Well being in Kenya by tfk2

GEOGRAPHIC DIMENSIONS OF WELL-BEING IN KENYA WHO AND WHERE ARE THE POOR? A CONSTITUENCY LEVEL PROFILE

VOLUME II

CBS The Central Bureau of Statistics (CBS) is a department in the Ministry of Planning and National Development that coordinates and supervises the national statistical system. The mission of CBS is to collect, collate, analyse, disseminate and store public socio-economic and spatial information, necessary for policy planning, monitoring and evaluation and for national, regional and international comparisons. The department’s website can be accessed at:

Central Bureau of Statistics

www.cbs.go.ke

THE WORLD BANK The World Bank group is one of the world’s largest sources of development assistance and leads in the provision of external funding for education, health and the fight against HIV/AIDS. Its primary focus is on helping the poorest people and the poorest countries. The World Bank is committed to working with the Government of Kenya, its development partners, academia and the civil society to improve and update knowledge regarding the economic and social status of the poor in Kenya and assist in designing, financing and implementing a pro-poor economic development agenda that responds

World Bank

to Kenya’s needs in a sustainable manner. The World Bank’s Kenya website can be accessed at: www.worldbank.org/kenya

SIDA Sida, the Swedish International Development Cooperation Agency, is a government agency that reports to the Ministry for Foreign Affairs. Sida is responsible for most of Sweden’s contributions to international development cooperation.The goal of Sweden’s development co-operation is to contribute to an environment supportive of poor people’s own efforts to improve their quality of life. In doing this, Sida is guided by a rights perspective based on international human rights conventions and the perspectives of the poor.

Swedish International Development Cooperation Agency

Sida website can be accessed at: www.sida.se

SID The Society for International Development (SID) is an international non-governmental association of individuals and organisations with an interest in development, research and dialogue. SID was founded in 1957 to promote social justice and foster democratic participation. Over the years, SID’s work has focused on generation and sharing of knowledge, facilitation of development dialogue, and strengthening of collective empowerment. Underpinning all of SID’s activities and linking its global initiatives and regional programmes, are the values that characterise SID’s programme portfolio: equity, respect for diversity and participation. SID works closely with

SID Society for International Development

governments, civil society organisations and academia. SID website can be accessed at: www.sidint.org

Contents Page 4 5 6 8 11 39 43 53 67 76 77

Colour coded chapters and sections Foreword Acknowledgements Chapter One: Introduction Chapter Two: Concepts, Methods and Data Chapter Three: Constituency Level Poverty Estimates Chapter Four: Constituency Development Fund (CDF) Allocations Chapter Five: Urban-Rural Perspectives on Poverty and Inequality Chapter Six: Socio-Economic Dimensions of Poverty Chapter Seven: North Eastern Province Poverty Profile – From Districts to Locations References Appendix 1: Expenditure-Based Small Area Estimation

Maps 7 10 10 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 42 68 69

Map 1: Map 2: Map 3: Map 4: Map 5: Map 6: Map 7: Map 8: Map 9: Map 10: Map 11: Map 12: Map 13: Map 14: Map 15: Map 16: Map 17: Map 18: Map 19: Map 20: Map 21: Map 22: Map 23: Map 24: Map 25:

Constituency Level Poverty Incidence – Kenya Constituency Level Poverty Incidence – Nairobi Constituency Level Contribution to Poverty – Nairobi Guide to Provinces and Sub-Provinces Mapped in the Atlas Constituency Level Poverty Incidence – Coast Province Constituency Level Contribution to Poverty – Coast Province Constituency Level Poverty Incidence – North Eastern Province Constituency Level Contribution to Poverty – North Eastern Province Constituency Level Poverty Incidence – Eastern Province (north) Constituency Level Contribution to Poverty – Eastern Province (north) Constituency Level Poverty Incidence – Eastern Province (south) Constituency Level Contribution to Poverty – Eastern Province (south) Constituency Level Poverty Incidence – Central Province Constituency Level Contribution to Poverty – Central Province Constituency Level Poverty Incidence – Rift Valley Province (north) Constituency Level Contribution to Poverty – Rift Valley Province (north) Constituency Level Poverty Incidence – Rift Valley Province (south) Constituency Level Contribution to Poverty – Rift Valley Province (south) Constituency Level Poverty Incidence – Western Province Constituency Level Contribution to Poverty – Western Province Constituency Level Poverty Incidence – Nyanza Province Constituency Level Contribution to Poverty – Nyanza Province Constituency Level Contribution to Poverty – Kenya Division and Location Level Poverty Incidence – North Eastern Province Division and Location Level Contribution to Poverty – North Eastern Province

Tables 34 45 47 50 55 58 61 64 67 67 70 74

Table 1: Table 2: Table 3: Table 4: Table 5: Table 6: Table 7: Table 8: Table 9: Table 10: Table 11: Table 12:

Constituency Level Poverty Estimates Distribution of Income and Consumption for Selected Countries Constituency Level Rural Poverty and Inequality Estimates Constituency Level Urban Poverty and Inequality Estimates Constituency Level Rural Poverty and Education Constituency Level Urban Poverty and Education Constituency Level Rural Poverty and Gender Constituency Level Urban Poverty and Gender Summary of Rural Poverty Estimates- North Eastern Province Summary of Urban Poverty Estimates- North Eastern Province North Eastern Province Rural Poverty Estimates - From Districts to Locations North Eastern Province Urban Poverty Estimates - From Districts to Sub-Locations

Figures 32-33

Figure 1:

Constituency Level Mountain of Poverty Incidence in Kenya

Foreword

Since independence, poverty reduction has been a key Government policy goal in Kenya. Poverty in Kenya has many faces that can vary substantially across space, time and various socio-economic groups. Obtaining comprehensive, disaggregated, reliable and timely indicators of poverty status across these dimensions is, therefore, a prerequisite to designing an all inclusive and effective pro-poor development agenda. This Report,Volume II of Geographic Dimensions of Well-Being in Kenya, focuses on poverty and inequality at the Constituency level. The new estimates of well-being presented in this report are based on statistical techniques to combine existing survey and census datasets. Within Constituencies, poverty and inequality are computed for urban and rural populations, as well as for some key socio-economic groups. The report also provides a detailed discussion of how these poverty statistics are applied to assist in making critical policy decisions such as resource allocation under the recently established Constituency Development Fund (CDF). At this critical juncture in planning and implementing Kenya’s development agenda, I wish to reiterate our Government’s pledge to reduce poverty through the creation of wealth and employment. This report provides critical indicators for evidence-based pro-poor policy making and key benchmarks for measuring our progress. Building systems and investing in research to gather and analyse statistics are an essential first step to stay on track in implementing the Investment Programme for Economic Recovery Strategy (IP-ERS). Indeed, the statistics contained in this report provide a road map with which the progress in the journey towards poverty reduction can be monitored and evaluated. However, the Report is also right in making clear that effective poverty and inequality reduction in Kenya is a very challenging task that requires a systematic and integrated approach. To successfully face this challenge requires substantial and sustained efforts by the Government and our development partners. Improving economic growth rates, though necessary are not always sufficient to reduce poverty. Therefore, for all to enjoy the fruits of our expanding economy, we must design pro-poor and targeted policies to provide the additional impetus needed to improve the well-being of the poor and the vulnerable. This requires responsible and effective governance at all levels in order to transform economic growth into delivering key essential services including better schools, a stronger health care system, and a safety net that effectively protects the marginalized and vulnerable. I am confident that this Report will contribute to poverty and inequality reduction efforts in Kenya. My thanks to the research team for their excellent work. My special thanks are extended to Mr. David S. O. Nalo, Permanent Secretary, MPND, for his vision and role in initiating the collaborative Rich and Poor Project (RAPP), and continued championing of policy relevant research and Mr. Anthony K.M. Kilele, Director of CBS, for his effective guidance throughout the preparation of this report. Last but not least, I would like to congratulate and thank our participating development partners particularly World Bank and SIDA for helping to fund this project and providing the required technical assistance.

Hon. Prof. Peter Anyang’ Nyong’o, EGH, MP MINISTER FOR PLANNING AND NATIONAL DEVELOPMENT

Acknowledgements

This report marks the end of a vigorous and innovative one-year research programme undertaken by the Poverty Analysis and Research Unit (PARU) within the Central Bureau of Statistics (CBS) in the Ministry of Planning and National Development (MPND). The research has been conducted in collaboration with the Society for International Development (SID) with financial and technical support from Swedish International Development Cooperation Agency (SIDA) and The World Bank respectively. Sincere appreciation is extended to Mr.Anthony K.M. Kilele, Director of Statistics CBS, for his support in facilitating the research team. For their commendable efforts and the technical expertise applied in producing this report, I would like to congratulate the core research team comprising team leader, Godfrey K. Ndengâ&#x20AC;&#x2122;e, the Head of Poverty Analysis and Research Unit, Collins Opiyo, Senior Demographic Statistician, PARU; George M. Kamula, Geographic Information Systems (GIS) Specialist; Piet Buys, GIS Specialist,World Bank; and last, but not least to, Johan Mistiaen, an Economist-Statistician with the Development Data Group of the World Bank, for providing exceptional technical guidance and assistance in capacity building of the PARU in terms of poverty and statistical analysis. Appreciation goes to Mary Wanyonyi, Joshua Musyimi, Stephen Macharia, Martha Atieno and Priscilla Owino for their high quality assistance to the core team. I also extend my appreciation to the CBS Population Division for assistance provided in matching sub locations and locations at Constituency level as well as the numerous District Statistical Officers and other field officers who helped validate some of the constituency poverty estimates and assisted in rectifying the constituency boundaries. I would like to congratulate and thank the Swedish International Development Cooperation Agency notably, Maria Stridsman, Head of Development Cooperation, David Wiking, Regional Adviser and Kalle Hellman, Economist for helping to fund the Rich and Poor Project (RAPP). I would also like to underline the excellent facilitation provided by the Society for International Development, in particular Mr. Duncan Okello, the Director of SID, Mr. J. M. Gitau, Programme Officer and Irene Omari,Accountant. I would also like to thank The World Bank for providing financial support and technical advice through Johan Mistiaen, Peter Lanjouw and Berk Ă&#x2013;zler of the Development Research Group and the Kenya based team headed by Country Director Colin Bruce and comprising Fred Kilby and Cosma Gatere. The excellent editorial assistance and layout work by the Regal Press team is also greatly appreciated. Finally, we express our deep appreciation for the interest and useful comments received from participants at various technical workshops and seminars organized throughout the research process. The May 2005 seminar organized jointly by the World Bank, DfID and GTZ in collaboration with the Poverty Analysis and Data Initiative chaired by the Ministry of Planning and National Development provided a stimulating forum which spurred the team to finalize this report and pursue further spatial analysis towards building integrated geo-referenced databases for policy planning and analysis.

David S. O. Nalo, CBS PERMANENT SECRETARY MINISTRY OF PLANNING AND NATIONAL DEVELOPMENT

Chapter One: This report provides maps and a geographic profile of poverty and inequality in Kenya at the Constituency level. A Constituency is a geo-political area that is represented in Parliament by an elected representative: a Member of Parliament (MP). The indicators of poverty and inequality were computed using small area estimation techniques which combine detailed information on household expenditures from the 1997 Welfare Monitoring Survey with data on socio-economic characteristics from the 1999 Population and Housing Census. This document is the second in a series of planned reports aimed at examining the “Geographic Dimensions of Well-Being in Kenya”. The recently launched Volume I presented poverty estimates for each administrative District, Division, Location (in rural areas) and sub-Location (in urban areas) in Kenya except for those in North-Eastern Province. This report, Volume II, complements the previous report by disaggregating indicators of well-being for all 210 political Constituencies in Kenya (see Map 1: Constituency-level poverty incidence in Kenya). In addition, Volume II also presents previously missing estimates of poverty for the North-Eastern Province from the District down to the Location level.

