 0.24 5.03 1.48 6 86 439.84 57.60 90.56 0.38 7.00 2.05 7 132 540.86 53.95 90.38 0.09 8.34 1.19 8 174 720.97 52.66 86.12 0.13 12.00 1.75 9 277 1030.48 46.09 82.61 0.18 12.75 4.45 10 382 2999.53 23.83 78.70 0.66 18.44 2.21 84 Chapter 3: Guide to Output Tables inside the home, own production, consumption away from home, and other sources combined). The data are disaggregated by income decile groups. Particularly in this table, the number of sampled households used to produce the estimates at income decile levels has to be analyzed to assess the reliability of the estimates. For instance, in table 1.8 the estimates of the first income decile group in the capital city are considered unreliable due to the low number of households used to derive the estimates (7 households). In general, a statistic obtained with data from fewer than 30 households is considered unreliable. Table 1.9: Food Consumption in Dietary Energy, Monetary, and Nutrient Content by Population Groups The human body requires energy for differ- ent purposes including metabolic processes, muscular activity, growth, and Table 1.9: Food Consumption in Dietary Energy, Monetary, and Nutrient Content by Population Groups Average Average food dietary energy consumption Average Average consumption in monetary protein Average fat carbohydrates (kcal/person/ value (LCU/ consumption consumption consumption day) person/day) (g/person/day) (g/person/day) (g/person/day) Total 2199 220.97 57.0 38.8 359.1 Quintiles of income Lowest quintile 1596 101.64 41.7 21.1 280.3 2 2043 166.18 53.8 32.0 340.9 3 2250 218.23 57.9 39.2 367.8 4 2604 299.82 65.9 50.3 412.3 Highest quintile 3051 448.89 79.8 69.5 462.9 Area Capital city 2064 395.73 53.4 52.1 328.0 Other urban areas 2179 291.85 55.8 48.9 347.2 Rural areas 2213 196.22 57.5 36.1 363.4 Household size One person 3667 529.04 95.7 76.4 512.7 Between 2 and 3 people 2667 307.82 69.1 51.4 413.9 Between 4 and 5 people 2253 237.00 58.8 40.5 364.0 Between 6 and 7 people 2097 198.68 53.2 35.0 349.6 More than 7 1951 170.34 51.2 32.8 330.1 Gender of the household head Male 2207 219.40 57.1 38.6 359.5 Female 2167 227.75 56.8 39.7 357.4 Age of the household head Less than 35 2310 240.19 60.2 42.0 375.5 Between 35 and 45 2155 222.74 55.5 38.1 352.1 Between 46 and 60 2206 212.94 57.2 38.8 360.0 More than 60 2089 198.75 54.6 34.5 343.4 85 Analyzing Food Security Using Household Survey Data synthesis of new tissues. Humans obtain the required energy through the intake of energy-yielding macronutrients from food consumption. These macronutrients are protein, fats, total carbohydrates (including fiber), and alcohol. Each of them contributes to the total dietary energy but in dif- ferent proportions. Because available carbohydrates are estimated as the difference between total carbohydrates and fiber combined with the use of the Atwater6 factors, the energy densities of the nutrients comprise the following: • Four calories per gram of protein • Nine calories per gram of fats • Four calories per gram of available carbohydrates • Two calories per gram of fiber • Seven calories per gram of alcohol This table shows protein, fats, and carbohydrates consumption expressed in grams per person per day. Note that the carbohydrates values, reported in table 1.9 (last column), are those corresponding to available carbohydrates. On average worldwide, people consume more carbohydrates per day than protein or fats. It is expected that macronutrient consumption increases with income, since food consumption is positively correlated with income. However, the pattern of the increase in macronutrient consumption varies among population groups because households with higher income can afford a more diverse diet (e.g., more protein from meat) than those with lower income. Table 1.10: Nutrient Contribution to Dietary Energy Consumption This table shows the proportion of dietary energy provided by each macronutri- ent. The proportion of calories from protein and fats are estimated as their respective consumption in grams times 4 and 9, respectively. Then the calories from total carbohydrates and alcohol are estimated as the differ- ence between total dietary energy consumption and the calories coming from protein and fats. The concept of a balanced diet is applied in more than one of the ADePT-FSM output tables. A joint WHO/FAO group of experts estab- lished guidelines for a “balanced diet,” described in terms of the pro- portions of total dietary energy provided by diverse sources of energy (WHO 2003). These guidelines are related to the effects of chronic 86 Chapter 3: Guide to Output Tables Table 1.10: Nutrient Contribution to Dietary Energy Consumption Average dietary Share of DEC from energy consumption Share of DEC from Share of DEC from total carbohydrates (kcal/person/day) protein (%) fat (%) and alcohol (%) Total 2199 10.37 15.87 73.75 Quintiles of income Lowest quintile 1596 10.46 11.88 77.66 2 2043 10.53 14.08 75.39 3 2250 10.30 15.67 74.03 4 2604 10.13 17.40 72.47 Highest quintile 3051 10.45 20.50 69.02 Area Capital city 2064 10.36 22.72 66.89 Other urban areas 2179 10.24 20.19 69.53 Rural areas 2213 10.40 14.68 74.92 Household size One person 3667 10.42 18.70 70.78 Between 2 and 3 people 2667 10.36 17.34 72.28 Between 4 and 5 people 2253 10.45 16.16 73.39 Between 6 and 7 people 2097 10.15 15.04 74.81 More than 7 1951 10.50 15.14 74.36 Gender of the household head Male 2207 10.35 15.73 73.91 Female 2167 10.48 16.48 73.04 Age of the household head Less than 35 2310 10.42 16.36 73.20 Between 35 and 45 2155 10.30 15.93 73.76 Between 46 and 60 2206 10.37 15.84 73.79 More than 60 2089 10.46 14.85 74.69 nondeficiency diseases. So, according to the experts, a diet is determined to be balanced when • The proportion of dietary energy provided by protein is in the range of 10–15 percent • The proportion of dietary energy provided by fats is in the range of 15–30 percent • The proportion of total dietary energy provided by the remaining macronutrients is in the range of 55–75 percent Table 1.11: Nutrient Contribution to Dietary Energy Consumption at Income Quintile Levels This table indicates whether households classified by income quintile groups have access to a balanced diet. The main sources of dietary energy are protein, fats, and total carbohydrates. Households have 87 Analyzing Food Security Using Household Survey Data Table 1.11: Nutrient Contribution to Dietary Energy Consumption at Income Quintile Levels Within range of Average Share Share of DEC Within range Within population total dietary energy of DEC Share from total of population range of carbohydrates consumption from of DEC carbohydrates protein population fat and alcohol (kcal/person/ protein from fat and alcohol intake goal: intake goal: intake goal: day) (%) (%) (%) 10%–15% 15%–30% 55%–75% Total Quantiles of income Lowest quintile 1596 10.46 11.88 77.66 OK LOW HIGH 2 2043 10.53 14.08 75.39 OK LOW HIGH 3 2250 10.30 15.67 74.03 OK OK OK 4 2604 10.13 17.40 72.47 OK OK OK Highest quintile 3051 10.45 20.50 69.02 OK OK OK Area Capital city Lowest quintile 938 9.86 15.51 74.63 LOW OK OK 2 1226 9.99 16.37 73.64 LOW OK OK 3 1434 10.02 20.45 69.52 OK OK OK 4 1922 10.32 22.47 67.21 OK OK OK Highest quintile 2793 10.52 24.31 65.12 OK OK OK Other urban areas Lowest quintile 1335 10.17 15.94 73.89 OK OK OK 2 1635 10.06 16.42 73.51 OK OK OK 3 1858 10.22 18.16 71.62 OK OK OK 4 2419 9.95 19.41 70.61 LOW OK OK Highest quintile 2905 10.59 24.08 65.26 OK OK OK Rural areas Lowest quintile 1621 10.48 11.63 77.89 OK LOW HIGH 2 2105 10.57 13.86 75.57 OK LOW HIGH 3 2382 10.32 15.10 74.57 OK OK OK 4 2749 10.16 16.39 73.45 OK OK OK Highest quintile 3193 10.38 18.06 71.55 OK OK OK access to a balanced diet if the proportion of dietary energy from each mac- ronutrient is within the experts’ recommendations.7 When OK appears for the three calorie-yielding macronutrients, we can consider that households in that population have access to a balanced diet. However, little else is known about households’ consuming a balanced diet because there is no information about how people combine the food they consume or about intrahousehold allocation of food. Table 1.12: Nutrient Density per 1,000 Kcal This table shows the grams of protein, carbohydrates, and fats per 1,000 kcals (kilocalories) of households’ consumption (macronutrient density). 88 Chapter 3: Guide to Output Tables Table 1.12: Nutrient Density per 1,000 Kcal Average dietary energy Average Average consumption protein carbohydrates Average fat (kcal/person/ consumption consumption consumption day) (g/1000 kcal) (g/1000 kcal) (g/1000 kcal) Total 2199 25.9 163.2 17.6 Quintiles of income Lowest quintile 1596 26.1 175.6 13.2 2 2043 26.3 166.8 15.6 3 2250 25.7 163.5 17.4 4 2604 25.3 158.3 19.3 Highest quintile 3051 26.1 151.7 22.8 Area Capital city 2064 25.9 158.9 25.2 Other urban areas 2179 25.6 159.3 22.4 Rural areas 2213 26.0 164.2 16.3 Household size One person 3667 26.1 139.5 20.8 Between 2 and 3 2667 25.9 155.2 19.3 people Between 4 and 5 2253 26.1 161.6 18.0 people Between 6 and 7 2097 25.4 166.7 16.7 people More than 7 1951 26.3 169.2 16.8 Gender of the household head Male 2207 25.9 162.9 17.5 Female 2167 26.2 164.9 18.3 Age of the household head Less than 35 2310 26.1 162.5 18.2 Between 35 and 45 2155 25.7 163.4 17.7 Between 46 and 60 2206 25.9 163.2 17.6 More than 60 2089 26.2 164.4 16.5 Table 1.13: Share of Animal Protein in Total Protein Consumption This table shows the proportion of protein consumption coming from food of animal origin (animal proteins). The food commodities considered to be of animal origin are meat (red and white), fish, eggs, milk, and cheese. When households are classified by income quintiles, an increasing trend in the proportion of protein of animal origin consumed as one moves from the first to the last income quintile is expected. This is mainly because richer households can afford more expensive food products such as meat and fish. However, such a trend probably is not present in pastoral regions where poor 89 Analyzing Food Security Using Household Survey Data Table 1.13: Share of Animal Protein in Total Protein Consumption Share of animal protein in total protein consumption (%) Total 21.2 Quintiles of income Lowest quintile 16.0 2 18.3 3 20.8 4 23.4 Highest quintile 28.0 Area Capital city 25.3 Other urban areas 24.2 Rural areas 20.5 Household size One person 26.1 Between 2 and 3 people 23.1 Between 4 and 5 people 21.4 Between 6 and 7 people 19.7 More than 7 20.8 Gender of the household head Male 21.3 Female 21.0 Age of the household head Less than 35 21.6 Between 35 and 45 21.9 Between 46 and 60 20.5 More than 60 20.7 communities/households derive a sizeable part of their consumption from livestock products (i.e., milk and cheese). Table 1.14: Within-Region Differences in Nutrient Consumption, by Regional Income Quintiles This table shows the average macronutrients con- sumption by region and income quintile providing information on the intraregional differences. Such disaggregation can be used to explore income-based disparities on macronutrient consumption within each region and identify regions where disparities due to income are more pro- nounced. Note that the first row of the table for each region corresponds to table 1.9. 90 Chapter 3: Guide to Output Tables Table 1.14: Within-Region Differences in Nutrient Consumption, by Regional Income Quintiles Average protein Average fat Average carbohydrates consumption consumption consumption (g/person/day) (g/person/day) (g/person/day) Region Region 1 Total 69.11 43.87 372.61 Lowest quintile 58.41 28.75 340.23 2 65.74 39.17 354.78 3 66.16 41.44 345.70 4 78.96 55.96 414.48 Highest quintile 81.41 61.05 428.31 Region 2 Total 52.38 39.78 316.28 Lowest quintile 40.42 21.95 267.46 2 45.01 29.18 273.65 3 49.31 34.52 289.86 4 62.47 52.19 380.20 Highest quintile 72.66 75.11 404.26 Region 3 Total 46.13 41.07 310.79 Lowest quintile 31.13 19.66 232.15 2 36.71 31.13 263.27 3 45.50 38.97 313.15 4 53.25 50.73 349.16 Highest quintile 73.79 76.22 438.10 Region 4 Total 50.35 37.53 380.04 Disaggregated by Food Commodity Group: Tables 2.1 to 2.9 The output tables showing statistics on consumption (grams/person/day) of protein, fats, and carbohydrates by food commodity group are useful in providing a picture of the consumption pattern. Note that this information also helps to identify which food commodity group contributes the most to the consumption of a given macronutrient. Note as well that the values of carbohydrates refer to available carbohydrates.8 Table 2.1: Food Consumption by Food Commodity Group This table shows the macronutrients (expressed in grams) and the food consumption (in dietary energy and monetary values) at the national level disaggregated by food commodity groups. 91 Analyzing Food Security Using Household Survey Data Table 2.1: Food Consumption by Food Commodity Groups Average food Average consumption dietary energy Average in monetary consumption Average protein carbohydrates Average fat value (LCU/ (kcal/person/ consumption consumption consumption person/day) day) (g/person/day) (g/person/day) (g/person/day) Food group Cereals 67.4 1201 29.5 231.0 12.6 Roots and tubers 14.9 258 2.6 58.7 0.5 Sugars and syrups 12.7 89 0.0 22.3 0.0 Pulses 12.5 73 5.5 9.8 0.3 Tree nuts 0.1 3 0.1 0.0 0.2 Oil crops 4.9 79 2.6 1.1 6.8 Vegetables 18.6 30 1.7 4.0 0.3 Fruits 10.2 69 0.6 15.4 0.2 Stimulants 2.0 5 0.1 0.9 0.1 Spices 2.9 2 0.0 0.2 0.0 Alcoholic beverages 7.8 114 0.1 1.7 0.0 Meat 23.1 71 5.3 0.0 5.5 Eggs 0.8 1 0.1 0.0 0.1 Fish 15.1 27 5.4 0.0 0.6 Milk and cheese 7.2 24 1.3 1.8 1.3 Oils and fats (vegetable) 8.4 76 0.0 0.0 8.4 Oils and fats (animal) 0.3 2 0.0 0.0 0.2 Nonalcoholic beverages 2.8 2 0.0 0.5 0.0 Miscellaneous and prepared food 9.4 74 2.0 11.8 1.5 Table 2.2: Contribution of Food Commodity Groups to Total Nutrient Consumption This table shows the contribution (in percentage) of each food commodity group to the total dietary energy, protein, available car- bohydrates, and fats consumed by the households at the national level. The disaggregation of these statistics by food commodity groups helps identify which food commodity groups are the main sources of calories, protein, total carbohydrates, and fats. Table 2.3: Food Consumption by Food Commodity Group and Income Quintile This table has the same indicators as table 2.1 except that the statistics are disaggregated by income quintile. This table helps to identify the food item groups that contribute the most in terms of calories and mac- ronutrients to the diet of population groups segmented according to their income level. Table 2.4: Food Consumption by Food Commodity Group and Area This table shows the urban/rural food consumption statistics in terms of 92 Chapter 3: Guide to Output Tables Table 2.2: Contribution of Food Commodity Groups to Total Nutrient Consumption Share of total Share of Share of total dietary energy total protein carbohydrates Share of total fat consumption (%) consumption (%) consumption (%) consumption (%) Food group Cereals 54.6 51.6 64.3 32.5 Roots and tubers 11.7 4.6 16.4 1.3 Sugars and syrups 4.1 0.0 6.2 0.0 Pulses 3.3 9.7 2.7 0.8 Tree nuts 0.1 0.2 0.0 0.6 Oil crops 3.6 4.6 0.3 17.5 Vegetables 1.3 3.0 1.1 0.8 Fruits 3.1 1.1 4.3 0.5 Stimulants 0.2 0.2 0.2 0.2 Spices 0.1 0.1 0.1 0.1 Alcoholic beverages 5.2 0.3 0.5 0.0 Meat 3.2 9.4 0.0 14.3 Eggs 0.1 0.2 0.0 0.2 Fish 1.2 9.5 0.0 1.7 Milk and cheese 1.1 2.3 0.5 3.5 Oils and fats (vegetable) 3.5 0.0 0.0 21.8 Oils and fats (animal) 0.1 0.0 0.0 0.5 Nonalcoholic beverages 0.1 0.0 0.1 0.0 Miscellaneous and prepared food 3.4 3.5 3.3 3.8 Table 2.3: Food Consumption by Food Commodity Group and Income Quintile Average food Average consumption dietary energy Average Average in monetary consumption protein carbohydrates Average fat value (LCU/ (kcal/person/ consumption consumption consumption person/day) day) (g/person/day) (g/person/day) (g/person/day) Quintiles of income Lowest quintile Cereals 35.75 987 24.6 188.5 10.5 Roots and tubers 11.62 267 2.6 60.9 0.5 Sugars and syrups 3.50 25 0.0 6.2 0.0 Pulses 6.76 53 4.0 7.1 0.2 Tree nuts 0.08 2 0.1 0.0 0.2 Oil crops 1.76 34 1.3 0.5 2.9 Vegetables 10.25 23 1.3 3.0 0.2 Fruits 3.34 30 0.3 6.8 0.1 Stimulants 0.47 2 0.0 0.4 0.0 Spices 1.61 0 0.0 0.0 0.0 Alcoholic beverages 2.25 59 0.1 0.8 0.0 Meat 8.06 29 2.4 0.0 2.2 Eggs 0.12 0 0.0 0.0 0.0 Fish 8.21 18 3.5 0.0 0.4 Milk and cheese 3.49 15 0.8 1.1 0.8 (continued) 93 Analyzing Food Security Using Household Survey Data Table 2.3: Food Consumption by Food Commodity Group and Income Quintile (continued) Average food Average consumption dietary energy Average Average in monetary consumption protein carbohydrates Average fat value (LCU/ (kcal/person/ consumption consumption consumption person/day) day) (g/person/day) (g/person/day) (g/person/day) Oils and fats (vegetable) 2.20 21 0.0 0.0 2.4 Oils and fats (animal) 0.08 1 0.0 0.0 0.1 Nonalcoholic beverages 0.22 0 0.0 0.0 0.0 Miscellaneous and prepared food 1.87 29 0.8 4.9 0.5 Quintile 2 Cereals 57.42 1192 29.4 228.9 12.4 Roots and tubers 14.53 265 2.6 60.6 0.5 Sugars and syrups 8.23 57 0.0 14.3 0.0 Pulses 10.63 65 4.9 8.7 0.3 Table 2.4: Food Consumption by Food Commodity Group and Area Average food Average consumption dietary energy Average Average in monetary consumption protein carbohydrates Average fat value (LCU/ (kcal/person/ consumption consumption consumption person/day) day) (g/person/day) (g/person/day) (g/person/day) Area Capital city Cereals 110.12 1029 24.7 200.2 10.6 Roots and tubers 8.95 50 0.7 11.2 0.1 Sugars and syrups 22.55 184 0.0 46.1 0.0 Pulses 15.27 51 3.8 6.8 0.2 Tree nuts 0.10 1 0.0 0.0 0.1 Oil crops 10.86 105 1.5 1.8 9.6 Vegetables 33.88 24 1.2 3.5 0.2 Fruits 19.88 44 0.4 9.8 0.1 Stimulants 3.63 5 0.1 1.0 0.1 Spices 2.94 4 0.1 0.5 0.1 Alcoholic beverages 12.99 8 0.0 0.4 0.0 Meat 40.76 91 6.2 0.0 7.3 Eggs 2.99 3 0.2 0.0 0.2 Fish 23.49 34 6.5 0.0 0.9 Milk and cheese 7.52 11 0.6 0.8 0.6 Oils and fats (vegetable) 14.71 132 0.0 0.0 14.6 Oils and fats (animal) 1.32 6 0.0 0.0 0.6 Nonalcoholic beverages 11.32 9 0.0 2.3 0.0 Miscellaneous and prepared food 52.44 274 7.3 43.6 6.6 Other urban areas Cereals 87.62 1217 29.4 234.3 13.1 Roots and tubers 11.06 134 1.6 30.2 0.3 Sugars and syrups 20.73 148 0.0 36.9 0.0 Pulses 12.87 57 4.2 7.6 0.3 94 Chapter 3: Guide to Output Tables macronutrients,9 dietary energy, and monetary values disaggregated by food commodity groups. This table allows the analyst to explore the macronutri- ent consumption patterns in urban and rural areas and detect differences, if any. Table 2.5: Contribution of Food Commodity Groups to Total Nutrient Consumption by Area This table shows the contribution (in percentage) of each food commodity group to the total dietary energy, protein, available carbohydrates,10 and fats consumed by the households in rural and urban areas. The disaggregation of these statistics by food commodity groups helps to identify which food commodity group(s) are the main sources of calories, protein, available carbohydrates, and fats within urban and rural areas and highlights urban/rural-based differences. Table 2.6: Food Consumption by Food Commodity Group and Region This table shows, for the first income quintile of each region, the food consumption statistics in terms of macronutrients, dietary energy, and monetary values disaggregated by food commodity groups. Because the food consumption pattern varies among regions, the analysis of the first income quintile group by region is important to identify the main sources of each macronutrient for the poorest population. Table 2.7: Food Consumption by Food Commodity Group and Region in the First Quintile This table shows, for the first income quintile of each region, the food consumption statistics in terms of macronutrients,11 dietary energy, and monetary values disaggregated by food commodity groups. Because the first income quintile refers to the poorest population and the food consumption pattern varies among regions, the analysis is important in helping to identify regional differences within the poorest part of the population. Table 2.8: Nutrient Costs by Food Commodity Group This table shows the unit value of dietary energy, protein, available carbohydrates,12 and fats disaggregated by food commodity groups. The objective of this table is to iden- tify the food commodity groups that are low-cost sources of dietary energy, protein, carbohydrates, or fats. The unit values are estimated using expenditures of each food commod- ity group as well as their contribution to total calories or nutrient content 95 Analyzing Food Security Using Household Survey Data Table 2.5: Contribution of Food Commodity Groups to Total Nutrient Consumption by Area Share of total Share of Share of total dietary energy total protein carbohydrates Share of total fat consumption (%) consumption (%) consumption (%) consumption (%) Area Capital city Cereals 49.8 46.3 61.0 20.4 Roots and tubers 2.4 1.3 3.4 0.2 Sugars and syrups 8.9 0.0 14.0 0.0 Pulses 2.5 7.1 2.1 0.4 Tree nuts 0.1 0.1 0.0 0.2 Oil crops 5.1 2.8 0.5 18.5 Vegetables 1.1 2.3 1.1 0.4 Fruits 2.2 0.8 3.0 0.2 Stimulants 0.2 0.2 0.3 0.2 Spices 0.2 0.2 0.2 0.2 Alcoholic beverages 0.4 0.1 0.1 0.0 Meat 4.4 11.6 0.0 14.0 Eggs 0.1 0.4 0.0 0.4 Fish 1.7 12.1 0.0 1.7 Milk and cheese 0.5 1.0 0.2 1.1 Oils and fats (vegetable) 6.4 0.0 0.0 28.1 Oils and fats (animal) 0.3 0.0 0.0 1.2 Nonalcoholic beverages 0.5 0.0 0.7 0.0 Miscellaneous and prepared food 13.3 13.6 13.3 12.6 Other urban areas Cereals 55.8 52.7 67.5 26.9 Roots and tubers 6.2 2.9 8.7 0.5 Sugars and syrups 6.8 0.0 10.6 0.0 Pulses 2.6 7.6 2.2 0.5 (in kcal or grams, respectively). Then the dietary energy unit value is expressed in local currency per 1,000 kcal, while the cost of each macronutrient is expressed in local currency per 100 grams of the respec- tive nutrient. Each time N/A replaces a unit value, it means that the dietary energy or nutrient content provided by the food commodity group is very low or null, or there was no acquisition of that food group. Table 2.9: Food Consumption by Food Commodity Group and Food Sources (in Dietary Energy) This table shows the contribution of each food source to the dietary energy consumption for each food commodity group. In table 2.9, own production contributes 47 percent to the total dietary energy consumption coming from cereals while most of the dietary energy consumption provided by vegetable oils and fats are coming from purchases (93.4 percent). This table makes it possible to identify the main sources of acquisition of the food group commodity being consumed. 96 Chapter 3: Guide to Output Tables Table 2.6: Food Consumption by Food Commodity Group and Region Average food Average Average Average consumption in dietary energy protein carbohydrates Average fat monetary value consumption consumption consumption consumption (LCU/person/day) (kcal/person/day) (g/person/day) (g/person/day) (g/person/day) Region Region 1 Cereals 69.53 1614 41.6 307.5 16.5 Roots and tubers 5.67 41 0.7 9.1 0.1 Sugars and syrups 9.62 67 0.0 16.7 0.0 Pulses 12.22 93 7.1 12.3 0.4 Tree nuts 0.05 1 0.0 0.0 0.1 Oil crops 5.36 124 5.2 1.6 10.3 Vegetables 22.22 47 2.7 5.6 0.5 Fruits 4.56 18 0.3 3.4 0.1 Stimulants 1.35 4 0.1 0.8 0.1 Spices 2.72 1 0.0 0.2 0.0 Alcoholic beverages 7.13 145 0.2 2.1 0.0 Meat 19.85 66 4.9 0.0 5.2 Eggs 0.65 1 0.1 0.0 0.1 Fish 6.53 12 2.4 0.0 0.3 Milk and cheese 7.53 33 1.8 2.4 1.8 Oils and fats (vegetable) 7.59 63 0.0 0.0 6.9 Oils and fats (animal) 0.22 1 0.0 0.0 0.1 Nonalcoholic beverages 1.24 1 0.0 0.2 0.0 Miscellaneous and 6.13 69 2.0 10.8 1.3 prepared food Region 2 Cereals 73.76 1275 31.9 241.3 14.7 Roots and tubers 6.45 40 0.8 8.4 0.1 Sugars and syrups 16.91 113 0.0 28.3 0.0 Pulses 11.50 54 4.0 7.3 0.3 Table 2.7: Food Consumption by Food Commodity Group and Region in the First Quintile Average food Average Average Average consumption in dietary energy protein carbohydrates Average fat monetary value consumption consumption consumption consumption (LCU/person/day) (kcal/person/day) (g/person/day) (g/person/day) (g/person/day) Region Region 1 Cereals 44.76 1584 42.0 301.2 15.7 Roots and tubers 2.93 31 0.4 6.9 0.1 Sugars and syrups 2.66 19 0.0 4.7 0.0 Pulses 5.83 56 4.3 7.4 0.2 Tree nuts 0.00 0 0.0 0.0 0.0 Oil crops 3.67 74 3.2 1.0 6.2 Vegetables 18.62 47 2.7 5.6 0.5 Fruits 2.14 10 0.2 1.7 0.1 (continued) 97 Analyzing Food Security Using Household Survey Data Table 2.7: Food Consumption by Food Commodity Group and Region in the First Quintile (continued) Average food Average Average Average consumption in dietary energy protein carbohydrates Average fat monetary value consumption consumption consumption consumption (LCU/person/day) (kcal/person/day) (g/person/day) (g/person/day) (g/person/day) Stimulants 0.65 9 0.1 1.9 0.1 Spices 1.60 0 0.0 0.1 0.0 Alcoholic beverages 3.66 98 0.1 1.4 0.0 Meat 5.31 20 1.5 0.0 1.6 Eggs 0.37 0 0.0 0.0 0.0 Fish 2.89 7 1.3 0.0 0.2 Milk and cheese 5.44 25 1.3 1.8 1.4 Oils and fats (vegetable) 2.52 20 0.0 0.0 2.2 Oils and fats (animal) 0.01 0 0.0 0.0 0.0 Nonalcoholic beverages 0.06 0 0.0 0.0 0.0 Miscellaneous and 1.96 41 1.2 6.6 0.6 prepared food Region 2 Cereals 46.80 1264 31.9 236.4 15.5 Roots and tubers 3.08 21 0.4 4.7 0.0 Sugars and syrups 4.09 30 0.0 7.6 0.0 Pulses 5.90 35 2.6 4.7 0.2 Table 2.8: Nutrient Costs by Food Commodity Group Average dietary Average Average Average fat energy unit value protein unit value carbohydrates unit unit value (LCU/1000 kcal) (LCU/100 g) value (LCU/100 g) (LCU/100 g) Food group Cereals 56.11 228.83 29.19 534.81 Roots and tubers 57.88 567.85 25.40 N/A Sugars and syrups 142.73 N/A 57.10 Pulses 169.82 225.32 127.47 N/A Tree nuts 49.99 146.84 343.23 58.57 Oil crops 61.88 186.93 431.55 71.81 Vegetables 626.42 N/A 462.75 N/A Fruits 147.45 N/A 66.18 N/A Stimulants 432.40 N/A 225.33 N/A Spices N/A N/A N/A N/A Alcoholic beverages 68.81 N/A 462.77 Meat 324.50 433.27 N/A 417.05 Eggs 683.22 814.44 N/A N/A Fish 551.73 279.98 N/A Milk and cheese 294.66 554.64 404.89 534.26 Oils and fats (vegetable) 110.11 99.10 Oils and fats (animal) 169.17 N/A 152.70 Nonalcoholic beverages N/A N/A 574.81 Miscellaneous and prepared food 126.07 469.16 79.66 635.46 Note: N/A: very low or no nutrient content or no consumption. 98 Table 2.9: Food Consumption by Food Commodity Group and Food Sources (in Dietary Energy) Food consumed away from Purchases Own Production home Other sources Average Share Average Average Average Share dietary in food dietary Share in food dietary Share in food dietary in food energy commodity energy commodity energy commodity energy commodity consumption group’s total consumption group’s total consumption group’s total consumption group’s total (kcal/person/ consumption (kcal/person/ consumption (kcal/person/ consumption (kcal/person/ consumption day) (%) day) (%) day) (%) day) (%) Food group Cereals 593.8 49.4 565.5 47.1 0.0 42.0 3.5 Roots and tubers 90.8 35.2 157.7 61.2 0.0 9.2 3.6 Sugars and syrups 85.5 95.9 0.8 0.9 0.0 2.8 3.2 Pulses 35.2 48.0 35.0 47.7 0.0 3.2 4.3 Tree nuts 1.2 44.5 1.1 40.4 0.0 0.4 15.1 Oil crops 43.0 54.7 30.9 39.3 0.0 4.7 6.0 Vegetables 11.8 39.8 16.7 56.4 0.0 1.1 3.8 Fruits 27.8 40.2 36.4 52.7 0.0 4.9 7.1 Stimulants 3.0 65.7 0.8 18.1 0.0 0.7 16.2 Spices 1.4 88.5 0.1 8.4 0.0 0.0 3.0 Alcoholic beverages 93.9 82.7 6.6 5.8 0.0 13.1 11.5 Meat 57.3 80.4 10.2 14.4 0.0 3.7 5.2 Eggs 0.7 64.1 0.3 30.7 0.0 0.1 5.2 Fish 25.8 94.3 0.7 2.6 0.0 0.9 3.1 Milk and cheese 10.3 42.3 13.1 53.6 0.0 1.0 4.1 Oils and fats (vegetable) 70.9 93.4 3.8 5.1 0.0 1.2 1.6 Oils and fats (animal) 1.6 86.4 0.2 10.9 0.0 0.1 2.7 Nonalcoholic beverages 1.8 89.7 0.0 0.2 0.0 0.2 10.1 Miscellaneous and prepared food 0.0 0.0 74.8 100.0 0.0 99 Analyzing Food Security Using Household Survey Data Disaggregated by Food Commodity: Tables 3.1 to 3.9 The food commodities analyzed are those collected in the survey excluding those consumed away from home. Therefore, the total protein consumed from the listed commodities is underestimated. The food commodity quan- tities refer to edible portions, which mean that they exclude the nonedible parts (peels, bones, etc.). Table 3.1: Consumption Statistics for Each Food Item at National Level This table shows national food consumption statistics by food commodities. The statistics comprise food commodity edible quantities and their respec- tive monetary value, the calories they provide, and the calorie costs. This table is useful to identify the food commodities providing more calories to the households’ consumption at the national level and how much an individual living in the country has to pay to acquire those calo- ries. Moreover, it enables one to do a comparison between food availability and food consumption. For instance, one could look for differences between daily calories13 consumption per person (third column) obtained from NHS and those available obtained from the FBS. Table 3.2: Food Item Protein Consumption at National Level This table shows national food consumption statistics by food commodities. The statistics comprise food commodity edible quantities and their respective monetary value, the amount of protein they provide, and the protein costs. This table is useful to identify the food commodities providing more protein to the households’ consumption at the national level and how much an individual living in the country has to pay to acquire that amount of pro- tein. Moreover, it enables one to do a comparison between food availability as compiled in the FBS and food consumption as collected in NHS. For instance, one could look for differences between daily protein consumption per person (third column) obtained from NHS and those available obtained from the FBS. Table 3.3: Consumption Statistics for Each Food Item by Area This table shows urban/rural food consumption statistics by food commodity. The statistics comprise food commodity edible quantities and their respective monetary value, the calories they provide, and the calorie costs. This table is useful to identify differences between urban and rural areas with respect to calorie consumption patterns and unit values. 100 Chapter 3: Guide to Output Tables Table 3.1: Consumption Statistics for Each Food Item at National Level Average food Average Average Average edible consumption in dietary energy dietary energy quantity consumed monetary value consumption unit value (g/person/day) (LCU/person/day) (kcal/person/day) (LCU/1000 kcals) Food item Rice paddy or rough 6.12 0.98 21.37 45.78 Rice husked 48.14 18.65 169.44 110.07 Maize cob fresh 9.29 1.71 6.10 279.42 Maize grain 72.72 7.01 263.81 26.57 Maize flour 163.94 27.63 586.57 47.10 Millet whole grain dried 1.68 0.33 5.34 62.75 Millet foxtail Italian whole grain 1.17 0.43 3.70 116.10 Sorghum whole grain brown 7.91 0.88 28.09 31.35 Sorghum average of all variety 20.38 2.68 72.43 36.97 Wheat durum whole grain 0.46 0.11 1.66 66.89 Wheat meal or flour unspecified 4.41 1.54 14.90 103.16 wheat Wheat 1.30 0.20 4.68 42.37 Bread 3.11 1.65 8.22 201.08 Baby cereals 0.09 0.04 0.33 128.53 Biscuits wheat from Europe 0.15 0.27 0.65 409.68 Buns cakes 3.25 3.10 10.50 295.24 Macaroni spaghetti 0.27 0.16 0.95 171.61 Oats 2.31 1.63 8.61 188.93 Cassava sweet roots raw 28.98 2.96 45.53 64.95 Cassava sweet roots dried 14.31 1.30 45.20 28.84 Cassava flour 35.67 4.18 112.69 37.12 Sweet potato 50.00 3.93 35.65 110.37 Coco yam tuber 5.45 0.65 5.76 112.69 Potatoes tubers raw 9.00 1.58 5.03 314.51 Banana cooking 42.28 5.41 52.93 102.12 Starch 2.17 0.31 7.93 39.20 Sugar refined white 21.67 12.21 86.60 141.02 Honey local product 0.58 0.17 1.92 88.47 Lemon sweet 0.22 0.34 0.62 551.97 Peas dry 4.15 1.13 13.34 84.99 Beans dry 36.36 9.40 38.36 244.94 Lentil seed dried whole 7.39 1.75 20.67 84.75 Pulse product 1.04 0.18 1.02 177.34 Table 3.4: Food Item Protein Consumption by Area This table shows urban/ rural food consumption statistics by food commodity. The statistics comprise food commodity edible quantities and their respective monetary value, the amount of protein they provide, and the protein costs. This table is useful to identify differences between rural and urban areas with respect to protein consumption and protein unit values. Table 3.5: Consumption Statistics for Each Food Item by Region This table shows regional food consumption statistics by food commodity. The statistics comprise food commodity edible quantities and their respective monetary 101 Analyzing Food Security Using Household Survey Data Table 3.2: Food Item Protein Consumption at National Level Average Average food Average edible quantity consumption in Average protein protein consumed monetary value consumption unit value (g/person/day) (LCU/person/day) (g/person/day) (LCU/100 g) Food item Rice paddy or rough 6.12 0.98 0.40 241.71 Rice husked 48.14 18.65 3.61 516.60 Maize cob fresh 9.29 1.71 0.17 1019.90 Maize grain 72.72 7.01 6.85 102.33 Maize flour 163.94 27.63 13.28 208.07 Millet whole grain dried 1.68 0.33 0.20 171.37 Millet foxtail Italian whole grain 1.17 0.43 0.08 556.05 Sorghum whole grain brown 7.91 0.88 0.89 98.58 Sorghum average of all varieties 20.38 2.68 2.30 116.25 Wheat durum whole grain 0.46 0.11 0.06 176.74 Wheat meal or flour unspecified wheat 4.41 1.54 0.60 254.43 Wheat 1.30 0.20 0.30 66.05 Bread 3.11 1.65 0.27 603.70 Baby cereals 0.09 0.04 0.01 345.18 Biscuits wheat from Europe 0.15 0.27 0.01 1886.31 Buns cakes 3.25 3.10 0.15 2027.08 Macaroni spaghetti 0.27 0.16 0.03 582.82 Oats 2.31 1.63 0.39 417.84 Cassava sweet roots raw 28.98 2.96 0.41 728.87 Cassava sweet roots dried 14.31 1.30 0.37 350.35 Cassava flour 35.67 4.18 0.93 451.02 Sweet potato 50.00 3.93 0.60 655.78 Coco yam tuber 5.45 0.65 0.08 793.35 Potatoes tubers raw 9.00 1.58 0.23 676.19 Banana cooking 42.28 5.41 0.34 1598.24 Starch 2.17 0.31 0.01 4775.90 Sugar refined white 21.67 12.21 0.00 Table 3.3: Consumption Statistics for Each Food Item by Area Average food Average Average Average edible consumption in dietary energy dietary energy quantity consumed monetary value consumption unit value (g/person/day) (LCU/person/day) (kcal/person/day) (LCU/1000 kcals) Area Capital city Rice paddy or rough 0.60 0.12 2.11 58.75 Rice husked 108.34 43.98 381.34 115.32 Maize cob fresh 0.68 0.27 0.45 594.81 Maize grain 7.68 1.35 27.86 48.48 Maize flour 125.27 28.35 448.23 63.24 Millet whole grain dried 0.42 0.19 1.32 145.65 Millet foxtail Italian whole grain 0.71 0.49 2.23 220.35 Sorghum whole grain brown 0.13 0.05 0.46 98.48 Sorghum average of all varieties 0.04 0.01 0.15 58.51 Wheat durum whole grain 0.24 0.08 0.87 92.83 (continued) 102 Chapter 3: Guide to Output Tables Table 3.3: Consumption Statistics for Each Food Item by Area (continued) Average food Average Average Average edible consumption in dietary energy dietary energy quantity consumed monetary value consumption unit value (g/person/day) (LCU/person/day) (kcal/person/day) (LCU/1000 kcals) Wheat meal or flour unspecified 10.40 3.22 35.14 91.71 wheat Wheat 0.21 0.09 0.77 115.73 Bread 16.83 9.18 44.46 206.59 Baby cereals 0.04 0.06 0.14 427.80 Biscuits wheat from Europe 0.50 0.78 2.13 365.65 Buns cakes 10.44 9.75 33.69 289.34 Macaroni spaghetti 2.18 1.30 7.79 167.22 Oats 10.69 11.05 39.93 276.67 Cassava sweet roots raw 13.06 2.24 20.52 109.14 Cassava sweet roots dried 1.09 0.13 3.43 38.41 Cassava flour 0.83 0.15 2.64 55.26 Sweet potato 13.23 2.20 9.43 233.14 Coco yam tuber 2.45 0.54 2.59 210.14 Potatoes tubers raw 10.96 3.26 6.12 531.81 Banana cooking 13.80 5.55 17.27 321.50 Table 3.4: Food Item Protein Consumption by Area Average food Average Average edible consumption in protein Average protein quantity consumed monetary value consumption unit value (g/person/day) (LCU/person/day) (g/person/day) (LCU/100 g) Area Capital city Rice paddy or rough 0.60 0.12 0.04 310.22 Rice husked 108.34 43.98 8.13 541.26 Maize cob fresh 0.68 0.27 0.01 2171.06 Maize grain 7.68 1.35 0.72 186.70 Maize flour 125.27 28.35 10.15 279.37 Millet whole grain dried 0.42 0.19 0.05 397.77 Millet foxtail Italian whole grain 0.71 0.49 0.05 1055.33 Sorghum whole grain brown 0.13 0.05 0.01 309.64 Sorghum average of all varieties 0.04 0.01 0.00 183.97 Wheat durum whole grain 0.24 0.08 0.03 245.30 Wheat meal or flour unspecified wheat 10.40 3.22 1.42 226.19 Wheat 0.21 0.09 0.05 180.40 Bread 16.83 9.18 1.48 620.23 Baby cereals 0.04 0.06 0.01 1148.95 Biscuits wheat from Europe 0.50 0.78 0.05 1683.56 Buns cakes 10.44 9.75 0.49 1986.58 Macaroni spaghetti 2.18 1.30 0.23 567.92 Oats 10.69 11.05 1.81 611.88 Cassava sweet roots raw 13.06 2.24 0.18 1224.71 Cassava sweet roots dried 1.09 0.13 0.03 466.67 Cassava flour 0.83 0.15 0.02 671.35 Sweet potato 13.23 2.20 0.16 1385.23 Coco yam tuber 2.45 0.54 0.04 1479.39 Potatoes tubers raw 10.96 3.26 0.28 1143.39 Banana cooking 13.80 5.55 0.11 5031.45 103 Analyzing Food Security Using Household Survey Data Table 3.5: Consumption Statistics for Each Food Item by Region Average food Average Average edible consumption in dietary energy Average dietary quantity consumed monetary value consumption energy unit value (g/person/day) (LCU/person/day) (kcal/person/day) (LCU/1000 kcals) Region Region 1 Rice paddy or rough 0.29 0.06 1.02 60.51 Rice husked 26.25 10.63 92.39 115.08 Maize cob fresh 11.73 2.57 7.70 333.78 Maize grain 50.15 4.09 181.92 22.47 Maize flour 240.10 33.01 859.08 38.43 Millet whole grain dried 1.68 0.26 5.32 48.86 Millet foxtail Italian whole grain 1.67 1.11 5.28 210.73 Sorghum whole grain brown 20.07 1.84 71.29 25.80 Sorghum average of all varieties 102.41 12.41 363.88 34.12 Wheat durum whole grain 0.05 0.02 0.19 110.07 Wheat meal or flour unspecified wheat 3.35 1.17 11.30 103.09 Wheat 1.40 0.24 5.05 48.00 Bread 1.07 0.70 2.84 247.73 Baby cereals 0.04 0.01 0.13 52.66 Biscuits wheat from Europe 0.09 0.17 0.40 428.68 Buns cakes 2.69 2.38 8.67 274.66 Macaroni spaghetti 0.12 0.10 0.43 225.61 Oats 1.38 1.28 5.15 249.02 Cassava sweet roots raw 11.73 1.63 18.42 88.51 Cassava sweet roots dried 0.16 0.03 0.50 63.99 Cassava flour 0.29 0.06 0.91 64.02 Sweet potato 22.52 2.32 16.06 144.43 Coco yam tuber 0.24 0.05 0.25 197.18 Potatoes tubers raw 7.74 1.52 4.33 351.12 Banana cooking 2.33 0.52 2.92 179.39 value, the calories they provide, and the calorie costs. This table is useful to identify differences across