95276 Mapping Subnational Poverty in Zambia March 2015 Republic of Zambia CENTRAL STATISTICAL OFFICE Mapping Subnational Poverty in Zambia Alejandro de la Fuente The World Bank Andreas Murr University of Oxford Ericka Rascón Middlesex University In collaboration with the Central Statistical Office of Zambia March 2015 Republic of Zambia CENTRAL STATISTICAL OFFICE Contents Acknowledgments.................................................................................................................................... vii 1. Introduction........................................................................................................................................1 2. Background..........................................................................................................................................5 Poverty Methodology..................................................................................................................................... 5 Consumption Aggregate......................................................................................................................... 5 Poverty Line Determination................................................................................................................... 6 Poverty Results................................................................................................................................................ 6 3. Small Area Estimation for Mapping Poverty.................................................................................9 Stage Zero. Comparability of Data................................................................................................................ 9 Stage One. Expenditure Modeling.............................................................................................................. 10 Stage Two. Computation of the Welfare Measure......................................................................................11 4. Data and Comparability...................................................................................................................13 Data.................................................................................................................................................................. 13 2010 Census of Population and Housing............................................................................................. 13 Living Conditions Monitoring Survey (LCMS) 2010......................................................................... 13 Auxiliary Data...................................................................................................................................... 14 Comparability of Census and Survey......................................................................................................... 14 5. Results.................................................................................................................................................23 Poverty Estimates at the Province, District and Constituency Levels................................................... 23 Poverty Estimates at the Ward Level.......................................................................................................... 33 Poverty Mapping Considering Spatial Differences.................................................................................. 38 Applications of the Poverty Map................................................................................................................ 41 Poverty Mapping and the Social Cash Transfer Scheme in Zambia...................................................... 41 Poverty Mapping and Socioeconomic Correlates..................................................................................... 44 6. Conclusions and Recommendations...............................................................................................47 References...................................................................................................................................................49 Annex...........................................................................................................................................................51 iii Mapping Subnational Poverty in Zambia TABLES Table 1: Descriptive Statistics: Dwelling Characteristics (Housing, roofs, walls, and lighting)................. 15 Table 2: Descriptive Statistics: Dwelling Characteristics (Cooking, garbage disposal, toilet, and house ownership)................................................................................................................................................ 16 Table 3: Descriptive Statistics: Assets.................................................................................................................. 17 Table 4: Descriptive Statistics: Sociodemographic Characteristics................................................................. 18 Table 5: Descriptive Statistics: Employment − Occupation............................................................................. 19 Table 6: Descriptive Statistics: Employment − Sector....................................................................................... 20 Table 7: Descriptive Statistics: Compounded Sociodemographic Characteristics....................................... 21 Table 8: Descriptive Statistics: Household Size, Maximum Age, and Maximum Education..................... 22 Table 9: Beta Model of Adult Equivalent Expenditure (logarithms).............................................................. 24 Table 10: Alpha Model of Residual (logarithms)................................................................................................ 25 Table 11: Poverty Estimates: Survey and Poverty Map Results at the Province Level.................................. 26 Table 12: Poverty Map Estimates and Rankings at the District Level (1/3).................................................... 27 Poverty Map Estimates and Rankings at the District Level (2/3).................................................... 28 Poverty Map Estimates and Rankings at the District Level (3/3).................................................... 29 Table 13: Poverty Map Estimates and Rankings at the Constituency Level: The Fifteen Constituencies with the Highest and Lowest Poverty Incidence................................ 30 Table 14: Poverty Map Estimates and Rankings at the Constituency Level: The Fifteen Constituencies with the Highest and Lowest Concentration of Poor Population.... 31 Table 15: The World Bank Poverty Measurement: Laspeyres Price Index by Province................................ 40 Table 16: CSO and World Bank National Poverty using the LCMS 2010........................................................ 40 FIGURES Figure 1: Significantly Different Pairwise Comparisons of Constituencies in the Country by Confidence Level............................................................................................................................ 33 Figure 2: Significantly Different Pairwise Comparisons of Constituencies in the Country by Confidence Level (Bonferroni Correction)................................................................................. 33 Figure 3: Significantly Different Pairwise Comparisons of Constituencies within District...................... 34 Figure 4: Significantly Different Comparisons of Constituency and National Poverty by Confidence Level............................................................................................................................ 35 Figure 5: Significantly Different Pairwise Comparisons of Wards in the Country by Confidence Level................................................................................................................................. 35 Figure 6: Significantly Different Pairwise Comparisons of Wards in the Country by Confidence Level (Bonferroni Correction)............................................................................................................ 35 Figure 7: Significantly Different Pairwise Comparisons of Wards within Constituency.......................... 36 Figure 8: Significantly Different Comparisons of Ward and Constituency Poverty.................................. 37 Figure 9: Significantly Different Comparisons of Ward and District Poverty............................................. 38 Figure 10: Significantly Different Pairwise Comparisons of Wards within District..................................... 39 Figure 11: Significantly Different Comparisons of Ward and National Poverty by Confidence Level..... 40 Figure 12: Confidence Intervals and Poverty Estimates at the Constituency Level using the CSO Poverty Measurement......................................................................................................... 43 Figure 13: Confidence Intervals and Poverty Estimates at the Constituency Level using the World Bank Poverty Measurement............................................................................................ 43 iv Contents Figure 14: Confidence Intervals and Poverty Estimates at the Ward Level using the CSO Poverty Measurement........................................................................................................................................ 44 Figure 15: Confidence Intervals and Poverty Estimates at the Ward Level using the World Bank Poverty Measurement......................................................................................................................... 44 MAPS Map 1: Poverty Map: Poverty Incidence at the Constituency Level using the CSO Poverty Measurement Method............................................................................................................................. 32 Map 2: Poverty Map: Concentration of Poor Population at the Constituency Level using the CSO Poverty Measurement Method.............................................................................................................. 32 Map 3: Poverty Map: Poverty Incidence at Ward Level using the CSO Poverty Measurement Method............................................................................................................................. 35 Map 4: Poverty Map: Poverty Incidence at the Constituency Level using the CSO Poverty Measurement Method and the World Bank Poverty Measurement Method................... 42 Map 5: Poverty Map: Concentration of Poor Population at the Constituency Level using the CSO Poverty Measurement Method and the World Bank Poverty Measurement Method ................. 42 Map 6: Poverty Map: Poverty Incidence at the Ward Level Using the CSO Poverty Measurement Method and the World Bank Poverty Measurement Method........................................................... 43 Map 7: Eligibility Criteria for the Social Cash Transfer Scheme at the Constituency Level....................... 44 Map 8: Eligibility Criteria for the Social Cash Transfer Scheme at the Ward Level.................................... 45 Map 9: Average of Maximum Education at the Ward Level........................................................................... 45 Map 10: Percentage of Individuals Working as Employees in the Agriculture Sector................................. 45 Map 11: Percentage of Individuals Working as Employees in the Sales Sector............................................. 45 Map 12: Average Distance to Primary Roads...................................................................................................... 46 Map 13: Average Distance to Tertiary Roads...................................................................................................... 46 Map 14: Precipitation of the Warmest Quarter of the Year............................................................................... 46 Map 15: Annual Precipitation................................................................................................................................ 46 v Acknowledgments This report is the result of a team effort between the Pov- sultant, GSPDR). We particularly thank Cornelia Tesliuc erty and the Social Protection Global Practices within the (Task Team Leader and Senior Social Protection Specialist, World Bank with the Central Statistical Office of Zambia GSPDR), who provided financial assistance and guidance (CSO). We thank the Director of CSO, John Kalumbi, for on the social protection inputs of the project to the report his enthusiasm and support for this project. The authors team. Brian Blankespoor (Environmental Specialist-GISP, also are most grateful to the CSO team for sharing data DECRG) gave useful suggestions to improve the appear- and useful inputs, and for their thoughtful comments and ance of maps and provided mapping inputs. We would advice during the course of the study. The CSO team was like especially to thank our peer reviewers, Peter Lanjouw led by Goodson Sinyenga (Deputy Director of Economic (Research Manager, DECPI) and Kenneth Simler (Senior and Financial Statistics), and comprised Frank Kakungu Economist, GPVDR), for their interest in this project and (IT Manager/Statistician), Owen Siyoto (Statistician), and sound recommendations to improve this report. Aaron Phiri (IT Analyst). Roy Katayama (Economist, GPVDR), Julio Revilla The Poverty Mapping methodology and its po- (Program Leader, AFCS2), and Isis Gaddis (Extended- tential applications for Zambia were discussed with Term Consultant, GPVDR) provided valuable comments representatives from multiple partners during a series to increase the team’s understanding of the Bank’s assess- of consultations held in Lusaka in September 2013 ment of poverty in Zambia based on the Living Conditions and May 2014. These partners included Government Monitoring Survey 2010 data. The report team also benefit- (Ministries of Finance and National Planning, Com- ted from the guidance of the management team, Kundhavi munity Development, Mother and Child Health, Labor Kadiresan (Country Director, AFCS3), Pablo Fajnzylber and Information, and the Office of the Vice President); (Practice Manager, GPVDR), John Panzer (Director, cooperating partners (Department for International GMFDR), and Praveen Kumar (Program Leader, AFCS3). Development-DFID, German Agency for International We thank Madeleine Chungkong (Senior Program Cooperation-GIZ, International Labour Organization- Assistant, GGODR) and Almaz Teklesenbet (Program ILO, The United Nations Children’s Fund-UNICEF, Assistant, AFTP1) for their assistance in organizing the and the United States Agency for International De- kick-off training workshop on Poverty Mapping held in velopment-USAID); and the research and academic Washington, D.C. in March 2014. We also are grateful to community (Indaba Agricultural Policy Research Peter Lanjouw (Research Manager, DECPI) and Qinghua Institute-IAPRI, Evidence-Based Development Institute Zhao (Senior Information Officer, DECCT) for their partici- of Southern Africa-EDISA, and the Zambia Institute of pation during the same kick-off training. Asumani Guloba Policy Analysis and Research-ZIPAR). The World Bank (Economist, GMFDR) and Helen Mbao (Senior Operations team extends its sincerest thanks to the teams from these Officer, AFCS3) provided excellent support throughout ministries, agencies, and institutes for their comments the project in coordinating the face-to-face dialogues with and suggestions. key individual stakeholders. Hellen Mungaila (Program The World Bank report team was led by Alejandro de Assistant, AFCS3) and Wisdom Mulenga (Office Assistant, la Fuente (Senior Economist and Co-Task Team Leader, AFCS3) also provided logistical support in the country GPVDR), and comprised Ericka Rascón (Main Research office. Finally, we thank Alicia Hetzner, who provided Consultant, GSPDR) and Andreas Murr (Research Con- valuable editing support. vii Introduction 1 The Republic of Zambia is a resource-rich country with massive mineral endowments (especially copper) and agricultural potential. It is geographically large but relatively sparsely populated with 13 million people. Zambia has capitalized on these factors. It is now a lower middle-income country that experienced robust growth in the past decade and was among the 10 fastest growing economies of Sub-Saharan Africa in 2012. Much of this economic growth was observed during the imple- mentation of the Fifth National Development Plan (FNDP) from 2006 to 2010. Yet, during that period, growth did not translate into commensurate improvement in living standards, especially in rural areas. The 2010 national poverty incidence of 60 percent was not much different from the 62.8 percent of 2006. With the vast majority of the population dependent on subsistence agriculture, Zambia’s rural poverty was as high as 78 percent in 2010, not much different from 80 percent in 2006. To address the challenges identified during the FNDP and realize pro-poor growth, the Sixth National Development Plan (SNDP) was set in motion. The SNDP, which covers 2011 to 2015, was designed to accomplish 3 goals:1 1. To accelerate infrastructure development (roads, bridges, air, water, rail and border infrastructure, feeder roads, water canals, tourist access roads). 2. To improve the provision of basic services including water and sanitation, electricity access, health, education and skills deve­ lopment to promote rural investment; and to accelerate poverty reduction, mainly through the continued implementation of the Rural Finance Program. 3. To enhance human development. 1 As a result of the change of Government administration in September 2011 from the Movement for Multiparty Democracy to the Patriotic Front Govern- ment, the SNDP was revised to become The Revised Sixth National Develop- ment Plan 2013-2016 (R-SNDP). The R-SDNP has similar objectives to the SNDP, but adds emphasis on promoting employment and job creation through targeted and strategic investments in selected sectors. 1 Mapping Subnational Poverty in Zambia In addition to continued growth, the govern- levels. Poverty can vary greatly among constituen- ment is committed to reduce poverty through larger cies within districts and among wards within the and well-targeted redistributive transfers via safety same constituency. Therefore, it is desirable to have net programs. Indeed, the government recently an appropriate tool to guide the allocation of the moved in the direction of withdrawing fuel and SNDP programs to maximize both the well-being maize subsidies and increasing resources for social of the Zambian population and the redistribution safety net programs. The government’s proposed of resources to the poor. funding for social cash transfers (the main uncon- To address the heterogeneity of poverty at ditional cash transfer scheme in the country) has subnational administrative levels, since the late nearly tripled from K72.1 billion in FY13 to K199.2 1990s, the World Bank Group (DECRG) and affili- billion in FY14, see The World Bank (2013). The Food ated researchers have been developing methods to Security Pack program (FSP) also is being scaled up.2 combine the detailed information of household However, this scaling up requires a roll-out plan to surveys with census data. One result is the “Poverty prioritize the selection of households. Map” method or Computed Small Area Estimation For this reason, the identification of poor ar- (CSAE) techniques, which produces estimators of eas and households has become one of the main welfare at a highly detailed level of spatial disag- concerns of the Government of the Republic of gregation. Zambia for targeting social safety net programs and Zambia’s Living Conditions Monitoring Survey transfers and, more generally, for allocating public (LCMS) is the primary source for estimations of pov- resources to finance projects. erty within the country. With the availability of the To reach the poorest areas and households, LCMS 2010 and the 2010 Census of Population and several existing public funds (such as the Constitu- Housing, poverty estimates can be disaggregated ency Development Fund, or CDF) and programs based on geographic and socioeconomic criteria rely, first, on the selection of the most deprived using CSAE techniques. geographic areas. For instance, the Social Cash The World Bank and the Central Statistical Transfer Scheme (SCT) is the flagship national Office teams recognized the overlap between social transfer program to address poverty. This census (collected every 10 years) and the LCMS program is scaling up for a national roll-out that 2010 data. They also recognized the critical role will reach approximately 500,000 households by that a Poverty Mapping exercise could play in 2016. The implementing agency is the Department prioritizing the poorest areas of Zambia as part of Social Welfare at the Ministry of Community De- of the national rollout of the Social Cash Transfer velopment, Mother and Child Health. Over the past Scheme. A Poverty Mapping also would help other decade, the ministry has piloted a combination of government programs better allocate resources different targeting methods to implement the SCT, across constituencies. As a result, the teams col- including geographic targeting based on poverty laborated to produce poverty indicators at the rates at the district level.3 Similarly, the CDF bases subnational level. Such an undertaking was long its allocations to finance projects in the financial overdue. Many reports have pointed out the need year on a formula applied at the constituency level. for this type of information.4 However, district-wide poverty rates often The remainder of this report is organized in mask extreme heterogeneity at lower geographic five sections. Section 2 briefly describes the official 2 The FSP was launched in 2000 as the flagship national social transfer program to address poverty and food insecurity. It provides small packages of seed and fertilizer to agricultural households that face food insecurity. 3 Zambia is divided into 10 provinces, which are subdivided into 74 districts. Administratively, the districts are further sub- divided into 150 constituencies, which are divided into 1,421 wards. Each ward consists of Census Supervisory Areas (CSA), which are further subdivided into Standard Enumeration Areas (SEAs). 4 The first Poverty Map using Zambian data was constructed in 2007 using the 2002–03 LCMS and the 2000 Census. For details, see Simler (2007). 2 Introduction methodology to estimate poverty in the country estimates of poverty incidence at three subnational and profiles Zambia’s poverty status based on the levels: district, constituency, and ward. This section LCMS 2010. Section 3 explains the methodology also discusses Poverty Map estimates that consider used to produce the Poverty Map. Section 4 de- spatial adjustments. Section 6 provides conclusions scribes the census and survey data and evaluates the and recommendations. comparability of their variables. Section 5 presents 3 Background 1 2 Zambia’s Central Statistical Office (CSO) has been carrying out com- prehensive poverty assessments since 1991. Many of the poverty as- sessments in the country have been based on the data from the Living Conditions Monitoring Survey (LCMS) rounds. The latest poverty assessment is based on the revised poverty estimation methodology, which has been applied to the LCMS 2010 survey data and comprises the following features. POVERTY METHODOLOGY Consumption Aggregate Since 1991, the Central Statistical Office (CSO) consistently has used household consumption expenditure as a measure of welfare. House- hold consumption expenditure comprises cash purchases (food and non-food), value of own-produce consumption (food and non-food), and value of consumable gifts. The consumption aggregate comprises food and non-food com- ponents. For the food component, CSO included all available items in the LCMS 2010 (128 items, including alcohol and tobacco) and all sources (purchases, consumption from own production, and transfers). For the non-food component, CSO also included all items, including hospital stays, payments to hospital/heath center/surgery, other health expenses, loan repayments, and all remittances.5 In addition, the non-food aggregates include imputed values of rent for all the households that had reported zero rent expenditure, mainly compris- ing owner-occupiers.6 Imputed use-values for household durable goods were not included in the consumption expenditure aggregates. The CSO poverty measure takes into account differences in household size and the age composition of the household members. All household expenditures were converted into monthly terms and then normalized using the CSO’s adult equivalent scale. 5 Item numbers 195, 198, 200, 210, 212, and 215–17 in the survey questionnaire. 6 The housing rent imputations were done through a weighted hedonic housing regression model that essentially relates rental values of households with non-zero expenditure on rent to key housing and location variables. 5 Mapping Subnational Poverty in Zambia Poverty Line Determination rate was 60 percent. Moreover, 42 percent of the population was living in extreme poverty with in- The Central Statistical Office (CSO) uses the Cost of sufficient consumption to meet their daily minimum Basic Needs (CBN) approach when measuring wel- food requirements. Although the poverty rate has fare outcomes of various households. This method declined marginally over time, due to population first determines the cost of a simple food basket growth, the absolute number of poor has increased that meets minimal nutritional requirements for a from approximately 6.0 million in 1991 to 7.9 million family of six. The cost of the food basket for 2010 in 2010. In 2010 the population of Zambia stood at was obtained by updating the prices for a 1991 food 13.3 million. Based on the annual growth rate of basket, which was constructed by the National Food 2.4 percent, by 2015, the population is projected to and Nutrition and Price and income Commissions increase to 15.5 million. At this rate, the population (NFNC/PIC) using the December item-specific is expected to double by 2030. average prices of a respective year. The 2010 food Poverty in Zambia is located overwhelmingly in basket was valued at K96,366. Therefore, the 2010 rural areas, in which the poverty rate is almost three absolute poverty line corresponds to the cost of times the level observed in urban areas. In 2010 rural the food basket. For purposes of its analysis, CSO poverty was estimated at 77.9 percent compared to designated this line the extreme poverty line. urban poverty levels of 27.5 percent. Similarly, more To account for non-food needs such as shelter, than half of the rural population (approximately 58 clothing, good health, and education, the food pov- percent) was afflicted by extreme levels of poverty erty line derived from the cost of the food basket whereas, in urban areas, the extreme poor remained was further adjusted using the Engels ratio.6 The at approximately 13 percent. method first identifies households whose per-adult Large and increasing discrepancies also exist equivalent food expenditure is very close to the across geographic areas. More than 50 percent of food poverty line. Households whose expenditure the population is not able to satisfy their basic food equivalents were within 30 percent of the poverty requirements in Luapula and the Western, Eastern line were chosen for this purpose. Next, using these and Northern provinces, which are predominantly chosen households, the average share of consump- rural. By contrast, in Lusaka and Copperbelt, less tion expenditure on food was estimated. This than 20 percent of the population is in this situation. method reveals typical non-food requirements for Apart from Lusaka and the Copperbelt provinces, households whose food expenditure corresponds to the rest of the regions are fairly remote. The degree the food poverty line (extreme poverty line). of remoteness increases the farther a province is The 2010 poverty estimates are based on a year- from the old railway line. The remote provinces are specific Engels ratio of 66 percent. The moderate characterized by mono economies with, among other poverty line was obtained by dividing the food hardships, poor infrastructure, poor access to social poverty line by its corresponding food budget and economic amenities, poor water and sanitation share (ratio). This division yielded a moderate conditions, and low levels of economic activities. poverty line of K146,009. Zambia’s poverty status Zambia also has one of the highest concentra- was evaluated based on these extreme and moder- tions of inequality in Sub-Saharan Africa. This ate poverty lines. high inequality is due partly to the huge gap that exists between the rural and urban areas of the country. Many of the gainful economic activities in POVERTY RESULTS the country are concentrated primarily along the rail line in the highly urbanized Copperbelt and Poverty and inequality in Zambia remain stub- Lusaka regions. The rest of the country is fairly bornly high. As of 2010, the poverty headcount underdeveloped, and its labor depends primarily 7 Engel’s Law is a law of economics stating that, with given tastes or preferences, the proportion of income spent on food diminishes as income increase. 