Public Disclosure Authorized POVERTY AND INEQUALITY ON THE

Public Disclosure Authorized MAP IN

An Application of Small Area Estimation

Public Disclosure Authorized Public Disclosure Authorized

POVERTY AND INEQUALITY ON THE MAP IN THE GAMBIA

November 2018

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This publication is prepared with the support of the Country Management Unit West Africa Poverty Monitoring Code (WAPMC - P164474).

Extracts may be published if source is duly acknowledged. Copyright © 2018 by The Gambia Bureau of Statistics The Statistician General P. O. Box 3504, Serekunda, The Gambia Tel. +220 4377847 Fax: +220 4377848

Authors

Rose Mungai Minh Cong Nguyen Tejesh Pradhan Supervisor

Andrew Dabalen Graphic presentation of the data

Minh Cong Nguyen Editor

Lauri Scherer

Table of Contents

Acknowledgments ...... 4 Abstract ...... 5 Abbreviations ...... 6 1. Introduction ...... 7 1.1 The Gambia country context ...... 8 2. Overview of the Methodology ...... 9 3. The Data ...... 12 3.1. Integrated Household Survey 2015/16 ...... 12 3.2. Population census ...... 13 4. Modeling for Monetary Poverty ...... 14 5. Model Selection ...... 18 5.1 Default stepwise ...... 19 5.2 Stepwise AIC ...... 19 5.3 Post-lasso ...... 19 5.4 Random forest ...... 20 6. Poverty Mapping Results ...... 30 7. Conclusion ...... 49 References ...... 50

List of Appendixes Appendix A: The Gambia: Administrative Boundaries ...... 53 Appendix B: Region Alpha Model Estimates ...... 54 Table B1: (Alpha Model, LGA 1) ...... 54 Table B2: (Alpha Model, LGA 2) ...... 54 Table B3: Brikama (Alpha Model, LGA 3) ...... 54 Table B4: Mansakonko (Alpha Model, LGA 4) ...... 54 Table B5: (Alpha Model, LGA 5) ...... 55 Table B6: (Alpha Model, LGA 6) ...... 55 Table B7: Janjangbureh (Alpha Model, LGA 7) ...... 55 Table B8: (Alpha Model, LGA 8) ...... 56

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Appendix C: Poverty Measures ...... 57 Appendix D: Census Poverty Measures by Administrative Units ...... 59 Table D1: Poverty Measures by Local Government Area and District ...... 59 Table D2: Poverty Measures by Ward ...... 61 Appendix E: Census Nonmonetary Indicators by Administrative Units ...... 66 Table E1: Population Characteristics by Local Government Area and District ...... 66 Table E2: Household Nonmonetary Measures by Local Government Area and District ...... 66 Appendix F: Census Population Poverty Profiles by Administrative Units ...... 72 Table F1: Population Poverty Profiles by Local Government Area and District ...... 72 Table F2: Household Poverty Profiles by Local Government Area and District ...... 74

List of Boxes Box 1: Step-by-Step Summary of the Modeling Approach ...... 11

List of Figures Figure 1: Households, Population, Household Size, and Total Fertility Rate by Census Year ...... 14 Figure 2: Distributions with Actual and Imputed Testing Sample ...... 29

List of Tables Table 1: Geographical Distribution between the Census (2013) and Survey (2015/16)...... 16 Table 2: Comparison of Household Characteristics between Census (2013) and Survey (2015/16), Weighted Average ...... 17 Table 3: Model Estimates Based on IHS 2015/16—Banjul (Beta Model, LGA 1) ...... 21 Table 4: Model Estimates Based on IHS 2015/16—Kanifing (Beta Model, LGA 2)...... 22 Table 5: Model Estimates Based on IHS 2015/16—Brikama (Beta Model, LGA 3) ...... 23 Table 6: Model Estimates Based on IHS 2015/16—Mansakonko (Beta Model, LGA 4) ...... 24 Table 7: Model Estimates Based on IHS 2015/16—Kerewan (Beta Model, LGA 5) ...... 25 Table 8: Model Estimates Based on IHS 2015/16—Kuntaur (Beta Model, LGA 6) ...... 26 Table 9: Model Estimates Based on IHS 2015/16—Janjangbureh (Beta Model, LGA 7) ...... 27 Table 10: Model Estimates Based on IHS 2015/16—Basse (Beta Model, LGA 8) ...... 28 Table 11: Poverty Estimates from Survey (Observed) and the Census (Small Area Estimation) ..... 30 Table 12: Census Small Area Estimation of Poverty and Gini at the National and Local Government Area ...... 30

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Table 13: Census Small Area Estimation of Poverty and Gini at the District Level ...... 34 Table 14: Census Small Area Estimation of Poverty and Gini at the Ward Level ...... 39

List of Maps Map 1: Census Small Area Estimation of Poverty at the Local Government Area ...... 31 Map 2: Census Small Area Estimation of Number of Poor at the Local Government Area...... 32 Map 3: Census Small Area Estimation of Poverty at the District Level ...... 36 Map 4: Census Small Area Estimation of Number of Poor at the District Level ...... 37 Map 5: Census Small Area Estimation of Gini at the District Level ...... 38 Map 6: Census Small Area Estimation of Poverty at the Ward Level ...... 43 Map 7: Census Small Area Estimation of Poverty at the Ward Level (Zoom in at Banjul and Kanifing) ...... 44 Map 8: Census Small Area Estimation of Number of Poor at the Ward Level ...... 45 Map 9: Census Small Area Estimation of Number of Poor at the Ward Level (Zoom in at Banjul and Kanifing) ...... 46 Map 10: Census Small Area Estimation of Gini at the Ward Level ...... 47 Map 11: Census Small Area Estimation of Gini at the Ward Level (Zoom in at Banjul and Kanifing) ...... 48

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Acknowledgments

This note was prepared by a core team of Rose Mungai (Senior Economist/Statistician, GPV07) and Minh Cong Nguyen (Economist, GPV03). Econometric and programming support was provided by Tejesh Pradhan (Consultant, GPV07). The shapefile data was provided by the Gambia Bureau of Statistics (GBoS) Geographic Information System (GIS) Mapping Unit. GIS visualization of the results was provided by Minh Cong Nguyen. Comments and suggestions were provided by Roy Van der Weide (Economist, DECPI) and Moritz Meyer (Economist, GPV07). The Gambia Bureau of Statistics validated the results in this Note through several discussions and training. The report was developed under the guidance of Andrew Dabalen (Practice Manager, GPV07).

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Abstract

This report documents the creation of the poverty map by combining the Integrated Household Survey 2015/16 with the 2013 Population and Housing Census. Monetary indicators are presented at three administrative levels: the Local Government Area, the district, and the ward. Nonmonetary indicators are presented at two administrative levels: the Local Government Area and the district. The authors apply Small Area Estimation techniques to drive estimates of geographical poverty and inequality. In addition to region-level welfare estimates, district-level (which are the first of their kind) and ward- level welfare measures are derived. The results clearly show there is a substantial spatial heterogeneity within the Local Government Area and the districts.

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Abbreviations

AIC Akaike Information Criterion CSD Central Statistics Department EA Enumeration Area EB Empirical Best ELL Model proposed by Khris Elbers, Jean Lanjouw, and Peter Lanjouw FAO Food and Agricultural Organization GBoS Gambia Bureau of Statistics GIS Geographic Information System GLS Generalized Least Squares GMD Gambian dalasi HH household IHS Integrated Household Survey LGA Local Government Area LSE Least Squares Estimation MSE Mean Square Error OLS Ordinary Least Squares PSU primary sampling unit RSS Residual Sum of Squares SAE Small Area Estimation SSA Sub-Saharan Africa VIF Variance Inflation Factor

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1. Introduction

The Government of The Gambia carried out its first comprehensive integrated household survey that captured consumption expenditure in 2015/16. Unlike previous region-level surveys, this one included district-level representation. A year later, the Gambia Bureau of Statistics (GBoS) and the World Bank started discussing the “Poverty and Inequality on the Map in The Gambia” project. The main objective of this project is to calculate the shares of population at risk of poverty at low geographical levels (wards).

The importance of household surveys cannot be understated because they have traditionally provided the basis for poverty measurement. More important, surveys have a direct impact on national policies, especially for the design of program targeting. The lack of frequent household surveys has made it difficult to assess the effects of any shocks on households. To allow for monitoring and to lower the costs of gathering detailed information, surveys select and sample a subset of the population. When this sample population is representative of a geographical level, welfare surveys provide reliable estimates of poverty incidence for the entire population at a fraction of what a national census would cost. This approach necessarily leads to sampling errors, however. Therefore, a typical household income or expenditure survey cannot produce statistically reliable poverty estimates for small geographic units.

Poverty maps have become a powerful method of measuring welfare for highly disaggregated geographic units as well as a tool for research. To regularly update these maps, short surveys must be designed in between the integrated surveys. These light surveys are administered quickly and are less broad in content but may cover a larger geographical unit. Despite this limitation, the light surveys have been used for policy design. Using the techniques of multiple imputation, poverty mapping analysts estimate poverty levels for small areas whose indicators would be impossible to construct with traditional survey data alone. The results are often used to target policies and assign resources to have greater poverty-reducing impact. Globally, poverty maps have been used to highlight geographic variations, simultaneously display different dimensions of poverty, understand poverty determinants, and both design and select interventions.

A variety of poverty mapping methods have been devised to overcome the increasing imprecision of poverty estimates as they are disaggregated. The standard approach, used in most cases when sufficient data are available, is described in Elbers, Lanjouw, and Lanjouw (2003)––henceforth referred to as ELL––and elaborated on in Bedi, Coudouel, and Simler (2007). Unlike surveys using direct interview methods, Small Area Estimation (SAE) produces information at lower geographical levels. This makes it necessary to combine data from sample surveys and additional sources like the population census or administrative data for estimation.

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This approach leverages the strengths of two data sources. First, the method uses traditional survey data that include detailed information on consumption and well-being. Second, the method looks at individual- or household-level information from the national 2013 Population and Housing Census. Although censuses usually provide less detail than surveys for any individual or household, the main advantage of census data is that they provide complete coverage of the entire population and are therefore free of sampling errors.

The basic idea of the mapping methodology is straightforward and is based on a model that simulates multiple vectors of census incomes. The standard strategy for estimating a poverty map involves (a) identifying a comparable set of variables that appear in both the census and household survey, (b) estimating consumption as a function of the comparable set of variables, and (c) computing welfare indicators on census records based on the parameters derived from the household survey data estimations.

1.1 The Gambia country context

Located in West Africa, The Gambia is the smallest country in mainland Sub-Saharan Africa, spanning just 10,120 square kilometers. It has a population density of about 201.43 persons per square kilometer of land area, ranking it among the top ten countries with the greatest population density. Its borders wind along a narrow strip of land that spans the Gambia River, and water covers 10 percent of its total surface area. The river runs from east to west, dividing the country into two banks, each of which is 25 to 50 kilometers wide and about 300 kilometers long. The unusual shape of the country is due to its colonial history. The Gambia is surrounded by Senegal, except along its western end, which borders the Atlantic Ocean. There is no distinct border to distinguish these two countries, which presents both challenges and opportunities.

The Gambia has access to international markets through the Port of Banjul and has close economic ties with its regional neighbors. Its geographic location between the metropolitan hub of Dakar to the north and the national capitals of Bissau and Conakry to the south forms an important overland transit corridor; thus, trade with these countries shapes economic opportunities. The Gambia is geographically and climatologically diverse in temperatures, altitudes, and weather. This terrain supports a rich diversity of plant and animal life, but income-generating activities tied to the country’s natural endowments remain underutilized.

The Gambia is rich in cultural diversity. Lifestyles vary from traditional groups in rural areas to cosmopolitan cities such as Banjul and Kanifing. Ethnicity transcends its political borders. Most ethnic groups share similar class hierarchies, which are often inherited from ancient societies, and interethnic marriages are common and widely accepted. Unlike other West African countries, The Gambia has enjoyed long spells of stability since independence. This stability, however, has not

8 | Page translated to economic prosperity. Tourism remains the most important source of foreign exchange as well as remittances from abroad.

Poverty mapping is a useful tool for countries with such strong regional diversity. It has been shown that large welfare disparities exist between the capital city of Banjul and the rural and urban areas in the rest of the country. The authors show that educational attainment and household demographics differ significantly depending on the area of residence. Such diversities can be observed even between different rural areas of the country. For instance, a rural area near the capital enjoys significantly better living standards than other rural areas.

National poverty rates alone do not present an accurate picture of the living standards in small local units of the country. The disaggregated indicators reported in this note can be used by policy makers to better target poverty and allocate resources in an objective and transparent manner. In The Gambia, no standard official poverty estimates are produced below the district and ward levels for this reason.

2. Overview of the Methodology

Numerous methods are available on poverty mapping methodology and have been documented by Bigman and Deichmann (2000) but selecting a specific poverty mapping methodology is a critical first step in deriving a poverty map. The SAE method developed by ELL has gained wide popularity among development practitioners and is preferred within the World Bank when enough data are available.

Because census microdata are available, the ELL model was used to develop the map reported in this note. The poverty mapping exercise relies on the unit-level model, which uses household-level data from the 2013 Population and Housing Census and the Integrated Household Survey (IHS) 2015/16. The ELL model relies on detailed income/consumption information from a household survey to estimate a welfare model, given a set of observable household characteristics. The parameter estimates from that model are then applied to the same set of characteristics in the population census to predict missing welfare information and then estimate expected levels of poverty across localities in the census. Although these poverty rates are estimated and are thus subject to error, they can be reasonably precise for purposes of informing policy choices (Bedi, Coudouel, and Simler 2007).

One advantage of this method is that it not only estimates poverty incidence but also estimates the standard errors. Poverty estimates are computed based on imputed consumption and therefore lead to imputation errors. The ELL method analyzes the properties of these errors in detail and derives a procedure to compute standard errors of poverty estimates. The section below provides greater detail on this method.

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Formally, the ELL method can be modeled as the (log) of per capita household consumption = +

𝑦𝑦𝑐𝑐ℎ 𝑋𝑋′𝑐𝑐ℎ𝜷𝜷 𝑢𝑢𝑐𝑐ℎ where is the per capita consumption of household h residing in area c, are household and area/location characteristics, and = + , representing the residual, which is composed of the 𝑦𝑦𝑐𝑐ℎ 𝑋𝑋𝑐𝑐ℎ area component and the household component . These two residual components have expected 𝑢𝑢𝑐𝑐ℎ 𝜇𝜇𝑐𝑐 𝜀𝜀𝑐𝑐ℎ values of zero and are independent of each other. It is assumed that ( ) = + . To estimate 𝜇𝜇𝑐𝑐 𝜀𝜀𝑐𝑐ℎ variance parameters, we rely on Henderson's method III, a commonly used2 estimator2 for2 the variance 𝐸𝐸 𝑢𝑢𝑐𝑐 𝜎𝜎𝜇𝜇 𝜎𝜎𝜀𝜀 parameters of a nested error model (Henderson 1953; Searle, Casella, and McCulloch 1992).

The variance of the remaining residual is modeled via a logistic transformation as a function of

𝑐𝑐ℎ = + household and area characteristics 𝜀𝜀 2 to obtain an estimate of the variance 𝑒𝑒𝑐𝑐ℎ 2 , . Note that this approach allows𝑙𝑙𝑙𝑙 �for𝐴𝐴− 𝑒𝑒𝑐𝑐heteroskedasticity,ℎ� 𝑍𝑍′𝑐𝑐ℎ𝛼𝛼 𝑟𝑟𝑐𝑐ℎ such that the model is subsequently reestimated2 to get a Generalized Least Squares (GLS) estimate of and of the variance-covariance 𝜎𝜎�𝜀𝜀 𝑐𝑐ℎ matrix. 𝜷𝜷 The small area estimates (and their standard errors) are obtained by means of simulation, which is ideally suited for estimating quantities that are nonlinear functions of y (and thus nonlinear functions of the errors and the model parameters), such as measures of poverty and inequality. Let R denote the number of simulations. The estimator then takes the form 1 = 𝑅𝑅 ( ) 𝑟𝑟 where ( ) is a function that converts the vector𝐻𝐻� y� withℎ (log)𝑦𝑦� incomes for all households into a poverty 𝑅𝑅 𝑟𝑟=1 measure (such as the head-count rate), and where denotes the r-th simulated vector with the ℎ 𝑦𝑦 following elements: 𝑟𝑟 𝑦𝑦� = + + 𝑟𝑟 ′ 𝑟𝑟 𝑟𝑟 𝑟𝑟 𝑦𝑦� 𝑋𝑋 𝛽𝛽� 𝜇𝜇�𝑐𝑐 𝜀𝜀𝑐𝑐̃ ℎ With each simulation, both the model parameters and the errors and are drawn from their estimated distributions. We draw by reestimating𝑟𝑟 the model parameters𝑟𝑟 using𝑟𝑟 the r-th bootstrap 𝛽𝛽� 𝜇𝜇�𝑐𝑐 𝜀𝜀𝑐𝑐̃ ℎ version of the survey sample. Alternatively,𝑟𝑟 may be drawn from its estimated asymptotic 𝛽𝛽� distribution (which we call parametric drawing). 𝑟𝑟The advantage of parametric drawing is that it is � computationally fast. A potential disadvantage 𝛽𝛽is that the true distribution of the estimator for the model parameter vector does not necessarily coincide with the asymptotic distribution. The use of bootstrapping, albeit more computationally intensive, provides a means of identifying the finite- sample distribution and is thus expected to provide more accurate results when the sample size is small. The IHS sample sizes that we use are large enough for the asymptotic results to apply, and we therefore expect to see little to no difference between estimates obtained with parametric drawing and those with bootstrapping.

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A further alternative at this stage is to employ Empirical Best (EB) estimation. The idea of EB estimation is that the residuals for households sampled in area c, = , are informative of the latent area error . This means that conditioning on the residuals observed for sampled 𝑒𝑒𝑐𝑐ℎ 𝑦𝑦𝑐𝑐ℎ − 𝑋𝑋′𝑐𝑐ℎ𝜷𝜷 households should enable us to tighten the distributions from which to simulate . It should be noted 𝜇𝜇𝑐𝑐 that EB only concerns the drawing of the area errors, and only areas that have been𝑐𝑐 sampled in the IHS benefit from the improvement offered by EB; for areas not sampled in the 𝜇𝜇survey, we still draw from the unconditional distribution, in which case EB will refer to it as ELL-EB and coincides with standard ELL.

Box 1: Step-by-Step Summary of the Modeling Approach 1. Bootstrap the survey (unless using parametric drawing). 2. Estimate by means of Ordinary Least Squares and extract the residuals. 3. Regress residuals from step 2 on the area dummies (estimate fixed-effects model) and extract the residuals.𝜷𝜷 4. Estimate the unconditional variance parameters of the nested error model ( and ) by applying Henderson’s method III,a which uses the residuals from both steps 2 and2 3. 2 𝜎𝜎𝜇𝜇 𝜎𝜎𝜀𝜀 5. If heteroskedastic household errors are assumed, then (a) derive estimates of the household errors by subtracting the area averages from the residuals (the deviations from the area mean residual); (b) apply a logistic transformation to the errors derived under (a) to obtain the left- hand side of the regression (also referred to as the alpha model) that will be used to predict the

conditional variance of household component , denoted by , ; and (c) ensure that the unconditional variance is still equal to (that is, [ ] = ). 2 𝜀𝜀𝑐𝑐ℎ , 𝜎𝜎𝜀𝜀 𝑐𝑐ℎ 6. Given estimates of the unconditional variance2 and2 conditional2 variance , we may 𝜎𝜎𝜀𝜀 𝐸𝐸 𝜎𝜎𝜀𝜀 𝑐𝑐ℎ 𝜎𝜎𝜀𝜀 , construct the covariance matrix , which is used2 to obtain the Generalized Least2 Squares 𝜎𝜎𝜀𝜀 𝜎𝜎𝜀𝜀 𝑐𝑐ℎ estimator for . Ω 7. At this stage we have estimates for all the model parameters: . Next, we draw the area errors 𝜷𝜷 and the household idiosyncratic errors (step 5) from their respective𝑟𝑟 normal distributions with � variances. 𝛽𝛽 8. We now have all we need to compute the round r simulated (log) household expenditure values for all households in the population census. 9. With the simulated household income data, we can now compute the poverty and inequality measures as if the population census came with household income data from the start. 10. This yields a simulated poverty and inequality measure for each of the R simulation rounds. The average and standard deviations give us, respectively, the poverty point estimate and the corresponding standard error. a. See Henderson (1953).