regions with respect to calorie consumption and unit values. Table 3.6: Food Item Protein Consumption by Region This table shows regional food consumption statistics by food commodity. The statistics com- prise food commodity edible quantities and their respective monetary value, the amount of protein they provide, and the protein costs. This table is use- ful to identify differences across regions with respect to protein consumption and unit values. Table 3.7: Food Item Quantities by Food Source For food fortification programs, the distinction of the food source plays an important role. Food that is home-produced is assumed not to have been fortified and 104 Chapter 3: Guide to Output Tables Table 3.6: Food Item Protein Consumption by Region Average food Average edible consumption in Average protein Average protein quantity consumed monetary value consumption unit value (g/person/day) (LCU/person/day) (g/person/day) (LCU/100 g) Region Region 1 Rice paddy or rough 0.29 0.06 0 319.53 Rice husked 26.25 10.63 2 540.13 Maize cob fresh 11.73 2.57 0 1218.31 Maize grain 50.15 4.09 5 86.53 Maize flour 240.10 33.01 19 169.75 Millet whole grain dried 1.68 0.26 0 133.43 Millet foxtail Italian whole grain 1.67 1.11 0 1009.28 Sorghum whole grain brown 20.07 1.84 2 81.11 Sorghum average of all varieties 102.41 12.41 12 107.27 Wheat durum whole grain 0.05 0.02 0 290.83 Wheat meal or flour unspecified wheat 3.35 1.17 0 254.26 Wheat 1.40 0.24 0 74.82 Bread 1.07 0.70 0 743.74 Baby cereals 0.04 0.01 0 141.42 Biscuits wheat from Europe 0.09 0.17 0 1973.79 Buns cakes 2.69 2.38 0 1885.79 Macaroni spaghetti 0.12 0.10 0 766.22 Oats 1.38 1.28 0 550.72 Cassava sweet roots raw 11.73 1.63 0 993.23 Cassava sweet roots dried 0.16 0.03 0 777.43 Cassava flour 0.29 0.06 0 777.84 Sweet potato 22.52 2.32 0 858.17 Coco yam tuber 0.24 0.05 0 1388.15 Potatoes tubers raw 7.74 1.52 0 754.90 Banana cooking 2.33 0.52 0 2807.42 Table 3.7: Food Item Quantities by Food Source Purchases Own production Other sources Quantity “as Proportion of Quantity “as Proportion of Quantity “as Proportion of purchased,” households produced,” households received,” households g/person/ in total g/person/ in total g/person/ in total day households (%) day households (%) day households (%) Food item Rice husked 56.1 67.4 63.7 14.4 19.2 8.9 Maize grain 134.6 30.6 82.1 25.9 73.6 5.0 Maize flour 113.3 52.7 189.3 52.2 29.8 11.3 Buns cakes 6.0 51.6 2.8 1.2 1.2 7.2 Oats 5.4 30.7 18.6 2.1 1.4 4.8 Sweet potato 48.7 28.4 124.2 24.0 25.4 6.5 Sugar refined white 28.4 72.5 5.2 1.5 8.6 7.2 Beans dry 30.2 66.8 40.5 34.3 14.6 9.0 Groundnuts shelled 7.8 31.0 15.7 15.5 4.1 5.1 Onion garden 8.8 70.8 5.1 5.8 2.5 5.0 (continued) 105 Analyzing Food Security Using Household Survey Data Table 3.7: Food Item Quantities by Food Source (continued) Purchases Own production Other sources Quantity “as Proportion of Quantity “as Proportion of Quantity “as Proportion of purchased,” households produced,” households received,” households g/person/ in total g/person/ in total g/person/ in total day households (%) day households (%) day households (%) Food item Spinach raw 12.9 43.4 10.9 24.9 5.6 4.0 Tomato raw ripe 23.7 73.8 12.2 11.7 7.5 7.6 whole Cattle meat 25.5 63.9 18.2 2.4 9.1 7.7 Fish average of all 32.3 34.7 24.4 3.0 13.6 4.9 kinds raw Sardine salted dried 17.0 77.7 4.6 3.8 4.9 8.4 Milk cow fluid whole 39.1 30.8 90.3 9.0 16.2 5.0 Cooking oil other 9.9 43.8 6.7 2.0 2.8 2.4 Salt 12.3 85.4 6.5 2.3 4.2 4.4 Tea common dried 1.0 47.0 0.9 0.7 0.3 1.9 black Soft drinks 15.7 28.7 3.6 0.3 6.4 7.4 not to be fortifiable. In assessing the potential coverage of a fortified or fortifiable food, only food that is purchased should be included in the analysis. Information on the proportion of households purchasing a food commodity is also relevant for food fortification programs. For instance, in some cases processed staple foods (e.g., flour) are purchased by households in larger quantities than the respective unprocessed food commodity (e.g., grain). So, from a fortification policy perspective, the processed food can be as important as, or more important, than the staple (Fiedler 2009). Table 3.7 shows information for the 20 food commodities most purchased by households at the national level. The information comprises (1) the food quantities acquired from purchases (i.e., at the market, from street vendors, at shops, etc.) and the percentage of households that purchased these food quantities; (2) the food quantities from own production and the percentage of households that reported own production; and (3) the food quantities from other sources and the percentage of households that reported other sources.14 Note that the sum of the proportion of households that acquired the product through purchase, or received in kind, or from own consumption does not necessarily equal 100 percent because not all households might have consumed the food. 106 Chapter 3: Guide to Output Tables Table 3.8: Food Item Quantities by Food Source and Area This table shows information for the 20 food commodities most purchased by households by rural and urban areas separately. The information comprises (1) the food quantities acquired from purchases (i.e., at the market, from street vendors, at shops, etc.) and the percentage of households that purchased these food quantities; (2) the food quantities from own production and the percentage of households that reported own production; and (3) the food quantities from other sources and the percentage of households that reported other sources.15 Note that the sum of the proportion of households that acquired the product through purchase, or received in kind, or from own consumption does not necessarily equal 100 percent because not all households might have consumed the food. Table 3.9: Food Item Quantities by Food Source and Region This table shows information for the 20 food commodities most purchased by house- holds at the regional level. The information comprises (1) the food quanti- ties acquired from purchases (i.e., at the market, from street vendors, at shops, etc.) and the percentage of households that purchased these food quantities; (2) the food quantities from own production and the percent- age of households that reported own production; and (3) the food quanti- ties from other sources and the percentage of households that reported other sources.16 Note that the sum of the proportion of households that acquired the product through purchase, or received in kind, or from own consumption does not necessarily equal 100 percent because not all households might have consumed the food. Inequality The dispersion ratios measure inequality between the two extreme income quintile groups. They are calculated using as reference the aver- age values corresponding to the first quintile. The following tables show dispersion ratios related to food and nonfood consumption, as well as between each income quintile and the first quintile. Since consumption is positively correlated with income, when the first income quintile is used as reference, all the ratios are expected to be greater than 1. In this case, a higher ratio value implies higher inequality between the poorest and the richest groups. For instance, a dietary energy dispersion ratio of 107 108 Table 3.8: Food Item Quantities by Food Source and Area Purchases Own production Other sources Quantity “as Proportion of Quantity “as Proportion of Quantity “as Proportion of purchased,” households in total produced,” households in total received,” households in total g/person/day households (%) g/person/day households (%) g/person/day households (%) Area Capital city Rice husked 110.6 90.6 44.9 0.5 43.2 6.9 Maize flour 128.8 91.1 18.4 1.3 35.6 6.9 Bread 20.9 74.0 5.7 0.5 4.1 1.6 Buns cakes 11.7 86.2 1.0 0.8 1.7 5.1 Oats 13.4 78.9 1.8 1.3 1.8 5.2 Potatoes tubers raw 22.8 56.9 3.4 0.1 17.7 1.2 Sugar refined white 44.8 93.2 3.2 1.0 22.0 4.0 Beans dry 32.1 86.6 4.3 0.2 8.4 3.8 Coconut mature kernel 52.7 77.7 44.8 0.6 16.2 3.0 Onion garden common 12.5 88.2 0.5 0.7 3.8 2.8 Spinach raw 18.4 78.8 4.7 1.7 5.6 3.4 Tomato raw ripe whole 45.5 92.2 5.0 1.0 10.9 3.3 Cattle meat 34.2 83.1 16.0 0.1 9.4 2.4 Fish dried 10.7 56.9 5.1 0.1 1.6 0.8 Sardine salted dried 9.3 67.1 12.6 0.3 4.3 2.0 Cooking oil other 15.2 73.0 0.7 0.9 2.4 1.5 Salt 9.9 77.2 3.3 0.3 2.1 1.0 Tea common dried black 1.1 78.2 0.0 0.5 0.1 0.4 Soft drinks 33.4 66.0 3.6 0.3 9.1 12.8 Orange sweet juice fresh 17.8 56.5 0.6 0.1 6.6 6.6 Other urban areas Rice husked 81.6 90.2 64.6 6.8 20.6 10.6 Maize flour 116.4 70.6 159.4 25.1 27.5 7.0 Table 3.9: Food Item Quantities by Food Source and Region Purchases Own production Other sources Quantity “as Proportion of Quantity “as Proportion of Quantity “as Proportion of purchased,” households in total produced,” households in total received,” households in total g/person/day households (%) g/person/day households (%) g/person/day households (%) Region Region 1 Rice husked 46.1 54.1 16.8 6.0 9.9 5.2 Maize flour 167.9 35.7 244.5 71.2 29.6 13.7 Buns cakes 5.6 44.3 2.2 1.0 0.6 6.9 Sweet potato 34.7 32.4 49.4 15.1 13.6 6.1 Potatoes tubers raw 26.2 25.4 38.7 4.1 14.6 5.4 Sugar refined white 23.8 59.1 4.8 2.6 7.4 6.5 Beans dry 25.5 63.6 26.1 22.7 8.0 8.2 Groundnuts shelled 12.2 31.5 18.8 34.7 4.9 6.8 Onion garden common 7.2 65.9 9.3 7.1 2.6 8.7 Spinach raw 9.3 32.0 9.2 23.4 4.5 3.5 Tomato raw ripe whole 17.0 59.8 10.6 15.0 2.8 8.0 Other fruit 38.0 27.4 11.0 3.5 21.3 9.5 Cattle meat 22.6 68.4 8.4 4.6 9.0 15.2 Sardine salted dried 8.9 71.4 4.0 2.1 1.8 7.8 Milk cow fluid whole 35.0 25.3 56.0 10.3 21.4 11.3 Yogurt made from 28.4 34.6 85.5 16.0 28.7 22.7 whole milk Oil sunflower seed 6.2 33.9 2.2 0.8 2.0 2.8 Cooking oil other 7.9 46.8 0.5 3.9 2.6 1.7 Salt 12.6 77.9 3.8 2.3 4.5 6.3 Tea common dried black 0.9 30.0 0.3 1.8 0.2 1.4 Region 2 Rice husked 53.8 70.3 15.3 3.1 14.0 3.8 Maize grain 160.0 67.0 155.5 35.7 105.7 13.9 109 Analyzing Food Security Using Household Survey Data 2 (between the fifth and first income groups) indicates that households belonging to the highest income quintile consume twice as many calories as those in the lowest quintile. As the first income quintile values are used as reference, the dispersion ratio of the first quintile using itself as reference is 1. Another way to measure inequality in food consumption is with elas- ticities. An income elasticity of demand is used to measure how sensitive the demand for food consumed is with respect to a change in income. The income elasticity of the demand of food could be measured through the responsiveness of dietary energy, food expenditure, or Engel ratio to a varia- tion in income. Disaggregated by Population Group: Tables 4.1 to 4.5 Table 4.1: Dispersion Ratio of Food Consumption by Income Quintile within Population Groups This table shows dispersion ratios related to food and nonfood consumption. These dispersion ratios measure the inequality between each income quintile and the first quintile. While the amount of calories consumed has a limit due to biological factors, expenditures and income do not. Thus, the dispersion ratios of dietary energy consumption are expected to be smaller than those related to monetary values. Table 4.2: Dispersion Ratios of Share of Food Consumption (in Dietary Energy) by Food Source, Income Quintile, and Population Groups This table shows the dispersion ratios of the percentage of total dietary energy provided by each of the four sources of food acquisition. These dispersion ratios mea- sure the inequality between each income quintile and the first quintile. Table 4.3: Dispersion Ratios of Share of Food Consumption (in Monetary Values) by Food Source and Income Quintile within Population Groups This table shows the dispersion ratios of the percentage of total food expenditure that each of the four sources of food acquisition represents. These dispersion ratios measure the inequality between each income quintile and the first quintile. It is expected that the dispersion ratio of the food consumed away from home increases with income in general, because rich people spend more on food outside the home than do poor ones. 110 Chapter 3: Guide to Output Tables Table 4.1: Dispersion Ratio of Food Consumption by Income Quintile within Population Groups Average food Average total Average dietary consumption in consumption in energy consumption monetary value monetary value (kcal/person/day) (LCU/person/day) (LCU/person/day) Ratio to Ratio to Ratio to Average the first the first the first household reference reference reference size Average group Average group Average group Total Quintiles of income Lowest quintile 6.6 1596.05 1.00 101.64 1.00 142.49 1.00 2 5.6 2043.27 1.28 166.18 1.64 241.69 1.70 3 5.0 2249.74 1.41 218.23 2.15 333.62 2.34 4 4.2 2604.28 1.63 299.82 2.95 480.80 3.37 Highest quintile 3.4 3051.03 1.91 448.89 4.42 812.53 5.70 Area Capital city Quintiles of income Lowest quintile 5.6 938.17 1.00 119.22 1.00 164.03 1.00 2 6.5 1226.18 1.31 176.55 1.48 262.98 1.60 3 5.8 1433.59 1.53 226.33 1.90 378.08 2.30 4 4.5 1921.70 2.05 342.83 2.88 592.30 3.61 Highest quintile 3.5 2793.36 2.98 597.27 5.01 1160.41 7.07 Other urban areas Quintiles of income Lowest quintile 6.5 1334.91 1.00 110.75 1.00 152.28 1.00 2 5.7 1634.69 1.22 164.35 1.48 244.85 1.61 3 5.2 1858.39 1.39 221.00 2.00 350.09 2.30 4 4.5 2418.62 1.81 308.50 2.79 517.64 3.40 Highest quintile 3.2 2904.60 2.18 491.51 4.44 894.70 5.88 Table 4.4: Dispersion Ratios of Food Dietary Energy Unit Values, Total Income, and Engel Ratio by Income Quintile within Population Groups This table shows dispersion ratios of dietary energy unit value and income. These dis- persion ratios measure the inequality between each income quintile and the first quintile. Table 4.5: Income Demand Elasticities by Income Decile within Population Groups This table shows values of the demand elasticity of food consump- tion with respect to income. The demand for food is analyzed in terms of dietary energy, monetary values, and Engel ratio. The elasticity values can be 0, negative, or positive. A value of 0 means the demand for food consumption is not sensitive to an income change 111 Analyzing Food Security Using Household Survey Data Table 4.2: Dispersion Ratios of Share of Food Consumption (in Dietary Energy) by Food Source, Income Quintile, and Population Groups Share of food Share of Share of own consumed away Share of food from purchased food produced food from home other sources in in total food in total food in total food total food consumption (%) consumption (%) consumption (%) consumption (%) Ratio to Ratio to Ratio to Ratio to Average the first the first the first the first household reference reference reference reference size Shares group Shares group Shares group Shares group Total Quintiles of income Lowest quintile 6.6 40.31 1.00 53.34 1.00 1.82 1.00 4.54 1.00 2 5.6 42.01 1.04 51.83 0.97 2.11 1.16 4.06 0.89 3 5.0 51.34 1.27 41.80 0.78 2.62 1.44 4.25 0.94 4 4.2 62.10 1.54 30.64 0.57 3.37 1.86 3.88 0.86 Highest quintile 3.4 67.84 1.68 21.44 0.40 7.19 3.96 3.54 0.78 Area Capital city Quintiles of income Lowest quintile 5.6 87.24 1.00 3.89 1.00 3.53 1.00 5.34 1.00 2 6.5 87.92 1.01 0.98 0.25 7.01 1.98 4.10 0.77 3 5.8 92.12 1.06 0.33 0.08 6.03 1.71 1.51 0.28 4 4.5 87.59 1.00 0.06 0.02 10.61 3.00 1.73 0.32 Highest quintile 3.5 80.00 0.92 0.48 0.12 17.36 4.91 2.17 0.41 Other urban areas Quintiles of income Lowest quintile 6.5 76.84 1.00 17.09 1.00 1.97 1.00 4.10 1.00 2 5.7 73.27 0.95 19.53 1.14 2.57 1.30 4.63 1.13 3 5.2 78.68 1.02 15.65 0.92 2.32 1.18 3.34 0.82 4 4.5 81.11 1.06 12.29 0.72 4.16 2.11 2.43 0.59 Highest quintile 3.2 81.68 1.06 7.97 0.47 7.68 3.90 2.66 0.65 (i.e., that the demand for food consumption is income inelastic). When the value is negative, it means that the demand for the current food con- sumed decreases with an increase of income. A positive value could be classified into less than 1 (necessary foods) or more than 1 (luxurious foods) and means that an increase in income would increase the demand for food consumption. As far as dietary energy is concerned, small values of calorie-income elasticity suggest that an increase in income would not affect much of the calorie intake, but it may improve the quality of the diet consumed by mov- ing from cheap to more expensive food. On the other hand, Engel’s law states that given a set of tastes and preferences, an increase in income will 112 Chapter 3: Guide to Output Tables Table 4.3: Dispersion Ratios of Share of Food Consumption (in Monetary Values) by Food Source and Income Quintile within Population Groups Share of food Share of Share of consumed away Share of food from purchased food own produced from home other sources in in total food food in total food in total food total food consumption (%) consumption (%) consumption (%) consumption (%) Ratio to Ratio to Ratio to Ratio to Average the first the first the first the first household reference reference reference reference size Shares group Shares group Shares group Shares group Total Quintiles of income Lowest quintile 6.6 50.15 1.00 42.89 1.00 1.84 1.00 5.12 1.00 2 5.6 53.50 1.07 40.11 0.94 2.19 1.19 4.20 0.82 3 5.0 62.04 1.24 30.88 0.72 2.76 1.50 4.33 0.85 4 4.2 71.56 1.43 20.92 0.49 3.79 2.06 3.73 0.73 Highest quintile 3.4 76.58 1.53 12.06 0.28 7.96 4.32 3.40 0.66 Area Capital city Quintiles of income Lowest quintile 5.6 88.97 1.00 3.10 1.00 3.46 1.00 4.47 1.00 2 6.5 88.32 0.99 1.49 0.48 7.32 2.12 2.88 0.64 3 5.8 91.80 1.03 0.32 0.10 6.09 1.76 1.79 0.40 4 4.5 87.64 0.99 0.12 0.04 10.69 3.09 1.55 0.35 Highest quintile 3.5 80.22 0.90 0.47 0.15 16.22 4.69 3.08 0.69 correspond to an increase in food expenditure, but at a slower rate than that of income. Regarding the Engel ratio, the proportion of income dedi- cated to acquiring food decreases with an increase in income. On the whole, the elasticity of food consumption with respect to income is higher for lower income groups of the population than for higher ones. However, the elasticity of food in dietary energy terms with respect to income is lower than its elasticity in monetary terms. In other words, for higher income groups the variation of dietary energy consumption due to a variation in income is lower than the variation of food expenditure with respect to the same income variation. Availability of Micronutrients The micronutrients analyzed in the ADePT-Food Security Module are vitamin A, ascorbic acid, thiamine (B1), riboflavin (B2), B6, and cobalamin (B12), as well as the minerals calcium and iron. It is important to remember 113 Analyzing Food Security Using Household Survey Data Table 4.4: Dispersion Ratios of Food Dietary Energy Unit Values, Total Income, and Engel Ratio by Income Quintile within Population Groups Average income Average dietary energy unit (LCU/person/day) value (LCU/1000 kcals) Share of food Ratio to the consumption in Ratio to the first first reference total income (%) Mean reference group Shares group (Engel ratio) Total Quintiles of income Lowest quintile 152.11 1.00 63.68 1.00 66.8 2 265.79 1.75 81.33 1.28 62.5 3 395.28 2.60 97.00 1.52 55.2 4 614.73 4.04 115.12 1.81 48.8 Highest quintile 1898.29 12.48 147.13 2.31 23.6 Area Capital city Quintiles of income Lowest quintile 172.46 1.00 127.08 1.00 69.1 2 271.33 1.57 143.99 1.13 65.1 3 396.97 2.30 157.87 1.24 57.0 4 645.43 3.74 178.40 1.40 53.1 Highest quintile 2034.94 11.80 213.82 1.68 29.4 Other urban areas Quintiles of income Lowest quintile 160.34 1.00 82.97 1.00 69.1 2 268.20 1.67 100.54 1.21 61.3 3 404.36 2.52 118.92 1.43 54.7 4 633.34 3.95 127.55 1.54 48.7 Highest quintile 2208.29 13.77 169.22 2.04 22.3 that the statistics on micronutrients shown in the tables exclude food con- sumed away from home. Therefore, the statistics of total available vitamins and minerals are underestimated. In the tables, the available amount of micronutrients (derived from national household survey data) is compared to the estimated average requirement (EAR) and recommended nutrient intake (RNI)17 through ratios. The available amount is the numerator, and the EAR or RNI are the denominators of the ratios. For instance, if the ratio of vitamin A available to vitamin A required is 100 percent, we could expect (under equal distribu- tion of vitamin A within the population) that half of the healthy individuals in the population meet its required level of vitamin A. As for the ratio of availability to recommend safe intake, if it is greater than 100 percent (under equal distribution of vitamin A within the population) we could expect that almost all apparently healthy individuals meet their requirement. 114 Chapter 3: Guide to Output Tables Table 4.5: Income Demand Elasticities by Income Decile within Population Groups Demand elasticity Demand elasticity Demand elasticity of of the share of Average income of food in dietary food consumption food consumption (LCU/person/day) energy consumption in monetary value in monetary value Total Deciles of income 1 122.00 0.36 2.28 0.91 2 188.26 0.31 1.15 0.91 3 240.31 0.29 0.90 0.90 4 294.60 0.27 0.76 0.90 5 355.73 0.26 0.66 0.90 6 436.89 0.25 0.58 0.90 7 539.38 0.24 0.52 0.90 8 698.55 0.22 0.46 0.89 9 1001.65 0.21 0.39 0.89 10 2927.19 0.17 0.28 0.87 Area Capital city Deciles of income 1 127.09 1.20 5.35 0.86 2 185.05 0.83 1.78 0.85 3 243.00 0.68 1.20 0.85 4 300.12 0.59 0.96 0.84 5 357.19 0.54 0.82 0.84 6 439.84 0.48 0.70 0.83 7 540.86 0.44 0.61 0.82 8 720.97 0.39 0.52 0.81 9 1030.48 0.34 0.44 0.80 10 2999.53 0.25 0.30 0.75 If the mean micronutrient intake is equal or exceeds mean micronutrient requirements, it cannot be concluded that group diets (group mean intakes, not individual diets) were adequate and conformed to recognized nutritional standards. The reason is that the prevalence of inadequacy depends on the shape and variation of the usual intake distribution, not on mean intake. If the mean intake equals the EAR, it is likely that a very high proportion of the population will have inadequate usual intake. In fact, roughly half of the population is expected to have intakes less than its requirement (except for energy). (NAS 2000) Disaggregated by Population Group: Tables 5.1 to 5.7 Table 5.1: Availability of Vitamin A This table shows the daily per person retinol, beta-carotene, and vitamin A available for human consumption. It also shows the vitamin A estimated average requirement and recommended nutrient intake for a representative individual of the population of analysis. 115 116 Table 5.1: Availability of Vitamin A Average Vitamin Vitamin A Ratio of Average Average vitamin A A mean Ratio of recommended vitamin A retinol Ratio of retinol beta-carotene availability requirement vitamin A safe intake available to availability available to availability (mcg RAE/ (mcg RAE/ available to (mcg RAE/ recommended (mcg/person/ vitamin A (mcg/person/ person/day) person/day) required (%) person/day) (%) day) available (%) day) Total 717 279 257 527 136 22 3.0 8320 Quintiles of income Lowest quintile 682 276 247 522 131 12 1.8 8011 2 705 279 252 526 134 18 2.6 8211 3 714 280 255 528 135 21 2.9 8285 4 785 281 279 531 148 28 3.6 9048 Highest quintile 729 282 259 535 136 39 5.3 8247 Area Capital city 313 285 110 537 58 22 6.9 3487 Other urban areas 581 283 205 531 109 23 3.9 6660 Rural areas 770 278 277 526 146 22 2.8 8953 Household size One person 797 291 274 569 140 36 4.5 9107 Between 2 and 3 people 766 281 272 536 143 27 3.5 8843 Between 4 and 5 people 677 273 248 520 130 22 3.2 7837 Between 6 and 7 people 711 279 255 525 135 19 2.6 8280 More than 7 731 283 258 529 138 21 2.9 8482 Gender of the household head Male 694 279 249 528 131 22 3.1 8035 Female 820 282 291 524 156 22 2.7 9556 Chapter 3: Guide to Output Tables Vitamin A is an essential nutrient needed in small amounts by humans for the normal functioning of vision, growth and development, mainte- nance of epithelial cellular integrity, immune system functioning, and reproduction (FAO/WHO 2004). It can be found in food of animal origin or under the form of a precursor of vitamin A in vegetal origin food. Low intake of vitamin A (as carotenoids) tends to reflect low intake of fruits and vegetables (USHHS/USDA 2005). The main consequence of vita- min A deficiency is night blindness, which can develop into irreversible blindness. Table 5.2: Availability of B Vitamins This table shows daily per person quantities of the vitamins B1 (thiamine), B2 (riboflavin), B6, and B12 (cobalamin) available for human consumption. It also shows their estimated average requirement and the cobalamin recommended nutrient intake for a representative individual of the population of analysis. Thiamine deficiency occurs when the diet consists mainly of milled white cereals, including polished rice and wheat flour, all of which are very poor sources of thiamine (WHO/UNHCR 1999). A deficiency of thiamine results in the disease beriberi, which provokes damage to the nervous system, heart failure, and gastrointestinal illnesses. A deficiency of riboflavin, which is present in a wide variety of food, results in the condition of hypo- or aribofla- vinosis. The major cause of the former is inadequate dietary intake, which is sometimes exacerbated by poor food storage or processing (FAO/WHO 2004). Vitamin B6 can be found in a wide variety of food, and its deficiency results in an impairment of the immune system. “A deficiency of vitamin B6 alone is uncommon because it usually occurs in association with a deficit in other B-complex vitamins” (FAO/WHO 2004). A deficiency in cobalamin could cause the autoimmune disease perni- cious anemia. “Products from herbivorous animals, such as milk, meat, and eggs, constitute important dietary sources of cobalamin, unless the animal is subsisting in one of the many regions known to be geochemically deficient in cobalt” (FAO/WHO 2004). Table 5.3: Availability of Vitamin C and Calcium This table shows the daily per person amount of vitamin C (ascorbic acid) and calcium available for human consumption, their recommended intake for a representative indi- vidual of the population of analysis, and the ratios between available and recommended quantities. 117 Table 5.2: Availability of B Vitamins 118 Ratio Average Vitamin Average Ratio Average Ratio Average vita- vitamin B12 Ratio Vitamin Ratio vitamin Vitamin vitamin vitamin Vitamin vitamin vitamin Vitamin min B6 B12 average vitamin B12 recom- vitamin B1 avail- B1 recom- B1 avail- B2 avail- B2 recom- B2 avail- B6 avail- B6 recom- avail- avail- require- B12 mended B12 ability mended able to ability mended able to ability mended able to ability ment avail- intake available (mg/ intake (mg/ recom- (mg/ intake (mg/ recom- (mg/ intake (mg/ recom- (mcg/ (mcg/ able to (mcg/ to recom- person/ person/ mended person/ person/ mended person/ person/ mended person/ person/ required person/ mended day) day) (%) day) day) (%) day) day) (%) day) day) (%) day) (%) Total 2.13 0.98 217 1.75 1.01 174 2.31 1.11 207 1.63 1.68 97 2.03 80 Quintiles of income Lowest 1.86 0.95 195 1.32 0.97 136 1.90 1.08 177 0.97 1.63 60 1.97 49 quintile 2 2.20 0.98 225 2.25 1.00 225 2.22 1.11 200 1.33 1.67 80 2.01 66 3 2.16 0.98 220 1.70 1.01 169 2.37 1.11 213 1.69 1.69 100 2.03 83 4 2.28 1.00 229 1.73 1.02 169 2.59 1.14 228 2.04 1.71 119 2.06 99 Highest 2.29 1.02 224 1.85 1.05 176 2.78 1.16 240 2.77 1.75 158 2.11 132 quintile Area Capital 1.41 1.03 137 1.19 1.05 113 1.49 1.16 128 1.79 1.77 102 2.12 84 city Other 1.80 1.00 180 1.43 1.02 139 2.15 1.13 189 1.76 1.72 102 2.07 85 urban areas Rural 2.23 0.97 229 1.85 1.00 185 2.39 1.11 216 1.59 1.67 95 2.01 79 areas Household size One 2.87 1.16 247 2.37 1.22 195 2.77 1.40 198 3.58 2.00 179 2.39 149 person Between 2.40 1.04 231 1.99 1.07 186 2.62 1.21 215 2.20 1.80 122 2.16 102 2 and 3 people Between 2.19 0.96 228 2.10 0.99 214 2.30 1.08 212 1.72 1.64 105 1.98 87 4 and 5 people Between 2.17 0.96 225 1.50 0.98 152 2.31 1.08 213 1.40 1.65 85 1.99 71 6 and 7 people More 1.88 0.98 193 1.52 1.00 152 2.15 1.10 195 1.36 1.67 82 2.01 68 than 7 Gender of the household head Male 2.13 0.98 217 1.74 1.01 172 2.31 1.11 208 1.62 1.68 97 2.02 80 Female 2.11 0.99 215 1.81 1.00 182 2.29 1.13 203 1.66 1.71 97 2.06 80 Chapter 3: Guide to Output Tables Table 5.3: Availability of Vitamin C and Calcium Average Average vitamin C Vitamin C Ratio vitamin calcium Calcium Ratio calcium availability recommended C available to availability recommended available to (mg/person/ intake (mg/ recommended (mg/person/ intake (mg/ recommended day) person/day) (%) day) person/day) (%) Total 92.42 39.57 233.58 295.91 747.00 39.61 Quintiles of income Lowest quintile 77.54 38.83 199.69 215.85 739.24 29.20 2 92.20 39.34 234.40 286.70 748.71 38.29 3 94.11 39.60 237.63 301.06 749.28 40.18 4 100.28 40.02 250.58 340.82 752.37 45.30 Highest quintile 109.37 40.75 268.39 403.01 749.18 53.79 Area Capital city 50.46 40.70 124.00 279.42 760.71 36.73 Other urban areas 66.81 40.06 166.76 251.53 756.31 33.26 Rural areas 99.83 39.40 253.39 304.71 744.41 40.93 Household size One person 109.56 44.92 243.92 514.17 762.83 67.40 Between 2 and 3 103.95 41.72 249.18 373.42 749.59 49.82 people Between 4 and 5 92.29 39.30 234.84 303.94 727.36 41.79 people Between 6 and 7 93.23 38.92 239.54 270.75 748.61 36.17 people More than 7 85.88 39.07 219.81 263.52 760.00 34.67 Gender of the household head Male 92.44 39.52 233.89 291.44 742.78 39.24 Female 92.31 39.75 232.23 315.23 765.26 41.19 Age of the household head Less than 35 92.49 38.82 238.24 298.52 696.02 42.89 Between 35 and 45 89.91 39.07 230.14 278.70 748.63 37.23 Between 46 and 60 92.93 40.12 231.61 311.90 774.21 40.29 More than 60 96.70 40.87 236.58 297.31 780.73 38.08 Ascorbic acid is an antioxidant, and it is found in many fruits and vege- tables. The vitamin C content of food is strongly influenced by many factors, including transport to market, storage, and cooking practices. A common feature of vitamin C deficiency is anemia, because ascorbic acid is a pro- moter of nonheme iron absorption (FAO/WHO 2004). It is not possible to relate servings of fruits and vegetables to an exact amount of vitamin C, but the WHO dietary goal of 400g of fruits and vegetables consumed per day (five portions of them) is aimed at providing sufficient vitamin C to meet the 1970 FAO/WHO guidelines (FAO/WHO 2004). Low intake of calcium tends to reflect low intake of milk and milk products (USHHS/USDA 2005). There is a wide variation in calcium intake between 119 Analyzing Food Security Using Household Survey Data countries, generally following the animal protein intake and depending largely on dairy product consumption. Calcium salts provide rigidity to the skeleton, and calcium ions play a role in many, if not most, metabolic processes (FAO/ WHO 2004). The populations at risk of calcium deficiency comprise children during the first two years of life, puberty, and adolescence; pregnant, lactating, and postmenopausal women; and, possibly, elderly men (FAO/WHO 2004). Table 5.4: Availability of Iron This table shows daily per person iron avail- ability for human consumption according to its source (animal or nonanimal origin) and its status (heme or nonheme). The food commodities considered animal origin are meat (red and white), fish, eggs, milk, and cheese. It also shows the median and the 95th percentile18 daily requirements of total iron intake for a representative individual of the population. Table 5.4: Availability of Iron Average iron Median of Average iron availability Average Average the average 95th percentile availability from heme iron nonheme iron absolute of the average from animal nonanimal availability availability iron intake absolute iron sources (mg/ sources (mg/ (mg/person/ (mg/person/ required (mg/ intake required person/day) person/day) day) day) person/day) (mg/person/day) Total 0.29 16.14 0.11 16.33 1.09 1.59 Quintiles of income Lowest quintile 0.17 13.45 0.07 13.56 1.06 1.52 2 0.24 16.58 0.09 16.73 1.10 1.57 3 0.30 16.44 0.11 16.64 1.10 1.60 4 0.36 17.55 0.13 17.79 1.11 1.62 Highest quintile 0.50 18.47 0.18 18.80 1.12 1.69 Area Capital city 0.35 11.13 0.12 11.36 1.17 1.74 Other urban areas 0.30 13.79 0.10 13.99 1.13 1.68 Rural areas 0.29 16.91 0.11 17.09 1.08 1.56 Household size One person 0.65 19.81 0.24 20.22 1.11 1.66 Between 2 and 3 people 0.39 18.89 0.14 19.14 1.12 1.73 Between 4 and 5 people 0.32 16.53 0.11 16.74 1.06 1.57 Between 6 and 7 people 0.25 15.47 0.09 15.63 1.09 1.55 More than 7 0.25 14.97 0.09 15.12 1.11 1.58 Gender of the household head Male 0.29 16.06 0.11 16.24 1.08 1.57 Female 0.29 16.53 0.10 16.72 1.13 1.68 Age of the household head Less than 35 0.33 16.68 0.12 16.89 1.02 1.55 Between 35 and 45 0.29 15.54 0.10 15.73 1.11 1.61 Between 46 and 60 0.28 16.38 0.10 16.56 1.13 1.64 More than 60 0.26 16.05 0.10 16.21 1.10 1.54 120 Chapter 3: Guide to Output Tables Iron has several vital functions in the body, including the transportation of oxygen to the tissues from the lungs by red blood cell hemoglobin (WHO 2004). There are two kinds of iron compounds in the diet with respect to the mechanism of absorption: heme iron (derived from hemoglobin and myoglo- bin) and nonheme iron (derived mainly from cereals, fruits, and vegetables). Heme iron forms a relatively minor part of iron intake. Even in diets with high meat content it accounts for only 10–15 percent of the total iron intake. Diets in developing countries usually contain negligible amounts of heme iron. Nonheme iron is thus the main source of dietary iron (Hallberg 1981). Table 5.5: Density of Calcium per 1,000 Kcal This table shows the nutrient density19 of calcium (mg/1,000 kcal) present in the food consumed by the population, the recommended intake, and the ratio between available and rec- ommended. The first is estimated based on calcium and calorie consumption (using the food consumption data from the survey), and the second is based Table 5.5: Density of Calcium per 1,000 Kcal Average calcium Calcium Ratio calcium Average dietary availability recommended intake available to energy requirement (mg/1000 kcal) (mg/1000 kcal) recommended (%) (kcal/person/day) Total 139 355 39 2106 Quintiles of income Lowest quintile 138 366 38 2018 2 143 360 40 2080 3 137 356 39 2107 4 135 349 39 2155 Highest quintile 142 332 43 2254 Area Capital city 156 337 46 2256 Other urban areas 121 349 35 2166 Rural areas 141 357 40 2085 Household size One person 171 287 59 2654 Between 2 and 3 people 145 328 44 2285 Between 4 and 5 people 139 351 40 2071 Between 6 and 7 people 133 367 36 2041 More than 7 138 366 38 2078 Gender of the household head Male 137 351 39 2117 Female 149 372 40 2059 Age of the household head Less than 35 135 344 39 2024 Between 35 and 45 134 361 37 2072 Between 46 and 60 145 354 41 2189 More than 60 146 361 41 2162 121 Analyzing Food Security Using Household Survey Data on the recommended calcium intake and the average dietary energy require- ment.20 The ratio compares the available and the recommended amounts and can be used to understand if (and to what extent) the amounts available are above or below the requirements. The notion of nutrient density is helpful for defining food-based dietary guidelines (FBDG) and evaluating the adequacy of diets. Unlike recom- mended intakes, FBDG can be used to educate the public through the mass media and provide a practical guide to selecting foods by defining dietary adequacy (WHO 2004). Table 5.6: Density of Vitamin A and Vitamin C per 1,000 Kcal This table shows the nutrient density21 of vitamins A and C (mcg retinol activ- ity equivalent [RAE] or mg/1,000 kcal) present in the food consumed by the population, the respective required densities, and the available to recommended ratios. The nutrient density in the food consumed is estimated based on the nutrient and calorie consumption. The required nutrient density considers the estimated nutrient average requirement,22 while the recommended one uses the recommended nutrient intake. Both required and recommended nutrient densities are based on the average dietary energy requirement. The notion of nutrient density is helpful for defining food-based dietary guidelines and evaluating the adequacy of diets. Unlike recommended intakes, FBDG can be used to educate the public through the mass media and provide a practical guide to selecting foods by defining dietary adequacy (WHO 2004). Table 5.7: Density of B Vitamins per 1,000 Kcal This table shows the nutrient density23 of vitamins B1, B2, and B6 (in mg/1,000 kcal), and B12 (in mcg/1,000 kcal) present in the food consumed by the population. It also shows the required densities of the vitamins, the recommended one for vitamin B12, and the respective available to recommended ratios. The nutrient density (grams of nutrient per 1,000 kcal) of the food con- sumed is estimated based on the nutrient and calorie consumption. Similarly, the required and recommended nutrient densities are calculated using the estimated nutrient average requirement and recommended nutrient intake, respectively. In both cases the 1,000 required calories are based on the average dietary energy requirements for a representative individual of the population.24 For instance, the protein density of the food consumed refers to how many grams of protein are consumed per 1,000 calories consumed. 