87733 Mozambique Then and Now An Atlas of Socio-Economic Statistics 1997–2007 THE WORLD BANK INSTITUTO NACIONAL DE ESTATÍSTICA ii Mozambique Then and Now An Atlas of Socio-Economic Statistics 1997–2007 Chad Djibouti Nigeria Sudan Adis Abeba Ethiopia Central African Republic Cameroon Bangui Malabo Yaounde Equatorial Guinea Somalia Equatorial Guinea Uganda Muqdisho Kisangani Mbandaka Kampala Kenya Gabon Congo Nairobi Port Gentil Rwanda Bujumbura RDC Burundi Mombasa Pointe Noire Kinshasa Brazzaville Kigoma Matadi Kananga Tanzania, United Republic of Dar es Salaam Kahemba Luanda Mtwara Lumumbashi Benguela Huambo Angola Malawi Zambia Lusaka Lilongwe C.Ilha Moçambique Livingstone Harare Antananarivo Zimbabwe Beira Madagascar Bulawayo Namibia Botswana Toliara Windhoek Gaborone Pretoria Maputo Mbabne Johannesburg Swaziland Kimberley Maseru South Africa Durban Lesotho East London Cape Town Port Elizabeth 0 250 500 1,000 Kilometers iv Mozambique Then and Now contents vi I Preface 39 access to services Access to electricity vii Acknowledgement Access to running water 1 Introduction Access to phones and internet Distance to major urban areas 3 The people of mozambique Population 45 education Demographic distribution by age and gender Trend in primary gross enrollment rates Main languages Primary enrollment by gender Religions Primary enrollment across Africa Distance to primary schools Trend in secondary gross enrollment rates 15 wealth Secondary enrollment by gender Poverty Secondary enrollment across Africa Inequality Distance to secondary schools Asset ownership Literacy rates Share of population with complete primary 21 healthy lives Share of population with complete secondary Infant mortality rate Infant mortality in Africa 57 land and agriculture Underweight, prevalence and concentration Topography Stunting, prevalence and concentration Land suitability Maternal mortality rate Rainfall by year Malnutrition Rainfall by month Distance to health facilities Temperature by month Fertility Ownership of animals Access to improved water Trend in accessing water from rivers and lakes Diarrhea and malaria 68 definition of indicators 70 image and map index 72 REFERENCES Preface The National Statistical Institute of Mozambique is responsible to collect, analyze, to be appropriately targeted. It is our wish that this Atlas will provide an important publish, and disseminate statistical information on a wide range of topics. The mis- source of information for all those who want to have the mission to promote the eco- sion of the National Statistical Institute is to provide and promote accurate, appropri- nomic and social development in Mozambique. ate, high-quality, and timely statistical information for use in both the public and the The maps are elaborated principally from the results of the 2007 Population Census private sectors for policy formulation, decision making, research, and general public and the 2008/09 Household Budget Survey (Inquerito ao Orcamento Familiar, IOF awareness for the advancement of the socio-economic status of Mozambicans. 2009). A central focus of the work of the INE is the data collection on living conditions of Funding for this publication was provided by the Trust Fund for Environmentally and the population of Mozambique. This Atlas was developed as part of efforts to increase Socially Sustainable Development (TF ESSD) at the World Bank and is gratefully ac- the understanding of the living conditions of Mozambicans and to serve as a basis for knowledged. We also gratefully acknowledge the excellent collaboration and support the preparation of the country’s development strategies. provided in the joint preparation of this Atlas. Taken in their entirety, the maps in this Atlas provide profound insights into the characteristics and living conditions of the population of Mozambique and how they João Dias Loureiro vary across the country, thereby enabling poverty reduction programs and policies President of INE vi acknowledgements This atlas has been prepared jointly by the National Statistics Office of Mozambique The atlas was produced by a team led by Mr Antonio Nucifora (World Bank, Lead (Instituto Nacional de Estatistica, INE) and the World Bank. Economist), and including Mr Thomas Pave Sohnesen (World Bank Senior Econo- mist, and main author of the Atlas), Mr Vasco Molini (World Bank, Senior Poverty INE provided the 1997 and 2007 Population Census data which is the basis for this Economist), Mr Antonio Adriano (INE Deputy Director for Census and Surveys), Mr Atlas, as well as contributed detailed input and comments that ensured the statistical Cassiano Chipembe (INE Director of Statistics on Demography and Living Condi- accuracy and improved its overall quality. tions ), Mr Paulo Covele (GIS specialist), and Ms Andrea Nieves (Graphic designer). Special thanks goes to INE’s President, Dr João Dias Loureiro, for providing over- Funding for the production was provided by the Trust Fund for Environmentally and all guidance and inspiration, and to the World Bank Country Director for Mozam- Socially Sustainable Development (TF ESSD) at the World Bank. bique, Mr Laurence Clarke, for encouraging and supporting this project. Invaluable comments and guidance was also received by INE Vice-President, Mr Manuel Gas- par, and the World Bank Sector Manager for Poverty Reduction and Economic Man- agement in southern Africa, Mr John Panzer. vii viii Mozambique Then and Now: An Atlas of Socio-Economic Statistics It is well known that Mozambique is characterized by wide variations in socio-economic indicators across provinces. According to available statistics, such differences have decreased in recent years. For instance, we know that there has been great progress in both primary and secondary enrollment rates the last decade; however, which areas have made the greatest progress, and which areas are still lacking behind? Infant mortality rates fell the last decade, but where did they decline the most, and where are they still high? This atlas provides maps and illustrations that give insights into these aspects and many more. It does so by showing a range of social and economic statistics at the level of Administrative Posts. For most of the indicators it includes maps which show the situation in 1997 and in 2007. In addition some of the maps specifically illustrate the change over the decade. Overall the Atlas provides a fascinating snapshot of recent socio-economic changes in Mozambique. Antonio Nucifora and Thomas Pave Sohnesen 2 the people of mozambique Population language religion Mozambique’s growing population speaks a wide range of tongues and belongs to a variety of religious denominations. population The population of Mozambique is still growing, reaching 20.5 million in 2007. The northern province of Nampu- 1.2 – Total population (in thousands), 2007 1.3 – Rural population per km2, 2007 la, the central parts of the country (Zambezia, Sofala, Manica and Tete provinces) and the south Eastern coastline (of Inhambane, Gaza and Maputo provinces) have the larg- est populations (fig 1.1 and 1.2) and the highest rural population densi- ties (fig 1.3). The population densi- ty refers to the number people liv- ing in a given Administrative Post divided by the size of that Adminis- trative Post measured in squared ki- lometers. 1.1 – Total population by province (in thousands), 1997 and 2007 1997 2007 0 1,000 2,000 3,000 4,000 Niassa Cabo Delgado Nampula Less than 25 Less than 10 Zambezia 25–50 10–20 Tete 51–75 21–30 Manica Sofala 76–100 31–40 Inhambane Greater than 100 Greater than 40 Gaza 0 75 150 300 0 75 150 300 Maputo province Maputo City Kilometers Kilometers 4 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE 1.4 – Change in population (percentage) between 1997 and 2007 1.5 – Change in population (in thousands) between 1997 and 2007 Population growth from 1997 to 2007 as a percentage was faster in the south and in coastal areas (fig 1.4). However in terms of absolute numbers the increase was greatest in the most populous areas of the country (fig 1.5). In general, popula- tion growth was must faster in rural areas (fig 1.6). 1.6 – Urban and rural population (in thousands), 1997 and 2007 8UEDQ 8UEDQ 5XUDO 5XUDO Less than –20     –20–0 Less than –50 1–20 –50–10 21–40 –9–0 41–60 1–10 61–80 11–20 81–100 21–30 Greater than 100 Greater than 30 0 75 150 300 0 75 150 300 Kilometers Kilometers 5 population (cont.) Mozambique has a very young population, particularly in the Center-north of the country. Most areas of the south are characterized by a larger share of women in the population. The population of Mozambique 1.8 – Share of population aged 15 or below, 2007 1.9 – Share of population aged 50 or above, 2007 is still expanding (fig 1.7) and the population under 15 years of age is greater than 50 percent in most of the center and the north of the country (fig 1.8). Only in the south and the extreme north there are dis- tricts with more than 15 percent of the population aged 50 or above (fig 1.9). 1.7 – Population pyramid by gender, 1997 and 2007 )HPDOHSRSXODWLRQ 0DOHSRSXODWLRQ )HPDOHSRSXODWLRQ 0DOHSRSXODWLRQ ï ï ï ï Less than 30 Less than 5 ï 30–40 5–10 ï 41–45 11–15 ï 46–50 16–20 ï ï Greater than 50 Greater than 20 ï 0 75 150 300 0 75 150 300      3RSXODWLRQ Kilometers Kilometers 6 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE 1.10 – Number of men per 100 women, 1997 1.11 – Number of men per 100 women, 2007 The ratio of men to women across Mozambique is not even across the country, with the provinces of In- hambane, Gaza and also the south- ern areas of the provinces of Man- ica and Sofala having more women than men (fig 1.11). The trend is not recent as a similar pattern is found in 1997 (fig 1.10). The pattern ap- pears to be age related, as only after age 17 there are more women than men (and this is true in all provinces mentioned above) (fig 1.12). 