6 Background on subsistence agriculture. Therefore, Zambia’s progressive redistribution of income that favors high inequality index of over 50 percent, as mea- the poor. sured by the Gini coefficient, does not come as a Given Zambia’s high levels of poverty and surprise because the gap between the rich and inequality and the wide disparities across regions poor remains quite wide. High income inequal- and urban/rural areas, it is clear that highly disag- ity erodes the gains associated with income or gregated measures of welfare would be extremely economic growth. Therefore, for economic growth useful to improve the allocation of resources in the to reach the poor, it should be accompanied by SNDP programs to the poor. 7 Small Area Estimation 3 1 for Mapping Poverty Since the late 1990s, the World Bank (DECRG) has engaged in an extensive program of research to produce estimators of welfare at geographic levels not represented in household surveys. At present, approximately 50 countries have completed “Poverty Maps.” More- over, in a growing number of countries, multiple-round Poverty Maps have been conducted or initiated to monitor the movement of poverty and inequality over time. The production of these maps increasingly is being implemented by other international organizations, partner- country statistical organizations, and academic researchers. The extensive use and production of Poverty Maps also has built a body of experience that has enabled Poverty Mapping, and extensions of the method, to receive a measure of validation.8 This report is based on the standard methodology of Small Area Estimation (CSAE) developed by Elbers and others (2000 and 2003) (henceforth ELL). The basic idea is to use detailed survey data to project welfare indicators into census records. The motivation for achieving this goal is to generate estimations in geographic partitions not allowed by household surveys and not available in census data. The stages suggested by ELL are (1) comparability between census and survey variables, (2) modeling the welfare indicator of interest using the survey, and (3) computing welfare indicators on census records (such as head count ratio, inequality) based on parameters derived from the survey. STAGE ZERO. COMPARABILITY OF DATA Prior to constructing the econometric model of expenditure, which projects the welfare indicator from survey to census, comparable covariates between the two data sources must be selected. To do this, one identifies the geographic partition for which this comparison must be made. This partitioning is country specific and is determined by the representativeness of the household survey. For instance, if the survey is representative at the region, provincial, or district level, the 8 A free software has been produced by the World Bank in parallel with the methodology to ease the computational burden of producing Poverty Maps. 9 Mapping Subnational Poverty in Zambia common practice is to model the welfare indicator ln Ych c the cluster =, X chβ + U ch represents household and dwell- at that geographic partition. However, the lower the ln Ych = X ing characteristics; 9 and ch β + U ch corresponds to the level of disaggregation, the smaller the number of error component. The latter may be decomposed observations, which, in turn, reduces the predictive into cluster and household effect: power of the model. In several cases, aggregating districts to define U ch = ηc + ∈ch (2) geographic regions has been convenient for mod- eling in Poverty Mapping. Aggregation enables where there is neither correlation between and U ch = ηc + ∈ch gathering administrative divisions with similar U ch = ηc + ∈ch , nor between household error components. socioeconomic features in advance. Aggregating The household effect refers to unobservable char- is useful when there are a reduced number of acteristics intrinsic to the family, such as ability observations at the level for which the survey is and motivation of household members, as well as representative (for instance, district or subdistrict socioeconomic variables not collected by the survey level), or when the survey is representative only at that may affect the level of expenditure. In addition, the regional level. cluster effect captures unobservable features at geo- After defining the geographic partition for graphic partitions above household level, such as modeling, comparable variables between census local prices, heterogeneity of returns to schooling, and survey are selected. To define a set of strictly and infrastructure. comparable variables, definitions of each vari- The identification of a geographic level to able should be compared between census and represent the cluster effect is a crucial decision survey, based on their respective questionnaires. that may affect the standard errors of the welfare Subsequently, the comparison of distributions and indicator projected in census data. Cluster effect statistics determines the final set of variables to be defines the partition in which the main geographi- used for the modeling stage. Statistical comparison cal variability exists. The empirical distribution of between census and survey is carried out controlling this partition is used for obtaining a similar vector for the survey sample design. For instance, standard in census records. Hence, the lower the level of errors must consider the clustering of primary disaggregation, the better it captures the hetero- sample units, or other stratification used as part of geneity at the target geographic level. However, the sample design. the definition of the cluster effect at lower levels of disaggregation comes at the cost of having a less reliable distribution derived from survey STAGE ONE. EXPENDITURE data. Elbers et al. (2003) suggest the definition of MODELING cluster effect at the primary sample unit (PSU) or enumeration area level. However, recent empirical Once comparable variables between census and sur- literature has shown that defining the cluster effect vey are identified, the welfare indicator is modeled at such level may produce “naive” standard errors using the survey. The basic idea is, first, to estimate of poverty estimates. This recent evidence suggests an Ordinary Least Squares (OLS) model with the that the cluster effect should be defined at the level set of comparable variables. The empirical model of the target indicator or at one additional level of follows this structure: disaggregation. After defining the cluster effect, assuming a ln Ych = X chβ + U ch (1) heteroskedastic structure, the ELL method sug- gests a model of the household error component. where ln Ych = denotes X chβ + the U chlogarithm of the house- Subsequently, household residuals, netted out from hold adult-equivalent expenditure h belonging to the cluster effect, are used to correct the standard 9 For the current report, the authors also consider constituency variables constructed from the census records. 10 Small Area Estimation for Mapping Poverty errors through a Generalized Least Squares (GLS) data, 100 random draws are obtained to generate estimation. final parameters based on the mean of these rep- lications. The selection of the error distribution will be made based on several tests using the best econometric models. Finally, using the betas of STAGE TWO. COMPUTATION OF the econometric model, we draw r-replications THE WELFARE MEASURE of betas, using their distributions, to estimate r-expenditures for each household record in the Once the selection of the best model is achieved, β, census. The cluster and household component are U ch U= ηc+ ch = and η∈ + ∈ch are simulated to obtain a final param- c ch treated as random effects for obtaining the projec- eter for the imputation in census records. Based on tion of household adult equivalent expenditure their empirical distributions obtained from survey into each census record. 11 Data and Comparability 1 4 DATA The Poverty Map exercise uses 3 types of information: (1) census data from the 2010 Census of Population and Housing, (2) household survey data from the Living Conditions Monitoring Survey (LCMS) 2010, and (3) auxiliary data that can be linked to survey and census mainly from administrative records or from census records at several geographic partitions (such as constituency and ward levels). 2010 Census of Population and Housing The Central Statistics Office of Zambia (CSO) is responsible for pro- ducing the Census of Population and Housing every 10 years. The 2010 Census marks the fifth National Census of Population and Hous- ing conducted in Zambia since independence in 1964. The country conducted censuses in 1969, 1980, 1990, 2000, and 2010. The 2010 Census of Population and Housing was carried out from 16 October to 15 November, 2010. However, field enumeration in all parts of the country was concluded only on 30 November 2010 (Central Statisti- cal Office, 2012). A single questionnaire was used to capture both individual and household information. Living Conditions Monitoring Survey (LCMS) 2010 CSO and its partners have been collecting nationally representative household survey data since 1996 through the Living Conditions Monitoring Survey (LCMS). The main purpose of these surveys is to assess the living conditions of Zambians, measure progress and results of development, and provide information on indicators contained in the National Development Plan. The 2010 survey also was designed to help in assessing whether the country is on course to achieve the Millennium Development Goals (MDGs), especially the first goal of halving the 1990 poverty levels by 2015. Since 1996, CSO has carried out 6 LCMS surveys starting in 1996, followed by the 1998, 2002/03, 2004, 2006, and 2010 surveys. With the exception of the LCMS III survey in 2002/03, which was conducted over 12 months using a rolling sample, the other rounds of the LCMS 13 Mapping Subnational Poverty in Zambia survey were conducted over a 2-month period near comparing distributions between both sources. The the end or beginning of the calendar year. The LCMS results of these comparisons are reported in the are representative on the provincial and rural-urban footnotes of the tables for those cases in which the level and, depending on the year, cover typically tests of distributions do not show significant differ- 10,000–20,000 households. The sampling frame of ences between survey and census. the LCMS 2010 survey was based on the 2000 Cen- Tables 1 to 8 present the variables that are sus of Population and Housing (Central Statistical available in both sources, including their means, Office, 2010). confidence interval, and comparability. These eight tables present dwelling, assets, and sociodemo- Auxiliary data graphic variables. Several definitions of schooling and household composition were constructed to These data are compounded by health (hospitals achieve comparability between census and survey. and clinics), education (primary and secondary For instance, household size, maximum education, schools), and infrastructure data (distance to hospi- and maximum age in the household were explored tals and distance to roads). All data is available at in continuous and categorical forms. different geographic levels. Some of these variables Dwelling and asset characteristics are repre- were used to provide a visual representation of the sented primarily by dummy variables. Approxi- spatial correlation between infrastructure and pov- mately 40 percent of these variables are comparable erty incidence at the constituency and ward levels. between census and survey (tables 1–3). This com- parability includes variables such as type of floor, roof, and walls in the dwelling, as well as the main COMPARABILITY OF CENSUS AND source of cooking and house ownership. With re- SURVEY gard to asset possession, some of the comparable variables are motorcycle, motor vehicle, bicycle, This section discusses stage zero using the Living computer, mobile phone, oxen, and plough. Vari- Conditions Monitoring Survey 2010 and the 2010 ables that presented a small percentage of obser- Census of Population and Housing. To determine vations, such as possession of donkeys, were not the comparability of a pair of variables between included in the modeling stage to prevent multicol- census and survey, we compare questionnaires and linearity. Finally, tables 4–8 present household-level the summary statistics. The comparison of summary variables based on individual characteristics such statistics between census and survey considers the as composition of the household, disability, maxi- sample design for calculating confidence intervals mum education, and maximum age in the house- for each variable in the survey.10,11 If the mean of hold. In addition, household head variables were the census falls inside the confidence interval of the constructed including civil status, economic status, survey (using 95 percent of confidence intervals), occupation, industry, disability, and education. the variable is considered comparable between both Although the number of individual variables sources. Otherwise, the variable is not considered that exist in both sources is quite large, their com- comparable between sources and, therefore, is not parability is poor. Few sociodemographic variables, considered for the modeling stage. In the case of such as tertiary education and number of children, continuous variables, we apply an additional check are comparable. However, employment variables 10 The interpretation of confidence interval can be better explained by using an example. Suppose Ȳ is the mean of a variable and its 95% confidence interval goes from –120 to +20, this will mean that if we were to repeatedly draw samples of the same size from this population, and each time a sample is selected we were to compute specific values for the random interval (–120, +20), then we would expect 95 percent of those computed intervals to contain the unknown mean (also known as population mean), see Canavos (1984). 11 The standard errors of survey variables were adjusted by considering the sample design: enumeration areas as primary sample units (PSU), stratification and household weights. The weights used for the comparability and modeling correspond to those adjusted by the CSO’s team to represent the census population of 2010. 14 Data and Comparability Table 1: Descriptive Statistics: Dwelling Characteristics (Housing, roofs, walls, and lighting) Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Housing: traditional or improved traditional housing/mixed 0.61 0.59 0.63 0.69 No Housing: flat/apartment/multi-unit 0.07 0.06 0.09 0.09 Yes Housing: conventional house 0.31 0.29 0.33 0.21 No Housing: other type of housing 0.01 0.00 0.01 0.01 Yes Roof: thatch/palm/wood/grass/straw 0.49 0.47 0.51 0.51 Yes Roof: metal/iron sheets 0.30 0.28 0.32 0.32 Yes Roof: asbestos 0.19 0.18 0.21 0.16 No Roof: non-asbestos 0.01 0.01 0.01 0.00 No Roof of cement 0.00 0.00 0.01 0.00 Yes Roof – other 0.00 0.00 0.01 0.02 No Wall: burnt pan bricks 0.25 0.23 0.27 0.24 Yes Wall: mud 0.30 0.28 0.33 0.40 No Wall: cement 0.25 0.22 0.27 0.26 Yes Wall: iron 0.00 0.00 0.00 0.00 Yes Wall: asbestos 0.00 0.00 0.00 0.00 Yes Wall: pole/dagga/mud 0.18 0.16 0.20 0.07 No Wall: grass 0.02 0.01 0.02 0.02 Yes Wall: other 0.00 0.00 0.00 0.01 No Floor: concrete 0.43 0.41 0.45 0.43 Yes Floor: mud 0.56 0.54 0.58 0.56 Yes Floor: wood 0.00 0.00 0.01 0.00 No Floor: other 0.01 0.01 0.01 0.01 Yes Number of rooms 2.98 2.92 3.04 2.77 No Light: paraffin/kerosene 0.27 0.25 0.29 0.20 No Light: electricity 0.22 0.20 0.24 0.22 Yes Light: solar panel 0.03 0.03 0.04 0.03 Yes Light: candle 0.26 0.24 0.28 0.28 Yes Light: diesel 0.04 0.03 0.04 0.02 No Light: none 0.01 0.01 0.01 0.01 Yes Light: other 0.18 0.16 0.19 0.24 No present a bigger set of comparable variables in- us to include in the models primary and lower cluding several classifications of occupation and secondary school as the maximum education in industry. To expand the set of comparable variables, the household. we constructed several dummies that represented The lack of comparability between census and ranges of values of continuous variables such as survey is likely to be explained by the differences maximum age, maximum education, and household between the definition of the Standard Enumera- size. The construction of these variables enabled tion Areas (SEAs) in Censuses 2000 and 2010. 15 Mapping Subnational Poverty in Zambia Table 2: Descriptive Statistics: Dwelling Characteristics (Cooking, garbage disposal, toilet, and house ownership) Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Cooking: wood 0.54 0.52 0.57 0.54 Yes Cooking: charcoal 0.29 0.27 0.31 0.29 Yes Cooking: coal 0.00 0.00 0.00 0.00 Yes Cooking: paraffin 0.00 0.00 0.00 0.00 Yes Cooking: gas 0.00 0.00 0.00 0.00 No Cooking: electricity 0.17 0.15 0.19 0.17 Yes Cooking: solar 0.00 0.00 0.00 0.00 Yes Cooking: other 0.00 0.00 0.00 0.00 Yes Cooking: coal/paraffin/gas/solar/other 0.00 0.00 0.00 0.01 No Garbage: collected 0.06 0.05 0.07 0.10 No Garbage: burnt 0.02 0.01 0.03 0.08 No Garbage: dumping 0.35 0.33 0.37 0.23 No Garbage: pit 0.57 0.55 0.59 0.56 Yes Garbage: other 0.00 0.00 0.01 0.04 No Toilet: flush 0.13 0.11 0.15 0.10 No Toilet: latrine 0.73 0.71 0.76 0.66 No Toilet: other 0.02 0.01 0.02 0.01 Yes Toilet: none 0.12 0.10 0.14 0.21 No Ownership: own house 0.72 0.70 0.73 0.68 No Ownership: provided free by employer 0.05 0.04 0.06 0.04 Yes Ownership: other free housing 0.03 0.03 0.04 0.07 No Ownership: rented from local government 0.00 0.00 0.00 0.00 Yes Ownership: rented from central government 0.01 0.01 0.01 0.00 No Ownership: rented from parastatal 0.00 0.00 0.00 0.00 Yes Ownership: rented from company or organisation 0.01 0.00 0.01 0.00 Yes Ownership: rented from other 0.18 0.16 0.20 0.20 Yes Ownership: rented from local government, central 0.02 0.02 0.02 0.01 No government, parastatal, or organisation According to the Central Statistical Office (2010), were reallocated into other provinces. To the best the SEAs form the frame upon which the sampling of our knowledge, the CSO has considered two of Zambian surveys is based. The demarcation of approaches to account for these issues: (1) the the country into SEAs is undertaken every 2–3 construction of new household weights for the years before a population census begins. In ad- survey to represent the Census 2010 population dition, between 2000 and 2010, a new province and (2) the definition of the new province, Much- and new wards appeared, and some districts inga, in the survey. 16 Data and Comparability Table 3: Descriptive Statistics: Assets Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Assets: radio 0.49 0.48 0.51 0.58 No Assets: tv 0.34 0.32 0.36 0.31 No Assets: refrigerator/freezer 0.19 0.17 0.20 0.16 No Assets: land phone 0.01 0.01 0.01 0.02 No Assets: bicycle 0.36 0.34 0.38 0.38 Yes Assets: motor vehicles 0.05 0.05 0.06 0.05 Yes Assets: computer and laptop (census)/computer (survey) 0.03 0.03 0.04 0.04 Yes Assets: motorcycle 0.01 0.01 0.01 0.01 Yes Assets: plough 0.09 0.08 0.10 0.09 Yes Assets: boat/canoe 0.02 0.02 0.03 0.04 No Assets: scotch cart 0.05 0.04 0.05 0.03 No Assets: donkey 0.00 0.00 0.00 0.00 Yes Assets: mobile phone 0.54 0.52 0.56 0.52 Yes Assets: oxen 0.06 0.05 0.07 0.07 Yes Assets: wheelbarrow 0.06 0.05 0.07 0.09 No 17 Mapping Subnational Poverty in Zambia Table 4: Descriptive Statistics: Sociodemographic Characteristics Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Members in HH 6 to 12 1.25 1.22 1.28 1.05 No Members in HH 13 to 18 0.95 0.93 0.98 0.75 No Members in HH 19 or older 2.86 2.82 2.89 1.35 No Members under 18 2.28 2.23 2.32 2.58 No Never attended school 0.09 0.09 0.10 0.17 No Education of HH 7.96 7.81 8.11 7.20 No Schooling attendance 6 to 12 0.38 0.36 0.40 0.03 No Schooling attendance 13 to 18 0.25 0.24 0.26 0.12 No Grade 0 to 7 of head 0.46 0.44 0.48 0.50 No Grade 8 to 9 of head 0.19 0.18 0.20 0.16 No Grade 10 to 13 of head 0.20 0.18 0.21 0.15 No Grade 14 to 16 of head 0.13 0.12 0.14 0.13 Yes No education of head 0.09 0.08 0.10 0.16 No Absent household head 0.06 0.05 0.07 0.08 No Father alive 0.16 0.14 0.17 0.28 No Mother alive 0.16 0.14 0.17 0.13 No Number of disable in the HH 0.04 0.04 0.04 0.06 No Total no. of own children 2.53 2.48 2.58 2.53 Yes Total no. of children 2.59 2.54 2.65 2.60 Yes Total no. of children <=5 0.19 0.18 0.21 0.94 No Total no. of children 6–12 0.97 0.94 1.00 0.89 No Total no. of children 13 to 18 0.65 0.63 0.67 0.57 No Total no. of adults 19 to 25 0.39 0.37 0.40 0.31 No Total no. of adults 26 to 35 0.19 0.18 0.20 0.15 No Total no. of adults 36 to 45 0.11 0.10 0.12 0.09 No Total no. of adults 46 to 60 0.10 0.09 0.10 0.08 No Total no. of adults older than 61 0.00 0.00 0.00 0.07 No Age of head 42.12 41.80 42.47 41.50 No Maximum age in the HH 45.10 44.73 45.47 42.98 No Minimum age in the HH 12.45 12.18 12.72 8.86 No Note: Age of household head is comparable between census and survey when their distributions are compared. 18 Data and Comparability Table 5: Descriptive Statistics: Employment − Occupation Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Unemployed 0.026 0.022 0.031 0.061 No Inactive 0.039 0.035 0.044 0.096 No Employed 0.934 0.927 0.941 0.843 No Employer 0.003 0.002 0.004 0.009 No Employee 0.257 0.241 0.272 0.245 Yes Self-employed 0.661 0.643 0.680 0.465 No Unpaid 0.009 0.005 0.012 0.125 No Occupation: Professional and Technicians 0.082 0.074 0.090 0.071 No Occupation: Administrative and Managerial 0.015 0.012 0.018 0.011 No Occupation: Clerical and Related Worker 0.012 0.010 0.014 0.009 No Occupation: Sales 0.072 0.064 0.080 0.067 Yes Occupation: Services 0.062 0.055 0.069 0.052 No Occupation: Agricultural and Fishery 0.552 0.530 0.574 0.483 No Occupation: Mining and Construction 0.039 0.035 0.044 0.058 No Occupation: Other trade related workers 0.034 0.030 0.037 0.031 Yes Occupation: Transport workers 0.040 0.036 0.045 0.041 Yes Occupation: Armed forces 0.001 0.000 0.001 0.005 No Sum at HH level: Administrative and Managerial 0.021 0.015 0.027 0.014 No Sum at HH level: Clerical and Related Worker 0.022 0.018 0.025 0.014 No Sum at HH level: Sales 0.136 0.125 0.148 0.117 No Sum at HH level: Services 0.107 0.097 0.117 0.078 No Sum at HH level: Agricultural and Fishery 1.237 1.183 1.291 0.977 No Sum at HH level: Mining and Construction 0.049 0.044 0.055 0.069 No Sum at HH level: Other trade related workers 0.051 0.046 0.057 0.045 No Sum at HH level: Transport workers 0.051 0.045 0.056 0.049 Yes 19 Mapping Subnational Poverty in Zambia Table 6: Descriptive Statistics: Employment − Sector Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Sector: Agriculture, Hunting and Fishing 0.559 0.537 0.581 0.485 No Sector: Mining 0.021 0.016 0.027 0.022 Yes Sector: Manufacturing 0.036 0.032 0.040 0.040 Yes Sector: Electricity, Gas, Water and Steam 0.005 0.004 0.006 0.005 Yes Sector: Construction 0.026 0.022 0.029 0.037 No Sector: Trade 0.103 0.094 0.113 0.082 No Sector: Transport 0.046 0.041 0.051 0.046 Yes Sector: Financial 0.028 0.022 0.033 0.005 No Sum at HH level: Administration 0.118 0.109 0.128 0.097 No Sum at HH level: Agriculture, Hunting and Fishing 1.242 1.189 1.295 0.964 No Sum at HH level: Mining 0.026 0.019 0.033 0.028 Yes Sum at HH level: Manufacturing 0.054 0.048 0.059 0.052 Yes Sum at HH level: Electricity, Gas, Water and Steam 0.007 0.005 0.009 0.006 Yes Sum at HH level: Construction 0.032 0.028 0.037 0.044 No Sum at HH level: Trade 0.189 0.175 0.204 0.142 No Sum at HH level: Transport 0.069 0.060 0.078 0.061 Yes Sum at HH level: Financial 0.034 0.027 0.040 0.007 No Sum at HH level: Administration 0.185 0.171 0.200 0.144 No 20 Data and Comparability Table 7: Descriptive Statistics: Compounded Sociodemographic Characteristics Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Total no. of children <=5: 0 children 0.82 0.81 0.83 0.46 No Total no. of children <=5: 1 child 0.16 0.15 0.17 0.25 No Total no. of children <=5: 2 children 0.02 0.01 0.02 0.22 No Total no. of children <=5: 3 or more children 0.00 0.00 0.00 0.08 No Total no. of children 6–12: 0 children 0.47 0.46 0.49 0.51 No Total no. of children 6–12: 1 child 0.23 0.22 0.24 0.22 Yes Total no. of children 6–12: 2 children 0.18 0.17 0.19 0.18 Yes Total no. of children 6–12: 3 or more children 0.12 0.11 0.12 0.10 No Total no. of children 13–18: 0 children 0.60 0.58 0.61 0.66 No Total no. of children 13–18: 1 child 0.22 0.21 0.23 0.18 No Total no. of children 13–18: 2 children 0.13 0.13 0.14 0.12 No Total no. of children 13–18: 3 or more children 0.05 0.05 0.06 0.05 Yes Total no. of children 19–25: 0 adult 0.72 0.71 0.73 0.79 No Total no. of children 19–25: 1 adult 0.19 0.18 0.20 0.13 No Total no. of children 19–25: 2 or more adults 0.09 0.08 0.09 0.07 No Total no. of children 26–35: 0 adult 0.84 0.83 0.85 0.90 No Total no. of children 26–35: 1 adult 0.14 0.13 0.15 0.07 No Total no. of children 26–35: 2 or more adults 0.02 0.02 0.03 0.03 Yes Total no. of children 36–45: 0 adult 0.89 0.88 0.90 0.94 No Total no. of children 36–45: 1 adult 0.11 0.10 0.11 0.04 No Total no. of children 36–45: 2 or more adults 0.01 0.00 0.01 0.02 No Total no. of children 46–60: 0 adult 0.90 0.90 0.91 0.96 No Total no. of children 46–60: 1 adult 0.09 0.09 0.10 0.03 No Total no. of children 46–60: 2 or more adults 0.00 0.00 0.01 0.02 No Total no. of children 61 or older: 0 adult 1.00 0.99 1.00 0.96 No Total no. of children 61 or older: 1 adult 0.00 0.00 0.00 0.02 No Total no. of children 