122 Table 5.6: Density of Vitamin A and Vitamin C per 1,000 Kcal Average Vitamin vitamin A A mean Ratio of Vitamin A Ratio of vitamin Average Vitamin C Ratio vitamin C availability requirement, vitamin A recommended A available to vitamin C recommended available to (mcg RAE/1000 mcg RAE/1000 available to safe intake, mcg recommended availability safe intake, recommended kcal) kcal required (%) RAE/1000 kcal (%) (mg/1000 kcal) mg/1000 kcal (%) Total 338 133 255 250 135 43 19 231 Quintiles of income Lowest quintile 435 137 318 259 168 49 19 257 2 352 134 262 253 139 46 19 244 3 326 133 245 250 130 43 19 229 4 312 131 239 246 127 40 19 215 Highest quintile 257 125 206 237 108 39 18 214 Area Capital city 175 126 138 238 74 28 18 156 Other urban areas 280 131 214 245 114 32 18 174 Rural areas 357 133 267 252 141 46 19 245 Household size One person 264 110 241 214 123 36 17 215 Between 2 and 3 people 298 123 242 234 127 40 18 221 Between 4 and 5 people 310 132 236 251 124 42 19 223 Between 6 and 7 people 349 137 255 257 136 46 19 240 More than 7 384 136 281 255 151 45 19 240 Gender of the household head Male 326 132 248 249 131 43 19 233 Female 388 137 284 255 153 44 19 226 Age of the household head Less than 35 342 130 263 252 136 42 19 219 Between 35 and 45 326 135 241 254 128 43 19 229 Between 46 and 60 316 132 240 245 129 43 18 236 More than 60 398 134 298 252 158 48 19 252 123 124 Table 5.7: Density of B Vitamins per 1,000 Kcal Vitamin Ratio Vitamin Ratio Vitamin Ratio Vitamin Ratio Vitamin Ratio B1 recom- vitamin Average B2 recom- vitamin Average B6 recom- vitamin Average B12 vitamin B12 vitamin Average mended B1 vitamin mended B2 vitamin mended B6 vitamin average B12 recom- B12 vitamin B1 safe available B2 avail- safe available B6 avail- safe available B12 avail- require- available mended available availability intake, to recom- ability intake, to recom- ability intake, to recom- ability ment, to safe intake to recom- (mg/1000 mg/1000 mended (mg/1000 mg/1000 mended (mg/1000 mg/1000 mended (mcg/1000 mcg/1000 required (mcg/1000 mended kcal) kcal (%) kcal) kcal (%) kcal) kcal (%) kcal) kcal (%) kcal) (%) Total 1.00 0.47 215 0.82 0.48 172 1.08 0.53 205 0.76 0.80 96 0.96 80 Quintiles of income Lowest 1.18 0.47 251 0.84 0.48 175 1.21 0.53 228 0.62 0.81 77 0.97 64 quintile 2 1.10 0.47 234 1.13 0.48 234 1.11 0.53 208 0.67 0.80 83 0.97 69 3 0.99 0.47 212 0.78 0.48 163 1.08 0.53 205 0.77 0.80 96 0.96 80 4 0.91 0.46 196 0.69 0.48 145 1.03 0.53 195 0.81 0.80 102 0.96 85 Highest 0.81 0.45 179 0.65 0.47 140 0.98 0.51 191 0.98 0.78 126 0.94 105 quintile Area Capital city 0.79 0.45 173 0.67 0.47 142 0.83 0.51 161 1.00 0.78 128 0.94 106 Other urban 0.87 0.46 188 0.69 0.47 145 1.03 0.52 198 0.85 0.80 107 0.96 89 areas Rural areas 1.04 0.47 222 0.86 0.48 179 1.11 0.53 209 0.74 0.80 92 0.96 76 Household size One person 0.95 0.44 218 0.79 0.46 171 0.92 0.53 175 1.19 0.75 158 0.90 132 Between 2 and 0.93 0.46 205 0.77 0.47 165 1.02 0.53 191 0.85 0.79 108 0.95 90 3 people Between 4 and 1.00 0.46 216 0.96 0.48 203 1.05 0.52 202 0.79 0.79 100 0.96 83 5 people Between 6 and 1.06 0.47 226 0.74 0.48 153 1.13 0.53 214 0.69 0.81 85 0.97 71 7 people More than 7 0.99 0.47 210 0.80 0.48 166 1.13 0.53 213 0.72 0.81 89 0.97 74 Gender of the household head Male 1.00 0.46 216 0.82 0.48 171 1.09 0.52 207 0.76 0.79 96 0.95 80 Female 1.00 0.48 209 0.86 0.48 178 1.08 0.55 198 0.79 0.83 94 1.00 78 Chapter 3: Guide to Output Tables Similarly, the nutrient density required/recommended of protein are the grams of protein required/recommended per 1,000 calories required. The notion of nutrient density is helpful for defining food-based dietary guidelines and evaluating the adequacy of diets. Unlike recommended intakes, FBDG can be used to educate the public through the mass media and provide a practical guide to selecting foods by defining dietary adequacy (WHO 2004). Disaggregated by Food Commodity Group: Tables 6.1 to 6.6 The micronutrients analyzed in the ADePT-Food Security Module are vita- min A, ascorbic acid, thiamine, riboflavin, B6, cobalamin, and the minerals calcium and iron. It is important to remember that the statistics shown in the tables exclude the food consumed away from home. Therefore, the val- ues of total available vitamins and minerals are underestimated. Table 6.1: Micronutrient Availability by Food Group This table shows how much each food commodity group contributes, in quantitative terms, to the total micronutrient availability at the national level. Each time N/A replaces a nutrient quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisition of that food group. Table 6.2: Micronutrient Availability by Food Group and Income Quintile This table shows how much each food commodity group contributes, in quanti- tative terms, to the total micronutrient availability in each income quintile group. Each time N/A replaces a nutrient quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisition of that food group. Table 6.3: Micronutrient Availability by Food Group and Area This table shows how much each food commodity group contributes, in quantitative terms, to the total micronutrient availability in urban and rural areas. Each time N/A replaces a nutrient quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisition of that food group. Table 6.4: Micronutrient Availability by Food Group and Region This table shows how much each food commodity group contributes, in quantitative terms, to the total micronutrient availability in each region. Each time N/A 125 126 Table 6.1: Micronutrient Availability by Food Group Average micronutrient availability RAE of Beta- vitamin Retinol carotene Vitamin Vitamin Vitamin Vitamin Vitamin Calcium Animal Nonanimal Heme Nonheme A (mcg/ (mcg/ (mcg/ B1 (mg/ B2 (mg/ B6 (mg/ B12 (mcg/ C (mg/ (mg/ iron (mg/ iron (mg/ iron (mg/ iron (mg/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ day) day) day) day) day) day) day) day) day) day) day) day) day) Food group Cereals 29.23 1.11 312.05 1.26 0.55 1.28 0.01 0.00 44.16 N/A 10.25 N/A 10.25 Roots and tubers 535.47 N/A 6425.59 0.23 0.14 0.51 N/A 52.23 42.72 N/A 1.54 N/A 1.54 Sugars and syrups 0.01 N/A 0.08 N/A 0.14 N/A N/A 0.02 0.30 N/A 0.03 N/A 0.03 Pulses 1.04 N/A 12.56 0.14 0.06 0.09 N/A 0.37 17.17 N/A 1.75 N/A 1.75 Tree nuts N/A N/A N/A 0.00 0.00 0.00 N/A 0.00 1.20 N/A 0.02 N/A 0.02 Oil crops 0.02 N/A 0.21 0.03 0.01 0.03 N/A 0.22 12.99 N/A 0.62 N/A 0.62 Vegetables 84.39 N/A 1011.89 0.37 0.67 0.11 N/A 14.65 58.88 N/A 1.23 N/A 1.23 Fruits 46.11 N/A 551.57 0.06 0.05 0.12 N/A 24.70 5.62 N/A 0.31 N/A 0.31 Stimulants 0.30 0.30 N/A 0.00 0.00 0.00 0.00 0.01 0.89 N/A 0.04 N/A 0.04 Spices 0.53 N/A 6.01 0.00 0.00 0.00 N/A 0.13 7.51 N/A 0.08 N/A 0.08 Alcoholic beverages N/A N/A N/A 0.00 0.00 0.05 N/A N/A 2.41 N/A 0.00 N/A 0.00 Meat 1.20 1.20 N/A 0.03 0.04 0.07 0.35 0.00 1.33 0.08 0.27 0.04 0.31 Eggs 1.27 1.27 N/A 0.00 0.00 0.00 0.01 N/A 0.38 0.01 N/A N/A 0.01 Fish 5.49 5.49 N/A 0.01 0.02 0.04 1.10 0.02 54.76 0.18 N/A 0.06 0.12 Milk and cheese 11.38 11.38 N/A N/A 0.06 0.00 0.16 0.07 45.41 0.03 N/A N/A 0.03 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A 0.00 N/A N/A 0.00 N/A 0.00 Oils and fats (animal) 1.03 1.03 N/A N/A 0.00 N/A 0.00 N/A 0.03 N/A N/A N/A N/A Nonalcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A 0.15 N/A N/A N/A N/A Miscellaneous and prepared food Table 6.2: Micronutrient Availability by Food Group and Income Quintile Average micronutrient availability RAE of Beta- vitamin Retinol carotene Vitamin Vitamin Vitamin Vitamin Vitamin Calcium Animal Nonanimal Heme Nonheme A (mcg/ (mcg/ (mcg/ B1 (mg/ B2 (mg/ B6 (mg/ B12 (mcg/ C (mg/ (mg/ iron (mg/ iron (mg/ iron (mg/ iron (mg/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ day) day) day) day) day) day) day) day) day) day) day) day) day) Quintiles of income Lowest quintile Cereals 26.23 0.27 288.14 1.04 0.49 1.05 0.00 0.00 28.30 N/A 8.99 N/A 8.99 Roots and tubers 566.08 N/A 6792.96 0.25 0.13 0.56 N/A 56.86 43.26 N/A 1.52 N/A 1.52 Sugars and syrups 0.00 N/A 0.02 N/A 0.04 N/A N/A 0.01 0.09 N/A 0.01 N/A 0.01 Pulses 0.73 N/A 8.80 0.10 0.04 0.06 N/A 0.27 12.51 N/A 1.27 N/A 1.27 Tree nuts N/A N/A N/A 0.00 0.00 0.00 N/A 0.00 0.90 N/A 0.01 N/A 0.01 Oil crops 0.02 N/A 0.21 0.02 0.01 0.02 N/A 0.05 8.08 N/A 0.30 N/A 0.30 Vegetables 55.92 N/A 670.31 0.40 0.52 0.08 N/A 9.04 53.36 N/A 1.07 N/A 1.07 Fruits 20.82 N/A 249.41 0.03 0.02 0.05 N/A 11.20 2.17 N/A 0.14 N/A 0.14 Stimulants 0.10 0.10 N/A 0.00 0.00 0.00 0.00 0.00 0.21 N/A 0.01 N/A 0.01 Spices 0.08 N/A 0.95 0.00 0.00 0.00 N/A 0.03 3.83 N/A 0.03 N/A 0.03 Alcoholic beverages N/A N/A N/A 0.00 0.00 0.02 N/A N/A 1.21 N/A 0.00 N/A 0.00 Meat 0.68 0.68 N/A 0.01 0.02 0.03 0.15 0.00 0.59 0.04 0.11 0.02 0.13 Eggs 0.19 0.19 N/A 0.00 0.00 0.00 0.00 N/A 0.06 0.00 N/A N/A 0.00 Fish 3.72 3.72 N/A 0.01 0.01 0.03 0.73 0.01 33.48 0.12 N/A 0.04 0.08 Milk and cheese 6.86 6.86 N/A N/A 0.03 0.00 0.09 0.06 27.77 0.01 N/A N/A 0.01 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A 0.00 N/A N/A 0.00 N/A 0.00 Oils and fats (animal) 0.56 0.56 N/A N/A 0.00 N/A 0.00 N/A 0.01 N/A N/A N/A N/A Nonalcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A 0.01 N/A N/A N/A N/A Quintile 2 Cereals 30.37 0.53 334.37 1.23 0.56 1.23 0.00 0.00 37.44 N/A 10.71 N/A 10.71 Roots and tubers 529.41 N/A 6352.88 0.24 0.13 0.52 N/A 53.83 42.79 N/A 1.51 N/A 1.51 Sugars and syrups 0.00 N/A 0.04 N/A 0.08 N/A N/A 0.01 0.19 N/A 0.02 N/A 0.02 Pulses 0.93 N/A 11.15 0.12 0.05 0.08 N/A 0.32 15.34 N/A 1.55 N/A 1.55 127 128 Table 6.3: Micronutrient Availability by Food Group and Area Average micronutrient availability Vitamin Animal RAE of Retinol Beta- Vitamin Vitamin Vitamin B12 Vitamin Calcium iron Nonanimal Heme Nonheme vitamin (mcg/ carotene B1 (mg/ B2 (mg/ B6 (mg/ (mcg/ C (mg/ (mg/ (mg/ iron (mg/ iron (mg/ iron (mg/ A (mcg/ person/ (mcg/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/day) day) person/day) day) day) day) day) day) day) day) day) day) day) Area Capital city Cereals 18.30 3.55 174.36 1.10 0.36 1.00 0.02 0.00 60.20 N/A 7.42 N/A 7.42 Roots and tubers 140.16 N/A 1681.92 0.03 0.06 0.08 N/A 7.81 9.99 N/A 0.49 N/A 0.49 Sugars and syrups 0.01 N/A 0.13 N/A 0.22 N/A N/A 0.04 0.65 N/A 0.06 N/A 0.06 Pulses 0.81 N/A 9.74 0.09 0.04 0.06 N/A 0.20 11.79 N/A 1.22 N/A 1.22 Tree nuts N/A N/A N/A 0.00 0.00 0.00 N/A 0.00 0.49 N/A 0.01 N/A 0.01 Oil crops 0.00 N/A 0.02 0.01 0.00 0.03 N/A 0.82 6.42 N/A 0.72 N/A 0.72 Vegetables 111.82 N/A 1341.86 0.07 0.35 0.10 N/A 17.56 20.21 N/A 0.54 N/A 0.54 Fruits 22.22 N/A 256.61 0.05 0.03 0.08 N/A 23.59 11.06 N/A 0.17 N/A 0.17 Stimulants 0.03 0.03 N/A 0.00 0.01 0.00 0.00 0.00 0.68 N/A 0.03 N/A 0.03 Spices 1.96 N/A 22.11 0.00 0.00 0.01 N/A 0.39 10.38 N/A 0.15 N/A 0.15 Alcoholic beverages N/A N/A N/A 0.00 0.00 0.01 N/A N/A 0.63 N/A 0.00 N/A 0.00 Meat 0.90 0.90 N/A 0.04 0.04 0.07 0.44 0.01 1.51 0.06 0.34 0.03 0.36 Eggs 3.22 3.22 N/A 0.00 0.01 0.00 0.02 N/A 0.95 0.02 N/A N/A 0.02 Fish 3.79 3.79 N/A 0.01 0.03 0.05 1.24 0.01 123.59 0.25 N/A 0.09 0.16 Milk and cheese 4.98 4.98 N/A N/A 0.03 0.00 0.07 0.01 20.02 0.02 N/A N/A 0.02 Oils and fats N/A N/A N/A N/A N/A N/A N/A 0.00 N/A N/A 0.00 N/A 0.00 (vegetable) Oils and fats (animal) 5.04 5.04 N/A N/A 0.00 N/A 0.00 N/A 0.13 N/A N/A N/A N/A Nonalcoholic N/A N/A N/A N/A N/A N/A N/A N/A 0.73 N/A N/A N/A N/A beverages Other urban areas Cereals 27.61 2.46 268.48 1.29 0.52 1.43 0.02 0.00 53.25 N/A 9.27 N/A 9.27 Roots and tubers 400.48 N/A 4805.70 0.12 0.11 0.27 N/A 26.31 25.01 N/A 1.03 N/A 1.03 Sugars and syrups 0.01 N/A 0.13 N/A 0.23 N/A N/A 0.03 0.48 N/A 0.04 N/A 0.04 Pulses 0.88 N/A 10.63 0.10 0.05 0.06 N/A 0.22 13.14 N/A 1.35 N/A 1.35 Table 6.4: Micronutrient Availability by Food Group and Region Average micronutrient availability Vitamin Animal Heme RAE of Retinol Beta- Vitamin Vitamin Vitamin B12 Vitamin Calcium iron Nonanimal iron Nonheme vitamin (mcg/ carotene B1 (mg/ B2 (mg/ B6 (mg/ (mcg/ C (mg/ (mg/ (mg/ iron (mg/ (mg/ iron (mg/ A (mcg/ person/ (mcg/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/day) day) person/day) day) day) day) day) day) day) day) day) day) day) Region Region 1 Cereals 40.61 0.91 458.79 1.56 0.73 1.44 0.01 0.00 53.45 N/A 15.65 N/A 15.65 Roots and tubers 238.05 N/A 2856.58 0.04 0.05 0.08 N/A 7.68 9.02 N/A 0.38 N/A 0.38 Sugars and syrups 0.00 N/A 0.05 N/A 0.09 N/A N/A 0.02 0.24 N/A 0.02 N/A 0.02 Pulses 1.04 N/A 12.49 0.17 0.07 0.12 N/A 0.70 22.12 N/A 2.22 N/A 2.22 Tree nuts N/A N/A N/A 0.00 0.00 0.00 N/A 0.00 0.47 N/A 0.01 N/A 0.01 Oil crops 0.00 N/A 0.03 0.06 0.02 0.06 N/A 0.04 19.44 N/A 0.96 N/A 0.96 Vegetables 95.08 N/A 1137.44 0.49 0.70 0.25 N/A 17.67 190.03 N/A 3.81 N/A 3.81 Fruits 8.78 N/A 104.99 0.02 0.01 0.03 N/A 23.13 4.33 N/A 0.07 N/A 0.07 Stimulants 0.17 0.17 N/A 0.00 0.00 0.00 0.00 0.01 0.50 N/A 0.03 N/A 0.03 Spices 0.74 N/A 8.33 0.00 0.00 0.00 N/A 0.12 6.45 N/A 0.04 N/A 0.04 Alcoholic beverages N/A N/A N/A 0.00 0.00 0.06 N/A N/A 2.98 N/A 0.00 N/A 0.00 Meat 0.89 0.89 N/A 0.03 0.04 0.06 0.33 0.01 1.16 0.06 0.26 0.03 0.29 Eggs 0.99 0.99 N/A 0.00 0.00 0.00 0.01 N/A 0.29 0.01 N/A N/A 0.01 Fish 1.70 1.70 N/A 0.00 0.01 0.02 0.56 0.00 33.65 0.08 N/A 0.03 0.05 Milk and cheese 14.84 14.84 N/A N/A 0.07 0.00 0.21 0.16 62.35 0.02 N/A N/A 0.02 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A 0.00 N/A N/A 0.00 N/A 0.00 Oils and fats (animal) 0.45 0.45 N/A N/A 0.00 N/A 0.00 N/A 0.01 N/A N/A N/A N/A Nonalcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A 0.06 N/A N/A N/A N/A 129 Analyzing Food Security Using Household Survey Data replaces a nutrient quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisi- tion of that food group. Table 6.5: Contribution of Food Groups to Micronutrient Availability This table shows how much each food commodity group contributes, in percent- age, to the total micronutrient availability at the national level. The total of each column is equal to 100 percent. The disaggregation of these statistics by food commodity groups helps identify the main food commodity group or groups as sources of each micronutrient. Table 6.6: Contribution of Food Groups to Micronutrient Availability by Area This table shows how much each food commodity group contributes, in percentage, to the total micronutrient availability in urban and rural areas. The total of each column is equal to 100 percent. The disaggregation of these statistics by food commodity groups helps identify differences in urban and rural areas for the main food commodity group or groups as sources of each micronutrient. Disaggregated by Food Commodity: Tables 6.7 to 6.9 The food commodities analyzed are those collected in the survey excluding those consumed away from home. The food commodity quantities refer to edi- ble portions, which mean they exclude the nonedible parts (peels, bones, etc.). Table 6.7: Micronutrient Availability by Food Item This table shows food commodity edible quantities and their contribution to the total micronutri- ent availability for human consumption at the national level. This table is useful to identify which food commodities are the main providers of micro- nutrients at the national level. Table 6.8: Micronutrient Availability by Food Item and Area This table shows food commodity edible quantities and their contribution to the total amount of micronutrients available for human consumption in urban and rural areas. This table is useful to identify which food commodities are the main providers of micronutrients within rural and urban areas as well as dif- ferences between rural and urban patterns. Table 6.9: Micronutrient Availability by Food Item and Region This table shows food commodity edible quantities and their contribution to the total 130 Table 6.5: Contribution of Food Groups to Micronutrient Availability Average micronutrient availability, % of total availability RAE of Beta- Vitamin Vitamin Vitamin Vitamin Vitamin Animal Nonanimal Heme Nonheme vitamin A Retinol carotene B1 B2 B6 B12 C Calcium iron iron iron iron Food group Cereals 4.07 5.09 3.75 59.10 31.32 55.69 0.46 0.00 14.92 0.00 63.46 0.00 62.73 Roots and tubers 74.63 0.00 77.23 11.02 7.77 22.26 0.00 56.51 14.44 0.00 9.54 0.00 9.43 Sugars and syrups 0.00 0.00 0.00 0.00 8.14 0.00 0.00 0.02 0.10 0.00 0.16 0.00 0.16 Pulses 0.15 0.00 0.15 6.38 3.42 3.76 0.00 0.40 5.80 0.00 10.87 0.00 10.74 Tree nuts 0.00 0.00 0.00 0.04 0.21 0.02 0.00 0.00 0.41 0.00 0.11 0.00 0.11 Oil crops 0.00 0.00 0.00 1.39 0.55 1.50 0.00 0.23 4.39 0.00 3.85 0.00 3.80 Vegetables 11.76 0.00 12.16 17.17 38.05 4.74 0.00 15.85 19.90 0.00 7.64 0.00 7.56 Fruits 6.43 0.00 6.63 2.80 2.90 5.11 0.00 26.73 1.90 0.00 1.91 0.00 1.89 Stimulants 0.04 1.37 0.00 0.02 0.26 0.05 0.16 0.01 0.30 0.00 0.25 0.00 0.24 Spices 0.07 0.00 0.07 0.03 0.06 0.12 0.00 0.14 2.54 0.00 0.51 0.00 0.51 Alcoholic beverages 0.00 0.00 0.00 0.00 0.00 2.04 0.00 0.00 0.82 0.00 0.00 0.00 0.00 Meat 0.17 5.51 0.00 1.58 2.30 2.89 21.73 0.00 0.45 26.40 1.68 40.32 1.88 Eggs 0.18 5.82 0.00 0.04 0.21 0.03 0.51 0.00 0.13 3.07 0.00 0.00 0.06 Fish 0.77 25.20 0.00 0.43 1.16 1.79 67.59 0.02 18.51 61.40 0.00 59.68 0.72 Milk and cheese 1.59 52.26 0.00 0.00 3.62 0.00 9.54 0.08 15.35 9.13 0.00 0.00 0.16 Oils and fats (vegetable) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oils and fats (animal) 0.14 4.74 0.00 0.00 0.02 0.00 0.02 0.00 0.01 0.00 0.00 0.00 0.00 Nonalcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 131 132 Table 6.6: Contribution of Food Groups to Micronutrient Availability by Area Average micronutrient availability, % of total availability RAE of Beta- Vitamin Vitamin Vitamin Vitamin Vitamin Animal Nonanimal Heme Nonheme vitamin A Retinol carotene B1 B2 B6 B12 C Calcium iron iron iron iron Food group/area Capital city Cereals 5.84 16.50 5.00 77.84 30.13 66.99 1.19 0.00 21.54 0.00 66.63 0.00 65.28 Roots and tubers 44.74 0.00 48.24 2.45 4.95 5.53 0.00 15.49 3.58 0.00 4.40 0.00 4.31 Sugars and syrups 0.00 0.00 0.00 0.00 18.89 0.00 0.00 0.09 0.23 0.00 0.50 0.00 0.49 Pulses 0.26 0.00 0.28 6.65 3.59 3.87 0.00 0.41 4.22 0.00 10.92 0.00 10.70 Tree nuts 0.00 0.00 0.00 0.03 0.13 0.01 0.00 0.00 0.17 0.00 0.07 0.00 0.07 Oil crops 0.00 0.00 0.00 0.58 0.23 2.15 0.00 1.62 2.30 0.00 6.48 0.00 6.35 Vegetables 35.70 0.00 38.48 4.84 29.43 6.56 0.00 34.79 7.23 0.00 4.84 0.00 4.75 Fruits 7.09 0.00 7.36 3.53 2.28 5.24 0.00 46.76 3.96 0.00 1.51 0.00 1.48 Stimulants 0.01 0.15 0.00 0.03 0.45 0.13 0.02 0.00 0.24 0.00 0.26 0.00 0.25 Spices 0.63 0.00 0.63 0.11 0.24 0.39 0.00 0.78 3.72 0.00 1.36 0.00 1.34 Alcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.61 0.00 0.00 0.23 0.00 0.00 0.00 0.00 Meat 0.29 4.18 0.00 2.89 3.48 4.99 24.30 0.03 0.54 17.18 3.03 27.37 3.21 Eggs 1.03 14.96 0.00 0.14 0.80 0.13 1.17 0.00 0.34 6.50 0.00 0.00 0.20 Fish 1.21 17.63 0.00 0.90 2.47 3.39 69.40 0.02 44.23 71.63 0.00 72.63 1.44 Milk and cheese 1.59 23.15 0.00 0.00 2.80 0.00 3.84 0.02 7.17 4.69 0.00 0.00 0.15 Oils and fats (vegetable) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 Oils and fats (animal) 1.61 23.44 0.00 0.00 0.12 0.00 0.08 0.00 0.04 0.00 0.00 0.00 0.00 Nonalcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.26 0.00 0.00 0.00 0.00 Other urban areas Cereals 4.75 10.85 4.03 71.65 36.19 66.48 0.90 0.01 21.17 0.00 67.24 0.00 66.30 Roots and tubers 68.97 0.00 72.16 6.53 7.91 12.40 0.00 39.39 9.94 0.00 7.48 0.00 7.37 Sugars and syrups 0.00 0.00 0.00 0.00 16.07 0.00 0.00 0.04 0.19 0.00 0.30 0.00 0.30 Pulses 0.15 0.00 0.16 5.82 3.35 2.98 0.00 0.34 5.22 0.00 9.82 0.00 9.68 Tree nuts 0.00 0.00 0.00 0.03 0.15 0.01 0.00 0.00 0.27 0.00 0.07 0.00 0.07 Oil crops 0.00 0.00 0.00 1.26 0.53 1.51 0.00 0.51 3.68 0.00 4.35 0.00 4.29 Vegetables 16.70 0.00 17.46 9.10 23.36 4.70 0.00 26.12 11.61 0.00 5.09 0.00 5.02 Fruits 5.76 0.00 6.00 2.66 2.68 4.57 0.00 33.12 2.65 0.00 1.68 0.00 1.65 Table 6.7: Micronutrient Availability by Food Item Average micronutrient availability Average edible RAE of Beta- Vitamin Animal Heme quantity vitamin Retinol carotene Vitamin Vitamin Vitamin B12 Vitamin Calcium iron Nonanimal iron Nonheme consumed A (mcg/ (mcg/ (mcg/ B1 (mg/ B2 (mg/ B6 (mg/ (mcg/ C (mg/ (mg/ (mg/ iron (mg/ (mg/ iron (mg/ (g/person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ day) day) day) day) day) day) day) day) day) day) day) day) day) day) Food item Rice paddy or rough 6.12 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.55 0.00 0.05 0.00 0.05 Rice husked 48.14 0.00 0.00 0.00 0.20 0.02 0.25 0.00 0.00 15.88 0.00 0.87 0.00 0.87 Maize cob fresh 9.29 0.65 0.00 7.80 0.01 0.00 0.00 0.00 0.28 0.09 0.00 0.03 0.00 0.03 Maize grain 72.72 8.00 0.00 70.54 0.28 0.15 0.45 0.00 0.00 5.09 0.00 1.97 0.00 1.97 Maize flour 163.94 18.03 0.00 216.40 0.66 0.33 0.49 0.00 0.00 9.84 0.00 5.74 0.00 5.74 Millet whole grain 1.68 0.34 0.00 4.04 0.01 0.00 0.01 0.00 0.00 0.71 0.00 0.13 0.00 0.13 dried Millet foxtail Italian 1.17 0.06 0.00 0.70 0.00 0.00 0.00 0.00 0.00 3.22 0.00 0.03 0.00 0.03 whole grain Sorghum whole grain 7.91 0.47 0.00 5.69 0.02 0.01 0.02 0.00 0.00 1.19 0.00 0.32 0.00 0.32 brown Sorghum average of 20.38 1.22 0.00 14.68 0.04 0.02 0.04 0.00 0.00 3.06 0.00 0.84 0.00 0.84 all varieties Wheat durum whole 0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.02 0.00 0.02 grain Wheat meal or flour 4.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.66 0.00 0.05 0.00 0.05 unspecied wheat Wheat 1.30 0.00 0.00 0.00 0.02 0.01 0.02 0.00 0.00 0.51 0.00 0.08 0.00 0.08 Bread 3.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00 0.02 0.00 0.02 Baby cereals 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.00 Biscuits wheat from 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00 0.00 Europe Buns cakes 3.25 1.11 1.11 0.00 0.00 0.00 0.00 0.01 0.00 1.24 0.00 0.01 0.00 0.01 Macaroni spaghetti 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 0.00 Oats 2.31 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 1.24 0.00 0.11 0.00 0.11 Cassava sweet roots 28.98 0.29 0.00 3.48 0.03 0.00 0.03 0.00 5.97 4.64 0.00 0.09 0.00 0.09 raw Cassava sweet roots 14.31 2.00 0.00 24.04 0.04 0.01 0.10 0.00 10.30 6.58 0.00 0.27 0.00 0.27 dried Cassava flour 35.67 4.99 0.00 59.93 0.11 0.04 0.25 0.00 25.68 16.41 0.00 0.68 0.00 0.68 Sweet potato 50.00 527.96 0.00 6335.53 0.05 0.05 0.10 0.00 9.00 10.00 0.00 0.20 0.00 0.20 Coco yam tuber 5.45 0.22 0.00 2.62 0.01 0.00 0.02 0.00 0.25 2.35 0.00 0.01 0.00 0.01 133 Table 6.8: Micronutrient Availability by Food Item and Area 134 Average micronutrient availability Average RAE of edible vitamin Beta- Vitamin Animal Heme quantity A Retinol carotene Vitamin Vitamin Vitamin B12 Vitamin Calcium iron Nonanimal iron Nonheme consumed (mcg/ (mcg/ (mcg/ B1 (mg/ B2 (mg/ B6 (mg/ (mcg/ C (mg/ (mg/ (mg/ iron (mg/ (mg/ iron (mg/ (g/person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ day) day) day) day) day) day) day) day) day) day) day) day) day) day) Food item/area Capital city Rice paddy or rough 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 Rice husked 108.34 0.00 0.00 0.00 0.45 0.05 0.55 0.00 0.00 35.75 0.00 1.95 0.00 1.95 Maize cob fresh 0.68 0.05 0.00 0.57 0.00 0.00 0.00 0.00 0.02 0.01 0.00 0.00 0.00 0.00 Maize grain 7.68 0.84 0.00 7.45 0.03 0.02 0.05 0.00 0.00 0.54 0.00 0.21 0.00 0.21 Maize flour 125.27 13.78 0.00 165.36 0.50 0.25 0.38 0.00 0.00 7.52 0.00 4.38 0.00 4.38 Millet whole grain 0.42 0.08 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.03 0.00 0.03 dried Millet foxtail Italian 0.71 0.04 0.00 0.42 0.00 0.00 0.00 0.00 0.00 1.94 0.00 0.02 0.00 0.02 whole grain Sorghum whole grain 0.13 0.01 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.01 0.00 0.01 brown Sorghum average of 0.04 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 all varieties Wheat durum whole 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.01 0.00 0.01 grain Wheat meal or flour 10.40 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 1.56 0.00 0.12 0.00 0.12 unspecified wheat Wheat 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.01 0.00 0.01 Bread 16.83 0.00 0.00 0.00 0.02 0.02 0.00 0.00 0.00 1.68 0.00 0.08 0.00 0.08 Baby cereals 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00 0.00 Biscuits wheat from 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.01 0.00 0.01 Europe Buns cakes 10.44 3.55 3.55 0.00 0.00 0.01 0.00 0.02 0.00 3.97 0.00 0.04 0.00 0.04 Macaroni spaghetti 2.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.03 0.00 0.03 Oats 10.69 0.00 0.00 0.00 0.08 0.01 0.01 0.00 0.00 5.77 0.00 0.50 0.00 0.50 Cassava sweet roots 13.06 0.13 0.00 1.57 0.01 0.00 0.01 0.00 2.69 2.09 0.00 0.04 0.00 0.04 raw Cassava sweet roots 1.09 0.15 0.00 1.83 0.00 0.00 0.01 0.00 0.78 0.50 0.00 0.02 0.00 0.02 dried Cassava flour 0.83 0.12 0.00 1.40 0.00 0.00 0.01 0.00 0.60 0.38 0.00 0.02 0.00 0.02 Sweet potato 13.23 139.66 0.00 1675.95 0.01 0.01 0.03 0.00 2.38 2.65 0.00 0.05 0.00 0.05 Coco yam tuber 2.45 0.10 0.00 1.18 0.00 0.00 0.01 0.00 0.11 1.06 0.00 0.00 0.00 0.00 Table 6.9: Micronutrient Availability by Food Item and Region Average micronutrient availability Average RAE of edible vitamin Beta- Vitamin Animal Heme quantity A Retinol carotene Vitamin Vitamin Vitamin B12 Vitamin Calcium iron Nonanimal iron Nonheme consumed (mcg/ (mcg/ (mcg/ B1 (mg/ B2 (mg/ B6 (mg/ (mcg/ C (mg/ (mg/ (mg/ iron (mg/ (mg/ iron (mg/ (g/person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ person/ day) day) day) day) day) day) day) day) day) day) day) day) day) day) Food item/region Region 1 Rice paddy or rough 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 Rice husked 26.25 0.00 0.00 0.00 0.11 0.01 0.13 0.00 0.00 8.66 0.00 0.47 0.00 0.47 Maize cob fresh 11.73 0.82 0.00 9.85 0.01 0.00 0.00 0.00 0.35 0.12 0.00 0.04 0.00 0.04 Maize grain 50.15 5.52 0.00 48.64 0.19 0.10 0.31 0.00 0.00 3.51 0.00 1.36 0.00 1.36 Maize flour 240.10 26.41 0.00 316.93 0.96 0.48 0.72 0.00 0.00 14.41 0.00 8.40 0.00 8.40 Millet whole grain 1.68 0.34 0.00 4.03 0.01 0.00 0.01 0.00 0.00 0.71 0.00 0.13 0.00 0.13 dried Millet foxtail Italian 1.67 0.08 0.00 1.00 0.01 0.00 0.00 0.00 0.00 4.59 0.00 0.05 0.00 0.05 whole grain Sorghum whole grain 20.07 1.20 0.00 14.45 0.04 0.02 0.04 0.00 0.00 3.01 0.00 0.82 0.00 0.82 brown Sorghum average of all 102.41 6.14 0.00 73.74 0.20 0.10 0.20 0.00 0.00 15.36 0.00 4.20 0.00 4.20 varieties Wheat durum whole 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 grain Wheat meal or flour 3.35 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.04 0.00 0.04 unspecified wheat Wheat 1.40 0.00 0.00 0.00 0.03 0.01 0.02 0.00 0.00 0.55 0.00 0.09 0.00 0.09 Bread 1.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.01 0.00 0.01 Baby cereals 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 Biscuits wheat from 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 Europe Buns cakes 2.69 0.91 0.91 0.00 0.00 0.00 0.00 0.01 0.00 1.02 0.00 0.01 0.00 0.01 Macaroni spaghetti 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 Oats 1.38 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.74 0.00 0.07 0.00 0.07 Cassava sweet roots 11.73 0.12 0.00 1.41 0.01 0.00 0.01 0.00 2.42 1.88 0.00 0.04 0.00 0.04 raw Cassava sweet roots 0.16 0.02 0.00 0.27 0.00 0.00 0.00 0.00 0.11 0.07 0.00 0.00 0.00 0.00 dried Cassava flour 0.29 0.04 0.00 0.48 0.00 0.00 0.00 0.00 0.21 0.13 0.00 0.01 0.00 0.01 Sweet potato 22.52 237.86 0.00 2854.31 0.02 0.02 0.05 0.00 4.05 4.50 0.00 0.09 0.00 0.09 Coco yam tuber 0.24 0.01 0.00 0.11 0.00 0.00 0.00 0.00 0.01 0.10 0.00 0.00 0.00 0.00 135 Analyzing Food Security Using Household Survey Data micronutrient availability for human consumption at the regional level. This table is useful to identify which food commodities are the main providers of micronutrients within each region and to detect differences across regions. Availability of Amino Acids Amino acids are the building blocks of proteins and have an important role in human bodies. Some of their functions include building cells, protecting the body from viruses or bacteria, repairing damaged tissue, providing nitrogen, and carrying oxygen throughout the body. They can be classified as dispensable or indispensable. The latter are also called essential amino acids and cannot be synthesized by the human body. Therefore, the indispensable amino acids should be supplied to the body through the consumption of proteins in food. Please note that the statistics on amino acids shown in the tables exclude the food consumed away from home. Therefore, the total available amino acids are underestimated. Disaggregated by Population Group: Tables 7.1 to 7.2 Table 7.1: Protein Consumption and Amino Acid Availability This table shows the availability of indispensable amino acids in terms of grams per person per day. Table 7.2: Amino Acid Availability per Gram of Protein This table shows the availability of indispensable amino acids in terms of milligrams per gram of protein. Disaggregated by Population Group: Tables 8.1 to 8.7 It is important to know that the statistics shown exclude the food consumed away from home. Therefore, the total available amino acids are underestimated. Table 8.1: Availability of Amino Acids by Food Group This table shows the available grams of amino acids provided by each food commodity group at the national level. Each time N/A replaces a nutrient quantity, it means that the amount of nutrient available from the food commod- ity group is very low or null, or there was no acquisition of that food group. 136 Table 7.1: Protein Consumption and Amino Acid Availability Average amino acid availability (g/person/day) Average food Average consumption protein in monetary consumption Methio- Phenyl- value (LCU/ (g/person/ nine and alanine and person/day) day) Lysine Valine Isoleucine Leucine cystine Threonine Histidine tyrosine Tryptophan Total 211.61 46.64 1.83 1.73 1.39 2.88 1.20 1.28 0.95 2.67 0.61 Quintiles of income Lowest quintile 99.77 34.03 1.17 1.16 0.91 1.99 0.82 0.85 0.62 1.78 0.47 2 162.53 44.18 1.59 1.55 1.24 2.60 1.07 1.14 0.84 2.39 0.56 3 212.21 47.62 1.85 1.76 1.41 2.95 1.22 1.31 0.97 2.73 0.62 4 288.45 54.36 2.24 2.10 1.70 3.47 1.46 1.56 1.17 3.26 0.71 Highest quintile 413.21 64.14 2.94 2.63 2.16 4.22 1.83 1.97 1.46 4.05 0.82 Area Capital city 343.33 40.96 1.89 1.70 1.39 2.61 1.17 1.26 0.92 2.61 0.43 Other urban areas 277.28 45.48 1.94 1.85 1.49 3.12 1.29 1.38 1.04 2.90 0.53 Rural areas 190.86 47.25 1.80 1.71 1.37 2.85 1.19 1.27 0.93 2.64 0.64 Household size One person 421.07 67.97 3.20 2.69 2.24 4.28 1.86 2.05 1.50 4.13 0.83 Between 2 and 3 people 295.05 56.93 2.33 2.13 1.73 3.46 1.46 1.59 1.17 3.28 0.72 Between 4 and 5 people 228.21 48.36 1.90 1.79 1.44 2.96 1.24 1.33 0.99 2.77 0.61 Between 6 and 7 people 191.95 43.41 1.65 1.59 1.27 2.71 1.12 1.18 0.88 2.48 0.59 More than 7 165.59 42.15 1.61 1.56 1.24 2.61 1.09 1.15 0.84 2.41 0.57 Gender of the household head Male 209.51 46.58 1.83 1.73 1.39 2.89 1.20 1.28 0.95 2.68 0.61 Female 220.73 46.90 1.81 1.71 1.37 2.84 1.18 1.27 0.93 2.64 0.60 Age of the household head Less than 35 225.51 48.67 1.92 1.80 1.45 3.00 1.25 1.34 0.99 2.78 0.63 Between 35 and 45 213.95 45.43 1.80 1.70 1.36 2.83 1.18 1.26 0.93 2.63 0.60 Between 46 and 60 206.17 47.06 1.81 1.75 1.39 2.89 1.20 1.28 0.94 2.70 0.61 More than 60 192.61 44.84 1.76 1.65 1.32 2.72 1.15 1.22 0.89 2.53 0.61 137 138 Table 7.2: Amino Acid Availability per Gram of Protein Mg amino acid/g protein Methionine Phenylalanine and Lysine Valine Isoleucine Leucine and cystine Threonine Histidine tyrosine Tryptophan Total 39.2 37.1 29.7 61.7 25.7 27.5 20.3 57.3 13.1 Quintiles of income Lowest quintile 34.4 34.0 26.6 58.4 24.0 25.0 18.1 52.3 13.7 2 36.1 35.2 28.0 58.8 24.1 25.8 18.9 54.2 12.7 3 38.9 37.0 29.6 61.9 25.6 27.4 20.3 57.3 13.1 4 41.2 38.7 31.2 63.8 26.8 28.8 21.5 60.1 13.0 Highest quintile 45.8 40.9 33.6 65.8 28.5 30.7 22.8 63.1 12.8 Area Capital city 46.2 41.6 34.1 63.7 28.5 30.7 22.5 63.6 10.5 Other urban areas 42.6 40.7 32.8 68.6 28.3 30.4 22.8 63.8 11.6 Rural areas 38.2 36.2 29.0 60.4 25.1 26.8 19.7 55.9 13.5 Household size One person 47.1 39.6 32.9 63.0 27.3 30.2 22.1 60.7 12.2 Between 2 and 3 people 40.9 37.4 30.4 60.7 25.6 27.9 20.5 57.6 12.6 Between 4 and 5 people 39.4 37.1 29.9 61.2 25.6 27.5 20.4 57.3 12.7 Between 6 and 7 people 38.0 36.7 29.2 62.5 25.7 27.3 20.3 57.1 13.5 More than 7 38.3 37.0 29.4 61.9 25.8 27.2 19.9 57.1 13.4 Gender of the household head Male 39.3 37.2 29.8 61.9 25.9 27.6 20.4 57.5 13.1 Female 38.5 36.5 29.3 60.5 25.2 27.1 19.9 56.4 12.8 Age of the household head Less than 35 39.5 36.9 29.7 61.7 25.7 27.6 20.4 57.1 12.9 Between 35 and 45 39.6 37.4 30.0 62.2 26.0 27.8 20.6 57.9 13.1 Between 46 and 60 38.5 37.1 29.6 61.5 25.5 27.3 20.1 57.3 12.9 More than 60 39.3 36.9 29.4 60.7 25.6 27.2 19.9 56.5 13.6 Table 8.1: Availability of Amino Acids by Food Group Average amino acid availability (g/person/day) Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Food group Cereals 499.01 778.65 559.99 1419.81 526.21 526.08 384.94 1237.03 140.35 Roots and tubers 13.25 13.08 9.50 15.53 57.50 10.90 7.50 20.14 209.93 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 215.13 152.61 131.89 248.01 78.85 121.70 96.17 281.47 37.57 Tree nuts 2.55 3.94 3.32 5.95 2.24 2.83 2.14 6.97 1.37 Oil crops 64.65 96.02 77.37 138.92 54.21 66.87 49.60 161.46 30.55 Vegetables 65.02 71.99 62.47 112.38 34.24 54.66 31.35 84.27 13.13 Fruits 8.26 5.17 3.31 5.40 14.16 3.28 27.98 6.14 43.48 Stimulants 2.76 2.19 1.93 2.75 1.13 1.79 0.88 3.21 0.47 Spices 2.09 2.53 1.87 2.71 1.26 1.57 0.97 2.73 0.62 Alcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Meat 419.64 245.40 230.07 392.42 187.63 217.53 168.80 363.80 55.49 Eggs 6.58 5.58 4.99 7.82 4.98 4.39 2.17 8.59 1.11 Fish 406.45 227.96 204.13 359.87 178.61 194.01 130.10 322.51 49.72 Milk and cheese 122.12 125.31 95.94 164.42 59.56 76.30 43.52 174.94 25.52 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A N/A N/A Oils and fats (animal) N/A N/A N/A N/A N/A N/A N/A N/A N/A Nonalcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A 139 Analyzing Food Security Using Household Survey Data Table 8.2: Availability of Amino Acids by Food Group and Income Quintile This table shows the available grams of amino acids provided by each food commodity group at the income quintile level. Each time N/A replaces a nutri- ent quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisition of that food group. Table 8.3: Availability of Amino Acids by Food Group and Area This table shows the available grams of amino acids provided by each food com- modity group at the urban/rural level. Each time N/A replaces a nutrient quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisition of that food group. Table 8.4: Availability of Amino Acids by Food Group and Region This table shows the available grams of amino acids provided by each food commodity group at the regional level. Each time N/A replaces a nutrient quantity, it means that the amount of nutrient available from the food commodity group is very low or null, or there was no acquisition of that food group. Table 8.5: Contribution of Food Groups to Amino Acid Availability This table shows how much each food commodity group contributes, in percent- age, to the total micronutrient availability at the national level. The total of each column is equal to 100 percent. This information is useful to identify the main food commodity groups that provide the available indispensable amino acids in the diet. Table 8.6: Contribution of Food Groups to Amino Acid Availability by Area This table shows how much each food commodity group contributes, in percentage, to the total micronutrient availability at the urban/rural level. The total of each column is equal to 100 percent. This information is useful to identify the main food commodity groups that provide the avail- able indispensable amino acids in the diet, and to highlight differences by area. 140 Table 8.2: Availability of Amino Acids by Food Group and Income Quintile Average amino acid availability (g/person/day) Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Quintiles of income Lowest quintile Cereals 0.37 0.58 0.41 1.10 0.39 0.40 0.29 0.91 0.10 Roots and tubers 0.01 0.01 0.00 0.01 0.06 0.01 0.00 0.01 0.23 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 0.15 0.11 0.09 0.18 0.06 0.09 0.07 0.20 0.03 Tree nuts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 Oil crops 0.03 0.05 0.04 0.07 0.03 0.03 0.02 0.08 0.02 Vegetables 0.05 0.06 0.05 0.10 0.03 0.05 0.03 0.07 0.01 Fruits 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00 0.02 Stimulants 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Spices 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Alcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Meat 0.18 0.11 0.10 0.17 0.08 0.10 0.07 0.16 0.02 Eggs 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Fish 0.26 0.15 0.13 0.23 0.12 0.13 0.08 0.21 0.03 Milk and cheese 0.09 0.09 0.07 0.12 0.04 0.05 0.03 0.12 0.02 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A N/A N/A Oils and fats (animal) N/A N/A N/A N/A N/A N/A N/A N/A N/A Nonalcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Quintile 2 Cereals 0.45 0.70 0.50 1.29 0.47 0.47 0.35 1.11 0.12 Roots and tubers 0.01 0.01 0.01 0.01 0.06 0.01 0.01 0.02 0.22 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 0.19 0.14 0.12 0.22 0.07 0.11 0.09 0.25 0.03 141 142 Table 8.3: Availability of Amino Acids by Food Group and Area Average amino acid availability (g/person/day) Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Area/food group Capital city Cereals 0.54 0.82 0.61 1.25 0.54 0.53 0.37 1.28 0.18 Roots and tubers 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.03 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 0.16 0.11 0.10 0.19 0.06 0.09 0.07 0.21 0.03 Tree nuts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oil crops 0.04 0.07 0.05 0.08 0.04 0.04 0.03 0.10 0.02 Vegetables 0.04 0.04 0.04 0.05 0.02 0.03 0.02 0.05 0.01 Fruits 0.02 0.01 0.01 0.01 0.01 0.01 0.03 0.01 0.02 Stimulants 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Spices 0.01 0.01 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Alcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Meat 0.48 0.28 0.26 0.45 0.22 0.25 0.20 0.42 0.06 Eggs 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.00 Fish 0.54 0.30 0.27 0.48 0.24 0.26 0.17 0.43 0.07 Milk and cheese 0.03 0.04 0.03 0.05 0.02 0.03 0.01 0.05 0.01 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A N/A N/A Oils and fats (animal) N/A N/A N/A N/A N/A N/A N/A N/A N/A Non alcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Other urban areas Cereals 0.59 0.93 0.67 1.70 0.63 0.63 0.47 1.50 0.18 Roots and tubers 0.02 0.02 0.01 0.02 0.03 0.01 0.01 0.02 0.11 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 0.18 0.13 0.11 0.21 0.07 0.10 0.08 0.24 0.03 Table 8.4: Availability of Amino Acids by Food Group and Region Average amino acid availability (g/person/day) Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Region Region 1 Cereals 0.59 0.92 0.65 1.56 0.62 0.60 0.42 1.40 0.16 Roots and tubers 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.03 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 0.21 0.15 0.13 0.25 0.08 0.12 0.09 0.27 0.04 Tree nuts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oil crops 0.12 0.18 0.15 0.27 0.10 0.13 0.10 0.32 0.06 Vegetables 0.10 0.12 0.10 0.18 0.06 0.09 0.05 0.15 0.03 Fruits 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.00 Stimulants 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Spices 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Alcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Meat 0.39 0.23 0.21 0.36 0.17 0.20 0.16 0.33 0.05 Eggs 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Fish 0.18 0.10 0.09 0.16 0.08 0.09 0.06 0.14 0.02 Milk and cheese 0.23 0.22 0.16 0.29 0.11 0.13 0.07 0.30 0.03 Oils and fats (vegetable) N/A N/A N/A N/A N/A N/A N/A N/A N/A Oils and fats (animal) N/A N/A N/A N/A N/A N/A N/A N/A N/A Nonalcoholic beverages N/A N/A N/A N/A N/A N/A N/A N/A N/A Region 2 Cereals 0.58 0.97 0.70 2.08 0.71 0.69 0.54 1.65 0.16 Roots and tubers 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.02 0.03 Sugars and syrups N/A N/A N/A N/A N/A N/A N/A N/A N/A Pulses 0.21 0.15 0.13 0.24 0.08 0.12 0.10 0.28 0.04 Tree nuts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oil crops 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.00 143 144 Table 8.5: Contribution of Food Groups to Amino Acid Availability Average amino acid availability, % of total availability Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Food group Cereals 27.31 45.00 40.38 49.37 43.83 41.04 40.69 46.27 23.03 Roots and tubers 0.73 0.76 0.69 0.54 4.79 0.85 0.79 0.75 34.45 Sugars and syrups 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pulses 11.77 8.82 9.51 8.62 6.57 9.49 10.17 10.53 6.17 Tree nuts 0.14 0.23 0.24 0.21 0.19 0.22 0.23 0.26 0.23 Oil crops 3.54 5.55 5.58 4.83 4.52 5.22 5.24 6.04 5.01 Vegetables 3.56 4.16 4.50 3.91 2.85 4.26 3.31 3.15 2.15 Fruits 0.45 0.30 0.24 0.19 1.18 0.26 2.96 0.23 7.14 Stimulants 0.15 0.13 0.14 0.10 0.09 0.14 0.09 0.12 0.08 Spices 0.11 0.15 0.14 0.09 0.11 0.12 0.10 0.10 0.10 Alcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Meat 22.96 14.18 16.59 13.64 15.63 16.97 17.84 13.61 9.11 Eggs 0.36 0.32 0.36 0.27 0.41 0.34 0.23 0.32 0.18 Fish 22.24 13.17 14.72 12.51 14.88 15.13 13.75 12.06 8.16 Milk and cheese 6.68 7.24 6.92 5.72 4.96 5.95 4.60 6.54 4.19 Oils and fats (vegetable) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oils and fats (animal) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nonalcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Table 8.6: Contribution of Food Groups to Amino Acid Availability by Area Average amino acid availability, % of total availability Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Area Capital city Cereals 28.71 48.03 43.76 48.10 46.26 41.94 40.60 49.23 42.14 Roots and tubers 0.70 0.75 0.66 0.55 1.19 0.70 0.60 0.73 7.62 Sugars and syrups 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pulses 8.53 6.73 7.07 7.11 5.10 7.26 7.92 8.19 6.66 Tree nuts 0.05 0.09 0.10 0.09 0.08 0.09 0.09 0.11 0.13 Oil crops 2.37 3.84 3.28 3.24 3.33 3.27 3.08 3.66 3.65 Vegetables 2.15 2.24 2.93 1.90 1.53 2.47 1.72 1.82 1.79 Fruits 0.88 0.68 0.53 0.39 0.95 0.48 3.65 0.53 3.84 Stimulants 0.06 0.06 0.06 0.04 0.04 0.06 0.04 0.06 0.05 Spices 0.29 0.39 0.35 0.27 0.29 0.33 0.27 0.27 0.38 Alcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Meat 25.50 16.55 18.88 17.43 18.47 20.08 21.27 16.13 15.05 Eggs 0.88 0.83 0.91 0.76 1.08 0.89 0.60 0.84 0.66 Fish 28.34 17.68 19.32 18.22 20.20 20.41 18.68 16.34 15.25 Milk and cheese 1.55 2.13 2.16 1.89 1.49 2.03 1.49 2.10 2.79 Oils and fats (vegetable) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oils and fats (animal) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nonalcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Other urban areas Cereals 30.23 49.97 45.06 54.42 49.14 45.33 44.86 51.66 33.30 Roots and tubers 0.87 0.87 0.79 0.58 2.57 0.85 0.74 0.83 20.01 Sugars and syrups 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pulses 9.36 6.94 7.40 6.67 5.19 7.39 7.88 8.25 6.09 145 Analyzing Food Security Using Household Survey Data Table 8.7: Contribution of Food Groups to Amino Acid Availability by Region This table shows how much each food commodity group contributes, in percentage, to the total micronutrient availability at the regional level. The total of each column is equal to 100 percent. This information is useful to identify the main food commodity groups that provide the available indispensable amino acids in the diet, and to highlight regional differences. Disaggregated by Food Commodity: Tables 8.8 to 8.10 The food commodities analyzed are those collected in the survey exclud- ing those consumed away from home. The food commodity quantities refer to edible portions, which mean they exclude the nonedible parts (peels, bones, etc.) Table 8.8: Availability of Amino Acid by Food Item This table shows food commodity edible quantities and the available grams of amino acids pro- vided by them at the national level. This table is useful to identify the main food commodities that provide indispensable amino acids at national level. Table 8.9: Availability of Amino Acid by Food Item and Area This table shows food commodity edible quantities and the available grams of amino acids provided by them at the urban/rural level. This table is useful to identify the main food commodities that provide indispensable amino acids within rural and urban areas as well as to highlight differences between rural and urban patterns. Table 8.10: Availability of Amino Acid by Food Item and Region This table shows food commodity edible quantities and the available grams of amino acids provided by them at the regional level. This table is useful to identify the main food commodity groups that provide the available of indispensable amino acids at the national level, and to highlight regional differences. 