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In the end, either option gives us R simulated poverty rates. The point estimates and their corresponding standard errors are obtained by computing the average and the standard deviation over these simulated values. Box 1 provides greater detail on this method.

3. The Data 3.1. Integrated Household Survey 2015/16

The Gambia’s government has collected six rounds of IHS data since 1992. The survey is part of the National Statistical Programme. The sampling frame for the IHS has always been the population census; the IHS 2015/16 was based on the 2013 Population and Housing Census. The sampling frame is a complete list of Enumeration Areas (EAs) containing a convenient number of households that serve as a counting unit for the census. The sampling frame contains information about the location, the administrative regions, the type of residence, and the number of residential households and population of each EA.

The Gambia is divided into eight Local Government Areas (LGAs), including two urban municipalities (Banjul and Kanifing). Each LGA is subdivided into districts, except for the two urban municipalities. Each district is divided into wards, and each ward is further divided into settlements. There is a total of 48 districts, excluding the two municipalities; 120 wards; and 4,096 EAs. Depending on the size (number of households) of the settlement, an EA can comprise one settlement, a group of small settlements, or part of a large settlement. Each EA is designated as either an urban or a rural area. Whereas the census included institutions, the unit of study for the IHS included residential households and their occupants in all districts but excluded institutions such as hospitals, prisons, orphanages, and military barracks. The estimates were to be representative at the district level, making up a total of 40 strata (38 districts plus the municipalities of Banjul and Kanifing).

The IHS 2015/16 was a comprehensive socioeconomic survey of the living standards of households in all districts of Gambia. This was a first-of-its-kind district-level survey administered by the national GBoS to about 14,000 households between April 2015 and April 2016. Two questionnaires were administered to each household, and another two were administered at the cluster (the community level). A two-stage Probability Proportional to Size approach, or PPS, stratified random sampling (size being number of households per EA) without replacement. At each stage, subsamples of equal size were independently drawn without replacement. Sampling units were selected for each subsample with simple random sampling without replacement. Each survey period (consisting of a quarter, or three months) was allocated one subsample.

The survey is the main source of information on poverty and living conditions of the Gambian population. The welfare measure is made up of two main components disaggregated into four classes

12 | Page and is normalized to annual consumption. This consumption included the total food consumption (purchased, own consumption, or gifts) reported by the household, the total nonfood consumption (education, health, or other nonfood), rent (both actual and imputed rent for nonpaying households), and an estimated use value of durable goods. Nonproduction items are excluded because households derive utility over a long period for durable goods and this leads to double counting, which is why use value was derived. Of the 13,821 households, 78 were dropped for the poverty analyses because they did not have any food expenditure; the data was reweighted to get the same population.

The 2015/16 poverty line was estimated using the Cost of Basic Needs approach. The bundle was based on the observed consumption of food (purchases, own consumption, gifts, or stocks). In the case of The Gambia, a national common basket was derived. Although such an assumption can be argued as adequate because consumption patterns may vary across space and time, it was acceptable here since The Gambia does not have large geographical and cultural variations. The food basket consisted of 59 food items consumed by 25–55 percent of the poorest population. This basket accounted for 93 percent of the household’s food consumption.

The caloric threshold used to define the minimum nutritional requirements tends to be arbitrary, varying from country to country. Whereas some countries use low thresholds, others use high ones (in Nigeria it is 3,000 kilocalories, or kcal; in Sierra Leone and Senegal, 2,700 kcal). Countries in West and Central Africa on average tend to use slightly lower caloric thresholds than 2,400 kcal per adult. However, there is no universally accepted threshold norm for a given country. Because there are no other available and comparable surveys to which the IHS could have been compared, the threshold set was based on the average estimated daily calories by activity.

Several poverty lines are derived to test robustness using different methodologies. For the food poverty line, three types of daily required calories were derived based on 25–55 percent of the poorest population. The calories used are for the West African countries produced by the Food and Agriculture Organization of the United Nations (FAO 2012). On the nonfood poverty line, a regression method, Engel’s curve, and the Ravallion (1998) nonparametric are tested. The Ravallion approach was selected due to its robustness. The extreme and absolute poverty lines for 2015/16 were GMD 982.89 and GMD 1,503.33 per month, respectively.

3.2. Population census

The 2013 Population and Housing Census is the second data set. It was administered in two phases: a housing census for one-week and a population census for 14 days. This was the fifth census taken since independence. Several questionnaires were administered to each household. The census was used to derive the sampling frame for the IHS 2015/16 survey. The census collects reliable, exhaustive, and detailed information on the number and main characteristics of the population as well

13 | Page as the housing (private and institutional) in the country. Figure 1 shows the distribution of census population over time.

Figure 1: Households, Population, Household Size, and Total Fertility Rate by Census Year 2,000,000 10.0 1,800,000 9.0 1,600,000 8.0 1,400,000 7.0 1,200,000 6.0 1,000,000 5.0

Number 800,000 4.0

600,000 3.0 Averagenumber 400,000 2.0 200,000 1.0 0 - 1973 1983 1993 2003 2013

Persons Households Household size Fertility rate

Source: Based on census reports.

The preceding four censuses had been conducted by the Central Statistics Department (CSD) under the provisions of the 1972 Statistics Act. A new Statistics Act was enacted in 2005 that transformed the CSD into the semiautonomous GBoS. By the provisions of the new act, the Statistician General is empowered to conduct the census every 10 years. The act also includes a confidentiality clause that protects a respondent’s personal information from being disclosed. This clause ensures that data collected during the census and surveys are strictly used for statistical purposes.

4. Modeling for Monetary Poverty

The ELL setup relies on estimating a welfare model based on the IHS data and applying it to the census data for prediction purposes. Therefore, one of the key parts of the model setup is the similarity between the variables in the IHS and the census. As part of building a welfare model, a two-stage process was undertaken: • Step 1: Comparison of the IHS and census questionnaires to identify “candidate variables” existing in both the survey and the census and which are generated from identical or similar questions; and

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• Step 2: Comparison of the distributions of the candidate variables identified in step 1 to examine whether they appear to capture the same underlying phenomena or whether, despite similar questions, their empirical distributions differ in any important ways.

Whereas the goal of model construction is to build a descriptive statistical model that explains the variation in per capita household expenditure (in the case of the household-level model), the choice of candidate variables is based on a heuristic model of per capita household expenditure. The per capita household expenditure is often assumed to be a function of the demographic characteristics of the household (for example, if it contains small children, working-age adults, or the elderly), the individual education and occupation characteristics of the household and its members (such as, the maximum level of education in the household, the education level and employment status of household members, and the type of employment for those who are employed).

In addition, the literature often shows the household’s dwelling type or its assets (for instance, if there is a bath or toilet in the dwelling). Access to basic services, such as water and electricity, is also assumed to describe or “reflect” the income level of the household and thus its expenditure. Furthermore, household expenditure may also vary based on the household’s location and its characteristics (for example, if it is rural or urban, its proximity to big cities, and if it is in an area with low or high employment rates).

The above list is not unique (or exhaustive), but the choice of characteristics is typically constrained by the overlap between the survey and census questionnaires. Based on the information available in the survey and census, these are the common variables:

• Demographic characteristics: Gender; age; marital status; household size; number of children, adults, and elderly in the household; dependency ratio • Education: Education level of the household head; highest level of education by any household member • Occupation: Employment status; occupation; sector of employment of household head • Housing characteristics: Type of housing unit; main construction material of wall; total area of land and dwelling; ownership and occupancy status of dwelling; source of drinking water and electricity; type of sewage and toilet • Productive and durable assets: Ownership of a cooler, refrigerator, freezer, electrical generator, cooker, television, washing machine, dishwasher, water heater, heater, electric fan, air conditioner, vacuum cleaner, motorcycle, car, and personal computer

The level at which regression models are run must be chosen carefully. If a single model is specified for the entire country, the implicit assumption is that the parameter estimates on the regressors are the same for all regions of the country. In other words, a national model assumes that the relationship between household expenditure and household characteristics are uniform throughout the country.

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This may not be a tenable assumption in a country like The Gambia, which has wide spatial heterogeneity in incidence of violence, endowment of natural resources, and robustness of factor markets. For example, returns on education are likely higher in capital areas where the formal job market is more robust than in the poorest governorates with thin labor markets.

By defining region-specific models, the relationship between expenditure and the explanatory variables can vary, and this reduces the standard error of poverty prediction due to modeling error. An alternative way to allow the coefficients to vary by region is to interact the variables with regional dummy variables in the regression. This approach is flexible enough to allow differential relationships across regions and minimizes the chances of overfitting.

This exercise proceeded in a top-down fashion to determine the level at which to model the relationship. The regression model started at the national level and went down to the LGA level. Table 1 displays the geographical distribution of the census and survey. One concern with running multiple models is the loss in degrees of freedom and the risk of overfitting (that is, the models are forced to explain the noise in the data in a small sample). To avoid the problem of overfitting, researchers recommended that the sample size be no smaller than 300 for each regression (Ahmed et al. 2014).

Table 1: Geographical Distribution between the Census (2013) and Survey (2015/16) Census Survey Sample size Weighted Number of households 217,610 13,189 281,283 Number of individuals 1,857,181 104,830 1,922,950 Male 913,755 49,538 915,640 Female 943,426 55,292 1,007,309 Regions 8 8 .. Districts 42 42 .. Enumeration Areas 4,096 666 .. Small areas (wards) 120 .. .. Source: Authors’ calculations based 2013 Population and Housing Census and IHS 2015/16.

Candidate variables were assigned by hand, comparing nationally representative means in the two data sources. Those variables deemed acceptable were included in the model selection process. For those that were shown to differ too greatly from one another—due, for instance, to slight differences in the wording of the question or the choice of respondent—the variable was excluded and not used in the model development process. Table 2 compares the averages of the variable subsets. Each was evaluated at the household level, including for questions that were gathered at the individual level in the questionnaire.

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Table 2: Comparison of Household Characteristics between Census (2013) and Survey (2015/16), Weighted Average Variable Description Census Survey employee_share Share of employees in the households 0.178 0.161 disability_head Household head with disability 0.033 0.019 married_head Household head married 0.859 0.879 female_head Female household head 0.209 0.185 age25t64_head Household head age 25–64 0.837 0.864 age65pl_head Household head age 65 plus 0.130 0.111 sec_com_head Household head with secondary completion education 0.276 0.234 unihi_com_head Household head with university and higher education 0.063 0.079 sec_abv_head Household head with secondary and above completion education 0.358 0.338 employed_head Household head employed 0.788 0.707 employee_head Household head employee 0.264 0.247 ethnicity2_head Household head with ethnicity—Fula/Tukulur/Lorobo 0.245 0.254 ethnicity5_head Household head with ethnicity—Serahulleh 0.048 0.068 ethnicity_rest_head Household head with ethnicity—others 0.095 0.087 religion1_head Household head with religion—Islam 0.940 0.958 religion2_head Household head with religion—Christianity 0.060 0.042 male_mean Average number of males in the household 0.525 0.501 female_mean Average number of females in the household 0.475 0.499 married_mean Average number of married individuals in the household 0.371 0.385 widowed_mean Average number of widowed individuals in the household 0.022 0.026 pri_com_mean Average number of individuals with primary completion in HH 0.185 0.178 unihi_com_mean Average number of individuals with university and higher in HH 0.035 0.042 employee_mean Average number of employees in the household 0.128 0.115 age0t6_mean Average number of children age 0–6 in the household 0.168 0.174 age1t14_mean Average number of children age 1–14 in the household 0.359 0.375 age15t24_mean Average number of children age 15–24 in the household 0.199 0.187 age25t64_mean Average number of children age 25–64 in the household 0.410 0.409 age65pl_mean Average number of children age 65 plus in the household 0.033 0.030 ethnicity1_mean Average number of individuals in HH with ethnicity—Mandinka/Jahanka 0.325 0.363 ethnicity2_mean Average number of individuals in HH with ethnicity—Fula/Ttukulur/Lorobo 0.269 0.264 ethnicity5_mean Average number of individuals in HH with ethnicity—Serahulleh 0.045 0.065 ethnicity_rest_mean Average number of individuals in HH with ethnicity—others 0.100 0.099 religion1_mean Average number of individuals in HH with religion—Islam 0.941 0.961 widowed_sum Number of widows in the HH 0.156 0.151 male_dummy Household with at least one male individual 0.962 0.958 married_dummy Household with at least one married individual 0.907 0.911 widowed_dummy Household with at least one widow 0.133 0.136 pri_com_dummy Household with at least one individual with primary completion 0.636 0.601 pri_abv_dummy Household with at least one individual with primary and above completion 0.863 0.836 unihi_com_dummy Household with at least one individual with university and higher 0.111 0.129 age0t6_dummy Household with at least one child age 0–6 0.661 0.659 age1t14_dummy Household with at least one child age 0–14 0.797 0.810 age15t24_dummy Household with at least one individual age 15–24 0.686 0.634 age25t64_dummy Household with at least one individual age 25–64 0.967 0.972 ethnicity1_dummy Household with at least one individual ethnicity—Mandinka/Jahanka 0.418 0.445 ethnicity2_dummy Household with at least one individual ethnicity—Fula/Tukulur/Lorobo 0.361 0.326 ethnicity5_dummy Household with at least one individual ethnicity—Serahulleh 0.061 0.075 ethnicity_rest_dummy Household with at least one individual ethnicity—others 0.155 0.140 religion1_dummy Household with at least one individual religion–Islam 0.952 0.966 radio HH with radio 0.831 0.467 mobile HH with mobile 0.938 0.937 landphone HH with land phone 0.032 0.010 Source: Authors’ calculations based on IHS 2015/16 and 2013 Population and Housing Census.

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Table 2: Comparison of Household Characteristics between Survey and Census (cont.) Census Survey motorcycle Household with motorcycle 0.077 0.054 elec6 Household with solar electricity 0.036 0.053 elec7 Household with battery-power light electricity 0.319 0.341 elec8 Household with other type electricity 0.003 0.003 elecgas1 Household with firewood 0.681 0.598 elecgas2 Household with kerosene or gas 0.043 0.014 elecgas3 Household with charcoal 0.268 0.321 elecgas4 Household with electricity or solar 0.000 0.000 watsrc3 Household with public stand pipe 0.214 0.251 watsrc5 Household with unprotected well in compound 0.065 0.049 watsrc7 Household with well without pump public 0.066 0.062 kitchen2 Household with shared kitchen 0.139 0.156 kitchen4 Household with no kitchen 0.068 0.077 kitchen5 Household with other type of kitchen 0.008 0.005 toilet1 Household with toilet (piped sewer system) 0.027 0.018 toilet2 Household with toilet (septic tank) 0.188 0.217 toilet3 Household with toilet (pit latrine) 0.711 0.732 sewagetyp4 Household with garbage–thrown into street/outside 0.123 0.121 sewagetyp5 Household with garbage–thrown into gutter 0.030 0.017 homeocc1 Home occupancy status—owner 0.585 0.560 homeocc2 Home occupancy status—renter 0.319 0.312 loghhsize Log of household size 1.801 1.684 hhsize_1 Household with 1 member 0.092 0.080 hhsize_2 Household with 2 members 0.063 0.062 hhsize_3 Household with 3 members 0.072 0.081 hhsize_4 Household with 4 members 0.082 0.098 hhsize_8 Household with 8 members 0.068 0.075 hhsize_9 Household with 9 members 0.059 0.064 hhsize_10 Household with 10 members 0.075 0.074 child_1 Household with 1 child 0.797 0.810 child_2 Household with 2 children 0.681 0.675 child_3p Household with 3 and more children 0.544 0.509 ln_hdage Log of household head age 3.782 3.778 Source: Authors’ calculations based on IHS 2015/16 and 2013 Population and Housing Census.

5. Model Selection

From the pool of variables that were not excluded for comparability concerns, a variety of model selection techniques were used to determine the best performing model and to evaluate performance based on several alternative criteria. The poverty map for The Gambia employs a range of automated variable selection routines borrowed from the machine-learning literature, all of which try to minimize overfitting by incorporating degrees of freedom into the evaluation. Four options were considered here: a default stepwise model, a stepwise model based on the Akaike Information Criteria (AIC), a post-lasso model, and a random forest model.

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5.1 Default stepwise

Stepwise regression uses a threshold value to determine which variables are included in the model. There are three main approaches to stepwise regression for model selection: forward, backward, and bidirectional. In this case, backward stepwise, the default option in the Stata software, is used. The process begins with all variables included in the model and removes those with p-values greater than the threshold value of 0.2. As stepwise is particularly sensitive to collinearity, an additional preprocessing step was taken to remove these variables from the model. The Variance Inflation Factor (VIF) is a measure of multicollinearity between variables in an Ordinary Least Squares (OLS) regression. Though a VIF of 10 or higher indicates the presence of severe collinearity, here a more conservative threshold of 5 is used to exclude variables.1

5.2 Stepwise AIC

As of version 13, the Stata software also contains a package (vselect) to use alternative variable elimination criteria, such as the AIC. Information-based criteria are like maximum likelihood methods, and a stepwise model using the AIC selection minimizes the AIC score of a regression model with r parameters where

= 2 log( ) + 2

The score estimates the expected𝐴𝐴𝐴𝐴𝐴𝐴 relative− distance𝑙𝑙𝑙𝑙𝑙𝑙 between𝑙𝑙𝑙𝑙𝑙𝑙ℎ𝑜𝑜𝑜𝑜𝑜𝑜 the fitted𝑟𝑟 model and the unknown true relationship. By using AIC instead of a simple p-value threshold, as in standard stepwise selection, there is a penalty for additional variables added to the model. This reduces the danger of overfitting, though it retains many of the stepwise vulnerabilities described above. Since the AIC is used instead of the p-value, it is not necessary to run the initial step of determining the optimal value of p.2

5.3 Post-lasso

The least absolute shrinkage and selection operator (lasso) uses shrinkage to reduce the coefficients of certain variables to zero.3 Whereas stepwise and joint significance minimize the Residual Sum of Squares (RSS), the lasso minimizes RSS plus an additional shrinkage penalty. This can be represented as the minimization of + . The size of the shrinkage penalty depends on the selection of the tuning parameter λ, which regulates𝑝𝑝 the relative trade-off between RSS minimization and the 𝑅𝑅𝑅𝑅𝑅𝑅 𝜆𝜆 ∑𝑗𝑗=1�𝛽𝛽𝑗𝑗� penalty. Each value of λ will produce a different set of coefficient estimates. When λ=0, the shrinkage coefficient is zero and the minimization reduces to least squares. When λ→∞, the coefficients all go

1 See Thompson (1995) for more information about the potential drawbacks to stepwise procedures for modeling. 2 See Lindsey and Sheather (2010) for further details on information-based selection and the vselect package. 3 See chapter 6 in Arthur et al. (2012) for additional discussion.

19 | Page to zero and the model becomes just the intercept. Therefore, for larger vales of λ, there will be fewer variables in the model. The value of λ is usually selected using cross validation in such a way as to minimize the Mean Square Error (MSE), which itself is composed of the variance and the square of the bias. As it is the combination that is minimized, the bias will be nonzero for lasso models. Therefore, when a lasso selection is used as the selection mechanism for imputation, the list of nonzero coefficient variables is retained and used as the list of variables for the imputation. This is because the use of the coefficients generated by the lasso model biases the model. The actual estimation of the values is done using OLS estimation. This model of selection is known as post- lasso. 𝛽𝛽 5.4 Random forest

Random forest differs completely from stepwise and post-lasso selection mechanisms as it is a tree- based (rather than linear) model. Tree-based methods sequentially divide the predictor space into simplified homogenous subregions. Using random forest, only a randomized subsample of predictors are considered at each node of the tree, with the objective of reducing the correlation between individual trees and therefore the variance of the aggregated (or “bagged”) trees.4 RSS is not a model selection method but rather a part of the process of estimation. It can be used to develop measures of variable importance, which can in turn be used to select which variables to include in the model. This is done through the “boruta” command in the R software package of the same name. Also, though the random forest approach is not susceptible to error from collinearity in the same way the stepwise routines are, the random forest model selection procedure still begins with the subset of variables remaining after all those with a VIF of 10 or higher have been removed. This is done for two reasons. First, it reduces the run time for the computationally intensive selection procedure. Second, the results are more robust; if the full set of variables is used, the subset that is selected by the random forest routine may contain collinear variables that are subsequently dropped in the OLS or GLS analysis. Since the model is designed to optimize for the full set of selected variables, these dropped variables would impact the effectiveness.