61 or older: 2 or more adults 0.00 –0.00 0.01 0.02 No 21 Mapping Subnational Poverty in Zambia Table 8: Descriptive Statistics: Household Size, Maximum Age, and Maximum Education Survey Census Description Mean Min CI-95% Max CI-95% Mean Comparability Household size (HH size) 5.245 5.181 5.309 5.275 Yes HH size: 1 individual 0.052 0.047 0.057 0.063 No HH size: 2 individuals 0.082 0.077 0.088 0.088 Yes HH size: 3 to 4 individuals 0.291 0.280 0.301 0.271 No HH size: 5 to 6 individuals 0.292 0.282 0.302 0.275 No HH size: 7 to 8 individuals 0.179 0.170 0.188 0.221 No HH size: 9 or more individuals 0.104 0.098 0.111 0.083 No Maximum age in HH: 12 to 24 0.038 0.033 0.042 0.066 No Maximum age in HH: 25 to 35 0.252 0.242 0.263 0.332 No Maximum age in HH: 36 to 45 0.268 0.258 0.277 0.238 No Maximum age in HH: 46 to 60 0.301 0.291 0.310 0.209 No Maximum age in HH: 61 to 75 0.105 0.098 0.112 0.113 No Maximum age: 76 or more 0.037 0.033 0.041 0.042 No Maximum education in HH: Primary 0.341 0.326 0.357 0.328 Yes Maximum education in HH: Lower Secondary 0.239 0.229 0.250 0.231 Yes Maximum education in HH: Upper Secondary 0.238 0.225 0.251 0.208 No Maximum education in HH: Tertiary 0.119 0.108 0.130 0.151 No Maximum education in HH: None 0.000 0.000 0.000 0.002 No Absent HH head 0.061 0.053 0.069 0.080 No 22 Results 1 5 POVERTY ESTIMATES AT THE PROVINCE, DISTRICT AND CONSTITUENCY LEVELS The ELL (2003) method was used to obtain poverty estimates at various subnational levels. Using the survey data, the authors mod- eled the adult-equivalent expenditure at the household level by using household and geographic controls. The construction of the adult-equivalent expenditure aggregate was carried out by the CSO to measure the official poverty at the national and provincial levels using the LCMS 2010. Tables 9 and 10 show the final econometric model used for the Poverty Map. Interacted variables, provincial, and district dummies were used to obtain the most predictive model. Several OLS models initially displayed significant coefficients of interacted variables by province and urban-rural region. However, after the authors adjusted the standard errors for heteroskedasticity, the parameters of these interactions ceased to be statistically significant. Table 9 presents the final model with an adjusted-R2 of 0.6968 and a root mean square error (RMSE) of 0.5475. Based on previous Poverty Mapping exercises, the prediction of this model is considered to be quite high for expenditure/income models. The cluster component of the error term corresponds to 5.7 percent of the total variance when it is defined at the constituency level. In other words, the inclusion of additional variables at the constituency or ward level may decrease the total variance of the error term by 5.7 percent at most, assuming no correlation of these variables with the household component. In addition, Table 10 presents the variables of the heteroskedastic model. In line with recent empirical evidence, the inclusion of geographic partition dummies provides more precise poverty estimates than stepwise selection of variables for this heteroskedastic model.12 12 Several econometric models with adjusted-R2 above 65% and RMSE between 0.5475 and 0.5536 were used to obtain poverty estimates at varying subnational levels. These estimates, as well as their rankings and coefficients of variation, were compared across models. The selected “good” models produced similar subnational poverty estimates and standard errors. All of these models consi­ dered alpha models with only district/regional variables and with district/re- 23 Mapping Subnational Poverty in Zambia Table 9: Beta Model of Adult Equivalent Expenditure (logarithms) Coefficient Std. Error P-value Variable (1) (2) (3) Intercept 13.17 0.02 0.000 Sociodemographics Household head: employee 0.08 0.01 0.000 Household size –0.25 0.00 0.000 Maximum education: primary –0.31 0.02 0.000 Maximum education: lower secondary –0.24 0.02 0.000 Assets and Dwelling characteristics Computer 0.40 0.02 0.000 Cooking: electricity 0.29 0.02 0.000 Cooking: wood –0.17 0.01 0.000 Floor: mud –0.21 0.01 0.000 Garbabe: pit 0.03 0.01 0.001 Lighting: electricity 0.34 0.02 0.000 Lighting: solar 0.41 0.02 0.000 Motor vehicle 0.68 0.02 0.000 Plough 0.20 0.02 0.000 Roof: metal –0.08 0.01 0.000 Roof: thatch –0.24 0.02 0.000 Wall: bricks 0.10 0.01 0.000 Wall: cement 0.08 0.02 0.000 Geographical partitions District: Mumbwa 0.24 0.04 0.000 District: Kalulushi 0.19 0.05 0.000 District: Masaiti 0.21 0.04 0.000 District: Katete –0.17 0.03 0.000 District: Petauke –0.14 0.03 0.000 District: Mansa 0.21 0.03 0.000 District: Samfya –0.15 0.04 0.000 District: Chama 0.34 0.05 0.000 District: Mpika 0.12 0.03 0.000 District: Kasama 0.20 0.03 0.000 District: Ikelenge 0.58 0.04 0.000 District: Mwinilunga 2.37 0.34 0.000 District: Solwezi 0.40 0.03 0.000 District: Itezhi-tezhi -0.15 0.04 0.000 District: Kazungula 0.36 0.05 0.000 District: Namwala 0.15 0.04 0.000 District: Shang’ombo –0.54 0.04 0.000 (continued on next page) 24 Results Table 9: Beta Model of Adult Equivalent Expenditure (logarithms) (continued) Coefficient Std. Error P-value Variable (1) (2) (3) Region: Rural –0.09 0.01 0.000 Interactions Age head * age head –0.00 0.00 0.000 Age head * district: Mwinilunga –0.03 0.01 0.001 Age head * province: Central 0.00 0.00 0.000 Age head * province: Copperbelt 0.00 0.00 0.000 Age head * province: Lusaka 0.00 0.00 0.000 Household size * household size 0.01 0.00 0.000 Household size * max. edu. secondary 0.02 0.00 0.000 Household size * max. edu. primary 0.02 0.00 0.000 Household size * province: Eastern 0.01 0.00 0.000 Note: Number of observations=19,014 SST=18801.2717 SSR=13114.7071. MSE=0.2998 RMSE=0.5475 F=994.2609 R2=0.6975 adjR2=0.6968. Table 10: Alpha Model of residual (logarithms) Coefficient Std. Error P-value Variable (1) (2) (3) Intercept –5.69 0.02 0.000 District: Chibombo –0.23 0.10 0.026 District: Kapiri-Mposhi 0.50 0.12 0.000 District: Mumbwa –0.50 0.14 0.000 District: Chililabombwe 0.42 0.18 0.019 District: Mpongwe 0.62 0.23 0.008 District: Mambwe –0.39 0.21 0.065 District: Nyimba 0.51 0.23 0.028 District: Kawambwa –0.36 0.12 0.002 District: Mansa –0.35 0.13 0.008 District: Nchelenge 0.48 0.15 0.001 District: Chongwe 0.26 0.12 0.029 District: Kafue 0.27 0.12 0.021 District: Chama 0.32 0.19 0.089 District: Mpika –0.49 0.12 0.000 District: Kaputa 0.88 0.27 0.001 District: Mporokoso 0.38 0.13 0.003 District: Ikelenge –0.50 0.18 0.006 District: Kabompo –0.67 0.17 0.000 District: Kasempa 0.78 0.21 0.000 (continued on next page) 25 Mapping Subnational Poverty in Zambia Table 10: Alpha Model of residual (logarithms) (continued) Coefficient Std. Error P-value Variable (1) (2) (3) District: Mwinilunga 0.89 0.34 0.009 District: Gwembe 0.29 0.16 0.077 District: Kalomo 0.68 0.19 0.001 District: Kaoma 0.39 0.17 0.021 District: Lukulu 0.32 0.13 0.014 District: Mongu 0.29 0.11 0.010 District: Senanga –0.37 0.21 0.072 District: Sesheke –0.75 0.19 0.000 Note: Number of observations=19,014 SST=93592.8926. SSR=1129.9469 MSE=4.8701 RMSE=2.2068 F=8.5933 R2=0.0121 adjR2=0.0107. Table 11: Poverty Estimates: Survey and Poverty Map Results at the Province Level Survey PovMap Province Name Code Poverty St. Error CI-Min 95 CI-Max 95 Poverty St. Error Central 1 0.61 0.03 0.56 0.66 0.65 0.026 Copperbelt 2 0.34 0.03 0.29 0.40 0.37 0.018 Eastern 3 0.79 0.02 0.75 0.82 0.80 0.022 Luapula 4 0.80 0.02 0.76 0.85 0.79 0.022 Lusaka 5 0.24 0.03 0.19 0.30 0.25 0.031 Muchinga 6 0.78 0.03 0.71 0.84 0.77 0.027 Northern 7 0.73 0.03 0.68 0.78 0.76 0.024 North Western 8 0.67 0.04 0.60 0.74 0.64 0.033 Southern 9 0.68 0.03 0.63 0.73 0.68 0.016 Western 10 0.80 0.02 0.76 0.85 0.84 0.013 National — 0.60 0.01 0.58 0.62 0.60 0.01 Note: The calculation of the survey variance considers the sample design using as primary sample units (PSUs) the enumeration areas, as well as the survey strata. Table 11 presents the moderate poverty es- represents a naive test, we use it as a starting point timates based on the survey and Poverty Map to show the differences of poverty estimates at ag- at province level. Although the comparison of gregate levels.13 The Poverty Map estimates at the province poverty estimates between both sources national level fall inside the confidence interval of gional and comparable variables. The selection of statistically significant variables was carried out using stepwise procedures. In the majority of cases, the alpha models with only district/regional variables provided poverty estimates with higher preci- sion than the alpha models with district/regional and comparable variables. The model selected to produce the final poverty estimates for this report uses district dummies in the alpha model as predictors. Details of the set of econometric models are available upon request at: adelafuente@worldbank.org. 13 Standard errors of poverty estimates based on survey data regularly are large due to small subsamples. As a result, it is common not to find significant differences in poverty estimates between census and survey. The authors thank one of the reviewers who suggested highlighting this point. 26 Results the survey estimates. Both estimates rate 60 percent over the total population in the province); Luapula, of the population as living in moderate poverty. Western, Eastern, and Muchinga present the highest Although the majority of Poverty Map estimates poverty incidence. present lower standard errors than the survey es- Table 12 presents the poverty estimates at the timates, the Poverty Map is less precise than the district level. These results show that the 15 poorest survey in three cases: Eastern, Luapula, and Lusaka. districts belong primarily to Western, North Western, The discrepancy amounts to one-fifth of a percent- and Northern provinces. These districts concentrate age point for Eastern and Luapala, and one-half 16.4 percent of the poor population in the country. of a percentage point for Lusaka. Both survey and The ranking based on poverty incidence (Headcount Poverty Map estimates provide the same conclu- HC) highlights as the poorest districts Shang’ombo, sion about provincial rankings based on poverty Smafya, Mafinga, Kabompo, and Milenge. The full incidence. Lusaka and Copperbelt present the low- set of poverty map estimates at district level is pre- est poverty incidence (that is, the number of poor sented in the Annex. Table 12: Poverty Map Estimates and Rankings at the District Level (1/3) Poverty Ranking Ranking Province District Code Headcount (HC) Std. Error No. Poor Prop. Poor (HC) (No.Poor) Western Shang’ombo 1007 0.95 0.04 90,396 0.011 1 37 Luapula Samfya 407 0.91 0.03 182,252 0.023 2 8 Muchinga Mafinga 604 0.91 0.03 60,260 0.008 3 57 North Western Kabompo 803 0.90 0.03 84,175 0.011 4 41 Luapula Milenge 404 0.88 0.04 38,573 0.005 5 68 Western Kalabo 1001 0.88 0.05 114,548 0.014 6 28 North Western Mufumbwe 805 0.87 0.04 51,281 0.006 7 63 North Western Zambezi 808 0.87 0.04 70,519 0.009 8 50 Western Senanga 1005 0.87 0.05 110,833 0.014 9 31 North Western Chavuma 801 0.87 0.04 30,622 0.004 10 70 Northern Chilubi 701 0.87 0.04 70,819 0.009 11 49 Northern Mungwi 708 0.86 0.04 132,510 0.017 12 20 Western Lukulu 1003 0.86 0.05 74,297 0.009 13 47 Northern Luwingu 704 0.86 0.04 105,296 0.013 14 32 Western Sesheke 1006 0.85 0.05 85,806 0.011 15 40 Muchinga Chinsali 602 0.85 0.04 125,498 0.016 16 24 Eastern Lundazi 304 0.84 0.04 278,353 0.035 17 3 Luapula Kawambwa 402 0.82 0.05 112,232 0.014 18 29 Southern Gwembe 902 0.82 0.05 44,103 0.006 19 66 Eastern Katete 303 0.82 0.06 201,890 0.025 20 6 Northern Mporokoso 706 0.82 0.04 82,180 0.010 21 42 Western Kaoma 1002 0.82 0.05 156,677 0.020 22 12 Eastern Petauke 307 0.82 0.06 259,929 0.033 23 4 Luapula Chienge 401 0.82 0.05 95,575 0.012 24 34 Northern Mbala 705 0.82 0.04 167,431 0.021 25 11 Note: District poverty estimates are calculated by defining the cluster effect at the district level. (continued on next page) 27 Mapping Subnational Poverty in Zambia Table 12: Poverty Map Estimates and Rankings at the District Level (2/3) (continued) Poverty Ranking Ranking Province District Code Headcount (HC) Std. Error No. Poor Prop. Poor (HC) (No.Poor) Eastern Mambwe 305 0.81 0.05 58,767 0.007 26 59 Muchinga Isoka 603 0.81 0.04 59,314 0.007 27 58 Eastern Chadiza 301 0.81 0.05 87,751 0.011 28 38 North Western Kasempa 804 0.81 0.04 56,802 0.007 29 60 Northern Mpulungu 707 0.81 0.04 79,907 0.010 30 43 Copperbelt Lufwanyama 206 0.80 0.06 63,386 0.008 31 56 Luapula Mwense 405 0.79 0.06 95,858 0.012 32 33 Northern Kaputa 702 0.79 0.05 95,529 0.012 33 35 Central Serenje 106 0.78 0.06 131,453 0.017 34 21 Eastern Nyimba 306 0.78 0.05 69,227 0.009 35 52 Luapula Nchelenge 406 0.77 0.05 118,718 0.015 36 26 Southern Sinazongwe 911 0.77 0.05 78,544 0.010 37 44 Southern Kalomo 904 0.75 0.05 197,306 0.025 38 7 Southern Monze 908 0.75 0.05 145,135 0.018 39 18 Muchinga Mpika 605 0.74 0.08 151,429 0.019 40 14 Central Chibombo 101 0.73 0.06 223,275 0.028 41 5 Southern Siavonga 910 0.72 0.05 65,874 0.008 43 55 Muchinga Nakonde 606 0.72 0.05 87,694 0.011 42 39 Southern Namwala 909 0.72 0.09 75,189 0.009 44 45 Eastern Chipata 302 0.72 0.05 331,336 0.042 45 1 Southern Choma 901 0.72 0.06 179,705 0.023 46 9 Central Mkushi 104 0.71 0.06 111,355 0.014 47 30 Copperbelt Mpongwe 208 0.71 0.06 67,232 0.008 48 54 Muchinga Chama 601 0.71 0.09 74,737 0.009 49 46 Western Mongu 1004 0.71 0.06 129,110 0.016 50 23 Note: District poverty estimates are calculated by defining the cluster effect at the district level. (continued on next page) Conversely, the ranking based on the concentra- Table 13 lists the constituencies with the high- tion of poor population highlights Chipata, Lusaka, est and lowest poverty incidences. The full set of Lundazi, Petauke, and Chibombo as the districts poverty map estimates at constituency level is pre- with the highest concentration. Eighteen percent sented in the Annex. The highest poverty rates at of the moderately poor population is concentrated the constituency level are located primarily in the in these 5 districts. It is important to highlight the provinces of Western and Luapula in the districts districts that have large numbers of poor people of Shang’ombo, Samfya, Senanga, and Sesheke. but relatively low poverty rates due to their high In contrast, the provinces with the lowest pov- populations. An example is Chipata, which stands erty incidences, based on the selection of the first in 45th position out of 74 districts by poverty head- 15 constituencies, are concentrated primarily in count rates but has the highest concentration of poor Cooperbelt and Lusaka in the districts of Chingola, people in the country (table 12). Ndola, and Lusaka. 28 Results Table 12: Poverty Map Estimates and Rankings at the District Level (3/3) (continued) Poverty Ranking Ranking Province District Code Headcount (HC) Std. Error No. Poor Prop. Poor (HC) (No.Poor) Southern Itezhi-tezhi 903 0.70 0.10 48,213 0.006 51 65 Lusaka Luangwa 503 0.70 0.08 16,888 0.002 52 74 Southern Kazungula 905 0.68 0.11 71,557 0.009 53 48 Central Kapiri-Mposhi 103 0.68 0.06 173,557 0.022 54 10 Luapula Mansa 403 0.65 0.09 151,248 0.019 55 15 Central Mumbwa 105 0.64 0.09 147,087 0.019 56 17 Southern Mazabuka 907 0.63 0.05 147,731 0.019 57 16 Lusaka Chongwe 501 0.61 0.07 117,793 0.015 58 27 North Western Ikelenge 802 0.59 0.11 19,722 0.002 59 73 Northern Kasama 703 0.51 0.10 120,427 0.015 60 25 Copperbelt Masaiti 207 0.51 0.13 53,060 0.007 61 61 North Western Solwezi 807 0.50 0.10 129,147 0.016 62 22 Lusaka Kafue 502 0.40 0.07 91,677 0.012 63 36 Central Kabwe 102 0.33 0.06 68,486 0.009 64 53 Copperbelt Luanshya 205 0.33 0.07 52,295 0.007 65 62 Copperbelt Chingola 202 0.32 0.06 70,028 0.009 66 51 Copperbelt Ndola 210 0.31 0.07 141,874 0.018 67 19 Copperbelt Kalulushi 203 0.30 0.12 30,952 0.004 68 69 Copperbelt Mufulira 209 0.30 0.06 50,032 0.006 69 64 Copperbelt Chililabombwe 201 0.30 0.06 27,946 0.004 70 72 Copperbelt Kitwe 204 0.29 0.07 151,762 0.019 71 13 North Western Mwinilunga 806 0.29 0.09 30,357 0.004 72 71 Southern Livingstone 906 0.28 0.06 39,266 0.005 73 67 Lusaka Lusaka 504 0.18 0.06 313,216 0.040 74 2 Total National — 0.6 0.01 7,946,668 — — — Map 1 presents the poverty incidence at the Because the poverty incidence presented in constituency level. The cut-points of this map cor- table 13 is constructed as a proportion relative to respond to equal counts of 30 constituencies per the population of the constituency, it is relevant category in which the total number of constituencies to complement table 13 with the concentration of is 150. The blue lines display the district boundaries. poor population across constituencies. The larg- The lightest color contains 30 constituencies that est proportions of the poor are concentrated in present poverty incidence from 9 percent–41 per- the constituencies of Kapiri Mposhi (2.2 percent), cent. The darkest color represents 30 constituencies Mungwi (1.7 percent), Nchelenge (1.5 percent), that display poverty rates between 85 percent and and Lundazi (1.4 percent) from the Central, North- 96 percent. This map shows that the highest poverty ern, Luapula, and Eastern provinces, respectively incidence at the constituency level (darkest color) (table 14). The 15 poorest constituencies ranked by is located in the Western and Luapula provinces, as the proportion of poor concentrate 19.6 percent of well as in the center of the Eastern province and the the total number of people living in poverty. The 15 east of the Northern province. constituencies with the lowest poverty incidence 29 Mapping Subnational Poverty in Zambia Table 13: Poverty Map Estimates and Rankings at the Constituency Level: The Fifteen Constituencies with the Highest and Lowest Poverty Incidence Poverty Headcount Ranking Ranking Province District Constituency Code (HC) Std. Error No. Poor Prop. Poor (HC) (No.Poor) Fifteen constituencies with the highest poverty incidence Western Shang'ombo Sinjembela 1007147 0.96 0.03 90,586 0.011 1 11 Luapula Samfya Chifunabuli 407068 0.92 0.04 77,421 0.010 2 26 Luapula Samfya Luapula 407069 0.92 0.04 22,291 0.003 3 138 North Western Kabompo Kabompo East 803104 0.92 0.03 44,814 0.006 4 86 Western Senanga Nalolo 1005145 0.92 0.03 51,582 0.006 5 69 Western Sesheke Mulobezi 1006148 0.91 0.04 28,099 0.004 6 126 North Western Zambezi Zambezi West 808114 0.91 0.04 20,089 0.003 7 142 Muchinga Mafinga Isoka East 604085 0.90 0.04 60,113 0.008 8 51 Western Kalabo Liuwa 1001135 0.90 0.03 24,022 0.003 9 132 Western Sesheke Mwandi 1006149 0.89 0.04 22,710 0.003 10 137 Western Kalabo Sikongo 1001136 0.89 0.03 41,139 0.005 11 94 Luapula Kawambwa Pambashe 402058 0.89 0.04 37,825 0.005 12 108 Luapula Samfya Bangweulu 407067 0.89 0.04 81,781 0.010 13 19 Western Lukulu Lukulu West 1003141 0.88 0.04 24,547 0.003 14 131 Muchinga Chinsali Shiwang'andu 602084 0.88 0.05 53,356 0.007 15 66 Fifteen constituencies with the lowest poverty incidence Southern Livingstone Livingstone 906123 0.28 0.05 38,884 0.005 136 103 Copperbelt Chingola Nchanga 202017 0.27 0.06 24,782 0.003 137 130 Central Kabwe Kabwe 102005 0.27 0.05 32,541 0.004 138 117 Copperbelt Luanshya Roan 205025 0.26 0.06 15,374 0.002 139 147 Copperbelt Ndola Kabushi 210035 0.25 0.07 23,527 0.003 140 135 Copperbelt Kitwe Wusakile 204023 0.24 0.05 23,736 0.003 141 133 Copperbelt Mufulira Kantanshi 209027 0.23 0.04 13,019 0.002 142 149 Lusaka Lusaka Mandevu 504079 0.22 0.07 80,020 0.010 143 20 Lusaka Lusaka Chawama 504075 0.21 0.07 40,049 0.005 144 97 Lusaka Lusaka Kanyama 504077 0.20 0.07 73,578 0.009 145 31 Lusaka Lusaka Matero 504080 0.19 0.07 55,507 0.007 146 61 Copperbelt Kitwe Nkana 204022 0.19 0.05 16,158 0.002 147 146 Lusaka Lusaka Munali 504081 0.15 0.05 39,835 0.005 148 99 Lusaka Lusaka Lusaka Central 504078 0.10 0.04 12,148 0.002 149 150 Lusaka Lusaka Kabwata 504076 0.09 0.04 16,722 0.002 150 145 Note: Constituency poverty estimates are calculated by defining the cluster effect at the constituency level. 30 Results Table 14: Poverty Map Estimates and Rankings at the Constituency Level: The Fifteen Constituencies with the Highest and Lowest Concentration of Poor Population Poverty Headcount Ranking Ranking Province District Constituency Code (HC) Std. Error No. Poor Prop. Poor (HC) (No. Poor) Fifteen constituencies with the highest concentration of poor Central Kapiri- Kapiri Mposhi 103006 0.68 0.05 175,504 0.022 101 1 Mposhi Northern Mungwi Malole 708092 0.85 0.05 131,462 0.017 31 2 Luapula Nchelenge Nchelenge 406066 0.77 0.05 118,718 0.015 78 3 Eastern Lundazi Lundazi 304050 0.81 0.05 108,056 0.014 61 4 Eastern Chipata Chipangali 302041 0.87 0.04 107,805 0.014 22 5 Eastern Petauke Kapoche 307053 0.85 0.06 107,271 0.014 35 6 Luapula Chienge Chienge 401065 0.81 0.06 94,780 0.012 58 7 Eastern Petauke Petauke 307054 0.76 0.07 94,700 0.012 81 8 Central Mkushi Mkushi North 104007 0.71 0.06 91,851 0.012 94 9 Central Chibombo Keembe 101003 0.74 0.07 90,672 0.011 84 10 Western Shang'ombo Sinjembela 1007147 0.96 0.03 90,586 0.011 1 11 Eastern Lundazi Chasefu 304048 0.87 0.04 88,888 0.011 18 12 Muchinga Nakonde Nakonde 606087 0.72 0.06 87,670 0.011 88 13 Northern Mbala Mbala 705095 0.78 0.05 87,486 0.011 75 14 Luapula Mansa Mansa 403061 0.61 0.10 83,906 0.011 108 15 Fifteen constituencies with the lowest concentration of poor Copperbelt Mufulira Mufulira 209028 0.38 0.06 23,390 0.003 122 136 Western Sesheke Mwandi 1006149 0.89 0.04 22,710 0.003 10 137 Luapula Samfya Luapula 407069 0.92 0.04 22,291 0.003 3 138 Muchinga Mpika Mfuwe 605099 0.82 0.06 22,101 0.003 52 139 Central Mkushi Mkushi South 104008 0.81 0.05 20,833 0.003 62 140 Luapula Mwense Mambilima 405063 0.78 0.06 20,225 0.003 76 141 North Western Zambezi Zambezi West 808114 0.91 0.04 20,089 0.003 7 142 North Western Ikelenge Mwinilunga 802109 0.58 0.12 19,267 0.002 112 143 West Lusaka Luangwa Feira 503071 0.70 0.07 17,019 0.002 95 144 Lusaka Lusaka Kabwata 504076 0.09 0.04 16,722 0.002 150 145 Copperbelt Kitwe Nkana 204022 0.19 0.05 16,158 0.002 147 146 Copperbelt Luanshya Roan 205025 0.26 0.06 15,374 0.002 139 147 Copperbelt Mufulira Kankoyo 209026 0.30 0.06 13,713 0.002 133 148 Copperbelt Mufulira Kantanshi 209027 0.23 0.04 13,019 0.002 142 149 Lusaka Lusaka Lusaka 504078 0.10 0.04 12,148 0.002 149 150 Central Note: Constituency poverty estimates are calculated by defining the cluster effect at the constituency level. 31 Mapping Subnational Poverty in Zambia Map 1. Poverty Map: Poverty Incidence at Map 2. Poverty Map: Concentration of Poor the Constituency Level Using the CSO Poverty Population at the Constituency Level Using the CSO Measurement Method Poverty Measurement Method Northern 0.96 0.03 Muchinga 0.85 Luapula 0.81 0.72 Copperbelt North Western 0.41 Eastern Central 0.009 0.007 Lusaka 0.005 0.004 Southern Western 0.09 0 Source: Author’s own calculations. Source: Author’s own calculations. Note: Muchinga province was separated from Northern province in 2011. concentrate only 3.5 percent of the total number we observe that 10 percent of the comparisons are living in poverty. statistically different at the 99 percent confidence Map 2 presents the concentration of poor popu- level (figure 2).14 This adjustment, known as the lation at constituency level in the country based Bonferroni correction, aims to minimize the Type I on 5 categories of equal size (30 constituencies per error (rejection of the null hypothesis when it is true). category). The highest concentrations of the poor For the constituency poverty rates, there are 11,175 population are in the Eastern, Central, and part of pairwise comparisons. Thus, the significance level the Northern provinces. at which to compare the p-value of each comparison All poverty estimates are accompanied by stan- corresponds to α/11,175. For instance, for the above dard errors defining the cluster effect at the level of 99 percent confidence level, the significance level the target population. To analyze the standard errors drops from 0.01 to 0.0000009. Thus, differences must is crucial to understand the significant differences be very large to reject the null hypothesis. Accord- within and between administrative units of interest. ingly, the Bonferroni correction comes at the cost of Having poverty ranges at the constituency level increasing the Type II error (non-rejection of the null could be a powerful tool for policymakers. hypothesis when it is false). We present both results To understand the statistical differences among to enable the reader to use his/her own decision rule. constituencies, figure 1 summarizes pairwise com- To better understand the differences among parisons among all constituencies in the country. constituencies, figure 3 shows the concentration of Figure 1 shows the percentage of constituencies significant pairwise comparisons within districts. that present significant differences among them. Thirty districts contain only 1 constituency; hence, For instance, 40 percent of constituencies differ figure 3 presents only the 44 districts that contain significantly at a confidence level of 99 percent. more than 1 constituency. The panel of the 90 percent However, after adjusting the significance level (α) by confidence level shows that 8 of 44 districts have the number of comparisons among constituencies, constituencies with significant differences among 14 At the 99% confidence level, 1% of the comparisons will test as significantly different even if none of them is. The reader should bear in mind that the same interpretation applies to the rest of comparisons at various confidence levels. 32 Results in 2 districts, all of them. This finding is relevant if Figure 1. Significantly Different Pairwise we consider that the Constituency Development Comparisons of Constituencies in the Country by Fund (CDF) allocates equal lump-sum transfers Confidence Level across constituencies.15 Additionally, figure 4 shows the proportion of significantly different comparisons between 60 Signi cantly di erent pairwise comparisons constituency and national poverty estimates. of constituencies within nation (in %) 50 Thirty-five percent of the constituencies signifi- 40 cantly differ from the national poverty rate of 60 percent at the 90 percent confidence level. 30 20 10 POVERTY ESTIMATES AT THE 0 WARD LEVEL 75 80 85 90 95 99 Con dence−level in % Based on the econometric model discussed earlier, the authors also calculated the poverty estimates and the concentration of poor population for the 1,421 wards in the country. The full set of poverty map Figure 2. Significantly Different Pairwise estimates at ward level is presented in the Annex. Comparisons of Constituencies in the Country by The majority of wards present a coefficient of varia- Confidence Level (Bonferroni Correction) tion (standard error/poverty estimate) lower than 15 percent (78 percent of the wards).16 Only 10 percent 14 of the total number of wards displays coefficients Signi cantly di erent pairwise comparisons 12 of variation between 15 percent and 25 percent. of constituencies within nation (in %) 10 Even though this measure is sensitive to the level of 8 poverty, wards that present coefficients of variation above 25 have an average poverty rate of 18 percent. 