146 Table 8.7: Contribution of Food Groups to Amino Acid Availability by Region Average amino acid availability, % of total availability Methionine Phenylalanine Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Region Region 1 Cereals 31.89 47.31 43.04 50.26 50.11 43.73 42.95 47.38 37.70 Roots and tubers 0.45 0.40 0.38 0.27 0.91 0.39 0.37 0.39 7.40 Sugars and syrups 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pulses 11.48 7.79 8.71 8.02 6.19 8.81 9.36 9.15 8.23 Tree nuts 0.05 0.08 0.08 0.07 0.07 0.08 0.08 0.09 0.12 Oil crops 6.42 9.42 10.06 8.84 8.41 9.54 10.03 10.88 14.62 Vegetables 5.60 6.05 6.42 5.94 4.67 6.63 5.12 5.17 6.03 Fruits 0.34 0.22 0.19 0.14 0.23 0.20 2.45 0.17 0.79 Stimulants 0.21 0.16 0.18 0.13 0.13 0.18 0.13 0.15 0.15 Spices 0.08 0.09 0.08 0.06 0.07 0.08 0.07 0.06 0.10 Alcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Meat 20.87 11.62 13.87 11.67 13.95 14.62 15.85 11.34 11.86 Eggs 0.28 0.22 0.26 0.20 0.31 0.25 0.17 0.23 0.20 Fish 9.76 5.22 5.96 5.15 6.43 6.29 5.88 4.85 5.12 Milk and cheese 12.56 11.43 10.77 9.25 8.53 9.21 7.55 10.14 7.68 Oils and fats (vegetable) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oils and fats (animal) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Nonalcoholic beverages 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Region 2 Cereals 32.94 52.72 47.93 61.11 55.97 49.94 51.90 55.59 37.04 Roots and tubers 1.03 0.90 0.83 0.53 1.13 0.80 0.68 0.82 6.57 Sugars and syrups 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pulses 11.90 7.97 8.61 6.98 6.15 8.48 9.18 9.38 8.66 147 148 Table 8.8: Availability of Amino Acid by Food Item Average amino acid availability (g/person/day) Average edible quantity Methio- Phenylala- consumed nine and nine and (g/person/day) Lysine Valine Isoleucine Leucine cystine Threonine Histidine tyrosine Tryptophan Food item Rice paddy or rough 6.12 0.01 0.02 0.02 0.03 0.02 0.01 0.01 0.03 0.00 Rice husked 48.14 0.13 0.19 0.14 0.27 0.11 0.12 0.08 0.29 0.04 Maize cob fresh 9.29 0.02 0.03 0.02 0.05 0.01 0.02 0.01 0.02 0.00 Maize grain 72.72 0.13 0.24 0.17 0.59 0.19 0.18 0.15 0.43 0.03 Maize flour 163.94 0.11 0.14 0.10 0.27 0.07 0.10 0.07 0.21 0.02 Millet whole grain dried 1.68 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Millet foxtail Italian whole grain 1.17 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Sorghum whole grain brown 7.91 0.01 0.02 0.02 0.03 0.02 0.01 0.01 0.03 0.00 Sorghum average of all varieties 20.38 0.04 0.06 0.04 0.08 0.04 0.04 0.02 0.09 0.01 Wheat durum whole grain 0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Wheat meal or flour unspecified wheat 4.41 0.02 0.03 0.02 0.04 0.02 0.02 0.01 0.04 0.01 Wheat 1.30 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.00 Bread 3.11 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.00 Baby cereals 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biscuits wheat from Europe 0.15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Buns cakes 3.25 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.01 0.00 Macaroni spaghetti 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oats 2.31 0.01 0.02 0.01 0.02 0.01 0.01 0.01 0.03 0.00 Cassava sweet roots raw 28.98 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.03 Cassava sweet roots dried 14.31 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.04 Cassava flour 35.67 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.10 Sweet potato 50.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.03 Coco yam tuber 5.45 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Potatoes tubers raw 9.00 0.01 0.01 0.01 0.01 0.00 0.01 0.00 0.01 0.00 Banana cooking 42.28 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.04 Starch 2.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Table 8.9: Availability of Amino Acid by Food Item and Area Average amino acid availability (g/person/day) Average edible quantity Methio- Phenylala- consumed nine and nine and (g/person/day) Lysine Valine Isoleucine Leucine cystine Threonine Histidine tyrosine Tryptophan Area/food item Capital city Rice paddy or rough 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rice husked 108.34 0.28 0.43 0.31 0.61 0.26 0.27 0.19 0.66 0.09 Maize cob fresh 0.68 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Maize grain 7.68 0.01 0.03 0.02 0.06 0.02 0.02 0.02 0.05 0.00 Maize flour 125.27 0.08 0.11 0.08 0.21 0.06 0.08 0.05 0.16 0.01 Millet whole grain dried 0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Millet foxtail Italian whole grain 0.71 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Sorghum whole grain brown 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sorghum average of all varieties 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Wheat durum whole grain 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Wheat meal or flour unspecified wheat 10.40 0.04 0.06 0.05 0.09 0.05 0.04 0.03 0.11 0.02 Wheat 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bread 16.83 0.03 0.06 0.05 0.10 0.06 0.04 0.03 0.11 0.02 Baby cereals 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biscuits wheat from Europe 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Buns cakes 10.44 0.02 0.02 0.02 0.03 0.02 0.01 0.01 0.04 0.01 Macaroni spaghetti 2.18 0.00 0.01 0.01 0.01 0.01 0.01 0.00 0.02 0.00 Oats 10.69 0.06 0.08 0.06 0.11 0.06 0.05 0.04 0.13 0.02 Cassava sweet roots raw 13.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 Cassava sweet roots dried 1.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cassava flour 0.83 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sweet potato 13.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Coco yam tuber 2.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Potatoes tubers raw 10.96 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.02 0.00 149 150 Table 8.10: Availability of Amino Acid by Food Item and Region Average Average amino acid availability (g/person/day) edible quantity consumed Methionine Phenylalanine (g/person/day) Lysine Valine Isoleucine Leucine and cystine Threonine Histidine and tyrosine Tryptophan Region/food item Region 1 Rice paddy or rough 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Rice husked 26.25 0.07 0.11 0.08 0.15 0.06 0.07 0.05 0.16 0.02 Maize cob fresh 11.73 0.03 0.04 0.02 0.07 0.02 0.02 0.02 0.03 0.00 Maize grain 50.15 0.09 0.17 0.12 0.41 0.13 0.12 0.10 0.30 0.02 Maize flour 240.10 0.16 0.21 0.15 0.40 0.11 0.15 0.10 0.31 0.03 Millet whole grain dried 1.68 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Millet foxtail Italian whole grain 1.67 0.00 0.01 0.01 0.02 0.01 0.01 0.00 0.01 0.00 Sorghum whole grain brown 20.07 0.04 0.06 0.04 0.08 0.04 0.03 0.02 0.08 0.01 Sorghum average of all varieties 102.41 0.18 0.30 0.22 0.41 0.22 0.18 0.12 0.43 0.06 Wheat durum whole grain 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Wheat meal or flour unspecified 3.35 0.01 0.02 0.02 0.03 0.02 0.01 0.01 0.03 0.01 wheat Wheat 1.40 0.02 0.02 0.01 0.02 0.01 0.01 0.01 0.02 0.00 Bread 1.07 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Baby cereals 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Biscuits wheat from Europe 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Buns cakes 2.69 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.01 0.00 Macaroni spaghetti 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Oats 1.38 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.02 0.00 Cassava sweet roots raw 11.73 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Cassava sweet roots dried 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cassava flour 0.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sweet potato 22.52 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Coco yam tuber 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Potatoes tubers raw 7.74 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.01 0.00 Chapter 3: Guide to Output Tables Glossary of Indicators Overall Food Consumption Indicators average carbohydrates Average quantity of available carbohydrates consumption (excluding fiber) consumed by the household. (g/person/day) See tables 1.9, 1.14, 2.1, 2.3, 2.4, 2.6, and 2.7. average carbohydrates Measures the cost of 100 grams of carbohydrates unit value by food groups. From this cost it is possible (LCU/100 g) to identify among the food groups that pro- vide carbohydrates those that provide low-cost carbohydrates. See table 2.8. average dietary Measures the amount of calories consumed by energy consumption the household. It is expressed in kilocalories per (kcal/person/day) person per day. The dietary energy consumption is estimated from the food quantities collected in the survey. Food quantities that are collected “as purchased” (including bones, peels, etc.) first are transformed into edible quantities by taking into consideration the respective food item refuse factor and then are expressed in grams. Once all edible quantities are transformed into grams of nutrients, the nutrient densities (grams of nutrient per gram of food product) of each food item are used to estimate the amount of calories consumed. The dietary energy consumption should be within reasonable ranges from 800 to 4,000 kcal (which- ever decile), and it tends to increase as income increases (although it is also possible that better-off households purchase more expensive and less ener- getic food). See tables 1.1, 1.3, 1.4, 1.9, 1.10, 1.11, 1.12, 2.1, 2.3, 2.4, 2.6, 2.7, 3.1, 3.3, 3.5, and 4.1. average dietary Proper normative reference for adequate nutrition energy requirement in the population. While it would be mistaken to (kcal/person/day) take the average dietary energy requirement value as the cutoff point to determine the prevalence of undernourishment, its value is used to calculate 151 Analyzing Food Security Using Household Survey Data the depth of the food deficit (FD), which is the amount of dietary energy needed to ensure that, if properly distributed, hunger would be elimi- nated. It is also used to estimate the nutrient recommended intake expressed in mg per 1000 kcal. See tables 1.1, 1.2, and 5.5. average dietary Measures the average cost of 1,000 kcal (in local energy unit value currency). It usually increases as income increases (LCU/1,000 kcals) because better-off households are more likely to buy food that is less caloric but more expensive (for instance meat instead of pulses) or to have meals in restaurants. Note that for all the tables presenting statistics by food commodity group or food commodity, the average dietary energy unit value refers to the median of the dietary energy value. See tables 1.3, 1.4, 2.8, 3.1, 3.3, 3.5, and 4.4. average edible For each food item, provides the quantity of food quantity consumed consumed in grams per person per day after the (g/person/day) nonedible portion has been removed. See tables 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 6.6, 6.8, 6.9, 8.8, 8.9, and 8.10. average fat Average quantity of fat consumed by the house- consumption hold (expressed in grams per person per day). (g/person/day) See tables 1.9, 1.14, 2.1, 2.3, 2.4, 2.6, and 2.7. average fat unit Measures the cost of 100 grams of fat by food value (LCU/100 g) groups. From this cost it is possible to identify among the food groups that provide fat those that provide low-cost fat (for instance, to obtain 100 grams of fats from milk and cheese may cost more than to get 100 grams of fat from vegetable oil). See table 2.8. average food Usually increases as income increases. It should consumption in be lower than the total consumption expenditure monetary value (which includes nonfood consumption expendi- (LCU/person/day) tures such as education, health, transport, durable goods, etc.). See tables 1.3, 1.4, 1.9, 2.1, 2.3, 2.4, 2.6, 2.7, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 4.1, and 7.1. 152 Chapter 3: Guide to Output Tables average household size Corresponds to the total number of household members, and usually wealthier households have fewer members than poor households. See tables 1.3, 1.4, 4.1, 4.2, and 4.3. average income Average per person per day income, expressed (LCU/person/day) in local unit of measurement. When the sur- vey does not collect information on income or this information is not reliable, total expen- ditures corresponding to the sum of total consumption and nonconsumption is used as a proxy of income. See tables 1.4, 1.6, 1.8, 4.4, and 4.5. average protein Average quantity of proteins consumed. See consumption tables 1.9, 1.14, 2.1, 2.3, 2.4, 2.6, 2.7, 3.2, 3.4, (g/person/day) 3.6, and 7.1. average protein unit Measures the cost of 100 grams of proteins by value (LCU/100 g) food groups. From this cost it is possible to iden- tify among the food groups that provide proteins those that provide low-cost proteins (for instance proteins from cereals may be less expensive than proteins coming from animal sources). See tables 2.8, 3.2, 3.4, and 3.6. average total Usually increases as income increases and should consumption in be lower than total expenditures and income. monetary value See tables 1.3, 1.4, and 4.1. (LCU/person/day) coefficient of variation Indicator of the dispersion of the dietary energy of dietary energy consumption within the general population. It consumption (%) should not be higher than 35 percent (maximum acceptable). A high CV should be corrected for excess variability. The CV should not be lower than 20 percent to account for at least the vari- ability of dietary energy consumption due to factors other than income/household character- istics. See tables 1.1 and 1.2. 153 Analyzing Food Security Using Household Survey Data contribution of Measures the contribution of each food group food groups to to the consumption of carbohydrates in percent total nutrient (e.g., a share of 55 percent from cereal indicates consumption: share that the group of cereals contributes 55 percent of total carbohydrates to the total carbohydrates consumption). See consumption (%) tables 2.2 and 2.5. contribution of Measures the contribution of each food group to food groups to total the total dietary energy consumption in percent nutrient consumption: (e.g., a share of 55 percent from cereals indicates share of total dietary that the group of cereals contributes 55 percent energy consumption to the total dietary energy consumption). See (%) tables 2.2, 2.5, and 2.9. contribution of Measures the contribution of each food group food groups to total to the consumption of fats in percent (e.g., a nutrient consumption: share of 17 percent from vegetable oils indicates share of total fat that the group of vegetable oils contributes consumption (%) 17 percent to the total fats consumption). See tables 2.2 and 2.5. contribution of Measures the contribution of each food group food groups to total to the total consumption of proteins in percent nutrient consumption: (e.g., a share of 20 percent from the group meat share of total protein and meat products indicates that the group of consumption (%) meat and products contributes 20 percent to the total protein consumption). See tables 2.2 and 2.5. density—average Measures the amount in grams of carbohydrates carbohydrates included in 1,000 kcal. See table 1.12. consumption (g/1,000 kcal) density—average fat Measures the amount in grams of fats contributing consumption (g/1,000 to 1,000 kcal. See table 1.12. kcal) density—average Measures the amount in grams of proteins protein consumption contributing to 1,000 kcal. See table 1.12. (g/1,000 kcal) 154 Chapter 3: Guide to Output Tables depth of food deficit Indicates how many calories would be needed (kcal/person/day) to lift the undernourished from its status, every- thing else being constant and considering food is equally distributed. The average intensity of food deprivation of the undernourished, estimated as the difference between the average dietary energy requirement and the average dietary energy consumption of the undernourished population (food-deprived), is multiplied by the number of undernourished to provide an estimate of the total food deficit in the country, and is then nor- malized by the total population. This usually is within the range of 100–400 kilocalories per day. When it is lower than 200 kcal, it is considered low; between 200 and 300, moderate; and above 300 kcal, high. See tables 1.1 and 1.2. dietary energy supply Corresponds to the dietary energy supply as adjusted for losses derived from the food balance sheets reduced by (kcal/person/day) losses that occur at the retail level. It is expressed in kilocalories per person per day. It is used in the calculation of the prevalence of undernourish- ment indicator 1.9 of the MDG. See table 1.2. estimated population Corresponds to the total number of people within a population group. It is calculated from the survey data as the sum of the product between household weight and number of house- hold members. See table 1.4. minimum dietary Amount of energy needed for light activity and energy requirement minimum acceptable body mass index (weight (MDER) (kcal/ for attained height). MDER is the cutoff point, or person/day) threshold, used to estimate the prevalence (percent- age) of the undernourished population in a coun- try. Dietary energy requirements differ by gender and age, and for different levels of physical activity. As a result, minimum dietary energy requirements vary by country, and from year to year, depending on the sex and age structure of the population. 155 Analyzing Food Security Using Household Survey Data In countries with a high prevalence of under- nourishment, a large proportion of the popula- tion typically consumes dietary energy levels close to the cutoff point, making the MDER a highly sensitive parameter. It is computed as a weighted average of the minimum energy requirements of different age/sex groups in the population.25 See tables 1.1, 1.2, and 1.3. The MDER shown at the national level in table 1.3 is calculated using the structure of the popu- lation as from the survey. Note that this MDER value is different from the MDER shown in the first row of tables 1.1 and 1.2. The MDER at the national level in tables 1.1 and 1.2 is based on the country population as published by the United Nations, biennially, and it is used to estimate the MDG 1.9 indicators. The differ- ence in the values of these two MDERs at the national level can be due to differences in (1) the structure of the population by age and sex groups; (2) the heights used by age and sex groups; and (3) the birthrate used. number of sampled Total should be equal to the size of the survey households sample: to obtain reliable estimates (at income decile levels) it is suggested to have more than 500 sampled households by category of analysis: region, household head’s characteristics, etc. A statistic obtained with fewer than 30 households is considered not reliable. See tables 1.3, 1.4, 1.5, 1.6, 1.7, and 1.8. other sources— Measures the percentage of households whose proportion of consumption of a food item is coming from households in total other sources (e.g., at national level, 5 percent households (%) associated to maize in grain means that 5 percent of households of this country have received maize in grain as a gift). See tables 3.7, 3.8, and 3.9. 156 Chapter 3: Guide to Output Tables own consumption— Measures the percentage of households whose proportion of consumption of a food item is coming from households in total their own production (e.g., at the national level households (%) 55 percent associated to wheat flour means that 55 percent of households of this country have consumed wheat flour coming from their own production). See tables 3.7, 3.8, and 3.9. population (’000s) Corresponds to the total number of people (expressed in thousands) within a population group. It is calculated from the survey data as the sum of the product between household weight and number of household members. At the national level, the population estimates should be close to those published by the UN for the same year. See tables 1.1 and 1.2. prevalence of Proportion of the population estimated to be undernourishment at risk of caloric inadequacy. A value less than (%) 5 percent is considered low, a value between 5 and 19 percent is considered moderate, and a value higher than 20 percent is considered high. For computing the official MDG 1.9 indica- tor FAO uses, as mean of the distribution, the dietary energy supply from food balance sheets minus waste of calories at the retail level. When the average dietary energy consumption from the national household surveys is used as the estimate of the mean of the distribution, the cor- responding indicator should not be considered the official MDG 1.9. However, the two indica- tors should be compared and critically evaluated. See tables 1.1 and 1.2. purchase—proportion Measures the percentage of households whose of households in total consumption of a food item is coming from households (%) purchases (e.g., at the national level, 55 percent associated to wheat flour means that 55 percent of households of this country have consumed wheat flour coming from purchases). 157 Analyzing Food Security Using Household Survey Data Note that the sum of the proportion of house- holds (HHs) that acquire the product through purchase, or received in kind, or from own con- sumption does not necessarily equal 100 percent because not all HH might have consumed the food. See tables 3.7, 3.8, and 3.9. quantity as Measures the quantity of food product consumed “produced” coming from own production. It is expressed in (g/person/day) grams per person per day. See tables 3.7, 3.8, and 3.9. quantity as Measures the quantity of food product con- “purchased” sumed coming from purchases. It is expressed (g/person/day) in grams per person per day. See tables 3.7, 3.8, and 3.9. quantity as “received” Measures the quantity of food product con- from other sources sumed from other sources. It is expressed in (g/person/day) grams per person per day. See tables 3.7, 3.8, and 3.9. ratio to the first Measures the inequality in the average dietary reference group of energy consumption between the first quintile average dietary energy (used as the reference group) and the other consumption quintiles. For instance, if the value of the ratio associated to quintile 4 is equal to 5, this means that the average dietary energy consumption in quintile 4 is five times higher than in quintile 1. It’s a measure of inequality easier to interpret than the Gini coefficient or the coefficient of variation. This ratio is computed for the average dietary energy value, average food consumption in monetary value, average total consumption in monetary value, average income, and share of food source in dietary and monetary value. See table 4.1. share of animal Proportion of protein consumed from food of protein in total protein animal origin (meat [red and white], fish, eggs, consumption (%) milk, and cheese). See table 1.13. 158 Chapter 3: Guide to Output Tables share of dietary Proportion of total calories from fats. The energy consumption experts from WHO/FAO/UNU recommend a from fats (%) consumption of calories from fats between 15 and 30 percent of total calories consumed. See tables 1.10 and 1.11. share of dietary Proportion of total calories from proteins. The energy consumption experts from WHO/FAO/UNU recommend a from protein (%) consumption of calories from proteins between 10 and 15 percent of total calories consumed. See tables 1.10 and 1.11. share of dietary Proportion of total calories from available carbo- energy consumption hydrates and alcohol. The experts from WHO/ from total FAO/UNU recommend a consumption of calo- carbohydrates and ries from available carbohydrates and alcohol alcohol (%) between 55 and 75 percent of total calories consumed. See tables 1.10 and 1.11. share of food Share of dietary energy coming from the food consumed away from eaten away from home (canteen at work, restau- home in total food rants, bars, street food, etc.). Usually is greater consumption (%) for better-off households. Yet this rule widely in dietary energy depends on the eating habits of the country. Indeed, street food (sometimes cheap and highly caloric) may contribute significantly to the diet of poor people; whereby restaurants may provide expensive but not highly caloric food. See tables 1.5, 1.6, and 4.2. share of food Contribution (expressed in monetary value) of consumed away from food consumed away from home in total food home in total food monetary value. Usually is higher in urban areas consumption (%) in and for higher income groups. Yet the rule widely monetary value depends on the eating habits of the country. See tables 1.7, 1.8, and 4.3. share of food According to Engel’s law, the higher the income, consumption in total the lower the proportion of income is spent income (%) (Engel on food. This ratio reflects the living stan- ratio) dard of a population group and its vulnerability 159 Analyzing Food Security Using Household Survey Data to food price increases. It can get close to 80 percent for low-income groups and 20 percent for high-income groups. See tables 1.7, 1.8, and 4.4. share of food from Share of dietary energy coming from food received other sources in total from other sources (e.g., received as payment, food consumption gift, aid, etc.). Usually is higher for low-income (%) in dietary energy groups as they are more likely to receive food aid, gifts, etc. See tables 1.5, 1.6, and 4.2. share of food from Contribution (expressed in monetary value) of other sources in total food received in kind to the total food monetary food consumption (%) value. Usually it is higher for lower income in monetary value deciles, which are mainly those receiving food in kind. See tables 1.7, 1.8, and 4.3. share of own Share of dietary energy coming from own pro- produced food in total duced food. Should be higher in rural areas food consumption than urban areas, and it is usually higher for (%) in dietary energy lower income groups. The greater the share is, the higher is the vulnerability to natural shocks affecting agricultural production. See tables 1.5, 1.6, and 4.2. share of own Contribution (expressed in monetary value) of produced food in total food taken from own production to the total food consumption (%) food monetary value. Should be higher in rural in monetary value areas than urban areas and it is usually higher for lower income groups. The greater the share is, the higher is the vulnerability to natural shocks affecting agricultural production. Indeed, farm- ing households will need to buy from the market the same amount of food they would have taken from their own production. See tables 1.7, 1.8, and 4.3. share of purchased Share of dietary energy coming from food pur- food in total food chased from the market. Usually higher in urban consumption (%) in areas. The greater the share is, the higher is the dietary energy vulnerability to price increase. This share can 160 Chapter 3: Guide to Output Tables be high for households living in urban areas and nonagricultural households. See tables 1.5, 1.6, and 4.2. share of purchased Contribution (expressed in monetary value) food in total food of purchased food to the total food monetary consumption (%) in value. Usually higher in urban areas where most monetary value people get food from the market. The greater the share is, the higher is the vulnerability to price increase. See tables 1.7, 1.8, and 4.3. skewness of dietary Skewness is a measure of the asymmetry of the energy consumption probability distribution of a real-valued random variable. It measures the length of the tail of the distribution. In a lognormal distribution, the skewness is a function of the coefficient of varia- tion: skewness = CV * (3 + CV2). In a skew(log) normal distribution, the skewness is indepen- dent from the CV’s values. For this reason, the adoption of a skew(log)normal model allows for greater flexibility and for a truer representation of the consumption distribution. See tables 1.1 and 1.2. within range of Indicates whether the proportion of total calories population fat intake available from fats is within the range of 15–30 goal: 15%–30% percent. See table 1.11. within range of Indicates whether the proportion of total calo- population protein ries available from protein is within the range of intake goal: 10–15 percent. See table 1.11. 10%–15% within range of Indicates whether the proportion of total calories population total available from carbohydrates is within the range carbohydrates and of 55–75 percent. See table 1.11. alcohol intake goal: 55%–75% 161 Analyzing Food Security Using Household Survey Data Indicators on Micronutrients 95th percentile of Total absolute iron requirements depend on sex, the average absolute age, and lactating and menopausal status (the lat- iron intakes required ter two for women only). Values are those reported (mg/person/day) in FAO/WHO (2004, 196). See table 5.4. average animal Average amount of iron from animal sources iron availability available for consumption. Iron has several vital (mg/person/day) functions in the body. It serves as a carrier of oxygen to the tissues from the lungs by red blood cell hemoglobin. Iron deficiency (sideropenia or hypoferremia) is one of the most common nutritional deficiencies. Symptoms of iron defi- ciency include fatigue, dizziness, pallor, hair loss, twitches, irritability, weakness, pica, brittle or grooved nails, Plummer-Vinson syndrome, impaired immune function, pagophagia, and restless legs syndrome. See tables 5.4, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average availability Relative contribution (percent) of each food of animal iron group to total animal iron. Table 6.5 provides provided by each food national values. Table 6.6 provides values disag- group, out of total gregated by area of residence. availability (%) average availability Relative contribution (percent) of each food of beta-carotene group to total beta-carotene availability. Table provided by each food 6.5 provides national values. Table 6.6 provides group, out of total values disaggregated by area of residence. availability (%) average availability of Relative contribution (percent) of each food calcium provided by group to total calcium availability. Table 6.5 pro- each food group, out vides national values. Table 6.6 provides values of total availability (%) disaggregated by area of residence. average availability of Relative contribution (percent) of each food heme iron provided by group to total heme iron. Table 6.5 provides each food group, out national values. Table 6.6 provides values disag- of total availability (%) gregated by area of residence. 