Using the implementation of the ELL methods in Stata (World Bank) to build the mode with these validation procedures, the final models are presented. The initial welfare models corresponding to equation 1 is presented in tables 3–10, for each LGA. The adjusted R-squared for LGA models is moderately high, ranging from 0.41 to 0.67, reflecting that the chosen model explains the variation on welfare moderately well. In addition to the variables present in both the census and IHS, variable means at the LGA and district levels are obtained from the census and introduced to the model. These variables are introduced to improve precision by reducing the unexplained variation in income due to location. With the inclusion of these variables, the ratios of the variance of over the LGA model

𝜂𝜂

4 See Arthur et al. (2012) for more information.

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MSE are from 4 to 11 percent. The low ratio illustrates the key role the variables play in improving precision of the estimates.

The coefficients estimated in the previous section provide the necessary inputs to estimate the first part of equation ( ) by combining coefficients with the census variables. The vectors of disturbances for households are unknown and must be estimated. The error component is then 𝑋𝑋′𝑐𝑐ℎ𝛽𝛽̂ decomposed using Henderson’s method III, and the coefficients, , are obtained by bootstrapped samples of the IHS data. The model chosen is where and are drawn from a normal distribution, 𝛽𝛽 with their respective variance structures. Finally, Empirical Best methods are chosen since these incorporate more information and are thus expected to𝜂𝜂 provide𝜀𝜀 a better fit. The model selection used was the stepwise with VIF using the AIC—based on the comparison of poverty estimates from surveys and the census at the national and LGA levels.

Table 3: Model Estimates Based on IHS 2015/16—Banjul (Beta Model, LGA 1) OLS GLS Variable coeff. se coeff. se age15t24_dummy -0.244*** 0.049 -0.236*** 0.048 car 0.324*** 0.112 0.308*** 0.105 child_1 -0.364*** 0.067 -0.352*** 0.063 child_3p -0.288*** 0.068 -0.286*** 0.063 computer 0.305*** 0.083 0.315*** 0.078 employee_mean 0.102 0.073 0.134* 0.076 hhsize_2 -0.214*** 0.081 -0.174** 0.081 hhsize_3 -0.245*** 0.076 -0.206*** 0.073 hhsize_4 -0.157** 0.078 -0.143** 0.072 kitchen4 0.179** 0.071 0.173** 0.070 male_dummy -0.309*** 0.099 -0.294*** 0.097 married_mean 0.066 0.069 0.049 0.071 sec_com_head 0.145*** 0.047 0.137*** 0.045 tv 0.299*** 0.055 0.291*** 0.055 unihi_com_head 0.169* 0.095 0.172* 0.090 Constant 10.912*** 0.122 10.886*** 0.122 Number of observations 355 Adjusted R-squared 0.506 Sigma eta sq. 0.006 Ratio of Sigma eta sq. over MSE 0.037 Variance of epsilon 0.154 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 4: Model Estimates Based on IHS 2015/16—Kanifing (Beta Model, LGA 2) OLS GLS Variable coeff. se coeff. se age15t24_dummy -0.189*** 0.041 -0.179*** 0.041 age1t14_dummy -0.307*** 0.052 -0.308*** 0.053 age65pl_dummy -0.133** 0.060 -0.123** 0.058 car 0.509*** 0.072 0.464*** 0.071 child_3p -0.208*** 0.047 -0.182*** 0.045 computer 0.201*** 0.062 0.194*** 0.061 employee_dummy -0.092** 0.040 -0.095** 0.040 evereduc_share 0.156** 0.077 0.147* 0.076 hhsize_10 -0.248*** 0.087 -0.223*** 0.084 male_dummy -0.431*** 0.086 -0.433*** 0.086 married_mean 0.289*** 0.074 0.282*** 0.077 sec_com_mean 0.258*** 0.095 0.259*** 0.096 sewagetyp5 0.258** 0.111 0.300*** 0.113 tv 0.206*** 0.053 0.234*** 0.053 unihi_com_mean 0.496*** 0.134 0.496*** 0.136 watsrc4 -0.296** 0.115 -0.220* 0.115 watsrc5 -0.401** 0.163 -0.318** 0.160 _cons 10.802*** 0.105 10.774*** 0.109 Number of observations 412 Adjusted R-squared 0.617 Sigma eta sq. 0.011 Ratio of Sigma eta sq. over MSE 0.081 Variance of epsilon 0.122 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 5: Model Estimates Based on IHS 2015/16—Brikama (Beta Model, LGA 3) OLS GLS Variable coeff. se coeff. se age15t24_sum -0.037*** 0.012 -0.038*** 0.010 age1t14_dummy -0.189*** 0.050 -0.181*** 0.039 age1t14_sum -0.027*** 0.005 -0.031*** 0.005 car 0.488*** 0.056 0.444*** 0.058 child_3p -0.124*** 0.037 -0.100*** 0.031 computer 0.206*** 0.050 0.202*** 0.042 depratio -0.063** 0.026 -0.048** 0.022 employee_sum -0.039*** 0.015 -0.045*** 0.012 female_head 0.107*** 0.034 0.102*** 0.029 hhsize_10 -0.129*** 0.046 -0.113*** 0.037 hhsize_2 0.294*** 0.065 0.329*** 0.056 hhsize_3 0.214*** 0.052 0.223*** 0.036 hhsize_8 -0.087** 0.043 -0.068* 0.035 hhsize_9 -0.130*** 0.050 -0.125*** 0.040 kitchen4 0.313*** 0.071 0.337*** 0.078 literacy2_share -0.166** 0.066 -0.155*** 0.053 literacy3_share -0.178*** 0.045 -0.180*** 0.037 religion1_dummy 0.212*** 0.063 0.242*** 0.053 sec_com_head 0.100*** 0.033 0.092*** 0.026 sec_com_sum -0.034*** 0.011 -0.020** 0.009 sewagetyp4 -0.079** 0.034 -0.107*** 0.027 toilet3 -0.243*** 0.036 -0.221*** 0.034 tv 0.264*** 0.027 0.260*** 0.022 unihi_com_head 0.191*** 0.057 0.226*** 0.050 watsrc6_m_dist_s -0.565*** 0.047 -0.564*** 0.062 widowed_mean -0.367** 0.157 -0.424*** 0.131 _cons 10.386*** 0.086 10.307*** 0.075 Number of observations 2892 Adjusted R-squared 0.652 Sigma ETA sq. 0.011 Ratio of sigma eta sq over MSE 0.073 Variance of epsilon 0.139 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 6: Model Estimates Based on IHS 2015/ 16—Mansakonko (Beta Model, LGA 4) OLS GLS Variable coeff. se coeff. se age0t6_dummy -0.123*** 0.031 -0.116*** 0.031 age15t24_dummy -0.100*** 0.026 -0.094*** 0.025 bicycle 0.053** 0.023 0.063*** 0.023 car 0.444*** 0.073 0.454*** 0.072 child_3p -0.205*** 0.031 -0.196*** 0.030 elec6 0.113*** 0.029 0.117*** 0.030 evereduc_share 0.193*** 0.044 0.181*** 0.043 female_dummy -0.304*** 0.091 -0.317*** 0.089 hhsize_10 -0.091** 0.038 -0.107*** 0.036 hhsize_2 0.132** 0.058 0.119** 0.061 hhsize_3 0.096** 0.046 0.063 0.048 hhsize_8 -0.063* 0.036 -0.067** 0.034 ln_hdage -0.174*** 0.038 -0.175*** 0.038 married_mean 0.182** 0.073 0.213*** 0.072 mobile 0.211*** 0.039 0.206*** 0.039 motorcycle 0.160*** 0.053 0.154*** 0.050 pri_com_dummy -0.073*** 0.028 -0.066** 0.029 pri_com_sum_m_dist_s -0.319*** 0.041 -0.338*** 0.054 radio 0.163*** 0.023 0.152*** 0.022 sec_abv_sum -0.034*** 0.012 -0.030*** 0.011 sec_com_sum_m_dist_s -0.240*** 0.062 -0.235*** 0.087 toilet2 0.220*** 0.074 0.235*** 0.068 tv 0.160*** 0.034 0.140*** 0.033 unihi_com_mean 0.454*** 0.143 0.475*** 0.114 urb_sh_emp 0.293*** 0.070 0.248*** 0.077 watsrc6 -0.055** 0.025 -0.075*** 0.029 widowed_dummy -0.245*** 0.045 -0.229*** 0.044 widowed_mean 0.513*** 0.184 0.500*** 0.181 _cons 11.595*** 0.201 11.632*** 0.227 Number of observations 1758 Adjusted R-squared 0.477 Sigma eta sq. 0.010 Ratio of Sigma eta sq. over MSE 0.063 Variance of epsilon 0.149 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 7: Model Estimates Based on IHS 2015/16—Kerewan (Beta Model, LGA 5) OLS GLS Variable coeff. se coeff. se age0t6_mean -0.248*** 0.078 -0.245*** 0.075 age15t24_mean -0.371*** 0.068 -0.355*** 0.064 age25t64_dummy -0.261*** 0.081 -0.275*** 0.073 age65pl_dummy -0.107*** 0.023 -0.097*** 0.022 bicycle 0.046** 0.021 0.041** 0.020 car 0.443*** 0.063 0.456*** 0.079 child_3p -0.210*** 0.029 -0.210*** 0.028 computer 0.214*** 0.068 0.225*** 0.060 elec7 -0.082*** 0.021 -0.066*** 0.022 employed_sum_m_dist_s -0.081*** 0.022 -0.081** 0.032 evereduc_share 0.279*** 0.040 0.268*** 0.038 female_dummy -0.483*** 0.090 -0.478*** 0.086 hhsize_10 -0.100*** 0.031 -0.092*** 0.030 hhsize_2 0.190*** 0.063 0.177*** 0.060 hhsize_3 0.213*** 0.055 0.221*** 0.064 hhsize_9 -0.077** 0.033 -0.074*** 0.028 kitchen4 -0.424*** 0.097 -0.401*** 0.089 married_dummy -0.159** 0.074 -0.176** 0.069 married_mean 0.368*** 0.078 0.394*** 0.074 mobile 0.119*** 0.037 0.123*** 0.041 pri_abv_sum -0.027*** 0.006 -0.028*** 0.006 pri_com_sum_m_dist_s -0.091*** 0.030 -0.101** 0.042 sec_com_sum_m_dist_s 0.176*** 0.039 0.190*** 0.055 sewagetyp4 0.073*** 0.026 0.087*** 0.025 tv 0.169*** 0.027 0.158*** 0.026 unihi_com_mean 0.364*** 0.097 0.363*** 0.089 watsrc4 -0.134*** 0.047 -0.125*** 0.048 widowed_dummy -0.123*** 0.045 -0.116*** 0.043 widowed_mean 0.638** 0.261 0.645** 0.262 _cons 10.853*** 0.162 10.852*** 0.197 Number of observations 2260 Adjusted R-squared 0.486 Sigma eta sq. 0.009 Ratio of Sigma eta sq. over MSE 0.064 Variance of epsilon 0.129 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 8: Model Estimates Based on IHS 2015/16—Kuntaur (Beta Model, LGA 6) OLS GLS Variable coeff. se coeff. se age0t6_mean -0.297*** 0.091 -0.303*** 0.090 age15t24_dummy -0.096*** 0.031 -0.093*** 0.031 age15t24_sum -0.055*** 0.010 -0.053*** 0.010 age25t64_sum -0.077*** 0.009 -0.077*** 0.008 bicycle 0.098*** 0.029 0.105*** 0.029 car 0.606*** 0.089 0.608*** 0.089 depratio -0.139*** 0.018 -0.137*** 0.018 elec7 -0.081*** 0.028 -0.084*** 0.028 ethnicity2_dummy -0.059** 0.024 -0.055** 0.026 ethnicity5_dummy -0.233 0.145 -0.232 0.144 female_head 0.147*** 0.050 0.133*** 0.050 female_mean 0.123* 0.066 0.126* 0.066 hhsize_2 0.097 0.087 0.093 0.087 hhsize_4 0.099** 0.047 0.101** 0.046 hhsize_m_dist_s 0.079*** 0.023 0.080*** 0.029 kitchen2 0.067** 0.029 0.077*** 0.029 landphone 0.456*** 0.146 0.432*** 0.142 literacy1_share 0.161*** 0.044 0.139*** 0.045 literacy2_share 0.198*** 0.052 0.176*** 0.053 ln_hdage -0.074* 0.042 -0.089** 0.041 married_mean 0.454*** 0.075 0.438*** 0.075 mobile 0.117*** 0.036 0.114*** 0.036 motorcycle 0.138*** 0.054 0.151*** 0.053 pri_abv_sum 0.034*** 0.008 0.030*** 0.008 pri_com_sum_m_dist_s -0.117** 0.059 -0.127 0.077 sec_com_dummy -0.086*** 0.032 -0.078** 0.032 sec_com_sum_m_dist_s 0.175*** 0.053 0.179*** 0.067 toilet2 0.305*** 0.108 0.309*** 0.112 tv 0.238*** 0.049 0.219*** 0.049 unihi_com_sum 0.182** 0.079 0.196** 0.093 watsrc3 -0.050** 0.024 -0.053* 0.028 watsrc7_m_dist_s 0.441*** 0.126 0.430*** 0.164 _cons 9.180*** 0.251 9.247*** 0.291 Number of observations 1521 Adjusted R-squared 0.419 Sigma eta sq. 0.007 Ratio of Sigma eta sq. over MSE 0.042 Variance of epsilon 0.152 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 9: Model Estimates Based on IHS 2015/16—Janjangbureh (Beta Model, LGA 7) OLS GLS Variable coeff. se coeff. se age0t6_mean -0.362*** 0.102 -0.355*** 0.100 age15t24_dummy -0.199*** 0.035 -0.196*** 0.034 age15t24_sum -0.036*** 0.011 -0.038*** 0.010 age25t64_sum -0.051*** 0.008 -0.049*** 0.008 bicycle 0.026 0.025 0.036 0.025 car 0.488*** 0.121 0.424*** 0.109 child_3p -0.104*** 0.039 -0.103*** 0.038 computer 0.220** 0.101 0.211** 0.097 depratio -0.099*** 0.023 -0.107*** 0.022 disability_head 0.107 0.066 0.105* 0.061 elec1 0.214*** 0.053 0.253*** 0.061 elec6 0.201*** 0.047 0.189*** 0.045 elecgas3 0.286*** 0.074 0.219*** 0.066 employed_sum_m_dist_s 0.093*** 0.022 0.090** 0.036 hhsize_10 -0.115*** 0.039 -0.096*** 0.036 hhsize_9 -0.065 0.048 -0.059 0.046 landphone 0.186 0.137 0.292** 0.128 ln_hdage -0.077* 0.044 -0.067 0.042 male_mean 0.168*** 0.064 0.125** 0.061 married_dummy -0.369*** 0.085 -0.326*** 0.081 married_mean 0.347*** 0.088 0.354*** 0.083 mobile 0.130*** 0.043 0.113*** 0.044 sec_com_mean 0.228*** 0.070 0.186*** 0.063 sewagetyp4 0.099*** 0.031 0.073** 0.030 tv 0.067 0.051 0.088* 0.048 watsrc3 -0.084*** 0.025 -0.091*** 0.032 watsrc4 -0.450*** 0.140 -0.293** 0.140 watsrc5 0.172 0.112 0.199* 0.115 widowed_mean -0.321** 0.131 -0.204 0.131 _cons 10.342*** 0.192 10.305*** 0.198 Number of observations 1621 Adjusted R-squared 0.514 Sigma eta sq. 0.018 Ratio of Sigma eta sq. over MSE 0.111 Variance of epsilon 0.144 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01

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Table 10: Model Estimates Based on IHS 2015/16—Basse (Beta Model, LGA 8) OLS GLS Variable coeff. se coeff. se age15t24_mean -0.414*** 0.083 -0.398*** 0.078 age1t14_dummy -0.192*** 0.049 -0.187*** 0.049 age25t64_dummy -0.212*** 0.075 -0.176** 0.068 car 0.401*** 0.072 0.415*** 0.068 child_2 -0.181*** 0.040 -0.174*** 0.035 child_3p -0.226*** 0.033 -0.210*** 0.032 depratio -0.069*** 0.018 -0.072*** 0.017 elec1_m_dist_s 0.702*** 0.084 0.727*** 0.105 elec7 -0.085*** 0.025 -0.090*** 0.025 employee_mean 0.167* 0.094 0.165* 0.092 female_mean -0.125* 0.067 -0.141** 0.063 hhsize_10 -0.058 0.043 -0.063 0.040 hhsize_9 -0.076 0.048 -0.076* 0.046 kitchen2 0.107*** 0.022 0.100*** 0.021 kitchen4 -0.186** 0.073 -0.179** 0.073 ln_hdage -0.250*** 0.036 -0.243*** 0.034 male_dummy -0.183** 0.084 -0.206*** 0.074 mobile 0.109*** 0.033 0.117*** 0.031 motorcycle 0.092*** 0.029 0.099*** 0.027 pri_com_mean 0.289*** 0.055 0.282*** 0.054 radio -0.035 0.022 -0.039* 0.021 sec_com_mean 0.196*** 0.073 0.205*** 0.068 toilet3 -0.124** 0.055 -0.143*** 0.053 tv 0.218*** 0.033 0.210*** 0.032 unihi_com_mean 0.272** 0.130 0.261** 0.121 urb_loghhsize -0.060*** 0.022 -0.069*** 0.026 urb_sh_emp 0.456*** 0.068 0.433*** 0.070 watsrc3 -0.056*** 0.021 -0.057** 0.023 watsrc3_m_dist_s 0.972*** 0.093 0.994*** 0.118 watsrc5 -0.169** 0.073 -0.163** 0.068 watsrc6_m_dist_s 1.193*** 0.213 1.165*** 0.268 widowed_mean 0.520*** 0.194 0.613*** 0.192 widowed_sum -0.126*** 0.036 -0.133*** 0.036 _cons 11.048*** 0.201 11.026*** 0.202 Number of observations 2110 Adjusted R-squared 0.547 Sigma eta sq. 0.008 Ratio of Sigma eta sq. over MSE 0.043 Variance of epsilon 0.166 Sampling variance of Sigma eta sq. 0.000 Error decomposition ELL Empirical best methods Yes Beta drawing Bootstrapped Eta drawing method Normal Epsilon drawing method Normal Alpha model Yes Source: Authors' calculations based on IHS 2015/16. Note: Model settings: alpha model; error decomposition: ELL; bootstrapped with normal distribution; empirical best methods. * p < .1 ** p < .05 *** p < .01 28 | Page

A visual assessment was conducted to compare the predicted and simulated consumption distributions as displayed in Figure 2. The results based on various training samples (beginning with 10 percent of the sample and continuing up to 90 percent) at each LGA are undertaken to ensure the robustness of the approach and the resulting distributions. This demonstrates a high level of statistical precision. However, this precision level declines as the degree of spatial disaggregation increases. This approach should be supplemented with complementary sources of information if further lower-level disaggregation is envisaged, but this should be done with a lot of caution.

Figure 2: Distributions with Actual and Imputed Testing Sample

.8 kdensity lnpcexp

kdensity yhat10

kdensity yhat20

kdensity yhat30 .6 kdensity yhat40

kdensity yhat50

kdensity yhat60

kdensity yhat70 .4 kdensity yhat80 Density kdensity yhat90

.2

0 4 6 8 10 12 Log of welfare (per capita)

Source: Authors calculations.