1.12 – Ratio of men to women by age, 2007 Inhambane Gaza Other provinces 120 100 Less than 70 Less than 70 80 71–80 70–80 81–90 81–90 60 91–100 91–100 Greater than 100 Greater than 100 0−5 6−10 11−15 16−20 21−25 26−30 31−35 36−40 41−45 46−50 51−55 56−60 61−65 0 75 150 300 0 75 150 300 Age group Kilometers Kilometers 7 language A variety of languages are spoken across Mozambique. Portuguese is the only one that is spoken in most parts of the country, and almost by everyone in the main urban centers. However, it is the first language for only a relatively small share of Mozambicans. The ability to speak Portuguese var- 1.13 – Share of population (aged 10 and above) that can speak Portu- ies from less than 20 percent of the guese, 2007 population in some rural areas to over 60 percent in the main urban centers. The ability to speak Por- tuguese also depends critically on age and gender. Most males under 55 speak Portuguese (more than 60 percent), while for females this crit- ically depends on age. For instance, 63 percent of women at age 11 to 15 speak Portuguese compared to 36 percent at age 31 to 35, and 17 per- cent at age 51 to 55 (fig 1.15). 1.15 – Share of population that can speak Portuguese by age group and gender, 2007 0DOH )HPDOH   Less than 20 20–40  41–60 61–80  Greater than 80 0 75 150 300 ï ï ï ï ï ï ï ï ï ï ï Kilometers $JHJURXS 8 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE 1.14a 1.14b Nine languages are spoken by at least 4 percent of the Mozambi- can population, and the geographic prevalence of each of these nine lan- guages is shown in the maps. Each maps show the share of population by whom that language is spoken as their main language ( figures 1.14a to 1.14i). The maps show that the common languages are generally geographically concentrated with the far majority in each area speak- ing the same language. It should be noted that the Census 2007 regis- 1.14c tered many variations of the same root language, and to create the maps some of these variations have been combined in the following way: Xitsonga includes the follow- ing variations: Xibila, Xidzonga, Xin’walungu, Xihlengue, and Xit- langanu. Sena includes the follow- ing variations: Bangwe, Gombe, Less than 1 1–30 31–60 61–90 1.14a–i – Language spoken (in percentage), 2007 Greater than 90 1.14a – Cindau and variations 0 75 150 300 1.14b – Cinyanja and variations Kilometers 1.14c – Echuwabo and variations 9 language (CONT.) Sena Care, Phondzo, and Tonga. 1.14d 1.14e Emakhuwa includes the following variations: Echirima, Emarevoni, Emetto, Enhara, Esaaka, and Esan- gagi. Echuwabo includes the fol- lowing variations: Ekarungu and Merenja. Cinyanja includes Cice- wa, Cingoni, and Cinsenga. Xitswa includes the following variations: Xidzivi, Xilhengwe, Xikhambani, and Ximhandla. Cindau includes the following variations: Cidanda, 1.14f Cimachanga, and Ciqwaka. Less than 1 1.14a–i – Language spoken (in percentage), 2007 1–30 1.14d – Elomwe 31–60 1.14e – Emakhuwa and variations 61–90 1.14f – Portuguese Greater than 90 1.14g – Sena and variations 0 75 150 300 1.14h – Xitsonga and variations Kilometers 1.14i – Xitswa and variations 10 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE 1.14g 1.14h Portuguese seems to be an excep- tion to this general pattern of geo- graphically concentrated languag- es. Portuguese is spoken as the main language across the country by a significant share of the popu- lation, even though it is almost no- where the main language for a ma- jority of the population ( fig 1.14f ). Figure 1.14f shows the share of the population which speaks Portu- 1.14i guese as their main languages, while Figure 1.13 showed the share of the population that is able to speak Portuguese. Less than 1 1–30 31–60 61–90 Greater than 90 0 75 150 300 Kilometers 11 religion Many denominations are found in Mozambique with distinct geographical patterns to popularity. Mozambique has a rare rich mix 1.17a of religious beliefs with no religion dominating the country. While all main religions can be found in ev- ery part of the country (fig 1.16), there are marked geographical pat- terns which characterize the pre- dominance of religious beliefs (fig 1.17a to 1.17f). 1.17b Less than 20 20–40 41–60 1.17a–f – Share of population belonging to a demonination, 2007 61–80 1.17a – Muslim Greater than 80 1.17b – Catholic 0 75 150 300 1.17c – Ciao Zion Kilometers 1.17d – Evangelical and Pentecostal 1.17e – Anglican 1.17f – Not a member of any religion 12 Mozambique Then and Now Section 1 - THE PEOPLE OF MOZAMBIQUE 1.17c 1.17e 1.17d 1.17f Less than 20 20–40 41–60 61–80 Greater than 80 0 75 150 300 Kilometers 13 14 wealth Poverty and inequality asset ownership Indicators of wealth and wellbeing as ownership of assets and consumption per capita show a complex pattern wealth across Mozambique. poverty and inequality Poverty and inequality indicators show an unexpected pattern. Wealth can be measured in many and non-food consumption per 2.1 – Poverty headcount by province, 2008/2009 ways. In this section we assess capita was calculated, and is re- households’ living standard by ferred to as the poverty line. The measuring their level of con- poverty headcount by province sumption per capita (which is was then calculated as the num- the accepted international in- ber of people who consume less dicator for measuring mone- than the poverty line, over the tary poverty) and also by look- total population in that province ing at the households ownership ( fig 2.1). According to the Third of physical assets. The informa- National Poverty Assessment the tion on consumption is taken northern provinces of Niassa, from the Household Budget Sur- Tete and Cabo Delgado were the vey 2008/09 (Inquerito do Orca- least poor provinces in Mozam- mento Familiar, IOF 2009), while bique in 2008/09, while Zambe- the data on ownership of assets zia and Maputo province were comes from the 2007 Population the poorest ( fig 2.1).This pattern Census. The Third National Pov- of poverty goes against the gen- erty Assessment (GOM, 2010) erally held view in Mozambique measured the level of poverty of that southern provinces are rel- Mozambican households using atively richer, and may reflect a data from the IOF 2008/09, by problem with the survey data or measuring the household’s total with the methodology used to Less than 35 consumption per capita. The level calculate the poverty line. Addi- 35–45 of expenditures needed to allow tional analysis is ongoing to veri- 46–55 households a basic level of food fy these preliminary results.” 56–65 Greater than 65 0 75 150 300 Kilometers 16 Mozambique Then and Now Section 2 - WEALTH 2.2 – Squared poverty gab by province, 2008/2009 2.3 – Inequality by province, 2008/2009 The level of consumption inequal- ity in each province (how much consumption per capita varies be- tween households within each prov- ince) was measured by calculating the Gini coefficient (GOM, 2010). A higher value of the Gini coefficient indicates that inequality is greater. Looking at inequality by province reveals a mixed pattern, with prov- inces in the north, Center and south that have high levels of inequali- ty, but also provinces with relatively low inequality both in the north and Center (fig 2.3). Less than 5 Less than 35 5–8 35–37 8–11 37–39 11–14 39 –41 Greater than 14 Greater than 41 0 75 150 300 0 75 150 300 Kilometers Kilometers 17 asset ownership Ownership of expensive assets is for the few and most of them live in urban areas and in the south, while ownership of less expensive assets is common throughout the country. 2.4 – Share of households owning a radio, 2007 2.5 – Share of households owning a bicycle, 2007 The most affordable and frequently owned assets are radios and bicycles. Ownership of radios is common in urban areas, but also widespread in rural areas (fig 2.4). It is worth to highlight that there is substantial variation across provinces and even bordering administrative posts; in many instances there is a difference of more than 20 percentage points in ownership of radios between neighboring administrative posts (fig 2.4). Ownership of bicycles is also spread across the country, but much lower in the south and in Ma- puto city, where ownership of cars is more common and the use of pub- lic transport is widespread (fig 2.5). Less than 30 Less than 10 30–40 10–20 41–50 21–40 51–60 41–60 Greater than 60 Greater than 60 0 75 150 300 0 75 150 300 Kilometers Kilometers 18 asset ownership (CONT.) Mozambique Then and Now Section 2 - WEALTH Ownership of expensive assets such 2.9a 2.7b as cars, motorcycles, TVs and com- puters is limited to very few house- holds (fig 2.7). Cars, TVs and com- puters are almost exclusively found in urban areas and in the southern part of the country, while motorcy- cles can be found in a few areas of the country (fig 2.7a–d). The percentage of households own- ing any of these assets is much great- er in urban areas compared to ru- ral areas, except for bicycles which are more common in rural areas (fig 2.6). 2.7c 2.7d 2.7a–d – Share of households owning specified assets, 2007 2.6 – Asset ownership by urban/ 2.9a – Car rural, 1997/2007 2.9b – Computer 2.9c – Television 2.9d – Motorcycle Less than 1 1–2 2–5 5–20 Greater than 20 0 75 150 300 Kilometers 19 20 healthy lives infant mortality rate maternal mortality rate malnutrition distance to health facilities fertility accessing water from rivers and lakes lack of access to toilet facilities diarrhea and malaria A healthy life depends on many aspects and there has been great progress in most of them from 1997 to 2007. infant mortality rate Infant mortality remains very high, but decreased significantly across Mozambique during the decade from 1997 to 2007, particularly in locations which had very high levels in 1997. The Infant Mortality Rate (IMR) is 3.1 – IMR per 1000 live births, 1997 3.2 – IMR per 1000 live births, 2007 defined as the number of newborns who die under one year of age di- vided by the number of live births during the year, multiplied by 1000. Data for Mozambique is from the 2007 Census while the internation- al comparison to other countries in Africa is based on data from the World health Organization online database (available at: www.who. int). There was a substantial reduc- tion in infant mortality from 1997 to 2007 (fig 3.1 and 3.2). The largest reductions over the de- cade took place in Administrative Posts that had the highest rates in 1997 (fig 3.4). As a result there is now less variation in infant mortal- ity rate across Administrative Posts in 2007, as is shown by the narrower (more concentrated) distribution of Less than 50 the infant mortality rate by Admin- 50–100 istrative Posts (fig 3.5). Despite prog- 101–150 ress, however, Mozambique’s infant 151–200 mortality rate is not among the low- Greater than 200 est for African countries (fig 3.3). 