162 Chapter 3: Guide to Output Tables average availability Relative contribution (percent) of each food of nonanimal iron group to total nonanimal iron. Table 6.5 pro- provided by each food vides national values. Table 6.6 provides values group, out of total disaggregated by area of residence. availability (%) average availability Relative contribution (percent) of each food of nonheme iron group to total nonheme iron. Table 6.5 provides provided by each food national values. Table 6.6 provides values disag- group, out of total gregated by area of residence. availability (%) average availability of Relative contribution (percent) of each food retinol provided by group to total retinol availability. Table 6.5 pro- each food group, out vides national values. Table 6.6 provides values of total availability disaggregated by area of residence. (%) average availability Relative contribution (percent) of each food of vitamin B1 group to total vitamin B1 availability. Table 6.5 provided by each food provides national values. Table 6.6 provides val- group, out of total ues disaggregated by area of residence. availability (%) average availability Relative contribution (percent) of each food of vitamin B12 group to total vitamin B12 availability. Table provided by each food 6.5 provides national values. Table 6.6 provides group, out of total values disaggregated by area of residence. availability (%) average availability Relative contribution (percent) of each food of vitamin B2 group to total vitamin B2 availability. Table provided by each food 6.5 provides national values. Table 6.6 provides group, out of total values disaggregated by area of residence. availability (%) average availability Relative contribution (percent) of each food of vitamin B6 group to total vitamin B6 availability. Table 6.5 provided by each food provides national level values. Table 6.6 pro- group, out of total vides values disaggregated by area of residence. availability (%) 163 Analyzing Food Security Using Household Survey Data average availability of Relative contribution (percent) of each food vitamin C provided by group to total vitamin C availability. Table 6.5 each food group, out provides national level values. Table 6.6 pro- of total availability (%) vides values disaggregated by area of residence. average beta-carotene Average amount of beta-carotene available, availability expressed in micrograms per person per day. (mcg/person/day) Beta-carotene (β-Carotene) is a strongly colored red-orange pigment abundant in plants and fruits. Its absorption is enhanced if eaten with fats, as carotenes are fat-soluble. See tables 5.1, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average calcium Average amount of calcium available for con- availability sumption. Calcium salts provide rigidity to the (mg/person/day) skeleton, and calcium ions play a role in many if not most metabolic processes. A positive calcium balance (i.e., net calcium retention) is required throughout growth, particularly during the first two years of life and during puberty and adoles- cence. See tables 5.3, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average calcium Average amount of calcium (expressed in mil- availability per ligrams) available in 1,000 kilocalories. Being 1,000 kcal a relative measure, we can talk about density of calcium per 1,000 kcal. See table 5.5. average heme Average amount of heme iron available for iron availability consumption. With respect to the mechanism of (mg/person/day) absorption, there are two kinds of dietary iron: heme iron and nonheme iron. Primary sources of heme iron are the hemoglobin and myoglobin from consumption of meat, poultry, and fish. Heme iron can be degraded and converted to nonheme iron if foods are cooked at a high tem- perature for a long time. See tables 5.4, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. 164 Chapter 3: Guide to Output Tables average nonanimal Average amount of iron from nonanimal sources iron availability available for consumption. Iron has several vital (mg/person/day) functions in the body. It serves as a carrier of oxygen to the tissues from the lungs by red blood cell hemoglobin. Iron deficiency (sideropenia or hypoferremia) is one of the most common nutritional deficiencies. Symptoms of iron defi- ciency include fatigue, dizziness, pallor, hair loss, twitches, irritability, weakness, pica, brittle or grooved nails, Plummer-Vinson syndrome, impaired immune function, pagophagia, and restless legs syndrome. See tables 5.4, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average nonheme Average amount of nonheme iron available for iron availability consumption. With respect to the mechanism (mg/person/day) of absorption, there are two kinds of dietary iron: heme iron and nonheme iron. Primary sources of nonheme iron are cereals, pulses, legumes, fruits, and vegetables. The absorption of nonheme iron is influenced by the individu- al’s iron status and the presence of some food components such as ascorbic acid, polyphenols, and phytates. See tables 5.4, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average retinol Average amount of retinol activity equivalent of activity equivalent of vitamin A available for consumption. There are vitamin A availability two sources of vitamin A: one is food from ani- (mcg/person/day) mal origin, which includes retinol, and the sec- ond one is food from plant origin, which includes beta-carotene. One unit of retinol is equivalent to one unit of vitamin A; however, in the case of carotenoids, the body converts them to vitamin A as shown in this formula: Vitamin A = mcg of retinol + (mcg of beta-carotene/12) + (mcg of other carotenoids)/24 165 Analyzing Food Security Using Household Survey Data Vitamin A is an essential nutrient needed for the normal functioning of the visual system, growth and development, maintenance of epithelial cel- lular integrity, immune system functioning, and reproduction. The main consequence of vitamin A deficiencies is night blindness, which could develop into irreversible blindness. See tables 5.1, 5.6, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average retinol Average amount of retinol available for con- availability sumption. See tables 5.1, 6.1, 6.2, 6.3, 6.4, 6.7, (mcg/person/day) 6.8, and 6.9 average vitamin Average amount of vitamin A (expressed in A availability in micrograms of retinol activity equivalents) avail- 1,000 kcal able in 1,000 kcal. Being a relative measure, we can talk about density of calcium per 1,000 kcal. See table 5.6. average vitamin Average amount of vitamin B1 (expressed in B1 availability in milligrams) available in 1,000 kcal. Being a rela- 1,000 kcal tive measure, we can talk about density of vita- min B1 per 1,000 kcal. See table 5.7. average vitamin Average amount of vitamin B1 available for B1 availability consumption. B1 (otherwise called thiamin) (mg/person/day) deficiency results in the disease called beri- beri. Beriberi occurs in breastfed infants whose nursing mothers are deficient. It also occurs in adults with high carbohydrate intake mainly from milled rice and with intake of antithiamin factors. See tables 5.2, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average vitamin Average amount of vitamin B12 (expressed in B12 availability in micrograms) available in 1,000 kcal. Being a 1,000 kcal relative measure, we can talk about density of vitamin B12 per 1,000 kcal. See table 5.7. average vitamin Average amount of vitamin B12 available for B12 availability consumption. Vitamin B12 (otherwise called (mcg/person/day) cobalamin) enters the human food chain 166 Chapter 3: Guide to Output Tables through food of animal origin. Products from herbivorous animals, such as milk, meat, and eggs, constitute important dietary sources of vita- min B12. Vitamin B12 deficiency can cause per- manent damage to nervous tissue if left untreated longer than six months. See tables 5.2, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average vitamin B2 Average amount of vitamin B2 (expressed in availability in 1,000 milligrams) available in 1,000 kcal. Being a rela- kcal tive measure, we can talk about density of vita- min B2 per 1,000 kcal. See table 5.7. average vitamin B2 Average amount of vitamin B2 available for availability consumption. B2 (otherwise called riboflavin) (mg/person/day) deficiency results into hypo- or ariboflavinosis, with sore throat, hyperemia, oedema of the pha- ryngeal and oral mucous membranes, cheilosis, angular stomatitis, glossitis, seborrheic dermati- tis, and normochromic, normocytic bone mar- row. The major cause of hyporiboflavinosis is inadequate dietary intake as a result of limited food supply, which is sometimes exacerbated by poor food storage or processing. See tables 5.2, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average vitamin B6 Average amount of vitamin B6 (expressed in availability in milligrams) available in 1,000 kcal. Being a rela- 1,000 kcal tive measure, we can talk about density of vita- min B6 per 1,000 kcal. See table 5.7. average vitamin B6 Average amount of vitamin B6 available for availability consumption. Vitamin B6 deficiency usu- (mg/person/day) ally occurs in association with a deficit in other B-complex vitamins. Infants are espe- cially susceptible to insufficient intakes, which can lead to epileptiform convulsions. Skin changes include dermatitis with cheilosis and glossitis. A decrease in the metabolism of glutamate in the brain, which is found in vitamin B6 insufficiency, reflects a nervous 167 Analyzing Food Security Using Household Survey Data system dysfunction. As is the case with other micronutrient deficiencies, vitamin B6 deficiency results in an impairment of the immune system. See tables 5.2, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. average vitamin C Average amount of vitamin C (expressed in mil- availability in ligrams) available in 1,000 kcal. Being a relative 1,000 kcal measure, we can talk about density of vitamin C per 1,000 kcal. See table 5.6. average vitamin C Average amount of vitamin C available for con- availability sumption. Vitamin C mainly works as an antioxi- (mg/person/day) dant. Therefore chronic lack of vitamin C in the diet can lead to a condition called scurvy (i.e., easy bruising, spontaneous bleeding, and the joint and muscle pains). The populations at risk of vitamin C deficiency are those for whom the fruit and vegetable supply is minimal. Epidemics of scurvy are associated with famine and war, when people are forced to become refugees and the food supply is small and irregular. In many developing countries, limitations in the supply of vitamin C are often determined by seasonal factors. See tables 5.3, 6.1, 6.2, 6.3, 6.4, 6.7, 6.8, and 6.9. calcium recommended Amount of recommended calcium intake per intake in 1,000 kcal 1,000 kcal. See table 5.5. calcium recommended Amount of recommended calcium intake to intake (mg/person/ meet the average daily nutrient intake needed by day) almost all apparently healthy individuals in the population group. Values are those reported in FAO/WHO (2004, 162). See table 5.3. median of the average Total absolute iron requirements depend on sex, absolute iron intake age, and lactating and menopausal status (the lat- required (mg/person/ ter two for women only). Values are those reported day) in FAO/WHO (2004, 196). See table 5.4. ratio of calcium Indicates whether the amount of calcium avail- available to able to the households is sufficient to meet the recommended (%) average daily nutrient intake needed by almost 168 Chapter 3: Guide to Output Tables all apparently healthy individuals in the popu- lation group. When the amount of available calcium exceeds the recommended amount, the ratio is above 1. However, we cannot talk about population out of risk of calcium deficiency because we do not have information of the actual intake. See tables 5.3 and 5.5. ratio of retinol Ratio between retinol and vitamin A available available to vitamin A for consumption, or the percentage of vitamin A available (%) that is due to the presence of retinol. See table 5.1. ratio of vitamin Indicates whether the amount of vitamin A A available to available to the households is sufficient to meet recommended (%) the average daily nutrient intake needed by almost all apparently healthy individuals in the population group. When the amount of available vitamin A exceeds the recommended amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin A defi- ciency because we do not have information of the actual intake. See tables 5.1 and 5.6. ratio of vitamin A Indicates whether the amount of vitamin A available to required available to the households is sufficient to meet (%) the average daily nutrient intake needed by 50 percent of the “healthy” individuals in the population group. When the amount of avail- able vitamin A exceeds the required amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin A deficiency because we do not have information of the actual intake. See tables 5.1 and 5.6. ratio of vitamin Indicates whether the amount of vitamin B1 B1 available to available to the households is sufficient to meet recommended (%) the average daily nutrient intake needed by almost all apparently healthy individuals in the population group. When the amount of available 169 Analyzing Food Security Using Household Survey Data vitamin B1 exceeds the recommended amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin B1 defi- ciency because we do not have information of the actual intake. See tables 5.2 and 5.7. ratio vitamin B12 Indicates whether the amount of vitamin B12 available to available to the households is sufficient to recommended (%) meet the average daily nutrient intake needed by almost all apparently healthy individuals in the population group. When the amount of available vitamin B12 exceeds the recom- mended amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin B12 deficiency because we do not have information of the actual intake. See tables 5.2 and 5.7. ratio vitamin B12 Indicates whether the amount of vitamin B12 available to required available to the households is sufficient to meet (%) the average daily nutrient intake needs by 50 percent of the “healthy” individuals in the population group. When the amount of available vitamin B12 exceeds the required amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin B12 deficiency because we do not have information of the actual intake. See tables 5.2 and 5.7. ratio vitamin B2 Indicates whether the amount of vitamin B2 available to available to the households is sufficient to meet recommended (%) the average daily nutrient intake needed by almost all apparently healthy individuals in the population group. When the amount of available vitamin B2 exceeds the recommended amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin B2 defi- ciency because we do not have information of the actual intake. See tables 5.2 and 5.7. 170 Chapter 3: Guide to Output Tables ratio vitamin B6 Indicates whether the amount of vitamin B6 available to available to the households is sufficient to recommended (%) meet the average daily nutrient intake needed by almost all apparently healthy individuals in the population group. When the amount of available vitamin B6 exceeds the recom- mended amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin B6 deficiency because we do not have information of the actual intake. See tables 5.2 and 5.7. ratio vitamin C Indicates whether the amount of vitamin C available to available to the households is sufficient to recommended (%) meet the average daily nutrient intake needed by almost all apparently healthy individuals in the population group. When the amount of available vitamin C exceeds the recommended amount, the ratio is above 1. However, we cannot talk about population out of risk of vitamin C deficiency because we do not have information of the actual intake. See tables 5.3 and 5.6. vitamin A mean Required amount of vitamin A (expressed in requirement in micrograms of retinol activity equivalent) per 1,000 kcal 1,000 kcalories. See table 5.6. vitamin A mean Amount of required vitamin A intake to meet requirement (mcg the average daily nutrient intake needed by 50 retinol activity percent of the healthy individuals in the popula- equivalent/person/day) tion group. Values are those reported in FAO/ WHO (2004, 100). See table 5.1. vitamin A Recommended amount of vitamin A (expressed recommended safe in micrograms of retino activity equivalent) per intake in 1,000 kcal 1,000 kcal. The difference between vitamin A requirements and vitamin A recommended safe intake is reported in FAO/WHO (2004). See table 5.6. 171 Analyzing Food Security Using Household Survey Data vitamin A Amount of recommended vitamin A intake to recommended safe meet the average daily nutrient intake needed by intake (mcg retinol almost all apparently healthy individuals in the activity equivalent/ population group. Values are those reported in person/day) FAO/WHO (2004, 100). See table 5.1. vitamin B1 Amount of recommended vitamin B1 intake to recommended intake meet the average daily nutrient intake needed by (mg/person/day) almost all apparently healthy individuals in the population group. Values are those reported in FAO/WHO (2004, 30). See table 5.2. vitamin B1 Recommended amount of vitamin B1 (expressed recommended safe in milligrams) per 1,000 kcal. See table 5.7. intake in 1,000 kcal vitamin B12 average Recommended amount of vitamin B12 (expressed requirement in in micrograms) per 1,000 kcal. See table 5.7. 1,000 kcal vitamin B12 Amount of required vitamin B12 intake to meet average requirement the average daily nutrient intake needed by 50 (mcg/person/day) percent of the healthy individuals in the popula- tion group. Values are those reported in FAO/ WHO (2004, 69). See table 5.2. vitamin B12 Amount of recommended vitamin B12 intake recommended intake to meet the average daily nutrient intake (mcg/person/day) needed by almost all apparently healthy indi- viduals in the population group. Values are those reported in FAO/WHO (2004, 69). See table 5.2. vitamin B12 Recommended amount of vitamin B12 (expressed recommended safe in micrograms) per 1,000 kcal. See table 5.7. intake in 1,000 kcal vitamin B2 Amount of recommended vitamin B2 intake to recommended intake meet the average daily nutrient intake needed by (mg/person/day) almost all apparently healthy individuals in the population group. Values are those reported in FAO/WHO (2004, 33). See table 5.2. 172 Chapter 3: Guide to Output Tables vitamin B2 Recommended amount of vitamin B2 (expressed recommended safe in milligrams) per 1,000 kcal. See table 5.7. intake in 1,000 kcal vitamin B6 Amount of recommended vitamin B6 intake to recommended intake meet the average daily nutrient intake needed by (mg/person/day) almost all apparently healthy individuals in the population group. Values are those reported in FAO/WHO (2004, 38). See table 5.2. vitamin B6 Recommended amount of vitamin B6 (expressed recommended safe in milligrams) per 1,000 kcal. See table 5.7. intake in 1,000 kcal vitamin C Amount of recommended vitamin C intake to recommended intake meet the average daily nutrient intake needed by (mg/person/day) almost all apparently healthy individuals in the population group. Values are those reported in FAO/WHO (2004, 79). See table 5.3. vitamin C Recommended amount of vitamin C (expressed recommended safe in milligrams) per 1,000 kcal. See table 5.6. intake in 1,000 kcal Indicators on Amino Acids amino acid availability Proportion of the essential amino acids (provided as percentage of total by a group of food items) in total availability of availability (%) the same amino acid, after correcting for protein digestibility. See tables 8.5, 8.6, and 8.7. amino acid availability Amount of the essential amino acids available per gram of protein for consumption per gram of protein after cor- (mg) recting for protein digestibility. See table 7.2. histidine—average Average amount of the essential amino acid amino acid availability histidine available for consumption after cor- (g/person/day) recting for protein digestibility. Statistics shown by population group are expressed in daily milligrams per person. Statistics shown at food group or food item level are expressed in daily grams per person. Histidine belongs to the aromatic amino acids and was accepted as an 173 Analyzing Food Security Using Household Survey Data indispensable amino acid in human adults, despite controversy regarding its essentiality (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. isoleucine—average Average amount of the essential amino acid amino acid availability isoleucine available for consumption after cor- (g/person/day) recting for protein digestibility. Statistics shown by population group are expressed in daily milli- grams per person. Statistics shown at food group or food item level are expressed in daily grams per person. See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. leucine—average Average amount of the essential amino acid leu- amino acid availability cine available for consumption after correcting (g/person/day) for protein digestibility. Statistics shown by pop- ulation group are expressed in daily milligrams per person. Statistics shown at food group or food item level are expressed in daily grams per per- son. Leucine is the most abundant amino acid in tissue and food proteins (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. lysine—average Average amount of the essential amino acid amino acid availability lysine available for consumption after correcting (g/person/day) for protein digestibility. Statistics shown by pop- ulation group are expressed in daily milligrams per person. Statistics shown at food group or food item level are expressed in daily grams per person. Lysine is the likely limiting amino acid in cereals, especially wheat (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. methionine and Average amount of the essential amino acids cystine—average methionine and cystine available for consumption amino acid availability after correcting for protein digestibility. Statistics (g/person/day) shown by population group are expressed in daily milligrams per person. Statistics shown at food group or food item level are expressed in 174 Chapter 3: Guide to Output Tables daily grams per person. Methionine and cystine are also called sulfur amino acids. The former is nutritionally indispensable while the latter, as a metabolic product of methionine catabolism, is dependent on there being sufficient methio- nine to supply the needs for both amino acids. Their concentrations are marginal in legume proteins, although they are equally abundant in cereal and animal proteins (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. phenylalanine and Average amount of the essential amino acids tyrosine—average phenylalanine and tyrosine available for con- amino acid availability sumption after correcting for protein digest- (g/person/day) ibility. Statistics shown by population group are expressed in daily milligrams per person. Statistics shown at food group or food item level are expressed in daily grams per person. These two amino acids belong to the aromatic amino acids. Phenylalanine is nutritionally indispens- able while tyrosine, as a metabolic product of phenylalanine catabolism, is dependent on there being sufficient phenylalanine to supply the needs for both amino acids (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. threonine—average Average amount of the essential amino acid amino acid availability threonine available for consumption after cor- (g/person/day) recting for protein digestibility. Statistics shown by population group are expressed in daily milli- grams per person. Statistics shown at food group or food item level are expressed in daily grams per person. Threonine is present at low concen- trations in cereal proteins (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. 175 Analyzing Food Security Using Household Survey Data tryptophan—average Average amount of the essential amino acid amino acid availability tryptophan available for consumption after (g/person/day) correcting for protein digestibility. Statistics shown by population group are expressed in daily milligrams per person. Statistics shown at food group or food item level are expressed in daily grams per person. Tryptophan belongs to the aromatic amino acids. The occurrence of tryptophan in proteins is generally less than many other amino acids because its content is low in cereals, especially maize (WHO 2002). See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. valine—average Average amount of the essential amino acid amino acid availability valine available for consumption after correct- (g/person/day) ing for protein digestibility. Statistics shown by population group are expressed in daily milli- grams per person. Statistics shown at food group or food item level are expressed in daily grams per person. See tables 7.1, 8.1, 8.2, 8.3, 8.4, 8.8, 8.9, and 8.10. Notes 1. Before executing ADePT-FSM, the user classifies the food commodities in different food groups. Further details can be found in chapter 2. 2. Further details can be found in chapter 2. 3. For the methodology applied at the subnational level, further details can be found in chapter 2. 4. Further details can be found in chapter 2. 5. See the following link: http://www.fao.org/economic/ess/ess-fs/fs -methods/adept-fsn/en/. 6. Further details can be found in chapter 2. 7. Further details can be found in chapter 2. 8. Available carbohydrates = total carbohydrates – fibers. 176 Chapter 3: Guide to Output Tables 9. Available carbohydrates = total carbohydrates – fibers. 10. Available carbohydrates = total carbohydrates – fibers. 11. Available carbohydrates = total carbohydrates – fibers. 12. Available carbohydrates = total carbohydrates – fibers. 13. The food commodity quantities cannot be used for this comparison unless refuse factors and technical conversion factors for agricultural commodities (the same used in FBS) are applied to the food quantities consumed. 14. All food quantities include both the edible and the nonedible parts (i.e., peels, bones, spines, etc.). 15. All food quantities include both the edible and the nonedible parts (i.e., peels, bones, spines, etc.). 16. All food quantities include both the edible and the nonedible parts (i.e., peels, bones, spines, etc.). 17. EAR is the average daily nutrient intake level that meets the needs of 50 percent of the “healthy” individuals in a particular age and gender group. The RNI is the daily intake, set at the EAR plus 2 standard deviations, which meets the nutrient requirements of almost all appar- ently healthy individuals in an age- and sex-specific population group (FAO/WHO 2004). To express nutrient requirements and recom- mended intakes for population groups, the requirements by sex and age are applied to individuals and then summed for each population group of analysis. The individual requirements were defined for gender-age population groups by a FAO/WHO group of experts in 1998 (WHO 2004). 18. Iron deficiency is defined as a hemoglobin concentration below the optimum value in an individual, whereas iron deficiency anemia implies that the hemoglobin concentration is below the 95th percentile of the distribution of hemoglobin concentration in a population (disregarding effects of altitude, age, sex, etc., on hemoglobin concentration) (WHO 2004). 19. Further details can be found in chapter 2. 20. Further details can be found in chapter 2. 21. Further details can be found in chapter 2. 22. Further details can be found in chapter 2. 23. Further details can be found in chapter 2. 24. Further details can be found in chapter 2. 25. Further details can be found in chapter 2. 177 Analyzing Food Security Using Household Survey Data References FAO, WHO (World Health Organization). 2004. Vitamin and Mineral Requirements in Human Nutrition, 2nd ed. Rome: FAO. Fiedler, J. L. 2009. “Strengthening Household Income and Expenditure Surveys as a Tool for Designing and Assessing Food Fortification Programs.” IHSN (International Household Survey Network) Working Paper 001. IHSN, United Nations, New York. Hallberg, L. 1981. “Bioavailability of Dietary Iron in Man.” Annual Review of Nutrition (1): 123–47. NAS (National Academy of Sciences). 2000. Dietary Reference Intakes: Applications in Dietary Assessment. Washington, DC: National Academy Press. http://www.nap.edu/catalog/9956.html. Schmidhuber, J. 2003. “Measurement and Assessment of Food Deprivation and Undernutrition: Household Expenditure Surveys.” Discussion Group Report, International Scientific Symposium, Rome, June 26–28. Smith, R. M. 1987. “Cobalt.” In Trace Elements in Human and Animal Nutrition, 5th ed., edited by W. Mertz, 143–84, San Diego: Academic Press. USHHS (United States Department of Health and Human Services), and USDA (United States Department of Agriculture). 2005. Dietary Guidelines for Americans 2005, 6th ed. Washington, DC: U.S. Government Printing Office. WHO. 2002. Protein and Amino Acid Requirements in Human Nutrition. Geneva: WHO. ⎯⎯⎯. 2003. Diet, Nutrition and the Prevention of Chronic Diseases. WHO Technical Report Series 961, Geneva: WHO. ⎯⎯⎯. 2004. Vitamin and Mineral Requirements in Human Nutrition. 2nd ed. Joint FAO/WHO Expert Consultation on Human Vitamin and Mineral Requirements. Bangkok: WHO. ⎯⎯⎯. 2007. Protein and Amino Acid Requirements in Human Nutrition. WHO Technical Report Series 935, Geneva: WHO. Bibliography Aromolaran, A. 2004. “Intra-Household Redistribution of Income and Calorie Consumption in South-Western Nigeria.” Discussion Paper 890. Yale University Economic Growth Center, New Haven, CT. 178 Chapter 3: Guide to Output Tables Cafiero, C. 2011. “Measuring Food Insecurity: Meaningful Concepts and Indicators for Evidence-Based Policy Making.” Paper presented at the Food and Agriculture Organization conference “Round Table on Monitoring Food Security,” Rome, September 12–13. FAO (Food and Agriculture Organization). 1996. The Sixth World Food Survey. Rome: FAO. ⎯⎯⎯. 1999–2013. The State of Food Insecurity in the World. Rome: FAO. McCormick, D. B. 1988. “Vitamin B6.” In Modern Nutrition in Health and Disease, 6th ed., edited by M. E. Shils and V. R. Young, 376–82. Philadelphia: Lea and Febiger. WHO, and UNHCR (United Nations High Commissioner for Refugees). 1999. Thiamine Deficiency and Its Prevention and Control in Major Emergencies. Geneva: WHO. 179 Chapter 4 Datasets Ana Moltedo, Andrea Borlizzi, Chiara Brunelli, Yassin Firas, Seevalingum Ramasawmy, Zurab Sajaia Introduction ADePT-FSM requires four datasets (loaded either in STATA or SPSS format). Three datasets contain data extracted mainly from the original national household surveys (NHS) files: • Dataset 1 (HOUSEHOLD), including mainly household character- istics. • Dataset 2 (INDIVIDUAL), with household member characteristics. • Dataset 3 (FOOD), ideally with quantities and monetary values of food commodities habitually consumed by households. However, just a few surveys, such as yearly panel surveys’ collecting information on food partakers, are designed to capture the household habitual food consumption. For this reason, in this book we refer to actual food consumed or acquired by households. These three datasets include a household identification code that allows for matching information among them. The fourth dataset contains data extracted from national and/or regional food composition tables (FCTs): • Dataset 4 (COUNTRY_NCT [nutrition conversion table]), with calorie and nutrient values for the food commodities collected in the survey. 181 Analyzing Food Security Using Household Survey Data Datasets Description Dataset 1 (HOUSEHOLD) Dataset 1 has one record for each household and provides information on household characteristics (household size, region and area of residence, total consumption expenditure, income, etc.), and price indexes (i.e., the con- sumer price index [CPI] and the food price index [FPI]). While household characteristics are extracted from national household survey (NHS) data, the FPI and CPI are provided by national or international organizations, such as the International Labour Organization (ILO). The household characteris- tics are mainly used to create groupings and produce subnational estimates. The FPI and CPI are instrumental for deflating the food expenditures and income/expenditure values, respectively, in the presence of one-year surveys. Table 4.1 shows the main characteristics of the variables included in dataset 1, the values they can assume, and the associated checks to be performed. Variable names depicted in the table are not mandatory; however com- parison of results intra- and intercountries is greatly facilitated if a common set of variable names is adopted. Each variable, and each value of categorical variables, has to be described by an appropriate label. Finally, none of the variables are allowed to have missing values. An important distinction has to be made between Household member and Food partaker. While only household members share the household income, the food acquired by the household can be distributed to nonhousehold members (such as guests and employees). Therefore, the number of food partakers corresponds to the number of people who actually consumed the food during the reference period. Example of a reference period for food consumption data for one month: • A household reported four members • One member was absent • One guest and one housekeeper with a child also consumed the food acquired by the household In this case, the number of partakers for the reference period will be six instead of four: four household members minus the absent member plus the guest, the housekeeper, and the child. 182 Chapter 4: Datasets Table 4.1: Dataset 1 (HOUSEHOLD) Variable name and format Rationale and values Remarks and checks Household number (hh_no) Identification code of the surveyed household. Each household has to be identified by a Format: Numeric or string Sequential numbers or a combination of unique code. geographical codes (district, area, village, Only households declaring food region, etc.). consumption should be included. Necessary to link dataset 1 with datasets 2 and 3. Location of the household Identification code of the district, province, or It is recommended that each location is (region) region of residence of the surveyed household. represented by about 500 households to Format: Numeric This variable has to include the labels have reliable estimates also at the income corresponding to the geographical groups. deciles level (a statistic obtained with fewer than 30 households is considered not reliable). Thus it may be necessary to group some locations into a new one. Area of residence of the Identification code of the area (urban, rural, It is recommended that each area is household (urb_rur) semiurban, etc.) of residence of the surveyed represented by about 500 households to Format: Numeric household. have reliable estimates also at the income This variable has to include the labels deciles level (a statistic obtained with fewer corresponding to the areas. Examples: than 30 households is considered not • Code = 1, Label: Urban reliable). Thus it may be necessary to group • Code = 2, Label: Rural some areas into a new one (for instance, urban with semiurban). Household size (hh_size) Number of people who usually live together Excludes Format: Numeric and share the household income. • Domestic workers, friends, or relatives who neither live in the house nor share the income • Domestic workers, friends, or relatives who live in the house but don’t share the household income Category of household size Identification code of category of household size. It is recommended that each category of (hhsizec) This variable has to include the labels. household size is represented by about 500 Format: Numeric Examples: households to have reliable estimates also at • Code = 1, Label: Less than three the income deciles level (a statistic obtained • Code = 2, Label: Three or four with fewer than 30 households is considered • Code = 3, Label: Five or six not reliable). • Code = 4, Label: More than six Number of food partakers Average number of people who shared the Partakers are individuals who shared the (partakers) food during the period of food data collection household food during the reference period. Format: Numeric (reference period). Includes housekeepers, friends, and relatives who may not live in the house but shared the food. Excludes household members who were absent during the food data reference period and therefore did not consume the food. Household weight (hh_wgt) The value of the household weight depends The sum of the product number of Format: Decimal on the sampling frame and is equal to the household members * household weight has expansion factor divided by the probability of to be close to the total country population at the household to be sampled. the year of the survey. Household weight should be adjusted for Only households declaring food nonresponding households. consumption should be included in this dataset. Therefore, after deleting households that did not declare food consumption, household weight should be amended accordingly. Details are provided at the bottom of the table. (continued) 183 Analyzing Food Security Using Household Survey Data Table 4.1: Dataset 1 (HOUSEHOLD) (continued) Variable name and format Rationale and values Remarks and checks Total household consumption Sum of household food and nonfood Monetary values should be expressed in expenditure (thh_cexp) consumption expenditures. daily basis. Format: Decimal Excludes all expenditures not related to Each household should have a positive value household consumption, such as investments, of total consumption expenditure. life insurance premiums, food for pets or given This value has to be greater than or at least away, etc. equal to the respective household total food expenditure. Total household income Sum of the income received by each household Monetary values should be expressed in (thh_inc) member; includes all the possible sources daily basis. Format: Decimal (wages, profit from self-employment, sales of If income data are either not available self-produced goods and services, income in or not reliable, total expenditure kind, transfers, rent received, etc.).a can be used as a proxy of income. Total expenditure includes consumption and nonconsumption expenditures such as direct taxes, insurance premiums, food given away or animal feed, etc. Each household should have a positive value of total income. Also, this value has to be greater than or at least equal to the respective household total consumption expenditure. Primary sampling unit (psu) Identification code of the smallest sampling Format: Numeric geographic unit from which households are selected. Month of food data collection Identification code of the month during which (month) the food consumption/acquisition data were Format: Numeric collected. This variable has to include labels, e.g., values of 1, 2, 3 . . . 