The clustering used for estimations is at the primary sampling unit level. The poverty mapping results are based on survey direct estimates, and the poverty line of GMD 1,503.33 per month per capita is used to measure the share of the poor. Table 11 shows the poverty head count for direct and poverty mapping at the national level. The results are similar to estimates obtained from the IHS. There is a very good match between the direct and small area estimates as the national-level estimate differs by approximately -0.1 percentage points. Furthermore, the differences between the survey and small area estimates across regions are significantly low.

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Table 11: Poverty Estimates from Survey (Observed) and the Census (Small Area Estimation) Survey Census 95% Head Standard No. of No. of Head Standard confidence count error HHs individuals count error interval THE GAMBIA 48.64 0.44 47.78 49.49 216,169 1,831,459 48.57 0.68 Banjul 10.76 1.65 7.54 13.99 6,638 29,478 10.54 1.75 Kanifing 17.27 1.85 13.65 20.89 59,717 373,075 16.50 1.50 Brikama 51.18 0.93 49.37 53.00 81,752 681,404 51.26 1.15 Mansakonko 60.14 1.16 57.87 62.40 9,491 78,430 60.00 1.42 Kerewan 59.85 1.02 57.85 61.85 22,391 216,053 60.46 1.58 Kuntaur 72.36 1.13 70.14 74.58 8,809 95,474 69.63 1.51 Janjangbureh 71.41 1.11 69.23 73.58 11,588 122,568 72.06 1.44 Basse 59.35 1.05 57.29 61.42 15,783 234,977 60.93 1.67 Source: Authors’ calculations based on IHS 2015/16 and 2013 Population and Housing Census. Note: HHs = households.

In summary, the methodology to compute monetary poverty indicators at a lower level of spatial disaggregation is consistent with poverty profile figures and permitted the computation of standard errors for these indicators. The poverty maps are compatible with poverty profile results and should be seen as natural extensions of the poverty profiles.

6. Poverty Mapping Results Table 12 presents a detailed estimated poverty rate and Gini at the national and LGA level using simulated welfares from the 2013 census. Map 1 presents poverty by region, and map 2 shows the number of poor by region. These estimates are close to the IHS estimates.

Table 12: Census Small Area Estimation of Poverty and Gini at the National and Local Government Area Number Poverty Gini No. of House- Indivi- Head poor Std. holds duals count Std. err Estimate err THE GAMBIA 216,169 1,831,459 48.57 0.68 889,589 0.36 0.01 Banjul 6,638 29,478 10.54 1.75 3,106 0.31 0.01 Kanifing 59,717 373,075 16.50 1.50 61,544 0.32 0.01 Brikama 81,752 681,404 51.26 1.15 349,279 0.35 0.01 Mansakonko 9,491 78,430 60.00 1.42 47,056 0.30 0.01 Kerewan 22,391 216,053 60.46 1.58 130,627 0.28 0.01 Kuntaur 8,809 95,474 69.63 1.51 66,481 0.31 0.01 Janjangbureh 11,588 122,568 72.06 1.44 88,324 0.30 0.01 Basse 15,783 234,977 60.93 1.67 143,172 0.30 0.01 Source: Authors’ calculations based on IHS 2015/16 and 2013 Population and Housing Census. Note: Std. err = standard error.

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Map 1: Census Small Area Estimation of Poverty at the Local Government Area

Source: Based on 2013 Population and Housing Census. Note: LGA refers to “Local Government Area.”

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Map 2: Census Small Area Estimation of Number of Poor at the Local Government Area

Source: Based on 2013 Population and Housing Census. Note: LGA refers to “Local Government Area.”

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Data in Table 13 show that there are districts with a high relative share of the poor in regions with relatively low poverty rates and vice versa. For example, the poverty rate ranges from 41.8 percent (Kombo North) to 90.7 percent () in Brikama. The two districts with the highest relative share of the poor, Foni Bondali (90.7 percent) and Foni Bintang Karanai (87.5 percent), are in Brikama Local Government Area. Despite such high poverty rates, inequality in these districts is relatively low compared to other districts. Map 3 shows the resulting map aggregated to the district level, and map 4 illustrates the number of the poor by district. These maps compare the poverty rates obtained from the SAE to the direct estimates from the IHS at the statistical area level. This comparison provides support to the quality of the census- based predictions obtained. Furthermore, the standard errors remain small.

At the ward level, the heterogeneity of poverty is more pronounced, as displayed in Table 14. Map 6 presents the relative share of the poor by ward. As the map shows, the pockets of poverty are similar results to those at the district level. Some of the less-poor wards are located in the poorest regions of the country.

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Table 13: Census Small Area Estimation of Poverty and Gini at the District Level Local Number Poverty Gini Government District Std. No. of poor Std. Area (LGA) Households Individuals Head count error Estimate error Banjul South 1,801 8,095 9.48 1.99 767 0.311 0.016 Banjul Central 2,522 10,956 10.49 2.08 1,149 0.309 0.015 Banjul Banjul North 2,315 10,427 11.41 1.93 1,190 0.301 0.014 Bakau 4,925 28,125 11.75 1.51 3,304 0.340 0.012 Jeshwang 10,245 66,127 15.07 1.67 9,967 0.323 0.012 Kanifing Serrekunda Central 12,239 75,025 16.10 1.60 12,076 0.301 0.011 Serrekunda East 15,717 109,736 19.63 1.71 21,539 0.306 0.012 Serrekunda West 16,591 94,062 15.58 1.54 14,657 0.323 0.012 Kombo North 43,507 335,455 41.80 1.28 140,224 0.351 0.015 11,804 105,929 56.71 1.59 60,074 0.315 0.013 15,856 138,883 51.79 1.46 71,932 0.323 0.013 4,345 41,773 66.22 1.60 27,664 0.293 0.012 Brikama 1,508 14,415 79.50 1.91 11,460 0.277 0.013 Foni Bintang Karanai 1,771 16,778 87.47 1.59 14,676 0.254 0.012 1,551 13,766 77.17 1.93 10,623 0.290 0.013 Foni Bondali 721 7,594 90.73 1.72 6,890 0.255 0.013 689 6,811 84.23 2.05 5,737 0.314 0.023 1,733 14,384 84.02 1.83 12,086 0.256 0.009 1,037 8,548 72.87 2.63 6,229 0.265 0.009 737 6,602 64.83 3.32 4,280 0.283 0.012 Mansakonko 3,481 25,793 40.25 2.84 10,381 0.287 0.011 908 8,163 65.84 2.52 5,374 0.281 0.011 1,595 14,940 58.27 2.68 8,705 0.278 0.013 6,345 56,389 48.88 2.39 27,562 0.284 0.012 2,742 30,754 68.83 2.18 21,168 0.251 0.009 1,998 22,225 70.76 2.62 15,727 0.252 0.011 Kerewan Lower Badibu 1,865 17,737 64.56 2.68 11,451 0.261 0.012 Central Badibu 1,968 18,777 67.89 2.72 12,748 0.267 0.014 Illiasa 5,463 47,071 50.44 2.07 23,741 0.284 0.012 Sabach Sanjar 2,010 23,100 78.92 2.41 18,231 0.245 0.013 1,583 15,260 73.56 2.09 11,226 0.300 0.012 1,718 18,357 74.64 2.44 13,702 0.289 0.012 Kuntaur 945 9,736 83.55 2.29 8,135 0.274 0.011 Niani 2,572 28,029 63.02 2.57 17,665 0.313 0.012 Sami 1,991 24,092 65.39 2.49 15,753 0.320 0.013 34 | Page

Local Number Poverty Gini Government District Std. No. of poor Std. Area (LGA) Households Individuals Head count error Estimate error 643 6,067 83.56 2.67 5,069 0.263 0.013 752 7,135 78.87 2.97 5,627 0.274 0.014 2,376 23,854 72.73 2.62 17,350 0.278 0.016 Janjangbureh Lower Fuladu West 3,191 39,202 75.57 1.92 29,623 0.300 0.011 Upper Fuladu West 4,213 43,040 66.99 1.74 28,834 0.315 0.012 413 3,270 55.67 3.60 1,820 0.282 0.014 Jimara 2,587 43,350 52.74 3.08 22,864 0.300 0.013 Basse 5,212 48,475 49.52 2.86 24,005 0.325 0.012 Tumana 2,098 36,896 52.06 3.28 19,209 0.268 0.011 Basse Kantora 1,837 37,927 63.30 2.63 24,009 0.273 0.012 Wuli West 1,361 21,601 78.86 2.16 17,035 0.280 0.014 Wuli East 1,294 23,386 80.30 2.37 18,778 0.262 0.011 Sandu 1,394 23,342 73.99 2.48 17,272 0.271 0.013 Source: Authors’ calculations based on IHS 2015/16 and 2013 Population and Housing Census. Note: Std. = standard.

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Map 3: Census Small Area Estimation of Poverty at the District Level

Source: Based on 2013 Population and Housing Census.

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Map 4: Census Small Area Estimation of Number of Poor at the District Level

Source: Based on 2013 Population and Housing Census.

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Map 5: Census Small Area Estimation of Gini at the District Level

Source: Based on 2013 Population and Housing Census.

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Table 14: Census Small Area Estimation of Poverty and Gini at the Ward Level Local Number Poverty Gini Ward Government District Std. No. of poor Std. code Area (LGA) Households Individuals Head count err. Estimate err. Banjul South 110101 719 3,334 9.62 2.62 321 0.313 0.020 Banjul South 110102 766 3,403 9.44 2.63 321 0.308 0.017 Banjul South 110103 316 1,358 9.23 3.84 125 0.305 0.019 Banjul Central 111111 1,100 4,740 12.43 2.72 589 0.303 0.016 Banjul Banjul Central 111112 1,116 4,910 9.65 2.54 474 0.313 0.017 Banjul Central 111113 306 1,306 6.59 2.81 86 0.296 0.020 Banjul North 112121 597 2,136 8.43 2.65 180 0.310 0.017 Banjul North 112122 1,201 6,258 13.21 2.58 827 0.290 0.015 Banjul North 112123 517 2,033 9.00 2.74 183 0.304 0.016 Bakau 220201 2,369 12,678 9.58 1.59 1,214 0.357 0.016 Bakau 220202 2,556 15,447 13.53 1.89 2,090 0.313 0.011 Jeshwang 221211 1,668 9,676 7.58 1.69 734 0.335 0.014 Jeshwang 221212 5,996 40,119 18.05 1.95 7,240 0.291 0.011 Jeshwang 221213 2,581 16,332 12.21 1.96 1,994 0.335 0.015 Serrekunda Central 222221 5,665 37,780 17.45 1.89 6,592 0.299 0.012 Serrekunda Central 222222 3,073 17,405 16.24 1.98 2,827 0.300 0.012 Serrekunda Central 222223 3,501 19,840 13.40 1.79 2,658 0.303 0.012 Kanifing Serrekunda East 223231 2,157 15,778 22.34 2.55 3,525 0.319 0.017 Serrekunda East 223232 5,346 37,909 19.58 2.03 7,421 0.304 0.012 Serrekunda East 223233 2,399 15,583 15.64 2.08 2,438 0.296 0.013 Serrekunda East 223234 5,815 40,466 20.15 1.86 8,155 0.305 0.013 Serrekunda West 224241 2,636 16,600 11.93 1.90 1,981 0.309 0.012 Serrekunda West 224242 3,968 22,483 16.02 2.12 3,603 0.302 0.012 Serrekunda West 224243 2,147 10,629 10.67 1.82 1,134 0.352 0.015 Serrekunda West 224244 2,270 12,809 14.69 2.06 1,881 0.325 0.013 Serrekunda West 224245 5,570 31,541 19.21 2.02 6,058 0.318 0.014 Kombo North 330301 4,546 35,244 43.98 1.91 15,502 0.327 0.014 Kombo North 330302 5,379 40,228 40.88 1.83 16,444 0.391 0.019 Kombo North 330303 5,791 48,052 48.66 1.66 23,380 0.319 0.012 Kombo North 330304 6,020 46,667 37.08 1.65 17,306 0.349 0.017 Brikama Kombo North 330305 5,479 43,070 40.56 1.81 17,470 0.333 0.014 Kombo North 330306 3,476 28,615 43.84 2.11 12,544 0.323 0.014 Kombo North 330307 1,706 13,047 53.92 2.36 7,035 0.313 0.016 Kombo North 330308 5,273 43,975 46.11 1.70 20,275 0.336 0.015

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Local Number Poverty Gini Ward Government District Std. No. of poor Std. code Area (LGA) Households Individuals Head count err. Estimate err. Kombo North 330309 5,837 36,557 28.08 1.48 10,267 0.369 0.017 Kombo South 331311 2,413 21,828 53.51 2.01 11,680 0.316 0.014 Kombo South 331312 1,846 16,869 64.54 2.44 10,888 0.311 0.016 Kombo South 331313 3,588 30,562 54.90 1.98 16,779 0.320 0.013 Kombo South 331314 3,957 36,670 56.52 2.09 20,727 0.308 0.013 Kombo Central 332321 4,307 38,076 52.30 2.05 19,915 0.312 0.012 Kombo Central 332322 2,702 26,115 65.07 2.09 16,993 0.289 0.014 Kombo Central 332323 4,491 36,004 43.52 1.80 15,670 0.333 0.016 Kombo Central 332324 4,356 38,688 50.03 1.75 19,354 0.324 0.015 Kombo East 333331 1,825 16,267 68.98 2.18 11,222 0.282 0.013 Kombo East 333332 1,338 14,087 67.33 2.15 9,485 0.297 0.015 Kombo East 333333 1,182 11,419 60.93 2.82 6,957 0.295 0.016 Foni Brefet 334341 833 8,098 80.33 2.15 6,505 0.269 0.014 Foni Brefet 334342 675 6,317 78.43 2.68 4,955 0.286 0.016 Foni Bintang Karanai 335351 683 6,741 89.91 1.72 6,061 0.243 0.014 Foni Bintang Karanai 335352 1,088 10,037 85.83 2.07 8,615 0.259 0.013 Foni Kansala 336361 820 6,759 73.73 2.48 4,983 0.302 0.018 Foni Kansala 336362 731 7,007 80.49 2.44 5,640 0.273 0.015 Foni Bondali 337332 6 44 79.39 18.68 35 0.16 0.050 Foni Bondali 337371 204 1,910 91.66 2.75 1,751 0.226 0.017 Foni Bondali 337372 511 5,640 90.50 1.91 5,104 0.264 0.014 Foni Jarrol 338381 408 3,553 80.95 2.71 2,876 0.318 0.030 Foni Jarrol 338382 281 3,258 87.81 2.52 2,861 0.303 0.022 Kiang West 440401 762 6,489 84.51 2.44 5,484 0.256 0.011 Kiang West 440402 971 7,895 83.63 2.18 6,602 0.256 0.010 Kiang Central 441411 525 4,096 71.74 2.95 2,938 0.261 0.011 Kiang Central 441412 512 4,452 73.92 3.69 3,291 0.268 0.011 Kiang East 442421 498 4,751 68.70 4.15 3,264 0.269 0.013 Kiang East 442422 239 1,851 54.91 4.12 1,016 0.299 0.020 Mansakonko Jarra West 443431 2,880 21,239 38.80 3.11 8,240 0.289 0.012 Jarra West 443432 601 4,554 47.01 4.43 2,141 0.269 0.013 Jarra central 444441 481 4,355 70.54 3.66 3,072 0.275 0.015 Jarra central 444442 427 3,808 60.46 3.61 2,302 0.282 0.013 Jarra East 445451 1,008 9,724 57.95 2.99 5,635 0.275 0.015 Jarra East 445452 587 5,216 58.85 3.98 3,069 0.284 0.023

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Local Number Poverty Gini Ward Government District Std. No. of poor Std. code Area (LGA) Households Individuals Head count err. Estimate err. Lower Niumi 550501 4,710 37,956 44.52 2.42 16,900 0.293 0.014 Lower Niumi 550502 1,635 18,433 57.84 3.28 10,662 0.250 0.015 Upper Niumi 551511 1,413 15,522 66.27 2.81 10,286 0.247 0.013 Upper Niumi 551512 1,329 15,232 71.44 2.69 10,881 0.255 0.010 Jokadu 552521 933 9,903 69.85 3.43 6,917 0.248 0.012 Jokadu 552522 1,065 12,322 71.50 2.88 8,810 0.255 0.012 Lower Badibu 553531 1,040 9,864 65.64 3.00 6,475 0.268 0.013 Lower Badibu 553532 825 7,873 63.20 3.59 4,976 0.253 0.013 Kerewan Central Badibu 554541 1,003 9,196 67.91 3.27 6,245 0.261 0.015 Central Badibu 554542 965 9,581 67.87 3.34 6,503 0.271 0.017 Illiasa 555551 651 5,544 56.46 3.42 3,130 0.264 0.012 Illiasa 555552 783 7,149 58.24 3.62 4,164 0.245 0.013 Illiasa 555553 3,302 26,071 44.46 2.40 11,592 0.298 0.015 Illiasa 555554 727 8,307 58.45 3.52 4,855 0.246 0.013 Sabach Sanjar 556561 707 8,401 80.02 3.50 6,722 0.232 0.014 Sabach Sanjar 556562 1,303 14,699 78.29 2.46 11,508 0.251 0.015 Lower Saloum 660601 346 3,875 87.01 2.61 3,371 0.275 0.014 Lower Saloum 660602 1,237 11,385 68.99 2.62 7,854 0.299 0.012 Upper Saloum 661611 958 10,590 75.70 2.73 8,016 0.299 0.014 Upper Saloum 661612 760 7,767 73.20 3.13 5,686 0.275 0.012 Nianija 662621 945 9,736 83.55 2.29 8,135 0.274 0.011 Kuntaur Niani 663631 1,550 15,729 57.74 3.04 9,081 0.315 0.015 Niani 663632 1,022 12,300 69.79 2.76 8,584 0.302 0.012 Sami 664641 843 9,880 62.38 3.46 6,164 0.322 0.015 Sami 664642 524 6,367 64.52 3.51 4,108 0.270 0.014 Sami 664643 624 7,845 69.88 2.83 5,482 0.353 0.025 Niamina Dankunku 770701 643 6,067 83.56 2.67 5,069 0.263 0.013 Niamina West 771711 752 7,135 78.87 2.97 5,627 0.274 0.014 Niamina East 772721 820 8,881 76.31 3.52 6,777 0.258 0.015 Niamina East 772722 1,556 14,973 70.61 2.74 10,573 0.288 0.018 Janjangbureh Lower Fuladu West 773731 1,217 15,074 75.32 2.62 11,353 0.310 0.014 Lower Fuladu West 773732 1,131 14,776 78.25 2.54 11,561 0.291 0.013 Lower Fuladu West 773733 843 9,352 71.73 3.10 6,708 0.274 0.015 Upper Fuladu West 774741 1,612 13,245 54.04 2.72 7,158 0.328 0.020 Upper Fuladu West 774742 641 9,437 78.91 3.07 7,447 0.275 0.014

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Local Number Poverty Gini Ward Government District Std. No. of poor Std. code Area (LGA) Households Individuals Head count err. Estimate err. Upper Fuladu West 774743 702 7,233 71.86 3.97 5,198 0.266 0.014 Upper Fuladu West 774744 1,258 13,125 68.81 2.46 9,031 0.279 0.013 Janjanbureh 775751 413 3,270 55.67 3.60 1,820 0.282 0.014 Jimara 880801 1,415 17,331 59.52 3.13 10,315 0.255 0.011 Jimara 880802 1,172 26,019 48.23 4.11 12,548 0.317 0.016 Basse 881811 4,490 33,738 44.25 3.11 14,931 0.332 0.012 Basse 881812 722 14,737 61.58 3.95 9,074 0.280 0.017 Tumana 882821 1,222 19,815 50.66 3.63 10,037 0.267 0.012 Tumana 882822 876 17,081 53.69 4.16 9,172 0.268 0.014 Kantora 883831 789 19,325 57.37 3.32 11,087 0.269 0.014 Basse Kantora 883832 1,048 18,602 69.47 3.04 12,922 0.268 0.013 Wuli West 884841 584 9,201 80.56 2.62 7,412 0.256 0.013 Wuli West 884842 777 12,400 77.61 2.65 9,624 0.296 0.018 Wuli East 885851 678 13,616 78.10 3.45 10,634 0.270 0.014 Wuli East 885852 616 9,770 83.36 2.26 8,144 0.247 0.012 Sandu 886861 726 11,763 74.42 3.37 8,755 0.272 0.015 Sandu 886862 668 11,579 73.56 2.94 8,517 0.269 0.015 Source: Authors’ calculations based on IHS 2015/16 and 2013 Population and Housing Census. Note: Std. err. = standard error.