0 75 150 300 Kilometers 22 Mozambique Then and Now Section 3 - HEALTHY LIVES 3.3 – IMR per 1000 live births across Africa, 2007 3.4 – IMR in 1997 and change be- tween 1997 and 2007 100 Change in infant mortality from 1997 to 2007 0 −100 −200 0 50 100 150 200 250 Infant mortality in 1997 3.5 – Distribution of IMR, 1997 and 2007 Infant mortality 1997 Infant mortality 2007 .03 Less than 30 30–60 61–90 .02 91–120 Greater than 120 No data .01 0 475 950 1,9 00 Kilometers 0 0 50 100 150 200 250 Infant mortality rate 23 maternal mortality rate Maternal mortality rates vary substantially between districts. The Maternal Mortality Rate 3.6– MMR by district, 2007 3.7 – MMR across Africa, 2007 (MMR) is defined as the death of a woman between 15 and 50 of age while pregnant, while giv- ing birth or within 60 days of termination of pregnancy, over 100.000 live births. The data for Mozambique is from the 2007 Population Census while the in- ternational comparison to oth- er countries in Africa is from the World Health Organization. Ma- ternal mortality rates are high- er in the north, compared to the center and south of the coun- try (fig 3.6). Maternal mortality rates also appear to vary greatly across districts. In part this could be due to few births and deaths related to births within each dis- trict making data susceptible to small changes. Less than 500 Less than 500 500–1000 500–1000 1001–1500 1001–1500 1501–2000 1501–2000 Greater than 2000 Greater than 2000 No data 0 75 150 300 0 475 950 1,9 00 Kilometers Kilometers 24 malnutrition Mozambique Then and Now Section 3 - HEALTHY LIVES The share of children underweight and stunted is gradually declining, but remains extremely high. 3.8 – Trend in malnutrition between 3.9 – Share of underweight children across Africa, 2007 Child anthropometric measures 1997 and 2008/2009 are widely used to analyze the prevalence of malnutrition among children. Anthropometric mea- sures are measures of height-for- age (stunting), weight-for-height (wasting), and weight-for-age (underweight). The different mea- sures capture different elements of malnutrition, with stunting re- flecting sustained past episode or episodes of undernutrition, wast- ing reflecting weight loss associat- ed with a recent period of starva- tion or disease, and underweight reflecting a current condition re- sulting from inadequate food in- take, past episodes of undernutri- tion or poor health conditions. Malnutrition indicators for Mozam- Less than 6 bican children have been calculat- 5–16 ed using data from the 1996/97and 17–26 2008/09 Household Budget Surveys 27–36 (IOF 1997 and IAF 2009). Measures Greater than 36 of stunting and underweight show a No data declining trend from 1997 to 2009 0 475 950 1,9 00 (fig 3.8). Compared to other African Kilometers countries Mozambique has an aver- age share of children being under- weight (fig 3.9). 25 malnutrition (CONT.) Malnutrition indicators (that is 3.10 – Share of stunted children, 2007 3.11 – Number of stunted children per km2, 2007 stunting, wasting and underweight) are usually not recorded during a census, and indeed the informa- tion was not collected as part of the 2007 Population Census (that went to all households in Mozambique). The data available from the House- hold Budget Surveys only refers to a relatively small sample of Mozam- bican households. In order to esti- mate how many children are stunted or malnourished in each Adminis- trative Post we used a methodolo- gy known as ‘Small Area Estimates’. This methodology combines the in- formation on malnutrition avail- able in the 2009 IOF survey with the household and child informa- tion available in both the IOF sur- vey and the 2007 Population Cen- sus to estimate how many children Less than 30 Less than 1 are malnourished in each Adminis- 30–40 2–5 trative Post (A full description of the 41–50 6–10 methodology and results is available 51–60 11–15 in Sohnesen (2011). These estimates Greater than 60 Greater than 15 have been used to produce the maps 0 75 150 300 0 75 150 300 of stunting and underweight indica- Kilometers Kilometers tors by administrative post (Fig 3.10 to 3.13) 26 Mozambique Then and Now Section 3 - HEALTHY LIVES 3.12 – Share of underweight children, 2007 3.13 – Number of underweight children per km2, 2007 Much larger shares of children are stunted and/or underweight in the northern and central provinces of Mozambique than in the south. In terms of absolute numbers of chil- dren stunted per km2 (which could be relevant if policy makers want to geographically target areas for inter- vention) the highest density of mal- nourished children are in the most populous areas in the north, Cen- ter and south, including along the densely populated southwestern coastline, and in the urban centers (figs 3.9, 3.10). A similar pattern is found for underweight children (figs 3.12, 3.13). Less than 10 11–15 Less than 1 16–20 2–5 21–25 6–10 26–30 11–15 Greater than 30 Greater than 15 0 75 150 300 0 75 150 300 Kilometers Kilometers 27 Distance to health facilities Most people reside relatively close to a health facility As part of the 2007 Population 3.14 – Location of health facilities across Mozambique, 2007 3.15 – Distance to nearest health facility, 2007 Census the National Statistical Office (INE) collected spatial data on the geographic location of all health facilities (fig 3.14). This has made it possible to cal- culate and map the average dis- tance of villages from health fa- cilities across the country, where distance to health facilities is measured by the distance as the bird flies between the center of the village to the nearest health facility of any type (health post, health center, or hospital). Most of the country is fairly close to a health facility, but there are also areas where households are lo- cated more than 35 kilometers from the nearest health facility (fig 3.15). Less than 5 6–15 16–25 26–35 + Health 2007 Greater than 35 0 75 150 300 0 75 150 300 Kilometers Kilometers 28 Access to safe water Mozambique Then and Now Section 3 - HEALTHY LIVES Despite lack of overall progress in access to clean and safe water, some of the worst areas did see improvements over the past decade. An important aspect of being able to 3.18 – Shared of households with improved water source live a healthy life is access to clean and safe water. The following sourc- es of water are considered improved water sources; household connec- tions, public stand pipes, bore- holes, protected dug wells, protect- ed springs, and rainwater collection. Map 3.x shows the share of house- holds that use an improved water source. The map shows that there are large variations between share of households with improved wa- ter source, from less than five per- cent to some areas with more than Less than 5 85 percent using improved water 5–15 sources. It’s noteworthy that unlike 16–25 many other social aspects illustrated 26–35 in this Atlas, the variation is largely 36–45 within districts more than between 46–55 north-south or East-West. 56–65 66–75 76–85 Greater than 85 0 75 150 300 Kilometers 29 Access to safe water (CONT.) Figure 3.20 shows location of water 3.20 – Rivers, lakes and water wells across Mozambique, 2007 3.21 - Share of households using water from rivers or lakes, 1997 wells, rivers and lakes across Mo- zambique. Rivers and lakes are not considered a clean and safe source of water. In 1997 and in 2007 around 17 percent of households fetched their drinking water from rivers and lakes. There is a big difference in the percentage of households us- ing rivers and lakes between urban and rural areas (fig 3.19). Adminis- trative Posts with the highest share of households using rivers and lakes as source of drinking water in 2007 are mostly in the Western part of the country (3.21, 3.23). 3.19 – Share of households using rivers as a source of water by urban/ national/rural, 2007 Less than 20 21–40 Water 41–60 Rivers 61–80 Lake Greater than 80 0 75 150 300 0 75 150 300 Kilometers Kilometers 30 Access to safe water (CONT.) Mozambique Then and Now Section 3 - HEALTHY LIVES Although on average no progress 3.23 – Share of households using water from rivers or lakes, 2007 3.24 – Change of river and lake usage between 1997 and 2007 was recorded in reducing the per- centage of households which fetched their drinking water from rivers and lakes, some areas particularly in the West of the country recorded sub- stantial progress (fig 3.24). In fact, the Administrative Posts with the highest share of households using rivers and lakes as a water source in 1997 have seen the most progress (fig 3.22). 3.22 – Share of households using rivers and lakes as water sources in 1997 and change between 1997 and 2007 Less than 20 Less than –15 .5 20–40 –15-–5 41–60 –5–5 Change from 1997 to 2007 61–80 5–15 0 Greater than 80 Greater than 15 0 75 150 300 0 75 150 300 −.5 Kilometers Kilometers −1 0 .2 .4 .6 .8 1 Share that fetch water from rivers and lakes in 1997 31 lack of access to toilet facilities There was wide progress in access to toilet facilities across the country during the past decade, especially in the north. The share of households that do not 3.26 – Share of households that do not have access to a toilet, 1997 3.27 – Share of households that do not have access to a toilet, 2007 have access to a toilet facility has fallen by 10 percentage points from 63 percent in 1997 to 53 percent in 2007 (figs 3.25, 3.26, 3.27). Never- theless there is a large gap between urban and rural areas, with more than 65 percent of the households in rural areas still having no access to a toilet in 2007. 