Prior to this study, poverty and inequality indicators were not available at the Constituency level.The statistics of well-being presented in this Volume are the culmination of rigorous analysis and diagnostics undertaken to deepen our understanding of the incidence and dynamics of poverty and inequality in Kenya against the backdrop of the Economic Recovery Strategy (ERS) framework. Its purpose is to guide national discourses and facilitate the design and implementation of poverty and inequality-reduction policies and programmes. While Volume I focused on administrative profiles from Districts to Locations, this Volume provides additional benchmark outcome indicators for tracking progress and evaluating the impact of policy and program interventions on the welfare of the poor at the Constituency level. For a number of years now, the Government of Kenya has been designing and implementing decentralized antipoverty programs that target the distribution of food, assets, funds and services to households, individuals and communities. Line Ministries, for instance, make numerous budget allocations at the District and other sub-National levels and local level officials, in turn, are then responsible for reaching the poor in their jurisdictions with the discretionary portion of these allocations. In other words, the Government of Kenya is engaging in de facto geographic targeting of budget allocations—including core poverty, development and recurrent expenditures—between and within Districts and Constituencies, and any discretionary budget allocations are then decided at the local level.

In principle, decentralizing targeting decisions facilitates exploitation of local level information to better reach the poor. However, in practice, efficient and effective targeting, monitoring and evaluation of decentralized budget allocations and propoor programmes requires good data and information systems down to the local level. There is thus a need for generating information to inform targeting decisions and monitor targeting performance of both line ministries and local level Government Offices, as well as initiatives supported by development partners. The poverty estimates produced at sub-District (Volume I) and Constituency (Volume II) levels thus provide a set of key statistics aimed at strengthening the database for pursuing evidence-based and targeted pro-poor resource allocations down to the local level. Targeting of resources at the local level, if successfully implemented, can optimise the amount of resources reaching the poor while minimising leakages. To date, detailed poverty maps have assisted policy makers to make more transparent decisions on resource allocation and are expected to raise further public awareness of poverty and elevate the dialogue on “Local level targeting of resources can help in better antipoverty programs.

reaching the poor while minimising leakages.”

Recently, the Government of Kenya created a number of alternative windows that allow allocation of resources directly to Districts and Communities. For instance, there has been a substantial increase in resources devoted to Constituency and Community based developments programs. These include development funds such as the Constituency Development Fund (CDF), the Community Development Trust Fund (CDTF), the Roads Fund, the AIDS Fund, the Local Authority Transfer Fund (LATF), and the Constituency Education

Introduction

Bursary Fund. The direct disbursement of these funds is intended to improve poverty targeting and project implementation by using local information and encouraging community participation especially in project identification, implementation and evaluation. The overarching goal is to improve development outcomes by involving local communities in the decision-making process and management of projects. In order to gauge the performance of these antipoverty and broader based development programmes, Government planners and policy makers as well as collaborating development partners need to rely on basic indicators for targeting resources and tailoring key features of these projects to challenges, needs and priorities that differ among communities. These outcome indicators also constitute benchmarks against which the effectiveness and impact of these interventions can be evaluated over time. Poverty maps provide useful inputs into further analysis aimed at better understanding the geographic variations in the levels, patterns and determinants of poverty. Such information can help in designing current and future poverty reduction policies and programmes. This report also presents measures of inequality at the constituency level to complement poverty incidence measures and capture the extent to which the distribution of well-being varies among and within Constituencies. Inequality within communities can influence policy and program outcomes in ways that, depending on the circumstance, are either harmful (e.g., elite capture of resources) or helpful (e.g., mobilization of collective action). Measures of inequality can thus also assist in the targeting and design of decentralised anti-poverty programmes and constituency-based development projects. A spatial framework also allows the use of new geographic units of analysis. Instead of using administrative boundaries, the approach facilitates the integration of data on well-being and socio-economic indicators within metalevel biophysical, environmental and agro-climatic boundaries. For example, watersheds typically interweave across administrative boundaries such as Districts and even provinces. Identifying spatial patterns of well-being within watersheds through the use of poverty maps can thus provide integrated databases for planning in such geographical meta-levels. Likewise, local level poverty maps can be complemented with spatial data on the location and quality of service delivery points such as schools and health centres, as well as infrastructure networks such as roads and telecommunication coverage. While the focus of this report is on spatial representation of poverty incidence among Constituencies, the methodology employed also allows us to further disaggregate poverty within areas by socio-economic characteristics. For instance, this report presents preliminary estimates that profile poverty incidence within Constituencies by gender and the level of educational achievement of the household head. The intended audience for this report is a broad one. It is aimed at Kenyan policy makers from national level down to the Constituency and Community levels by providing statistics to assist in taking an evidence-based approach towards addressing the economic and social development challenges facing the country. In particular, potentially important users of these Constituency poverty maps include all persons and institutions whose targeting base is the political constituency. More generally, the information in this report can contribute to a wider and informed policy debate regarding Kenya’s future development challenges and options. This report is organized in six further chapters. Chapter 2 provides an overview of the data, concepts and methods adopted and interpretation of poverty and inequality measures contained in this report. Chapter 3 details the results of the analyses of poverty incidence and geographic concentrations of poverty in all 210 Constituencies and provides overviews within each Province. Chapter 4 highlights and describes the current application of Constituency level poverty estimates in the allocation of the Constituency Development Funds (CDF). Chapter 5 presents perspectives on urban-rural poverty incidence and inequality within Constituencies. Chapter 6 presents a preliminary socio-economic profile of poverty within Constituencies by gender and educational attainment of the household head. And, finally, Chapter 7 presents previously missing estimates of poverty for the North-Eastern Province from the District down to the Location level.

Map 1: Constituency Level Poverty Incidence - Kenya

Chapter Two: This report focuses on the monetary dimensions of well-being captured via objective and quantitative measures of poverty and inequality. A key ingredient for constructing poverty and inequality measures is a relevant monetary indicator of well-being. In addition, for poverty measurement, one needs a threshold or poverty line below which people will be classified as poor. The most recent source of such information is the Welfare Monitoring Survey (WMS III) conducted in 1997. Quantitative measures of poverty are thus constructed as functions of consumption expenditures relative to a poverty line. This report focuses on three measures of poverty: poverty incidence, poverty contribution and poverty gap. Inequality is measured by computing the Gini index.

colour scheme that ranges from light peach indicating relatively smaller contributions to dark brown for relatively larger contributions. For instance, Map 3 shows the poverty contribution of each of the eight Constituencies in Nairobi to the overall number of poor in Nairobi. Map 3 shows that two Constituencies in Nairobi—Embakasi and Kasarani—together account for over 30 per cent of the total number of poor living in Nairobi (approximately 800,000 people). Table 1 contains the poverty contribution measures for each of the 210 Constituencies relative to both the total number of poor in Kenya and the total number of poor in their respective Provinces. In addition,Table 1 presents the total estimated population and the total number of poor for each Constituency.

Monetary Indicators of Well-Being and Poverty Lines

Poverty Gap Measure

The monetary indicator of well-being developed for measuring poverty in Kenya is based on detailed information regarding household consumption expenditures on food and a comprehensive range of non-food items such as schooling, health, transportation and rent (GoK, 2000). Regional price adjustments were applied to reflect relative differences in the cost of living between different areas, especially between urban and rural areas. To account for differences in needs among household members (e.g. relative to adults, children consume less food) an adult equivalence scale is applied. On average, the total monthly consumption expenditures per person (equivalence adjusted) in 1997 are estimated at about KSh. 1,846 in rural areas and KSh. 4,425 in urban areas (GoK, 2000). However, this average conceals important variations; the monthly expenditures of many people are substantially less.

The Poverty gap measure provides information on how much poorer the poor people are relative to the poverty line – that is the depth of poverty. This measure captures the average expenditure shortfall, or gap, for the poor in a given area relative to the poverty line. It is obtained by adding up all the shortfalls of the poor (ignoring the non-poor) and dividing this total by the population. The poverty gap measures the poverty deficit of the population, or the resources that would be needed to lift all the poor out of poverty through perfectly targeted cash transfers geared to closing the gap. In this sense, the poverty gap is a very crude measure of the minimum amount of resources necessary to eradicate poverty, that is, the amount that one would have to transfer to the poor to lift them up to the poverty line, under the assumption of perfect targeting. For instance, it was estimated that in 1997 the poverty gap for the rural population in Kenya was about 19.3 per cent (GoK, 2000). This implies that, on average, every poor person in a rural area would require an additional KSh. 240 per month to reach the poverty line (i.e., 19.3 per cent of the KSh. 1,239 rural poverty line). In other words, if about 53 per cent of the rural population was poor (according to the headcount index) in 1997, then this amounts to about 11.4 million people (GoK, 2000). This in turn implies that roughly KSh. 2.74 billion per month would have been needed, in perfectly targeted cash transfers, to eradicate rural poverty in 1997.

To determine how many people are poor, a monetary poverty line is derived which represents the cost of a basic basket of goods. This poverty line is determined and based on the expenditure required to purchase a food basket that allows minimum nutritional requirements to be met (set at 2,250 calories per adult equivalent per day) in addition to the costs of meeting basic non-food needs (GoK, 2000). In Kenya, this poverty line was estimated to be about KSh. 1,239 and KSh. 2,648 for rural and urban households respectively. Poverty Incidence or Headcount Index The share of the total population in a given area whose consumption is below the poverty line is a measure of poverty incidence—also known as the headcount index. In other words, the proportion of the population that cannot afford to purchase the basic basket of goods. Based on this measure, it was estimated that in 1997, about 53 per cent of the rural and some 50 per cent of the urban population in Kenya could be deemed poor (GoK, 2000). Map 2 shows the poverty incidence measure—i.e., the percentage of the population falling below the poverty line—for the eight Constituencies comprising Nairobi. The level of poverty in each Constituency (labelled by name) is mapped using a categorical 7-colour scheme that ranges from dark green indicating relatively wealthier areas (where the poverty rate is less than 24%) to dark brown for relatively poorer areas (where the poverty rate is greater than 74%). For instance, Map 2 shows that there is variation in poverty levels among Constituencies in Nairobi from between 54-64 per cent in Makadara to between 24-34 per cent in Westlands. Table 1 contains the poverty incidence measure for each of the 210 Constituencies as well as their national rank in terms of the least poor (1) to the poorest (210) measured in terms of the poverty headcount index. Poverty Contribution Measure Maps of poverty incidence do not provide an overview of the number of poor people in a given area. Some Constituencies can have relatively high poverty rates, but be inhabited by relatively few people. Since decision makers are often interested in the relative distribution of number of poor among Constituencies across Kenya or within its Provinces, the poverty contribution measure is mapped to bridge this gap. The poverty contribution measures the number of poor people in a Constituency as a percentage of the total number of poor in Kenya as a whole or within the Province in which the Constituency is located. The poverty contribution of each Constituency is mapped using a categorical 5-