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Map 6: Census Small Area Estimation of Poverty at the Ward Level

Source: Based on 2013 Population and Housing Census.

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Map 7: Census Small Area Estimation of Poverty at the Ward Level (Zoom in at Banjul and Kanifing)

Source: Based on 2013 Population and Housing Census.

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Map 8: Census Small Area Estimation of Number of Poor at the Ward Level

Source: Based on 2013 Population and Housing Census.

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Map 9: Census Small Area Estimation of Number of Poor at the Ward Level (Zoom in at Banjul and Kanifing)

Source: Based on 2013 Population and Housing Census.

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Map 10: Census Small Area Estimation of Gini at the Ward Level

Source: Based on 2013 Population and Housing Census.

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Map 11: Census Small Area Estimation of Gini at the Ward Level (Zoom in at Banjul and Kanifing)

Source: Based on 2013 Population and Housing Census.

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7. Conclusion

Unlike household surveys, poverty maps focus more on the spatial distribution of poverty and inequality at lower geographical levels. This analysis highlights the potential gains from a more aggregate-level to a lower-level geographical targeting. This may offer an effective approach for reaching the poor where there are substantial disparities in the living standards within and across geographical areas. Although beyond the scope of this note, this methodology can be complemented with other indicators of well-being, opportunity, and access for regional patterns.

The Gambia has undertaken several household surveys since the late 1990s. This note relies on the 2013 Population and Housing Census and IHS 2015/16 to estimate the various measures of welfare for small administrative units. Unlike the previous survey, the IHS 2015/16 is the first of its kind to estimate welfare at the district level. Furthermore, this analysis is the first to provide estimates of poverty and inequality for lower-level units of administration in The Gambia. The results show that poverty and inequality measures at both the province and the district level are comparable to those calculated using the IHS.

The findings also show a consistent measure of welfare across all administrative units. However, overall poverty masks regional disparities, and much heterogeneity existed within districts. Welfare rankings of lower administrative units further show large variation on various measures of poverty. Poverty remains a rural phenomenon, with rural areas being more heterogeneous than urban areas. Inequality measures remain low in all administrative units across The Gambia. This analysis implies that poverty maps can complement the 2015/16 poverty profile. As these maps examine lower geographical levels, they can help the Government of The Gambia make informed decisions about targeting poverty.

Poverty maps have many uses, but they must be interpreted well. The maps can be used to design budget allocations at the administrative level, taking into the account the lower-level characteristics for anti- poverty programs. The maps could be used as a tool for decentralization, which is currently important to the Government of The Gambia. For example, the Government can use the maps to distribute a budget to districts according to their level of monetary poverty. The local authority would then use that budget allocation to prioritize investment (for example, in health, education, infrastructure, and so forth) according to the local characteristics using nonmonetary indicators as guidelines.

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References

Ahmed, F., C. Cheku, S. Takamatsu, and N. Yoshida. 2014. “Hybrid Survey to Improve the Reliability of Poverty Statistics in a Cost-Effective Manner.” Policy Research Working Paper 6909, World Bank Group, Washington, DC. http://documents.worldbank.org/curated/en/364691468014449485/Hybrid-survey-to-improve-the- reliability-of-poverty-statistics-in-a-cost-effective-manner.

Arthur, J., M. Waring, R. Coe, and L.V. Hedges, eds. 2012. Research Methods and Methodologies in Education. Los Angeles: SAGE.

Bedi, T., A. Coudouel, and K. Simler, eds. 2007. More than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions. Washington DC: World Bank. http://documents.worldbank.org/curated/en/875491468320356640/More-than-a-pretty-picture-u.

Bigman, D., and U. Deichmann. 2000. “Geographic Targeting: A Review of Different Approaches.” In Geographical Targeting for Poverty Alleviation: Methodology and Applications, edited by D. Bigman and H. Fofack, 43–73. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/635661468743807218/Geographical-targeting-for- poverty-alleviation-methodology-and-applications.

Bigman, D., and H. Fofack, eds. 2000. Geographical Targeting for Poverty Alleviation: Methodology and Applications. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/635661468743807218/Geographical-targeting-for- poverty-alleviation-methodology-and-applications.

Coudouel, A., J.S. Hentschel, and Q.T. Wodon. 2002. “Poverty Measurement and Analysis.” In A Sourcebook for Poverty Reduction Strategies: Core Techniques and Cross-Cutting Issues, edited by J. Klugman, 27–74. Washington, DC: World Bank Group. http://documents.worldbank.org/curated/en/156931468138883186/Core-techniques-and-cross- cutting-issues.

Deaton, A., and S. Zaidi. 2002. “A Guide to Aggregating Consumption Expenditure.” Living Standards Measurement Study Working Paper 135, World Bank, Washington, DC.

Elbers C., J.O. Lanjouw, and P. Lanjouw. 2003. “Micro-Level Estimation of Poverty and Inequality.” Econometrica 71 (1): 355–64. https://doi.org/10.1111/1468-0262.00399.

50 | Page

Elbers, C., and R. Van der Weide. 2014. "Estimation of Normal Mixtures in a Nested Error Model with an Application to Small Area Estimation of Poverty and Inequality," Policy Research Working Paper 6962, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/712781468338974024/Estimation-of-normal-mixtures- in-a-nested-error-model-with-an-application-to-small-area-estimation-of-poverty-and-inequality.

FAO (Food and Agriculture Organization of the United Nations). 2012. West African Food Composition Table. Rome: FAO.

Foster, J.E., J. Greer, and E. Thorbecke. 1984. “A Class of Decomposable Poverty Indices.” Econometrica 52 (3): 761–66.

Haughton, J., and S.R. Khander. 2009. Handbook on Poverty and Inequality. Washington, DC: World Bank. http://documents.worldbank.org/curated/en/488081468157174849/Handbook-on-poverty-and- inequality.

Henderson, C.R. 1953. “Estimation of Variance and Covariance Components.” Biometrics 9 (2): 226– 52. https://doi.org/10.2307/3001853. Henderson, C. R. 1953. Estimation of variance and Hentschel, J., J.O. Lanjouw, P. Lanjouw, and J. Poggi. 2000. “Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador.” World Bank Economic Review 14 (1): 147–65. http://documents.worldbank.org/curated/en/586251468024830032/Combining- census-and-survey-data-to-trace-the-spatial-dimensions-of-poverty-a-case-study-of-Ecuador.

Lindsey, C., and S.J. Sheather. 2010. “Variable Selection in Linear Regression.” Stata Journal 10 (4): 650–69. https://www.stata-journal.com/sjpdf.html?articlenum=st0213.

Ravallion, M. 1994. “Poverty Comparisons: A Guide to Concepts and Methods.” Living Standards Measurement Study Working Paper 88, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/290531468766493135/Poverty-comparisons-a-guide-to- concepts-and-methods.

Ravallion, M. 1998. “Poverty Lines in Theory and Practice.” Living Standards Measurement Study Working Paper 135, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/916871468766156239/Poverty-lines-in-theory-and- practice.

Searle, S.R. 1968. “Another Look at Henderson's Methods of Estimating Variance Components.” Biometrics 24 (4): 749. https://doi.org/10.2307/2528870.

51 | Page

Searle, S.R., G. Casella, and C.E. McCulloch. 1992. Variance Components. New York: Wiley.

Thompson, B. 1995. “Stepwise Regression and Stepwise Discriminant Analysis Need Not Apply Here: A Guidelines Editorial.” Educational and Psychological Measurement 55: 525–34. https://doi.org/10.1177/0013164495055004001.

Van der Weide, R. 2014. "GLS Estimation and Empirical Bayes Prediction for Linear Mixed Models with Heteroskedasticity and Sampling Weights: A Background Study for the POVMAP Project." Policy Research Working Paper Series 7028, World Bank, Washington, DC. http://documents.worldbank.org/curated/en/397631468332346764/GLS-estimation-and-empirical- bayes-prediction-for-linear-mixed-models-with-Heteroskedasticity-and-sampling-weights-a- background-study-for-the-POVMAP-project.

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Appendix A: The Gambia: Administrative Boundaries

Source: Gambia Bureau of Statistics (GBoS). Note: LGA refers to “Local Government Area.”

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Appendix B: Region Alpha Model Estimates

Table B1: Banjul (Alpha Model, LGA 1) Variable coeff. se age15t24_dummy -0.241 0.264 child_1 -0.020 0.336 kitchen4 0.259 0.371 Constant -3.580 0.330

Table B2: Kanifing (Alpha Model, LGA 2) Variable coeff. se age1t14_dummy -0.35654 0.301974 child_3p 0.14219 0.280969 _cons -3.31815 0.225575

Table B3: Brikama (Alpha Model, LGA 3) Variable coeff. se age1t14_sum 0.019 0.026 car 0.677 0.320 child_3p 0.150 0.185 computer 0.124 0.284 female_head 0.256 0.186 hhsize_3 -0.422 0.295 kitchen4 0.927 0.364 sec_com_sum -0.072 0.041 toilet3 -0.438 0.202 tv -0.148 0.151 _cons -4.548 0.241

Table B4: Mansakonko (Alpha Model, LGA 4) Variable coeff. se evereduc_share -0.119 0.204 hhsize_3 0.148 0.244 married_mean 0.266 0.333 pri_com_dummy -0.206 0.149 sec_com_sum_m_dist_s -0.520 0.329 tv -0.067 0.170 unihi_com_mean -0.756 0.783 _cons -3.157 0.482

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Table B5: Kerewan (Alpha Model, LGA 5) Variable coeff. se age0t6_mean -0.311 0.449 age15t24_mean -0.500 0.361 car 0.720 0.377 child_3p 0.059 0.151 computer -0.179 0.395 employed_sum_m_dist_s -0.097 0.137 hhsize_3 0.429 0.328 hhsize_9 -0.330 0.193 kitchen4 -0.168 0.375 mobile -0.330 0.219 pri_abv_sum 0.004 0.026 sec_com_sum_m_dist_s -0.076 0.207 tv -0.202 0.145 watsrc4 -0.040 0.278 _cons -3.778 0.674

Table B6: Kuntaur (Alpha Model, LGA 6) Variable coeff. se age0t6_mean -0.009 0.463 age15t24_dummy -0.061 0.142 ln_hdage -0.052 0.224 pri_com_sum_m_dist_s 0.014 0.220 unihi_com_sum 0.380 0.441 _cons -3.877 0.939

Table B7: Janjangbureh (Alpha Model, LGA 7) Variable coeff. se age0t6_mean 0.499 0.512 age15t24_dummy -0.191 0.153 bicycle 0.079 0.138 child_3p -0.372 0.169 elecgas3 -0.215 0.399 hhsize_9 0.090 0.266 mobile -0.131 0.237 sec_com_mean -0.819 0.376 watsrc3 -0.070 0.134 _cons -4.106 0.268

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Table B8: Basse (Alpha Model, LGA 8) Variable coeff. se age1t14_dummy -0.797 0.233 child_2 0.452 0.168 elec1_m_dist_s 0.051 0.324 employee_mean -0.034 0.468 mobile -0.060 0.173 pri_com_mean 0.110 0.286 sec_com_mean -0.310 0.383 unihi_com_mean -0.378 0.694 watsrc3 0.158 0.112 _cons -4.073 0.252

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Appendix C: Poverty Measures

This section provides the mathematical expressions for the poverty measures used in the paper. Three poverty measures of the Foster-Greer-Thorbecke class (Foster, Greer, and Thorbecke 1984) are used, namely the head count, the poverty gap, and the squared poverty gap.5 The poverty head count is the share of the population which is poor; for example, the proportion of the population for whom consumption per equivalent adult y is less than the poverty line z. Suppose we have a population of size n in which q people are poor. Then the head count index is defined as q H = n

The poverty gap, which is often considered as representing the depth of poverty, is the mean distance separating the population from the poverty line, with the nonpoor being given a distance of zero. Arranging consumption in ascending order y1,...., yq < z < yq+1, ..., yn with the poorest household’s consumption denoted by y1, the next poorest y2, and so forth, and the richest household’s consumption by yn, the poverty gap is defined as q 1  z − yi  PG = ∑   n i=1  z 

where yi is the income of individual i, and the sum is taken only on those individuals who are poor (in practice, we often work with household rather than individual consumption). The poverty gap is thus a measure of the poverty deficit of the entire population, where the notion of “poverty deficit” captures the resources that would be needed (as a proportion of the poverty line) to lift all the poor out of poverty through perfectly targeted cash transfers.

The squared poverty gap is often described as a measure of the severity of poverty. Although the poverty gap considers the distance separating the poor from the poverty line, the squared poverty gap takes the square of that distance into account. When using the squared poverty gap, the poverty gap is weighted by itself, so as to give more weight to the very poor. Said differently, the squared poverty gap takes into account the inequality among the poor. It is defined as follows: 2 1 q  z − y  = i SPG ∑   n i=1 z 

The head count, the poverty gap, and the squared poverty gap are the first three measures of the Foster- Greer-Thorbecke class of poverty measures and a common structure is evident that suggests a generic class of additive measures (additive measures are such that aggregate poverty is equal to the population-

5 For a simple introduction to poverty measurement and profiles, see Coudouel, Hentschel, and Wodon (2002).

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weighted sum of poverty in various subgroups of society). The general formula for this class of poverty measures depends on a parameter α that takes a value of zero for the head count, one for the poverty gap, and two for the squared poverty gap in the following expression:

q α 1  z − yi  Pα = ∑  (α ≥ 0) n i=1  z 

In what follows, the discussion focuses on the head count index of poverty. Higher-order poverty measures (poverty gap and squared poverty gap) are provided as well.

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Appendix D: Census Poverty Measures by Administrative Units Table D1: Poverty Measures by Local Government Area and District Head Poverty Severity Population Contribution of poverty Population Number count gap of poverty share size of poor Head Poverty Severity of count gap poverty THE GAMBIA 48.57 15.95 7.14 100.00 100.00 100.00 100.00 1,831,459 889,589 Banjul 10.54 1.98 0.58 1.61 0.35 0.20 0.13 29,478 3,106 Banjul South 9.48 1.73 0.50 20.37 3.98 2.22 1.41 373,075 35,369 Banjul Central 10.49 1.99 0.59 37.21 8.03 4.65 3.09 681,404 71,465 Banjul North 11.41 2.15 0.63 4.28 1.01 0.58 0.38 78,430 8,947 Kanifing 16.50 3.42 1.08 20.37 6.92 4.37 3.08 373,075 61,544 Bakau 11.75 2.31 0.69 11.80 2.85 1.71 1.15 216,053 25,383 Jeshwang 15.07 3.06 0.94 5.21 1.62 1.00 0.69 95,474 14,391 Serrekunda Central 16.10 3.25 1.01 6.69 2.22 1.37 0.94 122,568 19,729 Serrekunda East 19.63 4.21 1.36 12.83 5.18 3.39 2.44 234,977 46,121 Serrekunda West 15.58 3.23 1.02 0.44 0.14 0.09 0.06 8,095 1,261 Brikama 51.26 16.82 7.54 37.21 39.26 39.23 39.33 681,404 349,279 Kombo North 41.80 12.58 5.33 0.60 0.51 0.47 0.45 10,956 4,580 Kombo South 56.71 18.44 8.25 0.57 0.66 0.66 0.66 10,427 5,913 Kombo Central 51.79 16.35 7.10 1.54 1.64 1.57 1.53 28,125 14,567 Kombo East 66.22 22.74 10.36 3.61 4.92 5.15 5.24 66,127 43,792 Foni Brefet 79.50 31.27 15.58 4.10 6.70 8.03 8.94 75,025 59,646 Foni Bintang Karanai 87.47 37.52 19.23 5.99 10.79 14.09 16.15 109,736 95,985 Foni Kansala 77.17 29.79 14.33 5.14 8.16 9.59 10.31 94,062 72,588 Foni Bondali 90.73 41.61 22.55 18.32 34.21 47.78 57.88 335,455 304,364 Foni Jarrol 84.23 38.13 20.90 5.78 10.03 13.83 16.94 105,929 89,224 Mansakonko 60.00 20.19 9.01 4.28 5.29 5.42 5.41 78,430 47,056 Kiang West 84.02 33.91 16.76 7.58 13.12 16.12 17.81 138,883 116,695 Kiang Central 72.87 26.08 11.98 2.28 3.42 3.73 3.83 41,773 30,442 Kiang East 64.83 21.70 9.52 0.79 1.05 1.07 1.05 14,415 9,346 Jarra West 40.25 10.57 3.98 0.92 0.76 0.61 0.51 16,778 6,752

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Jarra central 65.84 22.56 10.13 0.75 1.02 1.06 1.07 13,766 9,063 Jarra East 58.27 18.28 7.71 0.41 0.50 0.48 0.45 7,594 4,425 Kerewan 60.46 18.94 8.01 11.80 14.68 14.01 13.24 216,053 130,627 Lower Niumi 48.88 13.58 5.28 0.37 0.37 0.32 0.28 6,811 3,329 Upper Niumi 68.83 22.52 9.79 0.79 1.11 1.11 1.08 14,384 9,900 Jokadu 70.76 23.32 10.07 0.47 0.68 0.68 0.66 8,548 6,049 Lower Badibu 64.56 20.45 8.68 0.36 0.48 0.46 0.44 6,602 4,262 Central Badibu 67.89 22.80 10.09 1.41 1.97 2.01 1.99 25,793 17,512 Illiasa 50.44 14.06 5.45 0.45 0.46 0.39 0.34 8,163 4,117 Sabach Sanjar 78.92 28.72 13.30 0.82 1.33 1.47 1.52 14,940 11,791 Kuntaur 69.63 26.49 13.12 5.21 7.47 8.66 9.58 95,474 66,481 Lower Saloum 73.56 28.88 14.48 3.08 4.66 5.57 6.25 56,389 41,481 Upper Saloum 74.64 29.31 14.74 1.68 2.58 3.09 3.47 30,754 22,955 Nianija 83.55 35.39 18.48 1.21 2.09 2.69 3.14 22,225 18,570 Niani 63.02 22.39 10.57 0.97 1.26 1.36 1.43 17,737 11,179 Sami 65.39 23.99 11.82 1.03 1.38 1.54 1.70 18,777 12,278 Janjangbureh 72.06 27.34 13.38 6.69 9.93 11.47 12.55 122,568 88,324 Niamina Dankunku 83.56 34.44 17.42 2.57 4.42 5.55 6.27 47,071 39,331 Niamina West 78.87 31.01 15.28 1.26 2.05 2.45 2.70 23,100 18,218 Niamina East 72.73 26.77 12.73 0.83 1.25 1.40 1.49 15,260 11,099 Lower Fuladu West 75.57 30.34 15.66 1.00 1.56 1.91 2.20 18,357 13,872 Upper Fuladu West 66.99 24.09 11.26 0.53 0.73 0.80 0.84 9,736 6,522 Janjanbureh 55.67 17.08 7.16 1.53 1.75 1.64 1.54 28,029 15,604 Basse 60.93 20.68 9.28 12.83 16.09 16.64 16.68 234,977 143,172 Jimara 52.74 16.42 6.96 1.32 1.43 1.35 1.28 24,092 12,707 Basse 49.52 15.26 6.41 0.33 0.34 0.32 0.30 6,067 3,004 Tumana 52.06 15.10 6.03 0.39 0.42 0.37 0.33 7,135 3,715 Kantora 63.30 20.92 9.16 1.30 1.70 1.71 1.67 23,854 15,100 Wuli West 78.86 31.60 15.69 2.14 3.48 4.24 4.71 39,202 30,916 Wuli East 80.30 31.53 15.37 2.35 3.88 4.65 5.06 43,040 34,559 Sandu 73.99 27.34 12.84 0.18 0.27 0.31 0.32 3,270 2,420