3.25 – Share of households without access to a toilet by urban/national/ rural, 2007     Less than 20 20–40  41–60  61–80 Greater than 80  0 75 150 300  8UEDQ 1DWLRQDO 5XUDO Kilometers 32 lack of access to toilet facilities (CONT.) Mozambique Then and Now Section 3 - HEALTHY LIVES 3.28 – Change of households without access to a toilet between 1997 and 2007 In general more progress was ob- served in the north than the rest of the country (fig 3.28), but unlike other aspects like general fertility and infant mortality we don’t find a relationship between the progress made and the level in 1997 (fig 3.29). 3.29 – Share of households without access to toilet in 1997 and change between 1997 and 2007  &KDQJHLQDFFHVVWRWRLOHWWR  Less than –20 –20––10 –10–0 ï 0–10 Greater than 50 0 75 150 300 ï       Kilometers 6KDUHZLWKQRDFFHVVWRWRLOHWLQKRPHLQ 33 diarrhea and malaria More people are being treated for diarrhea and malaria today than in the past. The number of treated cases of 3.30 – Number of diarrhea cases per 1000 people, 1997 3.31 – Number of diarrhea cases per 1000 people, 2007 malaria and diarrhea are counted at health facilities throughout the country as reported by the Epide- miology Department of the Min- istry of Health (Gabinete de Epide- miologia, Ministério da Saúde).” Figures 3.30 to 3.33 show the num- ber of reported cases of diarrhea and malaria as a proportion of the population in health facilities in 1997–1998 and in 2007. They show a substantial increase in number of treated cases in most parts of the country. It is important to under- line, however, that the observed in- crease in number of cases treated in health facilities does not nec- essarily indicate that more people are suffering from these diseases— and in fact it is more likely to reflect an increase in the number of peo- Less than 10 Less than 10 ple seeking treatment as access to 10–20 10–20 health facilities has improved over 21–30 21–30 31–40 31–40 Greater than 40 Greater than 40 0 75 150 300 0 75 150 300 Kilometers Kilometers 34 diarrhea and malaria (CONT.) Mozambique Then and Now Section 3 - HEALTHY LIVES 3.32 – Number of malaria cases per 1000 people, 1998 3.33 – Number of malaria cases per 1000 people, 2007 time. The Report on the Millenni- um Development Goals by Min- istry of Planning and Develop- ment (GOM, 2010) indicate a sharp downward trend in on share of chil- dren under five suffering from ma- laria over time (fig 3.33)” with “The report on the Millennium Devel- opment Goals by the Ministry of Planning and Development (GOM, 2010) indicates a sharp downward trend in on share of children under five suffering from malaria.” Less than 50 Less than 50 50–100 50–100 101–150 101–150 151–200 151–200 201–250 201–250 Greater than 250 Greater than 250 0 75 150 300 0 75 150 300 Kilometers Kilometers 35 fertility Fertility rates are more homogenous across the country today than they were a decade ago. 3.16 – General fertility rate, 1997 3.17 – General fertility rate, 2007 The general fertility rate is defined as the annual number of live births per 1000 women of childbearing age (between 15 and 49 of age). The national average general fer- tility rate decreased very little over the decade from 174 in 1997 to 168 in 2007. This small reduction hides a process of equalization where ar- eas with high fertility rates in 1997 had decreasing fertility rates, while areas with very low fertility rates increased (figs 3.16, 3.17). Fertili- ty rates are higher on average in the center and in the north compared to the south, but it’s noteworthy that the fertility rate across the country is more homogeneous in 2007 than it was in 1997 (figs 3.16, 3.17). Less than 100 100–150 151–200 201–300 Greater than 300 0 75 150 300 Kilometers 36 Mozambique Then and Now Section 3 - HEALTHY LIVES 37 38 access to services electricity, running water, phones and internet distance to major urban areas Access to services such as electricity, fixed phone lines, and running water are mostly an urban privilege, while the use of cell phones has spread to the rural south. electricity, running water, phones and internet Access to basic services such as running water, landlines, internet, and electricity for lighting is the privilege of a few households, mainly in urban areas. Access to services as running wa- 4.2a 4.2b ter, phone landlines, internet, and electricity for lighting is generally a rare luxury. For both electricity and running water 32 percent of the ur- ban households use these services, while only 1 percent of households in rural areas do. In 2007 cell phones were owned by approximately 45 4.2a–d – Share of households using a service, 2007 percent of urban household and less 4.2a – Using electricity as the main source of light than 10 percent of rural households. 4.2b – Smainly using running water (available Electricity and running water were either within the house or outside the house) used by about one third of the urban 4.2c – Owning a fixed phone line households, and almost none in ru- 4.2d – Having used internet in last 12 months ral areas. (fig 4.1). 4.2c 4.2d 4.1 – Share of households using services in urban and rural areas, 2007 Cell phone Electricity Running water Internet Fixed phoneline 50% Less than 2 40% 2–4 5–6 30% 7–8 20% 9 –10 11–12 10% Greater than 12 0 37.5 75 150 0% Urban National Rural Kilometers 40 Mozambique Then and Now Section 4 - ACCESS TO SERVICES 4.3 – Share of households owning at least one cell phone 4.4 – Coverage of mcel masts across Mozambique, 2007 The ownership of cell phones is far more common in urban areas (48 percent of households have at least one cell phone), but it is also spread- ing to rural areas, notably in the south (fig 4.3). Figure 4.3 also re- veals that ownership of cell phones is not explained by access to cellular services, as there are many areas that have mcel towers (and therefore net- work coverage) in the north (fig 4.4), but which have very low density of cell phones. (Note: We were not pro- vided the location of cell phone tow- ers by Vodacom, which is the other main cell phone provider in Mo- zambique). Less than 2 2–4 5–6 7–8 9 –10 11–12 Greater than 12 0 75 150 300 0 75 150 300 Kilometers Kilometers 41 distance to major urban areas Vast areas of the country remain isolated from major markets PLEASE PROVIDE TITLE FOR FIGURE 4.5! Access to major urban centers in 4.5 – ??????????? 4.6 – Travel distance to urban centers (in minutes) part depends on road infrastructure and is often key for access to main markets with greater product avail- ability and favorable prices. Estimated travel time to nearest city of at least 50.000 people has been calculated based on available roads and assumptions about average trav- el speed by type of road, also tak- ing into account landscape and el- evation conditions (see Dorosh and Schmidt, 2008, for full details). The model calculates the quickest trav- el time from each village to the clos- est market within Mozambique. The Primary map is based on the 2007 Census Secondary map (fig 4.6). Tertiary Vicinal Less than 200 Unclassified 201–400 Ohter 401–600 Rail Greater than 600 Capitals 0 75 150 300 0 75 150 300 Kilometers Kilometers 42 Mozambique Then and Now Section 4 - ACCESS TO SERVICES 43 44 education Primary gross enrollment rates Secondary gross enrollment rates Distance to primary and secondary schools Education of the labor force There was substantial progress in primary and secondary enrollment over the decade from 1997 to 2007, both for boys and girls, and in urban and rural areas. primary gross enrollment rates Gross primary enrollment increased substantially during the past decade The Primary Gross Enrollment 5.1 – PGE rates, 2007 5.2 – Change in PGE between 1997 and 2007 (PGE) rate provides a measure of the share of primary school-age chil- dren which are actually enrolled in school. It is calculated as the num- ber of students attending primary school (grades 1 to 7, and including technical elementary ETE), divid- ed by number of children between 6 and 12 years of age. The Prima- ry Gross Enrollment rate increased substantially the last decade, from an average of 66 percent in 1997 to 99 percent in 2007. This reflects sub- stantial increases in primary enroll- ment both in urban and rural areas and for boys and girls. Geographi- cally there was progress in primary enrollment rates in almost all Ad- ministrative Posts, particularly in the central part of the country (figs 5.1, 5.2). In fact, a simple analysis shows that the administrative posts Less than 25 Less than 15 with the lowest enrollment rates in 25–50 15–30 1997 saw the largest improvements 51–75 31–45 between 1997 and 2007 (fig 5.5). It is 76–100 46–60 useful to keep in mind that the rel- Greater than 100 Greater than 60 atively small progress made in Ad- 0 75 150 300 0 75 150 300 ministrative Posts in the south and Kilometers Kilometers in Maputo City and other urban centers does not necessarily reflect 46 Mozambique Then and Now Section 5 - EDUCATION 5.4 – Male PGE rate, 2007 5.5 – Female PGE rate, 2007 lack of progress and could also in- dicate that enrollment rates are ap- proaching universal enrollment in those areas. It is also noteworthy that many Ad- ministrative Posts still have low pri- mary enrollment rates. Around 20 percent of Administrative Posts have primary gross enrollment rates below 75. Many of these areas with low enrollment rates are concentrat- ed in northern provinces as Niassa, Cabo Delgado, Nampula, and Tete. Further, female enrollment rates re- main lower than that for boys, par- ticularly in rural areas (figs 5.3, 5.4). 