Concepts, Methods and Data

The poverty gap measure, while informative, is subject to at least two caveats. First, attempting to reach the whole population through perfectly targeted cash transfers is neither practically feasible nor necessarily a recommendable policy option (e.g., financing transfers via excessive tax rates could stifle economic growth and, by extension, future poverty reduction). Second, while the poverty gap accounts for the average expenditure separating the poor from the poverty line, it does not measure inequality among poor people. In other words, the poverty gap measure still conceals the fact that some people might only be a few shillings short of the poverty line while others might only have a few shillings to spend. In this Volume, unlike its precursor, the poverty gap measures for each Constituency are not mapped due to space considerations, but these estimates are presented in Tables 3-4. Measuring Inequality using the Gini Index The poverty measures focus on where individuals find themselves in relation to the poverty line and therefore provide statistics summarizing the bottom of the consumption distribution (i.e., those that fall below the poverty line). Inequality means different things to different people and there are many ways of measuring inequality. In this report inequality refers to the dispersion of the distribution over the entire (estimated) consumption aggregate. The most widely used measure of inequality is the Gini index which ranges from zero (indicating perfect equality; i.e., where everyone in the population has the same expenditure or income) to one hundred (indicating perfect inequality; i.e., when all expenditure or income is accounted for by a single person in the population). For most developing countries, the Gini index ranges between point three and point six (World Development Indicators, 2005). Methodology for Estimating Constituency Poverty Measures Household surveys that sample a representative subset of the population and collect detailed information regarding consumption expenditures (e.g. the 1997 WMS III) can be used to estimate measures of urban and rural poverty at the

National level, the Provincial level and, albeit with less precision, at the District level. However, the small sample sizes of household surveys preclude estimation of meaningful poverty measures for smaller areas such as Divisions and Locations. Moreover, increasing the sample size of detailed household surveys such as the WMS IIIâ&#x20AC;&#x201D;or the Kenya Integrated Household Budget Survey (KIHBS) currently in the fieldâ&#x20AC;&#x201D;to produce representative statistics below the District level is neither practically feasible (because of prohibitively high costs) nor desirable (because of the likelihood of increased measurement errors). Data collected via instruments such as the 1999 Population and Housing Census (PHC) are sufficiently large (the PHC actually enumerates the entire population) to provide representative measurement below the District level. However, this data does not contain the detailed information on consumption expenditures required to estimate poverty indicators. Moreover, increasing the level of detail collected via census data is again neither practically feasible nor desirable. Nevertheless, implementing a recently developed methodological approach that combines the detailed information on well being from the 1997 Welfare Monitoring Survey with the complete geographic coverage provided by the 1999 Population and Housing Census can circumvent these problems.

The first step of the analysis involves exploring the relationship between a set of characteristics of households and the welfare level of the same households through an analysis of the WMS III data, which has detailed information about household expenditure and consumption. A regression equation is then estimated to explain daily per capita consumption and expenditure of a household using a number of socio-economic variables such as household size, education levels, housing characteristics and access to basic services. While the census does not contain household expenditure data, it does contain these socio-economic variables. Therefore, it is possible to statistically impute household expenditures for the census households by applying the socio-economic variables from the census data on the estimated relationship based on the survey data. This gives estimates of the welfare level of all households in the census, which in turn allows for estimation of the proportion of households that are poor and other poverty measures for relatively small geographic areas such as sub-Locations, Locations and Divisions, which are then plotted on maps to enhance their interpretation and utilisation. Additional details on the poverty mapping analysis and references are provided in Appendix 1.

Map 2: Constituency Level Poverty Incidence - Nairobi

Map 3: Constituency Level Contribution to Poverty - Nairobi

Chapter Three:

Constituency Level Poverty Estimates

Overall, among the 210 political Constituencies in Kenya, poverty incidence as measured by the headcount index ranges from about 16 per cent to about 84 per cent representing the least poor and the poorest Constituencies respectively. Kabete Constituency in Central Province is the least poor, while Ganze Constituency in Kilifi District, Coast Province, is the poorest in Kenya. This chapter presents a summary of the main findings for each Province and the complete set of poverty measures for each Constituency are presented at the end of this Chapter in Table 1.

The 36 political Constituencies in Eastern Province contribute just over 18 percent to total national poverty. With an estimated 2.62 million poor people, 42 percent of them are concentrated in 10 of the 36 Constituencies, namely: Makueni (5.1%), Nithi (4.5%), Kitui Central (4.4%), Mbooni (4.2%), Kangundo (4.2%), Kibwezi (4%), Igembe (3.9%), Mwingi North (3.9%), Mwala (3.7%), and and Machakos Town (3.6%). The least contribution to provincial poverty come from Saku and Laisamis Constituencies which contribute each less than 1%.

Nairobi Province Nairobi which doubles up as both a Province and district comprises eight political Constituencies (which correspond to the eight administrative divisions). Poverty incidence in Nairobi as a whole is 44 per cent. Among the Constituencies, poverty incidence ranges from 31 per cent in Westlands to 59 per cent in Makadara. Five of the eight Constituencies have a poverty headcount index that is above the provincial mean of 44 per cent. Poverty incidence in the poorest Constituency (Makadara) is about twice as large compared to that found in the least poor Constituency (Westlands).

North Eastern Province North Eastern Province comprises of 11 political Constituencies. Poverty incidence in North Eastern as a whole is 64 per cent. Among the Constituencies, poverty incidence ranges from 59 per cent in Dujis to 70 per cent in Wajir North Constituency. Four of the 11 Constituencies have a poverty headcount index that is above the provincial mean of 64 per cent. Poverty incidence in the poorest Constituency (Wajir North) is just over one times as large compared to that found in the least poor Constituency (Dujis).

The 8 political Constituencies in Nairobi Province contribute 6 percent to total national poverty. With an estimated 874,058 poor people, almost half (49%) of them are concentrated in 3 of the 8 Constituencies, namely: Embakasi (19.1%), Kasarani(17.3%) and Makadara (12.5%). The least contribution to provincial poverty comes from Westlands(6.7%) Constituency.

The 11 political Constituencies in North Eastern Province contribute less than 4 percent to total national poverty.With an estimated half a million poor people, almost two thirds (64.3%) of them are concentrated in 6 of the 11 Constituencies, namely: Mandera Central (12%), Wajir East (11.8%), Dujis (11.4%), Wajir South (10.2%), Mandera East (9.7%) and Lagdera (9.4%). Ijara Constituency contributes the least (4.5%) to provincial poverty.

Central Province Central Province comprises of 29 political Constituencies. Poverty incidence in Central as a whole is 31 per cent. Among the Constituencies, poverty incidence ranges from 16 per cent in Kabete to 43 per cent in Nyeri Town Constituency. Seventeen of the 29 Constituencies have a poverty headcount index that is above the provincial mean of 31 per cent. Poverty incidence in the poorest Constituency (Nyeri Town) is slighty over 21/2 times as large compared to that found in the least poor Constituency (Kabete).

Rift Valley Province Rift Valley Province comprises of 49 political Constituencies. Poverty incidence in Rift Valley as a whole is 48 per cent. Among the Constituencies, poverty incidence ranges from 34 per cent in Subukia to 64 per cent in Turkana Central Constituency. Twenty four of the 49 Constituencies have a poverty headcount index that is above the provincial mean of 48 per cent. Poverty incidence in the poorest Constituency (Turkana Central) is almost 2 times as large compared to that found in the least poor Constituency (Subukia).

The 29 political Constituencies in Central Province contribute almost 8 percent to total national poverty. With an estimated 1.1 million poor people, two fifths (41%) of them are concentrated in 8 of the 29 Constituencies, namely: Juja (8.4%), Gatanga (5.3%), Mwea (5.0%), Kandara (5.0%), Kinangop (4.8%), Kiharu (4.5%), Ol-Kalou (4.3%) and Kieni (3.7%). The smallest contribution to provincial poverty comes from Ndarangwa Constituency.

The 49 political Constituencies in Rift Valley Province contribute just over 22 percent to total national poverty.With an estimated 3.18million poor people, a third of them are concentrated in 10 of the 49 Constituencies, namely: Eldoret North (4.4%), Saboti (3.8%), Narok South (3.2%), Molo (3.1%), Nakuru Town (3.1%), Kilgoris (3.1%), Turkana Central (3.0%), Turkana North (2.9%), Naivasha (2.9%) and Bomet (2.8%). Samburu East Constituency which has a low population also has the smallest contribution of 0.4% to provincial poverty.

Coast Province Coast Province comprises of 21 political Constituencies curved from seven districts. Poverty incidence in Coast as a whole is 57.6 per cent. Among the Constituencies, poverty incidence ranges from 30 per cent in Bura to 84 per cent in Ganze Constituency. Half of the 21 Constituencies have a poverty headcount index that is above the provincial mean of 31 per cent. Poverty incidence in the poorest Constituency (Ganze) is almost 3 times as large compared to that found in the least poor Constituency (Bura). The 21 political Constituencies in Coast Province contribute almost 10 percent to total national poverty. With an estimated 1.363 million poor people, 60 percent of them are concentrated in a third of the 21 Constituencies, namely: Bahari (10.3%), Kaloleni (10.1%), Msambweni (9.0%), Kisauni (8.0%), Malindi (7.1%), Kinango (8.7%) and Ganze (6.7%). The smallest contribution to the provincial poverty comes from Lamu East Constituency which contributes only 1% percent. Eastern Province Eastern Province comprises of 36 political Constituencies. Poverty incidence in Eastern as a whole is 58 per cent. Among the Constituencies, poverty incidence ranges from 34 per cent in Ntonyiri to 76 per cent in Kitui South Constituency. Twenty four of the 36 Constituencies have a poverty headcount index that is above the provincial mean of 58 per cent. Poverty incidence in the poorest Constituency (Kitui South) is just over 2 times as large compared to that found in the least poor Constituency (Ntonyiri).

Western Province Western Province comprises of 24 political Constituencies. Poverty incidence in Western as a whole is 61 per cent. Among the Constituencies, poverty incidence ranges from 50 per cent in Amogoro to 72 per cent in Ikolomani Constituency. Ten of the 24 Constituencies have a poverty headcount index that is above the provincial mean of 61 per cent. Poverty incidence in the poorest Constituency (Ikolomani) is almost 1.5 times as large compared to that found in the least poor Constituency (Amagoro). The 24 political Constituencies in Western Province contribute almost 14 percent to total national poverty.With an estimated 1.99 million poor people, 63 percent of them are concentrated in half of the 24 Constituencies, namely: Lurambi (6.8%), Kimilili (6.7%), Malava (5.4%), Lugari (5.4%), Mumias (5.2%), Sirisia (5.2%), Webuye (5.0%), Nambale (4.9%), Emuhaya (4.8%), Kandunyi (4.7%), Shinyalu (4.5%) and Amogoro (4.4%). The smallest contributor to provincial poverty is Budalangi Constituency which accounts for 1.8%. Nyanza Province Nyanza Province comprises of 32 political Constituencies. Poverty incidence in Nyanza as a whole is 65 per cent. Among the Constituencies, poverty incidence ranges from 43 per cent in Migori to 80 per cent in Kuria Constituency. Eighteen of the 32 Constituencies have a poverty headcount index that is above the provincial mean of 65 per cent. Poverty incidence in the poorest Constituency (Kuria) is almost 2 times as large compared to that found in the least poor Constituency (Migori).

The 32 political Constituencies in Nyanza Province contribute 19 percent to total national poverty.With an estimated 2.73 million poor people, 43 percent of them are concentrated in 10 of the 32 Constituencies, namely: Kisumu Town West (5.8%), Kasipul-Kabondo (4.6%), Kuria (4.3%), Kitutu-Chache (4.3%),Alego

(4.0%), Kitutu-Masaba (4.0%) Rangwe (3.8%), Bomachoge (3.8%), Ugenya(3.8%) and Bomasi (3.6%). Kisumu Town East accounts for the smallest contribution to poverty accounting for only 1.5% of the total provincial poor.