6 Given the coefficients of variation and standard er- 4 rors of the poverty estimates at the ward level, we 2 consider the precision of our ward estimates as good. 0 Map 3 depicts the geographic dispersion of 75 80 85 90 95 99 poverty at the ward level, shedding light on several Con dence−level in % areas that were hidden by the constituency-level (Bonferroni Correction) estimates. A visual inspection ratifies the existence of various pockets of concentrated poverty at the ward level within some constituencies, but also them. This panel reveals that, in 2 districts, approxi- the notable dispersion of ward poverty rates in mately 40 percent of the pairwise comparisons are others. Figure 5 shows the percentage of pairwise statistically significant; in 3 districts, 60 percent; and ward comparisons that differ significantly in their 15 Zambia’s CDF was approved by Parliament in 1995 within a wider decentralization policy. The CDF is one of the country’s most significant transfers disbursed annually to the 150 constituencies. It has grown from approximately $13,000 per constitu- ency in 2006 to approximately $160,000 per constituency in 2011. These funds are under the control of the local member of parliament who is a member of the Constituency Development Committee. See A. Chileshe, “The Impact of the Constitu- ency Development Fund (CDF) in Zambia,” presented at Panel 73, Public Financial Management in African Countries, ECAS 2011–4th European Conference on African Studies: African Engagements: On Whose Terms?, Uppsala, June 15–18, 2011, The Nordic Africa Institute, Uppsala, Sweden, http://www.nai.uu.se/ecas-4/panels/61–80/panel-73/. 16 This level of coefficient of variation is reasonable based on previous Poverty Mapping exercises. 33 Mapping Subnational Poverty in Zambia Figure 3. Significantly Different Pairwise Comparisons of Constituencies within District 75 % Con dence−level 80 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 1.0 1.0 of const. within district (in %) of const. within district (in %) 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 10 20 30 40 0 10 20 30 40 Districts Districts 85 % Con dence−level 90 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 1.0 1.0 of const. within district (in %) of const. within district (in %) 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 10 20 30 40 0 10 20 30 40 Districts Districts 95 % Con dence−level 99 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 1.0 1.0 of const. within district (in %) of const. within district (in %) 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 10 20 30 40 0 10 20 30 40 Districts Districts poverty levels. At the 99 percent confidence level, the significance level at which to compare the p- 38 percent of the pairwise comparisons are signifi- value of each comparison is extremely conservative cantly different. in this case (α/1,008,910). Again, decreasing Type After considering the number of comparisons I error comes at the expense of increasing Type II to adjust the significance level, this percentage de- error, so we present both the uncorrected and cor- clines to 18 percent of the comparisons (figure 6). rected results of the multiple comparisons. As noted above, this adjustment is called Bonferroni Figure 7 displays the percentage of statistically correction. Because the total number of wards in the significant pairwise comparisons between wards country is 1,421, the total number of comparisons is within the same constituency at varying confidence approximately one million. This total implies that levels. The panel of the 90 percent confidence level 34 Results Figure 4. Significantly Different Comparisons of Map 3. Poverty Map: Poverty Incidence at Ward Constituency and National Poverty by Confidence Level using the CSO Poverty Measurement Method Level 0.97 0.87 0.84 40 of constituency and national estimate (in %) 0.76 Signi cantly di erent comparisons 30 20 0.51 10 0 75 80 85 90 95 99 Con dence−level in % (Bonferroni Correction) 0.01 Source: Author’s own calculations. shows that the wards of approximately 80 of 150 con- between ward and constituency poverty rates, as stituencies do not present significantly different pov- well as between ward and district poverty. Figure 8 erty rates among themselves. However, the wards of shows that, at the 90 percent confidence level, 50 approximately 50 constituencies present at least 20 per- constituencies have wards with significantly differ- cent of significantly different pairwise comparisons. ent poverty rates from the constituency poverty rate. To complement our understanding of how dif- Figure 9 reveals that, at the 90 percent confidence ferent ward poverty rates are from the poverty of level, 44 districts have wards with significantly dif- other administrative units, figures 8 and 9 present ferent poverty rates from the district poverty rate. the percentage of significantly different comparisons In approximately 20 districts, up to 10 percent of the Figure 5. Significantly Different Pairwise Figure 6. Significantly Different Pairwise Comparisons of Wards in the Country by Confidence Comparisons of Wards in the Country by Confidence Level Level (Bonferroni Correction) 60 20 Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 50 of wards within nation (in %) of wards within nation (in %) 15 40 30 10 20 5 10 0 0 75 80 85 90 95 99 75 80 85 90 95 99 Con dence−level in % Con dence−level in % (Bonferroni Correction) 35 Mapping Subnational Poverty in Zambia Figure 7. Significantly Different Pairwise Comparisons of Wards within Constituency 75 % Con dence−level 80 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 0.8 0.8 of wards within constituency (in %) of wards within constituency (in %) 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 50 100 150 Constituencies Constituencies 85 % Con dence−level 90 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 0.8 0.8 of wards within constituency (in %) of wards within constituency (in %) 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 50 100 150 Constituencies Constituencies 95 % Con dence−level 99 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 0.8 0.8 of wards within constituency (in %) of wards within constituency (in %) 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 50 100 150 Constituencies Constituencies wards differ significantly in their poverty rate from more than 10 percent of pairwise comparisons of the district rate. In approximately 10 districts, 10 wards that differ significantly at the 95 percent percent–30 percent of the wards do the same. confidence level. Additionally, figure 10 shows the percentage The authors also compared the ward poverty of pairwise comparisons of wards that differ sig- level with the national poverty level. Figure 11 shows nificantly within the district at varying confidence that, at the 90 percent confidence level, 30 percent levels. For instance, the panel of the 95 percent of the wards present significantly different poverty confidence level reveals that 50 of the 74 districts rates from the national poverty rate. present pairwise comparisons of wards that dif- These findings are relevant because the Social fer significantly. In particular, 30 districts present Cash Transfer Scheme (SCT) uses district-level pov- 36 Results Figure 8. Significantly Different Comparisons of Ward and Constituency Poverty 75 % Con dence−level 80 % Con dence−level (Bonferroni correction) (Bonferroni correction) 0.8 0.8 di erent from constituency (in %) di erent from constituency (in %) 0.6 0.6 Wards signi cantly Wards signi cantly 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 50 100 150 Constituencies Constituencies 85 % Con dence−level 90 % Con dence−level (Bonferroni correction) (Bonferroni correction) 0.8 0.8 di erent from constituency (in %) di erent from constituency (in %) 0.6 0.6 Wards signi cantly Wards signi cantly 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 50 100 150 Constituencies Constituencies 95 % Con dence−level 99 % Con dence−level (Bonferroni correction) (Bonferroni correction) 0.8 0.8 di erent from constituency (in %) di erent from constituency (in %) 0.6 0.6 Wards signi cantly Wards signi cantly 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 50 100 150 Constituencies Constituencies erty rates as the first filter to reach its beneficiaries. selected districts should have the same chances of Clearly, targeting the allocation of transfers beyond getting enrolled in the SCT program. The lottery districts makes sense because there are differential allocation rule probably would make more sense poverty rates at the ward level. Districts then select in districts all of whose ward poverty rates are wards based on a lottery system. However, irre- statistically similar.17 spective of their poverty rates, not all wards within The current Poverty Map have created poverty rates at the ward, constituency and district levels including their standard er- 17 rors. For each partition, the cluster effect has been identified at the target geographic level. For this reason, if the allocation of social benefits is planned at the ward level, the authors strongly recommend to use the ward poverty estimates and their standard errors derived from the Poverty Mapping exercise. In addition, if the allocation of resources requires as a first step a ranking of constitu- 37 Mapping Subnational Poverty in Zambia Figure 9. Significantly Different Comparisons of Wards and District Poverty 75 % Con dence−level 80 % Con dence−level (Bonferroni correction) (Bonferroni correction) 0.5 0.5 di erent from district (in %) di erent from district (in %) 0.4 0.4 Wards signi cantly Wards signi cantly 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 0 20 40 60 Districts Districts 85 % Con dence−level 90 % Con dence−level (Bonferroni correction) (Bonferroni correction) 0.5 0.5 di erent from district (in %) di erent from district (in %) 0.4 0.4 Wards signi cantly Wards signi cantly 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 0 20 40 60 Districts Districts 95 % Con dence−level 99 % Con dence−level (Bonferroni correction) (Bonferroni correction) 0.5 0.5 di erent from district (in %) di erent from district (in %) 0.4 0.4 Wards signi cantly Wards signi cantly 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 0 20 40 60 Districts Districts POVERTY MAPPING CONSIDERING ment’s official measurement of poverty in Zambia. The poverty line was defined in 2010 at the national SPATIAL DIFFERENCES level, and the consumption aggregates were deflated with national price indices. Because the cost of living The current Poverty Map uses the CSO poverty line is likely to be lower in rural areas than in urban areas, and the consumption aggregate used for the govern- a national poverty line may underestimate urban encies by poverty incidence, the user should use the constituency poverty rates and their standard errors estimated by the Poverty Map. The authors do not recommend to aggregate ward level poverty rates to calculate constituency or district poverty rates. 38 Results Figure 10. Significantly Different Pairwise Comparisons of Wards within District 75 % Con dence−level 80 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 0.5 0.5 of wards within district (in %) of wards within district (in %) 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 0 20 40 60 Districts Districts 85 % Con dence−level 90 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 0.5 0.5 of wards within district (in %) of wards within district (in %) 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 0 20 40 60 Districts Districts 95 % Con dence−level 99 % Con dence−level (Bonferroni correction) (Bonferroni correction) Signi cantly di erent pairwise comparisons Signi cantly di erent pairwise comparisons 0.5 0.5 of wards within district (in %) of wards within district (in %) 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 20 40 60 0 20 40 60 Districts Districts poverty if this line reflects primarily rural living To account for spatial differences in our analy- costs. At the moment, the Government of Zambia sis, we use the poverty measurement suggested by does not count with price data for 2010 based on the World Bank in 2012 to calculate a new Poverty community prices. To the best of our knowledge, Map.18 The aim of this exercise is to better under- the CSO partially collected these data parallel to the stand how the picture will change if we consider 2010 LCMS, but CSO has explained that such prices spatial differences in our poverty measurement. were not duly completed at the time. These spatial differences are mainly considered by 18 The final econometric models used for the Poverty Map with the poverty measurement methodology from the World Bank are available upon request. Please contact Alejandro de la Fuente at: adelafuente@worldbank.org. 39 Mapping Subnational Poverty in Zambia Figure 11. Significantly Different Comparisons of Table 15:  The World Bank Poverty Measurement: Ward and National Poverty by Confidence Level Laspeyres Price Index by Province Province Index Central 0.94 of ward and national estimate (in %) Signi cantly di erent comparisons 30 Copperbelt 1.03 Eastern 0.98 20 Luapula 0.98 Lusaka 1.10 10 Northern 0.95 North Western 1.07 0 Southern 0.96 75 80 85 90 95 99 Western 0.88 Con dence−level in % (Bonferroni Correction) Source: The World Bank (2012). Note: The CSO poverty line is 146,009 kwachas. The World Bank rural poverty line is 146,053.5 and the urban line is 180,551 kwachas. urban-rural poverty lines (180,551 and 146,053.5 kwachas, respectively) and spatial price adjust- ments across provinces. Table 16: CSO and World Bank National Poverty Similar to the CSO consumption aggregate, Using the 2010 LCMS the World Bank poverty measurement considers National Rural Urban food and non-food components. The food com- Poverty Measurement (1) (2) (3) ponent considered 128 items, excluding alcohol CSO 60.4 77.9 27.5 and tobacco (The World Bank, 2012). The non-food CSO (using the World Bank 63.5 78.0 30.3 component included all items excepting hospital poverty line) stays, payments to hospital/heath center/surgery, World Bank 60.5 73.3 35.3 other health expenses, loan repayments, and all Source: Author’s calculations. remittances. The imputed values calculated by CSO Note: The CSO poverty line is 146,009 kwachas. The World Bank rural poverty line is also were used by the World Bank team for rental 146,053.5 and the urban line is 180,551 kwachas. values, electricity, and water payments. The spatial adjustments to account for cost of living differences across provinces were constructed similar in both approaches. Considering the CSO by dividing the consumption aggregate by a Laspey- consumption aggregate and the World Bank poverty res index at the provincial level.19 This index was line, we observe that the CSO urban poverty in- constructed with median prices at the provincial creases by 3 percentage points. After considering the level for selected items used to construct the CPI price adjustment and the urban–rural poverty line, between January–April 2011. Table 15 presents the the rural poverty drops approximately 5 percentage price adjustments by province, wherein Copperbelt, points (from 77.9 to 73.3), and the urban poverty Lusaka, and North Western have indices above one. increases 8 percentage points (from 27.5 to 35.3). Using the LCMS 2010, table 16 summarizes the Map 4 displays the poverty incidence at con- main differences in national poverty rates between stituency level for both the CSO and World Bank both approaches. The national poverty rates are poverty measurement methods. Western, Eastern, 19 The Laspeyres price index is “the price index defined as a fixed weight, or fixed basket, index that uses the basket of goods and services of the base period. The base period serves as both the weight reference period and the price reference period.” (Organisation for Economic Cooperation and Development Glossary of Statistical Terms, stats.oecd.org/.../det). 40 Results Luapula and Lusaka provinces present the high- World Bank poverty measurements. Figures 12–15 est poverty incidences in both maps. However, in display the poverty rates and their corresponding the World Bank map, the Western province shows 95 percent confidence intervals. The CSO poverty fewer constituencies with high poverty incidences. estimates present narrower intervals than the World These are particularly the constituencies next to Bank estimates for both administrative units. How- the Southern province. This result is not surprising ever, the CSO and World Bank constituency poverty given the price adjustment that this province has estimates are highly imprecise for poverty rates been assigned (0.88 price index). between 50 percent and 70 percent. A similar result Using a simple means test between the CSO is found for the ward poverty estimates. and the World Bank Poverty Maps’ constituency These plots also help identify the statistically poverty, the authors find that, for 2 percent of the significant differences among subnational parti- constituencies (3 of 150), the 2 approaches differ sig- tions. For instance, Figure 14 shows that wards nificantly at the 90 percent confidence level. These with poverty rates of 80 percent–90 percent differ constituencies are Kalabo, Liuwa, and Sikongo significantly from the wards with poverty rates from the district of Kalabo in Western province. under 50 percent. The confidence interval of the 80 However, the percentage of constituencies that percent–90 percent poverty rate falls inside the con- differ significantly is smaller than the associated fidence interval of the 70 percent–80 percent poverty significance level of the test (a=0.10). Hence, the rates. Therefore, from figure 14 we can conclude that authors conclude there are no significant differences there are no significant differences among poverty in constituency rates between the two approaches. rates between 70 percent and 90 percent at the 95 Map 5 shows the concentration of the poor percent confidence level. population at the constituency level using the CSO In sum, the main finding of our comparisons and World Bank poverty measurement methods in this section is that we do not find significant again, respectively. A few visual differences are differences between the CSO and the World Bank found in the Western, and in the north of Central constituency and ward poverty rates. Hence, the and Northern, provinces. official CSO estimates are employed throughout Map 6 displays the poverty incidence at ward this report, except where explicitly stated otherwise. level for both the CSO and the World Bank poverty measurement methods. As was found in the constit- uency maps, CSO and World Bank ward estimates APPLICATIONS OF THE POVERTY show close similarities. Few differences are found MAP in the Southern province, whose poverty incidence is higher in the CSO ward map. After testing the The Poverty Map is a tool for obtaining welfare significant differences between the CSO and the indicators at geographic levels not representative World Bank wards, the authors find that 1.2 percent in survey data. The visual representation of the of wards (17 of 1,421) differ significantly between the Poverty Map results help in understanding the 2 approaches. As discussed above, this percentage is dispersion and concentration of poverty; however, lower than the significance level of the test (a=0.10). facilitating understanding should not be seen as the Hence, we conclude that there are no significant final goal of the Poverty Map. differences in wards between the two approaches. The means test that we applied relies on the differences in means and their standard errors. The POVERTY MAPPING AND THE larger the difference, the more likely it is to find sig- SOCIAL CASH TRANSFER SCHEME nificant differences. The smaller the standard errors, IN ZAMBIA the more likely it is to find significantly different means as well. Thus, the authors provide plots of The results derived from the Poverty Map could the poverty estimates and their confidence intervals be used as a first cut to target spatial areas that for constituencies and wards using the CSO and the are disadvantaged within a social safety net pro- 41 Mapping Subnational Poverty in Zambia Map 4. Poverty Map: Poverty Incidence at the Map 5. Poverty Map: Concentration of Poor Constituency Level Using the CSO Poverty Population at the Constituency Level Using the CSO Measurement Method (top) and the Word Bank Poverty Measurement Method (top) and the World Poverty Measurement Method (bottom) Bank Poverty Measurement Method (bottom) 0.96 0.03 0.85 0.81 0.72 0.41 0.009 0.007 0.005 0.004 0.09 0 0.92 0.03 0.81 0.76 0.69 0.47 0.009 0.007 0.006 0.004 0.18 0 gram. The results also could inform the potential The poverty estimates produced by the Poverty universe of eligible beneficiaries of a program Map are not reliable to produce indicators at the such as the Social Cash Transfer (SCT) Scheme. household level. Therefore, the authors constructed According to the SCT, households below the the percentage of eligible beneficiaries using the extreme poverty line that display a dependency following 4 steps: ratio of three or more are eligible for this scheme.20 1. Construct the dependency ratio variable in the Poverty Map estimates are aggregated above the census. household level and therefore cannot substitute 2. Use the new dataset to reproduce the poverty the granular identification of beneficiaries, which estimates at the subnational levels, as well as at requires household or individual-level targeting. the household level. 20 Dependency ratio is constructed by the sum of household members younger than 19 years old + older or equal to 65 years old + adults between 19–64 years old having a disability over the total number of household members between 19–64 who are not disabled. 42 Results Map 6. Poverty Map: Poverty Incidence at the Ward Figure 12. Confidence Intervals and Poverty Level Using the CSO Poverty Measurement Method Estimates at the Constituency Level Using the CSO (top) and the World Bank Poverty Measurement Poverty Measurement Method (bottom) 0.97 0.9 0.87 0.84 0.7 0.76 0.5 0.3 0.51 0.1 0 50 100 150 # constituencies CIMincso95/CIMaxcso95 Poverty_CSO 0.01 0.94 0.83 Figure 13. Confidence Intervals and Poverty 0.78 Estimates at the Constituency Level Using the 0.72 World Bank Poverty Measurement 0.54 0.9 0.7 0.5 0.3 0.04 0.1 0 50 100 150 # constituencies 3. Construct an identifier of eligible households CIMincso95wb/CIMaxcso95wb Poverty_WB based on 100 simulations of adult-equivalent consumption at the household level and based on the dependency ratio. This identifier follows the criteria of households with adult-equivalent to create national and subnational percentages expenditure lower than 96,366 kwachas and of eligible populations. dependency ratio equal to or greater than 3. 4. Construct the percentage of eligible beneficia- After obtaining Poverty Map estimates for ex- ries at the national, provincial, district, and treme poverty and using the dependency ratio as ward levels by adding up the number of times explained, the authors find that 13 percent of the the household is classified as eligible and creat- population lives in households of extreme poverty ing a proportion of the times the household is with a dependency ratio equal to or greater than 3. identified as such across the 100 replications. Using the same eligibility criteria in the survey, the This proportion at the household level is used authors find that 5 percent of the population will 43 Mapping Subnational Poverty in Zambia Figure 14. Confidence Intervals and Poverty Figure 15. Confidence Intervals and Poverty Estimates at the Ward Level Using the CSO Poverty Estimates at the Ward Level Using the World Bank Measurement Poverty Measurement 0.9 0.9 0.7 0.7 0.5 0.5 0.3 0.3 0.1 0.1 0 500 1000 1500 0 500 1000 1500 # wards # wards CIMincso95/CIMaxcso95 Poverty_CSO CIMinwb90/CIMaxwb Poverty_WB be eligible for the Social Cash Transfer Scheme. Notably, according to the Census data, the extreme Map 7. Eligibility Criteria for the Social Cash poverty estimated by the Poverty Map is 42 percent, Transfer Scheme at the Constituency Level and the percentage of people living in households 0.3 with dependency ratio equal to or greater than 3 is 22.4 percent.21 The latter coincides with the gov- ernment’s estimate that, based on the dependency 0.22 ratio, approximately 20 percent of households in Zambia meet the conditions of eligibility for the SCT 0.19 Scheme. Maps 7 and 8 show the eligibility criteria at 0.16 the constituency and ward levels. The darkest areas in Map 7, for instance, show that between 22 and 30 percent of households in those constituencies are eligible for the SCT program. 0.06 POVERTY MAPPING AND 0 SOCIOECONOMIC CORRELATES This subsection describes the geographic dispersion Maps 9 to 11 map separately the average maxi- of socioeconomic and climate variables that are likely mum education in the household and percentage of to correlate with poverty incidence at the ward level. individuals working as employees in the agriculture As discussed, the categories of these maps contain sector or in sales. The authors observe that the map of equal number of wards—approximately 355 wards maximum education is an inverse mirror of the map per category when 4 categories are considered, and of poverty incidence at the ward level. Wards with 284 wards when 5 categories are considered. high poverty rates also are those with low household 21 The standard error of the national extreme poverty calculated by the Poverty Map assuming the cluster effect at this level was 0.06. 44 Results Map 8. Eligibility Criteria for the Social Cash Map 9. Average of Maximum Education at the Ward Transfer Scheme at the Ward Level Level 0.39 14.18 0.23 0.20 9.55 0.17 8.11 7.49 0.08 6.89 0 4.91 Map 10. Percentage of Individuals Working as Map 11. Percentage of Individuals Working as Employees in the Agriculture Sector Employees in the Sales Sector 0.99 0.32 0.87 0.81 0.67 0.29 0.05 0.02 0.01 0 0 education. As mentioned before, the poorest wards are primary roads than from tertiary roads. The con- concentrated in the Southern, Luapula and Northern nection to roads is likely to correlate highly with provinces. Furthermore, wards with high percentages poverty incidence given that connection to roads of employees working in the agricultural sector also enables access to local markets and to the main present high poverty rates. The inverse relationship cities. Finally, Maps 14 and 15 present the precipita- is found in the map that displays the percentage of tion of the warmest quarter of the year and annual employees working in sales or related occupations. precipitation. These variables are likely to be related Maps 12 and 13 display the average distance to to local production of agricultural products. These primary and tertiary roads at the ward level. The maps reveal that wards with high concentrations of poorest wards are more likely to be farther from precipitation present low poverty levels. 