12 corresponding to the months January, February, March . . . December. Year of the food data Identification code of the year during which collection (year) the food consumption/acquisition data were Format: Numeric collected. Examples: Values of 1998, 1999, 2000, 2003 . . . etc. Consumer price index (cpi) Measures the changes in the purchasing Use the value corresponding to the month Format: Decimal power of a currency and the rate of inflation. and year in which the household food The consumer price index expresses the consumption data were collected. current prices of a basket of goods and If the monetary values are already deflated services in terms of the prices during the same (or the survey was conducted only over a period in a previous year, which shows the period of a few months), this variable is not effect of inflation on purchasing power. It is needed. one of the best-known lagging indicators. It is used to correct total consumption expenditure and total income for inflation or deflation. All the consumer price indexes should refer to the same base period.b (continued) 184 Chapter 4: Datasets Table 4.1: Dataset 1 (HOUSEHOLD) (continued) Variable name and format Rationale and values Remarks and checks Food price index (fpi) Measures the changes in the purchasing power Use the value corresponding to the month Format: Decimal of a currency and the rate of inflation. The food and year in which the household food price index expresses the current prices of a consumption data was collected. food basket in terms of the prices during the If the monetary values are already deflated same period in a previous year, which shows (or the survey was conducted only over a the effect of inflation on purchasing power. period of a few months), this variable is not It is used to correct food monetary values for needed. inflation or deflation. All the food price indexes should refer to the same base period.c a. For detailed information refer to the Canberra Handbook on Household Income Statistics (2nd ed., 2011) at http://www.unece.org/index.php?id=28894. b. Sources of data: National or international institutions such as ILO. This information can also be found in FAOSTAT: http://faostat.fao.org/site/683/Default.aspx#ancor. c. Sources of data: National or international institutions such as ILO. This information can also be found in FAOSTAT: http://faostat.fao.org/site/683/Default.aspx#ancor. The number of partakers is not always collected in household surveys. However, it is highly recommended to check if this information is available. If so, the variable Number of partakers has to be included in dataset 1. When deriving the statistics related to a variable of analysis such as the location or area of residence, ADePT-FSM excludes all records with missing values in that variable. The consequence of this could be to produce unreli- able statistics for that group of analysis. Therefore, it is important to avoid the presence of missing values as much as possible. Another crucial note regards the variable Location of the household. The analyst should always select the geographical domain(s) of which the survey data is representative.1 For instance, if the original NHS datasets include both the variable Province and the variable Region, and the survey was designed to be representative at the province level, then the analyst should select the province. Finally, only the households that declared food consumption should be in dataset 1; the other ones should be deleted. After the deletion, the house- hold weights should be amended, as follows: 1. Sum by enumeration area: (hh_size * hh_wgt); note that at the national level (hh_size * hh_wgt) = population_original (≈ total country population at the survey year). 2. Delete the households that did not declare food from dataset 1. 3. Sum by enumeration area: (hh_size * hh_wgt) = population_new. 4. Compute: hh_wgt_adj = hh_wgt * (population_original / population_new). 185 Analyzing Food Security Using Household Survey Data Screenshot 4.1: Example of Dataset 1 in SPSS Format (L: Data View, R: Variable View) Dataset 2 (INDIVIDUAL) Dataset 2 has one record for each member of the household and provides information on members’ characteristics such as gender, age, height, occu- pation, and education. Age, gender, and height are necessary to estimate the dietary energy requirements of the population. Even though some NHS collect data on height, this is usually done only for children under five years of age and/ or for women of reproductive age. Therefore, the distribution of height across the gender/age groups is usually derived from other sources such as demographic and health surveys, country reference tables, or specific publications (for example, James and Schofield 1990).2 Characteristics of the household members, particularly of the household head, can be used to disaggregate food consumption statistics by population groups (i.e., derive subnational estimates). In addition, the analyst can also define up to five “spare” variables to further disaggregate the food consumption statistics (hm_var1, hm_var2, ..., hm_var5). The spare variables can correspond to household/household head characteristics or can be a combination of them. When deriving statistics related to a variable such as education or occupation, ADePT-FSM excludes all records with missing values in that variable. The consequence of this could be to produce unreliable statistics for that group of analysis. Therefore, it is important to avoid the presence of missing values as much as possible. 186 Chapter 4: Datasets Gender Disaggregated Analysis Combining two variables into one is particularly useful in the context of gender analysis. ADePT automatically disaggregates all the statistics by gen- der of the household head. However, a more in-depth analysis can be carried out by combining the gender of the household head with other demographic and economic characteristics to produce a household typology. For instance, combining gender with the region/area of residence provides useful informa- tion for targeting aid and development programs. The following interaction effects are worth consideration: • Gender of the household head and area/region: Gender-based gaps might be very different in urban and rural areas. • Gender of the household head and household size: It is particularly inter- esting to assess gender disparities controlling for the household size. It is especially relevant to look at large and single-headed households that might be more exposed to poverty and food insecurity. • Gender of the household head and household composition: Similarly, it is important to interpret gender-based disparities in view of the house- hold demographic profile. This might include comparisons between male/female single parents, male/female-headed households with and without children under five years of age, etc. • Gender of the household head and presence of dependents in the household • Gender and age of the household head • Gender of the household head and household income group: It is particu- larly interesting to see if gender-based differences exist by controlling for the household income status. • Gender and education of the household head • Gender and marital status of the household head • Gender and economic sector/occupation of the household head In many countries, female-headed households are a small percentage of the entire sample. Therefore, the combination of two variables may result in a very low number of observations. This is particularly true when the survey sample is not very large. The analyst should take this issue into consider- ation and avoid creating categories with very few observations. A preliminary cross-tabulation helps to detect the combinations with a low number of cases. In screenshot 4.2, the cross-tabulation of gender and 187 Analyzing Food Security Using Household Survey Data education of the household head clearly suggests one should merge the edu- cational status into broader categories to build a combined variable whose categories have an acceptable number (a minimum of 500) of elements. It is not always possible to reach the suggested minimum number (a minimum of 500) of observations. For instance, in screenshot 4.2, the merging of no education and primary education gives 550 observations to the category female heads–no education or primary. But the merging of second- ary and more than secondary gives only 250 observations to the category female–secondary or more. Even though 250 is enough to obtain reliable estimates at the national level, it might not be sufficient to obtain reliable estimates across the income deciles. In fact, with only 250 cases, it is very likely to have fewer than 30 heads of households in one or more of the income decile groups (table 4.2). In such cases, it is important to keep in mind that the food consumption statistics with fewer than 30 heads of households have poor reliability. Table 4.3 shows the main characteristics of the variables included in dataset 2, the values they can assume, and the associated checks to be performed. Like dataset 1, variable names depicted in the table are not mandatory. Screenshot 4.2: Cross-Tabulation of Gender and Education of the Household Head Table 4.2: Review of the Number of Observations within the Population Groups Number of households DEC (kcal/person/day) Female—secondary or more 250 1650 Lowest 37 1400 2 30 1400 3 25 1450 4 25 1500 5 25 1670 6 28 1640 7 30 1800 8 25 1940 9 10 1800 Highest 15 1900 188 Chapter 4: Datasets Table 4.3: Dataset 2 (INDIVIDUAL) Variable name and format Rationale and values Remarks and checks Household number Identification code of the household. Sequential Each household has to be identified by (hh_no) numbers or a combination of geographical codes a unique code. Format: Numeric or (district, area, village, region, etc.). Only the households declaring food string Necessary for linking dataset 2 with datasets 1 and 3. consumption should be included in this dataset. Relationship between Identification code of the relationship between the All households must have a household head. the household member household member and the head of the household. There has to be only one head per household. and the head of the This variable has to include labels. household (hm_rel) The compulsory value code for the head of the Format: Numeric household is 1. Exclude all individuals who do not share the household income, such as housekeeper, guests, and relatives who do not live in the house or live in the household but do not share the household income. Gender of the Identification code of the gender of the household Missing data on gender are not valid; each household member member. household member has to have a value of (gender) This variable has to include labels corresponding to 1 or 2. Format: Numeric both sexes. Compulsory value codes: • Code = 1, Label: Male • Code = 2, Label: Female Age of the household Values are to be expressed in years. Missing data on age are not valid; each member (hm_age) For children less than one year of age, assign the household member has to have an age value. Format: Numeric value 0. Household member Identification code of the group to which the Records with missing values are deleted by age category (hmagec) household member belongs according to age. This the program, and this may cause unreliable Format: Numeric variable has to include the labels. Example: estimates for the variable. • Code = 1, Label: Less than 30 To have reliable estimates of the age of the • Code = 2, Label: Between 30 and 44 household head, it is recommended that • Code = 3, Label: Between 45 and 59 about 500 household heads are represented • Code = 4, Label: More than 59 in each age category. The reason for this is to also have reliable estimates by income deciles (a statistic obtained with fewer than 30 households is considered unreliable). Height of the Values are to be expressed in cm. Missing data on height are not valid; each household member household member must have a value (height) greater than 0 in this variable. Format: Decimal When height data are not collected in the survey, the median height by age/sex groups obtained from national reference tables, specific publications, or household demographic surveys should be used. Marital status of the Identification code of the group to which the Missing values are not allowed for the household member household member belongs according to marital household heads (hm_rel = 1). (hm_mar) status. To have reliable estimates, of the marital Format: Numeric This variable has to include the labels. Examples: status of the household head, it is • Code = 1, Label: Single recommended that about 500 household • Code = 2, Label: Married or living together heads are represented in each category of • Code = 3, Label: Widower marital status. The reason for this is to also • Code = 4, Label: Divorced or separated have reliable estimates by income deciles (a statistic obtained with fewer than 30 households is considered unreliable). (continued) 189 Analyzing Food Security Using Household Survey Data Table 4.3: Dataset 2 (INDIVIDUAL) (continued) Variable name and format Rationale and values Remarks and checks Economic activity Identification code of the group to which the Records with missing values are deleted by (hm_eact) household member belongs according to economic the program and this may cause unreliable Format: Numeric activity. Recode the economic activities collected estimates for the variable. in the survey into major economic activity groups To have reliable estimates, of by the defined by the first digit of national or international economic activity of the household head, it classifications such as ISIC (Rev. 4).a is recommended that about 500 household This variable has to include labels. Examples: heads are represented in each major activity. • Code = 1, Label: Primary (agriculture, fishing, The reason for this is to also have reliable hunting, and mining) estimates by income deciles (a statistic • Code = 2, Label: Secondary (manufacturing) obtained with fewer than 30 households is • Code = 3, Label: Services considered unreliable). • Code = 4, Label: Without an activity Occupation (hm_occ) Identification code of the group to which the Records with missing values are deleted by Format: Numeric household member belongs according to occupation. the program and this may cause unreliable It is highly recommended to recode the occupations estimates for the variable. collected in the survey into major occupation groups To have reliable estimates, of by the defined by the first digit of national/international occupation of the household head, it is classifications such as ISCO.b recommended that about 500 household This variable has to include labels. Examples: heads are represented in each major • Code = 1, Label: Managers and professionals occupation. The reason for this is to also • Code = 2, Label: Technicians and clerical support have reliable estimates by income deciles • Code = 3, Label: Service and sales workers (a statistic obtained with fewer than 30 • Code = 4, Label: Agricultural, forest, fishery workers households is considered unreliable). • Code = 5, Label: Without occupation Highest level of Identification code of the group to which the Records with missing values are deleted by education (hm_edu) household member belongs according to the highest the program and this may cause unreliable Format: Numeric level of education attended by them. estimates for the variable. This variable has to include labels. Examples: To have reliable estimates, of by the highest • Code = 1, Label: No education level of education the household head • Code = 2, Label: Primary school attended, it is recommended that about 500 • Code = 3, Label: Secondary school household heads are represented in each • Code = 4, Label: Tertiary education level of education. The reason for this is to also have reliable estimates by income deciles (a statistic obtained with fewer than 30 households is considered unreliable). Additional variables Identification code of the group to which the Records with missing values are deleted by with household/ household member belongs according to additional the program and this may cause unreliable household member variables. Some examples are Religion, Ethnic group, estimates for the variable. characteristics (hm_ Household with or without children under 5, Source To have reliable estimates, of the var1, . . ., hm_var5) of drinkable water, etc. characteristic of the household head, it is Format: Numeric These variables should include the labels. recommended that about 500 household heads are represented in each category of the group. The reason for this is to also have reliable estimates by income deciles (a statistic obtained with fewer than 30 households is considered unreliable). Note: ISCO - International Standard Classification of Occupations. a. For more information, see http://unstats.un.org/unsd/cr/registry/isic-4.asp. b. For more information, see http://www.ilo.org/public/english/bureau/stat/isco/index.htm. 190 Chapter 4: Datasets Screenshot 4.3: Example of Dataset 2 in SPSS Format (L: Data View, R: Variable View) Each variable/variable’s value has to be described by an appropriate label explaining its content of information. None of the variables are allowed to have missing values for the household head.3 Finally, for each household, the number of records in dataset 2 should be equal to the corresponding value of the variable hh_size (size of the house- hold) in dataset 1. This means only information about household members is required in this dataset. Therefore, records related to food partakers, such as housekeepers, friends, and relatives who are not household members, should be excluded from dataset 2. Dataset 3 (FOOD) Dataset 3 contains information on the household food consumption both in quantity and monetary terms, disaggregated by four main food sources. Each record corresponds to a food item consumed/acquired by the household through a specific source; the dataset may therefore have one or more entries of a given food item per household, depending on the number of sources from which the food item is obtained.4 Data in the food dataset should fulfill the following requirements: • All the food item quantities (including beverages) should be expressed in only one standard unit of measurement to be chosen among kilogram, gram, or pound. For this reason, the analyst has to transform all the food quantities into one unit. 191 Analyzing Food Security Using Household Survey Data • Food quantities and monetary values should be expressed on a daily basis. It is important to identify the actual reference period for which the households declared food consumption. The recall period is usu- ally clearly stated at the beginning of the food module in the ques- tionnaire, and the enumerators should have had the responsibility to convey the message as clearly as possible. This check is particularly relevant when food data are collected with a diary. In a consumption survey, if a diary is given to the households for a week, households may skip some days. In these cases, the reference period is the actual number of days the diary was filled in. However, in an acquisition survey, the same situation may require a different treatment. If a household is asked to report the food acquired in a week, and the diary is filled in for three days with considerable daily quantities, then it is likely that the food acquired in the three days also covers the four days with missing data. In this case, the most accurate reference period is still seven. • Food quantities must be related with the variable Number of food partakers or Household size. Food quantities should be expressed at the household level, not in “per person” amounts. ADePT-FSM automatically calculates the per person values by using the variable Number of food partakers, if available; otherwise, it uses the variable Household size. Also, food monetary values have to be expressed at the household level so that ADePT automatically calculates the per person values by using the variable Household size (note that the variable Food partakers is not taken into consideration when deriving food monetary values at the individual level). The preparation of the food dataset may require some computational steps to accurately estimate missing quantities of food consumed or monetary values. Estimate Accurate Quantities of Food Consumption Since the analysis is focused on the food consumed by the household (HH), the food given away, processed for resale, given to pets/livestock, and wasted has to be excluded. Such detailed data are rarely collected in the NHS, but if they are collected they should be subtracted from the total amount of food acquired. Details are provided in table 4.4. 192 Chapter 4: Datasets Table 4.4: Treatment of Food Acquired but Not Consumed by the Household • Food given away Subtract from the household food (e.g., to other households, consumption the food given away neighbors) • Food processed for resale (e.g., flour, Subtract from the household food sugar, eggs used for a cake to be consumption the food acquired for resale sold) • Food given to pets or for feeding Subtract from the household food livestock consumption the food given to pets or used for feeding livestock • Food thrown away (e.g., rotten, Subtract from the household food wasted, etc.) consumption the food thrown away Table 4.5: Dataset 3 (FOOD) Variable name and format Rationale and values Remarks and checks Household number Identification code of the household. Sequential Each household has to be identified by a unique (hh_no) numbers or a combination of geographical codes code. Format: Numeric or (district, area, village, region, etc.). Only the households declaring food consumption string Necessary to link dataset 3 with datasets 1 and 2. should be included in the dataset. Food item code Identification code of the food items listed in the Include alcoholic beverages and food consumed (item_cod) survey. away from home (canteens, bars, restaurants, etc.). Format: Numeric This variable should include labels. COICOPa or Exclude nonfood items, such as cigars, cigarettes, national classification codes can be used. tobacco, and drugs. Food item quantity Food quantities should reflect the food ADePT estimates the calories and nutrients (fd_qty) consumption or acquisition of the household. of missing food quantities only for the food Format: Decimal All food quantities should be expressed on a consumed away from home. If a household daily basis. declared expenditure for a food item with a food All food quantities, including beverages, should source different from 4 (consumed away), the be expressed in the same unit of measurement. quantity cannot be missing or 0. The analyst has The unit of measurement can only be grams, to estimate the missing/0 quantities based on the kilograms, or pounds. unit values. The estimation has to be carried out Keep track (by using labels or adding an extra before loading the dataset in ADePT. variable) of the unit of measurement used. Food item monetary Amount paid or estimated for the reported If a household declared a quantity for a food item, value in local currency quantity. the expenditure/monetary value cannot be missing (fd_mv) All food monetary values should be expressed on or 0. The analyst has to estimate the missing/0 Format: Decimal a daily basis. expenditure/monetary values based on the food item unit values. The estimation has to be carried out before loading the dataset in ADePT. Source of food item Identification code of the food source. ADePT analyzes four food sources. If there are (f_source) This variable should include labels. fewer than four food sources, keep this coding Format: Numeric Compulsory value codes. Examples: structure. • Code = 1, Label: Purchased and consumed at Food sources such as received free or as a gift, home from food aid, income in kind, gathering, or • Code = 2, Label: Own production fishing should be labeled as Received in kind with • Code = 3, Label: Received in kind code 3. • Code = 4, Label: Consumed away from home No missing values are allowed in this variable. Note: COICOP = Classification of Individual Consumption According to Purpose. a. For further information see http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=5. 193 Analyzing Food Security Using Household Survey Data Screenshot 4.4: Example of Dataset 3 in SPSS Format (L: Data View, R: Variable View) It is also important to check if information on the starting and ending levels of food stock are available, especially when the survey collects food acquisition data. If data on stocks are collected, they should be used as fol- lows to derive the household food consumption: HH food consumption = HH food acquired + HH starting food stock – HH ending food stock Estimate Missing Quantities and Expenditures For a food item reported by the household, a food quantity with a missing or 0 value is allowed only if the food item was consumed away from home (f_source = 4). For food expenditure, missing or 0 values are not accepted. Therefore, before loading the data in ADePT, the analyst must estimate the missing/0 values based on median food item unit values. See chapter 2 for a detailed account of such procedures. Table 4.5 illustrates the main characteristics of the variables included in dataset 3, the values they can assume, and the associated checks to be performed. Variable names depicted in the table are not mandatory. Each variable has to be described by an appropriate label explaining its content. Dataset 4 (COUNTRY_NCT) Dataset 4 contains information on the composition of each food item listed in the survey, in terms of energy and nutrients per 100 grams edible5 por- tion (nutrient values). This information is found in national or regional 194 Chapter 4: Datasets food composition tables (FCT) available either online (e.g., USDA FCT) or in hard copy (e.g., ASEAN FCT). To build dataset 4, the analyst has to match each food item listed in the survey with a food item described in the selected FCT. This section is divided into two parts. The first one describes the vari- ables to be included in the dataset; the second provides detailed guidelines on how to build it. Variables in Dataset 4 Dataset 4 includes three distinct groups of variables. • The first group includes calorie and macronutrient values, and it represents the minimum information required to execute the ADePT-Food Security Module. • The second group includes nutrient values for some vitamins and minerals, necessary to conduct a micronutrient analysis. • The third group includes nutrient values for essential amino acids, necessary to conduct an analysis of amino acids. The following tables show the main characteristics of the variables included in dataset 4 and the associated checks to be performed. Table 4.6 describes the minimum information required: Table 4.7 focuses on the micronutrient analysis. Finally, Table 4.8 regards the information needed for the amino acids analysis. Not all the food composition tables have information on amino acids. Information on amino acids can be found in the following sources: • U.S. Department of Agriculture: http://www.nal.usda.gov/fnic /foodcomp/search/index.html • FAO website for Amino-Acid Content of Foods and Biological Data on Proteins: http://www.fao.org/docrep/005/AC854T/AC854T00 .HTM • Tanzania Food Composition Table: http://www.fao.org/infoods /infoods/tables-and-databases/africa/en/ • Danish Food Composition Databank (Rev 5.0): http://www.fao.org /infoods/infoods/tables-and-databases/europe/en/ 195 Analyzing Food Security Using Household Survey Data Table 4.6: Dataset 4 (COUNTRY_NCT): Minimum Information Required Variable name and format Rationale and values Remarks and checks Food item code Identification code of the food Includes alcoholic beverages and food consumed away from (item_cod) item in the survey (e.g., COICOP or home (canteens, bars, restaurants, etc.). Format: Numeric national classification codes). Excludes cigars, cigarettes, tobacco, and drugs. This variable has to include labels There has to be one record for each food item collected in the corresponding to the food items. survey. No missing values are allowed for this variable. Food commodity Identification code of the food No missing values are allowed in this variable. group (item_grp) commodity group to which the Format: Numeric food item belongs. The file FOOD_GROUPS.xls suggests a classification of food items into food item groups.a This variable has to include labels. Refuse factor (refuse) Proportion of the nonedible portion The refuse factor has to be expressed in percentage: Format: Numeric of the food item.b • 0% if the food item is 100% edible (e.g., rice, milk, fillet of fish without spines, meat without bones, and peanuts without shell). • In the case of tea (in leaves) and coffee (in powder) it is suggested to assign 95%. This estimation is based on the assumption that only 1/20 of nutrients is going to the liquid tea/ coffee. • Between 1% and 95% for food items having nonedible portions (e.g., meat with bones, whole fish, peanuts in shell, bananas). Nutrient value for Grams of water per 100 grams Missing data are accepted only for food items for which it is water (water) edible portion of the food item. not possible to define their food composition, such as meals at Format: Decimal Values are compiled from food school or restaurant, lunch, and dinner (food consumed away composition tables. from home). Nutrient value for Grams of ash per 100 grams edible Missing data are accepted only for food items for which it is ash (ash) portion of the food item. not possible to define their food composition, such as meals at Format: Decimal Values are compiled from food school or restaurant, lunch, and dinner (food consumed away composition tables. from home). Nutrient value for Grams of protein per 100 grams Missing data are accepted only for food items for which it is protein (fd_pro) edible portion of the food item. not possible to define their food composition, such as meals at Format: Decimal Values are compiled from food school or restaurant, lunch, and dinner (food consumed away composition tables. from home). Nutrient value for Grams of fats per 100 grams edible Missing data are accepted only for food items for which it is fats (fd_fat) portion of the food item. not possible to define their food composition, such as meals at Format: Decimal Values are compiled from food school or restaurant, lunch, and dinner (food consumed away composition tables. from home). Nutrient value for Grams of total fiber per 100 grams Missing data are accepted only for food items for which it is fiber (fd_fib) edible portion of the food item. not possible to define their food composition, such as meals at Format: Decimal Values are compiled from food school or restaurant, lunch, and dinner (food consumed away composition tables. from home). Nutrient value for Grams of alcohol per 100 grams Missing data are accepted only for food items for which it is alcohol (fd_alc) edible portion of the food item. not possible to define their food composition, such as meals at Format: Decimal Values are compiled from food school or restaurant, lunch, and dinner (food consumed away composition tables. from home). (continued) 196 Chapter 4: Datasets Table 4.6: Dataset 4 (COUNTRY_NCT): Minimum Information Required (continued) Variable name and format Rationale and values Remarks and checks Nutrient value Grams of available carbohydrates Missing data are accepted only for food items for which it is not for available per 100 grams edible portion of possible to define their food composition, such as meals at school carbohydrates the food item. or restaurant, lunch, and dinner (food consumed away from home). (fd_car) Values are not compiled from Total carbohydrates are the sum of available carbohydrates and Format: Decimal food composition tables. They are total fibers. estimated with the formula: Before applying the formula: Available carbohydrates = • Check that none of the nutrient values involved in the formula 100 – grams of water – grams of are missing. ash – grams of protein – grams of After applying the formula: fats – grams of alcohol – grams • Ensure that the values of carbohydrates equal to 100 do not of total fiber. come from having missing data on all the nutrient values involved in the formula. Since food items have at least one macronutrient, it is impossible to have all missing values. For instance, mineral water has 100 grams of water, and salt has about 99.8 grams of ash. • Check for negative values (only nonnegative values are allowed). Dietary energy value Expressed in kilocalories per Missing data are accepted only for food items for which it is (fd_kcal) 100 grams edible portion of the not possible to define their food composition, such as meals at Format: Decimal food item. school or restaurant (food consumed away from home). Values are not compiled from Nutrient values are available for some food products classified as food composition tables. They are consumed away from home such as beer, carbonated beverage, calculated using the Atwater system roasted maize on the cob, and roasted chicken. coefficients with the formula: Therefore, for these food products consumed away from home, kilocalories = grams of protein it is possible to obtain the conversion factor for dietary energy * 4 + grams of fats * 9 + grams using the Atwater system coefficients. of available carbohydrates * 4 + Only very few food items have a calorie nutrient value equal 0 grams of alcohol * 7 + grams of (e.g., salt, water, and ice). fiber * 2. To detect errors: If the food item is classified as • Check for big differences between the dietary energy values food consumed away from home calculated with the formula and those reported in food and it is not possible to have the composition tables (note that there will always be differences nutrient values, the nutrient value between the two variables). of the dietary energy has to be • Check for big differences in calories among food items missing (not 0). belonging to the same food group. Note: COICOP - Classification of Individual Consumption According to Purpose. a. It can be downloaded from the FAO webpage of ADePT-FSM: http://www.fao.org/fileadmin/templates/ess/documents /food_security_statistics/Adept.zip. b. If no country specific data is available, refer to the file refuse factors.xls on the FAO webpage of ADePT-FSM. How to Build Dataset 4 Below are some guidelines to build the COUNTRY_NCT input dataset. Steps 7 and 8 can be skipped if micronutrients and amino acids analyses, respectively, are not conducted. Step 1 Open the template file COUNTRY_NCT_template.xlsx and save it on your computer. See also http://www.fao.org/fileadmin/templates/ess /documents/food_security_statistics/Adept.zip. 197 Analyzing Food Security Using Household Survey Data Table 4.7: Dataset 4 (COUNTRY_NCT): Micronutrient Analysis Variable name and format Rationale and values Remarks and checks Nutrient value for retinol Micrograms of retinol per 100 grams edible portion Missing data are accepted only for food (retinol) of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant, lunch, and dinner (food consumed away from home). Nutrient value for beta- Micrograms of beta-carotene per 100 grams edible Missing data are accepted only for food carotene (betacar) portion of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant, lunch, and dinner (food consumed away from home). Nutrient value for total Micrograms of vitamin A per 100 grams edible If the values are compiled from more than vitamin A (rae_vita) portion of the food item. The micrograms are one food composition table (FCT) it is Format: Decimal expressed in retinol activity equivalent (RAE) necessary to do a careful analysis of the NOT in retinol equivalent (RE). units in which the unit values of vitamin The difference between RAE and RE is the formula A are expressed in each food composition used to estimate the total amount of vitamin A: table (FCT). Vitamin A can be expressed Vitamin A (RAE) = mcg of retinol + (mcg of beta- in retinol equivalent, retinol activity carotene/12) + (mcg of other equivalent, or international units (IU). carotenoids)/24 Missing data are accepted only for food Vitamin A (RE) = mcg of retinol + (mcg of beta- items for which it is not possible to define carotene/6) + (mcg of other their food composition, such as meals at carotenoids)/12 school or restaurant, lunch, and dinner Values are compiled from food composition tables. (food consumed away from home). Nutrient value for vitamin Milligrams of vitamin C per 100 grams edible Missing data are accepted only for food C (vit_c) portion of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant (food consumed away from home). Nutrient value for vitamin Milligrams of vitamin B1 per 100 grams edible Missing data are accepted only for food B1 (thiamine) (vit_b1) portion of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant (food consumed away from home). Nutrient value for vitamin Milligrams of vitamin B2 per 100 grams edible Missing data are accepted only for food B2 (riboflavin) (vit_b2) portion of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant (food consumed away from home). Nutrient value for total Milligrams of total vitamin B6 per 100 grams edible Missing data are accepted only for food vitamin B6 (vit_b6) portion of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant (food consumed away from home). Nutrient value for vitamin Micrograms of vitamin B12 per 100 grams edible Missing data are accepted only for food B12 (cobalamin) (vit_b12) portion of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant (food consumed away from home). Nutrient value for iron of Milligrams of iron from animal origin per 100 grams Missing data are accepted only for food animal origin (fe_anim) edible portion of the food item. items for which it is not possible to define Format: Decimal Values of iron are compiled from food composition their food composition, such as meals at tables. Then the user classifies the iron as from school or restaurant (food consumed away animal origin if the food item is red or white meat, from home). milk, eggs, or their respective products. (continued) 198 Chapter 4: Datasets Table 4.7: Dataset 4 (COUNTRY_NCT): Micronutrient Analysis (continued) Variable name and format Rationale and values Remarks and checks Nutrient value for iron Milligrams of iron from nonanimal origin per 100 Missing data are accepted only for food of nonanimal origin grams edible portion of the food item. items for which it is not possible to define (fe_nanim) Values of iron are compiled from food composition their food composition, such as meals at Format: Decimal tables. Then the analyst classifies the iron as from school or restaurant (food consumed away nonanimal origin if the food item is different from from home). red or white meat, milk, eggs, or