Map 4: Guide to Provinces and Sub-Provinces Mapped in the Atlas

(south)

Map 5: Constituency Level Poverty Incidence - Coast Province

Map 6: Constituency Level Contribution to Poverty - Coast Province

Map 7: Constituency Level Poverty Incidence - North Eastern Province

Map 8: Constituency Level Contribution to Poverty - North Eastern Province

Map 9: Constituency Level Poverty Incidence - Eastern Province (north)

Map 10: Constituency Level Contribution to Poverty - Eastern Province (north)

Map 11: Constituency Level Poverty Incidence - Eastern Province (south)

Map 12: Constituency Level Contribution to Poverty - Eastern Province (south)

Map 13: Constituency Level Poverty Incidence - Central Province

Map 14: Constituency Level Contribution to Poverty - Central Province

Map 15: Constituency Level Poverty Incidence - Rift Valley Province (north)

Map 16: Constituency Level Contribution to Poverty - Rift Valley Province (north)

Map 17: Constituency Level Poverty Incidence - Rift Valley Province (south)

Map 18: Constituency Level Contribution to Poverty - Rift Valley Province (south)

Map 19: Constituency Level Poverty Incidence - Western Province

Map 20: Constituency Level Contribution to Poverty - Western Province

Map 21: Constituency Level Poverty Incidence - Nyanza Province

Map 22: Constituency Level Contribution to Poverty - Nyanza Province

Kabete Kiambaa Limuru Mathira Githunguri Ndaragwa Othaya Kieni Kiharu Mathioya Bura Gatundu South Lari Kigumo Westlands Mukurwe-inil Kangema Tetu Ndia Kerugoya/Kutus Ol-Kalou Subukia Gichugu Ntonyiri Mvita Gatanga Maragwa Kandara Gatundu North Kinangop Keiyo South Naivasha Juja Kajiado North Samburu East Langata Laisamis Kipipiri Embakasi Keiyo North Marakwet West Marakwet East Mwea Galole Lamu East Eldoret East Garsen Kuresoi Baringo North Baringo Central Molo South Imenti Nyeri Town Rongai Laikipia West Central Imenti Rongo Laikipia East Starehe North Imenti Likoni Ainamoi Nakuru Town Mosop Emgwen Changamwe Saku Kisauni Dagoretti Kamukunji Kipkelion Kacheliba Saboti Isiolo North Migori Eldama Ravine Kasarani Kajiado Central Nyatike Cherangany Aldai Buret Uriri Belgut Eldoret South Sotik Kajiado South Samburu West Amagoro Narok North Mogotio Kwanza Bomet Bumula Konoin Turkana South Kapenguria Matuga Narok South

Percent Poor (FGTO)

Fig 1: Constituency Level Moun

Name of C

Machakos Town Manyatta Sigor Eldoret North Isiolo South Kibwezi Taveta Mt. Elgon Lamu West Baringo East Malava Chepalungu Tinderet Igembe Nyaribari Chache Vihiga Muhoroni Nyaribari Masaba Sirisia Nithi Runyenjes Voi Kathiani Mwatate Kangundo Kilgoris Gachoka Hamisi Makadara Matungu Webuye Dujis Emuhaya Kimilili Sabatia Tigania East Kanduyi Ugenya Turkana North Malindi Msambweni Kilome Tigania West South Mugirango Fafi Kisumu Town East Moyale North Horr Mwingi North Mandera West Mumias Mwingi South Yatta Wajir West Lurambi Bomachoge Wajir East Bobasi Ijara Bahari Tharaka Nyakach Wundanyi Masinga Butere Lugari Khwisero Mwala Lagdera Turkana Central Kitutu Chache Mandera East Kitutu Masaba Gwassi Mbooni Kaiti Nyando Kisumu Town West Makueni Mandera Central Kitui West Shinyalu Mutito West Mugirango Alego Wajir South Mbita Gem Nambale Magarini Siakago Funyula Budalangi Kisumu Rural Butula Bondo Wajir North N. Mugirango Borabu Karachuonyo Ikolomani Kasipul-Kabondo Rangwe Kitui Central Ndhiwa Rarieda Bonchari Kaloleni Kinango Kitui South Kuria Ganze

tain of Poverty Incidence in Kenya

Constituency

Table 1: Constituency Code

Constituency Level Poverty Estimates Province/ Constituency Name

Estimated Population From 1999 Census

Estimated Number of Poor Individuals

Poverty Incidence Percent of Individuals below Poverty Line

Nairobi Province 1 Makadara 2 Kamukunji 3 Starehe 4 Langata 5 Dagoretti 6 Westlands 7 Kasarani 8 Embakasi

1,991,724 184,541 183,468 205,225 271,111 229,612 188,107 320,739 408,921

874,058 109,001 84,050 90,430 108,617 104,934 58,826 151,592 166,608

44 59 46 44 40 46 31 47 41

Coast Province 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

2,360,043 178,184 244,739 99,582 71,108 203,650 118,470 160,301 227,554 190,669 110,536 161,138 108,160 66,392 46,094 63,782 16,796 55,713 51,411 52,249 54,632 78,883

1,363,282 81,012 111,688 44,452 24,544 124,574 62,524 120,254 143,362 140,797 92,826 98,361 74,316 28,160 19,297 19,461 7,088 31,037 28,096 33,219 32,042 46,177

790,657 96,440 74,082 46,135 35,939 59,834 68,466 95,027 75,815 71,242 91,687 75,990 4,494,566 50,342 39,604 34,881 36,314 73,463 18,144 182,534 171,081 112,551 123,071 209,242 123,961 151,806 201,586 98,613 136,124 131,182 97,963 70,010

Changamwe Kisauni Likoni Mvita Msambweni Matuga Kinango Bahari Kaloleni Ganze Malindi Magarini Garsen Galole Bura Lamu East Lamu West Taveta Wundanyi Mwatate Voi

North Eastern Province 30 Dujis 31 Lagdera 32 Fafi 33 Ijara 34 Wajir North 35 Wajir West 36 Wajir East 37 Wajir South 38 Mandera West 39 Mandera Central 40 Mandera East Eastern Province 41 Moyale 42 North Horr 43 Saku 44 Laisamis 45 Isiolo North 46 Isiolo South 47 Igembe 48 Ntonyiri 49 Tigania West 50 Tigania East 51 North Imenti 52 Central Imenti 53 South Imenti 54 Nithi 55 Tharaka 56 Manyatta 57 Runyenjes 58 Gachoka 59 Siakago

Constituency National Poverty Rank (1 = richest, 210 = poorest)

Constituency Contribution to National Poverty (%)

Constituency Contribution to Provincial Poverty (%)

128 70 59 36 69 15 77 39

6.1 0.8 0.6 0.6 0.8 0.7 0.4 1.1 1.2

100.0 12.5 9.6 10.3 12.4 12.0 6.7 17.3 19.1

58 46 46 45 35 61 53 75 63 74 84 61 69 42 42 31 42 56 55 64 59 59

66 68 61 25 140 98 207 159 206 210 139 189 47 44 11 45 108 106 162 123 121

9.5 0.6 0.8 0.3 0.2 0.9 0.4 0.8 1.0 1.0 0.6 0.7 0.5 0.2 0.1 0.1 0.0 0.2 0.2 0.2 0.2 0.3

100.0 5.9 8.2 3.3 1.8 9.1 4.6 8.8 10.5 10.3 6.8 7.2 5.5 2.1 1.4 1.4 0.5 2.3 2.1 2.4 2.4 3.4

507,305 57,643 47,513 28,436 22,612 42,207 42,856 59,667 51,822 44,478 60,894 49,178

64 60 64 62 63 71 63 63 68 62 66 65

131 168 144 158 196 153 156 185 149 179 171

3.5 0.4 0.3 0.2 0.2 0.3 0.3 0.4 0.4 0.3 0.4 0.3

100.0 11.4 9.4 5.6 4.5 8.3 8.4 11.8 10.2 8.8 12.0 9.7

2,619,671 31,223 24,638 15,861 14,628 34,499 9,860 103,297 58,870 68,976 74,398 92,374 53,999 65,473 117,507 62,249 72,236 76,533 57,789 48,628

58 62 62 46 40 47 54 57 34 61 61 44 44 43 58 63 53 58 59 70

146 147 67 37 74 104 113 24 142 135 60 56 52 119 160 101 120 126 190

18.2 0.2 0.2 0.1 0.1 0.2 0.1 0.7 0.4 0.5 0.5 0.6 0.4 0.5 0.8 0.4 0.5 0.5 0.4 0.3

100.0 1.2 0.9 0.6 0.6 1.3 0.4 3.9 2.2 2.6 2.8 3.5 2.1 2.5 4.5 2.4 2.8 2.9 2.2 1.9

Table 1 contâ&#x20AC;&#x2122;d Constituency Code

Province/ Constituency Name

Estimated Population From 1999 Census

Estimated Number of Poor Individuals

Poverty Incidence Percent of Individuals below Poverty Line

Constituency National Poverty Rank (1 = richest, 210 = poorest)

Constituency Contribution to National Poverty (%)

Constituency Contribution to Provincial Poverty (%)

Eastern Province cont... 60 Mwingi North 61 Mwingi South 62 Kitui West 63 Kitui Central 64 Mutito 65 Kitui South 66 Masinga 67 Yatta 68 Kangundo 69 Kathiani 70 Machakos Town 71 Mwala 72 Mbooni 73 Kilome 74 Kaiti 75 Makueni 76 Kibwezi

162,447 136,171 139,770 159,372 89,021 115,574 104,681 122,707 186,145 136,131 176,346 150,057 168,698 80,688 110,594 200,865 192,827

101,185 85,073 94,686 114,696 60,512 87,597 66,581 76,797 109,643 79,772 93,548 96,233 110,347 49,378 72,557 132,988 105,040

62 63 68 72 68 76 64 63 59 59 53 64 65 61 66 66 55

148 151 180 202 182 208 163 152 124 122 100 167 174 140 175 178 105

0.7 0.6 0.7 0.8 0.4 0.6 0.5 0.5 0.8 0.6 0.7 0.7 0.8 0.3 0.5 0.9 0.7

3.9 3.2 3.6 4.4 2.3 3.3 2.5 2.9 4.2 3.0 3.6 3.7 4.2 1.9 2.8 5.1 4.0

Central Province 77 Kinangop 78 Kipipiri 79 Ol_Kalou 80 Ndaragwa 81 Tetu 82 Kieni 83 Mathira 84 Othaya 85 Mukurwe-Ini 86 Nyeri Town 87 Mwea 88 Gichugu 89 Ndia 90 Kerugoya/Kutus 91 Kangema 92 Mathioya 93 Kiharu 94 Kigumo 95 Maragwa 96 Kandara 97 Gatanga 98 Gatundu South 99 Gatundu North 100 Juja 101 Githunguri 102 Kiambaa 103 Kabete 104 Limuru 105 Lari

3,556,047 139,848 73,283 143,645 81,748 79,438 145,108 147,969 85,339 85,385 91,813 132,370 117,270 91,444 101,859 79,532 92,130 173,824 116,978 105,002 152,910 170,187 111,704 97,400 237,709 129,421 177,566 185,570 103,880 105,715

1,106,730 52,921 29,633 48,043 20,706 25,088 41,064 36,170 23,005 26,718 39,702 55,293 40,249 29,125 33,525 24,894 26,810 49,586 36,461 37,810 55,466 58,942 34,333 36,009 92,698 31,851 34,222 30,576 23,093 32,738

31 38 40 33 25 32 28 24 27 31 43 42 34 32 33 31 29 29 31 36 36 35 31 37 39 25 19 17 22 31

30 38 21 6 18 8 4 7 16 53 43 23 19 20 17 10 9 14 27 28 26 12 29 33 5 2 1 3 13

7.7 0.4 0.2 0.3 0.1 0.2 0.3 0.3 0.2 0.2 0.3 0.4 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.2 0.3 0.6 0.2 0.2 0.2 0.2 0.2