45 Mapping Subnational Poverty in Zambia Map 12. Average Distance to Primary Roads Map 13. Average Distance to Tertiary Roads 470.69 102.13 216.66 27.09 90.75 13.11 26.51 6.8 7.51 2.68 0 0.01 Map 14. Precipitation of the Warmest Quarter of Map 15. Annual Precipitation the Year 1501.94 493.9 425.27 1263.48 1108.32 328.09 278.09 980.48 205.17 834.29 657.49 73.35 46 Conclusions and 6 Recommendations This report presents consumption-based Poverty Map estimates at the district, constituency and ward levels in Zambia based on the Living Conditions Monitoring Survey (LCMS) 2010 and the 2010 Census of Population and Housing. Such poverty estimates are accompanied by standard errors. Therefore, what the Poverty Map has produced is a range of poverty rates for each of these administrative units of interest. The better the model and the quality of data, the smaller these errors and the more accurate the estimates are likely to be. After analyzing the coefficient of variation and standard errors of the poverty estimates at the constituency and ward levels, the au- thors conclude that the precision of constituency and ward estimates is good. The estimation of poverty rates considers the identification of the cluster effect in the target population (district, constituency, and ward levels). The estimates of poverty ranges at the constituency and ward levels could be a powerful tool for Zambian policymakers. The Constituency Development Fund allocates equal lump-sum trans- fers across constituencies. However, when the authors compare the poverty estimates among constituencies within districts, and among wards within districts and constituencies, they observe statistically significant differences. These differences should be considered for the allocation of resources to reduce poverty in Zambia. The Social Cash Transfer Scheme is the government’s flagship cash transfer program to address poverty. SCT relies on spatial target- ing at the district level as the first step to guide the program roll-out. Nevertheless, when the authors compare the district poverty rates with the poverty rates of the wards comprised by each district, they find that, in 30 percent of the ward pairwise comparisons, the poverty incidence is significantly different from the district poverty rate at the 90 percent confidence level. This finding justifies the need to apply targeting rules beyond the district level when allocating resources. Similarly, when we compare the ward poverty rates within dis- tricts, some districts show substantial differences in these rates among wards. Hence, not all wards within selected districts should be given equal opportunities to enroll in the SCT program, irrespective of their poverty rates. The wards with the higher poverty rates—and that are statistically similar across the wards—should be considered first. 47 Mapping Subnational Poverty in Zambia This report also emphasizes the need to comple- poverty among constituencies, and within them at ment the analysis of poverty incidence with the the ward level. This information holds important identification of areas with large concentrations of value for policymakers to prioritize the use of scarce poor people. In several cases, the rankings derived resources in areas that need them most. from both welfare indicators provided different con- The Poverty Mapping exercise also highlighted clusions as a result of the density of the poor popula- the relevance that strong geographic information tion in wards and constituencies. In addition, after systems (GIS) and human capital hold within this considering the World Bank poverty measurement type of undertaking. Looking ahead to the next that considers urban–rural poverty lines and price Poverty Map, strong investments and continuous adjustments at the provincial level, the authors do capacity building are fundamental to undertake not find significant differences between the World these tasks. Bank estimates and the official CSO estimates of the Finally, the current project brought unantici- constituency and ward poverty rates. pated positive externalities, such as the revision of In sum, the Poverty Map estimates shed light the sampling frames and the revision and improve- on the geographic dispersion and concentration of ment of shape files. 48 References George C. Canavos. Applied Probability and Statistical Methods. Little, Brown Boston, 1984. Central Statistical Office. Living Conditions Monitoring Survey. Main Report. CSO, Lusaka, Zambia, 2010. Central Statistical Office. 2010 Census of Population and Housing: Popu- lation Summary Report. Lusaka, Zambia, 2012. Chris Elbers, Jean O. Lanjouw, and Peter F. Lanjouw. Welfare in Vil- lages and Towns: Micro-estimation of Poverty and Inequality. Technical report, Discussion Paper TI 2000–029/2, Tinbergen Institute, Amsterdam (http:/www.tinbergen.nl), 2000. Chris Elbers, Jean O. Lanjouw, and Peter F. Lanjouw. Micro–level Estimation of Poverty and Inequality. Econometrica, 71(1):355–364, 2003. ISSN 1468–0262. Kenneth R. Simler. Micro-level Estimates of Poverty in Zambia. Tech- nical report, 2007. Republic of Zambia. Sixth National Development Plan 2011–2015 – Sustained Economic Growth and Poverty Reduction. January 2011. Republic of Zambia. Revised Sixth National Development Plan 2013–2016 – People Centered Economic Growth and Develop- ment. Ministry of Finance. Lusaka, Zambia, 2014. The World Bank. Zambia Poverty Assessment: Stagnant Poverty and Inequality in a Natural Resource-based Economy. Technical report, 2012. The World Bank. Country Partnership Strategy for the Republic of Zambia for the Period FY13–FY16. The World Bank, February, 2013. 49 Annex 51 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the District Level Poverty Province District Province Code District Code Headcount Std. Error No. Poor Central Chibombo 1 101 0.73 0.06 223,275 Central Kabwe 1 102 0.33 0.06 68,486 Central Kapiri-Mposhi 1 103 0.68 0.06 173,557 Central Mkushi 1 104 0.71 0.06 111,355 Central Mumbwa 1 105 0.64 0.09 147,087 Central Serenje 1 106 0.78 0.06 131,453 Copperbelt Chililabombwe 2 201 0.30 0.06 27,946 Copperbelt Chingola 2 202 0.32 0.06 70,028 Copperbelt Kalulushi 2 203 0.30 0.12 30,952 Copperbelt Kitwe 2 204 0.29 0.07 151,762 Copperbelt Luanshya 2 205 0.33 0.07 52,295 Copperbelt Lufwanyama 2 206 0.80 0.06 63,386 Copperbelt Masaiti 2 207 0.51 0.13 53,060 Copperbelt Mpongwe 2 208 0.71 0.06 67,232 Copperbelt Mufulira 2 209 0.30 0.06 50,032 Copperbelt Ndola 2 210 0.31 0.07 141,874 Eastern Chadiza 3 301 0.81 0.05 87,751 Eastern Chipata 3 302 0.72 0.05 331,336 Eastern Katete 3 303 0.82 0.06 201,890 Eastern Lundazi 3 304 0.84 0.04 278,353 Eastern Mambwe 3 305 0.81 0.05 58,767 Eastern Nyimba 3 306 0.78 0.05 69,227 Eastern Petauke 3 307 0.82 0.06 259,929 Luapula Chienge 4 401 0.82 0.05 95,575 Luapula Kawambwa 4 402 0.82 0.05 112,232 Luapula Mansa 4 403 0.65 0.09 151,248 Luapula Milenge 4 404 0.88 0.04 38,573 Luapula Mwense 4 405 0.79 0.06 95,858 Luapula Nchelenge 4 406 0.77 0.05 118,718 Luapula Samfya 4 407 0.91 0.03 182,252 Lusaka Chongwe 5 501 0.61 0.07 117,793 Lusaka Kafue 5 502 0.40 0.07 91,677 Lusaka Luangwa 5 503 0.70 0.08 16,888 Lusaka Lusaka 5 504 0.18 0.06 313,216 Muchinga Chama 6 601 0.71 0.09 74,737 Muchinga Chinsali 6 602 0.85 0.04 125,498 Muchinga Isoka 6 603 0.81 0.04 59,314 Muchinga Mafinga 6 604 0.91 0.03 60,260 (continued on next page) 52 Annex Poverty Map Estimates at the District Level (continued) Poverty Province District Province Code District Code Headcount Std. Error No. Poor Muchinga Mpika 6 605 0.74 0.08 151,429 Muchinga Nakonde 6 606 0.72 0.05 87,694 Northern Chilubi 7 701 0.87 0.04 70,819 Northern Kaputa 7 702 0.79 0.05 95,529 Northern Kasama 7 703 0.51 0.10 120,427 Northern Luwingu 7 704 0.86 0.04 105,296 Northern Mbala 7 705 0.82 0.04 167,431 Northern Mporokoso 7 706 0.82 0.04 82,180 Northern Mpulungu 7 707 0.81 0.04 79,907 Northern Mungwi 7 708 0.86 0.04 132,510 North Western Chavuma 8 801 0.87 0.04 30,622 North Western Ikelenge 8 802 0.59 0.11 19,722 North Western Kabompo 8 803 0.90 0.03 84,175 North Western Kasempa 8 804 0.81 0.04 56,802 North Western Mufumbwe 8 805 0.87 0.04 51,281 North Western Mwinilunga 8 806 0.29 0.09 30,357 North Western Solwezi 8 807 0.50 0.10 129,147 North Western Zambezi 8 808 0.87 0.04 70,519 Southern Choma 9 901 0.72 0.06 179,705 Southern Gwembe 9 902 0.82 0.05 44,103 Southern Itezhi-tezhi 9 903 0.70 0.10 48,213 Southern Kalomo 9 904 0.75 0.05 197,306 Southern Kazungula 9 905 0.68 0.11 71,557 Southern Livingstone 9 906 0.28 0.06 39,266 Southern Mazabuka 9 907 0.63 0.05 147,731 Southern Monze 9 908 0.75 0.05 145,135 Southern Namwala 9 909 0.72 0.09 75,189 Southern Siavonga 9 910 0.72 0.05 65,874 Southern Sinazongwe 9 911 0.77 0.05 78,544 Western Kalabo 10 1001 0.88 0.05 114,548 Western Kaoma 10 1002 0.82 0.05 156,677 Western Lukulu 10 1003 0.86 0.05 74,297 Western Mongu 10 1004 0.71 0.06 129,110 Western Senanga 10 1005 0.87 0.05 110,833 Western Sesheke 10 1006 0.85 0.05 85,806 Western Shang'ombo 10 1007 0.95 0.04 90,396 53 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Constituency Level Constituency Poverty Province District Constituency Province Code District Code Code Headcount Std. Error No. Poor Central Chibombo Chisamba 1 101 1 0.71 0.07 74,965 Central Chibombo Katuba 1 101 2 0.70 0.08 55,786 Central Chibombo Keembe 1 101 3 0.74 0.07 90,672 Central Kabwe Bwacha 1 102 4 0.42 0.07 35,677 Central Kabwe Kabwe 1 102 5 0.27 0.05 32,541 Central Kapiri-Mposhi Kapiri Mposhi 1 103 6 0.68 0.05 175,504 Central Mkushi Mkushi North 1 104 7 0.71 0.06 91,851 Central Mkushi Mkushi South 1 104 8 0.81 0.05 20,833 Central Mumbwa Mumbezhi 1 105 9 0.62 0.10 32,997 Central Mumbwa Mumbwa 1 105 10 0.60 0.09 53,809 Central Mumbwa Nangoma 1 105 11 0.71 0.10 60,355 Central Serenje Chitambo 1 106 12 0.83 0.06 40,609 Central Serenje Muchinga 1 106 13 0.82 0.06 43,815 Central Serenje Serenje 1 106 14 0.73 0.06 47,689 Copperbelt Chililabombwe Chililabombwe 2 201 15 0.31 0.05 28,875 Copperbelt Chingola Chingola 2 202 16 0.37 0.07 47,235 Copperbelt Chingola Nchanga 2 202 17 0.27 0.06 24,782 Copperbelt Kalulushi Kalulushi 2 203 18 0.31 0.11 31,653 Copperbelt Kitwe Chimwemwe 2 204 19 0.36 0.07 42,654 Copperbelt Kitwe Kamfinsa 2 204 20 0.33 0.06 28,759 Copperbelt Kitwe Kwacha 2 204 21 0.31 0.06 41,741 Copperbelt Kitwe Nkana 2 204 22 0.19 0.05 16,158 Copperbelt Kitwe Wusakile 2 204 23 0.24 0.05 23,736 Copperbelt Luanshya Luanshya 2 205 24 0.39 0.06 38,570 Copperbelt Luanshya Roan 2 205 25 0.26 0.06 15,374 Copperbelt Lufwanyama Lufwanyama 2 206 30 0.81 0.05 64,100 Copperbelt Masaiti Kafulafuta 2 207 29 0.54 0.11 23,538 Copperbelt Masaiti Masaiti 2 207 31 0.50 0.11 30,886 Copperbelt Mpongwe Mpongwe 2 208 32 0.73 0.05 68,344 Copperbelt Mufulira Kankoyo 2 209 26 0.30 0.06 13,713 Copperbelt Mufulira Kantanshi 2 209 27 0.23 0.04 13,019 Copperbelt Mufulira Mufulira 2 209 28 0.38 0.06 23,390 Copperbelt Ndola Bwana Mkubwa 2 210 33 0.33 0.07 39,953 Copperbelt Ndola Chifubu 2 210 34 0.29 0.07 28,544 Copperbelt Ndola Kabushi 2 210 35 0.25 0.07 23,527 Copperbelt Ndola Ndola 2 210 36 0.38 0.07 53,590 Eastern Chadiza Chadiza 3 301 37 0.80 0.05 50,644 Eastern Chadiza Vubwi 3 301 38 0.85 0.05 38,209 (continued on next page) 54 Annex Poverty Map Estimates at the Constituency Level (continued) Constituency Poverty Province District Constituency Province Code District Code Code Headcount Std. Error No. Poor Eastern Chipata Chipangali 3 302 41 0.87 0.04 107,805 Eastern Chipata Chipangali 3 302 42 0.51 0.06 82,814 Eastern Chipata Kasenengwa 3 302 43 0.83 0.05 82,369 Eastern Chipata Luangeni 3 302 44 0.82 0.05 62,722 Eastern Katete Milanzi 3 303 45 0.86 0.05 56,149 Eastern Katete Mkaika 3 303 46 0.79 0.06 76,280 Eastern Katete Sinda 3 303 47 0.85 0.06 70,521 Eastern Lundazi Chasefu 3 304 48 0.87 0.04 88,888 Eastern Lundazi Lumezi 3 304 49 0.87 0.04 82,619 Eastern Lundazi Lundazi 3 304 50 0.81 0.05 108,056 Eastern Mambwe Malambo 3 305 51 0.82 0.06 59,352 Eastern Nyimba Nyimba 3 306 52 0.78 0.06 70,049 Eastern Petauke Kapoche 3 307 53 0.85 0.06 107,271 Eastern Petauke Petauke 3 307 54 0.76 0.07 94,700 Eastern Petauke Msanzala 3 307 55 0.86 0.06 57,335 Luapula Chienge Chienge 4 401 65 0.81 0.06 94,780 Luapula Kawambwa Kawambwa 4 402 56 0.77 0.06 36,685 Luapula Kawambwa Mwansabombwe 4 402 57 0.82 0.06 37,380 Luapula Kawambwa Pambashe 4 402 58 0.89 0.04 37,825 Luapula Mansa Bahati 4 403 59 0.69 0.10 64,824 Luapula Mansa Mansa 4 403 61 0.61 0.10 83,906 Luapula Milenge Chembe 4 404 60 0.87 0.05 38,141 Luapula Mwense Chipili 4 405 62 0.84 0.05 27,739 Luapula Mwense Mambilima 4 405 63 0.78 0.06 20,225 Luapula Mwense Mwense 4 405 64 0.78 0.06 48,558 Luapula Nchelenge Nchelenge 4 406 66 0.77 0.05 118,718 Luapula Samfya Bangweulu 4 407 67 0.89 0.04 81,781 Luapula Samfya Chifunabuli 4 407 68 0.92 0.04 77,421 Luapula Samfya Luapula 4 407 69 0.92 0.04 22,291 Lusaka Chongwe Chongwe 5 501 73 0.54 0.06 77,670 Lusaka Chongwe Rufunsa 5 501 74 0.79 0.05 40,561 Lusaka Kafue Kafue 5 502 70 0.43 0.06 52,477 Lusaka Kafue Chilanga 5 502 72 0.36 0.06 39,084 Lusaka Luangwa Feira 5 503 71 0.70 0.07 17,019 Lusaka Lusaka Chawama 5 504 75 0.21 0.07 40,049 Lusaka Lusaka Kabwata 5 504 76 0.09 0.04 16,722 Lusaka Lusaka Kanyama 5 504 77 0.20 0.07 73,578 Lusaka Lusaka Lusaka Central 5 504 78 0.10 0.04 12,148 (continued on next page) 55 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Constituency Level (continued) Constituency Poverty Province District Constituency Province Code District Code Code Headcount Std. Error No. Poor Lusaka Lusaka Mandevu 5 504 79 0.22 0.07 80,020 Lusaka Lusaka Matero 5 504 80 0.19 0.07 55,507 Lusaka Lusaka Munali 5 504 81 0.15 0.05 39,835 Muchinga Chama Chama North 6 601 39 0.70 0.09 37,676 Muchinga Chama Chama South 6 601 40 0.73 0.09 37,181 Muchinga Chinsali Chinsali 6 602 83 0.82 0.06 71,822 Muchinga Chinsali Shiwang'andu 6 602 84 0.88 0.05 53,356 Muchinga Isoka Isoka West 6 603 86 0.82 0.04 59,708 Muchinga Mafinga Isoka East 6 604 85 0.90 0.04 60,113 Muchinga Mpika Kanchibiya 6 605 98 0.85 0.06 70,222 Muchinga Mpika Mfuwe 6 605 99 0.82 0.06 22,101 Muchinga Mpika Mpika 6 605 100 0.63 0.10 60,444 Muchinga Nakonde Nakonde 6 606 87 0.72 0.06 87,670 Northern Chilubi Chilubi 7 701 82 0.87 0.04 70,836 Northern Kaputa Chimbamilonga 7 702 88 0.80 0.05 39,456 Northern Kaputa Kaputa 7 702 89 0.76 0.06 54,805 Northern Kasama Kasama 7 703 90 0.45 0.09 73,529 Northern Kasama Lukashya 7 703 91 0.68 0.10 49,115 Northern Luwingu Lubansenshi 7 704 93 0.82 0.05 43,526 Northern Luwingu Lupososhi 7 704 94 0.88 0.05 61,357 Northern Mbala Mbala 7 705 95 0.78 0.05 87,486 Northern Mbala Senga Hill 7 705 97 0.84 0.05 78,607 Northern Mporokoso Lunte 7 706 101 0.85 0.05 49,653 Northern Mporokoso Mporokoso 7 706 102 0.78 0.05 32,479 Northern Mpulungu Mpulungu 7 707 96 0.81 0.05 80,016 Northern Mungwi Malole 7 708 92 0.85 0.05 131,462 North Western Chavuma Chavuma 8 801 103 0.86 0.05 30,282 North Western Ikelenge Mwinilunga West 8 802 109 0.58 0.12 19,267 North Western Kabompo Kabompo East 8 803 104 0.92 0.03 44,814 North Western Kabompo Kabompo West 8 803 105 0.88 0.04 39,239 North Western Kasempa Kasempa 8 804 106 0.81 0.05 56,619 North Western Mufumbwe Mufumbwe 8 805 107 0.87 0.04 51,222 North Western Mwinilunga Mwinilunga East 8 806 108 0.29 0.09 30,610 North Western Solwezi Solwezi Central 8 807 110 0.35 0.09 47,632 North Western Solwezi Solwezi East 8 807 111 0.69 0.10 25,154 North Western Solwezi Solwezi West 8 807 112 0.62 0.10 53,678 North Western Zambezi Zambezi East 8 808 113 0.85 0.04 49,962 North Western Zambezi Zambezi West 8 808 114 0.91 0.04 20,089 (continued on next page) 56 Annex Poverty Map Estimates at the Constituency Level (continued) Constituency Poverty Province District Constituency Province Code District Code Code Headcount Std. Error No. Poor Southern Choma Choma 9 901 115 0.61 0.06 75,452 Southern Choma Mbabala 9 901 116 0.81 0.05 47,756 Southern Choma Pemba 9 901 117 0.82 0.05 55,743 Southern Gwembe Gwembe 9 902 118 0.83 0.04 44,370 Southern Itezhi-tezhi Itezhi Tezhi 9 903 130 0.70 0.10 48,331 Southern Kalomo Dundumwenze 9 904 119 0.79 0.05 64,501 Southern Kalomo Kalomo 9 904 120 0.72 0.05 78,957 Southern Kalomo Mapatizya 9 904 122 0.79 0.05 55,833 Southern Kazungula Katombola 9 905 121 0.67 0.10 70,343 Southern Livingstone Livingstone 9 906 123 0.28 0.05 38,884 Southern Mazabuka Chikankata 9 907 124 0.78 0.05 46,921 Southern Mazabuka Magoye 9 907 125 0.80 0.05 57,979 Southern Mazabuka Mazabuka 9 907 126 0.41 0.06 41,659 Southern Monze Bweengwa 9 908 127 0.82 0.05 50,177 Southern Monze Monze 9 908 128 0.67 0.05 68,718 Southern Monze Moomba 9 908 129 0.83 0.05 25,252 Southern Namwala Namwala 9 909 131 0.72 0.10 74,524 Southern Siavonga Siavonga 9 910 132 0.71 0.05 64,872 Southern Sinazongwe Sinazongwe 9 911 133 0.76 0.05 78,144 Western Kalabo Kalabo 10 1001 134 0.86 0.03 49,621 Western Kalabo Liuwa 10 1001 135 0.90 0.03 24,022 Western Kalabo Sikongo 10 1001 136 0.89 0.03 41,139 Western Kaoma Kaoma 10 1002 137 0.77 0.05 67,479 Western Kaoma Luampa 10 1002 138 0.86 0.04 38,293 Western Kaoma Mangango 10 1002 139 0.87 0.04 51,008 Western Lukulu Lukulu East 10 1003 140 0.85 0.04 50,207 Western Lukulu Lukulu West 10 1003 141 0.88 0.04 24,547 Western Mongu Luena 10 1004 142 0.84 0.05 42,851 Western Mongu Mongu 10 1004 143 0.58 0.06 52,539 Western Mongu Nalikwanda 10 1004 144 0.86 0.05 34,825 Western Senanga Nalolo 10 1005 145 0.92 0.03 51,582 Western Senanga Senanga 10 1005 146 0.85 0.04 60,657 Western Sesheke Mulobezi 10 1006 148 0.91 0.04 28,099 Western Sesheke Mwandi 10 1006 149 0.89 0.04 22,710 Western Sesheke Sesheke 10 1006 150 0.80 0.05 35,287 Western Shang'ombo Sinjembela 10 1007 147 0.96 0.03 90,586 57 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Central Chibombo Chisamba Muswishi 1 101 1 1 0.78 0.07 12,932 Central Chibombo Chisamba Mulungushi 1 101 1 2 0.77 0.07 5,328 Central Chibombo Chisamba Chikokomene 1 101 1 3 0.80 0.07 12,812 Central Chibombo Chisamba Chamuka 1 101 1 4 0.70 0.07 15,009 Central Chibombo Chisamba Chisamba 1 101 1 5 0.57 0.08 13,612 Central Chibombo Chisamba Liteta 1 101 1 6 0.72 0.07 14,545 Central Chibombo Katuba Katuba 1 101 2 7 0.70 0.07 11,744 Central Chibombo Katuba Chunga 1 101 2 8 0.37 0.08 2,997 Central Chibombo Katuba Mungule 1 101 2 9 0.72 0.07 18,583 Central Chibombo Katuba Muchenje 1 101 2 10 0.80 0.06 8,999 Central Chibombo Katuba Chalochabalenge 1 101 2 11 0.81 0.07 6,173 Central Chibombo Katuba Kabile 1 101 2 12 0.83 0.06 8,500 Central Chibombo Keembe Chaloshi 1 101 3 13 0.36 0.06 3,354 Central Chibombo Keembe Kalola 1 101 3 14 0.74 0.07 10,465 Central Chibombo Keembe Kakoma 1 101 3 15 0.76 0.06 14,429 Central Chibombo Keembe Chikobo 1 101 3 16 0.75 0.06 4,384 Central Chibombo Keembe Chibombo 1 101 3 17 0.65 0.06 6,343 Central Chibombo Keembe Chitanda 1 101 3 18 0.82 0.06 10,480 Central Chibombo Keembe Mashikili 1 101 3 19 0.82 0.05 19,045 Central Chibombo Keembe Keembe 1 101 3 20 0.82 0.06 7,628 Central Chibombo Keembe Lunjofwa 1 101 3 21 0.79 0.06 6,719 Central Chibombo Keembe Ipongo 1 101 3 22 0.80 0.06 7,993 Central Kabwe Bwacha Muwowo 1 102 4 1 0.69 0.08 1,266 Central Kabwe Bwacha Muwowo East 1 102 4 2 0.38 0.06 1,603 Central Kabwe Bwacha Ngungu 1 102 4 3 0.09 0.04 558 Central Kabwe Bwacha Chimanimani 1 102 4 4 0.12 0.04 577 Central Kabwe Bwacha Bwacha 1 102 4 5 0.16 0.05 1,548 Central Kabwe Bwacha Kawama 1 102 4 6 0.41 0.08 4,154 Central Kabwe Bwacha Ben Kapufi 1 102 4 7 0.40 0.08 2,051 Central Kabwe Bwacha Makululu 1 102 4 8 0.45 0.09 1,732 Central Kabwe Bwacha Moomba 1 102 4 9 0.55 0.09 6,188 Central Kabwe Bwacha Zambezi 1 102 4 10 0.65 0.09 5,398 Central Kabwe Bwacha Kangomba 1 102 4 11 0.68 0.08 3,322 Central Kabwe Bwacha Chinyanja 1 102 4 12 0.61 0.07 2,000 Central Kabwe Bwacha Munyama 1 102 4 13 0.58 0.08 1,250 Central Kabwe Bwacha Munga 1 102 4 14 0.37 0.07 1,276 Central Kabwe Bwacha Chililalila 1 102 4 15 0.55 0.09 3,013 Central Kabwe Kabwe Mpima 1 102 5 16 0.32 0.06 2,115 (continued on next page) 58 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Central Kabwe Kabwe Luansase 1 102 5 17 0.65 0.07 1,984 Central Kabwe Kabwe Waya 1 102 5 18 0.50 0.06 2,855 Central Kabwe Kabwe Chirwa 1 102 5 19 0.36 0.07 9,777 Central Kabwe Kabwe Njanji 1 102 5 20 0.09 0.04 590 Central Kabwe Kabwe Justine Kabwe 1 102 5 21 0.09 0.04 500 Central Kabwe Kabwe David Ramushu 1 102 5 22 0.39 0.08 3,256 Central Kabwe Kabwe Highridge 1 102 5 23 0.05 0.02 348 Central Kabwe Kabwe Kalonga 1 102 5 24 0.26 0.06 2,836 Central Kabwe Kabwe Luangwa 1 102 5 25 0.14 0.04 2,268 Central Kabwe Kabwe Nakoli 1 102 5 26 0.42 0.09 4,233 Central Kabwe Kabwe Kaputula 1 102 5 27 0.19 0.05 2,494 Central Kapiri-Mposhi Kapiri Mposhi Ngabwe 1 103 6 1 0.81 0.06 4,679 Central Kapiri-Mposhi Kapiri Mposhi Mukumbwe 1 103 6 2 0.80 0.06 12,640 Central Kapiri-Mposhi Kapiri Mposhi Luanchele 1 103 6 3 0.79 0.06 12,014 Central Kapiri-Mposhi Kapiri Mposhi Chipepo 1 103 6 4 0.76 0.06 10,964 Central Kapiri-Mposhi Kapiri Mposhi Kapandwe 1 103 6 5 0.77 0.06 3,000 Central Kapiri-Mposhi Kapiri Mposhi Mpunde 1 103 6 6 0.70 0.06 18,218 Central Kapiri-Mposhi Kapiri Mposhi Chimbwelo 1 103 6 7 0.39 0.07 17,588 Central Kapiri-Mposhi Kapiri Mposhi Kapiri Central 1 103 6 8 0.68 0.07 10,234 Central Kapiri-Mposhi Kapiri Mposhi Kashitu 1 103 6 9 0.77 0.06 5,861 Central Kapiri-Mposhi Kapiri Mposhi Mushimbili 1 103 6 10 0.74 0.06 13,926 Central Kapiri-Mposhi Kapiri Mposhi Lunchu 1 103 6 11 0.73 0.06 23,882 Central Kapiri-Mposhi Kapiri Mposhi Chang’ondo 1 103 6 12 0.70 0.06 16,933 Central Kapiri-Mposhi Kapiri Mposhi Kakwelesa 1 103 6 13 0.75 0.06 19,226 Central Kapiri-Mposhi Kapiri Mposhi Kapumba 1 103 6 14 0.71 0.06 4,368 Central Mkushi Mkushi North Masofu 1 104 7 1 0.80 0.07 3,423 Central Mkushi Mkushi North Upper Lunsefwa 1 104 7 2 0.79 0.07 9,985 Central Mkushi Mkushi North Chalata 1 104 7 3 0.72 0.07 10,418 Central Mkushi Mkushi North Chibefwe 1 104 7 4 0.41 0.08 7,963 Central Mkushi Mkushi North Nkumbi 1 104 7 5 0.73 0.07 9,188 Central Mkushi Mkushi North Mushibemba 1 104 7 6 0.66 0.08 7,731 Central Mkushi Mkushi North Nshinso 1 104 7 7 0.82 0.07 7,693 Central Mkushi Mkushi North Matuka 1 104 7 8 0.79 0.07 12,743 Central Mkushi Mkushi North Tembwe 1 104 7 9 0.57 0.09 5,630 Central Mkushi Mkushi North Munda 1 104 7 10 0.77 0.07 12,249 Central Mkushi Mkushi North Chikanda 1 104 7 11 0.83 0.06 2,953 Central Mkushi Mkushi South Kalwa 1 104 8 12 0.88 0.05 3,612 Central Mkushi Mkushi South Chingombe 1 104 8 13 0.83 0.05 1,858 (continued on next page) 59 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Central Mkushi Mkushi South Nkomashi 1 104 8 14 0.74 0.06 7,814 Central Mkushi Mkushi South Kamimbya 1 104 8 15 0.80 0.05 4,007 Central Mkushi Mkushi South Chapaba 1 104 8 16 0.84 0.05 1,445 Central Mkushi Mkushi South Mwalala 1 104 8 17 0.89 0.04 1,868 Central Mumbwa Mumbezhi Kapyanga 1 105 9 17 0.73 0.10 4,029 Central Mumbwa Mumbezhi Chabota 1 105 9 18 0.72 0.10 4,493 Central Mumbwa Mumbezhi Kalundu 1 105 9 19 0.73 0.10 5,713 Central Mumbwa Mumbezhi Milandu 1 105 9 20 0.69 0.11 7,940 Central Mumbwa Mumbezhi Makombwe 1 105 9 21 0.62 0.12 3,337 Central Mumbwa Mumbezhi Nampundwe 1 105 9 22 0.45 0.09 7,737 Central Mumbwa Mumbwa Nalusanga 1 105 10 1 0.65 0.08 6,844 Central Mumbwa Mumbwa Lutale 1 105 10 2 0.75 0.08 7,805 Central Mumbwa Mumbwa Kalwanyembe 1 105 10 3 0.72 0.08 6,060 Central Mumbwa Mumbwa Mpusu 1 105 10 4 0.78 0.07 5,582 Central Mumbwa Mumbwa Mumba 1 105 10 5 0.73 0.08 10,868 Central Mumbwa Mumbwa Mupona 1 105 10 6 0.26 0.07 5,429 Central Mumbwa Mumbwa Chibolyo 1 105 10 7 0.71 0.09 4,088 Central Mumbwa Mumbwa Nambala 1 105 10 8 0.71 0.08 4,148 Central Mumbwa Mumbwa Shimbizhi 1 105 10 9 0.68 0.08 4,119 Central Mumbwa Nangoma Nakasaka 1 105 11 10 0.61 0.08 7,932 Central Mumbwa Nangoma Nangoma 1 105 11 11 0.71 0.09 11,130 Central Mumbwa Nangoma Myooye 1 105 11 12 0.69 0.08 3,234 Central Mumbwa Nangoma Shichanzu 1 105 11 13 0.70 0.09 8,561 Central Mumbwa Nangoma Nalubanda 1 105 11 14 0.74 0.09 8,885 Central Mumbwa Nangoma Choma 1 105 11 15 0.70 0.09 5,914 Central Mumbwa Nangoma Chisalu 1 105 11 16 0.72 0.09 14,075 Central Serenje Chitambo Lulimala 1 106 12 1 0.82 0.06 7,564 Central Serenje Chitambo Mpelembe 1 106 12 2 0.86 0.05 1,916 Central Serenje Chitambo Chipundu 1 106 12 3 0.84 0.05 6,403 Central Serenje Chitambo Luombwa 1 106 12 4 0.85 0.06 1,662 Central Serenje Chitambo Chalilo 1 106 12 5 0.83 0.05 6,379 Central Serenje Chitambo Chitambo 1 106 12 6 0.81 0.05 8,069 Central Serenje Chitambo Muchinka 1 106 12 7 0.82 0.05 8,587 Central Serenje Muchinga Mailo 1 106 13 8 0.83 0.05 6,333 Central Serenje Muchinga Kanona 1 106 13 9 0.69 0.05 4,460 Central Serenje Muchinga Serenje 1 106 13 10 0.82 0.05 4,310 Central Serenje Muchinga Kabansa 1 106 13 11 0.85 0.06 689 Central Serenje Muchinga Chisomo 1 106 13 12 0.85 0.06 2,148 (continued on next page) 60 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Central Serenje Muchinga Lukunsanshi 1 106 13 13 0.86 0.05 3,277 Central Serenje Muchinga Sancha 1 106 13 14 0.85 0.06 4,735 Central Serenje Muchinga Chibale 1 106 13 15 0.83 0.06 6,686 Central Serenje Muchinga Masaninga 1 106 13 16 0.81 0.06 10,941 Central Serenje Serenje Kabamba 1 106 14 17 0.81 0.06 6,117 Central Serenje Serenje Ibolelo 1 106 14 18 0.53 0.07 12,077 Central Serenje Serenje Muchinga 1 106 14 19 0.82 0.06 13,699 Central Serenje Serenje Nganswa 1 106 14 20 0.85 0.06 6,464 Central Serenje Serenje Lupiya 1 106 14 21 0.83 0.06 6,380 Central Serenje Serenje Musangashi 1 106 14 22 0.89 0.05 2,373 Copperbelt Chililabombwe Chililabombwe Chilimina 2 201 15 1 0.83 0.05 2,338 Copperbelt Chililabombwe Chililabombwe Miyanda 2 201 15 2 0.79 0.06 1,899 Copperbelt Chililabombwe Chililabombwe Joseph Mwila 2 201 15 3 0.44 0.06 1,701 Copperbelt Chililabombwe Chililabombwe Anoya Zulu 2 201 15 4 0.24 0.06 334 Copperbelt Chililabombwe Chililabombwe Kawama 2 201 15 5 0.73 0.06 990 Copperbelt Chililabombwe Chililabombwe Miteta 2 201 15 6 0.64 0.06 3,949 Copperbelt Chililabombwe Chililabombwe Chitambi 2 201 15 7 0.72 0.07 453 Copperbelt Chililabombwe Chililabombwe Kakoso 2 201 15 8 0.33 0.07 2,072 Copperbelt Chililabombwe Chililabombwe Kafue 2 201 15 9 0.07 0.02 254 Copperbelt Chililabombwe Chililabombwe Mvula 2 201 15 10 0.11 0.04 302 Copperbelt Chililabombwe Chililabombwe Mathew 2 201 15 11 0.17 0.05 919 Nkoloma Copperbelt Chililabombwe Chililabombwe James Phiri 2 201 15 12 0.14 0.04 1,058 Copperbelt Chililabombwe Chililabombwe Silwizya 2 201 15 13 0.65 0.07 524 Copperbelt Chililabombwe Chililabombwe Helen Kaunda 2 201 15 14 0.14 0.05 453 Copperbelt Chililabombwe Chililabombwe Chitimukulu 2 201 15 15 0.15 0.05 437 Copperbelt Chililabombwe Chililabombwe Yeta 2 201 15 16 0.14 0.05 637 Copperbelt Chililabombwe Chililabombwe Ngebe 2 201 15 17 0.15 0.05 698 Copperbelt Chililabombwe Chililabombwe Kamina 2 201 15 18 0.15 0.05 603 Copperbelt Chililabombwe Chililabombwe Mukuka 2 201 15 19 0.26 0.06 1,556 Copperbelt Chililabombwe Chililabombwe Yotam Muleya 2 201 15 20 0.49 0.07 5,969 Copperbelt Chililabombwe Chililabombwe Nakatindi 2 201 15 21 0.19 0.05 1,394 Copperbelt Chililabombwe Chililabombwe Mumba 2 201 15 22 0.07 0.03 170 Copperbelt Chingola Chingola Musenga 2 202 16 11 0.48 0.06 1,343 Copperbelt Chingola Chingola Kasompe 2 202 16 12 0.36 0.07 1,875 Copperbelt Chingola Chingola Mimbula 2 202 16 13 0.40 0.06 2,674 Copperbelt Chingola Chingola Lulamba 2 202 16 14 0.33 0.05 5,030 Copperbelt Chingola Chingola Twatasha 2 202 16 15 0.33 0.07 2,736 (continued on next page) 61 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Copperbelt Chingola Chingola Chifwembe 2 202 16 16 0.28 0.07 1,114 Copperbelt Chingola Chingola Chabanyama 2 202 16 17 0.31 0.07 2,274 Copperbelt Chingola Chingola Maiteneke 2 202 16 18 0.27 0.06 3,186 Copperbelt Chingola Chingola Chitimukulu 2 202 16 19 0.25 0.06 1,882 Copperbelt Chingola Chingola Chikola 2 202 16 20 0.12 0.04 1,054 Copperbelt Chingola Chingola Chiwempala 2 202 16 21 0.29 0.07 3,678 Copperbelt Chingola Chingola Kabungo 2 202 16 22 0.28 0.07 1,989 