5.5 – PGE rates in 1997 and change between 1997 and 2007  &KDQJHLQHQUROOPHQWUDWHIURPWR Less than 25 Less than 25 25–50 25–50 51–75 51–75  76–100 76–100 Greater than 100 Greater than 100 0 75 150 300 0 75 150 300  Kilometers Kilometers ï     (QUROOPHQWUDWHLQ 47 primary gross enrollment rates (CONT.) Access to primary schools is good in most of the country Distance to primary schools is mea- 5.6 – Distance to nearest primary school, 2007 sured as the bird flies, e.i. the geo- graphical distance between any lo- cation and the nearest primary school, irrespective of the existence of a road or other means of trans- portation. A better measure would be the actual travel distance given available paths and roads, however, the map still gives a good indication of how well the country is covered by primary schools. The map shows that in most parts of the country, there is a primary school within six kilometers. There are however, also some areas with much longer dis- tance to the nearest primary school. Less than 2 3–4 5–6 7–8 Greater than 8 0 75 150 300 Kilometers 48 Mozambique Then and Now Section 5 - EDUCATION 5.7 – PGE across Africa, 2007 With primary enrollment levels ap- proaching universal enrollment Mo- zambique does well compared to many other African countries (fig 2.5). Less than 70 70–90 91–110 111–130 Greater than 130 No data 0 475 950 1,9 00 Kilometers 49 secondary gross enrollment rates Gross secondary enrollment increased around four fold during the last decade with progress concentrated in urban areas. The Secondary Gross Enrollment 5.8 – SGE rates, 2007 5.9 – Change in SGE between 1997 and 2007 (SGE) rate provides a measure of the share of secondary school-age children which are actually enrolled in secondary school. It is defined as number of students attending sec- ondary school (grade 8 to 12, in- cluding technical schools ETB and ETM), divided by the number of children between 13 and 16 years of age. The gross secondary enroll- ment rate increased four fold from an average of 10 percent in 1997 to 42 percent in 2007. Progress across administrative posts was relative- ly equal with almost all administra- tive posts making progress (figs 5.8, 5.9). However the increase in enroll- ment was concentrated in urban ar- eas and mostly took place in areas that already had higher enrollment Less than 10 Less than 5 in 1997 (fig 5.12). 10–20 21–25 5–15 26–45 16–25 46–65 26–35 66–85 36–45 86–100 46–55 Greater than 100 Greater than 55 0 75 150 300 0 75 150 300 Kilometers Kilometers 50 Mozambique Then and Now Section 5 - EDUCATION 5.10 – Male SGE rate, 2007 5.11 – Female SGE rate, 2007 A gender gap prevails in secondary education. In 2007, approximate- ly 47 percent of boys were enrolled compared to 37 percent of girls. Un- like some other social aspects there is not a clear North-South differ- ence in secondary enrollment rates for both genders. There is however disparities between administrative posts, and within all provinces there are wide difference across Adminis- trative Posts (figs 5.10, 5.11). 5.12 – SGE rates in 1997 and change between 1997 and 2007  &KDQJHLQHQUROOPHQWUDWHIURPWR Less than 10 Less than 10 10–20 10–20  21–25 21–25 26–45 26–45  46–65 46–65  66–85 66–85 86–100 86–100  Greater than 100 Greater than 100 0 75 150 300 0 75 150 300      Kilometers Kilometers (QUROOPHQWUDWHLQ 51 secondary gross enrollment rates (CONT.) Access to secondary schools remains limited Distance to secondary schools is 5.13 – Distance to nearest secondary school, 2007 measured as the bird flies, e.i. the geographical distance between any location and the nearest secondary school, irrespective of the existence of a road or other means of trans- portation. A better measure would be the actual travel distance giv- en available paths and roads, how- ever, the map still gives a good in- dication of how well the country is covered by secondary schools. The map shows that in most parts of the country there is no secondary school within 40 kilometers. Less than 10 10–20 21–30 31–40 Greater than 40 0 75 150 300 Kilometers 52 Mozambique Then and Now Section 5 - EDUCATION 5.14 – SGE rates across Africa, 2007 Secondary enrollment rates are on average lower in Mozambique than in most other African countries (fig 5.14). Less than 10 10–30 31–50 51–70 Greater than 70 No data 0 475 950 1,9 00 Kilometers 53 Education of labor force Educated labor is scarce An educated labor force is a key 5.19 – Adult male literacy rate, 2007 5.20 – Adult female literacy rate, 2007 element in development and growth. Unfortunately, in most rural areas only about half the men can read and write (i.e. they are literate), and only very few women are literate (fig 5.19, 5.20). Less than 25 Less than 25 25–40 25–40 41–60 41–60 61–80 61–80 Greater than 80 Greater than 80 0 75 150 300 0 75 150 300 Kilometers Kilometers 54 Mozambique Then and Now Section 5 - EDUCATION 5.21 – Share of labor force with completed primary education or 5.22 – Share of labor force with completed secondary education or The population of labor force age above, 2007 above, 2007 (15 to 65) with some formal educa- tion (primary education or above) is mostly concentrated in urban areas. There is hardly anyone of labor force age (with secondary (or above) out- side urban areas (fig 5.21, 5.22, 5.23). 5.23 – Share of labor (aged 15 to 65) with complete primary or above, and secondary and above, by gender and urban/rural, 2007 3ULPDU\ 6HFRQGDU\   Less than 25 Less than 25 25–40 25–40  41–60 41–60  61–80 61–80 Greater than 80 Greater than 80  0 75 150 300 0 75 150 300  Kilometers Kilometers 8UEDQ 5XUDO 8UEDQ 5XUDO 0DOH )HPDOH 55 56 land and agriculture land weather animals Land and agriculture is of great importance to most in Mozambique. This section shows how the land is distributed across the country and how rain and temperature varied in 2007 and over the last decade. It also shows where ownership of animals is more common and how it has changed over time. land and weather Mozambique’s topography varies mostly from East to West while rain patterns often vary mostly from north to south Elevation generally increas- 6.1 – Topography of Mozambique 6.2 – Land suitability es as you move from East to West in the Northern parts of the country ( fig 6.1). Land capabilities and its suitabili- ty for use vary mostly from North to South as land suit- ed for intensive agricultur- al use is concentrated in the North ( fig 6.2). Land suitable for intensive agricultural use Water bodies –112 – 100 Suitable for all uses 101 – 200 Suitable for all uses, but requires some conservation 201 – 300 Suitable for all uses, but requires intensive conservation 301 – 400 Land not suitable for agricultural use, but suitable for 401 – 500 pasture, reforestation or natural vegetation. 501 – 600 Suitable for several uses 601 – 1,000 Suitable for pasture, reforestation or wild life 1,010 – 2,420 Suitable for reforestation or wild life, inadequate for grass 0 75 150 300 0 75 150 300 Kilometers Kilometers 58 Mozambique Then and Now Section 6 - LAND & AGRICULTURE The north-south pattern 6.3a – 1997 6.3b – 1998 6.3c – 1999 6.3d – 2000 6.3e – 2001 6.3f – 2002 seen in agricultural suit- ability is also seen in rain- fall with the north receiv- ing much more rain than the south. Figures 6.3a–l show how rain varies across years and location. In particular the floods in 2000 and the droughts around 2002 and 2005 are easily observable The rainfall and tempera- tures were produced using daily NASA weather sat- ellite data, over the period 6.3g – 2003 6.3h - 2004 6.3i – 2005 6.3j – 2006 6.3k – 2007 6.3l – 2008 from 1997 to 2008.. 6.3a–l – Rainfall by year, 1997–2008 400mL 500mL 600mL 700mL 800mL 900mL 1000mL 1300mL 1600mL 1900mL 0 75 150 300 Kilometers 59 land and weather (CONT.) The 12 maps in fig- 6.4a – January 6.4b – February 6.4c – March 6.4d – April 6.4e – May 6.4f – June ures 6.4a–l dramatical- ly illustrate the seasonal rainfall in Mozambique as it was in 2007. The rain season started in November in the south and Center, covering the whole country by De- cember, and declining from the south towards the north in January, February and March. 6.4a–l – Rainfall by Month, 2007 6.4g – July 6.4h – August 6.4i – September 6.4j – October 6.4k – November 6.4l – December 5mL 10mL 20mL 40mL 60mL 80mL 100mL 200mL 400mL 700mL 0 75 150 300 Kilometers 60 Mozambique Then and Now Section 6 - LAND & AGRICULTURE 6.5a – January 6.5b – February 6.5c – March 6.5d – April 6.5e – May 6.5f – June Figures 6.5a–l show varia- tion in mean day tempera- tures in Celsius across the same year. Compared to rain, temperatures do not vary as drastically over the year or across the country. The higher elevated plains in the West do see some temperature swings around the winter months of June and July that are up to 10 degrees Celsius colder than the summer months 6.5g – July 6.5h – August 6.5i – September 6.5j – October 6.5k – November 6.5l – December 6.5a–l – Average temperature by month, 2007 <18° C 19 ° C 20 ° C 21° C 22 ° C 23° C 24° C 25° C 26° C 27° C 28° C 0 75 150 300 Kilometers 61 ownership of animals Ownership of animals varies with location, but is generally very little, except for poultry There are geographical patterns in 6.6 – Share of households owning sheep 6.7 – Share of households owning cattle ownership of animals. Sheep own- ers are mostly found in the south (fig 6.6), cattle owners are mostly found in the south and the West (fig 6.7), ownership of swine is most com- mon in southern part of Inhambane and the central part of the country (fig 6.8), while ownership of poultry is common throughout the coun- try, except in the Maputo province (fig 6.9). Less than 1 Less than 1 1–5 1–5 6–10 6–10 11–15 11–15 16–20 16–20 21–25 21–25 26–30 26–30 Greater than 30 Greater than 30 0 75 150 300 0 75 150 300 Kilometers Kilometers 62 Mozambique Then and Now Section 6 - LAND & AGRICULTURE 6.8 – Share of households owning swine 6.9 – Share of households owning poultry Less than 1 Less than 1 1–5 1–5 6–10 6–10 11–15 11–15 16–20 16–20 21–25 21–25 26–30 26–30 Greater than 30 Greater than 30 0 75 150 300 0 75 150 300 Kilometers Kilometers 63 ownership of animals (CONT.) 6.13 – Percentage point change in share Ownership of most animals is the 6.11 – Share of households owning owning cattle between 1997 and 2007 priviledge of few households; in sheep between 1997 and 2007 2007 only 4 percent of households owned cattle, 2 percent sheep, and 10 percent swine (figs 6.10, 6.11, 6.12). Poultry on the other hand is relatively common with more than 50 percent of households owning some poultry. Compared to 1997, some households have shifted away from sheep (–10 percentage points from 1997 to 2007), while there has been little change in the percentage of household owning swine (–2 per- centage points) and cattle (+1 per- centage point) (figs 6.13, 6.14, 6.15). 