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Table D2: Poverty Measures by Ward Head Poverty Severity Population Contribution of poverty Population Number count gap of poverty share size of poor Head Poverty Severity of count gap poverty THE GAMBIA 48.57 15.95 7.14 100.00 100.00 100.00 100.00 1,831,459 889,589 Banjul 10.54 1.98 0.58 1.61 0.35 0.20 0.13 29,478 3,106 Banjul South 9.62 1.69 0.46 2.37 0.47 0.25 0.15 43,350 4,172 Banjul South 9.44 1.75 0.51 2.65 0.51 0.29 0.19 48,475 4,576 Banjul South 9.23 1.81 0.55 2.01 0.38 0.23 0.15 36,896 3,405 Banjul Central 12.43 2.43 0.74 2.07 0.53 0.32 0.21 37,927 4,716 Banjul Central 9.65 1.79 0.52 1.18 0.23 0.13 0.09 21,601 2,084 Banjul Central 6.59 1.15 0.32 1.28 0.17 0.09 0.06 23,386 1,542 Banjul North 8.43 1.55 0.45 1.27 0.22 0.12 0.08 23,342 1,967 Banjul North 13.21 2.52 0.74 0.18 0.05 0.03 0.02 3,334 440 Banjul North 9.00 1.65 0.47 0.19 0.03 0.02 0.01 3,403 306 Kanifing 16.50 3.42 1.08 20.37 6.92 4.37 3.08 373,075 61,544 Bakau 9.58 1.83 0.54 0.07 0.01 0.01 0.01 1,358 130 Bakau 13.53 2.70 0.82 0.26 0.07 0.04 0.03 4,740 641 Jeshwang 7.58 1.46 0.43 0.27 0.04 0.02 0.02 4,910 372 Jeshwang 18.05 3.69 1.15 0.07 0.03 0.02 0.01 1,306 236 Jeshwang 12.21 2.46 0.75 0.12 0.03 0.02 0.01 2,136 261 Serrekunda Central 17.45 3.54 1.10 0.34 0.12 0.08 0.05 6,258 1,092 Serrekunda Central 16.24 3.36 1.06 0.11 0.04 0.02 0.02 2,033 330 Serrekunda Central 13.40 2.61 0.79 0.69 0.19 0.11 0.08 12,678 1,699 Serrekunda East 22.34 5.21 1.80 0.84 0.39 0.28 0.21 15,447 3,451 Serrekunda East 19.58 4.05 1.26 0.53 0.21 0.13 0.09 9,676 1,894 Serrekunda East 15.64 3.14 0.96 2.19 0.71 0.43 0.29 40,119 6,276 Serrekunda East 20.15 4.38 1.43 0.89 0.37 0.25 0.18 16,332 3,291 Serrekunda West 11.93 2.33 0.71 2.06 0.51 0.30 0.21 37,780 4,509 Serrekunda West 16.02 3.20 0.98 0.95 0.31 0.19 0.13 17,405 2,789 Serrekunda West 10.67 2.10 0.63 1.08 0.24 0.14 0.10 19,840 2,116

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Serrekunda West 14.69 3.07 0.97 0.86 0.26 0.17 0.12 15,778 2,317 Serrekunda West 19.21 4.16 1.36 2.07 0.82 0.54 0.39 37,909 7,281 Brikama 51.26 16.82 7.54 37.21 39.26 39.23 39.33 681,404 349,279 Kombo North 43.98 12.98 5.34 0.85 0.77 0.69 0.64 15,583 6,854 Kombo North 40.88 12.89 5.76 2.21 1.86 1.79 1.79 40,466 16,542 Kombo North 48.66 14.98 6.35 0.91 0.91 0.85 0.81 16,600 8,077 Kombo North 37.08 10.72 4.43 1.23 0.94 0.83 0.76 22,483 8,338 Kombo North 40.56 12.03 5.04 0.58 0.48 0.44 0.41 10,629 4,311 Kombo North 43.84 12.79 5.27 0.70 0.63 0.56 0.52 12,809 5,615 Kombo North 53.92 16.51 6.89 1.72 1.91 1.78 1.66 31,541 17,007 Kombo North 46.11 14.25 6.16 1.92 1.83 1.72 1.66 35,244 16,250 Kombo North 28.08 8.14 3.49 2.20 1.27 1.12 1.07 40,228 11,298 Kombo South 53.51 16.87 7.40 2.62 2.89 2.78 2.72 48,052 25,713 Kombo South 64.54 22.48 10.48 2.55 3.39 3.59 3.74 46,667 30,120 Kombo South 54.90 17.83 8.00 2.35 2.66 2.63 2.64 43,070 23,646 Kombo South 56.52 18.02 7.94 1.56 1.82 1.77 1.74 28,615 16,174 Kombo Central 52.30 16.17 6.95 0.71 0.77 0.72 0.69 13,047 6,824 Kombo Central 65.07 21.95 9.88 2.40 3.22 3.30 3.33 43,975 28,614 Kombo Central 43.52 12.98 5.34 2.00 1.79 1.62 1.49 36,557 15,910 Kombo Central 50.03 15.89 6.99 1.19 1.23 1.19 1.17 21,828 10,919 Kombo East 68.98 24.13 11.15 0.92 1.31 1.39 1.44 16,869 11,637 Kombo East 67.33 23.95 11.21 1.67 2.31 2.51 2.62 30,562 20,577 Kombo East 60.93 19.26 8.20 2.00 2.51 2.42 2.30 36,670 22,342 Foni Brefet 80.33 31.56 15.74 2.08 3.44 4.11 4.59 38,076 30,588 Foni Brefet 78.43 30.90 15.38 1.43 2.30 2.76 3.07 26,115 20,483 Foni Bintang Karanai 89.91 39.55 20.59 1.97 3.64 4.88 5.67 36,004 32,371 Foni Bintang Karanai 85.83 36.15 18.32 2.11 3.73 4.79 5.42 38,688 33,206 Foni Kansala 73.73 27.43 12.78 0.89 1.35 1.53 1.59 16,267 11,994 Foni Kansala 80.49 32.06 15.82 0.77 1.27 1.55 1.71 14,087 11,338 Foni Bondali 91.66 40.08 20.50 0.62 1.18 1.57 1.79 11,419 10,467 Foni Bondali 90.42 42.12 23.24 0.44 0.82 1.17 1.44 8,098 7,322 Foni Jarrol 80.95 35.38 18.59 0.34 0.57 0.77 0.90 6,317 5,114 62 | Page

Foni Jarrol 87.81 41.14 23.41 0.37 0.67 0.95 1.21 6,741 5,919 Mansakonko 60.00 20.19 9.01 4.28 5.29 5.42 5.41 78,430 47,056 Kiang West 84.51 34.57 17.25 0.55 0.95 1.19 1.32 10,037 8,482 Kiang West 83.63 33.36 16.36 0.37 0.64 0.77 0.85 6,759 5,652 Kiang Central 71.74 25.00 11.24 0.38 0.57 0.60 0.60 7,007 5,027 Kiang Central 73.92 27.07 12.66 0.10 0.16 0.18 0.19 1,910 1,412 Kiang East 68.70 23.33 10.30 0.31 0.44 0.45 0.45 5,684 3,905 Kiang East 54.91 17.51 7.54 0.19 0.22 0.21 0.20 3,553 1,951 Jarra West 38.80 9.96 3.68 0.18 0.14 0.11 0.09 3,258 1,264 Jarra West 47.01 13.40 5.36 0.35 0.34 0.30 0.27 6,489 3,051 Jarra central 70.54 25.07 11.51 0.43 0.63 0.68 0.70 7,895 5,569 Jarra central 60.46 19.69 8.56 0.22 0.28 0.28 0.27 4,096 2,476 Jarra East 57.95 18.07 7.59 0.24 0.29 0.28 0.26 4,452 2,580 Jarra East 58.85 18.67 7.91 0.26 0.31 0.30 0.29 4,751 2,796 Kerewan 60.46 18.94 8.01 11.80 14.68 14.01 13.24 216,053 130,627 Lower Niumi 44.52 12.16 4.71 0.10 0.09 0.08 0.07 1,851 824 Lower Niumi 57.84 16.51 6.47 1.16 1.38 1.20 1.05 21,239 12,285 Upper Niumi 66.27 20.73 8.65 0.25 0.34 0.32 0.30 4,554 3,018 Upper Niumi 71.44 24.35 10.95 0.24 0.35 0.36 0.36 4,355 3,111 Jokadu 69.85 22.60 9.59 0.21 0.30 0.29 0.28 3,808 2,660 Jokadu 71.50 23.90 10.46 0.53 0.78 0.80 0.78 9,724 6,953 Lower Badibu 65.64 21.39 9.32 0.28 0.38 0.38 0.37 5,216 3,424 Lower Badibu 63.20 19.27 7.88 2.07 2.70 2.50 2.29 37,956 23,990 Central Badibu 67.91 22.79 10.07 1.01 1.41 1.44 1.42 18,433 12,519 Central Badibu 67.87 22.80 10.11 0.85 1.18 1.21 1.20 15,522 10,535 Illiasa 56.46 16.53 6.64 0.83 0.97 0.86 0.77 15,232 8,599 Illiasa 58.24 16.78 6.60 0.54 0.65 0.57 0.50 9,903 5,768 Illiasa 44.46 11.92 4.53 0.67 0.62 0.50 0.43 12,322 5,479 Illiasa 58.45 16.78 6.56 0.54 0.65 0.57 0.50 9,864 5,765 Sabach Sanjar 80.02 28.77 13.18 0.43 0.71 0.78 0.79 7,873 6,300 Sabach Sanjar 78.29 28.69 13.37 0.50 0.81 0.90 0.94 9,196 7,200

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Kuntaur 69.63 26.49 13.12 5.21 7.47 8.66 9.58 95,474 66,481 Lower Saloum 87.01 39.25 21.42 0.52 0.94 1.29 1.57 9,581 8,336 Lower Saloum 68.99 25.35 12.12 0.30 0.43 0.48 0.51 5,544 3,825 Upper Saloum 75.70 30.57 15.74 0.39 0.61 0.75 0.86 7,149 5,411 Upper Saloum 73.20 27.59 13.38 1.42 2.15 2.46 2.67 26,071 19,085 Nianija 83.55 35.39 18.48 0.45 0.78 1.01 1.17 8,307 6,941 Niani 57.74 19.57 9.06 0.46 0.55 0.56 0.58 8,401 4,850 Niani 69.79 26.00 12.49 0.80 1.15 1.31 1.40 14,699 10,258 Sami 62.38 22.87 11.38 0.21 0.27 0.30 0.34 3,875 2,417 Sami 64.52 21.53 9.58 0.62 0.83 0.84 0.83 11,385 7,345 Sami 69.88 27.41 14.21 0.58 0.83 0.99 1.15 10,590 7,400 Janjangbureh 72.06 27.34 13.38 6.69 9.93 11.47 12.55 122,568 88,324 Niamina Dankunku 83.56 34.44 17.42 0.42 0.73 0.92 1.04 7,767 6,490 Niamina West 78.87 31.01 15.28 0.53 0.86 1.03 1.14 9,736 7,679 Niamina East 76.31 27.90 12.98 0.86 1.35 1.50 1.56 15,729 12,003 Niamina East 70.61 26.09 12.59 0.67 0.98 1.10 1.18 12,300 8,685 Lower Fuladu West 75.32 31.17 16.59 0.54 0.84 1.05 1.25 9,880 7,441 Lower Fuladu West 78.25 32.21 16.86 0.35 0.56 0.70 0.82 6,367 4,982 Lower Fuladu West 71.73 26.07 12.25 0.43 0.63 0.70 0.74 7,845 5,627 Upper Fuladu West 54.04 17.81 7.90 0.33 0.37 0.37 0.37 6,067 3,279 Upper Fuladu West 78.91 31.49 15.73 0.39 0.63 0.77 0.86 7,135 5,630 Upper Fuladu West 71.86 25.62 11.81 0.48 0.72 0.78 0.80 8,881 6,382 Upper Fuladu West 68.81 24.25 11.11 0.82 1.16 1.24 1.27 14,973 10,303 Janjanbureh 55.67 17.08 7.16 0.82 0.94 0.88 0.83 15,074 8,392 Basse 60.93 20.68 9.28 12.83 16.09 16.64 16.68 234,977 143,172 Jimara 59.52 18.04 7.39 0.81 0.99 0.91 0.84 14,776 8,795 Jimara 48.23 15.34 6.67 0.51 0.51 0.49 0.48 9,352 4,510 Basse 44.25 13.04 5.32 0.72 0.66 0.59 0.54 13,245 5,861 Basse 61.58 20.34 8.90 0.52 0.65 0.66 0.64 9,437 5,811 Tumana 50.66 14.43 5.69 0.39 0.41 0.36 0.32 7,233 3,664 Tumana 53.69 15.88 6.42 0.72 0.79 0.71 0.64 13,125 7,047 Kantora 57.37 17.70 7.37 0.18 0.21 0.20 0.18 3,270 1,876 64 | Page

Kantora 69.47 24.26 11.01 0.95 1.35 1.44 1.46 17,331 12,039 Wuli West 80.56 31.49 15.39 1.42 2.36 2.81 3.06 26,019 20,960 Wuli West 77.61 31.68 15.92 1.84 2.94 3.66 4.11 33,738 26,184 Wuli East 78.10 30.62 15.00 0.80 1.29 1.54 1.69 14,737 11,509 Wuli East 83.36 32.80 15.89 1.08 1.86 2.22 2.41 19,815 16,518 Sandu 74.42 27.75 13.15 0.93 1.43 1.62 1.72 17,081 12,712 Sandu 73.56 26.92 12.53 1.06 1.60 1.78 1.85 19,325 14,215

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Appendix E: Census Nonmonetary Indicators by Administrative Units Table E1: Population Characteristics by Local Government Area and District Sex Population structure Literacy Education level 0–14 15–64 65+ Adult Youth Pri- Secon- Teri- Voca- Male Female years years years (15+) (15–24) None mary dary tary tional THE GAMBIA 49.2 50.8 42.7 54.2 3.1 55.1 71.9 49.0 22.3 25.6 2.2 1.0 Banjul City Council 54.1 45.9 30.0 66.4 3.6 69.8 82.6 33.6 19.7 41.3 4.0 1.4 Banjul South 53.1 46.9 30.5 65.5 4.0 69.5 82.1 33.3 20.0 40.6 3.8 2.2 Banjul Central 52.4 47.6 30.6 65.7 3.8 68.9 82.2 34.5 19.9 39.5 4.7 1.4 Banjul North 56.6 43.4 29.0 67.9 3.1 70.9 83.4 32.8 19.4 43.5 3.4 0.8 Kanifing Municipal 50.2 49.8 35.7 61.7 2.6 69.4 82.5 36.0 20.6 37.1 4.8 1.6 Bakau 50.4 49.6 32.0 64.4 3.7 75.5 87.7 28.8 18.7 43.0 7.6 2.0 New Jeshwang 49.8 50.2 35.1 62.0 2.9 71.0 82.7 34.6 20.0 36.7 7.2 1.4 Sere Kunda Central 50.6 49.4 36.2 61.4 2.3 68.2 80.9 37.2 20.8 37.3 3.3 1.5 Sere Kunda East 49.9 50.1 37.5 60.4 2.2 67.9 82.4 37.4 21.8 35.8 3.5 1.4 Sere Kunda West 50.3 49.7 34.9 62.5 2.6 69.2 82.1 36.6 19.9 36.7 4.7 2.0 Brikama 50.0 50.0 42.0 55.3 2.8 61.1 79.5 42.4 23.6 30.4 2.5 1.1 Kombo North 50.1 49.9 40.8 56.9 2.3 63.7 79.1 41.2 22.4 32.2 3.0 1.2 Kombo South 50.3 49.7 43.9 53.2 2.9 58.0 78.2 44.2 25.0 28.1 1.7 0.9 Kombo Central 49.7 50.3 41.4 55.9 2.7 63.0 81.0 40.9 23.7 31.1 2.8 1.4 Kombo East 49.9 50.1 45.6 50.7 3.6 55.5 79.1 45.3 27.2 25.8 1.0 0.8 Foni Brefet 49.3 50.7 44.8 50.5 4.7 52.3 80.5 44.5 28.6 25.3 1.2 0.5 Foni Bintang Karanai 49.3 50.7 44.2 50.6 5.2 48.4 82.9 48.0 26.2 24.7 0.9 0.3 Foni Kansala 49.8 50.2 40.6 54.8 4.7 51.0 81.5 46.1 23.0 28.6 1.4 1.0 Foni Bondali 51.2 48.8 45.0 50.8 4.2 51.1 80.9 48.6 24.8 25.0 1.2 0.3 Foni Jarrol 53.0 47.0 44.0 51.6 4.4 51.2 78.4 49.3 22.0 26.9 1.2 0.6 Mansakonko 47.8 52.2 46.3 49.3 4.4 49.7 74.0 52.4 27.1 18.9 0.8 0.7 Kiang West 48.3 51.7 48.8 46.0 5.1 52.4 84.2 42.2 34.6 21.7 0.6 1.0 Kiang Central 46.9 53.1 46.7 47.8 5.5 44.0 71.6 50.8 29.5 18.2 0.7 0.8 Kiang East 46.1 53.9 47.5 47.8 4.6 48.2 70.9 57.1 26.5 15.4 0.5 0.5 Jarra West 47.1 52.9 43.3 52.8 3.9 53.0 75.0 49.7 24.6 23.5 1.3 0.9 66 | Page

Jarra Central 48.4 51.6 46.7 49.1 4.2 40.0 62.3 56.9 26.7 15.5 0.6 0.4 Jarra East 49.6 50.4 47.8 48.1 4.1 50.1 71.7 63.0 23.5 12.5 0.4 0.5 Kerewan 48.1 51.9 47.0 49.3 3.7 46.9 66.6 56.7 23.4 18.5 0.5 0.9 Lower Niumi 49.0 51.0 44.9 51.7 3.4 54.0 75.6 48.1 25.8 24.3 0.5 1.3 Upper Niumi 49.3 50.7 48.1 48.2 3.7 46.6 65.5 58.5 23.6 16.5 0.4 1.0 Jokadu 47.4 52.6 49.6 46.9 3.5 42.8 61.7 57.6 25.5 15.3 0.2 1.3 Lower Badibu 46.8 53.2 47.6 47.7 4.7 51.7 75.9 50.3 27.6 20.8 0.5 0.8 Central Badibu 48.3 51.7 49.4 46.4 4.2 39.9 60.6 59.8 25.5 13.8 0.5 0.4 Illiasa 47.9 52.1 45.1 51.2 3.7 46.6 64.6 57.3 21.4 19.9 0.8 0.5 Sabach Sanjar 46.5 53.5 49.4 46.8 3.8 35.0 51.1 75.4 14.9 9.0 0.3 0.4 Kuntaur 47.2 52.8 49.2 47.3 3.5 31.9 46.2 73.2 15.9 10.3 0.3 0.3 Lower Saloum 44.8 55.2 46.7 49.3 4.0 33.4 52.7 72.2 13.3 13.6 0.6 0.4 Upper Saloum 46.0 54.0 51.0 46.3 2.8 26.9 35.8 81.1 12.5 6.0 0.1 0.2 Nianija 48.6 51.4 50.8 45.2 4.0 28.8 42.3 73.6 18.0 7.9 0.2 0.4 Niani 47.9 52.1 49.0 47.9 3.1 35.5 49.5 69.5 17.9 11.9 0.5 0.3 Sami 48.4 51.6 48.9 47.2 3.9 31.6 47.2 71.8 16.9 10.6 0.2 0.5 Janjanbureh 47.9 52.1 46.8 49.6 3.7 38.5 55.6 64.7 19.2 15.0 0.7 0.4 Niamina Dankunku 46.1 53.9 48.5 47.1 4.4 29.6 48.8 70.4 16.7 12.3 0.1 0.5 Niamina West 48.1 51.9 49.5 45.6 4.9 34.8 54.7 69.4 19.1 10.4 0.3 0.7 Niamina East 47.1 52.9 48.4 47.9 3.7 35.6 48.9 71.9 15.1 12.1 0.5 0.4 Lower Fuladu West 47.6 52.4 46.0 50.3 3.7 37.7 55.4 61.8 22.6 14.9 0.5 0.3 Upper Fuladu West 48.7 51.4 46.7 49.9 3.3 39.4 55.6 64.5 18.6 15.4 0.9 0.5 Janjanbureh 50.9 49.1 36.3 59.2 4.5 70.7 91.1 35.1 19.1 42.3 2.9 0.6 Jimara 47.4 52.6 47.1 49.2 3.7 31.7 46.8 64.4 25.2 9.7 0.4 0.4 Basse 47.6 52.4 47.5 49.0 3.5 32.8 48.7 64.8 23.1 11.4 0.4 0.3 Basse 49.0 51.0 44.3 52.5 3.2 42.8 58.3 56.9 23.8 18.1 0.9 0.4 Tumana 47.3 52.7 48.9 47.9 3.2 28.3 43.4 69.2 21.0 9.3 0.2 0.2 Kantora 47.0 53.0 48.7 47.6 3.7 29.2 45.6 69.3 21.3 9.1 0.2 0.1 Wuli West 46.7 53.3 47.4 48.5 4.1 28.9 45.4 68.2 20.9 10.2 0.5 0.3 Wuli East 47.2 52.9 50.2 46.4 3.4 28.5 44.3 65.8 25.3 8.5 0.1 0.2 Sandu 47.4 52.6 47.9 48.3 3.8 33.6 51.3 64.0 23.9 11.4 0.4 0.3 67 | Page