100.0 4.8 2.7 4.3 1.9 2.3 3.7 3.3 2.1 2.4 3.6 5.0 3.6 2.6 3.0 2.2 2.4 4.5 3.3 3.4 5.0 5.3 3.1 3.3 8.4 2.9 3.1 2.8 2.1 3.0

Rift Valley Province 106 Turkana North 107 Turkana Central 108 Turkana South 109 Kacheliba 110 Kapenguria 111 Sigor 112 Samburu West 113 Samburu East 114 Kwanza 115 Saboti 116 Cherangany 117 Eldoret North 118 Eldoret East 119 Eldoret South 120 Marakwet East

6,648,288 153,793 147,691 68,835 58,991 127,836 114,469 98,365 32,746 147,829 256,627 140,129 262,884 161,680 173,731 63,640

3,182,219 93,610 94,928 36,181 27,466 67,232 61,117 49,273 13,033 75,507 120,170 67,908 141,558 68,298 84,808 26,483

48 61 64 53 47 53 53 50 40 51 47 49 54 42 49 42

138 169 96 72 97 102 88 35 92 73 80 103 46 85 42

22.1 0.7 0.7 0.3 0.2 0.5 0.4 0.3 0.1 0.5 0.8 0.5 1.0 0.5 0.6 0.2

100.0 2.9 3.0 1.1 0.9 2.1 1.9 1.5 0.4 2.4 3.8 2.1 4.4 2.1 2.7 0.8

Table 1 contâ&#x20AC;&#x2122;d Constituency Code

Province/ Constituency Name

Rift Valley Province cont... 121 Marakwet West 122 Keiyo North 123 Keiyo South 124 Mosop 125 Aldai 126 Emgwen 127 Tinderet 128 Baringo East 129 Baringo North 130 Baringo Central 131 Mogotio 132 Eldama Ravine 133 Laikipia West 134 Laikipia East 135 Naivasha 136 Nakuru Town 137 Kuresoi 138 Molo 139 Rongai 140 Subukia 141 Kilgoris 142 Narok North 143 Narok South 144 Kajiado North 145 Kajiado Central 146 Kajiado South 147 Bomet 148 Chepalungu 149 Sotik 150 Konoin 151 Buret 152 Belgut 153 Ainamoi 154 Kipkelion

Estimated Population From 1999 Census

Estimated Number of Poor Individuals

Poverty Incidence Percent of Individuals below Poverty Line

Constituency National Poverty Rank (1 = richest, 210 = poorest)

Constituency Contribution to National Poverty (%)

Constituency Contribution to Provincial Poverty (%)

74,188 54,984 85,764 117,456 124,687 162,775 154,617 61,743 79,277 114,606 44,364 89,762 182,997 121,152 236,519 221,467 170,711 230,127 135,714 151,756 165,790 151,198 194,765 185,591 84,444 113,348 174,471 116,757 133,529 127,536 121,517 159,449 126,828 169,153

30,733 22,426 32,538 53,034 60,620 73,519 87,278 34,438 33,758 49,121 22,611 42,326 79,574 53,322 90,989 99,737 72,692 99,078 58,853 50,922 97,683 76,630 103,040 73,219 40,241 56,666 90,059 65,495 65,779 66,092 59,151 77,836 56,778 78,410

41 41 38 45 49 45 56 56 43 43 51 47 44 44 39 45 43 43 43 34 59 51 53 40 48 50 52 56 49 52 49 49 45 46

41 40 31 64 81 65 112 109 49 50 91 76 55 58 32 63 48 51 54 22 125 90 99 34 78 87 93 111 86 95 82 84 62 71

0.2 0.2 0.2 0.4 0.4 0.5 0.6 0.2 0.2 0.3 0.2 0.3 0.6 0.4 0.6 0.7 0.5 0.7 0.4 0.4 0.7 0.5 0.7 0.5 0.3 0.4 0.6 0.5 0.5 0.5 0.4 0.5 0.4 0.5

1.0 0.7 1.0 1.7 1.9 2.3 2.7 1.1 1.1 1.5 0.7 1.3 2.5 1.7 2.9 3.1 2.3 3.1 1.8 1.6 3.1 2.4 3.2 2.3 1.3 1.8 2.8 2.1 2.1 2.1 1.9 2.4 1.8 2.5

Western Province 155 Malava 156 Lugari 157 Mumias 158 Matungu 159 Lurambi 160 Shinyalu 161 Ikolomani 162 Butere 163 Khwisero 164 Emuhaya 165 Sabatia 166 Vihiga 167 Hamisi 168 Mt Elgon 169 Kimilili 170 Webuye 171 Sirisia 172 Kanduyi 173 Bumula 174 Amagoro 175 Nambale 176 Butula 177 Funyula 178 Budalangi

3,276,348 191,438 167,536 164,783 107,425 216,004 132,177 90,407 109,782 87,361 158,876 114,419 81,048 133,354 133,353 223,898 166,484 177,793 155,205 127,240 175,877 143,244 94,441 73,111 51,092

1,993,506 107,248 106,695 102,882 63,463 135,304 89,658 64,847 69,840 55,692 95,196 68,711 46,242 78,733 73,399 134,450 99,164 103,242 94,054 65,908 88,443 97,963 65,991 50,824 35,557

61 56 64 62 59 63 68 72 64 64 60 60 57 59 55 60 60 58 61 52 50 68 70 70 70

110 165 150 129 154 181 199 164 166 132 134 115 127 107 133 130 118 136 94 89 188 194 191 192

13.9 0.7 0.7 0.7 0.4 0.9 0.6 0.5 0.5 0.4 0.7 0.5 0.3 0.5 0.5 0.9 0.7 0.7 0.7 0.5 0.6 0.7 0.5 0.4 0.2

100.0 5.4 5.4 5.2 3.2 6.8 4.5 3.3 3.5 2.8 4.8 3.4 2.3 3.9 3.7 6.7 5.0 5.2 4.7 3.3 4.4 4.9 3.3 2.5 1.8

Nyanza Province 179 Ugenya 180 Alego 181 Gem

4,246,969 171,801 161,413 134,360

2,738,898 104,558 110,266 91,879

65 61 68 68

137 184 187

19.0 0.7 0.8 0.6

19.0 3.8 4.0 3.4

Table 1 contâ&#x20AC;&#x2122;d Constituency Code

Province/ Constituency Name

Nyanza Province cont... 182 Bondo 183 Rarieda 184 Kisumu Town East 185 Kisumu Town West 186 Kisumu Rural 187 Nyando 188 Muhoroni 189 Nyakach 190 Kasipul-Kabondo 191 Karachuonyo 192 Rangwe 193 Ndhiwa 194 Rongo 195 Migori 196 Uriri 197 Nyatike 198 Mbita 199 Gwassi 200 Kuria 201 Bonchari 202 South Mugirango 203 Bomachoge 204 Bobasi 205 Nyaribari Masaba 206 Nyaribari Chache 207 Kitutu Chache 208 Kitutu Masaba 209 West Mugirango 210 N.Mugirango Borabu National

Estimated Population From 1999 Census

Estimated Number of Poor Individuals

Poverty Incidence Percent of Individuals below Poverty Line

118,265 110,220 200,466 123,971 121,343 106,329 122,739 109,744 174,324 123,202 145,274 128,462 161,081 139,077 85,666 106,502 78,576 73,212 145,250 84,182 122,806 166,189 158,340 103,172 106,920 180,922 166,752 130,249 186,160

83,139 81,008 124,088 81,821 84,513 69,761 70,670 69,758 125,059 88,284 104,335 93,244 70,415 65,487 41,779 51,585 53,720 47,846 117,414 61,881 75,630 104,292 99,456 59,591 60,996 116,795 108,469 88,909 132,250

70 74 62 66 70 66 58 64 72 72 72 73 44 47 49 48 68 65 81 74 62 63 63 58 57 65 65 68 71

27,364,642

14,385,670

Constituency National Poverty Rank (1 = richest, 210 = poorest)

195 204 145 177 193 176 116 161 200 198 201 203 57 75 83 79 186 173 209 205 143 155 157 117 114 170 172 183 197

Constituency Contribution to National Poverty (%)

0.6 0.6 0.9 0.6 0.6 0.5 0.5 0.5 0.9 0.6 0.7 0.6 0.5 0.5 0.3 0.4 0.4 0.3 0.8 0.4 0.5 0.7 0.7 0.4 0.4 0.8 0.8 0.6 0.9

Constituency Contribution to Provincial Poverty (%)

3.0 3.0 4.5 3.0 3.1 2.5 2.6 2.5 4.6 3.2 3.8 3.4 2.6 2.4 1.5 1.9 2.0 1.7 4.3 2.3 2.8 3.8 3.6 2.2 2.2 4.3 4.0 3.2 4.8

100

Chapter Four:

Constituency Development Fund (CDF) Allocations

Background This chapter provides a detailed description of a recent policy application of the small area poverty estimation methodology in Kenya: the formula used to allocate the recently established Constituency Development Fund (CDF). The chapter highlights data sources, methodologies and the criteria used to apportion the CDF among Constituencies. The chapter also describes the positive and negative consequences that could arise as a result of distribution of funds based on imprecise and/or inaccurate data. Finally, the chapter concludes with some suggested relevant policy directions and recommendations targeted at the various institutions engaged in formulating and implementing development policies in Kenya. As already mentioned in chapter one, the Government of Kenya has engaged in the design and implementation of decentralized anti-poverty programs since independence.These interventions were intended to distribute food, assets, cash or services to needy households, individuals and communities. For instance, line Ministries are allocated funds and these then decide on how to apportion these resources to Districts and Communities. Following forty years of project/programme targeting and implementation through line Ministries, it became apparent that channelling of anti-poverty funds through line Ministries was not always effective. Problems arose in the form of imperfect coverage, whereby certain poor communities were sometimes marginalized and not reached by some of the anti-poverty programmes, or leakages occurred in the sense that only a portion of the allocated funds actually reached the targeted population. In order to ensure effective targeting and evaluation of poverty interventions, development planners, policy makers and programme implementers in the Government and the wider development community require key input and output indicators to tailor, monitor and evaluate project and programme features to community level needs and priorities that vary widely across Kenya. Likewise required are benchmark outcome indicators against which the efficiency, effectiveness and impact of program interventions can be evaluated across space and time. Poverty estimates at various sub-National administrative levels provide key outcome indicators in this context. Against this background, in addition to strengthening monitoring and evaluation systems, the Government of Kenya has recently also taken further steps to create complementary windows and programs to facilitate direct allocation of additional resources to Districts and Communities. In the past few years for example, there has been a substantial increase in resources devoted to Constituency and Community based development programs. These include development funds such as the Constituency Development Fund (CDF), the Community Development Trust Fund (CDTF), the Roads Fund, the AIDS Fund, the Local Authority Transfer Fund (LATF), and the Constituency Education Bursary Fund. The direct disbursement of funds at the Community level is intended to improve pro-poor targeting and project implementation by using local information and encouraging community participation especially in project identification, implementation and evaluation. The underlying objective is thus to strengthen targeting by extending coverage whilst minimizing leakages, as well as improving development outcomes by involving local communities in the decision-making process and management of projects. In a recent comprehensive review of the community-based development approach, Mansuri and Rao (2004) warn that although potential gains are large, there are also risks inherent in the basic precepts of the approach. For instance, experiences in some countries indicate there is a risk that the potential benefits of local level involvement may be outweighed by the possibility of resources being captured by local elites. Local level inequality can therefore be important. In the past the natural assumption was that where livelihood conditions are on average at or near subsistence levels, there is little likelihood that well-being would vary much across households and individuals at the local level. However, as demonstrated recently by Elbers et al. (2004), there should be no presumption that inequality is less pronounced within poor communities. This issue will be examined in the Kenya context in a forthcoming report. In the meantime, policy

makers in Kenya must confront the challenge of implementing anti-poverty programmes that target discretionary resources to the local level whilst taking steps to ensure transparency and reporting on subsequent local-level allocation of these resources.