their respective products. Nutrient value for calcium Milligrams of calcium per 100 grams edible portion Missing data are accepted only for food (calcium) of the food item. items for which it is not possible to define Format: Decimal Values are compiled from food composition tables. their food composition, such as meals at school or restaurant (food consumed away from home). Table 4.8: Dataset 4 (COUNTRY_NCT): Amino Acids Analysis Variable name and format Rationale and values Remarks and checks Nutrient value for iIsoleucine Grams of isoleucine per 100 grams edible Missing data are accepted only for food items (isoleuc) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for leucine Grams of leucine per 100 grams edible Missing data are accepted only for food items (leucine) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for lysine Grams of lysine per 100 grams edible Missing data are accepted only for food items (lysine) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for methionine Grams of methionine per 100 grams edible Missing data are accepted only for food items (methion) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for Grams of phenylalanine per 100 grams Missing data are accepted only for food items phenylalanine (phenyl) edible portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for threonine Grams of threonine per 100 grams edible Missing data are accepted only for food items (threon) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for tryptophan Grams of tryptophan per 100 grams edible Missing data are accepted only for food items (trypto) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for valine Grams of valine per 100 grams edible Missing data are accepted only for food items (valine) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). (continued) 199 Analyzing Food Security Using Household Survey Data Table 4.8: Dataset 4 (COUNTRY_NCT): Amino Acids Analysis (continued) Variable name and format Rationale and values Remarks and checks Nutrient value for histidine Grams of histidine per 100 grams edible Missing data are accepted only for food items (histid) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for cystine Grams of cystine per 100 grams edible Missing data are accepted only for food items (cistyne) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Nutrient value for tyrosine Grams of tyrosine per 100 grams edible Missing data are accepted only for food items (tyrosine) portion of the food item. for which it is not possible to define their Format: Decimal Values are compiled from food composition food composition, such as meals at school or tables. restaurant (food consumed away from home). Protein digestibility (pro_dig) Values are expressed as a percentage.a Missing data are accepted only for food items Format: Numeric for which it is not possible to define their food composition, such as meals at school or restaurant (food consumed away from home). a. For protein digestibility values refer to the file Protein Digestibility Values.xls available on the FAO web page of ADePT-FSM at the link: http://www.fao.org/fileadmin/templates/ess/documents/food_security_statistics/Adept.zip. The template file is composed of different worksheets; one of these is named Archival. Go to Archival and list here all the food items collected in the NHS, inserting their survey code and description in columns A, Food item code in household survey (item_cod), and B, Food item description in house- hold survey (desc). All the food items collected during the survey should be included in the list, including the food items consumed away from home. Step 2: 2a. Selecting the Food Composition Table Identify the most suitable national or regional food composition table or database (reference file) for matching the food items in the survey with those described in the selected FCT. Some criteria that should be taken into consideration in the selec- tion of a FCT are the year of publication, the completeness of information (especially for macronutrients), geographic/cultural proximity between the country under study, and those countries/regions for which the food compo- sition table is written. Some FCT are available on the web at the following addresses: • U.S. Department of Agriculture FCT: http://www.nal.usda.gov/fnic /foodcomp/search/index.html • European FCT: http://www.eurofir.net/eurofir_knowledge/european _databases • Latin Foods: http://www.inta.cl/latinfoods/ 200 Chapter 4: Datasets • INFOODS databases: http://www.fao.org/infoods/infoods/tables-and -databases/en/ • LANGUAL: http://www.langual.org/langual_linkcategory.asp? Category ID=4&Category=Food+Composition 2b. Food Matching Once the FCT is identified, insert its name in col- umn C, Reference food composition table (FCT). After matching a food item listed in the Archival worksheet with a food item in the FCT, insert the reference food item’s code and descrip- tion in columns D, Food code in FCT, and E, Food description in FCT, respectively.6 It may happen that a food product listed in the NHS cannot be matched directly with any of the foods in the reference table. Reasons could be: (1) the food item does not exist in the FCT or (2) the food product listed in the NHS includes more than one food item of the FCT or is broadly described. In the first case, the food matching (step 2b) for that specific item is done using another FCT (selected using the criteria mentioned in step 2a) to find out the appropriate food product of reference. In the second case, a weighted average of the nutritional values of all the relevant (i.e., similar, corresponding) food products should be performed. By default, all food items involved have equal weight factor, unless their respective proportion of con- sumption in the country is known.7 Examples: • The food item in the NHS is broadly described, for example rice. In this case, the color (brown or white) of the rice is not specified. Therefore, a weighted average of the nutritional values of different types of rice is needed. If the food item description in the survey is rice and in the list of food items in the survey there is no mention of rice flour, then not only rice grain food commodities but also rice flour has to be included in the calculation of the average nutritional values. • Different types of the same food product or different food products are listed together in the NHS as if they were one food item (for example, white rice, grain or flour, wheat or corn flour, and eggplant, cau- liflower, broccoli). If the proportions of consumption are not known, a simple average of the nutritional values is done. • Fresh and dry food items are listed together (for example, fresh or powdered milk, whole milk and fresh or dried salmon). If the proportions 201 Analyzing Food Security Using Household Survey Data of consumption are not known, a weighted average of the nutritional values is done assigning a maximum weight factor of 10 percent to the dry product.8 For instance, in tables 4.9 and 4.10, the protein value of the food item collected in the survey is obtained averaging the protein values of similar food items from the FCT. In tables 4.9 and 4.10 the total number of food items from the FCT is five. In table 4.9, equal weights9 are applied so the weight factor for each food item is 0.2. Table 4.10 shows an example when the applied weights are different (e.g., they could be obtained from previous analysis of food consumption from household survey data). Once the matching between the food items in the NHS list and those in the FCT is done, insert the food item index matching in column F, Food Item Index Matching, of the Archival worksheet. The values indicate Table 4.9: Content of Protein in Rice Applying Equal Weights Grams of Item protein Name of Item code weight from the the FCT in the FCT Item description in the FCT factor FCT USDA 20036 Rice, brown, long-grain, raw 0.2 7.94 USDA 20040 Rice, brown, medium-grain, raw 0.2 7.5 USDA 20444 Rice, white, long-grain, regular, raw, unenriched 0.2 7.13 USDA 20450 Rice, white, medium-grain, raw, unenriched 0.2 6.61 USDA 20052 Rice, white, short-grain, raw 0.2 6.5 Item code in the survey Item description in the survey Grams of protein 4002 Rice grain 0.2 * 7.94 + 0.2 * 7.5 + 0.2 * 7.13 + 0.2 * 6.61 + 0.2 * 6.5 = 7.136 Table 4.10: Content of Protein in Rice Applying Different Weights Name of Item code Item weight Grams of protein the FCT in the FCT Item description in the FCT factor from the FCT Bolivia FCT A77 Wheat flour 0.759 8.03 Bolivia FCT A80 Corn flour 0.241 8.5 Item code in the survey Item description in the survey Grams of protein 4005 Wheat or corn flour 0.759 * 8.03 + 0.241 * 8.5 = 8.143 202 Chapter 4: Datasets the type of matching between the food item listed in the survey and the food item selected from the FCT. These are the codes: A = Single, perfect match, no modifications required (apart from edible portion, if indicated) A2 = Exact match with multiple selections requiring average computation B = Similar, single match B2 = Similar match with multiple selections requiring average computation C = Poor, single match C2 = Poor match with multiple selections requiring average computation D = Calories estimated by ADePT using unit calorie cost (applies only to food consumed away from home for which it is not pos- sible to know its composition, such as lunch, dinner or meal, other foods, etc.) Step 3 In the worksheet Archival, in column G, Refuse factor (refuse), insert the food item’s refuse factor.10 In column H, Item group (item_grp), insert the food item group to which the food item belongs. Step 4 In the worksheet Archival, fill all the columns highlighted in gray with the information available in the FCT corresponding to each food item, including total carbohydrates for further data-checking purposes. If a nutrient of a food item is missing in the selected FCT, look for the respec- tive value in another FCT. Insert a comment in the Excel cell of the miss- ing nutrient mentioning the name of the FCT from which the value was obtained as well as the food item code and description in the FCT. In the specific case of missing ash content, the value found in another FCT has to be adjusted by the total content of solids using the formula: [ Ash ( g ) in other FCT ∗ (100 − Water ( g ) in the FCT )] Ash ( g ) = 100 − Water( g )in other FCT As for the nutrient values of the food items consumed away from home for which it is not possible to know their composition (meal, lunch, etc.), blank cells are allowed, because their respective nutrient values will be estimated by the ADePT-FSS Module. 203 Analyzing Food Security Using Household Survey Data The cells of the following columns should not be filled in the archival sheet: • P: Available carbohydrates by difference (fd_car) • R: Computed calories (kcal) (fd_kcal) • U: Animal iron (milligrams) (fe_anim) • V: Nonanimal iron (milligrams) (fe_nanim) Once all the required information is inserted in the Archival worksheet, copy it to the Reference worksheet. Step 5 In the Reference worksheet, compute the grams of available carbohy- drates by difference in column P, Available carbohydrates by difference (grams) (fd_car), as: fd_car (column P) = 100 − Water (column I) − Ash (column J) − Protein (column K) − Fat (column L) − Fiber (column M) − Alcohol (column N) Suggested checks: • The sum of the values in columns M, fd_fib, and P, fd_car, should be similar to the value in column O, Carbohydrates including fiber (Total) (grams). • The values in column fd_car should be positive or equal to 0. If one value is negative and there was no data entry error in any of the nutri- ents involved in the computation, assign a value of 0. • Ensure that the values of fd_car = 100 do not come from having miss- ing data on all the nutrient values involved in the formula. Since food items have at least one macronutrient, it is impossible to have all missing values.11 Step 6 In the Reference worksheet, compute the dietary energy value in column R, Computed calories (kcal) (fd_kcal), as: fd_kcal (column R) = Protein (column K) * 4 + Fat (column L) * 9 + Fiber (column M) * 2 + Alcohol (column N) * 7 + Avail- able carbohydrates by difference (column P) * 4 204 Chapter 4: Datasets Verify that the computed dietary energy values in column R, Computed calories (kcal) (fd_kcal), are similar to those compiled from the FCT in col- umn Q, Calories (kcal). There will always be differences between the values of these two columns, but if there are big differences, verify that the nutri- ent values used in the computation are correct.12 Two of the most common errors are wrong data entry of the food item nutrient content and wrong estimation of available carbohydrates. Step 7 If the food item in the NHS is of animal origin (as previously defined in this document), in the Reference worksheet, copy the values of column T, Iron (milligrams) (iron), to column U, Animal iron (milligrams) (fe_anim). Similarly, if the food item is not of animal origin, copy the values of column T to column V, Nonanimal iron (milligrams) (fe_nanim). Step 8 In the Reference worksheet, insert the percentage of digestible pro- tein in the food item in column AP, Protein digestibility (%) (pro_dig). Step 9 When all the above steps are completed, copy all the informa- tion of the Reference worksheet and paste it to the Upload worksheet. To paste the information, select the function paste special > values from the menu. Only the columns whose variable name is red in the Upload worksheet are needed in dataset 4 and should be uploaded. An example of a completed COUNTRY_NCT template for a coun- try is available at http://www.fao.org/fileadmin/templates/ess/documents /food_security_statistics/Adept.zip. Exogenous Parameters To Estimate Dietary Energy Requirements13 The minimum and average dietary energy requirements (MDER and ADER, respectively) are produced by ADePT-FSM. To estimate the energy require- ments, the values for the under-five mortality rate and birthrate are needed. Therefore, ADePT-FSM requires the user to insert these two country-specific parameters. Both parameters are computed at the country level and should refer to the year in which the survey was conducted. 205 Analyzing Food Security Using Household Survey Data Under-Five Mortality Rate UNICEF defines the under-five mortality rate as the probability of dying between birth and exactly five years of age expressed per 1,000 live births. Estimates of the under-five mortality rate are available at http://www .childinfo.org/mortality_ufmrcountrydata.php. Birthrate The crude birthrate is the number of births over a given period of time divided by the person–years lived by the population over that period (UN 2011). Estimates of crude birthrate, expressed as the number of births per 1,000 people, are available at http://esa.un.org/unpd/wpp/unpp/panel _indicators.htm. In the ADePT module, the value of the birthrate param- eter should be expressed per person. To Estimate the Prevalence of Undernourishment14 The prevalence of undernourishment (PoU) is computed using a parametric approach under the assumption of a skewed normal distribution of dietary energy consumption. Such a distribution is defined by three parameters: • The average dietary energy habitually consumed by a representative individual over one year • The coefficient of variation of dietary energy consumption within the population • The skewness, which is an indicator of the asymmetry of the distribution The average dietary energy habitually consumed by a representative individual over one year can be estimated either from food balance sheets, which provide information on food available in a given country for human consumption (dietary energy supply [DES]), or directly from food consump- tion data obtained from NHS (dietary energy consumption). The MDG 1.9 indicator uses the DES for human consumption after having subtracted the calories lost at the retail level.15 The cutoff point used in the calculation of the PoU is the MDER. The depth of food deficit is estimated using the ADER. While ADePT-FSM 206 Chapter 4: Datasets computes the MDER and ADER using the structure of the population obtained from the survey, the MDER used to obtain the MDG 1.9 indica- tor and the ADER used to estimate the depth of food deficit consider the structure of the population published by the UN. From the above, to estimate the PoU and depth of hunger using not only data from the survey but also from other sources, the user has to select the following parameters that are included in the software ADePT-FSM: • Dietary energy consumption (estimated as the average DES from food balance sheets minus the calories lost at the retail level) • Minimum dietary energy requirement • Average dietary energy requirements The values of these parameters used to estimate the MDG 1.9 indicator published in The State of Food Insecurity in the World16 can be accessed at the FAO Statistics Division’s website.17 Finally, while it is possible to infer at the population level estimates of the average consumption of calories and nutrients, at the subnational level the PoU and depth of food deficit can be inferred only for those popula- tion groups for which the survey has representativeness. Usually, surveys are designed in such a way that the sample is representative at national, regional, and/or urban/rural levels. Notes 1. The information can be extrapolated from the survey documentation. 2. It is recommended to use the median height of each sex and age group, instead of the mean. 3. The analysis is done by characteristics of the household head. 4. For example: a household consumed potatoes; they were partly purchased on the market and partly obtained from own production. This household will have two entries (i.e., two lines) for the food item potatoes: one with quantity and expenditure related to the purchase, and the other with quantity and monetary value related to own production. 5. For example, without considering inedible parts such as peels, bones, etc. 207 Analyzing Food Security Using Household Survey Data 6. For the food matching, consult FAO/INFOODS Guidelines for Food Matching (2012) available at http://www.fao.org/infoods/infoods /standards-guidelines/en/. 7. For example, from the analysis of previous national consumption surveys in the country, the milk consumption pattern is whole, 90 per- cent; partially skimmed, 7 percent; skimmed, 3 percent. 8. The figure 10 percent, though arbitrary, is used to avoid overestimation of nutrient content, as nutrients are more concentrated in dry foods, leading to higher nutrient values per 100 grams edible portion (FAO forthcoming). 9. A weighted average performed by applying equal weight factors is equal to a simple average. 10. In the survey, households report food quantities as purchased/acquired. But many foods have edible and nonedible parts. FCT report nutrients on edible quantities. Therefore, a refuse factor is needed to calculate the edible quantities contained in the quantities reported as purchased/ acquired. Only if we do so, can we apply the nutrients from the house- holds to the food item list. 11. For example, mineral water has 100 grams of water; salt has about 99.8 grams of ash, etc. 12. A hypothetical example is that the value of calories for rice white raw published in the FCT is 346 kcal, while the value of calories estimated with the formula is 260 kcal. 13. Further details can be found in chapter 2. 14. Further details can be found in chapter 2. 15. Food waste within households is not subtracted. 16. The website is available at http://www.fao.org/publications/sofi/en/. 17. The website is available at http://www.fao.org/economic/ess/ess-fs /fs-methods/adept-fsn/en/. References FAO (Food and Agriculture Organization). Forthcoming. AGN Proposition for an Improved Methodology to Attribute Nutrition Values to Foods in the FAO Commodity List (FCL). Rome: FAO. 208 Chapter 4: Datasets FAO, and INFOODS (International Network of Food Data Systems). 2012. Guidelines for Food Matching Version 1.2. Rome: FAO. http://www.fao .org/infoods/infoods/standards-guidelines/en/. James, W. P. T., and E. C. Schofield. 1990. Human Energy Requirements: A Manual for Planners and Nutritionists. Oxford, UK, Oxford Medical Publications under arrangement with FAO. 209 Chapter 5 Guide to Using ADePT-FSM Ana Moltedo, Michael Lokshin, Zurab Sajaia Introduction Once the required four input data files are created they are used to execute the ADePT-Food Security Module (FSM). This chapter provides comprehensive instructions for installing and using the ADePT-Food Security Module. The instructions cover system requirements, installation, registration, updates, steps to launch the software, and the main characteristics of the ADePT-FSM. System Requirements To execute ADePT-FSM some requirements are needed for the system and the datasets. These requirements are shown in table 5.1 below. Table 5.1: System Requirements PC running Microsoft Windows XP (SP1 or later), Windows Vista, Windows Server 2003 and later, or Windows 7. ADePT runs in 32- and 64-bit environments. .NET 2.0 or later (included with recent Windows installations), and all updates and patches. 80MB disk space to install, plus space for temporary data set copies. At least 512MB RAM. At least 1024 x 768 screen resolution. At least one printer driver must be installed (even if no computer is connected). Microsoft® Excel® for Windows® (XP or later), Microsoft® Excel Viewer or a compatible spreadsheet program for viewing reports generated by ADePT. A Web browser and Internet access are needed to download ADePT. Internet access is needed for program updates and to load Web-based datasets into ADePT. Otherwise, ADePT runs without needing Internet access. ADePT can process data in Stata (.dta) and SPSS (.sav) formats. 211 Analyzing Food Security Using Household Survey Data Installing ADePT There are six main steps to installing ADePT: 1. Download the ADePT installer by clicking Download the software ADePT-FSM (.exe) located at http://www.fao.org/economic/ess /ess-fs/fs-methods/adept-fsn/en/. 2. Click the Run button and launch the installer immediately, or click the Save button and launch the installer later. 3. After the installer is launched, read the License Agreement dialog, then click the I Agree button. 4. In the Installation Folder dialog: a. If desired, click the Browse... button to change the default instal- lation folder. b. Click the Install button. 212 Chapter 5: Guide to Using ADePT-FSM Note: If a message mentioning that .NET is not installed cancel the ADePT installation, install the latest version of .NET (free download from the Microsoft® Website), then restart the ADePT installation. 5. Wait while ADePT is installed. 6. In the Setup Completed dialog, click the Close button. ADePT is automatically launched after installation. Registering ADePT When installation is complete, the user is invited to register as an ADePT user in the Welcome to ADePT! dialog. 213 Analyzing Food Security Using Household Survey Data 1. Select one of the registration options: To receive notifications about program updates and new releases: a. Click the Send this email to the developers... option. b. Enter the e-mail address. c. Click the Register >> button. To register anonymously: a. Click the Send an anonymous... option. b. Click the Register >> button. To skip the registration process, click the Close button in the upper right corner. Tip: The user can register for notifications later by using the Help Register... command to reopen the Welcome to ADePT! dialog. 2. In the Select ADePT Module dialog, double-click the name of the module to use. Launching ADePT After completing the installation and optional registration steps, the user is ready to launch the ADePT-FSM software. This can be done with the following two steps: 1. Click the ADePT icon in the Windows Start menu. 2. In the Select ADePT Module window, double-click Food Security (see arrow in the screenshot on the next page). 214 Chapter 5: Guide to Using ADePT-FSM The Select ADePT Module dialog lists currently available modules. To work only with the Food Security module, suppress the Select ADePT Module dialog by activating the Don’t show this window at startup option. ADePT will then automatically load the last-used module when it’s launched. 3. Now the ADePT-FSM main window is shown. Using the ADePT-FSM Main Window In the screenshot below, the main window of the ADePT-FSM is divided into four areas; below the picture there is a description of each of these areas. The four areas correspond to the four general steps in the analysis process. • Area 1 contains the datasets and dataset variables where the user can load, remove, and examine datasets. The variable labels shown in the right column are read from the dataset. 215 Analyzing Food Security Using Household Survey Data • Area 2 contains seven tab pages where the user maps the dataset variables and the exogenous parameters. The tab pages are classified according to the information required on: i) household characteristics, ii) household member characteristics, iii) food consumption, iv) macronutrient values, v) micronutrient values, vi) amino acid values and vii) exogenous parameters needed to estimate the prevalence of undernourishment. • Area 3 contains the list of tables the user can select to be generated. • Area 4 contains the description of the tables and the ADePT-FSM notifications created during the execution of the program. 216 Chapter 5: Guide to Using ADePT-FSM Using ADePT-FSM After completing the preliminary steps and becoming familiar with the user- friendly interactive window of the software, the user is ready to complete a comprehensive food security analysis. There are six main steps in performing an analysis: 1. Specify the four datasets needed to execute the software. 2. Map dataset variables. 3. Set parameters. 4. Select tables. 5. Generate the tables. 6. Analyze the notifications. 1. Specify Datasets The first task in performing an analysis is to specify the four datasets. ADePT can process data in Stata (.dta) and SPSS (.sav) formats. Operations in this section take place in the upper left corner of the ADePT main window where the • First data file (Household) contains the household characteristics • Second data file (Individual) contains household member characteristics • Third data file (Food) contains the household food consumption • Fourth data file (Country) contains nutrient values of the food To add a dataset click the Select button. In the Open dataset dialog, locate and click the dataset to be analyzed, and then click the Open button. 217 Analyzing Food Security Using Household Survey Data Repeat the step to specify each additional dataset. To remove a dataset: Click the dataset, and then click the Clear button. Viewing a Dataset’s Data and Variable Details To view the content of a dataset: (1) in the Datasets tab click to the dataset to be examined, and (2) double-click in the text defining the dataset (e.g., Household). This opens the ADePT Data Browser. 218 Chapter 5: Guide to Using ADePT-FSM Data View Tab • The Data View tab lists observations in rows and variables in columns. 219 Analyzing Food Security Using Household Survey Data Note: Applying a filter in the Data Browser does not affect calculations. This filter only reduces the number of observations visible in the Browser according to the filter criteria in order to make it easier to examine the dataset. Tip: The status bar in the Data Browser windows indicates whether the filter and value labels are on or off. Right-click in the table to open this context menu: Table 5.2: Description of the Commands Displayed in the Menu Command Description Copy Copies the contents of the selected cell(s) to the clipboard. Hide Hides the column containing the selected variable. (Unhide columns in the Data Browser’s Variable View tab.). Statistics Opens the Statistics window for the selected variable. Tabulate variable Opens the Frequency tabulation window for the selected variable. Encoding Opens a submenu listing character encoding for various languages. Click an encoding to properly display characters in the Variables tab. Variable View Tab The Data Browser’s Variable View tab lists detailed information about the dataset’s variables. Maximize the window or scroll to see additional columns. 220 Chapter 5: Guide to Using ADePT-FSM 2. Map Dataset Variables ADePT-FSM needs to know which variables in the datasets correspond to each type of information. In the second step of an ADePT-FSM analysis, the user manually maps the dataset variables to the corresponding field. The operations described in this section take place on the left-hand side of the ADePT-FSM main window. At the bottom left of the main window, there are seven tab pages; in six of them the user has to map dataset variables, according to the type of analysis (table 5.3): Table 5.3 Variables to Map According to the Type of Analysis BASIC ANALYSIS Map variables in the tab pages: Household, Individual, Food, and Main factors BASIC AND MICRONUTRIENT ANALYSIS Map variables in the tab pages: Household, Individual, Food, Main factors, and Micronutrients BASIC AND AMINO ACIDS ANALYSIS Map variables in the tab pages: Household, Individual, Food, Main factors, and Amino acids COMPLETE ANALYSIS Map variables in the tab pages: Household, Individual, Food, Main factors, Micronutrients, and Amino acids 221 Analyzing Food Security Using Household Survey Data Brief Description of the Tab Pages • Household: maps dataset variables pertaining to household characteristics. • Individual: maps dataset variables pertaining to household member characteristics. • Food: maps dataset variables pertaining to household food consumption. • Main factors: maps dataset variables pertaining to food commodity characteristics such as refuse factors and the contents of macronutrients for each food commodity listed in the survey. • Micronutrients: maps dataset variables pertaining to the content of micronutrients for each food commodity listed in the survey. • Amino acids: maps dataset variables pertaining to the content of amino acids for each food commodity listed in the survey. There are two methods for mapping variables: Method 1 To illustrate the first method for mapping variables, an example is shown for the Household tab. In the lower Household tab, open the variable’s list, then click the corresponding dataset variable, as shown here for the Household size variable. 222 Chapter 5: Guide to Using ADePT-FSM To navigate a drop-down list quickly: Type a letter or two in the variable field, then open the drop-down list. The most closely matching variable name will be highlighted. Method 2 One can also use a second method to map variables, and this method is illustrated here again using the Household tab as an example. In the middle left of the main window, the list of variables is shown and their description is included in the dataset selected above. Drag the variable name and drop it in the corresponding field in the lower Household tab. 223 Analyzing Food Security Using Household Survey Data Tip: This method may be more efficient than method 1 when datasets have a large number of variables. Note: Dataset variable names can be typed in the variable fields. The above methods are preferred since typing may introduce errors. A spelling error, syntax error, missing variable, or other problem is indicated by a red exclamation point next to the input variable field. However, point- ing the cursor over the exclamation point allows one to see information about the error. 224 Chapter 5: Guide to Using ADePT-FSM To remove a mapping: Select the variable name in the variable field, then press DELETE. To locate a variable in the selected dataset: In the Search field, type a few char- acters in the variable name or variable label. Custom Variables In the tab page Individual, there is the possibility to customize variables to be analyzed. This means that the user can analyze country-specific groups of the population (e.g., ethnicity) or specific household characteristics (e.g., whether or not the household is receiving aid or the type of access the household has to drinkable water). Independently of the type of analysis (household or household member characteristics), the custom variable has to be in the dataset containing the household members’ characteristics. Below, two different examples are shown for including an additional variable in the analysis. In the example on the left, the variable hm_var1 is used to analyze differ- ences between households. In the example on the right, the variable hm_var1 is used to analyze characteristics of household members, and the variable can take on different values for different members of the same household. 225 Analyzing Food Security Using Household Survey Data Example where the variable hm_var1 has a household Example where the variable hm_var1 has characteristic: household members characteristic: As mentioned before, the ADePT-FSM has multiple variable tab pages; therefore, be sure to visit all tabs to map variables before starting the analysis. 226 Chapter 5: Guide to Using ADePT-FSM 3. Set Parameters For the third step of a food security analysis using the ADePT-FSM, the values of exogenous parameters are assigned. In the Parameters tab, the data required are exogenous from the datasets. 227 Analyzing Food Security Using Household Survey Data The tab page is split according to three types of information used to estimate (1) dietary energy requirements from household survey data, (2) the coefficient of variation due to sources different from income, and (3) the prevalence of undernourishment used to estimate the FAO MDG 1.9 indicator as in SOFI. The layout of the Parameters tab is shown and described below. 228 Chapter 5: Guide to Using ADePT-FSM An example is shown for the selection of the Coefficient of variation for Australia in 2003, using the magnifying glass. 4. Select Tables After mapping variables, the user is ready to complete step 4 of the food security analysis by selecting the tables to be generated by ADePT. The operations described in this section take place in the right side of the main window. 229 Analyzing Food Security Using Household Survey Data To see a description of a table: Click the name. Its description is displayed in the Table description and if-condition tab in the lower right corner of the main window. 230 Chapter 5: Guide to Using ADePT-FSM 5. Generate the Tables After completing the four initial steps described above, the user is ready to generate the tables selected previously. Click the Generate button. 231 Analyzing Food Security Using Household Survey Data To stop calculations: Click the Stop button. (The selected tables are not generated if the user stops the calculations.) 6. Analyze the Notifications It is possible that an error was committed in one of the steps above to generate the analysis, so it is important to analyze any notifications displayed after the generation of the output tables. Potential data problems can also be illuminated with these notifications. 1. Examine items in the Messages tab. ADePT-FSM lists potential problems in this tab. ADePT can identify three kinds of problems: Notification provides information that may be of interest to the user. Notifications do not affect the content of reports generated by ADePT-FSM. 232 Chapter 5: Guide to Using ADePT-FSM Warning indicates a suspicious situation in the data. Warnings are issued when ADePT-FSM cannot determine whether it is an impossible situation. Examples include presence of missing values or potential outliers in the datasets, inconsistent data, and inconsistent category definitions. Error prevents the use of a variable in the analysis. For example, a variable may not exist in a dataset (in this case, ADePT-FSM continues its calculations as if the variable wasn’t specified). If ADePT can match the problem to a particular variable field, that field is highlighted in the input Variables tab. 2. As needed, correct problems, then generate the report again. If some problems were solved within a dataset, this dataset has to be uploaded again to refresh the information. Note: Notifications, warnings, and errors can negatively affect the results ADePT produces. Carefully review messages and correct critical problems before drawing conclusions from the tables. If a problem is found in a particular variable, an exclamation point is displayed next to the field in the input Variables tab in the lower left corner of the main window. 233 Analyzing Food Security Using Household Survey Data Examples of Notifications When the Generate button is clicked ADePT checks the following: Note: Send any inquiry related to the notifications displayed in the Messages box to the FAO Statistics Division: Food-Security-Statistics@ fao.org. 234 Chapter 5: Guide to Using ADePT-FSM Examining the Tables When the analysis is complete, ADePT-FSM automatically opens the results as a spreadsheet in the spreadsheet program or viewer installed on the computer. This section will provide instructions on how to examine and interpret the output tables. The tables are organized in multiple worksheets, as follows: The Contents worksheet lists all the other worksheets, including titles for tables. Click a link to open a worksheet. The Notifications worksheet lists errors, warnings, and notifications ADePT identified during its analysis. This worksheet may be more useful than the Messages tab in the main window because the problems are organized according to the relevant dataset. 235 Analyzing Food Security Using Household Survey Data The Table worksheets display tables generated by ADePT. Tip: ADePT formats table data with a reasonable number of deci- mal places. Click in a cell to see the data with full resolution in the formula bar. Viewing Basic Information about a Dataset’s Variables In addition to viewing the default output in the tables generated by the ADePT-FSM software, the user may wish to examine observations according to a specific set of criteria. Instructions on how to select specific variables, create new variables, and drop variables are given here; in addition, basic statistics and case frequencies will be generated for the variables selected or defined by the user. 1. Click the dataset to be examined. The list of variables within the dataset selected is displayed below. An example is shown for the input dataset Household. Note: Variable labels (in the right column) are read from the dataset file. To search for a variable: In the Search field, type a few characters of the variable name or variable label. 