Copperbelt Chingola Chingola Kalilo 2 202 16 23 0.77 0.06 4,848 Copperbelt Chingola Chingola Ipafu 2 202 16 24 0.77 0.06 4,500 Copperbelt Chingola Chingola Muchinshi 2 202 16 25 0.80 0.05 3,524 Copperbelt Chingola Chingola Mutenda 2 202 16 26 0.81 0.05 4,758 Copperbelt Chingola Chingola Chingola 2 202 16 27 0.07 0.02 569 Copperbelt Chingola Nchanga Kwacha 2 202 17 1 0.05 0.02 272 Copperbelt Chingola Nchanga Nchanga 2 202 17 2 0.09 0.03 466 Copperbelt Chingola Nchanga Sekela 2 202 17 3 0.10 0.04 585 Copperbelt Chingola Nchanga Nsansa 2 202 17 4 0.12 0.04 1,216 Copperbelt Chingola Nchanga Buntungwa 2 202 17 5 0.13 0.04 1,656 Copperbelt Chingola Nchanga Kabundi 2 202 17 6 0.07 0.03 509 Copperbelt Chingola Nchanga Kasala 2 202 17 7 0.10 0.04 740 Copperbelt Chingola Nchanga Kapisha 2 202 17 8 0.50 0.08 16,837 Copperbelt Chingola Nchanga Luano 2 202 17 9 0.74 0.06 614 Copperbelt Chingola Nchanga Bupalo 2 202 17 10 0.78 0.06 1,883 Copperbelt Kalulushi Kalulushi Musakashi 2 203 18 1 0.71 0.12 716 Copperbelt Kalulushi Kalulushi Chambishi 2 203 18 2 0.13 0.13 12 Copperbelt Kalulushi Kalulushi Twaiteka 2 203 18 3 0.27 0.12 6,476 Copperbelt Kalulushi Kalulushi Lukoshi 2 203 18 4 0.45 0.12 700 Copperbelt Kalulushi Kalulushi Lulamba 2 203 18 5 0.35 0.12 1,582 Copperbelt Kalulushi Kalulushi Mwambashi 2 203 18 6 0.68 0.14 2,349 Copperbelt Kalulushi Kalulushi Ichimpe 2 203 18 7 0.44 0.14 1,545 Copperbelt Kalulushi Kalulushi Kalanga 2 203 18 8 0.13 0.06 955 Copperbelt Kalulushi Kalulushi Lubuto 2 203 18 9 0.10 0.08 425 Copperbelt Kalulushi Kalulushi Kalungwishi 2 203 18 10 0.11 0.09 384 Copperbelt Kalulushi Kalulushi Luapula 2 203 18 11 0.11 0.09 343 Copperbelt Kalulushi Kalulushi Ngweshi 2 203 18 12 0.15 0.10 484 Copperbelt Kalulushi Kalulushi Kalengwa 2 203 18 13 0.28 0.13 531 Copperbelt Kalulushi Kalulushi Chibuluma 2 203 18 14 0.25 0.13 849 Copperbelt Kalulushi Kalulushi Kankonshi 2 203 18 15 0.51 0.16 722 Copperbelt Kalulushi Kalulushi Chisupa 2 203 18 16 0.42 0.15 1,780 (continued on next page) 62 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Copperbelt Kalulushi Kalulushi Buseko 2 203 18 17 0.26 0.13 933 Copperbelt Kalulushi Kalulushi Dongwe 2 203 18 18 0.12 0.10 188 Copperbelt Kalulushi Kalulushi Kafue 2 203 18 19 0.26 0.12 2,935 Copperbelt Kalulushi Kalulushi Chankalamo 2 203 18 20 0.59 0.14 1,866 Copperbelt Kalulushi Kalulushi Chati 2 203 18 21 0.51 0.13 4,475 Copperbelt Kalulushi Kalulushi Chembe 2 203 18 22 0.62 0.14 1,274 Copperbelt Kitwe Chimwemwe Itimpi 2 204 19 1 0.49 0.07 5,766 Copperbelt Kitwe Chimwemwe Twatasha 2 204 19 2 0.45 0.08 10,707 Copperbelt Kitwe Chimwemwe Kawama 2 204 19 3 0.47 0.08 18,070 Copperbelt Kitwe Chimwemwe Buntungwa 2 204 19 4 0.22 0.06 3,838 Copperbelt Kitwe Chimwemwe Lubuto 2 204 19 5 0.16 0.05 1,986 Copperbelt Kitwe Chimwemwe Chimwemwe 2 204 19 6 0.20 0.06 2,858 Copperbelt Kitwe Kamfinsa Bupe 2 204 20 13 0.11 0.04 2,031 Copperbelt Kitwe Kamfinsa Ndeke 2 204 20 14 0.35 0.07 18,069 Copperbelt Kitwe Kamfinsa Kafue 2 204 20 15 0.62 0.08 4,396 Copperbelt Kitwe Kamfinsa Kamfinsa 2 204 20 16 0.37 0.05 4,534 Copperbelt Kitwe Kwacha Kwacha 2 204 21 7 0.26 0.07 4,808 Copperbelt Kitwe Kwacha Bulangililo 2 204 21 8 0.26 0.06 7,281 Copperbelt Kitwe Kwacha Ipusukilo 2 204 21 9 0.46 0.08 20,000 Copperbelt Kitwe Kwacha Chantete 2 204 21 10 0.77 0.07 2,402 Copperbelt Kitwe Kwacha Riverside 2 204 21 11 0.24 0.05 6,935 Copperbelt Kitwe Kwacha Lubwa 2 204 21 12 0.17 0.03 2,109 Copperbelt Kitwe Nkana Rokana 2 204 22 22 0.27 0.06 3,107 Copperbelt Kitwe Nkana Parklands 2 204 22 23 0.03 0.02 203 Copperbelt Kitwe Nkana Buchi 2 204 22 24 0.23 0.07 5,715 Copperbelt Kitwe Nkana Mukuba 2 204 22 25 0.05 0.02 321 Copperbelt Kitwe Nkana Miseshi 2 204 22 26 0.11 0.04 1,965 Copperbelt Kitwe Nkana Mindolo 2 204 22 27 0.19 0.05 2,723 Copperbelt Kitwe Nkana Kamakonde 2 204 22 28 0.63 0.08 2,501 Copperbelt Kitwe Wusakile Limaposa 2 204 23 17 0.53 0.07 1,187 Copperbelt Kitwe Wusakile Luangwa 2 204 23 18 0.48 0.08 14,812 Copperbelt Kitwe Wusakile Chamboli 2 204 23 19 0.13 0.06 2,948 Copperbelt Kitwe Wusakile Chibote 2 204 23 20 0.11 0.05 1,888 Copperbelt Kitwe Wusakile Wusakile 2 204 23 21 0.16 0.06 4,026 Copperbelt Luanshya Luanshya Misaka 2 205 24 1 0.71 0.06 7,781 Copperbelt Luanshya Luanshya Fisenge 2 205 24 2 0.66 0.07 4,707 Copperbelt Luanshya Luanshya Twashuka 2 205 24 3 0.76 0.06 599 Copperbelt Luanshya Luanshya Chitwi 2 205 24 4 0.81 0.06 1,845 (continued on next page) 63 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Copperbelt Luanshya Luanshya Chifulube 2 205 24 5 0.75 0.06 603 Copperbelt Luanshya Luanshya Buntungwa 2 205 24 6 0.59 0.08 9,125 Copperbelt Luanshya Luanshya Mpelembe 2 205 24 7 0.07 0.03 435 Copperbelt Luanshya Luanshya Buteko 2 205 24 8 0.11 0.03 1,168 Copperbelt Luanshya Luanshya James Phiri 2 205 24 9 0.41 0.05 3,527 Copperbelt Luanshya Luanshya Levi Chiko 2 205 24 10 0.24 0.06 1,731 Copperbelt Luanshya Luanshya Mikomfwa 2 205 24 11 0.33 0.07 3,894 Copperbelt Luanshya Luanshya Zambezi 2 205 24 12 0.31 0.07 2,070 Copperbelt Luanshya Luanshya Mulungushi 2 205 24 13 0.16 0.05 863 Copperbelt Luanshya Luanshya Mipundu 2 205 24 14 0.17 0.05 971 Copperbelt Luanshya Roan Kafubu 2 205 25 15 0.19 0.05 615 Copperbelt Luanshya Roan Nkoloma 2 205 25 16 0.16 0.05 451 Copperbelt Luanshya Roan Lumumba 2 205 25 17 0.14 0.04 1,044 Copperbelt Luanshya Roan Kafue 2 205 25 18 0.28 0.06 2,295 Copperbelt Luanshya Roan Chilabula 2 205 25 19 0.79 0.06 1,416 Copperbelt Luanshya Roan Mpatamatu 2 205 25 20 0.14 0.04 750 Copperbelt Luanshya Roan Justine Kabwe 2 205 25 21 0.16 0.05 691 Copperbelt Luanshya Roan Nkulumashimba 2 205 25 22 0.17 0.05 491 Copperbelt Luanshya Roan Baluba 2 205 25 23 0.22 0.06 776 Copperbelt Luanshya Roan Milyashi 2 205 25 24 0.34 0.06 2,221 Copperbelt Luanshya Roan Ngebe 2 205 25 25 0.13 0.04 566 Copperbelt Luanshya Roan Kansengu 2 205 25 26 0.19 0.05 1,013 Copperbelt Luanshya Roan Kawama 2 205 25 27 0.77 0.06 1,547 Copperbelt Luanshya Roan Muva Hill 2 205 25 28 0.68 0.08 331 Copperbelt Lufwanyama Lufwanyama Kasanta 2 206 30 1 0.86 0.05 5,664 Copperbelt Lufwanyama Lufwanyama Kabundia 2 206 30 2 0.83 0.05 5,649 Copperbelt Lufwanyama Lufwanyama Boso 2 206 30 3 0.81 0.06 5,061 Copperbelt Lufwanyama Lufwanyama Kansonka 2 206 30 4 0.78 0.06 5,042 Copperbelt Lufwanyama Lufwanyama Kafubu 2 206 30 5 0.78 0.06 5,378 Copperbelt Lufwanyama Lufwanyama Chinbanga 2 206 30 6 0.74 0.06 9,031 Copperbelt Lufwanyama Lufwanyama Sokotwe 2 206 30 7 0.75 0.06 3,667 Copperbelt Lufwanyama Lufwanyama Mibenge 2 206 30 8 0.77 0.07 2,679 Copperbelt Lufwanyama Lufwanyama Chantete 2 206 30 9 0.78 0.08 671 Copperbelt Lufwanyama Lufwanyama Bulaya 2 206 30 10 0.78 0.08 443 Copperbelt Lufwanyama Lufwanyama Mukumbo 2 206 30 11 0.86 0.05 3,904 Copperbelt Lufwanyama Lufwanyama Mwelushi 2 206 30 12 0.81 0.05 3,577 Copperbelt Lufwanyama Lufwanyama Mpindi 2 206 30 13 0.84 0.05 3,220 Copperbelt Lufwanyama Lufwanyama Lufwanyama 2 206 30 14 0.83 0.05 2,704 (continued on next page) 64 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Copperbelt Lufwanyama Lufwanyama Luswishi 2 206 30 15 0.87 0.05 3,175 Copperbelt Lufwanyama Lufwanyama Mushingashi 2 206 30 16 0.85 0.05 3,917 Copperbelt Masaiti Kafulafuta Mwatishi 2 207 29 1 0.54 0.12 3,106 Copperbelt Masaiti Kafulafuta Majaliwa 2 207 29 2 0.56 0.12 3,829 Copperbelt Masaiti Kafulafuta Chondwe 2 207 29 3 0.49 0.11 4,637 Copperbelt Masaiti Kafulafuta Mutaba 2 207 29 4 0.60 0.12 4,095 Copperbelt Masaiti Kafulafuta Miengwe 2 207 29 5 0.54 0.12 4,742 Copperbelt Masaiti Kafulafuta Katonte 2 207 29 6 0.62 0.12 513 Copperbelt Masaiti Kafulafuta Ishitwe 2 207 29 7 0.56 0.12 2,688 Copperbelt Masaiti Masaiti Kashitu 2 207 31 8 0.54 0.11 4,119 Copperbelt Masaiti Masaiti Luansobe 2 207 31 9 0.54 0.11 2,983 Copperbelt Masaiti Masaiti Chinondo 2 207 31 10 0.60 0.11 2,018 Copperbelt Masaiti Masaiti Mishikishi 2 207 31 11 0.49 0.11 3,019 Copperbelt Masaiti Masaiti Masangano 2 207 31 12 0.47 0.11 4,287 Copperbelt Masaiti Masaiti Lumano 2 207 31 13 0.49 0.11 4,188 Copperbelt Masaiti Masaiti Katuba 2 207 31 14 0.57 0.11 3,237 Copperbelt Masaiti Masaiti Shimibanga 2 207 31 15 0.47 0.10 3,386 Copperbelt Masaiti Masaiti Chilulu 2 207 31 16 0.45 0.10 1,509 Copperbelt Masaiti Masaiti Miputu 2 207 31 17 0.53 0.11 2,395 Copperbelt Mpongwe Mpongwe Luswishi 2 208 32 1 0.83 0.05 1,433 Copperbelt Mpongwe Mpongwe Kasonga 2 208 32 2 0.79 0.06 1,813 Copperbelt Mpongwe Mpongwe Munkumpu 2 208 32 3 0.72 0.06 6,142 Copperbelt Mpongwe Mpongwe Kashiba 2 208 32 4 0.76 0.05 7,277 Copperbelt Mpongwe Mpongwe Mpongwe 2 208 32 5 0.67 0.05 7,535 Copperbelt Mpongwe Mpongwe Kanyenda 2 208 32 6 0.77 0.06 6,678 Copperbelt Mpongwe Mpongwe Kasamba 2 208 32 7 0.80 0.05 1,532 Copperbelt Mpongwe Mpongwe Ibenga 2 208 32 8 0.71 0.05 11,230 Copperbelt Mpongwe Mpongwe Kalweo 2 208 32 9 0.77 0.05 6,173 Copperbelt Mpongwe Mpongwe Mikata 2 208 32 10 0.74 0.05 7,713 Copperbelt Mpongwe Mpongwe Nampamba 2 208 32 11 0.62 0.05 7,899 Copperbelt Mpongwe Mpongwe Musofu 2 208 32 12 0.76 0.06 2,381 Copperbelt Mufulira Kankoyo John Kapengele 2 209 26 21 0.29 0.08 1,688 Copperbelt Mufulira Kankoyo Buntungwa 2 209 26 22 0.25 0.07 1,058 Copperbelt Mufulira Kankoyo Kangwa Nsuluka 2 209 26 23 0.66 0.07 717 Copperbelt Mufulira Kankoyo Luansobe 2 209 26 24 0.73 0.08 4,869 Copperbelt Mufulira Kankoyo Butondo 2 209 26 25 0.17 0.05 1,434 Copperbelt Mufulira Kankoyo Kwacha 2 209 26 26 0.18 0.06 976 Copperbelt Mufulira Kankoyo Fibusa 2 209 26 27 0.23 0.07 1,380 (continued on next page) 65 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Copperbelt Mufulira Kankoyo Mpelembe 2 209 26 28 0.25 0.07 1,930 Copperbelt Mufulira Kantanshi Minambe 2 209 27 1 0.74 0.06 2,765 Copperbelt Mufulira Kantanshi Mulundu 2 209 27 2 0.65 0.07 5,021 Copperbelt Mufulira Kantanshi Francis Mukuka 2 209 27 3 0.17 0.05 1,360 Copperbelt Mufulira Kantanshi Maina Soko 2 209 27 4 0.05 0.02 248 Copperbelt Mufulira Kantanshi Bwafwano 2 209 27 5 0.12 0.04 491 Copperbelt Mufulira Kantanshi Mulungushi 2 209 27 6 0.12 0.05 1,071 Copperbelt Mufulira Kantanshi Shinde 2 209 27 7 0.12 0.05 1,255 Copperbelt Mufulira Kantanshi Bwembya 2 209 27 8 0.15 0.05 538 Silwizya Copperbelt Mufulira Kantanshi Leya Mukutu 2 209 27 9 0.16 0.06 665 Copperbelt Mufulira Kantanshi David Kaunda 2 209 27 10 0.05 0.02 79 Copperbelt Mufulira Mufulira Mutundu 2 209 28 11 0.78 0.06 3,808 Copperbelt Mufulira Mufulira Bwananyina 2 209 28 12 0.63 0.08 7,314 Copperbelt Mufulira Mufulira David Kaunda 2 209 28 13 0.06 0.03 275 Copperbelt Mufulira Mufulira Chachacha 2 209 28 14 0.21 0.06 2,191 Copperbelt Mufulira Mufulira Kamuchanga 2 209 28 15 0.17 0.05 702 Copperbelt Mufulira Mufulira Kasempa 2 209 28 16 0.18 0.06 836 Copperbelt Mufulira Mufulira Kansuswa 2 209 28 17 0.33 0.07 1,989 Copperbelt Mufulira Mufulira Kafue 2 209 28 18 0.45 0.07 452 Copperbelt Mufulira Mufulira Kawama 2 209 28 19 0.60 0.09 4,213 Copperbelt Mufulira Mufulira Hanky Kalanga 2 209 28 20 0.24 0.07 1,851 Copperbelt Ndola Bwana Mkubwa Itawa 2 210 33 11 0.17 0.04 3,635 Copperbelt Ndola Bwana Mkubwa Munkulungwe 2 210 33 12 0.40 0.05 6,706 Copperbelt Ndola Bwana Mkubwa Twashuka 2 210 33 13 0.59 0.07 8,427 Copperbelt Ndola Bwana Mkubwa Kavu 2 210 33 14 0.49 0.06 2,491 Copperbelt Ndola Bwana Mkubwa Mushili 2 210 33 15 0.26 0.06 12,236 Copperbelt Ndola Bwana Mkubwa Chichele 2 210 33 16 0.49 0.05 3,914 Copperbelt Ndola Bwana Mkubwa Kantolomba 2 210 33 17 0.45 0.07 3,287 Copperbelt Ndola Chifubu Pamodzi 2 210 34 1 0.30 0.07 12,329 Copperbelt Ndola Chifubu Kawama 2 210 34 2 0.49 0.09 9,042 Copperbelt Ndola Chifubu Fibobe 2 210 34 3 0.20 0.06 3,082 Copperbelt Ndola Chifubu Chifubu 2 210 34 4 0.22 0.07 2,918 Copperbelt Ndola Chifubu Kamba 2 210 34 5 0.21 0.06 2,461 Copperbelt Ndola Kabushi Lubuto 2 210 35 18 0.20 0.06 4,741 Copperbelt Ndola Kabushi Mukuba 2 210 35 19 0.30 0.07 2,932 Copperbelt Ndola Kabushi Toka 2 210 35 20 0.27 0.06 2,242 Copperbelt Ndola Kabushi Kaloko 2 210 35 21 0.26 0.07 2,360 (continued on next page) 66 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Copperbelt Ndola Kabushi Kabushi 2 210 35 22 0.32 0.07 3,964 Copperbelt Ndola Kabushi Kafubu 2 210 35 23 0.30 0.07 2,078 Copperbelt Ndola Kabushi Skyways 2 210 35 24 0.27 0.06 4,284 Copperbelt Ndola Kabushi Masala 2 210 35 25 0.19 0.05 1,733 Copperbelt Ndola Ndola Kanseshi 2 210 36 6 0.04 0.02 497 Copperbelt Ndola Ndola Nkwazi 2 210 36 7 0.54 0.09 12,015 Copperbelt Ndola Ndola Yengwe 2 210 36 8 0.04 0.02 687 Copperbelt Ndola Ndola Chipulukusu 2 210 36 9 0.51 0.09 19,391 Copperbelt Ndola Ndola Kanini 2 210 36 10 0.11 0.03 1,238 Copperbelt Ndola Ndola Twapia 2 210 36 26 0.40 0.08 11,989 Copperbelt Ndola Ndola Dag 2 210 36 27 0.76 0.07 7,463 Hammersjoed Copperbelt Ndola Ndola Kaniki 2 210 36 28 0.64 0.07 879 Eastern Chadiza Chadiza Mangwe 3 301 37 1 0.78 0.06 8,101 Eastern Chadiza Chadiza Nsadzu 3 301 37 2 0.82 0.06 4,754 Eastern Chadiza Chadiza Manje 3 301 37 3 0.84 0.05 5,789 Eastern Chadiza Chadiza Chanjowe 3 301 37 4 0.81 0.05 2,150 Eastern Chadiza Chadiza Kapachi 3 301 37 5 0.83 0.05 3,374 Eastern Chadiza Chadiza Chadiza 3 301 37 6 0.52 0.06 2,960 Eastern Chadiza Chadiza Chilenga 3 301 37 7 0.86 0.04 3,897 Eastern Chadiza Chadiza Naviluri 3 301 37 8 0.82 0.05 5,284 Eastern Chadiza Chadiza Chamandala 3 301 37 9 0.87 0.04 3,604 Eastern Chadiza Chadiza Kandabwako 3 301 37 10 0.85 0.05 3,480 Eastern Chadiza Chadiza Kampini 3 301 37 11 0.80 0.05 3,543 Eastern Chadiza Chadiza Taferansoni 3 301 37 12 0.85 0.05 3,451 Eastern Chadiza Vubwi Ambidzi 3 301 38 13 0.84 0.06 2,472 Eastern Chadiza Vubwi Kabvumo 3 301 38 14 0.83 0.06 2,233 Eastern Chadiza Vubwi Nkhumba 3 301 38 15 0.82 0.06 2,928 Eastern Chadiza Vubwi Mwangazi 3 301 38 16 0.83 0.06 4,172 Eastern Chadiza Vubwi Mbozi 3 301 38 17 0.86 0.05 3,608 Eastern Chadiza Vubwi Vubwi 3 301 38 18 0.83 0.05 7,180 Eastern Chadiza Vubwi Chisiya 3 301 38 19 0.84 0.05 2,085 Eastern Chadiza Vubwi Chimphanje 3 301 38 20 0.84 0.06 1,974 Eastern Chadiza Vubwi Mlawe 3 301 38 21 0.86 0.05 4,067 Eastern Chadiza Vubwi Dzozwe 3 301 38 22 0.83 0.06 6,910 Eastern Chipata Chipangali Sisinje 3 302 41 13 0.86 0.05 16,142 Eastern Chipata Chipangali Nthope 3 302 41 14 0.88 0.05 29,198 Eastern Chipata Chipangali Chipangali 3 302 41 15 0.85 0.05 17,760 (continued on next page) 67 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Eastern Chipata Chipangali Kasenga 3 302 41 16 0.87 0.05 12,582 Eastern Chipata Chipangali Rukuzye 3 302 41 17 0.86 0.05 17,301 Eastern Chipata Chipangali Msandile 3 302 41 18 0.87 0.05 14,500 Eastern Chipata Chipangali Msanga 3 302 42 19 0.52 0.06 34,864 Eastern Chipata Chipangali Kanjala 3 302 42 20 0.53 0.06 19,244 Eastern Chipata Chipangali Dilika 3 302 42 21 0.67 0.06 15,747 Eastern Chipata Chipangali Kapata 3 302 42 22 0.35 0.07 12,023 Eastern Chipata Kasenengwa Makungwa 3 302 43 6 0.82 0.05 10,642 Eastern Chipata Kasenengwa Chingazi 3 302 43 7 0.85 0.05 12,887 Eastern Chipata Kasenengwa Kwenje 3 302 43 8 0.81 0.05 23,858 Eastern Chipata Kasenengwa Ng’ongwe 3 302 43 9 0.83 0.05 12,921 Eastern Chipata Kasenengwa Mkowe 3 302 43 10 0.88 0.04 1,787 Eastern Chipata Kasenengwa Mboza 3 302 43 11 0.85 0.05 10,060 Eastern Chipata Kasenengwa Chiparamba 3 302 43 12 0.84 0.05 9,840 Eastern Chipata Luangeni Nsingo 3 302 44 1 0.79 0.05 16,335 Eastern Chipata Luangeni Khova 3 302 44 2 0.85 0.05 9,070 Eastern Chipata Luangeni Makangila 3 302 44 3 0.82 0.05 6,206 Eastern Chipata Luangeni Chikando 3 302 44 4 0.84 0.05 21,575 Eastern Chipata Luangeni Kazimule 3 302 44 5 0.81 0.05 9,392 Eastern Katete Milanzi Kafumbwe 3 303 45 1 0.86 0.06 6,396 Eastern Katete Milanzi Kazala 3 303 45 2 0.85 0.06 7,370 Eastern Katete Milanzi Milanzi 3 303 45 3 0.87 0.05 4,861 Eastern Katete Milanzi Kapoche 3 303 45 4 0.85 0.06 2,054 Eastern Katete Milanzi Chindwale 3 303 45 5 0.87 0.05 4,019 Eastern Katete Milanzi Kapangulula 3 303 45 6 0.88 0.05 8,027 Eastern Katete Milanzi Dole 3 303 45 7 0.83 0.06 7,497 Eastern Katete Milanzi Chimwa 3 303 45 8 0.86 0.06 1,588 Eastern Katete Milanzi Mwandafisi 3 303 45 9 0.85 0.06 5,076 Eastern Katete Milanzi Katiula 3 303 45 10 0.86 0.06 9,232 Eastern Katete Mkaika Mphangwe 3 303 46 11 0.52 0.08 9,910 Eastern Katete Mkaika Chavuka 3 303 46 12 0.86 0.05 8,899 Eastern Katete Mkaika Kadula 3 303 46 13 0.86 0.05 8,096 Eastern Katete Mkaika Vulamkoko 3 303 46 14 0.87 0.05 9,382 Eastern Katete Mkaika Chimtende 3 303 46 15 0.87 0.05 8,585 Eastern Katete Mkaika Lukweta 3 303 46 16 0.88 0.05 5,422 Eastern Katete Mkaika Mkaika 3 303 46 17 0.83 0.05 20,193 Eastern Katete Mkaika Matunga 3 303 46 18 0.84 0.05 5,391 Eastern Katete Sinda Kamwaza 3 303 47 19 0.89 0.05 6,367 (continued on next page) 68 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Eastern Katete Sinda Nchingilizya 3 303 47 20 0.88 0.06 4,530 Eastern Katete Sinda Luandazi 3 303 47 21 0.87 0.06 6,877 Eastern Katete Sinda Chiwuyu 3 303 47 22 0.88 0.06 7,501 Eastern Katete Sinda Sinda 3 303 47 23 0.77 0.07 10,436 Eastern Katete Sinda Mnyamanzi 3 303 47 24 0.84 0.07 4,612 Eastern Katete Sinda Nyamasonkho 3 303 47 25 0.84 0.06 8,646 Eastern Katete Sinda Mngo’mba 3 303 47 26 0.85 0.07 8,981 Eastern Katete Sinda Kasangazi 3 303 47 27 0.89 0.05 9,107 Eastern Katete Sinda Chitawe 3 303 47 28 0.88 0.05 3,839 Eastern Lundazi Chasefu Manda Hill 3 304 48 1 0.90 0.04 3,992 Eastern Lundazi Chasefu Magodi 3 304 48 2 0.88 0.05 15,914 Eastern Lundazi Chasefu Susa 3 304 48 3 0.89 0.04 9,540 Eastern Lundazi Chasefu Luwerezi 3 304 48 4 0.86 0.05 4,358 Eastern Lundazi Chasefu Kajilime 3 304 48 5 0.89 0.04 16,613 Eastern Lundazi Chasefu Kapirinsanga 3 304 48 6 0.87 0.05 8,961 Eastern Lundazi Chasefu Nkhanga 3 304 48 7 0.89 0.04 18,232 Eastern Lundazi Chasefu Membe 3 304 48 8 0.86 0.05 4,424 Eastern Lundazi Chasefu Chaboli 3 304 48 10 0.85 0.05 7,330 Eastern Lundazi Lumezi Chamtowa 3 304 49 19 0.84 0.05 11,072 Eastern Lundazi Lumezi Kachama 3 304 49 20 0.88 0.05 8,120 Eastern Lundazi Lumezi Wachitangachi 3 304 49 21 0.88 0.04 7,010 Eastern Lundazi Lumezi Kamimba 3 304 49 22 0.87 0.05 9,905 Eastern Lundazi Lumezi Kazembe 3 304 49 23 0.88 0.04 7,875 Eastern Lundazi Lumezi Lumimba 3 304 49 24 0.87 0.04 7,670 Eastern Lundazi Lumezi Lukusuzi 3 304 49 25 0.85 0.05 1,832 Eastern Lundazi Lumezi Diwa 3 304 49 26 0.87 0.04 20,627 Eastern Lundazi Lumezi Chibande 3 304 49 27 0.86 0.05 8,107 Eastern Lundazi Lundazi Vuu 3 304 50 9 0.87 0.05 17,825 Eastern Lundazi Lundazi Mnyamazi 3 304 50 11 0.63 0.06 22,268 Eastern Lundazi Lundazi Ndonda 3 304 50 12 0.85 0.05 8,464 Eastern Lundazi Lundazi Msuzi 3 304 50 13 0.87 0.05 14,633 Eastern Lundazi Lundazi Mkomba 3 304 50 14 0.85 0.05 4,529 Eastern Lundazi Lundazi Chilola 3 304 50 15 0.87 0.05 9,167 Eastern Lundazi Lundazi Chimaliro 3 304 50 16 0.87 0.05 9,066 Eastern Lundazi Lundazi Nthintimila 3 304 50 17 0.87 0.05 7,378 Eastern Lundazi Lundazi Lunevwa 3 304 50 18 0.87 0.04 14,489 Eastern Mambwe Malambo Nsefu 3 305 51 1 0.87 0.06 7,236 Eastern Mambwe Malambo Jumbe 3 305 51 2 0.85 0.06 4,365 (continued on next page) 69 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Eastern Mambwe Malambo Chipapa 3 305 51 3 0.69 0.07 3,503 Eastern Mambwe Malambo Mphomwa 3 305 51 4 0.89 0.05 7,676 Eastern Mambwe Malambo Chikowa 3 305 51 5 0.89 0.05 5,194 Eastern Mambwe Malambo Mnkhanya 3 305 51 6 0.74 0.06 6,393 Eastern Mambwe Malambo Kakumbi 3 305 51 7 0.74 0.07 11,014 Eastern Mambwe Malambo Ncheka 3 305 51 8 0.90 0.05 507 Eastern Mambwe Malambo Malama 3 305 51 9 0.74 0.09 288 Eastern Mambwe Malambo Ndima 3 305 51 10 0.86 0.06 8,470 Eastern Mambwe Malambo Msoro 3 305 51 11 0.85 0.07 735 Eastern Mambwe Malambo Kasamanda 3 305 51 12 0.85 0.06 2,441 Eastern Mambwe Malambo Nyakatokoli 3 305 51 13 0.88 0.05 1,095 Eastern Nyimba Nyimba Chinsimbwe 3 306 52 1 0.80 0.07 683 Eastern Nyimba Nyimba Katipa 3 306 52 2 0.80 0.07 795 Eastern Nyimba Nyimba Vizimumba 3 306 52 3 0.82 0.06 11,156 Eastern Nyimba Nyimba Ngozi 3 306 52 4 0.79 0.06 7,244 Eastern Nyimba Nyimba Lwezi 3 306 52 5 0.79 0.06 4,206 Eastern Nyimba Nyimba Mtilizi 3 306 52 6 0.81 0.06 4,816 Eastern Nyimba Nyimba Nyimba 3 306 52 7 0.54 0.07 5,049 Eastern Nyimba Nyimba Kaliwe 3 306 52 8 0.82 0.06 8,184 Eastern Nyimba Nyimba Chiweza 3 306 52 9 0.81 0.06 10,792 Eastern Nyimba Nyimba Mombe 3 306 52 10 0.79 0.06 3,883 Eastern Nyimba Nyimba Chamilala 3 306 52 11 0.84 0.06 3,371 Eastern Nyimba Nyimba Chinambi 3 306 52 12 0.84 0.06 7,752 Eastern Nyimba Nyimba Luangwa 3 306 52 13 0.86 0.06 1,987 Eastern Petauke Kapoche Kapoche 3 307 53 1 0.77 0.07 8,214 Eastern Petauke Kapoche Chingombe 3 307 53 2 0.86 0.06 15,571 Eastern Petauke Kapoche Mwangaila 3 307 53 3 0.87 0.06 18,754 Eastern Petauke Kapoche Matambazi 3 307 53 4 0.86 0.06 18,778 Eastern Petauke Kapoche Kaumbwe 3 307 53 5 0.88 0.05 15,656 Eastern Petauke Kapoche Lusinde 3 307 53 6 0.87 0.06 9,961 Eastern Petauke Kapoche Manjazi 3 307 53 7 0.89 0.05 8,190 Eastern Petauke Kapoche Manyane 3 307 53 8 0.89 0.05 14,033 Eastern Petauke Petauke Msumbazi 3 307 54 9 0.85 0.06 14,618 Eastern Petauke Petauke Ongolwe 3 307 54 10 0.87 0.06 14,232 Eastern Petauke Petauke Kovyane 3 307 54 11 0.86 0.06 10,840 Eastern Petauke Petauke Mbala 3 307 54 12 0.83 0.07 9,968 Eastern Petauke Petauke Nsimbo 3 307 54 13 0.88 0.06 7,464 Eastern Petauke Petauke Nyika 3 307 54 15 0.59 0.09 22,723 (continued on next page) 70 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Eastern Petauke Petauke Chalimanyana 3 307 54 16 0.85 0.06 15,732 Eastern Petauke Msanzala Nyakawise 3 307 55 14 0.86 0.06 9,342 Eastern Petauke Msanzala Singozi 3 307 55 17 0.87 0.06 11,248 Eastern Petauke Msanzala Mateyo Mzeka 3 307 55 18 0.88 0.06 8,935 Eastern Petauke Msanzala Mawanda 3 307 55 19 0.86 0.06 10,208 Eastern Petauke Msanzala Lusangazi 3 307 55 20 0.89 0.05 1,995 Eastern Petauke Msanzala Ukwimi 3 307 55 21 0.86 0.06 8,764 Eastern Petauke Msanzala Chisangu 3 307 55 22 0.87 0.06 7,524 Luapula Chienge Chienge Lunchinda 4 401 65 1 0.79 0.07 5,981 Luapula Chienge Chienge Chipungu 4 401 65 2 0.80 0.07 12,455 Luapula Chienge Chienge Luau 4 401 65 3 0.81 0.06 5,477 Luapula Chienge Chienge Lambwe Chomba 4 401 65 4 0.80 0.07 3,140 Luapula Chienge Chienge Chiengi 4 401 65 5 0.75 0.07 7,248 Luapula Chienge Chienge Katete 4 401 65 6 0.79 0.06 8,895 Luapula Chienge Chienge Ifuna 4 401 65 7 0.83 0.06 6,677 Luapula Chienge Chienge Chipamba 4 401 65 8 0.83 0.06 13,651 Luapula Chienge Chienge Kalobwa 4 401 65 9 0.83 0.06 4,047 Luapula Chienge Chienge Chitutu 4 401 65 10 0.84 0.06 5,485 Luapula Chienge Chienge Munwa 4 401 65 11 0.82 0.06 8,753 Luapula Chienge Chienge Kulungwishi 4 401 65 12 0.82 0.06 5,023 Luapula Chienge Chienge Munungu 4 401 65 13 0.82 0.06 7,681 Luapula Kawambwa Kawambwa Senga 4 402 56 1 0.83 0.05 9,322 Luapula Kawambwa Kawambwa Luela 4 402 56 3 0.74 0.07 2,095 Luapula Kawambwa Kawambwa Nthumbachushi 4 402 56 4 0.85 0.06 1,919 Luapula Kawambwa Kawambwa Kawambwa 4 402 56 5 0.64 0.07 6,073 Luapula Kawambwa Kawambwa Ngo’ona 4 402 56 6 0.70 0.06 8,518 Luapula Kawambwa Kawambwa Fisaka 4 402 56 7 0.88 0.05 4,668 Luapula Kawambwa Kawambwa Iyanga 4 402 56 14 0.88 0.05 3,985 Luapula Kawambwa Mwansabombwe Mununshi 4 402 57 15 0.83 0.07 4,048 Luapula Kawambwa Mwansabombwe Mulele 4 402 57 16 0.84 0.07 9,133 Luapula Kawambwa Mwansabombwe Lufubu 4 402 57 17 0.85 0.06 2,843 Luapula Kawambwa Mwansabombwe Mwansabombwe 4 402 57 18 0.77 0.07 4,308 Luapula Kawambwa Mwansabombwe Kakose 4 402 57 19 0.77 0.08 4,172 Luapula Kawambwa Mwansabombwe Chipita 4 402 57 20 0.86 0.06 654 Luapula Kawambwa Mwansabombwe Kayo 4 402 57 21 0.88 0.05 7,411 Luapula Kawambwa Mwansabombwe Mbereshi 4 402 57 22 0.74 0.06 4,759 Luapula Kawambwa Pambashe Kabanse 4 402 58 2 0.88 0.05 6,265 Luapula Kawambwa Pambashe Ilombe 4 402 58 8 0.87 0.05 10,802 (continued on next page) 71 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Luapula Kawambwa Pambashe Mulundu 4 402 58 9 0.90 0.05 10,098 Luapula Kawambwa Pambashe Chibote 4 402 58 10 0.88 0.05 2,458 Luapula Kawambwa Pambashe Chipili 4 402 58 11 0.90 0.05 2,292 Luapula Kawambwa Pambashe Luongo 4 402 58 12 0.89 0.05 3,532 Luapula Kawambwa Pambashe Pambashe 4 402 58 13 0.88 0.05 2,085 Luapula Mansa Bahati Mutuna 4 403 59 1 0.75 0.09 10,636 Luapula Mansa Bahati Misakalala 4 403 59 2 0.79 0.09 8,470 Luapula Mansa Bahati Kaole 4 403 59 3 0.79 0.10 2,438 Luapula Mansa Bahati Mushipashi 4 403 59 4 0.76 0.10 6,484 Luapula Mansa Bahati Mansa 4 403 59 5 0.51 0.11 15,269 Luapula Mansa Bahati Myulu 4 403 59 6 0.80 0.09 15,929 Luapula Mansa Bahati Mulenshi 4 403 59 7 0.81 0.09 6,163 Luapula Mansa Mansa Muchinka 4 403 61 8 0.43 0.11 14,387 Luapula Mansa Mansa Mulelenshi 4 403 61 9 0.32 0.09 7,022 Luapula Mansa Mansa Lukangaba 4 403 61 10 0.78 0.10 14,150 Luapula Mansa Mansa Chilyapa 4 403 61 11 0.67 0.12 4,105 Luapula Mansa Mansa Chansunsu 4 403 61 12 0.70 0.10 6,876 Luapula Mansa Mansa Lwingishi 4 403 61 13 0.79 0.10 13,253 Luapula Mansa Mansa Chibeleka 4 403 61 14 0.78 0.10 6,502 Luapula Mansa Mansa Lukola 4 403 61 15 0.78 0.10 5,660 Luapula Mansa Mansa Luapula 4 403 61 16 0.74 0.11 12,067 Luapula Milenge Chembe Chiswishi 4 404 60 1 0.86 0.06 1,833 Luapula Milenge Chembe Mulumbi 4 404 60 2 0.86 0.06 2,012 Luapula Milenge Chembe Itembo 4 404 60 3 0.85 0.05 4,062 Luapula Milenge Chembe Lusumbwe 4 404 60 4 0.89 0.05 2,169 Luapula Milenge Chembe Milambo 4 404 60 5 0.88 0.05 3,399 Luapula Milenge Chembe Nsaka 4 404 60 6 0.90 0.06 271 Luapula Milenge Chembe Fibalala 4 404 60 7 0.86 0.05 4,904 Luapula Milenge Chembe Nsunga 4 404 60 8 0.91 0.04 1,953 Luapula Milenge Chembe Chipundu 4 404 60 9 0.90 0.04 733 Luapula Milenge Chembe Sokontwe 4 404 60 10 0.91 0.04 4,659 Luapula Milenge Chembe Mumbotuta 4 404 60 11 0.91 0.04 2,685 Luapula Milenge Chembe Kapalala 4 404 60 12 0.92 0.04 1,210 Luapula Milenge Chembe Mikula 4 404 60 13 0.85 0.05 8,309 Luapula Mwense Chipili Nsenga 4 405 62 1 0.82 0.05 4,483 Luapula Mwense Chipili Mweshi 4 405 62 2 0.83 0.05 3,026 Luapula Mwense Chipili Mumbwe 4 405 62 3 0.84 0.05 3,820 Luapula Mwense Chipili Chibalashi 4 405 62 4 0.83 0.05 4,555 (continued on next page) 72 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Luapula Mwense Chipili Nalumpembe 4 405 62 5 0.84 0.04 6,789 Luapula Mwense Chipili Nkonge 4 405 62 6 0.84 0.05 4,885 Luapula Mwense Mambilima Mpasa 4 405 63 7 0.87 0.04 4,132 Luapula Mwense Mambilima Chibembe 4 405 63 8 0.80 0.06 1,782 Luapula Mwense Mambilima Musonda 4 405 63 9 0.77 0.05 3,054 Luapula Mwense Mambilima Lundashi 4 405 63 10 0.73 0.06 4,732 Luapula Mwense Mambilima Mambilima 4 405 63 11 0.77 0.06 1,367 Luapula Mwense Mambilima Munwa 4 405 63 12 0.77 0.06 947 Luapula Mwense Mambilima Nsomfi 4 405 63 13 0.74 0.06 4,075 Luapula Mwense Mwense Kalanga 4 405 64 14 0.75 0.07 4,742 Luapula Mwense Mwense Kasengu 4 405 64 15 0.57 0.07 4,304 Luapula Mwense Mwense Katiti 4 405 64 16 0.83 0.06 5,405 Luapula Mwense Mwense Chachacha 4 405 64 17 0.84 0.06 3,811 Luapula Mwense Mwense Kapela 4 405 64 18 0.79 0.07 2,931 Luapula Mwense Mwense Pabe kabesa 4 405 64 19 0.80 0.06 7,227 Luapula Mwense Mwense Luche 4 405 64 20 0.80 0.06 10,139 Luapula Mwense Mwense Nkanga 4 405 64 21 0.84 0.06 5,307 Luapula Mwense Mwense Kaombe 4 405 64 22 0.82 0.06 4,361 Luapula Nchelenge Nchelenge Kabuta 4 406 66 1 0.80 0.05 6,805 Luapula Nchelenge Nchelenge Mwatishi 4 406 66 2 0.80 0.05 13,231 Luapula Nchelenge Nchelenge Munkombwe 4 406 66 3 0.82 0.05 1,366 Luapula Nchelenge Nchelenge Kilwa 4 406 66 4 0.84 0.05 7,353 Luapula Nchelenge Nchelenge Kashikishi 4 406 66 5 0.77 0.06 19,419 Luapula Nchelenge Nchelenge Nchelenge 4 406 66 6 0.30 0.07 398 Luapula Nchelenge Nchelenge Chilongo 4 406 66 7 0.71 0.06 