Table E2: Household Nonmonetary Measures by Local Government Area and District Sanitation Water Own house Toilet Improved shared Improved Piped Yes No Yes No Yes No Yes No Own Rent Free THE GAMBIA 60.4 39.6 50.7 49.3 81.6 18.4 53.2 46.8 58.5 31.9 9.5 Banjul City Council 98.2 1.8 59.2 40.8 98.1 1.9 98.0 2.0 20.4 72.1 7.5 Banjul city council 97.1 2.9 50.6 49.4 98.6 1.4 98.6 1.4 25.1 66.5 8.4 Banjul city council 99.7 0.3 61.6 38.4 97.2 2.8 97.2 2.8 20.5 74.1 5.4 Banjul city council 97.4 2.6 63.4 36.6 98.5 1.5 98.3 1.7 16.6 74.2 9.2 Kanifing Municipal 84.3 15.7 49.7 50.3 93.3 6.7 90.3 9.7 32.1 59.4 8.5 Bakau 91.2 8.8 38.7 61.3 99.6 0.4 98.3 1.7 28.2 54.5 17.4 New Jeshwang 86.1 13.9 44.9 55.1 93.7 6.3 92.7 7.3 40.2 51.9 8.0 Sere Kunda Central 85.9 14.1 56.3 43.7 95.2 4.8 92.7 7.3 24.0 70.4 5.5 Sere Kunda East 80.0 20.0 51.2 48.8 89.8 10.3 84.8 15.2 34.2 58.2 7.6 Sere Kunda West 84.7 15.3 49.4 50.6 93.6 6.4 90.9 9.1 32.2 58.6 9.2 Brikama 64.2 35.8 52.6 47.4 77.7 22.3 58.2 41.8 60.0 25.8 14.2 Kombo North 73.0 27.0 47.1 52.9 82.6 17.4 69.4 30.6 51.9 33.0 15.1 Kombo South 52.2 47.8 51.0 49.0 62.9 37.1 41.6 58.4 71.6 12.5 15.9 Kombo Central 61.2 38.8 60.9 39.1 74.3 25.7 54.3 45.7 59.3 28.5 12.2 Kombo East 56.5 43.5 42.4 57.6 77.9 22.1 59.5 40.5 84.7 4.5 10.9 Foni Brefet 51.1 48.9 78.6 21.4 74.5 25.5 40.1 59.9 82.0 9.0 9.1 Foni Bintang Karanai 45.2 54.8 77.4 22.6 87.0 13.0 24.9 75.1 80.0 3.2 16.8 Foni Kansala 62.8 37.2 75.6 24.4 84.3 15.7 44.0 56.0 73.7 13.1 13.3 Foni Bondali 39.4 60.6 56.7 43.3 86.4 13.6 11.5 88.5 85.6 5.7 8.7 Foni Jarrol 29.4 70.6 75.6 24.4 91.6 8.4 38.3 61.7 74.3 13.9 11.7 Mansakonko 45.9 54.1 50.0 50.0 85.7 14.3 35.0 65.0 83.6 10.1 6.4 Kiang West 45.8 54.2 56.4 43.6 88.1 11.9 11.1 88.9 93.0 4.7 2.4 Kiang Central 44.8 55.2 61.8 38.2 87.8 12.2 3.0 97.0 82.0 8.8 9.1 Kiang East 44.5 55.5 51.6 48.4 92.7 7.3 63.7 36.3 93.1 3.6 3.3 Jarra West 59.1 40.9 50.9 49.1 88.9 11.1 76.8 23.2 71.8 17.7 10.4 68 | Page

Jarra Central 23.6 76.4 49.5 50.5 76.9 23.1 1.4 98.6 93.8 0.4 5.8 Jarra East 36.6 63.4 34.4 65.6 78.2 21.8 10.5 89.5 89.5 8.7 1.8 Kerewan 51.0 49.0 50.9 49.1 80.9 19.1 45.2 54.8 81.0 12.3 6.6 Lower Niumi 49.1 50.9 49.0 51.0 80.1 19.9 52.4 47.6 75.4 17.7 6.9 Upper Niumi 41.0 59.0 35.6 64.4 80.0 20.0 61.9 38.1 92.8 3.7 3.5 Jokadu 25.6 74.4 53.5 46.5 70.7 29.3 32.5 67.5 92.3 3.6 4.1 Lower Badibu 66.8 33.2 49.9 50.1 82.1 17.9 13.3 86.7 85.6 3.0 11.4 Central Badibu 51.1 48.9 54.1 45.9 91.9 8.1 13.1 86.9 91.0 0.8 8.2 Illiasa 66.3 33.7 57.5 42.5 86.5 13.5 73.3 26.7 66.6 25.4 8.0 Sabach Sanjar 49.8 50.2 58.0 42.0 73.1 26.9 10.7 89.3 96.9 0.0 3.1 Kuntaur 35.1 64.9 56.2 43.8 75.2 24.8 18.3 81.7 93.5 3.8 2.7 Lower Saloum 50.6 49.4 64.3 35.7 95.5 4.5 56.1 43.9 89.1 8.1 2.8 Upper Saloum 43.5 56.5 59.0 41.0 67.7 32.3 28.8 71.2 98.9 0.1 1.0 Nianija 30.0 70.0 79.0 21.0 88.1 11.9 21.7 78.3 95.7 0.0 4.3 Niani 31.6 68.4 56.5 43.5 62.6 37.4 1.6 98.4 87.8 7.2 5.0 Sami 25.0 75.0 40.1 59.9 77.3 22.7 4.2 95.8 99.0 0.8 0.2 Janjanbureh 35.8 64.2 56.9 43.1 71.4 28.6 27.9 72.1 89.1 7.0 3.9 Niamina Dankunku 8.4 91.6 41.5 58.5 81.6 18.4 16.5 83.5 98.1 0.5 1.4 Niamina West 32.1 67.9 50.8 49.2 61.8 38.2 5.3 94.7 97.6 0.8 1.6 Niamina East 29.8 70.2 63.2 36.8 58.3 41.7 5.5 94.5 94.5 3.4 2.1 Lower Fuladu West 35.9 64.1 46.8 53.2 76.6 23.4 46.0 54.0 90.5 5.5 4.0 Upper Fuladu West 42.5 57.5 67.7 32.3 72.2 27.8 23.7 76.3 83.0 12.1 4.9 Janjanbureh 48.9 51.1 30.9 69.1 99.4 0.6 99.4 0.6 80.6 7.9 11.4 Jimara 40.2 59.8 30.2 69.8 83.2 16.8 51.0 49.0 88.1 8.0 3.9 Basse 43.4 56.6 40.4 59.6 79.8 20.2 15.5 84.5 77.1 18.5 4.4 Basse 41.5 58.5 45.6 54.4 82.2 17.8 9.3 90.7 45.3 48.2 6.6 Tumana 48.2 51.8 47.9 52.1 82.6 17.4 6.4 93.6 93.6 2.1 4.3 Kantora 48.8 51.2 39.5 60.5 79.6 20.4 7.9 92.1 95.1 4.0 0.9 Wuli West 45.7 54.3 56.6 43.4 77.6 22.4 5.5 94.5 93.8 1.3 4.8 Wuli East 45.1 54.9 30.0 70.0 75.0 25.0 2.0 98.0 94.0 4.9 1.2 Sandu 33.4 66.6 33.3 66.7 71.8 28.2 11.7 88.3 95.1 0.6 4.2 69 | Page

Table E2: Household Nonmonetary Measures by Local Government Area and District (cont.) Asset ownership Car Bicycle Motorcycle Radio Television Computer Yes No Yes No Yes No Yes No Yes No Yes No THE GAMBIA 12.4 87.6 42.3 57.7 7.6 92.4 83.1 16.9 51.7 48.3 13.2 86.8 Banjul City Council 5.9 94.1 21.4 78.6 2.1 97.9 81.3 18.7 78.6 21.4 16.2 83.8 Banjul city council 4.1 95.9 22.8 77.2 1.3 98.7 79.7 20.3 78.0 22.0 22.9 77.1 Banjul city council 8.9 91.1 19.0 81.0 2.7 97.3 77.9 22.1 74.6 25.4 14.6 85.4 Banjul city council 4.1 95.9 23.0 77.0 2.1 97.9 86.4 13.6 83.6 16.4 12.9 87.1 Kanifing Municipal 17.8 82.2 28.5 71.5 4.1 95.9 82.8 17.2 76.8 23.2 21.2 78.8 Bakau 24.8 75.2 27.4 72.6 4.3 95.7 88.1 11.9 82.4 17.6 33.9 66.1 New Jeshwang 20.9 79.1 30.9 69.1 4.2 95.8 86.8 13.2 81.2 18.8 26.7 73.3 Sere Kunda Central 13.6 86.4 26.1 73.9 5.0 95.0 80.9 19.1 78.4 21.6 17.5 82.5 Sere Kunda East 16.2 83.8 29.9 70.1 4.1 95.9 81.2 18.8 73.0 27.0 17.6 82.4 Sere Kunda West 18.2 81.8 27.8 72.2 3.2 96.8 81.7 18.3 74.7 25.3 20.1 79.9 Brikama 15.3 84.7 46.8 53.2 6.1 93.9 84.0 16.0 51.8 48.2 14.9 85.1 Kombo North 19.8 80.2 39.6 60.4 5.5 94.5 85.0 15.0 61.7 38.3 18.8 81.2 Kombo South 10.8 89.2 56.0 44.0 5.9 94.1 81.3 18.7 37.1 62.9 11.6 88.4 Kombo Central 13.9 86.1 54.4 45.6 7.2 92.8 83.8 16.2 51.2 48.8 14.0 86.0 Kombo East 6.3 93.7 55.3 44.7 7.2 92.8 81.3 18.7 35.6 64.4 4.9 95.1 Foni Brefet 0.4 99.6 57.3 42.7 8.5 91.5 80.9 19.1 32.1 67.9 0.7 99.3 Foni Bintang Karanai 0.6 99.4 49.6 50.4 3.1 96.9 83.9 16.1 14.0 86.0 6.4 93.6 Foni Kansala 5.0 95.0 50.9 49.1 9.8 90.2 87.6 12.4 37.4 62.6 4.0 96.0 Foni Bondali 0.8 99.2 69.2 30.8 9.3 90.7 87.8 12.2 9.8 90.2 0.7 99.3 Foni Jarrol 2.3 97.7 60.3 39.7 12.3 87.7 83.9 16.1 11.6 88.4 0.7 99.3 Mansakonko 4.4 95.6 48.7 51.3 7.9 92.1 75.9 24.1 27.8 72.2 3.9 96.1 Kiang West 2.6 97.4 52.7 47.3 4.4 95.6 82.2 17.8 13.3 86.7 0.7 99.3 Kiang Central 1.2 98.8 54.2 45.8 3.8 96.2 80.7 19.3 12.0 88.0 1.8 98.2 Kiang East 10.7 89.3 34.3 65.7 6.1 93.9 80.5 19.5 10.5 89.5 0.9 99.1 Jarra West 4.0 96.0 50.5 49.5 12.0 88.0 75.8 24.2 54.5 45.5 7.3 92.7 Jarra Central 7.5 92.5 49.7 50.3 3.7 96.3 66.3 33.7 13.2 86.8 1.7 98.3 70 | Page

Jarra East 4.5 95.5 43.0 57.0 8.9 91.1 69.5 30.5 12.6 87.4 4.2 95.8 Kerewan 5.4 94.6 37.8 62.2 7.9 92.1 85.7 14.3 32.0 68.0 4.2 95.8 Lower Niumi 8.9 91.1 38.7 61.3 11.9 88.1 88.5 11.5 41.5 58.5 4.9 95.1 Upper Niumi 1.6 98.4 40.2 59.8 8.8 91.2 86.1 13.9 20.7 79.3 3.0 97.0 Jokadu 4.2 95.8 35.2 64.8 8.8 91.2 88.8 11.2 17.8 82.2 2.0 98.0 Lower Badibu 1.4 98.6 36.9 63.1 6.6 93.4 83.5 16.5 32.3 67.7 2.1 97.9 Central Badibu 2.6 97.4 38.6 61.4 7.2 92.8 80.8 19.2 25.7 74.3 1.7 98.3 Illiasa 7.1 92.9 36.8 63.2 4.8 95.2 84.0 16.0 41.9 58.1 6.9 93.1 Sabach Sanjar 2.2 97.8 37.5 62.5 4.3 95.7 85.3 14.7 10.7 89.3 2.6 97.4 Kuntaur 2.6 97.4 39.3 60.7 6.3 93.7 76.7 23.3 14.1 85.9 2.2 97.8 Lower Saloum 0.7 99.3 30.6 69.4 5.9 94.1 78.1 21.9 28.8 71.2 0.7 99.3 Upper Saloum 2.4 97.6 18.8 81.2 2.9 97.1 74.5 25.5 3.9 96.1 0.1 99.9 Nianija 0.4 99.6 9.7 90.3 0.1 99.9 69.9 30.1 2.2 97.8 0.3 99.7 Niani 3.6 96.4 55.6 44.4 8.2 91.8 77.2 22.8 18.4 81.6 4.8 95.2 Sami 3.8 96.2 56.9 43.1 10.1 89.9 80.0 20.0 11.1 88.9 2.5 97.5 Janjanbureh 3.1 96.9 59.4 40.6 10.9 89.1 79.8 20.2 18.8 81.2 3.6 96.4 Niamina Dankunku 0.2 99.8 44.1 55.9 1.2 98.8 73.0 27.0 9.6 90.4 0.2 99.8 Niamina West 0.3 99.7 53.3 46.7 4.1 95.9 71.0 29.0 8.4 91.6 1.3 98.7 Niamina East 1.6 98.4 48.2 51.8 6.4 93.6 77.0 23.0 13.0 87.0 0.8 99.2 Lower Fuladu West 2.8 97.2 75.4 24.6 13.4 86.6 83.5 16.5 13.0 87.0 4.1 95.9 Upper Fuladu West 5.1 94.9 57.9 42.1 14.8 85.2 81.0 19.0 25.3 74.7 5.5 94.5 Janjanbureh 2.8 97.2 51.6 48.4 4.0 96.0 82.3 17.7 61.9 38.1 5.1 94.9 Jimara 8.5 91.5 64.6 35.4 24.9 75.1 84.4 15.6 23.8 76.2 3.0 97.0 Basse 7.4 92.6 70.8 29.2 29.1 70.9 86.9 13.1 33.3 66.7 4.0 96.0 Basse 8.3 91.7 57.2 42.8 22.7 77.3 85.5 14.5 48.3 51.7 7.0 93.0 Tumana 6.3 93.7 77.3 22.7 38.8 61.2 89.1 10.9 35.9 64.1 1.5 98.5 Kantora 9.0 91.0 85.9 14.1 33.4 66.6 91.0 9.0 32.6 67.4 2.8 97.2 Wuli West 7.3 92.7 80.9 19.1 31.6 68.4 87.1 12.9 23.5 76.5 5.4 94.6 Wuli East 1.9 98.1 87.0 13.0 36.6 63.4 94.7 5.3 15.4 84.6 0.7 99.3 Sandu 6.2 93.8 78.4 21.6 31.5 68.5 80.8 19.2 17.9 82.1 2.3 97.7

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Appendix F: Census Population Poverty Profiles by Administrative Units Table F1: Population Poverty Profiles by Local Government Area and District Sex Population structure Literacy Education level 0–14 15–64 65+ Adult Youth Pri- Secon- Teri- Voca- Male Female years years years (15+) (15–24) None mary dary tary tional THE GAMBIA 47.8 49.3 53.5 44.6 52.3 38.9 44.1 54.1 49.8 38.7 20.0 33.1 Banjul City Council 9.8 11.4 14.2 9.0 7.8 8.4 10.7 11.3 12.7 8.9 5.5 8.2 Banjul South 8.7 10.3 12.8 8.1 6.3 7.4 9.5 10.4 11.5 7.7 4.1 7.5 Banjul Central 9.9 11.2 14.1 9.0 7.8 8.4 10.8 11.1 12.5 9.0 5.2 9.0 Banjul North 10.6 12.4 15.2 9.7 9.4 9.2 11.6 12.1 13.7 9.6 7.3 8.5 Kanifing Municipal 16.1 16.9 19.3 14.8 18.8 13.3 15.7 19.5 18.3 13.7 7.0 12.1 Bakau 11.4 12.1 14.4 10.4 12.6 9.4 11.7 14.6 13.8 10.3 3.5 9.6 New Jeshwang 14.7 15.4 17.9 13.4 15.5 11.6 13.9 18.6 16.8 12.3 5.6 10.7 Sere Kunda Central 15.5 16.7 18.7 14.5 20.1 13.2 15.3 18.6 17.4 13.3 8.4 12.5 Sere Kunda East 19.3 20.0 21.9 18.1 24.3 16.7 18.9 22.3 21.0 16.8 10.6 16.4 Sere Kunda West 15.0 16.1 18.7 13.8 17.6 12.2 14.8 18.4 17.9 12.6 6.2 9.5 Brikama 50.9 51.6 55.0 48.4 54.1 46.0 51.2 53.6 53.6 47.7 31.3 41.7 Kombo North 41.4 42.2 45.4 39.3 41.9 37.0 42.1 44.5 43.9 38.3 25.2 31.9 Kombo South 56.4 57.1 59.7 54.4 55.4 53.2 57.2 58.0 57.4 54.6 41.9 51.3 Kombo Central 51.5 52.1 55.1 49.4 53.1 48.4 52.4 52.7 54.1 50.1 35.4 47.8 Kombo East 65.3 65.3 67.8 63.3 62.3 62.4 65.2 66.0 65.7 63.9 50.8 64.2 Foni Brefet 79.6 79.4 81.9 77.6 76.8 77.4 79.0 79.4 80.1 78.8 68.2 74.6 Foni Bintang Karanai 87.2 87.7 89.5 85.9 85.6 85.6 87.5 87.4 88.2 86.8 73.3 86.3 Foni Kansala 75.9 78.4 80.9 74.4 79.0 71.6 77.6 79.1 78.1 74.4 44.1 66.1 Foni Bondali 85.2 85.0 86.5 82.8 91.8 80.9 85.5 86.5 85.0 84.2 69.1 83.9 Foni Jarrol 83.3 85.2 87.2 81.8 83.3 80.1 83.3 85.6 85.0 82.3 60.3 61.9 Mansakonko 59.9 60.1 62.8 57.4 61.4 56.5 59.6 59.9 62.3 58.1 33.0 49.9 Kiang West 84.0 84.0 85.9 82.2 82.3 81.9 83.6 84.1 84.9 82.8 58.9 80.7 Kiang Central 72.3 73.3 74.9 71.2 71.9 71.1 73.1 72.8 73.6 73.2 43.0 58.0 Kiang East 64.8 64.9 66.5 63.4 62.9 63.3 65.0 64.9 65.3 65.2 39.9 46.4 Jarra West 39.9 40.5 42.7 38.3 41.0 37.8 41.0 41.1 41.1 38.7 23.3 29.9 72 | Page