Statistics in the Context of the Constituency Development Fund (CDF) Act The Constituency Development Fund Act, 2003 and the subsequent establishment of the Fund by legislation through Kenya Gazette Supplement No. 107 (ACTS No.11) of 9th January, 2004, ensures that a specific portion of the national annual budget is devoted to the Constituencies for purposes of development. Specifically, an amount of money equal to not less than 2.5% of all Government ordinary revenue collected in every financial year is targeted and set aside for the CDF as a strategy in the fight against poverty at the Constituency level.

The enactment of the 2004 “Constituency Development Fund Act” called for the use of poverty estimates for constituencies to allocate close to 2.5% of the ordinary revenues annually for development programs at the Constituency level. The 2004 Act necessitated the Central Bureau of Statistics (CBS) to produce Constituency level poverty estimates which have not previously been computed in Kenya. The most recent data source for producing poverty estimates in Kenya is the Welfare Monitoring Survey III of 1997, but sample size restrictions inhibit the computation of representative statistics at the Constituency level. Because there is no single recent sample data source that can provide the poverty estimates at the constituency level, the Poverty Analysis and Research Unit (PARU) at CBS undertook to estimate these by using statistical modelling techniques—the small area estimation approach developed by Elbers, Lanjouw and Lanjouw (2003)—that relies on combining data from the 1997 WMS and the 1999 Population and Housing Census. Ultimately, it will be possible to develop more accurate and timely poverty estimates for small areas by using new and improved sources of data from household surveys (e.g., the forthcoming Kenya Integrated Household Budget Survey) and updated population census data. However, while overall poverty levels are changing, to the extent that the geographic distribution of poverty among Constituencies has remained relatively stable, these statistics remain relevant for planning purposes. The small area estimation methodology used in the estimation of poverty at various administrative and political units is described in some detail in Appendix1. In addition to computing the Constituency level poverty estimates using standard small area statistical regression models, the PARU team in CBS also had to develop an additional extrapolation model because no rural households in the North-Eastern province where sampled in the 1997 WMS. In the regional (provincial) regression models, the survey’s direct estimate of household consumption (or income) for the reference year (1997) is the dependent variable and the predictor variables are those variables that are both common and comparable in both census and survey. The Constituency level poverty estimates in the North-Eastern Province are therefore based on regression coefficients and error structures calibrated on 1997 WMS data from the Coast Province combined with Population and Housing Census data from the North-Eastern Province. An evaluation of the computed small area geographic poverty estimates vis-à-vis Provincial and District level poverty measures indicate that the former are generally at least as statistically accurate (based on standard error calculations) as the latter; which have been routinely relied on for sub-National planning purposes. However, this analysis also found that the standard errors for some areas are sizable, particularly for rural communities with small populations. Moreover, the incomplete coverage of the survey data, particularly in the North Eastern Province also contributed to Constituency level poverty estimates that are relatively less precise in statistical terms. For instance, in Tana-River District, the District and Constituency level poverty estimates constitute lower bounds (because the 1997 WMS only covered relatively better off urbanized settlements in the District), while field verification undertaken in Kuria District suggests the poverty estimates constitute upper bounds. Specifically, in Tana River District,

poverty levels were anticipated to fall in the 60-70% range (as opposed to 3040%) while in Kuria poverty levels were expected to fall in the 50-60% range (as opposed to 70-80%). In some areas one should therefore take note of upper and lower bounds on the poverty estimates. With these caveats in mind, the computed Constituency poverty estimates were used in apportioning the Constituency Development Fund for the financial years 2004/05 and 2005/06.

Constituency Development Fund Allocation Criteria According to the Gazette Supplement No.107 referred to above, the budget ceiling for each constituency shall be: “(a) three quarters of the net total CDF divided equally among all constituencies (netting out 5% emergency and 3% administrative takedown), and (b) a quarter of the net total CDF divided by the national poverty index multiplied by the constituency poverty index”.

From a statistical standpoint, this raises two principal challenges. First, while the implementation of component (a) is straightforward, component (b) requires the estimation of poverty index for all 210 Constituencies. Since all Constituencies in the North Eastern province and some in newly established Districts were not covered by the 1997 WMS, substantial statistical extrapolation was required to estimate poverty incidence in these Constituencies and the statistics computed for these areas are therefore relatively less accurate. Secondly, because the CDF was designed in principal to assist development in rural areas (which, relative to most urban areas, are generally characterized by higher poverty incidence, but lower population densities), an additional weighting factor was designed to adjust the allocation formula for population distribution. In line with the above, CBS designed the following formula to allocate the CDF as equitably as possible to each of the 210 political constituencies for financial year 2004/2005:

where CDFund allocated is the Constituency Development Fund allocation to each Constituency, CDF is the total net CDF allocation (after netting out 3% administrative budget and 5% Constituency Emergency Budget) and the Weighted Contribution Poverty is the weighted poverty contribution of each Constituency to national poverty.The un-weighted poverty contribution of each Constituency to National poverty is presented in Table 1 (see Chapter 3) and is shown on Map 23 at the end of this Chapter. The weighting factor is derived from the ratio of urban-rural poor population and is derived from the 1999 population and housing census. The weighting favours rural areas by attaching a weight factor of 0.23 to urban areas vis-à-vis unity for rural Constituencies. The absolute contribution to national poverty of each urban Constituency is thus multiplied by this adjusted weighting factor. Several aspects were considered in the derivation of the weighting factor that favours rural areas in the allocation of the Constituency Development Fund. Firstly, from the analysis of poverty based on the 1999 Kenya population and housing census, it was established that the share of urban poor to rural poor population was about 19 and 81 per cent respectively. Secondly, it was assumed that majority of Kenyans who live and derive their livelihoods in urban areas, have a home and/or family in their rural communities of origin where they visit occasionally and often ultimately migrate back to. Thirdly, it was further postulated that the concentration of people in slum settlements with very poor social amenities in urban areas could be an indication that the living conditions and economic opportunities in their respective rural areas are probably worse than what they experience in their urban areas. And, finally, if rural areas are better developed and more capable of absorbing the growing population, then fewer people might be attracted to migrate into urban slums. Improved rural access to social and economic facilities/opportunities and services such as clinics, schools, access roads, water and factories/commercial centres is therefore

among the highest of development priorities for a large majority of the population. The allocation scheme further recognises that although some pure urban constituencies contribute more poor people to national poverty than their rural counterparts, most of the people found in these urban areas (especially those in slum and informal settlements) have a base in the rural areas where they would like to go back to. Thus, while there are socio-economic pull and push factors, the apparent urban concentration of people in slum like settlements arises out of the economic fact that the living conditions in rural areas are deplorable and unattractive. There is therefore need to develop the rural areas in order to reduce the rural-urban influx. This is premised on the assumption that no one would like to live in a slum area that is worse off than the rural area of origin and that over time there has been over-concentration of social amenities and development of infrastructure in the urban areas which has attracted people from rural areas to the urban. The general observable impact of this trend has been the mushrooming of urban slums and emerging pitiable living conditions in “Based on the above formula and most of Kenya’s urban areas. criteria, a total of KSh 5.6 billion and Based on the above formula KSh. 7.25 billion were earmarked for and criteria, a total of KSh 5.6 the CDF in the fiscal year 2004/2005 billion and KSh. 7.25 billion were earmarked for the CDF and 2005/2006 respectively.” in the fiscal year 2004/2005 and 2005/2006 respectively.

Challenges and Opportunities Allocation Formulas: The use of small-area income and poverty estimates for allocating funds or related programme purposes imposes significant requirements if the estimates are to satisfy the intentions of programme legislation. Such requirements include the desired concept or definition of poverty or income measured, the level of geographic detail, the level of population or demographic detail, the timeliness of production and updating contingent on new survey and census data, and the accuracy of measurement. The selection of a set of estimates for use in a given programme will generally involve trade-offs among competing goals. In the context of CDF allocations, it is important to consider features of the specific allocation criteria and formula, some of which may be sensitive to the level of accuracy in the estimates. For example, if a formula embodies an eligibility threshold, an area that is erroneously estimated to be below the threshold may not receive any funds, even if the degree of underestimation is small. Policy makers thus need to be informed about the consequences of alternative estimates and formula provisions. For this purpose, policy makers are urged to commission regular assessments of formulas and the estimates used in them with a view to identifying key issues and provide guidance for research to strengthen the underlying statistics. Data Constraints: For programme purposes, policy makers should consider the advantages and disadvantages of alternative sources of income and poverty estimates and evaluate estimates and indicators that are in consonance with programme goals. Data from the decennial census have the advantage of providing the opportunity to compute small-area poverty estimates, but updated census estimates are only available every 10 years. As more data becomes available from such sources as the KIHBS 2004/05, the possible use of cutting edge statistical modelling techniques that make use of multiple years of data from the same survey, separate surveys, administrative data or all, could be potentially useful in updating the underlying statistical database. Further research on such approaches should be supported. Program Implementation: As described earlier, initiatives such as the CDF are intended to improve poverty targeting and project implementation by using local information and encouraging community participation. In practice, however, the potential benefits of local involvement may be outweighed by the possibility of resources being captured by local elites.Thus, although potential gains are large, there are also important risks inherent in the basic precepts of the approach. Large scale projects seeking to administer country-wide cash transfers to

communities, such as the CDF, cannot at the central level take into account the full range of local constituency characteristics that could possibly affect project performance. Policymakers must therefore confront the challenge of designing schemes that take critical local information into account but are not prohibitively costly to implement. On the other hand, new programmes such as the CDF, provide an opportunity for commissioning research towards gaining a better

understanding of implementation and targeting effectiveness, identifying bestpractices at the Community level and developing guidelines and monitoring systems to safeguard and stimulate transparent and effective allocation of project funds at the Constituency level amongst competing projects, communities and implementing agencies and firms.