26,963 Luapula Nchelenge Nchelenge Chisenga 4 406 66 8 0.82 0.05 8,506 Luapula Nchelenge Nchelenge Kasamba 4 406 66 9 0.79 0.06 10,318 Luapula Nchelenge Nchelenge Mulwe 4 406 66 10 0.80 0.05 10,467 Luapula Nchelenge Nchelenge Shabo 4 406 66 11 0.81 0.05 3,297 Luapula Nchelenge Nchelenge Mofwe 4 406 66 12 0.79 0.06 5,334 Luapula Nchelenge Nchelenge Katofwo 4 406 66 13 0.81 0.05 5,655 Luapula Samfya Bangweulu Chimana 4 407 67 11 0.76 0.06 15,156 Luapula Samfya Bangweulu Mano 4 407 67 12 0.90 0.03 16,292 Luapula Samfya Bangweulu Katanshya 4 407 67 13 0.94 0.03 9,558 Luapula Samfya Bangweulu Isamba 4 407 67 16 0.94 0.03 8,283 Luapula Samfya Bangweulu Kapata 4 407 67 17 0.92 0.03 15,154 Luapula Samfya Bangweulu Musaba 4 407 67 20 0.93 0.03 8,857 Luapula Samfya Bangweulu Kapilibila 4 407 67 21 0.92 0.03 3,417 (continued on next page) 73 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Luapula Samfya Bangweulu Lumamya 4 407 67 22 0.94 0.03 5,413 Luapula Samfya Chifunabuli Masonde 4 407 68 1 0.95 0.03 8,554 Luapula Samfya Chifunabuli Kasansa 4 407 68 2 0.94 0.03 6,506 Luapula Samfya Chifunabuli Kasaba 4 407 68 3 0.91 0.04 10,471 Luapula Samfya Chifunabuli Kapamba 4 407 68 4 0.95 0.03 4,976 Luapula Samfya Chifunabuli Kasongole 4 407 68 5 0.93 0.03 6,215 Luapula Samfya Chifunabuli Kafumbo 4 407 68 6 0.94 0.03 7,280 Luapula Samfya Chifunabuli Chinkutila 4 407 68 7 0.93 0.04 9,430 Luapula Samfya Chifunabuli Chishi 4 407 68 8 0.95 0.03 5,161 Luapula Samfya Chifunabuli Chifunabuli 4 407 68 9 0.89 0.04 14,411 Luapula Samfya Chifunabuli Mbabala 4 407 68 10 0.95 0.03 4,928 Luapula Samfya Luapula Nkutila 4 407 69 14 0.92 0.04 8,936 Luapula Samfya Luapula Lunga 4 407 69 15 0.90 0.05 3,217 Luapula Samfya Luapula Ncheta 4 407 69 18 0.93 0.04 4,711 Luapula Samfya Luapula Nsalushi 4 407 69 19 0.93 0.04 5,433 Lusaka Chongwe Chongwe Kapwayambale 5 501 73 1 0.38 0.06 6,638 Lusaka Chongwe Chongwe Chinkuli 5 501 73 2 0.51 0.05 11,576 Lusaka Chongwe Chongwe Ntandabale 5 501 73 3 0.40 0.05 7,957 Lusaka Chongwe Chongwe Chongwe 5 501 73 4 0.50 0.06 13,268 Lusaka Chongwe Chongwe Kanakantapa 5 501 73 5 0.68 0.06 9,579 Lusaka Chongwe Chongwe Chalimbana 5 501 73 6 0.64 0.05 6,775 Lusaka Chongwe Chongwe Nakatindi 5 501 73 7 0.42 0.05 2,797 Lusaka Chongwe Chongwe Lukoshi 5 501 73 8 0.76 0.06 6,695 Lusaka Chongwe Chongwe Manyika 5 501 73 9 0.76 0.06 8,153 Lusaka Chongwe Chongwe Lwimba 5 501 73 10 0.78 0.06 4,007 Lusaka Chongwe Rufunsa Mwachilele 5 501 74 11 0.77 0.07 3,414 Lusaka Chongwe Rufunsa Nyangwena 5 501 74 12 0.77 0.07 5,946 Lusaka Chongwe Rufunsa Bunda Bunda 5 501 74 13 0.78 0.06 11,309 Lusaka Chongwe Rufunsa Nyamanongo 5 501 74 14 0.83 0.06 2,522 Lusaka Chongwe Rufunsa Rufunsa 5 501 74 15 0.81 0.06 12,827 Lusaka Chongwe Rufunsa Mankanda 5 501 74 16 0.75 0.07 3,081 Lusaka Chongwe Rufunsa Shikabeta 5 501 74 17 0.82 0.06 1,599 Lusaka Kafue Kafue Chiyaba 5 502 70 1 0.67 0.08 4,021 Lusaka Kafue Kafue Kambale 5 502 70 2 0.69 0.08 3,521 Lusaka Kafue Kafue Malundu 5 502 70 3 0.77 0.07 7,577 Lusaka Kafue Kafue Chisankane 5 502 70 4 0.58 0.07 5,029 Lusaka Kafue Kafue Lukolongo 5 502 70 5 0.62 0.08 1,974 Lusaka Kafue Kafue Kafue 5 502 70 6 0.35 0.07 2,968 (continued on next page) 74 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Lusaka Kafue Kafue Matanda 5 502 70 7 0.24 0.06 2,690 Lusaka Kafue Kafue Shabusale 5 502 70 8 0.37 0.08 11,339 Lusaka Kafue Kafue Kasenje 5 502 70 9 0.14 0.05 3,067 Lusaka Kafue Kafue Muungu 5 502 70 10 0.66 0.08 10,338 Lusaka Kafue Kafue Chikupi 5 502 70 11 0.76 0.08 796 Lusaka Kafue Chilanga Chilanga 5 502 72 12 0.30 0.06 9,835 Lusaka Kafue Chilanga Chilongolo 5 502 72 13 0.37 0.06 7,118 Lusaka Kafue Chilanga Namalombwe 5 502 72 14 0.31 0.06 10,274 Lusaka Kafue Chilanga Nyemba 5 502 72 15 0.39 0.05 3,979 Lusaka Kafue Chilanga Nakachenje 5 502 72 16 0.69 0.07 1,514 Lusaka Kafue Chilanga Chinyanja 5 502 72 17 0.58 0.06 6,710 Lusaka Luangwa Feira Dzalo 5 503 71 1 0.52 0.08 2,650 Lusaka Luangwa Feira Mkaliva 5 503 71 2 0.67 0.09 586 Lusaka Luangwa Feira Mandombe 5 503 71 3 0.71 0.09 523 Lusaka Luangwa Feira Phwazi 5 503 71 4 0.76 0.07 702 Lusaka Luangwa Feira Mphuka 5 503 71 5 0.73 0.08 850 Lusaka Luangwa Feira Kabowo 5 503 71 6 0.84 0.08 172 Lusaka Luangwa Feira Kapoche 5 503 71 7 0.83 0.06 1,233 Lusaka Luangwa Feira Chiriwe 5 503 71 8 0.81 0.08 245 Lusaka Luangwa Feira Lunya 5 503 71 9 0.77 0.08 485 Lusaka Luangwa Feira Katondwe 5 503 71 10 0.68 0.07 1,604 Lusaka Luangwa Feira Chikoma 5 503 71 11 0.81 0.06 1,853 Lusaka Luangwa Feira Mburuma 5 503 71 12 0.69 0.08 1,989 Lusaka Luangwa Feira Mwalilia 5 503 71 13 0.76 0.07 1,383 Lusaka Luangwa Feira Kaunga 5 503 71 14 0.75 0.07 2,113 Lusaka Luangwa Feira Mankhokwe 5 503 71 15 0.78 0.07 529 Lusaka Lusaka Chawama Nkoloma 5 504 75 1 0.22 0.07 16,634 Lusaka Lusaka Chawama Chawama 5 504 75 2 0.20 0.07 14,444 Lusaka Lusaka Chawama John Howard 5 504 75 3 0.20 0.07 5,845 Lusaka Lusaka Chawama Lilayi 5 504 75 4 0.17 0.05 2,240 Lusaka Lusaka Kabwata Kamwala 5 504 76 5 0.11 0.04 5,878 Lusaka Lusaka Kabwata Kabwata 5 504 76 6 0.04 0.02 882 Lusaka Lusaka Kabwata Libala 5 504 76 7 0.06 0.02 1,433 Lusaka Lusaka Kabwata Chilenje 5 504 76 8 0.06 0.03 3,333 Lusaka Lusaka Kabwata Kamulanga 5 504 76 9 0.17 0.05 4,409 Lusaka Lusaka Kanyama Kanyama 5 504 77 10 0.22 0.07 37,324 Lusaka Lusaka Kanyama Harry Mwaanga 5 504 77 11 0.19 0.06 32,029 Nkumbula (continued on next page) 75 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Lusaka Lusaka Kanyama Munkolo 5 504 77 12 0.19 0.05 5,308 Lusaka Lusaka Lusaka Central Silwizya 5 504 78 13 0.02 0.01 102 Lusaka Lusaka Lusaka Central Independence 5 504 78 14 0.02 0.01 398 Lusaka Lusaka Lusaka Central Lubwa 5 504 78 15 0.04 0.02 1,388 Lusaka Lusaka Lusaka Central Kabulonga 5 504 78 16 0.17 0.05 9,632 Lusaka Lusaka Mandevu Roma 5 504 79 17 0.20 0.05 13,668 Lusaka Lusaka Mandevu Mulungushi 5 504 79 18 0.03 0.01 462 Lusaka Lusaka Mandevu Ngwerere 5 504 79 19 0.19 0.05 12,591 Lusaka Lusaka Mandevu Chaisa 5 504 79 20 0.27 0.07 5,429 Lusaka Lusaka Mandevu Justine Kabwe 5 504 79 21 0.22 0.06 9,082 Lusaka Lusaka Mandevu Raphael Chota 5 504 79 22 0.24 0.06 22,900 Lusaka Lusaka Mandevu Mpulungu 5 504 79 23 0.27 0.07 15,959 Lusaka Lusaka Matero Muchinga 5 504 80 24 0.14 0.05 5,679 Lusaka Lusaka Matero Kapwepwe 5 504 80 25 0.21 0.06 11,510 Lusaka Lusaka Matero Lima 5 504 80 26 0.28 0.08 17,977 Lusaka Lusaka Matero Mwembeshi 5 504 80 27 0.19 0.06 13,391 Lusaka Lusaka Matero Matero 5 504 80 28 0.15 0.05 8,678 Lusaka Lusaka Munali Chainda 5 504 81 29 0.13 0.04 4,977 Lusaka Lusaka Munali Mtendere 5 504 81 30 0.17 0.06 18,554 Lusaka Lusaka Munali Kalingalinga 5 504 81 31 0.15 0.06 5,962 Lusaka Lusaka Munali Chakunkula 5 504 81 32 0.13 0.04 4,234 Lusaka Lusaka Munali Munali 5 504 81 33 0.12 0.05 5,653 Muchinga Chama Chama North Mazonde 6 601 39 1 0.74 0.10 1,191 Muchinga Chama Chama North Nkhankha 6 601 39 2 0.71 0.10 3,823 Muchinga Chama Chama North Luangwa 6 601 39 3 0.72 0.10 2,815 Muchinga Chama Chama North Chisunga 6 601 39 4 0.74 0.10 2,845 Muchinga Chama Chama North Ndunda 6 601 39 5 0.71 0.10 1,375 Muchinga Chama Chama North Mbazi 6 601 39 6 0.73 0.10 2,193 Muchinga Chama Chama North Manthepa 6 601 39 7 0.74 0.10 1,631 Muchinga Chama Chama North Mphalausenga 6 601 39 8 0.75 0.10 4,863 Muchinga Chama Chama North Kalinkhu 6 601 39 9 0.76 0.10 1,684 Muchinga Chama Chama North Kamphemba 6 601 39 10 0.60 0.10 8,286 Muchinga Chama Chama North Mwalala 6 601 39 11 0.75 0.10 4,835 Muchinga Chama Chama North Muchinga 6 601 39 12 0.68 0.11 2,134 Muchinga Chama Chama South Chipala 6 601 40 13 0.70 0.09 1,820 Muchinga Chama Chama South Bazimu 6 601 40 14 0.71 0.09 4,280 Muchinga Chama Chama South Mabinga 6 601 40 15 0.71 0.09 3,975 Muchinga Chama Chama South Lupamazi 6 601 40 16 0.74 0.09 1,798 (continued on next page) 76 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Muchinga Chama Chama South Lumezi 6 601 40 17 0.75 0.09 3,987 Muchinga Chama Chama South Chibungwe 6 601 40 18 0.72 0.09 3,871 Muchinga Chama Chama South Lunzi 6 601 40 19 0.72 0.09 5,647 Muchinga Chama Chama South Vilimukulu 6 601 40 20 0.71 0.09 3,980 Muchinga Chama Chama South Chilenje 6 601 40 21 0.71 0.09 4,347 Muchinga Chama Chama South Mapamba 6 601 40 22 0.71 0.10 2,982 Muchinga Chinsali Chinsali Itapa 6 602 83 1 0.87 0.05 8,728 Muchinga Chinsali Chinsali Chilunda 6 602 83 2 0.89 0.05 1,153 Muchinga Chinsali Chinsali Chilinda 6 602 83 3 0.85 0.05 7,830 Muchinga Chinsali Chinsali Kaunga 6 602 83 4 0.88 0.05 7,993 Muchinga Chinsali Chinsali Malalo 6 602 83 5 0.86 0.05 3,822 Muchinga Chinsali Chinsali Chipanga 6 602 83 6 0.88 0.04 9,140 Muchinga Chinsali Chinsali Chambeshi 6 602 83 7 0.88 0.05 1,483 Muchinga Chinsali Chinsali Lubwa 6 602 83 8 0.86 0.05 9,018 Muchinga Chinsali Chinsali Ichinga 6 602 83 9 0.69 0.06 11,370 Muchinga Chinsali Chinsali Nkakula 6 602 83 10 0.56 0.06 2,726 Muchinga Chinsali Chinsali Mwanuakabili 6 602 83 11 0.84 0.05 8,159 Muchinga Chinsali Shiwang’andu Chamusenga 6 602 84 12 0.89 0.04 7,043 Muchinga Chinsali Shiwang’andu Muchinga 6 602 84 13 0.86 0.04 5,508 Muchinga Chinsali Shiwang’andu Chandaula 6 602 84 14 0.85 0.05 3,534 Muchinga Chinsali Shiwang’andu Mukumbi 6 602 84 15 0.87 0.04 6,188 Muchinga Chinsali Shiwang’andu Mwila Kabuswe 6 602 84 16 0.90 0.04 6,373 Muchinga Chinsali Shiwang’andu Chibinda 6 602 84 17 0.93 0.03 1,062 Muchinga Chinsali Shiwang’andu Mayembe 6 602 84 18 0.92 0.03 2,752 Muchinga Chinsali Shiwang’andu Mwiche 6 602 84 19 0.91 0.03 3,309 Muchinga Chinsali Shiwang’andu Ichingo 6 602 84 20 0.91 0.03 4,132 Muchinga Chinsali Shiwang’andu Chimpundu 6 602 84 21 0.87 0.04 7,565 Muchinga Chinsali Shiwang’andu Nkulungwe 6 602 84 22 0.89 0.04 5,957 Muchinga Isoka Isoka West Kasoka 6 603 86 1 0.60 0.07 10,514 Muchinga Isoka Isoka West Kantenshya 6 603 86 2 0.91 0.04 6,835 Muchinga Isoka Isoka West Sasamwenje 6 603 86 3 0.88 0.05 11,436 Muchinga Isoka Isoka West Kapililonga 6 603 86 4 0.85 0.05 7,409 Muchinga Isoka Isoka West Itukuta 6 603 86 5 0.87 0.05 5,008 Muchinga Isoka Isoka West Milongo 6 603 86 6 0.90 0.05 1,902 Muchinga Isoka Isoka West Nkombwa 6 603 86 7 0.91 0.04 6,015 Muchinga Isoka Isoka West Luangwa 6 603 86 8 0.91 0.04 3,375 Muchinga Isoka Isoka West Mpungu 6 603 86 9 0.90 0.04 7,235 Muchinga Mafinga Isoka East Mafinga 6 604 85 10 0.91 0.03 5,504 (continued on next page) 77 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Muchinga Mafinga Isoka East Ntonga 6 604 85 11 0.90 0.03 10,496 Muchinga Mafinga Isoka East Mukutu 6 604 85 12 0.91 0.03 6,481 Muchinga Mafinga Isoka East Thendere 6 604 85 13 0.89 0.03 9,036 Muchinga Mafinga Isoka East Bemba 6 604 85 14 0.91 0.03 9,198 Muchinga Mafinga Isoka East Kakoma 6 604 85 15 0.91 0.03 3,810 Muchinga Mafinga Isoka East Luhoka 6 604 85 16 0.91 0.03 3,899 Muchinga Mafinga Isoka East Kalanga 6 604 85 17 0.89 0.03 11,598 Muchinga Mpika Kanchibiya Chambeshi 6 605 98 1 0.82 0.08 5,211 Muchinga Mpika Kanchibiya Mansha 6 605 98 2 0.82 0.07 5,492 Muchinga Mpika Kanchibiya Mumbubu 6 605 98 3 0.86 0.06 7,339 Muchinga Mpika Kanchibiya Lubaleshi 6 605 98 4 0.84 0.07 10,514 Muchinga Mpika Kanchibiya Lulingila 6 605 98 5 0.83 0.08 8,458 Muchinga Mpika Kanchibiya Munikashi 6 605 98 6 0.83 0.07 6,506 Muchinga Mpika Kanchibiya Chinama 6 605 98 7 0.84 0.07 4,920 Muchinga Mpika Kanchibiya Lukulu 6 605 98 8 0.82 0.08 6,951 Muchinga Mpika Kanchibiya Lulimala 6 605 98 9 0.82 0.08 8,684 Muchinga Mpika Kanchibiya Chimbwa 6 605 98 10 0.84 0.07 5,130 Muchinga Mpika Mfuwe Chifungwe 6 605 99 19 0.84 0.08 4,976 Muchinga Mpika Mfuwe Muchinga 6 605 99 20 0.82 0.08 3,656 Muchinga Mpika Mfuwe Chikanda 6 605 99 21 0.85 0.07 7,015 Muchinga Mpika Mfuwe Munpamazi 6 605 99 22 0.78 0.07 6,455 Muchinga Mpika Mpika Mukungwa 6 605 100 11 0.81 0.08 6,494 Muchinga Mpika Mpika Lwitikila 6 605 100 12 0.69 0.09 7,300 Muchinga Mpika Mpika Musakanya 6 605 100 13 0.41 0.10 9,484 Muchinga Mpika Mpika Lubambala 6 605 100 14 0.36 0.09 5,956 Muchinga Mpika Mpika Chishibe Isonde 6 605 100 15 0.72 0.09 11,280 Muchinga Mpika Mpika Nachikufu 6 605 100 16 0.83 0.07 6,727 Muchinga Mpika Mpika Mutekwe 6 605 100 17 0.81 0.08 4,162 Muchinga Mpika Mpika Chipembele 6 605 100 18 0.85 0.07 7,503 Muchinga Nakonde Nakonde Mulalo 6 606 87 1 0.87 0.05 8,510 Muchinga Nakonde Nakonde Luchinde 6 606 87 2 0.88 0.05 5,121 Muchinga Nakonde Nakonde Ngumba 6 606 87 3 0.85 0.05 5,801 Muchinga Nakonde Nakonde Musyani 6 606 87 4 0.79 0.06 5,742 Muchinga Nakonde Nakonde Popomozi 6 606 87 5 0.86 0.05 5,607 Muchinga Nakonde Nakonde Chiwanza 6 606 87 6 0.89 0.04 6,992 Muchinga Nakonde Nakonde Ilonda 6 606 87 7 0.87 0.05 4,399 Muchinga Nakonde Nakonde Isunga 6 606 87 8 0.85 0.05 5,630 Muchinga Nakonde Nakonde Nakonde 6 606 87 9 0.53 0.08 20,654 (continued on next page) 78 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Muchinga Nakonde Nakonde Old Fife 6 606 87 10 0.59 0.07 7,197 Muchinga Nakonde Nakonde Musele 6 606 87 11 0.87 0.05 3,607 Muchinga Nakonde Nakonde Mpanda 6 606 87 12 0.88 0.04 6,492 Muchinga Nakonde Nakonde Mukulika 6 606 87 13 0.88 0.05 2,264 Northern Chilubi Chilubi Mulanda 7 701 82 1 0.88 0.05 4,869 Northern Chilubi Chilubi Chifwenge 7 701 82 2 0.87 0.05 7,136 Northern Chilubi Chilubi Bulilo 7 701 82 3 0.87 0.05 5,695 Northern Chilubi Chilubi Chisupa 7 701 82 4 0.86 0.05 3,172 Northern Chilubi Chilubi Ndela 7 701 82 5 0.86 0.05 2,866 Northern Chilubi Chilubi Katamba 7 701 82 6 0.88 0.05 2,840 Northern Chilubi Chilubi Muteka 7 701 82 7 0.87 0.05 2,611 Northern Chilubi Chilubi Lwenda 7 701 82 8 0.87 0.05 3,259 Northern Chilubi Chilubi Mofu 7 701 82 9 0.87 0.05 2,637 Northern Chilubi Chilubi Mpanshya 7 701 82 10 0.78 0.06 1,276 Northern Chilubi Chilubi Kashitu 7 701 82 11 0.82 0.06 2,187 Northern Chilubi Chilubi Kambashi 7 701 82 12 0.85 0.05 2,629 Northern Chilubi Chilubi Nguni 7 701 82 13 0.84 0.05 1,010 Northern Chilubi Chilubi Chinkundu 7 701 82 14 0.88 0.05 1,430 Northern Chilubi Chilubi Chiloba 7 701 82 15 0.86 0.05 3,762 Northern Chilubi Chilubi Kanchindi 7 701 82 16 0.81 0.05 5,907 Northern Chilubi Chilubi Kapoka 7 701 82 17 0.86 0.05 1,986 Northern Chilubi Chilubi Kawena 7 701 82 18 0.86 0.05 4,915 Northern Chilubi Chilubi Kanama 7 701 82 19 0.87 0.05 1,289 Northern Chilubi Chilubi Bumba 7 701 82 20 0.88 0.05 5,191 Northern Chilubi Chilubi Mubemba 7 701 82 21 0.87 0.05 1,710 Northern Chilubi Chilubi Luangwa 7 701 82 22 0.84 0.06 1,789 Northern Kaputa Chimbamilonga Kapisha 7 702 88 1 0.78 0.05 7,257 Northern Kaputa Chimbamilonga Nsumbu 7 702 88 2 0.78 0.05 3,484 Northern Kaputa Chimbamilonga Chisela 7 702 88 3 0.82 0.05 1,686 Northern Kaputa Chimbamilonga Munwa 7 702 88 4 0.83 0.05 6,565 Northern Kaputa Chimbamilonga Kampinda 7 702 88 5 0.82 0.05 5,605 Northern Kaputa Chimbamilonga Kakusu 7 702 88 6 0.82 0.05 982 Northern Kaputa Chimbamilonga Kashikishi 7 702 88 7 0.81 0.05 3,348 Northern Kaputa Chimbamilonga Mwambeshi 7 702 88 8 0.83 0.05 1,517 Northern Kaputa Chimbamilonga Chubo 7 702 88 9 0.83 0.05 2,568 Northern Kaputa Chimbamilonga Fungwa 7 702 88 10 0.81 0.05 3,674 Northern Kaputa Chimbamilonga Mukubwe 7 702 88 11 0.77 0.05 3,032 Northern Kaputa Kaputa Chiyilunda 7 702 89 12 0.80 0.05 2,460 (continued on next page) 79 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Northern Kaputa Kaputa Mofwe 7 702 89 13 0.80 0.05 1,183 Northern Kaputa Kaputa Kalungwishi 7 702 89 14 0.80 0.05 4,112 Northern Kaputa Kaputa Nkota 7 702 89 15 0.77 0.05 4,521 Northern Kaputa Kaputa Chipili 7 702 89 16 0.80 0.04 8,048 Northern Kaputa Kaputa Choma 7 702 89 17 0.62 0.05 5,807 Northern Kaputa Kaputa Mowa 7 702 89 18 0.76 0.05 9,880 Northern Kaputa Kaputa Kaleulu 7 702 89 19 0.80 0.05 7,711 Northern Kaputa Kaputa Mwawe 7 702 89 20 0.82 0.05 2,008 Northern Kaputa Kaputa Kapulwa 7 702 89 21 0.84 0.04 4,658 Northern Kaputa Kaputa Munkonge 7 702 89 22 0.82 0.04 5,172 Northern Kasama Kasama Kasenga 7 703 90 1 0.64 0.10 8,115 Northern Kasama Kasama Bululu 7 703 90 2 0.68 0.10 13,132 Northern Kasama Kasama Chilunga 7 703 90 3 0.67 0.10 8,235 Northern Kasama Kasama Lukulu 7 703 90 4 0.70 0.10 2,759 Northern Kasama Kasama Julia 7 703 90 5 0.71 0.10 2,002 Chikamoneka Northern Kasama Kasama Lukupa 7 703 90 6 0.58 0.10 5,519 Northern Kasama Kasama Mulilansolo 7 703 90 7 0.22 0.06 8,062 Northern Kasama Kasama Buseko 7 703 90 8 0.38 0.10 24,774 Northern Kasama Lukashya Lusenga 7 703 91 9 0.68 0.10 7,593 Northern Kasama Lukashya Mukanga 7 703 91 10 0.67 0.10 5,294 Northern Kasama Lukashya Lualuo 7 703 91 11 0.71 0.10 6,096 Northern Kasama Lukashya Chiba 7 703 91 12 0.57 0.11 4,511 Northern Kasama Lukashya Kapumaula 7 703 91 13 0.66 0.10 6,471 Northern Kasama Lukashya Chibundu 7 703 91 14 0.68 0.10 6,220 Northern Kasama Lukashya Kapongolo 7 703 91 15 0.69 0.10 4,678 Northern Kasama Lukashya Musowa 7 703 91 16 0.71 0.10 4,119 Northern Kasama Lukashya Chumba 7 703 91 17 0.71 0.10 3,600 Northern Luwingu Lubansenshi Ipusukilo 7 704 93 14 0.86 0.05 3,483 Northern Luwingu Lubansenshi Katopola 7 704 93 15 0.60 0.07 6,473 Northern Luwingu Lubansenshi Namukolo 7 704 93 16 0.84 0.05 967 Northern Luwingu Lubansenshi Chulungoma 7 704 93 17 0.83 0.05 5,618 Northern Luwingu Lubansenshi Masonde 7 704 93 18 0.84 0.05 5,547 Northern Luwingu Lubansenshi Chifwile 7 704 93 19 0.88 0.04 4,150 Northern Luwingu Lubansenshi Mushituwamboo 7 704 93 20 0.88 0.05 5,614 Northern Luwingu Lubansenshi Lwata 7 704 93 21 0.88 0.04 4,637 Northern Luwingu Lubansenshi Isangano 7 704 93 22 0.89 0.04 6,586 Northern Luwingu Lupososhi Itandashi 7 704 94 1 0.90 0.03 4,633 (continued on next page) 80 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Northern Luwingu Lupososhi Kaela 7 704 94 2 0.87 0.04 5,130 Northern Luwingu Lupososhi Munshinga 7 704 94 3 0.88 0.04 5,020 Northern Luwingu Lupososhi Katilye 7 704 94 4 0.88 0.04 3,594 Northern Luwingu Lupososhi Kanfinsa 7 704 94 5 0.89 0.04 3,105 Northern Luwingu Lupososhi Kampemba 7 704 94 6 0.88 0.04 6,015 Northern Luwingu Lupososhi Mulalashi 7 704 94 7 0.86 0.04 2,307 Northern Luwingu Lupososhi Mufili 7 704 94 8 0.88 0.04 4,304 Northern Luwingu Lupososhi Ilambo 7 704 94 9 0.88 0.04 3,753 Northern Luwingu Lupososhi Ibale 7 704 94 10 0.88 0.04 2,345 Northern Luwingu Lupososhi Bwalinde 7 704 94 11 0.86 0.04 8,618 Northern Luwingu Lupososhi Mwelawamangu 7 704 94 12 0.88 0.04 7,794 Northern Luwingu Lupososhi Isansa 7 704 94 13 0.88 0.04 4,688 Northern Mbala Mbala Itala 7 705 95 1 0.87 0.04 20,351 Northern Mbala Mbala Moto Moto 7 705 95 2 0.72 0.06 2,077 Northern Mbala Mbala Kazimolwa 7 705 95 3 0.62 0.07 15,178 Northern Mbala Mbala Mwambezi 7 705 95 4 0.79 0.05 6,488 Northern Mbala Mbala Nsunzu 7 705 95 5 0.76 0.05 11,613 Northern Mbala Mbala Kawimbe 7 705 95 6 0.88 0.04 7,743 Northern Mbala Mbala Mwamba 7 705 95 7 0.86 0.05 5,284 Northern Mbala Mbala Luandi 7 705 95 8 0.85 0.05 19,403 Northern Mbala Senga Hill Mukololo 7 705 97 9 0.85 0.04 11,251 Northern Mbala Senga Hill Lapisha 7 705 97 10 0.85 0.04 10,603 Northern Mbala Senga Hill Malamba 7 705 97 11 0.86 0.04 6,906 Northern Mbala Senga Hill Chimbili 7 705 97 12 0.86 0.04 10,855 Northern Mbala Senga Hill Chela 7 705 97 13 0.85 0.04 14,103 Northern Mbala Senga Hill Mwiluzi 7 705 97 14 0.84 0.05 5,889 Northern Mbala Senga Hill Chinyika 7 705 97 15 0.86 0.04 8,278 Northern Mbala Senga Hill Ipembe 7 705 97 16 0.87 0.04 5,766 Northern Mbala Senga Hill Chozi 7 705 97 17 0.76 0.05 5,508 Northern Mporokoso Lunte Kansanshi 7 706 101 11 0.87 0.05 3,237 Northern Mporokoso Lunte Isenga 7 706 101 12 0.84 0.05 4,286 Northern Mporokoso Lunte Nchelenge 7 706 101 13 0.83 0.05 5,002 Northern Mporokoso Lunte Malambwa 7 706 101 14 0.87 0.05 3,163 Northern Mporokoso Lunte Malaila 7 706 101 15 0.88 0.05 3,811 Northern Mporokoso Lunte Bwandela 7 706 101 16 0.86 0.05 4,230 Northern Mporokoso Lunte Masonde 7 706 101 17 0.85 0.05 1,462 Northern Mporokoso Lunte Luangwa 7 706 101 18 0.86 0.05 5,686 Northern Mporokoso Lunte Kalungwishi 7 706 101 19 0.84 0.05 6,355 (continued on next page) 81 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Northern Mporokoso Lunte Lunte 7 706 101 20 0.83 0.06 5,932 Northern Mporokoso Lunte Kanyanta 7 706 101 21 0.84 0.06 3,776 Northern Mporokoso Lunte Lubushi 7 706 101 22 0.82 0.05 2,605 Northern Mporokoso Mporokoso Lumangwe 7 706 102 1 0.85 0.05 3,869 Northern Mporokoso Mporokoso Chikulu 7 706 102 2 0.86 0.04 1,980 Northern Mporokoso Mporokoso Mumbuluma 7 706 102 3 0.83 0.05 2,087 Northern Mporokoso Mporokoso Chisha Mwamba 7 706 102 4 0.83 0.04 6,988 Northern Mporokoso Mporokoso Mikomba 7 706 102 5 0.48 0.06 2,634 Northern Mporokoso Mporokoso Kapumo 7 706 102 6 0.75 0.06 2,847 Northern Mporokoso Mporokoso Muchinga 7 706 102 7 0.78 0.05 3,483 Northern Mporokoso Mporokoso Chimpolenge 7 706 102 8 0.84 0.05 3,135 Northern Mporokoso Mporokoso Mutotoshi 7 706 102 9 0.85 0.04 2,570 Northern Mporokoso Mporokoso Mabale 7 706 102 10 0.87 0.04 2,738 Northern Mpulungu Mpulungu Kapembwa 7 707 96 1 0.89 0.04 1,911 Northern Mpulungu Mpulungu Katwe 7 707 96 2 0.90 0.04 2,949 Northern Mpulungu Mpulungu Chibulula 7 707 96 3 0.81 0.06 19,919 Northern Mpulungu Mpulungu Mpulungu 7 707 96 4 0.54 0.07 11,759 Central Northern Mpulungu Mpulungu Tanganyika 7 707 96 5 0.87 0.05 2,494 Northern Mpulungu Mpulungu Chilumba 7 707 96 6 0.88 0.05 690 Northern Mpulungu Mpulungu Isoko 7 707 96 7 0.88 0.04 8,050 Northern Mpulungu Mpulungu Iyendwe 7 707 96 8 0.91 0.03 3,768 Northern Mpulungu Mpulungu Mumila 7 707 96 9 0.91 0.04 3,915 Northern Mpulungu Mpulungu Itimbwe 7 707 96 10 0.90 0.04 2,163 Northern Mpulungu Mpulungu Vyamba 7 707 96 11 0.89 0.04 9,389 Northern Mpulungu Mpulungu Chisha 7 707 96 12 0.90 0.04 9,227 Northern Mpulungu Mpulungu Isunga 7 707 96 13 0.90 0.04 2,810 Northern Mungwi Malole Lubanda 7 708 92 1 0.87 0.05 11,864 Northern Mungwi Malole Mpanda 7 708 92 2 0.86 0.05 11,072 Northern Mungwi Malole Kabisha 7 708 92 3 0.88 0.04 7,825 Northern Mungwi Malole Fibwe 7 708 92 4 0.89 0.04 9,701 Northern Mungwi Malole Chibamba 7 708 92 5 0.87 0.04 16,947 Northern Mungwi Malole Iyaya 7 708 92 6 0.88 0.04 10,377 Northern Mungwi Malole Kalunga 7 708 92 7 0.87 0.05 12,576 Northern Mungwi Malole Fube 7 708 92 8 0.87 0.04 12,575 Northern Mungwi Malole Ngulula 7 708 92 9 0.88 0.04 8,822 Northern Mungwi Malole Mungwi 7 708 92 10 0.76 0.05 9,968 Northern Mungwi Malole Chambeshi 7 708 92 11 0.88 0.04 12,323 (continued on next page) 82 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Northern Mungwi Malole Mabula 7 708 92 12 0.87 0.05 4,749 Northern Mungwi Malole Musensenshe 7 708 92 13 0.87 0.05 4,412 North Chavuma Chavuma Chambi 8 801 103 1 0.90 0.04 2,265 Western North Chavuma Chavuma Sewe 8 801 103 2 0.87 0.04 2,176 Western North Chavuma Chavuma Lingelengenda 8 801 103 3 0.83 0.04 1,419 Western North Chavuma Chavuma Chiyeke 8 801 103 4 0.87 0.04 5,142 Western North Chavuma Chavuma Kalombo 8 801 103 5 0.85 0.05 1,954 Western Kamisamba North Chavuma Chavuma Chivombo 8 801 103 6 0.89 0.04 1,230 Western Mbalango North Chavuma Chavuma Chavuma 8 801 103 7 0.80 0.05 5,869 Western North Chavuma Chavuma Sanjongo 8 801 103 8 0.93 0.03 1,314 Western North Chavuma Chavuma Lingundu 8 801 103 9 0.91 0.04 966 Western North Chavuma Chavuma Lukolwe/ 8 801 103 10 0.92 0.04 1,069 Western musonda North Chavuma Chavuma Kambuwa 8 801 103 11 0.87 0.04 3,423 Western Mukelangombe North Chavuma Chavuma Nyatanda 8 801 103 12 0.92 0.03 2,446 Western Nyambingila North Chavuma Chavuma Nguvu 8 801 103 13 0.91 0.04 1,381 Western North Ikelenge Mwinilunga Chana- 8 802 109 1 0.65 0.12 2,118 Western West chamuhinga North Ikelenge Mwinilunga Jimbe 8 802 109 2 0.57 0.12 2,182 Western West North Ikelenge Mwinilunga Nyakaseya 8 802 109 3 0.54 0.12 4,459 Western West North Ikelenge Mwinilunga Ikelenge 8 802 109 4 0.59 0.11 4,779 Western West North Ikelenge Mwinilunga Mwininyilamba 8 802 109 5 0.59 0.12 2,975 Western West North Ikelenge Mwinilunga Mukangala 8 802 109 7 0.57 0.11 2,607 Western West North Kabompo Kabompo East Dihamba 8 803 104 1 0.96 0.03 2,423 Western (continued on next page) 83 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor North Kabompo Kabompo East Lunsuna 8 803 104 2 0.95 0.03 5,005 Western North Kabompo Kabompo East Kashinakaji 8 803 104 3 0.87 0.03 443 Western North Kabompo Kabompo East Chiteve 8 803 104 4 0.95 0.03 1,337 Western North Kabompo Kabompo East Manyinga 8 803 104 5 0.95 0.03 1,419 Western North Kabompo Kabompo East Loloma 8 803 104 6 0.87 0.04 11,233 Western North Kabompo Kabompo East Kawanda 8 803 104 7 0.92 0.04 6,060 Western North Kabompo Kabompo East Kaula 8 803 104 8 0.93 0.03 14,895 Western North Kabompo Kabompo East Chongo 8 803 104 9 0.95 0.03 1,896 Western North Kabompo Kabompo West Kamafwafwa 8 803 105 10 0.93 0.02 3,497 Western North Kabompo Kabompo West Kabompo 8 803 105 11 0.67 0.04 6,670 Western North Kabompo Kabompo West Litoya 8 803 105 12 0.95 0.03 1,500 Western North Kabompo Kabompo West Kamisombo 8 803 105 13 0.95 0.03 3,961 Western North Kabompo Kabompo West Kabulamena 8 803 105 14 0.94 0.03 3,972 Western North Kabompo Kabompo West Mumbeji 8 803 105 15 0.93 0.03 3,750 Western North Kabompo Kabompo West Luli 8 803 105 16 0.95 0.03 2,531 Western North Kabompo Kabompo West Katuva 8 803 105 17 0.94 0.02 2,289 Western North Kabompo Kabompo West Maveve 8 803 105 18 0.95 0.03 1,698 Western North Kabompo Kabompo West Chikenge 8 803 105 19 0.95 0.02 2,688 Western North Kabompo Kabompo West Lunyiwe 8 803 105 20 0.96 0.02 1,484 Western North Kabompo Kabompo West Kayombo 8 803 105 21 0.93 0.03 1,768 Western North Kabompo Kabompo West Chikonkwelo 8 803 105 22 0.94 0.03 3,504 Western (continued on next page) 84 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor North Kasempa Kasempa Kamakoku 8 804 106 1 0.84 0.05 882 Western North Kasempa Kasempa Nselauke 8 804 106 2 0.86 0.04 3,170 Western North Kasempa Kasempa Ingwe 8 804 106 3 0.87 0.05 513 Western North Kasempa Kasempa Kaimbwe 8 804 106 4 0.83 0.05 2,682 Western North Kasempa Kasempa Mukema 8 804 106 5 0.86 0.04 3,832 Western North Kasempa Kasempa Kamatete 8 804 106 6 0.86 0.04 3,751 Western North Kasempa Kasempa Dengwe 8 804 106 7 0.83 0.05 3,122 Western North Kasempa Kasempa Njenga 8 804 106 8 0.86 0.04 5,395 Western North Kasempa Kasempa Kalombe 8 804 106 9 0.85 0.04 3,173 Western North Kasempa Kasempa Kamusongolwa 8 804 106 10 0.32 0.07 383 Western North Kasempa Kasempa Kikonkomeme 8 804 106 11 0.50 0.06 2,305 Western North Kasempa Kasempa Nkenyauna 8 804 106 12 0.71 0.05 4,796 Western North Kasempa Kasempa Mukinge 8 804 106 13 0.84 0.05 1,181 Western North Kasempa Kasempa Katenda 8 804 106 14 0.85 0.04 4,220 Western North Kasempa Kasempa Lubofu 8 804 106 15 0.85 0.05 1,922 Western North Kasempa Kasempa Mpungu 8 804 106 16 0.87 0.05 1,201 Western North Kasempa Kasempa Nyoka 8 804 106 17 0.86 0.04 3,226 Western North Kasempa Kasempa Kelongwa 8 804 106 18 0.86 0.04 2,389 Western North Kasempa Kasempa Mukunanshi 8 804 106 19 0.86 0.04 3,169 Western North Kasempa Kasempa Kanongo 8 804 106 20 0.84 0.04 1,438 Western North Kasempa Kasempa Kamankechi 8 804 106 21 0.82 0.05 2,922 Western (continued on next page) 85 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor North Kasempa Kasempa Jifumpa 8 804 106 22 0.75 0.06 859 Western North Mufumbwe Mufumbwe Kashima West 8 805 107 1 0.90 0.04 6,244 Western North Mufumbwe Mufumbwe Kashima East 8 805 107 2 0.89 0.04 1,818 Western North Mufumbwe Mufumbwe Matushi 8 805 107 3 0.91 0.04 8,090 Western North Mufumbwe Mufumbwe Kamabuta 8 805 107 4 0.92 0.03 3,079 Western North Mufumbwe Mufumbwe Kalambu 8 805 107 5 0.71 0.06 7,750 Western North Mufumbwe Mufumbwe Chizera 8 805 107 6 0.87 0.05 2,733 