236 Chapter 5: Guide to Using ADePT-FSM 2. Right-click in the variable’s row and a pop-up menu appears. Table 5.4: Description of the Commands Displayed in the Pop-Up Menu Command Description Add variable Opens the Generate/Replace Variable dialog. Drop variable [name] Asks for confirmation that you want to remove the selected variable from the loaded dataset. Applies to generated variables and original variables, but does not remove original variables from the dataset. Display statistics for variable [name] Opens the Statistics window for the selected variable. Tabulate values of variable [name] Opens the Frequency tabulation window for the selected variable. Select encoding Opens a submenu listing character encoding for various languages. Click an encoding to properly display characters in the Variables tab. Add a Variable The user can create new numeric variables based on variables present in a dataset. When in the pop-up menu the user selects Add variable the Generate/ Replace Variable dialog box is opened: 237 Analyzing Food Security Using Household Survey Data Table 5.5: Operators That Can Be Used in Expressions Operator Description + − * / basic mathematical operators abs sign = == equality check operators A pow sqrt exponent (e.g., x^2 is x squared), power (e.g., pow(4,2) is 42 = 16) and square root round truncate shortenting operators min max range operators exp log log10 exponential and log operators indicates a missing value Variable expressions can include constants, and strings can be used for vari- ables that are of string type. Table 5.6: Examples of Expressions Expression Effect x=1 sets all variable x observations to 1 x=y+z sets variable x observations to y observation plus z observation x=y=1 sets variable x observations to 1 (true) if y is 1, otherwise sets to 0 (false) x = 23 if z ==. sets variable x observations to 23 if z is missing ( . ), otherwise sets to . x = Log(y) if z = 1 sets variable x observations to log of y observation if z is 1, otherwise sets to. s = “test” sets all variable x observations to the string “test” In the Expression field, define the new variable using the following syntax: = [if <filter_expression>] where • is a unique name not already in the dataset(s) • calculates new data for the variable • <filter_expression> (optional) filters observations that take account in the calculation Click the Generate button. In the Information dialog, click the OK button. The new variable will be listed in the Variables | [dataset label] tab, and in the Data Browser.” You can change it with: The new variable will be added to the list of variables shown in the main window and listed in the Data Browser. When the project is saved, the variable expressions are saved with the project. The variables are regenerated when that project is opened. Generating new variables does not change original datasets. 238 Chapter 5: Guide to Using ADePT-FSM Note: To replace a variable, specify an existing variable name instead of a new variable name. As with generated variables, these expressions are saved with a project and the variables are regenerated when the project is opened. Replacing variables does not change original datasets. Drop a Variable Variables can be removed from the working copy of a dataset that ADePT uses for its calculations. This operation does not change the original dataset. Native variables, as well as generated and replaced variables, can be deleted. 1. In the dataset Variables tab, right-click in the row containing the variable to be deleted, then click Drop Variable [variable name] in the pop-up menu. 2. In the Confirmation dialog, click the Yes button. Display Statistics for a Variable • When in the pop-up menu the user selects Display statistics for vari- able [name], the window Statistics is opened and shows statistics for the selected variable. 239 Analyzing Food Security Using Household Survey Data Tabulate Values of a Variable When in the pop-up menu the user selects Tabulates values of vari- able [name] the window Frequency tabulation is opened and shows the frequency of values for the selected variable. Working with Projects A project is an ADePT configuration file that contains • Paths for datasets and URLs for Web-based datasets • Dataset transformations: generated, replaced, and dropped variables; variable mappings • Global and dataset-specific filters • Missing variable definitions • Expressions used in the global filter Projects do not retain table selections, corresponding if-conditions, and frequencies, because these are related to analysis outputs. 240 Chapter 5: Guide to Using ADePT-FSM After specifying datasets and mapping variables the user can save the configuration for future use. Using a Project File on a Different Computer The saved project files can be used on a different computer. ADePT projects contain absolute (not relative) paths to the data files. ADePT tries to load data files first from the locations stored in the project file; if this fails, it loads them from the directory where the project file is located. Thus, to use a proj- ect file in a situation where the locations of the data files are different from those saved in the project file, place the data files in the directory where the project file is located. Replicating Results Obtained with ADePT To reproduce the results obtained with ADePT, give the following to the person who will replicate the work: • The link to download ADePT: http://www.fao.org/economic/ess /ess-fs/fs-methods/adept-fsn/en/. They will need to install ADePT. • The project file with the input specifications used to generate the results. • Datasets used to generate the results. (Datasets are not stored in project files. Only links to datasets are stored in project files.) 241 Analyzing Food Security Using Household Survey Data Note: If the person who is using the files is unable or unwilling to re- create the same folder structure on their computer, instruct him or her to place the datasets in the same folder as the project files. Tip: The size of the transfer can be reduced by packaging the files in a single archive (e.g., a .zip file). The recipient will need to unzip the archive to access the files. Exiting ADePT The user cannot exit ADePT when it is performing computations. To close ADePT during its calculations, click the Stop button (which replaces the Generate button when computations are in progress). When the user relaunches ADePT it will be in the same state as when it was closed, including the last-used module, settings, and contents of the input variable fields. However, the content of the input variable fields will be restored only if ADePT successfully generated output tables in the previous session. Using ADePT in a Batch Mode ADePT supports batch operations. This can be helpful when the user needs to produce several reports for many countries, or a set of reports with dif- ferent parameters for the same country. Batch mode minimizes the effort by creating reports automatically based on settings that the user saved in a project file. Here’s how to set up and run a batch file: For each analysis, prepare a project file in ADePT: 1. Load the dataset(s). 2. Map variables. 3. Set parameters. 4. Save the project (Project Save Project). Note: The user does not select tables when using batch mode. ADePT automatically determines which tables can be built based on the inputs. It always creates all feasible tables during batch processing. 242 Chapter 5: Guide to Using ADePT-FSM 5. Using a text editor (such as Windows Notepad), create a batch file (with extension .bat) containing one line for each analysis. Each line must have the following syntax: \ADePT.exe \ \ where \ADePT.exe is the full path to the ADePT program \ file that the user created in step 1 \ file that ADePT will produce Example: C:\ADePT\ADePT.exe C:\Projects\FirstProject.adept C:\Reports\ FirstReport.xls If a path or file name contains one or more spaces, enclose the entire path\name in DOUBLE QUOTES. For example: “C:\Program Files\ADePT.exe” “C:\My Projects\First Project.adept” “C:\My Reports\First Report.xls” 6. Save the batch file. Be sure the file has the .bat extension. 7. Run the batch file by locating the batch file in Windows Explorer and double-clicking the batch file name. The user should see ADePT running. If batch processing takes a long time, the user can use Windows® Task Scheduler to run the batch at night or some other time when the user is not using the computer. On a Windows® 7 computer, Task Scheduler can usually be found in the Start All Programs Accessories System Tools folder. Batch Processing Tips • Be sure to create the batch files using a text editor (i.e., not Microsoft® Word), and save them with the .bat extension so that the Windows® operating system can recognize them as batch files. 243 Analyzing Food Security Using Household Survey Data • To show the path where ADePT is installed, right-click its icon in the Start menu and then click Properties in the pop-up menu. In the ADePT Properties dialog, copy the text in the Target field, and then paste it in the batch file to specify the path to the ADePT program. • Organize the files. Projects, reports, and data can be located in different folders, but it’s a good idea to logically organize them. For example, store the prepared projects in one folder with data files in subfolders, and generate reports in a special output folder. Good file organization helps to find and back up the files more easily. • Associate the project and its report with a common name. If the project file is First.adept, for example, then name the report First.xls. • ADePT can be configured to run under another account in the background. Be sure to run it at least once interactively to correctly initialize all global parameters. Debug Mode ADePT is a complex computer program, and—as in any program—bugs and errors can occur. If the user experiences anything strange during the computations (in particular, if some tables are not generated or there are possible bugs), activate ADePT’s debug mode. In debug mode, ADePT logs the commands issued during computations. This log can help identify problems with the algorithms on which ADePT is based. Here’s how to use debug mode: Tools Debug mode. 244 Chapter 5: Guide to Using ADePT-FSM Generate a report following the normal procedure. Click the Generate button. After the report is displayed, a Save As dialog will appear. Save the log file (ErrorReport.zip). The file name and folder can be changed as needed. Send the log file for analysis, as an e-mail attachment, to the ADePT Team at adept@worldbank.org. The error report file includes the following items: • Information entered in the ADePT main window • Messages ADePT produced while checking the data and performing calculations • Any reports (possibly incomplete) ADePT managed to generate before an error occurred • Trace of the commands ADePT executed to transform the data and compute the indicators The error report file does not include any unit-record data or user’s datasets, which were used when the error occurred, for confidentiality reasons. However, this information would be useful for the developers in attempting to reproduce the problem. All the information can be checked in the error report before sending it to the ADePT Team—just open each file in the zip archive using a text editor. Reference World Bank. 2013. ADePT Version 5 Technical User’s Guide. Washington, DC: World Bank.http://siteresources.worldbank.org/EXTADEPT /Resources/adept_ug.pdf or http://issuu.com/world.bank.publications /docs/adept_user_guide. 245 Index Figures, notes, and tables are indicated by f, n, and t following the page number. A in batch mode, 242–44 access, in food security, 2 batch processing tips for, 243–44 acquisition. See also stocks Data View tab in, 219–20 coefficient of variation and, 46 debug mode in, 244–45 consumption vs., 6, 7, 11n9 development of, xi–xii in household surveys, 38 dropping variables in, 239 in kind, 16, 17, 18, 80–81, 106, 107, exiting, 242 158, 160, 194t expressions in, 238, 238t produced food, 158, 160 food security indicators produced by, food balance sheets and, 5 33–34, 33t, 34t fortification and, 104, 106 installation of, 212–13 nutritional dietary surveys and, 8 launching, 214–15 purchased food main window of, 215–16 away from home consumption of, 17 mapping dataset variables in, 221–27 as consumption source in household national household surveys and, 10 surveys, 16, 17, 157–58 notification analysis in, 232–34 for in-house consumption, 17 population groups in, 33, 33t, 35t in monetary value, as share of total prevalence of undernourishment in, 58 consumption, 161 project files on different computers in, 241 received food, 17, 45, 158, 160 registration of, 213–14 sources, consumption statistics by, 45 replicating results obtained with, 241–42 ADePT-Food Security Module setting parameters in, 227–29 (ADePT-FSM) specifying datasets in, 217–21 adding variables in, 237–39 statistics for variables in, 239 247 Index system requirements for, 211t list of, 37 table examination in, 235–36 as micronutrients, 37 table generation in, 231–32 nutrient values for, 199–200t table selection in, 229–31 animal protein tabulating variable values in, 240 amino acids and, 69n14, 175 variable information in, 236–40 calcium and, 120 Variable View tab in, 220–21 defined, 89 viewing datasets in, 218–20 as share of total protein consumption, viewing variables in, 220–21 89–90, 90t, 158 working with projects in, 240–42 area ADER. See average dietary energy requirement amino acid availability by, 140, 142t, age 145t, 146, 149t dietary energy requirements and, 186 commodity item consumption by, 100, estimated average requirement and, 102–3t 177n17 commodity item quantities by food of household member, 189t source and, 107, 108t minimum dietary energy requirements consumption by commodity groups and, and, 155–56 92, 94t, 95 alcohol food item protein consumption by, 101, in Atwater system, 21, 21t 103t calories from, estimation of, 29, 62t, 63t micronutrient availability by, 125, 128t, dietary energy contribution from, 86–87, 130, 132t, 134t 87t, 159 nutrient contribution from commodity by income, 87–88, 88t groups by, 95, 96t estimation of missing, 29 as variable in household dataset, 183t nutrient value for, 196t ascorbic acid, 119. See also vitamin C within range of population goal for ash, 196t intake of, 161 asymmetry measures, 49 amino acids. See also micronutrient(s) Atwater formula, 21, 21t, 26, 29, 86, 197t availability of availability, food average, 173–76 consumption vs., 100, 101t, 102t by food group, 136, 139t in food balance sheets, 5, 101 and area, 140, 142t in food security, 2 and income, 140, 141t in household surveys, 101 and region, 140, 143t average dietary energy requirement food groups contribution to, 140, 144t (ADER), 51, 60, 205, 206–7 by area, 140, 145t by region, 140, 147t B by food item, 146, 148t balanced diet and area, 146, 149t defined, 11, 40, 87 and region, 146, 150t as indicator, 40–41 by gram of protein, 136, 138t, 173 in output tables, 86–87 as percentage of total availability, 173 beta carotene. See vitamin A protein consumption and, 136, 137t birthrate, 156, 205, 206 essential, 39–40, 69n14, 136 density of, per 1,000 kcal, 122, 124t, 125 estimation of consumption of, 40 nutrient value for, 198t functions of, 39, 136 recommended intake of, 172–73 information sources for, 195 safe intake of, 172–73 248 Index C micronutrient deficiency and, 51 calcium minimum dietary energy requirement for, availability, 117, 119–20, 119t 50–51 average, 164 cobalamin deficiency, 117. See also vitamins by food group, 125, 126t, 162 B1, B2, B6, B12 and area, 125, 128t coefficient of variation (COV), 45–49, 66t, and income, 125, 127t 153 and region, 125, 129t, 130 Committee on Food Security, 3 food group contribution to, 131t commodity groups by area, 132t amino acid availability by, 136, 139t by food item, 133t and area, 140, 142t and area, 134t and income, 140, 141t and region, 135t and region, 140, 143t recommended intake vs., 168–69 amino acid availability contribution deficiency, 119–20 from, 140, 144t density per 1,000 kcal, 121–22, 121t by area, 140, 145t nutrient value for, 199t by region, 140, 147t recommended intake of, 168 consumption by, 91, 92t calories and area, 92, 94t, 95 estimation of, 22–29, 62t, 63t and food sources, 96, 99t from expenditure, 27–29, 64t, 65t and income, 92, 93t from fats, 21t, 27–28 and region, 95, 97t by nutrient, 26 by region, 101, 104, 104t per household, 26–27 in datasets, 196t from protein, 21t, 27–28 in indicators, 33–34, 34t, 36t from quantities, 22–27 micronutrient availability by, 125, 126t as unit of measurement, 21 and area, 125, 128t carbohydrates. See also macronutrient(s) and income, 125, 127t in Atwater system, 21t and region, 125, 129t, 130 average consumption of, 151 micronutrient availability contribution average unit value, 151 by, 130, 131t calories from, estimation of, 29, 62t, 63t by area, 130, 132t by commodity groups, 92, 93t by nutrient, contribution to, 92, 93t costs, by commodity group, 95, 98t by area, 95, 96t dietary energy contribution from, 86–87, nutrient costs by, 95, 98t 87t, 159 protein consumption by, 100, 102t by income, 87–88, 88t by region, 104, 105t estimation of missing, 29 total nutrient consumption from food group contribution to total nutrient share of total carbohydrates consumption, share of, 154 consumption, 154 nutrient value for, 197t share of total dietary energy per 1,000 kcal, 88, 89t, 154 consumption, 154 within range of population goal for share of total fat consumption, 154 intake of, 161 share of total protein consumption, 154 within-region differences in consumption commodity items of, by income, 90, 91t amino acid availability by, 146, 148t children and area, 146, 149t ages of, in household member age, 189t and region, 146, 150t 249 Index codes for, 194t, 196t in monetary value, by population groups, consumption of 85–86, 85t by area, 100, 102–3t in nutrient content, by population at national level, 100, 101t groups, 85–86, 85t by region, 101, 104, 104t other sources in, 156 by food source, quantities of, 104, own, 5, 17, 18, 106, 107, 157, 158 105–6t, 106 by population groups, 77, 78t, 85–86, 85t and area, 107, 108t sources of, 16–18 and region, 107, 109t by sources of acquisition, 45 in indicators, 34, 36t COV. See coefficient of variation micronutrient availability by, 130, 133t CPI. See consumer price index and area, 130, 134t cystine, 69n14, 174–75. See also amino acids and region, 130, 135t protein consumption, by area, 101, 103t D consumer price index (CPI), 31, 65t, 182, 184t datasets consumption COUNTRY_NCT, 193, 194f, 194t, 195, accuracy in estimation of, 192, 193t 196–97t, 197, 198–200t, 201–5, 202t acquisition vs., in household surveys, 6 FOOD, 191–93, 193t availability vs., 100, 101t, 102t HOUSEHOLD, 182, 183–84t, 185, 186f by commodity group, 91, 92t INDIVIDUAL, 186–88, 188f, 188t, and area, 92, 94t, 95 189–90t, 191, 191f and food sources, 96, 99t mapping variables in, 221–27 and income, 92, 93t specifying, 217–21 and region, 95, 97t viewing, 218–20 by region, 101, 104, 104t data sources, of food consumption, 4–9 by commodity item DEC. See dietary energy consumption by area, 100, 102–3t deficit depth, 155 at national level, 100, 101t defined, 59 by region, 101, 104, 104t dietary energy requirement and, 151–52 data sources for, 4–9 estimation of, 59–60, 206–7 in dietary energy, by population groups, representativeness and, 207 85–86, 85t demand, responsiveness of, to income, dispersion ratios of 42–44, 44f by food source and income within demand elasticity, by income within population groups population groups, 111, 115, 115t in dietary energy, 110, 112t DER. See dietary energy requirements in monetary value, 110, 113t DES. See dietary energy supply by income, 110, 111t dietary energy consumption (DEC) by food sources, 80–81, 80t average, 151 and commodity group, 96, 99t coefficient of variation of, 153 by income, 81, 82t consumption in, by population group, in monetary value, 81, 83–84, 83t 85–86, 85t by income, 84–85, 84t food groups contribution to in household surveys, 16–18 share of total dietary energy by income deciles, 77, 79t, 80 consumption, 154 for items by area, 100, 102–3t total nutrient consumption, share of, monetary/quantitative data collection consumption of, 154 for, 18–19 nutrient contribution to, 86–87, 87t 250 Index by income, 87–88, 88t estimated average requirement (EAR), 52, prevalence of undernourishment and, 76 53, 54, 114, 177n17 produced food in, as share of total exogenous parameters, 205–7 consumption, 160 expenditure. See also monetary value; purchased food in, as share of total prices consumption, 160–61 estimation of, 30–31, 41–42 ratio to first reference group of, 158 estimation of calories and nutrients from, skewness of, 161 27–29, 64t, 65t variability in, 45–49 as indicator, 41–42 dietary energy requirements (DER), 48, price variability and, 31 49–51, 67t total household consumption, 184t average, 60, 151–52, 205, 206–7 exogenous parameters in estimation of, F 205–6 famine, in historical conception of food individual dataset and, 186 security, 2 minimum, 50–51, 67t, 76–77, 155–56, FAO. See Food and Agriculture Organization 206–7 fat(s). See also macronutrient(s) dietary energy supply (DES) in Atwater system, 21t estimation of, indicators, 35–37 average consumption of, 152 food balance sheets and, 4 calories from, estimation of, 21t, 27–28, in household surveys, 20–29 29, 62t, 63t losses and, 4–5, 155 by commodity groups, 92, 93t, 95, 98t MDG 1.9 indicator and, 58 dietary energy contribution from, prevalence of undernourishment and, 58, 86–87, 87t, 159 76, 206 by income, 87–88, 88t units of measurement for, 21 estimation of missing, 29 dietary energy unit value, average, 152 food group contribution to total nutrient dietary energy value, 197t consumption, share of, 154 dispersion ratios, 45, 107, 110, 111, 112t, nutrient value for, 196t 113t, 114t per 1,000 kcal, 88, 89t, 154 within range of population goal for E intake of, 161 EAAs. See essential amino acids within-region differences in consumption EAR. See estimated average requirement of, by income, 90, 91t economic activity, 190t FBDG. See Food-Based Dietary Guidelines edible quantity consumed, average, 152 FBS. See food balance sheets educational attainment, 190t FCT. See food composition table energy. See calories; dietary energy fiber consumption; dietary energy in Atwater system, 21, 21t supply calorie estimation and, 29 Engel ratio, 159–60 estimation of missing, 29 defined, 45 nutrient value for, 196t dispersion ratios of dietary energy, Food and Agriculture Organization (FAO), income and, by income, 111, 114t 1–2 income elasticity of demand and, 42–43 food balance sheets (FBS) Engel’s law, 45, 83, 112, 159–60 as data source, 4–5 essential amino acids (EAA), 39–40, 69n14, dietary diversity and, 5 136. See also amino acids dietary energy supply and, 4–5, 76 251 Index national household surveys vs., 9t, 100 hemoglobin concentration, 177n18 nutritional dietary surveys and, 9t histidine, 173–74. See also amino acids seasonality and, 5 household head Food-Based Dietary Guidelines (FBDG) in datasets, 189t, 190t micronutrients and, 53–54 food not consumed by, 16 nutrient density and, 122, 125 gender and, 187 food composition table (FCT), 200–203 household location, 183t, 185 food groups. See commodity groups household member(s) food items. See commodity items absence of, 6 food price index (FPI), 31, 65t, 182, 185t. age of, 189t See also prices in amino acid availability, 40 food security in demand responsiveness to income, 43 access in, 2 in dietary energy consumption, 37 availability in, 2 estimated average requirement and, 52–53 defined, 3 gender of, 189t historical conception of, 2 head of household and, 189t indicators, 32–60 height of, 189t nonfood factors in, 2 in kind acquisition by, 16, 17, 18, 80–81, nutrition in, 3 106, 107, 158, 160, 194t stability in, 3 marital status of, 189t utilization in, 2–3 in micronutrient availability, 38 Food Security Statistics Module (FSSM), in monetary values, 41 xi–xii partaker vs., 182 food sources in population weight, 32 consumption by, 80–81, 80t household number, 183t, 189t, 194t and commodity group, 96, 99t households, number of sampled, 156 by income, 81, 82t household size in monetary value, 81, 83–84, 83t average, 153 by income, 84–85, 84t food quantities and, 192 consumption dispersion ratios by gender and, 187 in dietary energy, 110, 112t as variable in dataset, 183t in monetary values, 110, 113t variations due to, 32 in datasets, 194t household surveys. See national household item quantities by, 104, 105–6t, 106 surveys and area, 107, 108t household weight, 32, 37, 38, 40, 41, 43, and region, 107, 109t 155, 183t, 185 fortification, 104, 106 FPI. See food price index I FSSM. See Food Security Statistics income Module amino acid availability by, and food group, 140, 141t G average, 153 gender disaggregated analysis, 187–88 consumption dispersion ratios by, 110, grams, 21–22, 22–29 111t, 113t groups. See commodity groups consumption statistics by commodity group and, 92, 93t H by food sources by, 81, 82t height, of household member, 189t in monetary value, 84–85, 84t 252 Index of population groups by, 77, 79t, 80 by food item, 133t demand elasticities by, 111, 115, 115t and area, 134t dispersion ratios of dietary energy by, and region, 135t 111, 114t deficiency, 177n18 micronutrient availability by, 125, 127t functions of, 121 nutrient consumption by, differences in, heme, 39, 68n13, 121, 198t 90, 91t intake requirement, 168 nutrient contribution to dietary energy measurement of, 39, 68n13 consumption by, 87–88, 88t nonheme, 39, 68n13, 121, 165, 199t responsiveness of demand to, 42–44, 44f nutrient value for, 198t, 199t share of food consumption in total, isoleucine, 174. See also amino acids 159–60 items. See commodity items total household, 184t waste and, 7 J indicators, 32–60 joules, 21 amino acids in, 39–40 L asymmetry measures and, 49 leucine, 174. See also amino acids balanced diet in, 40 liters, 22 commodity groups in, 33–34, 34t, 36t Living Standard Measurement Studies, 5 commodity items in, 34, 36t losses. See also refuse factor; waste consumption by acquisition source in, 45 consumption estimation accuracy and, deficit depth in, 59–60 192, 193t demand responsiveness to income in, dietary energy supply and, 4–5, 155 42–44, 44f lysine, 174. See also amino acids dietary energy in, 35–37 dietary energy requirements in, 49–51 M disaggregation of, 73 macronutrient(s). See also alcohol; estimation methods with, 34–60 carbohydrates; fat(s); nutrients; glossary of, 151–76 protein groups of analysis in, 33 consumption, 36–37 inequality measures and, 45–49 density, 88, 89t macronutrient consumption in, 35–37 micronutrients vs., 37 micronutrient availability in, 37–39 monetary values, 42 monetary value in, 41–42 marital status, of household member, 189t population groups in, 33, 33t, 35t MDER. See minimum dietary energy inequality measures, 45–49, 107–13, requirement 111–15t MDG. See Millennium Development Goal iron member. See household member(s) 95th percentile of intake required, 162 methionine, 69n14, 174–75. See also amino availability, 120–21, 120t acids average, 164, 165 micronutrient(s). See also amino acids; average animal, 162 calcium; iron; nutrients; and various by food group, 125, 126t, 162, 163 vitamins and area, 125, 128t availability, 37–39 and income, 125, 127t by food group, 125, 126t and region, 125, 129t, 130 and area, 125, 128t food group contribution to, 131t and income, 125, 127t by area, 132t and region, 125, 129t, 130 253 Index food group contribution to, 130, 131t ADePT-Food Security Module and, 10 by area, 130, 132t consumption in, 5–6 by food item, 130, 133t consumption sources in, 16–18 and area, 130, 134t and conversion in per person per day, 32 and region, 130, 135t data collected in, 15–20, 20t recommended/required intakes vs., as data source, 5–8 51–54 household size vs. partakers in, 6–7 deficiencies, 51–52 micronutrients and, 38 Food-Based Dietary Guidelines and, 53–54 minimal requirements for, 8 household surveys and, 38 monetary and quantitative data list of, 37 collection in, 18–19 macronutrients vs., 37 national and subnational inference Millennium Declaration, 2 from, 32 Millennium Development Goal (MDG) 1.9 nonpurchased food in, 17–18 indicator, 58, 74–75, 206 nutritional dietary surveys vs., 9t milliliters, 22, 23–25 price variability and, 31 minimum dietary energy requirement purchased food in, 16, 17 (MDER), 50–51, 67t, 76–77, reference periods in, 6 155–56, 206–7 standardization procedures in, 20–32 monetary value. See also expenditure; stocks in, 17, 18 prices takeaway food in, 19 average food consumption in, 152 telescoping errors in, 6 average total consumption in, 153 units of measurement in, 19–20 consumption by food sources in, 81, waste and, 7 83–84, 83t NDS. See nutritional dietary surveys by income, 84–85, 84t NHS. See national household surveys consumption by population groups in, nonedible portions, 22, 25–26, 37, 40, 100, 85–86, 85t 130,146, 152, 208n10. See also refuse consumption dispersion ratios in, by factor food source and income within nonpurchased food population groups, 110, 113t as consumption source in household daily expression of, 192 surveys, 17–18 in datasets, 194t in kind, 16, 17, 18, 80–81, 106, 107, 158, food from other sources as share of total 160, 194t in, 160 for own consumption, 17–18 as indicator, 41–42. See also expenditure; nutrients. See also macronutrient(s); prices micronutrient(s) produced food in, as share of total calories from, estimation of, 26 consumption, 160 to commodity groups, contribution of, purchased food in, as share of total 92, 93t consumption, 161 by area, 95, 96t takeaway food in, as share of total consumption in content of, by population consumption, 159 groups, 85–86, 85t costs, by commodity group, 95, 98t N density per 1,000 kcal, 88, 89t national household surveys (NHS) dietary energy consumption contribution acquisition vs. consumption in, 6, 7, from, 86–87, 87t 11n9 by income, 87–88, 88t 254 Index from expenditure, 27–29 with survey data, 74–75, 74t per household, estimation of, 26 prices. See also expenditure; food from quantities, 22–27 price index; income; monetary within-region differences in consumption value of, by income, 90, 91t consumer price index, 31, 65t, nutritional dietary surveys (NDS) 182, 184t collection method in, 8, 11n10 food price index, 31, 65t, 182, 185t complexity of, 9 in food quantity estimation, 19 as data source, 8–9 as indicator, 42 food balance sheets vs., 9t nutrient, by commodity group, food intake outside of home and, 8 95, 98t national household surveys vs., 9t shocks in, 81 seasonality and, 8 variability of, 31 primary sampling unit (psu), 184t O produced food, 158, 160 occupation, 190t food balance sheets and, 5 fortification and, 104, 106 P nutritional dietary surveys and, 8 partakers protein. See also amino acids; in conversion in per person per day, 32 macronutrient(s) defined, 6 amino acid availability food quantities and, 192 and consumption of, 136, 137t household member vs., 182 by gram of, 136, 138t, 173 as variable in dataset, 183t, 185, 191 animal phenylalanine, 175. See also amino acids amino acids and, 69n14, 175 population, estimated, 155, 157 calcium and, 120 population groups defined, 89 consumption dispersion ration within, by as share of total protein consumption, income, 110, 111t 89–90, 90t, 158 and food source in Atwater system, 21t in dietary energy, 110, 112t average consumption of, 153 in monetary values, 110, 113t calories from, estimation of, 27–28, 29, consumption statistics by, 77, 78t, 62t, 63t 85–86, 85t by commodity groups, 92, 93t demand elasticities in, by income, 111, commodity items by consumption of, 115, 115t 100, 102t in indicators, 33, 33t, 35t by area, 101, 103t Poverty Reduction Strategy Papers, 2, 11n3 by region, 104, 105t prevalence of undernourishment (PoU), costs, by commodity group, 95, 98t 54–58, 56f, 157 dietary energy contribution from, 86–87, deficit depth and, 59 87t, 159 dietary energy requirement and, 151 by income, 87–88, 88t dietary energy supply and, 4, 155 estimation of missing, 29 exogenous parameters in estimation of, food group contribution to total 206–7 nutrient consumption, share with external sources, 75–77, 75t of, 154 minimum dietary energy requirement nutrient value for, 196t and, 156 per 1,000 kcal, 88, 89t, 154 255 Index within-region differences in consumption retinol equivalent (RE), 39 of, by income, 90, 91t riboflavin. See vitamins B1, B2, B6, B12 psu. See primary sampling unit RNI. See recommended nutrient intake purchased food Rural Development Strategies, 2 away from home consumption of, 17 as consumption source in household S surveys, 16, 17, 157–58 seasonality for in-house consumption, 17 food balance sheets and, 5, 9t in monetary value, as share of total national household surveys and, 5, 9t consumption, 161 nutritional dietary surveys and, 8, 9t vitamin C and, 168 R self-production, 18. See also consumption, RAE. See retinol activity equivalent own; produced food RE. See retinol equivalent skewness received food, 17, 45, 158, 160 defined, 49, 161 recommended nutrient intake (RNI), estimation of, 49 52, 53, 54, 114, 177n17. See also expression of, 69n16 micronutrient(s) with greater than 1 value, 75–76 reference period in prevalence of undernourishment, 206 in food balance sheets, 4 SOFI. See State of Food Insecurity in the in household surveys, 6 World in nutritional dietary surveys, 11n10 sources. See data sources; food sources partakers and, 182, 183t stability, in food security, 3 refuse factor, 25, 196t, 208n10. See also standardization procedures, in household nonedible portions; waste surveys, 20–32 region starvation, in historical conception of food amino acid availability, 140, 143t, 146, security, 2 147t, 150t State of Food Insecurity in the World (SOFI), commodity item consumption by, 101, 54–55, 75, 207 104, 104t stocks. See also acquisition commodity item quantities by food as consumption source, 16, 17, 18 source and, 107, 109t in food balance sheets, 4, 5 consumption by commodity groups and, household surveys and, 7 95, 97t overview of, 18 micronutrient availability storage by commodity groups and, 125, household surveys and, 6 129t, 130 micronutrients and, 38, 52 by commodity items and, 130, 135t riboflavin and, 167 nutrient consumption differences within, thiamine and, 117 90, 91t vitamin C and, 119 protein consumption by commodity waste and, 5, 7 groups and, 104, 105t surveys. See national household as variable in household data set, 183t surveys; nutritional dietary retinol. See vitamin A surveys retinol activity equivalent (RAE), 39, 165–66 T retinol availability, vitamin A availability takeaway food, 19, 100, 110, 125, 136, 159, vs., 169 193, 203 256 Index thiamine deficiency, 117. See also vitamins retinol availability vs., 169 B1, B2, B6, B12 safe intake of, 171, 172 threonine, 175. See also amino acids vitamin C tryptophan, 176. See also amino acids availability, 117, 119–20, 119t tyrosine, 175. See also amino acids in 1,000 kcal, 168 average, 168 by food group, 125, 126t, 163 U and area, 125, 128t under-five mortality rate, 206 and income, 125, 127t undernourishment. See prevalence of and region, 125, 129t, 130 undernourishment food group contribution to, 131t units of measurement by area, 132t calories as, 21 by food item, 133t for energy, 21 and area, 134t examples of, 61t and region, 135t in household surveys, 19–20, 21 recommended intake vs., 171 joules as, 21 deficiency of, 119 local, 23–24 density of, per 1,000 kcal, 122, 123t for vitamin A, 39 nutrient value for, 198t utilization, in food security, 2–3 vitamins B1, B2, B6, B12 availability, 117, 118t V average, 166–68 valine, 176. See also amino acids by food group, 125, 126t, 163 variability, indicators and, 45–49 and area, 125, 128t vitamin A and income, 125, 127t availability, 115, 116t, 117 and region, 125, 129t, 130 in 1,000 kcal, 166 food group contribution to, 131t average, 164, 166 by area, 132t by food group, 125, 126t, 162, 163 by food item, 133t and area, 125, 128t and area, 134t and income, 125, 127t and region, 135t and region, 125, 129t, 130 recommended intake vs., 169–71 food group contribution to, 131t density of, per 1,000 Kcal, 122, 124t, 125 by area, 132t nutrient value for, 198t by food item, 133t recommended intake of, 172–73 and area, 134t safe intake of, 172–73 and region, 135t importance of, 117 W measurement of, 39 waste. See also refuse factor recommended intake vs., 169 consumption estimation accuracy and, required intake vs., 169 192, 193t retinol activity equivalent and, dietary energy supply and, 4–5 165–66 in FAO current practice, 57 density of, per 1,000 kcal, 122, 123t household surveys and, 7 intake requirement, 171 income and, 7 nutrient value for, 198t water, nutrient value for, 196t requirement in 1,000 kcal, 171 World Food Summit, 1–2, 3, 54 257 ECO-AUDIT Environmental Benefits Statement The World Bank is committed to preserving Saved: endangered forests and natural resources. • 7 trees The Office of the Publisher has chosen to print • 4 million BTUs Analyzing Food Security Using Household of total energy Survey Data on recycled paper with • 665 pounds of net 50 percent postconsumer fiber in accordance greenhouse gases with the recommended standards for paper • 3,604 gallons of waste usage set by the Green Press Initiative, a water nonprofit program supporting publishers in • 241 pounds of solid using fiber that is not sourced from endangered waste forests. 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