Map 23: Constituency Level Contribution to Poverty - Kenya

Chapter Five: Urban-Rural Perspectives on poverty and Inequality Rural Poverty Profile at the Constituency Level The rural poverty rate across the 198 Constituencies with rural populations varies from a low of 16 per cent in Kabete Constituency, Central Province, to a high of 84 per cent in Ganze Constituency in Coast Province. The poverty gap index among rural Constituencies likewise varies extensively from lows of 4 and 5 per cent in Kiambaa and Kabete Constituencies respectively, to highs of 34 and 39 per cent in Kuria and Ganze Constituencies. The results thus indicate there is substantial variation in poverty incidence and depth among rural Constituencies in Kenya. Moreover, even within Provinces, there are some marked differences in well being among Constituencies. Central Province Compared to the Central Province average rural poverty rate of 31 per cent, rural poverty incidence among Constituencies varies from a low of 16 per cent in Kabete Constituency to a high of 44 per cent in Mwea Constituency. Rural residents in Mwea Constituency are thus 2.8 times more likely to be poor compared to those living in Kabete Constituency. Compared to the Central Province average rural poverty gap of 8.3 per cent, the rural depth of poverty ranges from a low of 4 per cent in Kiambaa Constituency to a high of 14 per cent in Mwea Constituency. This implies that, on average, every poor person in Mwea Constituency would require KSh 173 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Kiambaa Constituency would require almost 4 times fewer resources. Coast Province Compared to the Coast Province average rural poverty rate of 63 per cent, rural poverty incidence among Constituencies varies from a low of 30 per cent in Bura Constituency to a high of 84 per cent in Ganze Constituency. Rural residents in Ganze Constituency are thus almost 3 times more likely to be poor compared to those living in Bura Constituency. Compared to the Coast Province average rural poverty gap of 24 per cent, the rural depth of poverty ranges from a low of 9 per cent in Bura Constituency to 39 per cent in Ganze. This implies that, on average, every poor person in Ganze Constituency would require KSh 485 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Bura Constituency would require just over 4 times fewer resources. Eastern Province Compared to the Eastern Province average rural poverty rate of 63 per cent, rural poverty incidence among Constituencies varies from a low of 34 per cent in Ntonyiri Constituency to a high of 76 per cent in Kitui South Constituency. Rural residents in Kitui South Constituency are thus just over 2 times more likely to be poor compared to those living in Ntonyiri Constituency. Compared to the Eastern Province average rural poverty gap of 21 per cent, the rural depth of poverty ranges from a low of 9 per cent in Ntonyiri Constituency to 30 per cent in Kitui South. This implies that, on average, every poor person in Kitui South Constituency would require KSh 377 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Ntonyiri Constituency would require just over 3 times fewer resources. North Eastern Province Compared to the North Eastern Province average rural poverty rate of 64 per cent, rural poverty incidence among Constituencies varies from 61 per cent in Fafi Constituency to 71 per cent in Wajir North Constituency. Rural dwellers in Wajir North Constituency are thus 1.2 times more likely to be poor compared to those living in Fafi Constituency. Compared to the North Eastern Province

average rural poverty gap of 22 per cent, the rural depth of poverty ranges from a low of 21 per cent in Fafi Constituency to 25 per cent in Wajir North. This implies that, on average, every poor person in Wajir North Constituency would require KSh 309 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Fafi Constituency would require about 1.2 times fewer resources. Nyanza Province Compared to the Nyanza Province average rural poverty rate of 65 per cent, rural poverty incidence among Constituencies varies from 44 per cent in Rongo Constituency in Migori District to a high of 80 per cent in Kuria Constituency. Rural constituents in Kuria are thus almost 2 times more likely to be poor compared to those living in Rongo Constituency. Compared to the Nyanza Province average rural poverty gap of 24 per cent, the rural depth of poverty ranges from a low of 14 per cent in Rongo Constituency to 34 per cent in Kuria. This implies that, on average, every poor person in Kuria Constituency would require KSh 419 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Rongo Constituency would require close to2.5 times fewer resources.

Rift Valley Province Compared to the Rift Valley Province average rural poverty rate of 48 per cent, rural poverty incidence among Constituencies varies from a low of 32 per cent in Subukia Constituency to a high of 64 per cent in Turkana Central Constituency. Rural dwellers in Turkana Central Constituency are thus 2 times more likely to be poor compared to those living in Subukia Constituency. Compared to the Rift Valley Province average rural poverty gap of 16 per cent, the rural depth of poverty ranges from a low of 9 per cent in Subukia Constituency to 26 per cent in Turkana Central. This implies that, on average, every poor person in Turkana Central Constituency would require KSh 326 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Subukia Constituency would require almost 3 times fewer resources.

Western Province Compared to the Western Province average rural poverty rate of 60 per cent, rural poverty incidence among Constituencies varies from a low of 48 per cent in Amagoro Constituency in Teso District to a high of 72 per cent in Ikolomani Constituency in Kakamega district. Rural constituents in Ikolomani are thus 1.5 times more likely to be poor compared to their counterparts living in Amagoro Constituency. Compared to the Western Province average rural poverty gap of 22 per cent, the rural depth of poverty ranges from a low of 15 per cent in Amagoro Constituency to 30 per cent in Ikolomani. This implies that, on average, every poor person in Amagoro Constituency would require KSh 374 (in 1997 KSh) to climb above the rural poverty line of KSh 1,239 per month per person (in 1997 KSh); by contrast, the poor in Ikolomani Constituency would require 2 times fewer resources.

Urban Poverty Profile at the Constituency Level Nairobi Province Compared to the Nairobi Province average urban poverty rate of 44 per cent, urban poverty incidence among Constituencies ranges from 31 per cent in Westlands Constituency to a high of 59 per cent in Makadara Constituency. Urban constituents in Makadara are thus just over 2 times more likely to be poor compared to their counterparts living in Westlands Constituency. Compared to the Nairobi Province average urban poverty gap of 14 per cent, the urban depth of poverty widely varies from a low of 10 per cent in Westlands Constituency to 23 per cent in Makadara. The poverty gap differential implies

that, on average, every poor person in Makadara Constituency would require 609 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Westlands Constituency would require just over 2 times less resources. Central Province Compared to the Central Province average urban poverty rate of 49 per cent, urban poverty incidence among Constituencies ranges from a low of 9 per cent in Maragwa Constituency to a high of 84.6 per cent in Kangema Constituency. Urban residents in Kangema are thus 10 times more likely to be poor compared to their counterparts living in Maragwa Constituency. Compared to the Central Province average urban poverty gap of 21 per cent, the urban depth of poverty widely varies from a low of 3 per cent in Maragwa Constituency to 54 per cent in Kangema. The poverty gap differential implies that, on average, every poor person in Kangema Constituency would require 1,430 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Maragwa Constituency would require 16 times fewer resources.

(in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Dujis Constituency would require almost 2 times fewer resources. Nyanza Province Compared to the Nyanza Province average urban poverty rate of 63 per cent, urban poverty incidence among Constituencies varies from a low of 8 per cent in Kitutu Masaba Constituency to a high of 99 per cent in Nyaribari Masaba Constituency. Urban dwellers in Nyaribari Masaba are thus 13 times more likely to be poor compared to their counterparts living in Kitutu Masaba Constituency. Compared to the Nyanza Province average urban poverty gap of 25 per cent, the urban depth of poverty widely varies from a low of 2 per cent in Kitutu Masaba Constituency to 68 per cent in Nyaribari Masaba. The poverty gap differential implies that, on average, every poor person in Nyaribari Masaba Constituency would require 1,801 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Kitutu Masaba Constituency would require 34 times fewer resources.

Coast Province Compared to the Coast Province average urban poverty rate of 47 per cent, urban poverty incidence among Constituencies widely varies from 34 per cent in Mvita Constituency to a high of 66 per cent in Garsen Constituency. Urban dwellers in Garsen are thus almost 2 times more likely to be poor compared to those living in Mvita Constituency. Compared to the Coast Province average urban poverty gap of 17 per cent, the urban depth of poverty ranges from 11 per cent in Mvita Constituency to 27 per cent in Garsen. This poverty gap differential implies that, on average, every poor person in Garsen Constituency would require 723 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Mvita Constituency would require 2.4 times fewer resources.

Rift Valley Province Compared to the Rift Valley Province average urban poverty rate of 54 per cent, urban poverty incidence among Constituencies varies from a low of 19 per cent in Ainamoi Constituency to a high of 71 per cent in Turkana South Constituency. Urban residents in Turkana South are thus about 4 times more likely to be poor compared to their counterparts living in Ainamoi Constituency. Compared to the Rift Valley Province average urban poverty gap of 19 per cent, the urban depth of poverty widely varies from a low of 5 per cent in Ainamoi Constituency to 26 per cent in Turkana South. The poverty gap differential implies that, on average, every poor person in urban Turkana South Constituency would require 678 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Ainamoi Constituency would require just over 5 times fewer resources.

Eastern Province Compared to the Eastern Province average urban poverty rate of 51 per cent, urban poverty incidence among Constituencies varies from a low of 19 per cent in Saku Constituency to a high of 95 per cent in Laisamis Constituency. Urban residents in Laisamis are thus about 9 times more likely to be poor compared to their counterparts living in Saku Constituency. Compared to the Eastern Province average urban poverty gap of 21 per cent, the urban depth of poverty widely varies from a low of 7 per cent in Saku Constituency to 62 per cent in Laisamis. The poverty gap differential implies that, on average, every poor person in Laisamis Constituency would require 1,639 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Saku Constituency would require almost 9 times fewer resources.

Western Province Compared to the Western Province average urban poverty rate of 68 per cent, urban poverty incidence among Constituencies varies from 59 per cent in Matungu Constituency to 84 per cent in Funyula Constituency. Urban residents in Funyula are thus about 1.4 times more likely to be poor compared to their counterparts living in Matungu Constituency. Compared to the Western Province average urban poverty gap of 34 per cent, the urban depth of poverty varies from 27 per cent in Matungu Constituency to 46 per cent in Funyula. The poverty gap differential implies that, on average, every poor person in urban Funyula Constituency would require 1,223 KSh (in 1997 KSh) to climb above the urban poverty line of KSh 2,648 per month per person (in 1997 KSh); by contrast, the poor in Matungu Constituency would require almost 2 times fewer resources.

North Eastern Province Compared to the North Eastern Province average urban poverty rate of 61 per cent, urban poverty incidence among Constituencies ranges from 52 per cent in Dujis Constituency to 75 per cent in Ijara Constituency. Urban constituents in Ijara are thus almost 1.5 times more likely to be poor compared to their counterparts living in Dujis Constituency. Compared to the North Eastern Province average urban poverty gap of 24 per cent, the urban depth of poverty varies from 19 per cent in Dujis Constituency to 33 per cent in Ijara. This implies that, on average, every poor person in Ijara Constituency would require 871 KSh

Inequality Profile at the Constituency Level While poverty measures provide statistics pertaining to the distribution of consumption below the poverty line, inequality measures summarize the variation in the distribution as a whole; including both the rich and the poor. For instance, the Gini index measures the extent to which the distribution of income or consumption among the population deviates from a perfectly equal distribution (i.e., when all people earn or consume exactly the same amount). A Gini index measure of 0 represents prefect equality, while a Gini index of 100

Table 2: Distribution of Income and Consumption for Selected Countries Country

Survey

Gini

Year

Index

Denmark

1997

Sweden Norway

Percentage Share of Income or Consumption Lowest 10%

Lowest 20%

Second 20%

Third 20%

24.7

2.6

8.3

14.7

18.2

22.9

35.8

21.3

2000

25.0

3.6

9.1

14.0

17.6

22.7

36.6

22.2

2000

25.8

3.9

9.6

14.0

17.2

37.2

23.4

United Kingdom 1999

36.0

2.1

6.1

11.4

16.0

22.5

44.0

28.5

Tanzania

1993

38.2

2.8

6.8

11.0

15.1

21.6

45.5

30.1

U. S.A.

2000

40.8

1.9

5.4

10.7

15.7

22.4

45.8

29.9

Ghana

1998/98

40.8

2.1

5.6

10.1

14.9

22.9

46.6

30.0

Kenya

1997

42.5

2.5

6.0

9.8

14.3

20.8

49.1

Uganda

1999

43.0

2.3

5.9

10.0

14.0

20.3

49.7

Nigeria

1996/97

50.6

1.6

4.4

8.2

12.5

19.3

55.7

South Africa

2000

57.8

1.4

3.5

6.3

10.0

18.0

62.2

Namibia

1993

70.7

0.5

1.4

3.0

5.4

11.5

78.7

Source: 2005 World Development Indicators implies perfect inequality. In 1997, the Gini index measure of inequality for Kenya was estimated at 42.5, which is comparable to its East African neighbours Tanzania (38.2) and Uganda (43.0), but indicates substantially higher levels of inequality vis-à-vis, for instance, Scandinavian economies such as Denmark (24.7) and Sweden (25.0) which boast among the lowest inequality rates worldwide (see Table 2). Kenya’s richest 10 per cent of households are responsible for 33.9 per cent of the total consumption, in contrast to the mere 2.5 per cent attributable to the poorest 10 per cent of the population (World Development Indicators, 2005). Within Kenya, the Gini index at the Constituency level varies from a low level of 22 in Wajir South and Mandera West to a high level of 42 in Kajiado North among the rural population. Among the urban population at the Constituency level, the Gini index varies from 14 to 51 and is indicative of relatively (vis-à-vis rural population) higher levels of inequality both among and within urban areas across Constituencies. The sections below summarize the Constituency inequality profiles by place of residence (rural and urban) within each Province. Nairobi Province The average Gini Index for Constituencies in the all urban Nairobi Province is 38.The Gini Index varies from 34 in Kasarani Constituency to 40 in both Langata and Westlands Constituencies. Kasarani residents are therefore the least unequal, while the most unequal residents are found in both Langata and Westlands Constituencies. Central Province The average Gini Index for Constituencies in rural Central Province is 37. The Gini Index widely varies from the least unequal rural Constituencies Gatundu South and Kangema where the Gini Index is 29, to the most unequal Constituencies Kabete and Limuru with a Gini Index of 39. The average Gini Index among Central Province urban Constituencies is 48 which is far much higher compared to the rural average Gini Index. The urban Gini index for the

Fourth 20% Highest 20% Highest 10%

5 33.9 34.9

40.8

44.7 64.5

Province widely varies from a low of 35 in Kiharu Constituency to 51 in Kinangop Constituency.