Western North Mufumbwe Mufumbwe Shukwe 8 805 107 7 0.93 0.03 2,214 Western North Mufumbwe Mufumbwe Kikonge 8 805 107 8 0.92 0.04 1,705 Western North Mufumbwe Mufumbwe Munyambala 8 805 107 9 0.93 0.04 1,499 Western North Mufumbwe Mufumbwe Kalengwa 8 805 107 10 0.88 0.05 1,843 Western North Mufumbwe Mufumbwe Kabi Pupu 8 805 107 11 0.92 0.04 1,355 Western North Mufumbwe Mufumbwe Mushima 8 805 107 12 0.92 0.04 4,083 Western North Mufumbwe Mufumbwe Musonweji 8 805 107 13 0.94 0.03 1,973 Western North Mufumbwe Mufumbwe Kaminzekenzeke 8 805 107 14 0.89 0.05 1,894 Western North Mufumbwe Mufumbwe Lalafuta 8 805 107 15 0.91 0.04 912 Western North Mufumbwe Mufumbwe Miluji 8 805 107 16 0.90 0.05 3,871 Western North Mwinilunga Mwinilunga Kanongesha 8 806 108 6 0.29 0.09 3,967 Western East North Mwinilunga Mwinilunga Kawiku 8 806 108 8 0.30 0.08 950 Western East North Mwinilunga Mwinilunga Mulumbi 8 806 108 9 0.16 0.06 2,611 Western East North Mwinilunga Mwinilunga Mundwinji 8 806 108 10 0.31 0.09 2,578 Western East (continued on next page) 86 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor North Mwinilunga Mwinilunga Kapundu 8 806 108 11 0.29 0.09 1,467 Western East North Mwinilunga Mwinilunga Kanyama 8 806 108 12 0.30 0.09 1,402 Western East North Mwinilunga Mwinilunga Kakoma 8 806 108 13 0.28 0.09 1,981 Western East North Mwinilunga Mwinilunga Kasambula 8 806 108 14 0.27 0.09 302 Western East North Mwinilunga Mwinilunga Lumwana 8 806 108 15 0.30 0.09 1,488 Western East North Mwinilunga Mwinilunga Sailunga 8 806 108 16 0.32 0.09 991 Western East North Mwinilunga Mwinilunga Chisasa 8 806 108 17 0.29 0.09 1,147 Western East North Mwinilunga Mwinilunga Ntambu 8 806 108 18 0.31 0.09 2,862 Western East North Mwinilunga Mwinilunga Samuteba 8 806 108 19 0.33 0.09 2,304 Western East North Mwinilunga Mwinilunga Mudyanyama 8 806 108 20 0.30 0.09 962 Western East North Mwinilunga Mwinilunga Chibwika 8 806 108 21 0.30 0.09 2,985 Western East North Mwinilunga Mwinilunga Kamapanda 8 806 108 22 0.28 0.09 1,383 Western East North Solwezi Solwezi Central Kapijimpanga 8 807 110 8 0.61 0.11 11,186 Western North Solwezi Solwezi Central Sandangombe 8 807 110 9 0.57 0.11 10,585 Western North Solwezi Solwezi Central Kamalamba 8 807 110 10 0.32 0.07 6,294 Western North Solwezi Solwezi Central Tuvwanganai 8 807 110 11 0.26 0.08 10,441 Western North Solwezi Solwezi Central Kimasala 8 807 110 12 0.26 0.08 9,784 Western North Solwezi Solwezi East Musaka 8 807 111 1 0.65 0.11 5,776 Western North Solwezi Solwezi East Chikola 8 807 111 2 0.71 0.10 2,840 Western North Solwezi Solwezi East Kangwena 8 807 111 3 0.72 0.10 2,958 Western North Solwezi Solwezi East Kalilele 8 807 111 4 0.68 0.10 2,091 Western (continued on next page) 87 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor North Solwezi Solwezi East Mulonga 8 807 111 5 0.71 0.10 7,112 Western North Solwezi Solwezi East Mapunga 8 807 111 6 0.71 0.09 2,748 Western North Solwezi Solwezi East Mujimanzovu 8 807 111 7 0.75 0.09 2,088 Western North Solwezi Solwezi West Mumena 8 807 112 13 0.68 0.11 8,105 Western North Solwezi Solwezi West Mwajimambwe 8 807 112 14 0.60 0.11 7,654 Western North Solwezi Solwezi West Kibanza 8 807 112 15 0.69 0.11 3,743 Western North Solwezi Solwezi West Mukumbi 8 807 112 16 0.48 0.10 4,377 Western North Solwezi Solwezi West Matebo 8 807 112 17 0.72 0.11 1,725 Western North Solwezi Solwezi West Shilenda 8 807 112 18 0.55 0.11 9,242 Western North Solwezi Solwezi West Lumwana 8 807 112 19 0.60 0.11 4,541 Western North Solwezi Solwezi West Mumbezhi 8 807 112 20 0.58 0.11 939 Western North Solwezi Solwezi West Musele 8 807 112 21 0.71 0.11 8,475 Western North Solwezi Solwezi West Chovwe 8 807 112 22 0.74 0.11 4,994 Western North Zambezi Zambezi East Lukunyi 8 808 113 1 0.92 0.04 4,076 Western North Zambezi Zambezi East Nyakulenga 8 808 113 2 0.92 0.04 3,664 Western North Zambezi Zambezi East Dipalata 8 808 113 3 0.92 0.04 5,355 Western North Zambezi Zambezi East Mukanda 8 808 113 4 0.90 0.04 6,850 Western Nkunda North Zambezi Zambezi East Chileng’a 8 808 113 5 0.91 0.04 4,719 Western Chizenzi North Zambezi Zambezi East Lwitadi 8 808 113 6 0.92 0.04 2,941 Western Lwatembo North Zambezi Zambezi East Mpidi Kakonga 8 808 113 7 0.90 0.05 6,900 Western North Zambezi Zambezi East Chitokoloki 8 808 113 8 0.84 0.05 3,599 Western (continued on next page) 88 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor North Zambezi Zambezi East Chivweti Kasesi 8 808 113 9 0.89 0.05 5,655 Western North Zambezi Zambezi East Zambezi 8 808 113 10 0.58 0.07 6,048 Western North Zambezi Zambezi West Mapachi 8 808 114 11 0.87 0.04 3,294 Western Chinyingi North Zambezi Zambezi West Likungu 8 808 114 12 0.92 0.03 4,413 Western North Zambezi Zambezi West Liyovu 8 808 114 13 0.91 0.03 4,603 Western North Zambezi Zambezi West Muyembe 8 808 114 14 0.91 0.03 1,809 Western North Zambezi Zambezi West Mwange 8 808 114 15 0.93 0.03 3,477 Western Nyawanda North Zambezi Zambezi West Matondo 8 808 114 16 0.91 0.03 2,538 Western nyachikai Southern Choma Choma Batoka 9 901 115 15 0.74 0.05 5,344 Southern Choma Choma Sikalongo 9 901 115 16 0.81 0.05 6,652 Southern Choma Choma Simamvwa 9 901 115 17 0.77 0.06 8,948 Southern Choma Choma Stateland 9 901 115 18 0.65 0.06 4,296 Southern Choma Choma Nakeempa 9 901 115 19 0.85 0.04 4,848 Southern Choma Choma Moomba 9 901 115 20 0.68 0.07 2,292 Southern Choma Choma Kulundana 9 901 115 21 0.41 0.06 5,437 Southern Choma Choma Simacheche 9 901 115 22 0.58 0.07 4,965 Southern Choma Choma Sikalundu 9 901 115 23 0.16 0.04 1,505 Southern Choma Choma Mubula 9 901 115 24 0.35 0.07 7,505 Southern Choma Choma Singani 9 901 115 25 0.80 0.05 7,219 Southern Choma Choma Siasikabole 9 901 115 26 0.85 0.05 7,209 Southern Choma Choma Namuswa 9 901 115 27 0.82 0.04 9,626 Southern Choma Mbabala Simaumbi 9 901 116 1 0.86 0.05 10,344 Southern Choma Mbabala Mapanza 9 901 116 2 0.80 0.05 4,878 Southern Choma Mbabala Mang’unza 9 901 116 3 0.85 0.04 5,273 Southern Choma Mbabala Chilalantambo 9 901 116 4 0.84 0.05 8,140 Southern Choma Mbabala Macha 9 901 116 5 0.73 0.05 5,588 Southern Choma Mbabala Kabimba 9 901 116 6 0.84 0.05 1,732 Southern Choma Mbabala Mbabala 9 901 116 7 0.81 0.05 12,032 Southern Choma Pemba Kasiya 9 901 117 8 0.84 0.05 10,991 Southern Choma Pemba Pemba 9 901 117 9 0.46 0.07 1,078 Southern Choma Pemba Hamaundu 9 901 117 10 0.80 0.06 14,904 Southern Choma Pemba Maambo 9 901 117 11 0.87 0.05 11,171 (continued on next page) 89 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Southern Choma Pemba Kauba 9 901 117 12 0.84 0.06 6,106 Southern Choma Pemba Habunkululu 9 901 117 13 0.86 0.05 3,854 Southern Choma Pemba Nachibanga 9 901 117 14 0.84 0.06 7,853 Southern Gwembe Gwembe Chisanga 9 902 118 1 0.86 0.05 2,457 Southern Gwembe Gwembe Sinafala 9 902 118 2 0.85 0.05 1,780 Southern Gwembe Gwembe Jumbo 9 902 118 3 0.87 0.04 2,088 Southern Gwembe Gwembe Kkoma 9 902 118 4 0.82 0.04 4,936 Southern Gwembe Gwembe Chibuwe 9 902 118 5 0.81 0.05 4,434 Southern Gwembe Gwembe Siampande 9 902 118 6 0.87 0.04 1,268 Southern Gwembe Gwembe Kota Kota 9 902 118 7 0.88 0.05 1,023 Southern Gwembe Gwembe Luumbo 9 902 118 8 0.86 0.04 3,203 Southern Gwembe Gwembe Kkole 9 902 118 9 0.87 0.04 2,404 Southern Gwembe Gwembe Bbondo 9 902 118 10 0.86 0.04 7,575 Southern Gwembe Gwembe Chaamwe 9 902 118 11 0.86 0.04 2,498 Southern Gwembe Gwembe Fumbo 9 902 118 12 0.80 0.05 7,292 Southern Gwembe Gwembe Jongola 9 902 118 13 0.90 0.05 594 Southern Gwembe Gwembe Lukonde 9 902 118 14 0.63 0.05 2,707 Southern Itezhi-tezhi Itezhi Tezhi Itezhi-tezhi 9 903 130 1 0.31 0.08 2,576 Southern Itezhi-tezhi Itezhi Tezhi Itumbi 9 903 130 2 0.79 0.08 4,628 Southern Itezhi-tezhi Itezhi Tezhi Kaanzwa 9 903 130 3 0.77 0.08 4,869 Southern Itezhi-tezhi Itezhi Tezhi Banamwaze 9 903 130 4 0.76 0.09 2,158 Southern Itezhi-tezhi Itezhi Tezhi Makunku 9 903 130 5 0.79 0.08 2,897 Southern Itezhi-tezhi Itezhi Tezhi Nyambo 9 903 130 6 0.77 0.08 1,967 Southern Itezhi-tezhi Itezhi Tezhi Kabulungwe 9 903 130 7 0.71 0.10 1,253 Southern Itezhi-tezhi Itezhi Tezhi Lubanda 9 903 130 8 0.76 0.09 5,109 Southern Itezhi-tezhi Itezhi Tezhi Masemu 9 903 130 9 0.71 0.10 7,015 Southern Itezhi-tezhi Itezhi Tezhi Luubwe 9 903 130 10 0.76 0.09 2,568 Southern Itezhi-tezhi Itezhi Tezhi Basanga 9 903 130 11 0.75 0.09 5,521 Southern Itezhi-tezhi Itezhi Tezhi Luchena 9 903 130 12 0.75 0.09 1,652 Southern Itezhi-tezhi Itezhi Tezhi Mbila 9 903 130 13 0.75 0.09 6,071 Southern Kalomo Dundumwenze Chikanta 9 904 119 1 0.79 0.05 16,549 Southern Kalomo Dundumwenze Chamuka 9 904 119 2 0.77 0.05 7,753 Southern Kalomo Dundumwenze Kasukwe 9 904 119 3 0.77 0.05 11,710 Southern Kalomo Dundumwenze Omba 9 904 119 4 0.79 0.05 8,510 Southern Kalomo Dundumwenze Bbilili 9 904 119 5 0.80 0.05 8,086 Southern Kalomo Dundumwenze Naluja 9 904 119 6 0.79 0.05 10,943 Southern Kalomo Kalomo Siachitema 9 904 120 7 0.78 0.06 21,068 Southern Kalomo Kalomo Kalonda 9 904 120 8 0.74 0.06 10,188 (continued on next page) 90 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Southern Kalomo Kalomo Choonga 9 904 120 9 0.50 0.06 10,872 Southern Kalomo Kalomo Mayoba 9 904 120 10 0.76 0.06 8,681 Southern Kalomo Kalomo Namwianga 9 904 120 11 0.65 0.06 6,471 Southern Kalomo Kalomo Simayakwe 9 904 120 12 0.79 0.05 4,036 Southern Kalomo Kalomo Chawila 9 904 120 13 0.78 0.06 6,335 Southern Kalomo Kalomo Sipatunyana 9 904 120 14 0.78 0.06 3,028 Southern Kalomo Kalomo Nachikungu 9 904 120 15 0.79 0.06 6,680 Southern Kalomo Mapatizya Chidi 9 904 122 16 0.79 0.05 8,221 Southern Kalomo Mapatizya Mulamfu 9 904 122 17 0.81 0.05 5,390 Southern Kalomo Mapatizya Simwatachela 9 904 122 18 0.77 0.06 3,943 Southern Kalomo Mapatizya Luyaba 9 904 122 19 0.79 0.05 12,194 Southern Kalomo Mapatizya Zimba 9 904 122 20 0.73 0.05 9,696 Southern Kalomo Mapatizya Siamafumba 9 904 122 21 0.79 0.05 8,327 Southern Kalomo Mapatizya Mbwiko 9 904 122 22 0.79 0.05 7,321 Southern Kazungula Katombola Moomba 9 905 121 1 0.73 0.11 1,786 Southern Kazungula Katombola Chooma 9 905 121 2 0.67 0.12 4,900 Southern Kazungula Katombola Nguba 9 905 121 3 0.71 0.11 8,193 Southern Kazungula Katombola Kauwe 9 905 121 4 0.65 0.12 4,279 Southern Kazungula Katombola Nyawa 9 905 121 5 0.69 0.12 7,688 Southern Kazungula Katombola Ngwezi 9 905 121 6 0.71 0.11 7,252 Southern Kazungula Katombola Sikaunzwe 9 905 121 7 0.68 0.12 5,142 Southern Kazungula Katombola Mandia 9 905 121 8 0.53 0.10 4,991 Southern Kazungula Katombola Sekute 9 905 121 9 0.71 0.11 2,317 Southern Kazungula Katombola Kanchele 9 905 121 10 0.72 0.11 7,815 Southern Kazungula Katombola Simango 9 905 121 11 0.68 0.11 3,255 Southern Kazungula Katombola Musokotwane 9 905 121 12 0.69 0.11 3,466 Southern Kazungula Katombola Katapazi 9 905 121 13 0.65 0.11 4,169 Southern Kazungula Katombola Mukuni 9 905 121 14 0.66 0.11 5,913 Southern Livingstone Livingstone Freedom 9 906 123 1 0.18 0.05 2,230 Southern Livingstone Livingstone Musi-o-tunya 9 906 123 2 0.07 0.03 606 Southern Livingstone Livingstone Dr.mubitana 9 906 123 3 0.17 0.04 1,123 Southern Livingstone Livingstone Namatama 9 906 123 4 0.36 0.07 4,179 Southern Livingstone Livingstone Kasiya 9 906 123 5 0.54 0.07 5,013 Southern Livingstone Livingstone Libuyu 9 906 123 6 0.34 0.07 2,657 Southern Livingstone Livingstone Mwalibonena 9 906 123 7 0.27 0.06 2,704 Southern Livingstone Livingstone Mulungushi 9 906 123 8 0.32 0.07 2,669 Southern Livingstone Livingstone Maramba 9 906 123 9 0.14 0.05 1,410 Southern Livingstone Livingstone Akapelwa 9 906 123 10 0.08 0.03 237 (continued on next page) 91 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Southern Livingstone Livingstone Lizuma 9 906 123 11 0.11 0.04 312 Southern Livingstone Livingstone Simonga 9 906 123 12 0.38 0.06 4,969 Southern Livingstone Livingstone Dambwa Central 9 906 123 13 0.19 0.05 1,198 Southern Livingstone Livingstone Zambezi 9 906 123 14 0.23 0.06 3,662 Southern Livingstone Livingstone Kariba 9 906 123 15 0.22 0.06 1,006 Southern Livingstone Livingstone Nansanzu 9 906 123 16 0.29 0.07 2,058 Southern Livingstone Livingstone Shungu 9 906 123 17 0.53 0.05 1,922 Southern Mazabuka Chikankata Kasengo 9 907 124 1 0.85 0.05 5,829 Southern Mazabuka Chikankata Namalundu 9 907 124 2 0.52 0.05 4,223 Southern Mazabuka Chikankata Musaya 9 907 124 3 0.86 0.06 1,006 Southern Mazabuka Chikankata Nansenga 9 907 124 4 0.83 0.06 2,257 Southern Mazabuka Chikankata Chitete 9 907 124 5 0.82 0.06 8,672 Southern Mazabuka Chikankata Malala 9 907 124 6 0.85 0.05 5,521 Southern Mazabuka Chikankata Mabwetuba 9 907 124 7 0.81 0.05 10,496 Southern Mazabuka Chikankata Upper Kaleya 9 907 124 8 0.79 0.06 9,076 Southern Mazabuka Magoye Nkonkola 9 907 125 9 0.86 0.05 5,601 Southern Mazabuka Magoye Chivuna 9 907 125 10 0.82 0.06 16,140 Southern Mazabuka Magoye Musuma 9 907 125 11 0.85 0.05 4,722 Southern Mazabuka Magoye Munjile 9 907 125 12 0.86 0.05 2,193 Southern Mazabuka Magoye Ngwezi 9 907 125 13 0.78 0.06 13,031 Southern Mazabuka Magoye Kalama 9 907 125 14 0.84 0.06 2,857 Southern Mazabuka Magoye Munenga 9 907 125 15 0.85 0.05 2,321 Southern Mazabuka Magoye Mwanachingwala 9 907 125 16 0.73 0.07 8,726 Southern Mazabuka Magoye Itebe 9 907 125 17 0.80 0.05 2,323 Southern Mazabuka Mazabuka Mazabuka 9 907 126 18 0.32 0.06 18,725 Central Southern Mazabuka Mazabuka Nakambala 9 907 126 19 0.36 0.05 3,128 Southern Mazabuka Mazabuka Chizobo 9 907 126 20 0.64 0.07 3,928 Southern Mazabuka Mazabuka Lubombo 9 907 126 21 0.55 0.05 11,891 Southern Mazabuka Mazabuka Nenga Nenga 9 907 126 22 0.79 0.06 3,796 Southern Monze Bweengwa Malundu 9 908 127 1 0.80 0.06 6,759 Southern Monze Bweengwa Kaila 9 908 127 2 0.85 0.05 6,370 Southern Monze Bweengwa Keemba 9 908 127 3 0.85 0.05 10,700 Southern Monze Bweengwa Choongo West 9 908 127 4 0.81 0.06 3,023 Southern Monze Bweengwa Bweengwa 9 908 127 5 0.84 0.06 6,125 Southern Monze Bweengwa Hamangaba 9 908 127 6 0.85 0.05 7,266 Southern Monze Bweengwa Choongo East 9 908 127 7 0.75 0.05 9,711 Southern Monze Monze Chipembele 9 908 128 8 0.84 0.05 6,097 (continued on next page) 92 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Southern Monze Monze Ufwenuka 9 908 128 9 0.78 0.05 7,539 Southern Monze Monze Manungu 9 908 128 10 0.45 0.07 17,535 Southern Monze Monze Chisekesi 9 908 128 11 0.78 0.06 5,678 Southern Monze Monze Mayaba 9 908 128 12 0.81 0.06 3,812 Southern Monze Monze Hufwa/ 9 908 128 13 0.85 0.05 3,080 hamapande Southern Monze Monze Hamamvwa 9 908 128 14 0.87 0.05 3,773 Southern Monze Monze Katimba 9 908 128 15 0.86 0.05 6,115 Southern Monze Monze Hantotola 9 908 128 16 0.86 0.05 10,421 Southern Monze Monze Bbombo 9 908 128 17 0.86 0.05 4,979 Southern Monze Monze Monze Urban 9 908 128 18 0.73 0.07 1,110 Southern Monze Moomba Mwanza West 9 908 129 19 0.83 0.05 10,931 Southern Monze Moomba Chona 9 908 129 20 0.84 0.05 8,221 Southern Monze Moomba Mwanza East 9 908 129 21 0.82 0.06 5,162 Southern Monze Moomba Moomba 9 908 129 22 0.85 0.06 881 Southern Namwala Namwala Namwala Central 9 909 131 1 0.55 0.09 5,719 Southern Namwala Namwala Ngabo 9 909 131 2 0.80 0.09 2,228 Southern Namwala Namwala Baambwe 9 909 131 3 0.77 0.09 3,180 Southern Namwala Namwala Maala 9 909 131 4 0.72 0.09 4,156 Southern Namwala Namwala Kantengwa 9 909 131 5 0.76 0.09 3,470 Southern Namwala Namwala Kabulamwanda 9 909 131 6 0.76 0.09 5,563 Southern Namwala Namwala Chitongo 9 909 131 7 0.80 0.09 4,018 Southern Namwala Namwala Mandondo 9 909 131 8 0.75 0.08 3,383 Southern Namwala Namwala Nakamboma 9 909 131 9 0.75 0.10 10,356 Southern Namwala Namwala Mbeza 9 909 131 10 0.71 0.10 3,710 Southern Namwala Namwala Ndema 9 909 131 11 0.77 0.09 7,294 Southern Namwala Namwala Namakube 9 909 131 12 0.74 0.10 8,153 Southern Namwala Namwala Itapa 9 909 131 13 0.76 0.09 5,462 Southern Namwala Namwala Moobola 9 909 131 14 0.72 0.10 9,317 Southern Siavonga Siavonga Ibwe Munyama 9 910 132 1 0.88 0.04 2,936 Southern Siavonga Siavonga Musaya 9 910 132 2 0.81 0.05 4,344 Southern Siavonga Siavonga Chirundu 9 910 132 3 0.47 0.06 7,125 Southern Siavonga Siavonga Ngombe Ilende 9 910 132 4 0.85 0.05 10,095 Southern Siavonga Siavonga Sikongo 9 910 132 5 0.87 0.04 3,407 Southern Siavonga Siavonga Lusitu 9 910 132 6 0.85 0.04 6,963 Southern Siavonga Siavonga Nanyanga 9 910 132 7 0.85 0.05 2,241 Southern Siavonga Siavonga Kariba 9 910 132 8 0.46 0.06 7,702 Southern Siavonga Siavonga Simamba 9 910 132 9 0.83 0.05 5,251 (continued on next page) 93 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Southern Siavonga Siavonga Mulima 9 910 132 10 0.88 0.04 4,822 Southern Siavonga Siavonga Manchanvwa 9 910 132 11 0.86 0.05 4,105 Southern Siavonga Siavonga Sinadambwe 9 910 132 12 0.90 0.04 3,905 Southern Siavonga Siavonga Lusangazi 9 910 132 13 0.91 0.04 2,822 Southern Sinazongwe Sinazongwe Mabinga 9 911 133 1 0.84 0.07 653 Southern Sinazongwe Sinazongwe Namazambwe 9 911 133 2 0.84 0.06 4,370 Southern Sinazongwe Sinazongwe Mweenda 9 911 133 3 0.87 0.06 4,749 Southern Sinazongwe Sinazongwe Muuka 9 911 133 4 0.86 0.06 4,226 Southern Sinazongwe Sinazongwe Tekelo 9 911 133 5 0.87 0.05 1,686 Southern Sinazongwe Sinazongwe Mweemba 9 911 133 6 0.82 0.06 9,400 Southern Sinazongwe Sinazongwe Muchekwa 9 911 133 7 0.77 0.06 5,286 Southern Sinazongwe Sinazongwe Maamba 9 911 133 8 0.42 0.07 4,379 Southern Sinazongwe Sinazongwe Mweezya 9 911 133 9 0.75 0.07 13,030 Southern Sinazongwe Sinazongwe Nkamdabwe 9 911 133 10 0.77 0.07 5,046 Southern Sinazongwe Sinazongwe Sinazongwe 9 911 133 11 0.76 0.07 8,136 Southern Sinazongwe Sinazongwe Nangombe 9 911 133 12 0.82 0.07 5,242 Southern Sinazongwe Sinazongwe Sinenge 9 911 133 13 0.80 0.07 6,643 Southern Sinazongwe Sinazongwe Malima 9 911 133 14 0.84 0.06 5,210 Western Kalabo Kalabo Mapungu 10 1001 134 8 0.89 0.04 3,358 Western Kalabo Kalabo Luanginga 10 1001 134 9 0.65 0.06 5,757 Western Kalabo Kalabo Liumba 10 1001 134 10 0.91 0.03 3,351 Western Kalabo Kalabo Yuka 10 1001 134 11 0.89 0.04 6,392 Western Kalabo Kalabo Buleya 10 1001 134 12 0.89 0.04 3,765 Western Kalabo Kalabo Lutwi 10 1001 134 13 0.90 0.04 5,242 Western Kalabo Kalabo Ndoka 10 1001 134 14 0.90 0.04 7,073 Western Kalabo Kalabo Namulilo 10 1001 134 15 0.89 0.04 7,133 Western Kalabo Kalabo Nguma 10 1001 134 16 0.91 0.04 3,923 Western Kalabo Kalabo Kandambo 10 1001 134 17 0.91 0.03 3,520 Western Kalabo Liuwa Siluwe 10 1001 135 1 0.89 0.05 1,942 Western Kalabo Liuwa Likulundundu 10 1001 135 2 0.90 0.05 2,049 Western Kalabo Liuwa Luola 10 1001 135 3 0.90 0.05 1,605 Western Kalabo Liuwa Salunda 10 1001 135 4 0.89 0.05 2,709 Western Kalabo Liuwa Sishekanu 10 1001 135 5 0.90 0.05 5,061 Western Kalabo Liuwa Kuuli 10 1001 135 6 0.91 0.04 3,624 Western Kalabo Liuwa Libonda 10 1001 135 7 0.87 0.05 6,928 Western Kalabo Sikongo Lueti 10 1001 136 18 0.88 0.05 7,904 Western Kalabo Sikongo Lulangunyi 10 1001 136 19 0.87 0.05 2,112 Western Kalabo Sikongo Tuuwa 10 1001 136 20 0.89 0.05 4,199 (continued on next page) 94 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Western Kalabo Sikongo Maala 10 1001 136 21 0.87 0.05 5,071 Western Kalabo Sikongo Liumena 10 1001 136 22 0.90 0.04 5,990 Western Kalabo Sikongo Licha 10 1001 136 23 0.88 0.05 7,350 Western Kalabo Sikongo Lwambi 10 1001 136 24 0.91 0.05 1,564 Western Kalabo Sikongo Nengu 10 1001 136 25 0.91 0.05 2,178 Western Kalabo Sikongo Mutala 10 1001 136 26 0.89 0.05 1,373 Western Kalabo Sikongo Mwenyi 10 1001 136 27 0.89 0.05 3,124 Western Kaoma Kaoma Lalafuta 10 1002 137 1 0.88 0.04 6,604 Western Kaoma Kaoma Shitwa 10 1002 137 2 0.87 0.04 7,364 Western Kaoma Kaoma Namilangi 10 1002 137 3 0.87 0.04 18,629 Western Kaoma Kaoma Nkeyama 10 1002 137 4 0.83 0.04 10,064 Western Kaoma Kaoma Litoya 10 1002 137 5 0.86 0.04 7,929 Western Kaoma Kaoma Mulamatila 10 1002 137 6 0.49 0.06 10,575 Western Kaoma Kaoma Longe 10 1002 137 7 0.87 0.04 6,661 Western Kaoma Luampa Naliele 10 1002 138 8 0.84 0.04 6,019 Western Kaoma Luampa Namando 10 1002 138 9 0.88 0.04 1,067 Western Kaoma Luampa Nyambi 10 1002 138 10 0.86 0.04 5,315 Western Kaoma Luampa Mulwa 10 1002 138 11 0.87 0.04 3,676 Western Kaoma Luampa Lui 10 1002 138 12 0.88 0.04 6,916 Western Kaoma Luampa Nkenga 10 1002 138 13 0.84 0.04 7,510 Western Kaoma Luampa Mbanyutu 10 1002 138 14 0.89 0.04 1,830 Western Kaoma Luampa Luampa 10 1002 138 15 0.87 0.04 5,733 Western Kaoma Mangango Namafulo 10 1002 139 16 0.87 0.04 14,191 Western Kaoma Mangango Luambuwa 10 1002 139 17 0.88 0.04 6,793 Western Kaoma Mangango Mushwala 10 1002 139 18 0.87 0.04 14,870 Western Kaoma Mangango Kapili 10 1002 139 19 0.86 0.05 1,964 Western Kaoma Mangango Mangango 10 1002 139 20 0.81 0.05 4,343 Western Kaoma Mangango Kanabilumbu 10 1002 139 21 0.86 0.05 2,738 Western Kaoma Mangango Shinkombwe 10 1002 139 22 0.87 0.04 6,182 Western Lukulu Lukulu East Simakumba 10 1003 140 1 0.88 0.04 4,213 Western Lukulu Lukulu East Kamilende 10 1003 140 2 0.88 0.04 2,420 Western Lukulu Lukulu East Dongwe 10 1003 140 3 0.89 0.04 2,179 Western Lukulu Lukulu East Kashamba 10 1003 140 4 0.88 0.04 2,288 Western Lukulu Lukulu East Mwito 10 1003 140 5 0.88 0.04 3,515 Western Lukulu Lukulu East Kang’oti 10 1003 140 6 0.88 0.04 5,812 Western Lukulu Lukulu East Lukau 10 1003 140 7 0.88 0.04 3,150 Western Lukulu Lukulu East Likapai 10 1003 140 8 0.89 0.04 1,478 Western Lukulu Lukulu East Mbanga 10 1003 140 9 0.88 0.04 4,483 (continued on next page) 95 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Western Lukulu Lukulu East Kawaya 10 1003 140 10 0.87 0.04 3,590 Western Lukulu Lukulu East Mwandi 10 1003 140 11 0.68 0.05 7,384 Western Lukulu Lukulu East Namayula 10 1003 140 12 0.87 0.04 3,379 Western Lukulu Lukulu East Luanchuma 10 1003 140 13 0.87 0.04 5,825 Western Lukulu Lukulu West Muyondoti 10 1003 141 14 0.87 0.04 2,615 Western Lukulu Lukulu West Nyaala 10 1003 141 15 0.88 0.04 2,951 Western Lukulu Lukulu West Mataba 10 1003 141 16 0.88 0.04 3,214 Western Lukulu Lukulu West Lupui 10 1003 141 17 0.87 0.04 2,443 Western Lukulu Lukulu West Kakwacha 10 1003 141 18 0.86 0.05 1,438 Western Lukulu Lukulu West Lutembwe 10 1003 141 19 0.88 0.04 878 Western Lukulu Lukulu West Mitete 10 1003 141 20 0.88 0.04 2,945 Western Lukulu Lukulu West Kanshizhi 10 1003 141 21 0.88 0.04 5,786 Western Lukulu Lukulu West Chin’onwe 10 1003 141 22 0.88 0.04 2,102 Western Mongu Luena Limulunga 10 1004 142 1 0.76 0.05 10,922 Western Mongu Luena Mabili 10 1004 142 2 0.87 0.05 3,533 Western Mongu Luena Ikwichi 10 1004 142 3 0.87 0.05 3,019 Western Mongu Luena Namboma 10 1004 142 4 0.83 0.05 4,387 Western Mongu Luena Nangula 10 1004 142 5 0.86 0.04 10,545 Western Mongu Luena Ushaa 10 1004 142 6 0.88 0.04 4,682 Western Mongu Luena Simaa 10 1004 142 7 0.90 0.04 4,430 Western Mongu Luena Ndanda 10 1004 142 8 0.87 0.05 1,353 Western Mongu Mongu Namushakende 10 1004 143 16 0.77 0.05 4,222 Western Mongu Mongu Yeta 10 1004 143 17 0.75 0.06 5,003 Western Mongu Mongu Kama 10 1004 143 18 0.84 0.05 2,486 Western Mongu Mongu Lumbo 10 1004 143 19 0.85 0.05 3,916 Western Mongu Mongu Katongo 10 1004 143 20 0.70 0.07 6,911 Western Mongu Mongu Kanyonyo 10 1004 143 21 0.51 0.07 5,906 Western Mongu Mongu Kambule 10 1004 143 22 0.40 0.06 3,833 Western Mongu Mongu Lewanika 10 1004 143 23 0.39 0.06 1,182 Western Mongu Mongu Mulambwa 10 1004 143 24 0.44 0.06 4,397 Western Mongu Mongu Imwiko 10 1004 143 25 0.41 0.06 7,107 Western Mongu Mongu Lealui 10 1004 143 26 0.80 0.06 3,899 Western Mongu Mongu Mabumbu 10 1004 143 27 0.81 0.06 1,806 Western Mongu Mongu Kaande 10 1004 143 28 0.83 0.06 1,456 Western Mongu Nalikwanda Lui 10 1004 144 9 0.86 0.04 8,871 Western Mongu Nalikwanda Imalyo 10 1004 144 10 0.86 0.04 5,409 Western Mongu Nalikwanda Mutondo 10 1004 144 11 0.86 0.04 2,945 Western Mongu Nalikwanda Namengo 10 1004 144 12 0.86 0.04 2,844 (continued on next page) 96 Annex Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Western Mongu Nalikwanda Nakanyaa 10 1004 144 13 0.88 0.04 6,130 Western Mongu Nalikwanda Mbekise 10 1004 144 14 0.86 0.04 3,549 Western Mongu Nalikwanda Nakato 10 1004 144 15 0.88 0.04 5,165 Western Senanga Nalolo Lyamakumbi 10 1005 145 1 0.92 0.04 11,119 Western Senanga Nalolo Silowana 10 1005 145 2 0.92 0.04 6,368 Western Senanga Nalolo Shekela 10 1005 145 3 0.93 0.04 6,836 Western Senanga Nalolo Makoka 10 1005 145 4 0.92 0.04 6,086 Western Senanga Nalolo Kambai 10 1005 145 5 0.92 0.04 6,148 Western Senanga Nalolo Kataba 10 1005 145 6 0.91 0.04 4,158 Western Senanga Nalolo Muoyo 10 1005 145 7 0.84 0.05 4,188 Western Senanga Nalolo Nanjucha 10 1005 145 8 0.91 0.04 6,203 Western Senanga Senanga Mwanambuyu 10 1005 146 9 0.93 0.03 10,777 Western Senanga Senanga Imatongo 10 1005 146 10 0.89 0.04 6,720 Western Senanga Senanga Imatanda 10 1005 146 11 0.60 0.06 10,631 Western Senanga Senanga Wanyau 10 1005 146 12 0.91 0.04 4,408 Western Senanga Senanga Lipuwe 10 1005 146 13 0.93 0.04 8,507 Western Senanga Senanga Naluywa 10 1005 146 14 0.92 0.04 5,704 Western Senanga Senanga Lumbe 10 1005 146 15 0.91 0.04 3,786 Western Senanga Senanga Sibukali 10 1005 146 16 0.92 0.04 4,625 Western Senanga Senanga Mata 10 1005 146 17 0.92 0.04 5,058 Western Sesheke Mulobezi Nawinda 10 1006 148 1 0.94 0.04 4,921 Western Sesheke Mulobezi Kamanga 10 1006 148 2 0.95 0.03 3,382 Western Sesheke Mulobezi Lwamuloba 10 1006 148 3 0.94 0.03 3,814 Western Sesheke Mulobezi Sichili 10 1006 148 4 0.90 0.04 8,956 Western Sesheke Mulobezi Mulobezi 10 1006 148 5 0.85 0.06 3,775 Western Sesheke Mulobezi Machile 10 1006 148 6 0.91 0.04 3,283 Western Sesheke Mwandi Magumwi 10 1006 149 7 0.92 0.04 2,946 Western Sesheke Mwandi Sankolonga 10 1006 149 8 0.94 0.04 1,494 Western Sesheke Mwandi Mabumbu 10 1006 149 9 0.91 0.04 3,636 Western Sesheke Mwandi Mwandi 10 1006 149 10 0.77 0.06 3,350 Western Sesheke Mwandi Simungoma 10 1006 149 11 0.91 0.04 951 Western Sesheke Mwandi Lwanja 10 1006 149 12 0.91 0.05 2,862 Western Sesheke Mwandi Lwazamba 10 1006 149 13 0.93 0.04 4,331 Western Sesheke Mwandi Mushukula 10 1006 149 14 0.91 0.05 3,082 Western Sesheke Sesheke Maondo 10 1006 150 15 0.91 0.03 7,838 Western Sesheke Sesheke Mulimambango 10 1006 150 16 0.67 0.05 13,367 Western Sesheke Sesheke Lusu 10 1006 150 17 0.91 0.04 2,739 Western Sesheke Sesheke Luampungu 10 1006 150 18 0.94 0.03 4,987 (continued on next page) 97 Mapping Subnational Poverty in Zambia Poverty Map Estimates at the Ward Level (continued) Province District Constituency Ward Poverty Std. No. Province District Constituency Ward Code Code Code Code Headcount Error Poor Western Sesheke Sesheke Kalobolelwa 10 1006 150 19 0.90 0.04 4,663 Western Sesheke Sesheke Imusho 10 1006 150 20 0.91 0.04 1,935 Western Shang’ombo Sinjembela Mambolomoka 10 1007 147 1 0.96 0.04 10,724 Western Shang’ombo Sinjembela Kayena 10 1007 147 2 0.95 0.04 5,973 Western Shang’ombo Sinjembela Nalwashi 10 1007 147 3 0.97 0.03 3,255 Western Shang’ombo Sinjembela Kalongola 10 1007 147 4 0.96 0.03 2,759 Western Shang’ombo Sinjembela Mbeta 10 1007 147 5 0.95 0.04 12,166 Western Shang’ombo Sinjembela Sioma 10 1007 147 6 0.95 0.04 5,515 Western Shang’ombo Sinjembela Sikabenga 10 1007 147 7 0.97 0.03 9,034 Western Shang’ombo Sinjembela Mutomena 10 1007 147 8 0.97 0.03 10,674 Western Shang’ombo Sinjembela Mulamba 10 1007 147 9 0.96 0.04 1,177 Western Shang’ombo Sinjembela Kaunga Mashi 10 1007 147 10 0.96 0.04 4,376 Western Shang’ombo Sinjembela Beshe 10 1007 147 11 0.96 0.04 2,687 Western Shang’ombo Sinjembela Sipuma 10 1007 147 12 0.95 0.04 7,129 Western Shang’ombo Sinjembela Mulonga 10 1007 147 13 0.96 0.04 8,415 Western Shang’ombo Sinjembela Simu 10 1007 147 14 0.91 0.05 6,455 98 http://www.worldbank.org/zambia/mappingpoverty