6.10 – Share of households owning 6.12 – Share of households owning cattle between 1997 and 2007 swine between 1997 and 2007 Less than –6 –6––2 –2–2 2–6 6–10 Greater than 10 0 75 150 300 Kilometers 64 Mozambique Then and Now Section 6 - LAND & AGRICULTURE 6.14 – Percentage point change in share 6.15 – Percentage point change in share owning sheep between 1997 and 2007 owning swine between 1997 and 2007 Less than –6 Less than –25 –6––2 –25––15 –2–2 –15––5 2–6 –5–5 6–10 Greater than 5 Greater than 10 0 75 150 300 0 75 150 300 Kilometers Kilometers 65 Cod Adm. Posts District Province Cod Adm. Posts District Province 1 CIDADE DE LICHINGA CIDADE DE LICHINGA NIASSA 73 IMBUO MUEDA CABO DELGADO 2 CUAMBA CUAMBA NIASSA 74 NEGOMANO MUEDA CABO DELGADO Location of 3 ETARARA CUAMBA NIASSA 75 N’GAPA MUEDA CABO DELGADO 4 LURIO CUAMBA NIASSA 76 MUIDUMBE MUIDUMBE CABO DELGADO 5 METANGULA LAGO NIASSA 77 CHITUNDA MUIDUMBE CABO DELGADO 6 COBUE LAGO NIASSA 78 MITEDA MUIDUMBE CABO DELGADO Administration Posts 7 MANIAMBA LAGO NIASSA 79 NAMUNO NAMUNO CABO DELGADO 8 CHIMBONILA LICHINGA NIASSA 80 HUCULA NAMUNO CABO DELGADO 9 LIONE LICHINGA NIASSA 81 MACHOCA NAMUNO CABO DELGADO 10 MEPONDA LICHINGA NIASSA 82 MELOCO NAMUNO CABO DELGADO 11 MALANGA MAJUNE NIASSA 83 N’CUMPE NAMUNO CABO DELGADO 12 MUAQUIA MAJUNE NIASSA 84 PAPAI NAMUNO CABO DELGADO 13 NAIRRUBI MAJUNE NIASSA 85 NANGADE NANGADE CABO DELGADO 87 14 MANDIMBA MANDIMBA NIASSA 86 M’TAMBA NANGADE CABO DELGADO United Republic of Tanzania 85 89 88 15 MITANDE MANDIMBA NIASSA 87 PALMA PALMA CABO DELGADO 75 86 63 16 MARRUPA MARRUPA NIASSA 88 OLUMBI PALMA CABO DELGADO 64 38 74 71 73 76 65 17 MARANGIRA MARRUPA NIASSA 89 PUNDANHAR PALMA CABO DELGADO 25 77 58 6 22 26 72 78 56 18 NUNGO MARRUPA NIASSA 90 QUIONGA PALMA CABO DELGADO 37 21 69 55 57 19 MAUA MAUA NIASSA 91 METUGE PEMBA CABO DELGADO 93 53 Zambia 61 62 20 MAIACA MAUA NIASSA 92 MIEZE PEMBA CABO DELGADO 7 30 17 94 95 54 5 35 68 40 91 21 MAVAGO MAVAGO NIASSA 93 QUISSANGA QUISSANGA CABO DELGADO 36 29 11 16 46 66 7042 41 60 9239 22 M’SAWIZE MAVAGO NIASSA 94 BILIBIZA QUISSANGA CABO DELGADO 10 1 8 12 18 44 43 67 49 47 48 50 59 23 INSACA MECANHELAS NIASSA 95 MAHATE QUISSANGA CABO DELGADO Malawi 9 32 13 34 45 83 79 82 51 52 126 24 CHIUTA MECANHELAS NIASSA 96 URBANO CENTRAL CIDADE DE NAMPULA NAMPULA 31 80 106 125 19 84 81 107 127 25 MECULA MECULA NIASSA 97 MUATALA CIDADE DE NAMPULA NAMPULA 15 27 20 33 122 108 Nampula 26 MATONDOVELA MECULA NIASSA 98 MUHALA CIDADE DE NAMPULA NAMPULA 227 14 160 124 121 228 28 159 111 129 114 238 219 218 2 4 120 114 112 158 157 154 149 27 METARICA METARICA NIASSA 99 NAMIKOPO CIDADE DE NAMPULA NAMPULA 231 24 143 156 226 232 247 115 165 123 150 148 144 147 403 405 28 NACUMUA METARICA NIASSA 100 NAPIPIME CIDADE DE NAMPULA NAMPULA 250 236 113 167 116 142 145 400 251 163 16199 237 248 23 3 180 152 164 119 110 29 MUEMBE MUEMBE NIASSA 101 NATIKIRE CIDADE DE NAMPULA NAMPULA 166 162 402 249 239 230 229 179 173 151 117 131 146 404 30 CHICONONO MUEMBE NIASSA 102 CIDADE DE ANGOCHE ANGOCHE NAMPULA 220 242 181 134 401 234 233 222 197 211 184 178 153 133 136 132 128 139 139 31 MASSANGULO NGAUMA NIASSA 103 AUBE ANGOCHE NAMPULA 172 135 129 235 225 217 240 241 190 210 182 139 137 104 32 ITEPELA NGAUMA NIASSA 104 NAMAPONDA ANGOCHE NAMPULA 221 177 105 102 196 195 188 187 183 141 103 33 NIPEPE NIPEPE NIASSA 105 BOILA_NAMITORIA ANGOCHE NAMPULA 224 198 189 264 245 200 216 138 140 34 MUIPITE NIPEPE NIASSA 106 NAMAPA ERATI NAMPULA 266 205 223 286 284 201 215 35 UNANGO SANGA NIASSA 107 ALUA ERATI NAMPULA 193 265 299 243 206 199 214 263 285 298 244 204 194 192 36 LUSSIMBESSE SANGA NIASSA 108 NAMIROA ERATI NAMPULA 300 207 191 255 316 297 246 208 37 MACALOGE SANGA NIASSA 109 C. ILHA DE MOÇAMBIQUE C. ILHA DE MOÇAMBIQUE NAMPULA 212 209 271 269 315 296 202 203 169213 38 MATCHEDJE SANGA NIASSA 110 LUMBO C. ILHA DE MOÇAMBIQUE NAMPULA 295 254 317 319 185 39 CIDADE DE PEMBA CIDADE DE PEMBA CABO DELGADO 111 LALAUA LALAUA NAMPULA 253 302 186 270 311 175 176 40 ANCUABE ANCUABE CABO DELGADO 112 METI LALAUA NAMPULA 275 310 301 318 174 41 METORO ANCUABE CABO DELGADO 113 MALEMA MALEMA NAMPULA 273 276 261 309 272274 252 258 321 320 42 MESA ANCUABE CABO DELGADO 114 CHIHULO MALEMA NAMPULA 257 Zimbabwe 262 322 43 BALAMA BALAMA CABO DELGADO 115 MUTUALI MALEMA NAMPULA 282 260 259 283 280 323 324307 308 Beira 44 IMPIRI BALAMA CABO DELGADO 116 MECONTA MECONTA NAMPULA 306291 428 429 429 281 292 289290 45 KUEKUE BALAMA CABO DELGADO 117 CORRANE MECONTA NAMPULA 304 293 425 46 MAVALA BALAMA CABO DELGADO 118 NAMIALO MECONTA NAMPULA 279 303 294 423 47 CHIURE CHIURE CABO DELGADO 119 7 DE ABRIL MECONTA NAMPULA 277 278 305 313314 424 48 CHIURE VELHO CHIURE CABO DELGADO 120 MECUBURI MECUBURI NAMPULA 267 312 373 422 49 KATAPUA CHIURE CABO DELGADO 121 MILHANA MECUBURI NAMPULA 421 328 50 MAZEZE CHIURE CABO DELGADO 122 MUITE MECUBURI NAMPULA 268 329 340 51 NAMOGELIA CHIURE CABO DELGADO 123 NAMINA MECUBURI NAMPULA 390 335 389 334 52 OCUA CHIURE CABO DELGADO 124 MEMBA MEMBA NAMPULA 338 349 53 IBO IBO CABO DELGADO 125 CHIPENE MEMBA NAMPULA 366 339 369 350 54 QUIRIMBA IBO CABO DELGADO 126 LURIO MEMBA NAMPULA 367 327 342 55 MACOMIA MACOMIA CABO DELGADO 127 MAZUA MEMBA NAMPULA 56 CHAI MACOMIA CABO DELGADO 128 NAMIGE MOGINCUAL NAMPULA 368 380 326 341 57 MUCOJO MACOMIA CABO DELGADO 129 QUINGA MOGINCUAL NAMPULA 370 345 344 58 QUITERAJO MACOMIA CABO DELGADO 130 CHUNGA MOGINCUAL NAMPULA 381 392 379 348 331 343 59 MECUFI MECUFI CABO DELGADO 131 QUIXAXE MOGINCUAL NAMPULA 363 347 330 325 391 393 378 346 336 60 MURREBUE MECUFI CABO DELGADO 132 LIUPO MOGINCUAL NAMPULA 377 361 386 337 0 75 150 300 61 MELUCO MELUCO CABO DELGADO 133 NAMETIL MOGOVOLAS NAMPULA 373 376 333 332 403 405 South Africa 404 372 362364 365387 384352 351 Maputo Matola Km 62 MUAGUIDE MELUCO CABO DELGADO 134 CALIPO MOGOVOLAS NAMPULA 406 356 385 402 355394 395388 361 63 MOCIMBOA DA PRAIA MOCIMBOA DA PRAIA CABO DELGADO 135 IULUTI MOGOVOLAS NAMPULA 353 423 412359 396 407 408358 382 64 DIACA MOCIMBOA DA PRAIA CABO DELGADO 136 MUATUA MOGOVOLAS NAMPULA 420 422 420 410414 418 65 MBAU MOCIMBOA DA PRAIA CABO DELGADO 137 NANHUPO RIO MOGOVOLAS NAMPULA 421413 424401399 407408 66 MONTEPUEZ MONTEPUEZ CABO DELGADO 138 MOMA MOMA NAMPULA 432 400 416418 363419 406 410 410 67 MAPUPULO MONTEPUEZ CABO DELGADO 139 CHALAUA MOMA NAMPULA 425 427 415 68 MIRATE MONTEPUEZ CABO DELGADO 140 LARDE MOMA NAMPULA Swaziland417 364 409 69 NAIROTO MONTEPUEZ CABO DELGADO 141 MUCUALI MOMA NAMPULA 419 70 NAMANHUMBIR MONTEPUEZ CABO DELGADO 142 MONAPO MONAPO NAMPULA 71 MUEDA MUEDA CABO DELGADO 143 ITOCULO MONAPO NAMPULA 72 CHAPA MUEDA CABO DELGADO 144 NETIA MONAPO NAMPULA 66 Cod Adm. Posts District Province Cod Adm. Posts District Province Cod Adm. Posts District Province Cod Adm. Posts District Province 145 MOSSURIL MOSSURIL NAMPULA 217 CIDADE DE TETE CIDADE DE TETE TETE 289 URBANO Nº3 CIDADE DA BEIRA SOFALA 361 ALTO CHANGANE CHIBUTO GAZA 146 LUNGA MOSSURIL NAMPULA 218 ULONGUE ANGONIA TETE 290 URBANO Nº4 CIDADE DA BEIRA SOFALA 362 CHAIMITE CHIBUTO GAZA 147 MATIBANE MOSSURIL NAMPULA 219 DOMUE ANGONIA TETE 291 URBANO Nº5 CIDADE DA BEIRA SOFALA 363 CHANGANINE CHIBUTO GAZA 148 MUECATE MUECATE NAMPULA 220 SONGO CAHORA BASSA TETE 292 BUZI BUZI SOFALA 364 GODIDE CHIBUTO GAZA 149 IMALA MUECATE NAMPULA 221 CHINTHOLO CAHORA BASSA TETE 293 ESTAQUINHA BUZI SOFALA 365 MALEHICE CHIBUTO GAZA 150 MUCULOENE MUECATE NAMPULA 222 CHITIMA CAHORA BASSA TETE 294 NOVA-SOFALA BUZI SOFALA 366 VILA EDUARDO MONDLANE CHICUALACUALA GAZA 151 MURRUPULA MURRUPULA NAMPULA 223 LUENHA CHANGARA TETE 295 CAIA CAIA SOFALA 367 MAPAI CHICUALACUALA GAZA 152 CHINGA MURRUPULA NAMPULA 224 CHIPEMBERE (CHIOCO) CHANGARA TETE 296 MURRAÇA CAIA SOFALA 368 PAFURI CHICUALACUALA GAZA 153 NIHESSIUE MURRUPULA NAMPULA 225 KACHEMBE (MARARA) CHANGARA TETE 297 SENA CAIA SOFALA 369 CHIGUBO CHIGUBO GAZA 154 MAIAIA NACALA-PORTO NAMPULA 226 CHIFUNDE CHIFUNDE TETE 298 CHEMBA CHEMBA SOFALA 370 DINDIZA CHIGUBO GAZA 155 MUANONA NACALA-PORTO NAMPULA 227 MUALADZE CHIFUNDE TETE 299 CHIRAMBA CHEMBA SOFALA 371 CIDADE DE CHOKWE CHOKWE GAZA 156 NACALA-A-VELHA NACALA-A-VELHA NAMPULA 228 NSADZU CHIFUNDE TETE 300 MULIMA CHEMBA SOFALA 372 LIONDE CHOKWE GAZA 157 COVO NACALA-A-VELHA NAMPULA 229 KAZULA CHIUTA TETE 301 INHAMINGA CHERINGOMA SOFALA 373 MACARRETANE CHOKWE GAZA 158 NACAROA NACAROA NAMPULA 230 MANJE CHIUTA TETE 302 INHAMITANGA CHERINGOMA SOFALA 374 XILEMBENE CHOKWE GAZA 159 INTETE NACAROA NAMPULA 231 FURANCUNGO MACANGA TETE 303 CHIBABAVA CHIBABAVA SOFALA 375 CANIÇADO GUIJA GAZA 160 SAUA-SAUA NACAROA NAMPULA 232 CHIDZOLOMONDO MACANGA TETE 304 GOONDA CHIBABAVA SOFALA 376 CHIVONGOENE GUIJA GAZA 161 RAPALE RAPALE-NAMPULA NAMPULA 233 MPHENDE MAGOE TETE 305 MUXUNGUE CHIBABAVA SOFALA 377 MUBANGOENE GUIJA GAZA 162 ANCHILO RAPALE-NAMPULA NAMPULA 234 CHINTHOPO MAGOE TETE 306 CIDADE DE DONDO DONDO SOFALA 378 NALAZI GUIJA GAZA 163 MUTIVASSE RAPALE-NAMPULA NAMPULA 235 MUKUMBURA MAGOE TETE 307 MAFAMBISSE DONDO SOFALA 379 MABALANE MABALANE GAZA 164 NAMAITA RAPALE-NAMPULA NAMPULA 236 CHIPUTU MARAVIA TETE 308 SAVANE DONDO SOFALA 380 COMBOMUNE MABALANE GAZA 165 RIBAUE RIBAUE NAMPULA 237 FINGOE MARAVIA TETE 309 GORONGOSA GORONGOSA SOFALA 381 NTLAVENE MABALANE GAZA 166 CUNLE RIBAUE NAMPULA 238 MALOWERA MARAVIA TETE 310 NHAMADZE GORONGOSA SOFALA 382 MANDLAKAZE MANDLAKAZE GAZA 167 IAPALA RIBAUE NAMPULA 239 CHIPERA MARAVIA TETE 311 VANDUZI GORONGOSA SOFALA 383 XHALALA MANDLAKAZE GAZA 168 Urbano Nº1 QUELIMANE ZAMBEZIA 240 MOATIZE MOATIZE TETE 312 MACHANGA MACHANGA SOFALA 384 CHIBONZANE MANDLAKAZE GAZA 169 Urbano Nº2 QUELIMANE ZAMBEZIA 241 KAMBULATSISI MOATIZE TETE 313 DIVINHE MACHANGA SOFALA 385 CHIDENGUELE MANDLAKAZE GAZA 170 Urbano Nº3 QUELIMANE ZAMBEZIA 242 ZOBUE MOATIZE TETE 314 CHILOANE MACHANGA SOFALA 386 MACUACUA MANDLAKAZE GAZA 171 Urbano Nº4 QUELIMANE ZAMBEZIA 243 NHAMAYABUE MUTARARA TETE 315 MARINGUE MARINGUE SOFALA 387 MAZUCANE MANDLAKAZE GAZA 172 ALTO MOLOCUE ALTO MOLOCUE ZAMBEZIA 244 CHARRE MUTARARA TETE 316 CANXIXE MARINGUE SOFALA 388 NGUZENE MANDLAKAZE GAZA 173 NAUELA ALTO MOLOCUE ZAMBEZIA 245 DOA MUTARARA TETE 317 SUBWE MARINGUE SOFALA 389 MASSANGENA MASSANGENA GAZA 174 CHINDE CHINDE ZAMBEZIA 246 INHANGOMA MUTARARA TETE 318 MARROMEU MARROMEU SOFALA 390 MAVUE MASSANGENA GAZA 175 LUABO CHINDE ZAMBEZIA 247 NTENGO-WA-MBALAME TSANGANO