Jarra Central 65.6 66.1 67.3 64.5 66.5 63.8 66.8 67.0 64.0 66.3 41.4 59.7 Jarra East 58.3 58.3 60.4 56.2 57.8 55.2 58.6 58.3 58.0 58.6 40.3 48.4 Kerewan 60.1 60.8 63.4 57.9 60.8 56.1 59.0 61.8 61.5 55.2 39.2 51.1 Lower Niumi 48.2 49.5 51.7 46.5 50.9 45.8 48.9 49.7 50.5 45.8 31.6 41.8 Upper Niumi 68.8 68.8 71.0 66.9 67.6 66.9 68.8 68.5 70.5 67.7 58.0 58.8 Jokadu 70.3 71.2 72.3 69.3 71.3 68.9 71.7 70.7 71.5 70.1 58.2 65.1 Lower Badibu 65.4 63.9 67.4 62.2 60.8 61.9 64.0 64.9 66.0 61.7 44.4 52.7 Central Badibu 68.3 67.6 70.9 65.5 63.0 63.8 66.7 68.3 69.0 64.6 41.5 55.9 Illiasa 49.9 50.9 53.6 47.6 52.5 46.1 49.7 50.9 53.1 46.3 27.2 40.2 Sabach Sanjar 78.8 79.0 80.3 77.6 78.9 78.3 80.0 78.8 80.0 77.3 63.5 77.5 Kuntaur 63.5 70.2 62.2 14.4 45.4 61.5 41.6 61.5 60.2 43.4 37.7 34.0 Lower Saloum 74.7 72.7 75.8 71.7 71.7 64.4 65.8 77.4 66.1 61.9 30.2 60.8 Upper Saloum 75.2 74.2 76.5 72.7 74.1 69.2 69.3 76.0 68.2 69.3 47.1 54.5 Nianija 83.8 83.3 85.5 81.7 81.5 79.5 82.5 85.5 78.8 78.5 22.9 56.5 Niani 63.4 62.6 65.5 60.8 59.5 54.5 56.7 66.8 55.2 52.5 20.8 56.9 Sami 66.2 64.6 67.3 63.9 62.3 60.3 60.9 67.4 61.0 57.6 32.2 54.9 Janjanbureh 70.0 71.5 67.9 4.7 73.2 41.3 21.9 41.3 60.1 0.0 26.6 53.4 Niamina Dankunku 83.1 84.0 85.5 82.1 78.3 81.6 83.5 83.5 84.0 81.8 79.7 65.9 Niamina West 78.5 79.2 81.6 76.7 72.2 75.9 77.7 79.1 78.9 74.6 63.0 79.1 Niamina East 72.1 73.3 75.1 70.8 68.6 71.4 72.7 73.0 73.3 70.3 62.6 61.5 Lower Fuladu West 75.0 76.0 77.8 73.9 71.3 73.6 75.4 75.5 76.8 73.2 59.6 65.1 Upper Fuladu West 66.6 67.3 70.5 64.1 63.4 61.0 63.2 68.6 67.5 59.3 38.4 51.2 Janjanbureh 53.7 57.5 59.9 53.1 49.9 52.0 56.2 57.3 57.2 53.1 40.3 42.5 Jimara 52.4 53.1 53.7 51.6 55.4 48.0 49.2 54.9 49.3 46.9 34.5 41.4 Basse 57.0 60.2 54.2 23.9 43.2 0.0 22.7 0.0 55.7 32.0 25.7 41.6 Basse 47.7 51.3 53.4 46.1 53.5 42.2 47.2 51.7 49.8 42.3 18.3 32.6 Tumana 51.7 52.4 53.0 50.9 54.9 47.7 48.3 53.4 48.4 49.8 38.6 48.1 Kantora 62.9 63.6 63.9 62.3 66.3 58.1 59.0 65.3 59.1 57.1 42.9 46.7 Wuli West 78.3 79.3 79.8 77.7 80.8 74.0 75.6 79.4 78.1 75.3 60.4 77.3 Wuli East 80.3 80.3 81.0 79.5 80.3 76.1 78.0 82.0 76.9 76.8 83.9 79.2 Sandu 73.9 74.1 75.1 72.7 77.0 69.8 70.6 75.7 70.5 70.4 67.3 75.0 73 | Page

Table F2: Household Poverty Profiles by Local Government Area and District Sanitation Water Housing tenure Toilet Improved shared Improved Piped Yes No Yes No Yes No Yes No Own Rent Free THE GAMBIA 40.0 61.6 50.9 46.2 46.0 60.2 36.2 62.7 42.8 17.1 33.3 Banjul City Council 10.5 11.6 7.8 12.4 10.5 14.7 10.4 15.5 5.0 7.0 5.5 Banjul city council 9.5 8.8 6.7 12.1 9.5 11.5 9.5 11.5 4.9 6.7 5.7 Banjul city council 10.5 10.9 7.5 12.4 10.2 19.3 10.2 19.3 4.5 7.1 6.7 Banjul city council 11.3 14.3 9.3 12.6 11.5 7.1 11.4 10.9 6.0 7.2 4.6 Kanifing Municipal 15.2 23.3 12.5 20.6 15.4 31.0 14.8 32.4 10.6 10.7 13.1 Bakau 10.8 21.9 8.6 16.7 11.7 18.8 11.5 28.4 5.5 8.4 7.1 New Jeshwang 13.7 23.7 11.0 20.1 14.3 26.0 14.2 26.4 9.1 10.0 13.7 Sere Kunda Central 15.1 21.9 12.2 19.1 15.4 29.2 14.7 33.5 10.5 10.5 11.7 Sere Kunda East 18.1 25.8 16.0 23.0 18.1 33.1 17.1 33.9 14.7 12.3 17.8 Sere Kunda West 14.7 20.6 11.2 20.0 14.5 32.0 13.9 32.5 8.9 10.2 13.0 Brikama 45.6 61.4 47.0 55.1 49.4 57.8 45.5 59.3 40.9 26.0 40.3 Kombo North 37.7 52.9 38.9 45.0 39.5 52.8 37.8 50.9 29.7 22.8 35.0 Kombo South 52.3 61.5 54.4 59.0 55.5 58.8 54.1 58.6 45.0 35.2 42.8 Kombo Central 46.5 60.1 48.9 53.7 50.7 54.9 49.0 55.1 41.1 29.2 43.0 Kombo East 61.6 70.2 65.0 66.1 64.6 69.0 65.3 65.7 53.6 49.1 55.6 Foni Brefet 80.8 78.1 76.1 80.4 79.1 80.7 78.7 80.0 69.7 57.4 61.8 Foni Bintang Karanai 89.1 86.1 82.9 88.8 87.5 87.4 79.2 90.2 79.7 64.2 75.6 Foni Kansala 76.6 78.1 67.1 80.4 77.1 77.6 70.2 82.8 68.4 47.8 46.4 Foni Bondali 82.6 92.1 83.0 91.9 91.2 84.7 93.5 84.9 76.1 85.2 75.6 Foni Jarrol 86.6 83.2 84.4 84.2 83.6 91.1 81.8 85.8 77.5 36.9 61.0 Mansakonko 56.6 62.9 59.3 60.7 60.2 58.7 45.3 68.0 53.9 29.2 35.4 Kiang West 83.4 84.5 84.8 83.4 84.4 80.2 75.3 85.1 77.9 66.6 41.8 Kiang Central 73.3 72.5 74.9 71.6 73.5 68.0 68.8 73.0 65.6 59.4 64.6 Kiang East 63.4 66.0 65.9 63.8 65.7 53.7 65.3 64.1 57.6 40.9 70.5 Jarra West 39.0 42.1 40.9 39.6 40.1 41.5 37.6 48.8 34.2 15.2 22.9 74 | Page

Jarra Central 59.9 67.7 63.6 68.1 68.2 58.0 65.4 65.8 56.9 17.8 46.4 Jarra East 57.6 58.6 57.3 60.1 57.1 62.7 51.9 59.0 51.0 47.4 39.0 Kerewan 56.6 64.4 60.1 60.9 59.4 65.0 52.8 66.8 51.7 29.1 43.7 Lower Niumi 43.2 54.4 47.7 50.1 47.9 52.8 44.3 53.9 39.0 24.4 37.0 Upper Niumi 68.0 69.4 68.4 69.7 67.7 73.5 65.4 74.3 58.8 43.1 55.9 Jokadu 70.4 70.9 71.4 70.2 70.3 71.8 70.6 70.8 62.1 69.7 57.1 Lower Badibu 62.3 69.1 62.0 67.2 65.2 61.4 48.4 67.0 52.2 47.1 51.2 Central Badibu 66.7 69.1 67.5 68.2 67.2 75.6 61.2 68.9 57.9 45.7 56.7 Illiasa 47.8 55.6 50.3 50.5 49.6 56.0 47.1 59.5 43.9 28.9 33.6 Sabach Sanjar 79.4 78.4 79.5 78.5 78.5 80.2 79.2 78.9 72.6 94.0 66.9 Kuntaur 63.8 72.8 69.3 69.9 69.2 71.0 70.9 69.4 61.2 34.8 41.3 Lower Saloum 68.3 78.9 70.6 75.2 73.3 80.3 69.5 78.7 65.6 37.9 49.7 Upper Saloum 69.9 78.2 77.4 72.7 73.8 76.3 72.8 75.4 65.3 86.0 52.2 Nianija 83.1 83.7 85.7 82.7 82.9 88.3 89.7 81.9 76.7 0.0 61.8 Niani 58.3 65.2 65.4 61.1 60.3 67.6 25.1 63.6 54.3 33.6 30.9 Sami 48.9 70.9 64.7 66.4 64.9 67.2 52.6 65.9 55.1 23.4 18.3 Janjanbureh 68.0 74.3 74.3 70.4 72.6 70.7 70.1 72.8 61.6 33.5 43.4 Niamina Dankunku 75.8 84.3 83.6 83.5 83.5 83.7 76.6 84.9 74.9 12.0 43.8 Niamina West 79.5 78.6 79.7 78.0 80.6 75.9 84.2 78.6 69.5 24.5 47.9 Niamina East 71.9 73.1 69.6 74.5 74.7 70.0 71.0 72.8 62.2 36.4 54.3 Lower Fuladu West 76.7 74.9 77.6 73.3 77.3 69.7 78.2 73.3 62.7 44.5 58.3 Upper Fuladu West 59.7 72.4 71.5 64.9 66.0 69.7 59.1 69.4 57.9 31.4 34.6 Janjanbureh 49.8 61.4 55.2 56.8 55.7 0.0 55.7 0.0 44.2 23.5 24.1 Jimara 45.4 57.7 52.2 54.1 51.1 60.9 49.1 56.6 47.8 39.0 42.3 Basse 57.2 63.8 61.1 60.7 59.3 67.3 53.8 62.2 56.3 25.9 49.5 Basse 44.6 53.0 49.9 49.1 48.7 53.3 45.3 50.0 40.1 22.9 43.2 Tumana 51.3 52.8 51.6 52.6 51.1 56.8 59.8 51.5 48.4 42.6 42.1 Kantora 62.1 64.5 64.2 61.9 62.8 65.3 62.9 63.3 61.5 34.2 33.1 Wuli West 73.5 83.3 76.2 80.9 78.5 80.0 85.7 78.5 72.8 48.6 86.0 Wuli East 79.2 81.2 81.9 76.5 78.4 86.2 75.7 80.4 75.2 69.4 55.8 Sandu 67.7 77.1 73.4 75.2 71.9 79.5 73.6 74.1 71.0 53.2 71.2 75 | Page

Table F2: Household Poverty Profiles by Local Government Area and District (cont.)

Car Bicycle Motorcycle Radio Television Computer Yes No Yes No Yes No Yes No Yes No Yes No THE GAMBIA 6.7 37.5 37.4 31.0 33.9 33.7 33.0 37.4 16.5 52.1 9.1 37.4 Banjul City Council 0.8 6.9 5.3 6.8 2.9 6.6 6.0 8.6 4.3 14.6 0.9 7.6 Banjul city council 0.3 6.4 4.5 6.6 1.3 6.2 6.0 6.6 4.2 13.1 1.1 7.6 Banjul city council 0.9 7.1 4.7 7.0 2.7 6.7 5.8 9.1 4.0 13.9 0.7 7.6 Banjul city council 1.1 7.0 6.3 6.8 4.1 6.8 6.2 10.1 4.7 17.2 1.0 7.6 Kanifing Municipal 0.9 13.0 9.6 11.3 8.0 11.0 10.1 14.5 7.2 22.7 2.8 13.0 Bakau 0.6 9.5 5.0 8.2 2.9 7.5 6.7 12.2 5.3 17.0 1.5 10.3 New Jeshwang 0.6 12.4 9.2 10.3 9.4 10.0 9.5 12.9 6.7 24.0 2.6 12.7 Sere Kunda Central 1.2 12.1 10.0 10.8 7.1 10.8 9.7 14.2 7.6 21.4 3.0 12.2 Sere Kunda East 1.3 15.9 11.7 14.3 9.5 13.7 12.8 16.7 8.6 26.8 3.3 15.7 Sere Kunda West 0.8 12.1 8.9 10.5 7.8 10.1 9.2 13.9 6.7 20.1 3.0 11.9 Brikama 9.0 42.0 38.3 35.9 30.9 37.4 36.2 41.1 21.7 53.4 11.9 41.4 Kombo North 7.4 33.3 27.3 28.8 20.3 28.7 27.6 31.8 17.3 45.8 8.6 32.8 Kombo South 12.7 47.1 44.4 42.1 40.0 43.6 42.9 45.6 27.1 53.0 20.8 46.4 Kombo Central 11.3 42.3 38.3 37.5 28.2 38.7 37.2 42.2 24.0 52.6 14.6 41.8 Kombo East 15.4 56.3 51.6 56.8 41.8 54.4 52.0 61.0 40.4 62.4 20.4 55.2 Foni Brefet 33.2 68.0 65.8 70.6 63.7 68.2 66.1 75.2 54.0 74.4 25.5 68.2 Foni Bintang Karanai 35.6 78.8 77.0 80.1 76.7 78.6 78.4 79.5 59.4 81.7 61.5 79.7 Foni Kansala 25.4 64.7 63.4 62.1 52.5 63.9 62.0 68.3 47.9 71.7 31.9 64.1 Foni Bondali 64.2 75.9 73.4 87.6 86.2 73.7 73.5 87.3 68.0 87.7 48.0 75.9 Foni Jarrol 32.1 70.9 72.8 65.6 81.2 68.4 69.1 74.6 62.2 71.0 46.2 70.1 Mansakonko 12.7 52.0 47.3 53.0 25.6 52.4 47.9 57.8 27.7 58.9 26.1 51.2 Kiang West 23.4 78.0 74.8 78.5 57.4 77.4 75.4 81.8 58.9 79.0 45.9 76.8 Kiang Central 23.1 65.5 61.8 68.7 39.8 65.9 62.6 75.1 54.7 66.3 69.9 64.8 Kiang East 13.2 62.8 45.1 63.9 26.0 59.6 53.3 74.9 32.0 60.5 36.0 57.7 Jarra West 5.0 30.6 27.7 31.5 16.2 31.4 26.5 39.4 22.0 38.7 13.2 30.9 Jarra Central 17.5 59.3 51.5 60.8 21.4 57.5 50.3 67.7 22.0 61.4 37.7 56.5 76 | Page

Jarra East 13.8 52.2 47.7 52.5 33.0 52.1 47.8 56.5 33.7 52.9 54.2 50.3 Kerewan 16.1 50.3 45.7 50.1 43.9 48.8 48.4 48.8 30.6 56.8 21.0 49.6 Lower Niumi 12.9 38.5 33.5 38.0 34.6 36.5 36.4 35.1 23.0 45.7 12.3 37.5 Upper Niumi 24.1 58.7 56.8 59.0 52.2 58.7 57.9 59.5 43.6 62.0 38.9 58.8 Jokadu 29.2 63.6 58.7 64.1 56.7 62.7 62.6 58.6 48.2 65.2 44.4 62.6 Lower Badibu 22.6 52.3 50.3 52.8 45.5 52.3 51.6 53.1 36.9 59.0 19.1 52.6 Central Badibu 28.0 58.5 54.5 59.8 53.7 58.0 59.4 50.8 40.5 63.7 45.7 57.9 Illiasa 12.0 41.3 35.7 41.3 40.9 39.2 38.8 41.6 26.0 48.8 12.8 41.2 Sabach Sanjar 41.3 73.1 70.2 73.8 65.3 72.8 72.1 74.4 70.0 72.7 67.5 72.6 Kuntaur 10.5 61.0 51.2 65.2 37.3 61.2 57.9 65.4 34.3 63.8 52.0 59.9 Lower Saloum 25.5 63.4 61.7 63.8 54.0 63.7 61.6 68.7 44.6 70.6 38.3 63.3 Upper Saloum 19.9 66.3 55.1 67.6 59.3 65.4 64.2 68.1 46.4 66.0 8.0 65.2 Nianija 23.3 76.3 75.5 76.1 30.0 76.1 76.0 76.3 86.4 75.8 43.0 76.2 Niani 9.4 53.2 44.6 60.3 29.6 53.6 49.6 58.5 26.3 57.2 54.2 51.5 Sami 3.6 56.8 51.8 58.7 32.2 57.4 53.0 61.8 21.7 58.9 51.2 54.9 Janjanbureh 30.0 60.4 59.4 59.4 55.9 59.8 58.7 62.4 41.3 63.5 34.5 60.3 Niamina Dankunku 47.0 74.6 73.7 75.2 61.9 74.7 72.9 79.0 79.1 74.1 98.0 74.5 Niamina West 27.0 69.0 63.5 74.9 76.1 68.5 66.0 75.7 59.3 69.7 41.2 69.2 Niamina East 38.4 62.1 58.9 64.3 59.1 61.8 61.5 62.2 52.6 62.9 44.3 61.8 Lower Fuladu West 43.7 62.5 63.0 58.5 63.4 61.7 62.5 58.9 54.3 63.1 48.9 62.5 Upper Fuladu West 23.8 55.7 55.7 51.6 49.3 54.9 52.9 58.9 31.0 61.7 26.8 55.6 Janjanbureh 4.2 41.8 38.0 44.0 46.0 40.7 39.9 45.6 36.8 47.8 7.7 42.3 Jimara 13.8 50.0 44.8 50.8 33.2 51.4 46.7 47.8 25.9 53.5 38.3 47.2 Basse 16.3 53.0 52.4 45.3 43.1 53.3 50.6 48.9 29.0 61.0 36.2 50.9 Basse 12.5 33.8 32.7 31.1 29.4 32.8 32.6 28.7 20.4 42.8 19.9 32.9 Tumana 11.9 50.5 47.3 50.4 38.0 54.4 48.0 48.1 29.6 58.4 59.0 47.9 Kantora 22.4 63.9 60.3 59.7 47.4 66.6 60.0 62.7 41.3 69.3 63.3 60.1 Wuli West 25.5 76.9 73.6 70.9 59.7 79.4 72.5 77.1 48.4 80.7 73.2 73.1 Wuli East 24.7 75.7 74.6 75.4 65.4 80.1 74.4 79.5 53.2 78.6 55.4 74.8 Sandu 24.5 74.0 69.8 75.1 56.9 77.4 70.1 74.5 48.0 75.9 59.4 71.2

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