TETE 319 CHUPANGA MARROMEU SOFALA 391 MASSINGIR MASSINGIR GAZA 176 MICAUNE CHINDE ZAMBEZIA 248 TSANGANO TSANGANO TETE 320 MALINGAPASSE MARROMEU SOFALA 392 MAVODZE MASSINGIR GAZA 177 GILE GILE ZAMBEZIA 249 ZUMBU ZUMBU TETE 321 MUANZA MUANZA SOFALA 393 ZULO MASSINGIR GAZA 178 ALTO LIGONHA GILE ZAMBEZIA 250 MUZE ZUMBU TETE 322 GALINHA MUANZA SOFALA 394 CHICUMBANE XAI-XAI GAZA 179 CIDADE DE GURUE GURUE ZAMBEZIA 251 ZAMBUE ZUMBU TETE 323 NHAMATANDA NHAMATANDA SOFALA 395 CHONGOENE XAI-XAI GAZA 180 LIOMA GURUE ZAMBEZIA 252 CIDADE DE CHIMOIO CIDADE DE CHIMOIO MANICA 324 TICA NHAMATANDA SOFALA 396 ZONGOENE XAI-XAI GAZA 181 MEPUAGIUA GURUE ZAMBEZIA 253 CATANDICA BARUE MANICA 325 CIDADE DE INHAMBANE CIDADE DE INHAMBANE INHAMBANE 397 MATOLA CIDADE DA MATOLA MAPUTO 182 ILE ILE ZAMBEZIA 254 CHOA BARUE MANICA 326 FUNHALOURO FUNHALOURO INHAMBANE 398 MACHAVA CIDADE DA MATOLA MAPUTO 183 MULEVALA (NAMIGONHA) ILE ZAMBEZIA 255 NHAMPASSA BARUE MANICA 327 TOME FUNHALOURO INHAMBANE 399 INFULENE CIDADE DA MATOLA MAPUTO 184 SOCONE ILE ZAMBEZIA 256 GONDOLA GONDOLA MANICA 328 NOVA MAMBONE GOVURO INHAMBANE 400 BOANE BOANE MAPUTO 185 INHASSUNGE (MUCUPIA) INHASSUNGE ZAMBEZIA 257 AMATONGAS GONDOLA MANICA 329 SAVE GOVURO INHAMBANE 401 MATOLA RIO BOANE MAPUTO 186 GONHANE INHASSUNGE ZAMBEZIA 258 CAFUMPE GONDOLA MANICA 330 HOMOINE HOMOINE INHAMBANE 402 MAGUDE MAGUDE MAPUTO 187 LUGELA LUGELA ZAMBEZIA 259 INCHOPE GONDOLA MANICA 331 PEMBE HOMOINE INHAMBANE 403 MAPULANGUENE MAGUDE MAPUTO 188 TACUANE LUGELA ZAMBEZIA 260 MACATE GONDOLA MANICA 332 INHARRIME INHARRIME INHAMBANE 404 MOTAZE MAGUDE MAPUTO 189 MUNHAMADE LUGELA ZAMBEZIA 261 MATSINHO GONDOLA MANICA 333 MOCUMBI INHARRIME INHAMBANE 405 MAHELE MAGUDE MAPUTO 190 MUABANAMA LUGELA ZAMBEZIA 262 ZEMBE GONDOLA MANICA 334 INHASSORO INHASSORO INHAMBANE 406 PANJANE MAGUDE MAPUTO 191 MAGANJA DA COSTA MAGANJA DA COSTA ZAMBEZIA 263 GURO GURO MANICA 335 BAZARUTO INHASSORO INHAMBANE 407 MANHIÇA MANHIÇA MAPUTO 192 BAJONE MAGANJA DA COSTA ZAMBEZIA 264 MANDIE GURO MANICA 336 JANGAMO JANGAMO INHAMBANE 408 CALANGA MANHIÇA MAPUTO 193 MOCUBELA MAGANJA DA COSTA ZAMBEZIA 265 MUNGARI GURO MANICA 337 CUMBANA JANGAMO INHAMBANE 409 ILHA JOSINA MACHEL MANHIÇA MAPUTO 194 NANTE MAGANJA DA COSTA ZAMBEZIA 266 NHAMASSONGE GURO MANICA 338 MABOTE MABOTE INHAMBANE 410 MALUANA MANHIÇA MAPUTO 195 MILANGE MILANGE ZAMBEZIA 267 CHITOBE (MACHAZE) MACHAZE MANICA 339 ZIMANE MABOTE INHAMBANE 411 XINAVANE MANHIÇA MAPUTO 196 MAJAUA MILANGE ZAMBEZIA 268 SAVE MACHAZE MANICA 340 ZINAVE MABOTE INHAMBANE 412 PALMEIRA (3 DE FEVEREIRO) MANHIÇA MAPUTO 197 MOLUMBO MILANGE ZAMBEZIA 269 MACOSSA MACOSSA MANICA 341 MASSINGA MASSINGA INHAMBANE 413 MARRACUENE MARRACUENE MAPUTO 198 MONGUE MILANGE ZAMBEZIA 270 NGUAWALA MACOSSA MANICA 342 CHICOMO MASSINGA INHAMBANE 414 MACHUBO MARRACUENE MAPUTO 199 CIDADE DE MOCUBA MOCUBA ZAMBEZIA 271 NHAMAGUA MACOSSA MANICA 343 CIDADE DE MAXIXE CIDADE DE MAXIXE INHAMBANE 415 BELA VISTA MATUTUINE MAPUTO 200 MUGEBA MOCUBA ZAMBEZIA 272 CIDADE DE MANICA MANICA MANICA 344 MORRUMBENE MORRUMBENE INHAMBANE 416 MUGAZINE (CATEMBE) MATUTUINE MAPUTO 201 NAMANJAVIRA MOCUBA ZAMBEZIA 273 MACHIPANDA MANICA MANICA 345 MUCODUENE MORRUMBENE INHAMBANE 417 CATUANE MATUTUINE MAPUTO 202 MOPEIA MOPEIA ZAMBEZIA 274 MESSICA MANICA MANICA 346 PANDA PANDA INHAMBANE 418 NDELANE (MACHAMGULO) MATUTUINE MAPUTO 203 CAMPO MOPEIA ZAMBEZIA 275 MAVONDE MANICA MANICA 347 MAWAYELA PANDA INHAMBANE 419 ZITUNDO MATUTUINE MAPUTO 204 MORRUMBALA MORRUMBALA ZAMBEZIA 276 VANDUZI MANICA MANICA 348 URRENE PANDA INHAMBANE 420 MOAMBA MOAMBA MAPUTO 205 CHIRE MORRUMBALA ZAMBEZIA 277 ESPUNGABERA MOSSURIZE MANICA 349 VILANKULO VILANKULO INHAMBANE 421 PESSENE MOAMBA MAPUTO 206 DERRE MORRUMBALA ZAMBEZIA 278 CHIURAIRUE MOSSURIZE MANICA 350 MAPINHANE VILANKULO INHAMBANE 422 RESSANO GARCIA MOAMBA MAPUTO 207 MEGAZA MORRUMBALA ZAMBEZIA 279 DACATA MOSSURIZE MANICA 351 QUISSICO ZAVALA INHAMBANE 423 SABIE MOAMBA MAPUTO 208 NAMACURRA NAMACURRA ZAMBEZIA 280 SUSSUNDENGA SUSSUNDENGA MANICA 352 ZANDAMELA ZAVALA INHAMBANE 424 NAMAACHA NAMAACHA MAPUTO 209 MACUZE NAMACURRA ZAMBEZIA 281 DOMBE SUSSUNDENGA MANICA 353 CIDADE DE XAI-XAI CIDADE DE XAI-XAI GAZA 425 CHANGALANE NAMAACHA MAPUTO 210 NAMARROI NAMARROI ZAMBEZIA 282 MUOHA SUSSUNDENGA MANICA 354 MACIA BILENE GAZA 426 DISTRITO URBANO Nº1 DISTRITO URBANO Nº1 CIDADE DE MAPUTO 211 REGONE NAMARROI ZAMBEZIA 283 ROTANDA SUSSUNDENGA MANICA 355 CHISSANO BILENE GAZA 427 DISTRITO URBANO Nº2 DISTRITO URBANO Nº2 CIDADE DE MAPUTO 212 NICOADALA NICOADALA ZAMBEZIA 284 NHACOLO TAMBARA MANICA 356 MAZIVILA BILENE GAZA 428 DISTRITO URBANO Nº3 DISTRITO URBANO Nº3 CIDADE DE MAPUTO 213 MAQUIVAL NICOADALA ZAMBEZIA 285 BUZUA TAMBARA MANICA 357 MESSANO BILENE GAZA 429 DISTRITO URBANO Nº4 DISTRITO URBANO Nº4 CIDADE DE MAPUTO 214 PEBANE PEBANE ZAMBEZIA 286 NHACAFULA TAMBARA MANICA 358 PRAIA DE BILENE BILENE GAZA 430 DISTRITO URBANO Nº5 DISTRITO URBANO Nº5 CIDADE DE MAPUTO 215 MUALAMA (MULELA) PEBANE ZAMBEZIA 287 URBANO Nº1 CIDADE DA BEIRA SOFALA 359 MAKLUANE BILENE GAZA 431 DISTRITO URBANO Nº6 DISTRITO URBANO Nº6 CIDADE DE MAPUTO 216 NABURI PEBANE ZAMBEZIA 288 URBANO Nº2 CIDADE DA BEIRA SOFALA 360 CIDADE DE CHIBUTO CHIBUTO GAZA 432 DISTRITO URBANO Nº7 DISTRITO URBANO Nº7 CIDADE DE MAPUTO 67 definitions of indicators this needs to be updated Section 1 – Healthy Lives indicators as stunting and underweight are usually not recorded during a census and they were not part of the 2007 census. In order to estimate how many children are stunted or malnourished Infant Mortality Rate is defined as number of newborns dying under a year of age divided by the in each administrative post a method known as Small Area Estimates was used. This method number of live births during the year times 1000. combines the information available on malnutrition in the 2008/09 IAF survey with the household and child information available in both the AIF survey and the 2007 census to estimate Maternal Mortality Rate is defined as the death of a woman between 15 and 50 of age while how many children are malnourished in each Administrative post. A full description of the pregnant, while giving birth or within 60 days of termination of pregnancy, over 100.000 live method and results can be found in Sohnesen et all. births. Note that the international standard definition usually only include the first 42 days after birth and not the 2 months used in the Mozambique census. Underweight. A child is considered underweight if it has a Weight-for-Age z-score that is 2 standard deviations below the reference population. General fertility rate is defined as the annual number of live births per 1000 women of childbearing age (between 15 and 49 of age). Accessing water from rivers and lakes is the share of households that uses rivers or lakes as their source of drinking water. Stunting. A child is considered stunted if it has a Height-for-Age z-score that is 2 standard deviations below the reference population. Distance to health facilities is measured as the distance as the bird flies between the center of the village to the nearest health facility of any type (health post, health center, or hospital) Malnutrition. Child anthropometric measures are widely used to analyze the prevalence malnutrition among children under five. Anthropometric measures are measures of height for Lack of access to toilet facilities is the share of households that do not have access to a toilet. age (stunting), weight for height (wasting), and weight for age (underweight). These measures are compared to what you would expect to find in a healthy population, and a child is considered moderate malnourished if it has a value that is lower than two standard deviations from Section 2 – Access to Education the mean of the healthy reference population. The different measures can be said to capture different elements of malnutrition, with stunting reflecting sustained past episode or episodes Gross Primary Enrollment Rate is defined as number of students attending primary school of undernutrition, wasting reflecting weight loss associated with a recent period of starvation or (grade 1 to 7), including technical elementary (ETE), divided by number of children between 6 disease, and underweight ref lecting a current condition resulting from inadequate food intake, and 12 years of age. past episodes of undernutrition or poor health conditions. Gross Secondary Enrollment Rate is defined as number of students attending secondary school Small Area Estimates of Malnutrition: Most of the other social indicators shown in this Atlas (grade 8 to 12), including technical schools (ETB and ETM) and primary school teachers, divided were recorded in the 2007 census that went to all households in Mozambique. Malnutrition by number of children between 13 and 16 years of age. 68 Section 3 – Access to Services Section 5 – Land and Agriculture Estimated travel time to nearest city with over 50.000 people. This is an estimate of travel time The rainfall and temperature maps in section 5 were produced using daily NASA weather satellite based on location and available roads among other aspects, please see Dorosh and Schmidt for data, over the period from 1997 to 2007. full details. Share of households with cell phones. A household is defined as being a call phone if at least one Section 6 – The People of Mozambique person within the household has a cell phone. Population density is defined as number people living in an administrative post divided by the Share of household using electricity. A household is defined as using electricity if this is its main size measured km2 of the Administrative post. source of light. Share that speak Portuguese is defined for ages 10 and up. Share of households with running water. A household I defined to have access to running water if this its main source of water either within the house or outside the house. Section 7 – Wealth Section 4 – Labor Force and Employment Poverty headcount is defined as the number of peopled considered poor over total population. Share active in labor market is defined as any person over 15 that during the last week of Squared poverty gap measures the depth of poverty. A high value indicates that many people are June 2007 said that they 1) are working, 2) wasn’t working, but has a job, Worked with a family very poor. For more details see MEPD 2010. business, 3) worked at home, or was looking for a job. Following the practice of INE young people looking for their first job are not included as active in the labor market. Inequality is measured by the Gini coefficient. Share literate is any person that can read and write. Share of labor aged with primary or above is the share of adults between 15 and 65 that have primary education or above. Share of labor aged with secondary or above is the share of adults between 15 and 65 that have secondary education or above. 69 image and map index needs update when all changes are implemented Section 1 - Healthy Lives 1.31 - Share of households that do not have access to a toilet by administrative post - 2007 1.1 - IMR by administrative post per 1000 live births - 1997 1.32 - Change in share of households with no access to toilets 1997–2007 1.2 - IMR by administrative post per 1000 live births - 2007 1.3 - Distribution of IMR in administrative posts - 1997 and 2007 Section 2 - Access to Education 1.4 - Box plot of IMR in administrative posts - 1997 and 2007 2.1 - Male gross primary enrollment by administrative post - 1997 1.5 - IMR in 1997 and change between 1997 and 2007, by administrative post 2.2 - Male gross primary enrollment by administrative post - 2007 1.6 - IMR per 1000 live births across Africa- 2007 2.3 - Female gross primary enrollment by administrative post - 1997 1.7 - Malnutrition by region and urban/rural - 2008/09 2.4 - Female gross primary enrollment by administrative post - 2007 1.8 - Malnutrition by province - 2008/09 2.5 - Gross primary enrollment across Africa - 2007 1.9 - Trend in malnutrition - 1997–2008/09 2.6 - Male gross secondary enrollment by administrative post - 1997 1.10 - Share of underweight children across Africa - 2007 2.7 - Male gross secondary enrollment by administrative post - 2007 Share of stunted children by administrative post, Share of stunted children per km2 by administrative 1.11 -  2.8 - Female gross secondary enrollment by administrative post - 1997 post - 2007 2.9 - Female gross secondary enrollment by administrative post - 2007 Share of underweight children by administrative posts, Share of underweight children per km2 by 1.12 -  2.10 - Gross secondary enrollment across Africa - 2007 administrative post - 2007 2.11 - Absolute change of male gross primary enrollment by administrative post - 1997–2007 1.13 - MMR across Africa - 2007 2.12 - Absolute change of female gross primary enrollment by administrative post - 1997–2007 1.14 - MMR in urban and rural areas - 2007 2.13 - Absolute change of male gross secondary enrollment by administrative post - 1997–2007 1.15 - MMR by province - 2007 2.14 - Absolute change of female gross secondary enrollment by administrative post - 1997–2007 1.16 - MMR by district - 2007 2.15 - Primary gross enrollment rate in urban and rural areas, 1997 and 2007 General fertility rate [defined as the annual number of live births per 1000 women of childbearing age 1.17 -  2.16 - Secondary gross enrollment rate in urban and rural areas, 1997 and 2007 (15 to 49)] - 1997 2.17 - Increase in total number of students in primary and secondary from 1997 to 2007, by gender General fertility rate [defined as the annual number of live births per 1000 women of childbearing age 1.18 -  2.18 - Primary gross enrollment rate for boys and girls, 1997 and 2007 (15 to 49)] - 2007 2.19 - Secondary gross enrollment rate for boys and girls, 1997 and 2007 1.19 - General fertility rates in 1997 and change between 1997 and 2007, by administrative post 2.20 - Increase in total number of students in primary and secondary from 1997 to 2007, by urban/rural 1.20 - General fertility rate by province - 1997 and 2007 2.21 - Gross enrollment rates by administrative post - 1997 and 2007 1.21 - Distribution of general fertility rates in administrative post - 1997 and 2007 2.22 - Primary gross enrollment rates in 1997 and change between 1997 and 2007, by administrative post 1.22 - Absolute change of fertility rate by administrative post - 1997–2007 2.23 - Secondary gross enrollment rates in 1997 and change between 1997 and 2007, by administrative post 1.23 - Rivers, lakes and water wells across Mozambique - 2007 1.24 - Share of households using water from rivers or lakes by administrative post - 2007 Section 3 - Access to Services 1.25 - Absolute change of river and lake usage by administrative post - 1997–2007 3.1 - Share of households using electricity as main source of light by administrative post - 2007 1.26 -  Share of households using rivers and lakes as water source in 1997 and change between 1997 and 2007, by administrative post 3.2 - Share of households having running water by administrative post - 2007 1.27 - Location of health facilities across Mozambique - 2007 3.3 - Share of households with at least one cell phone by administrative post - 2007 1.28 - Distance to nearest health facility, as birds fly - 2007 3.4 - Share of households that own a fixed phoneline by administrative post - 2007 1.29 - Absolute change of households without access to a toilet by administrative post between 1997–2007 3.5 - Share of households that have used internet in the last year at least once by administrative post - 2007 1.30 - Share of households that do not have access to a toilet by administrative post - 1997 3.6 - Share of household using service in urban and rural areas - 2007 3.7 - Travel distance to urban centers by administrative post – 2007 70 Section 4 - Labor Force and Employment Section 6 - The People of Mozambique 4.1 - Adult female literacy rate by administrative post - 2007 6.1 - Change in population (percentage) - 1997–2007 4.2 - Adult male literacy rate by administrative post - 2007 6.2 - Change in population by administrative post - 1997–2007 4.3 - Share of labor force with primary education or above by administrative post - 2007 6.3 - Ratio of men to women by age and province - 2007 4.4 - Share of labor force with secondary education or above by administrative post - 2007 6.4 - Population by province - 1997 and 2007 4.5 - S  hare of labor (aged 15 to 65) with complete primary or above, and secondary and above, by gender and 6.5 - Population by gender - 1997 and 2007 urban/rural - 2007 6.6 - Share of population aged 15 or below by administrative post - 2007  hare of adults active in labor market in 1997 and change between 1997 and 2007, by administrative post 4.6 - S 6.7 - Share of population aged 50 or above by administrative post - 2007 4.7 - Box plot share of adults active in labor market by administrative post - 1997 and 2007 6.8 - Total population by administrative post - 2007 4.8 - Labor force participation by gender and urban/rural - 2007 6.9 - Rural population per km2 by administrative post - 2007 4.9 - Labor force participation by gender and education level - 2007 6.10 - Number of men per 100 women by administrative post - 1997 4.10 - Female labor force participation rate by administrative post - 2007 6.11 - Number of men per 100 women by administrative post - 2007 4.11 - Male labor force participation rate by administrative post - 2007 6.12 - Main languages spoken in northern provinces - 2007 4.12 - Labor force participation rate by administrative post - 1997 6.13 - Main languages spoken in central provinces - 2007 4.13 - Labor force participation rate by administrative post - 2007 6.14 - Main languages spoken in southern provinces - 2007 4.14 - Change in labor force participation rate by administrative post - 1997–2007 6.15 - Share of population that know Portuguese by age group and gender - 2007 4.15 - Type of employer by urban/rural - 2007 6.16 - Share of population (aged 10 and above) that speak Portuguese by administrative post - 2007 4.16 - Type of employer by gender - 2007 6.17 - Share of population belonging to denomination - 2007 4.17 - Type of employer by province - 2007 6.18 - Religion by province - 2007 Section 5 - Land and Agriculture Section 7 - Wealth 5.1 - Share of households owning cows - 1997 and 2007 7.1 - Share of households that own a car by administrative post - 2007 5.2 - Share of households owning sheep - 1997 and 2007 7.2 - Share of households that own a TV by administrative post - 2007 5.3 - Share of households owning swine - 1997 and 2007 7.3 - Share of households that own a computer by administrative post - 2007 5.4 - Absolute change of households owning swine by administrative post - 1997–2007 7.4 - Asset ownership by urban/rural 5.5 - Absolute change of households owning sheep by administrative post - 1997–2007 7.5 - Share of households that own a radio by administrative post - 2007 5.6 - Absolute change of households owning cows by administrative post - 1997–2007 7.6 - Share of households that own a bicycle by administrative post - 2007 5.7 - Share of households that own sheep by administrative post - 2007 7.7 - Share of households that own a motorcycle by administrative post - 2007 5.8 - Share of households that own cattle by administrative post - 2007 7.8 - Poverty headcount by province - 2008/09 5.9 - Share of households that own swine by administrative post - 2007 7.9 - Poverty headcount by urban/rural - 2008/09 5.10 - Topography of Mozambique 7.10 - Squared poverty gab by province - 2008/09 5.11 - Temperature per month in Mozambique - 2007 7.11 - Inequality by province - 2008/09 5.12 - Rainfall per year in Mozambique - 1997–2008 5.13 - Share of cashew farmers by administrative post - 2007 5.14 - Share of fish farmers by administrative post - 2007 71 References Third National Poverty Assessment, Ministry of Planning and Development (GOM, 2010) Report on the Millennium Development Goals, Ministry of Planning and Develop- ment (GOM, 2010) Sohnesen, Thomas Pave (2011), Children’s Health on a Map: Infant Mortality and Malnutrition at Local Levels in Mozambique. Unpublished manuscript. The World Bank, Washington DC Dorosh, Paul, Schmidt, Emily. 2008. Mozambique Corridors: Implications of Invest- ments in Feeder Roads. Unpublished manuscript. The World Bank, Washington DC 72 THE WORLD BANK 1818 H Street, N.W. Washington, DC 20433 76