Coast Province The average Gini index for Constituencies in rural Coast Province is 36.The Gini index widely varies from the least unequal rural Constituency Ganze with an index of 29, to the most unequal Constituency Lamu East with a Gini index of 39.The average Gini index among Coast Province urban Constituencies is 36 and is the same as that of the rural Constituencies. The urban Gini index for the Province varies minimally from 34 in Mwatate Constituency to 38 in Ganze Constituency. Eastern Province The average Gini index for Constituencies in rural Eastern Province is 34. The Gini index widely varies from 29 in Moyale Constituency representing the least unequal rural Constituency to 40 in Gachoka Constituency representing the most unequal rural Constituency in the Province.The average Gini index among Eastern Province urban Constituencies is 44 and thus substantially higher compared to the rural average Gini Index.The urban Gini index for the Province widely varies from a low of 30 in Laisamis Constituency to 44 in Moyale Constituency. North Eastern Province The average Gini index for Constituencies in rural North Eastern Province is 29. The Gini index varies minimally from a low of 22 among residents of Mandera East Constituency representing the least unequal rural Constituency to 31 in Fafi Constituency representing the most unequal rural Constituency in the Province. The average Gini index among North Eastern Province urban Constituencies is 36 which is higher compared to the rural average Gini Index. The urban Gini index for the Province varies only minimally from a low of 30 in Ijara Constituency to a high of 36 in Wajir East Constituency.

Western Province The average Gini index for Constituencies in rural Western Province is 34.The Gini index widely varies from a low of 28 in Butula Constituency representing the least unequal rural Constituency to a high of 41 in Hamisi Constituency representing the most unequal rural Constituency in the Province.The average Gini index among Western Province urban Constituencies is 45 which is higher than the rural average Gini Index. The urban Gini index within the Province widely varies from a low of 38 in Funyula Constituency to 45 in Lurambi and Sabatia Constituencies. Rift Valley Province The average Gini index for Constituencies in rural Rift Valley Province is 35.The Gini index widely varies from a low of 31 in Baringo North Constituency representing the least unequal rural Constituency to a high of 42 in Kajiado North Constituency representing the most unequal rural Constituency in the

Province. The average Gini index among Rift Valley Province urban Constituencies is 35 which is comparable to the rural average Gini Index. The urban Gini index within the Province widely varies from a low of 25 in Turkana South Constituency to 46 in Laikipia East Constituency. Nyanza Province The average Gini index for Constituencies in rural Nyanza Province is 34. The Gini index widely varies from a low of 29 in Kuria Constituency representing the least unequal rural Constituency to a high of 39 in Rongo Constituency representing the most unequal rural Constituency in the Province.The average Gini index among Nyanza Province urban Constituencies is 39.

Table 3:

Constituency Level Rural Poverty and Inequality Estimates

Constituency Province/ Code Constituency Name

Poverty Incidence Percent of Individuals below Poverty Line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Gini Index Inequality measure (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Coast Province Msambweni Matuga Kinango Bahari Kaloleni Ganze Malindi Magarini Garsen Galole Bura Lamu East Lamu West Taveta Wundanyi Mwatate Voi

63 62 53 75 65 75 84 65 69 41 37 31 42 58 54 65 59 61

(2) (5) (4) (4) (4) (4) (4) (4) (4) (7) (5) (9) (7) (5) (5) (5) (5) (4)

24 23 18 31 25 32 39 25 26 13 11 9 13 22 18 25 21 23

(1) (3) (2) (3) (2) (3) (4) (3) (3) (3) (2) (3) (3) (3) (2) (3) (3) (3)

36 34 35 32 35 33 31 35 32 35 36 34 36 39 33 33 34 37

(1) (1) (1) (1) (2) (2) (1) (1) (1) (2) (2) (2) (2) (2) (1) (2) (1) (2)

1,519,527 147,686 114,474 158,675 176,695 176,705 109,229 110,038 104,652 61,507 36,711 63,782 32,148 74,900 40,192 48,194 51,246 63,501

957,302 91,084 60,349 119,192 114,720 132,924 92,073 71,884 72,239 24,940 13,496 19,461 13,575 43,632 21,721 31,279 30,109 38,503

(22,018) (4,401) (2,644) (4,316) (4,403) (5,574) (3,716) (3,092) (3,035) (1,714) (740) (1,693) (905) (2,067) (1,048) (1,644) (1,597) (1,728)

30 31 32 33 34 35 36 37 38 39 40

North Eastern Province Dujis Lagdera Fafi Ijara Wajir North Wajir West Wajir East Wajir South Mandera West Mandera Central Mandera East

64 67 63 61 62 71 62 64 68 62 66 63

(4) (20) (23) (22) (21) (18) (24) (23) (23) (22) (21) (22)

22 24 22 21 21 25 21 22 24 21 22 21

(2) (12) (13) (12) (11) (11) (13) (13) (14) (13) (12) (12)

29 25 28 31 24 23 23 23 22 22 23 23

(2) (1) (5) (8) (1) (1) (1) (1) (1) (1) (1) (1)

654,715 49,480 65,436 43,708 34,735 55,485 61,517 75,752 75,815 70,208 74,486 48,093

421,636 33,340 41,551 26,786 21,703 39,357 37,962 48,636 51,822 43,789 48,848 30,476

(18,130) (6,797) (9,730) (5,940) (4,602) (6,983) (9,171) (11,176) (11,882) (9,616) (10,059) (6,739)

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

Eastern Province Moyale North Horr Saku Laisamis Isiolo North Isiolo South Igembe Ntonyiri Tigania West Tigania East North Imenti Central Imenti South Imenti Nithi Tharaka Manyatta Runyenjes Gachoka Siakago Mwingi North Mwingi South Kitui West Kitui Central Mutito Kitui South Masinga Yatta Kangundo Kathiani Machakos Town Mwala Mbooni Kilome Kaiti Makueni Kibwezi

59 71 62 57 40 51 54 58 34 61 60 41 44 42 59 63 55 58 59 70 62 63 68 73 68 76 64 62 59 56 54 64 65 61 66 67 54

(2) (4) (4) (4) (6) (4) (7) (3) (7) (3) (3) (3) (4) (4) (3) (4) (3) (3) (4) (3) (3) (3) (3) (4) (5) (4) (3) (3) (3) (3) (3) (3) (3) (3) (3) (2) (3)

21 26 22 20 12 17 18 19 9 21 21 13 14 13 21 22 19 21 22 29 22 22 26 30 26 30 23 22 21 20 18 23 24 22 24 25 18

(1) (3) (2) (2) (2) (2) (3) (1) (3) (2) (2) (1) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (3) (3) (3) (2) (2) (1) (1) (2) (2) (2) (2) (2) (1) (2)

34 29 30 33 33 36 32 31 30 30 31 36 34 34 34 31 34 33 40 37 31 31 32 33 31 30 32 32 33 39 34 32 32 33 31 32 33

(1) (1) (1) (2) (2) (2) (2) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (3) (2) (1) (1) (1) (1) (1) (1) (1) (1) (1) (3) (1) (1) (1) (1) (1) (1) (1)

4,231,468 37,519 39,278 24,262 36,071 51,971 14,896 173,795 169,186 112,551 123,071 168,583 123,961 143,755 193,716 98,613 107,642 129,667 97,963 67,698 153,166 135,738 138,292 148,284 89,021 114,150 104,027 117,794 177,625 113,989 149,908 146,866 165,007 78,840 110,093 192,891 181,579

2483872 26,657 24,338 13,800 14,397 26,249 8,059 100,021 57,667 68,976 74,398 68,692 53,999 60,605 114,405 62,249 58,863 75,838 57,789 47,495 95,699 84,906 93,844 108,932 60,512 86,753 66,256 73,349 104,636 63,795 81,042 93,847 107,688 48,132 72,184 128,385 97,772

(39,742) (1,073) (923) (613) (844) (1,119) (548) (2,945) (4,324) (2,088) (2,287) (2,370) (2,047) (2,223) (3,255) (2,324) (1,982) (2,188) (2,396) (1,450) (3,113) (2,309) (2,841) (4,287) (2,737) (3,184) (1,886) (2,484) (2,805) (1,637) (2,480) (2,612) (3,041) (1,421) (2,361) (2,713) (3,051)

Central Province Kinangop

31 (2) 36 (16)

8 10

(1) (6)

37 31

(1) (1)

3,228,306 131,354

1,004,003 47,257

(17,068) (7,670)

Table 3 contâ&#x20AC;&#x2122;d Constituency Province/ Code Constituency Name

Central Province cont... 78 Kipipiri 79 Ol_Kalou 80 Ndaragwa 81 Tetu 82 Kieni 83 Mathira 84 Othaya 85 Mukurwe-Ini 86 Nyeri Town 87 Mwea 88 Gichugu 89 Ndia 90 Kerugoya/Kutus 91 Kangema 92 Mathioya 93 Kiharu 94 Kigumo 95 Maragwa 96 Kandara 97 Gatanga 98 Gatundu South 99 Gatundu North 100 Juja 101 Githunguri 102 Kiambaa 103 Kabete 104 Limuru 105 Lari

106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146

Rift Valley Province Turkana North Turkana Central Turkana South Kacheliba Kapenguria Sigor Samburu West Samburu East Kwanza Saboti Cherangany Eldoret North Eldoret East Eldoret South Marakwet East Marakwet West Keiyo North Keiyo South Mosop Aldai Emgwen Tinderet Baringo East Baringo North Baringo Central Mogotio Eldama Ravine Laikipia West Laikipia East Naivasha Nakuru Town Kuresoi Molo Rongai Subukia Kilgoris Narok North Narok South Kajiado North Kajiado Central Kajiado South

Poverty Incidence Percent of Individuals below Poverty Line (Std. error)

40 31 25 32 27 24 26 31 33 44 34 31 32 29 29 27 31 37 37 35 31 37 34 20 17 16 22 31

Poverty Gap As a percent of the poverty line (Std. error)

Gini Index Inequality measure (Std. error)

Estimated Population From 1999 census

(16) (14) (15) (15) (11) (14) (15) (15) (13) (18) (15) (14) (16) (15) (15) (14) (17) (15) (16) (17) (17) (15) (17) (13) (10) (11) (12) (14)

12 8 6 9 8 7 7 8 9 14 9 8 9 8 8 7 8 11 10 10 8 10 10 5 4 5 6 9

(7) (5) (5) (6) (4) (5) (5) (6) (5) (9) (6) (5) (6) (5) (5) (5) (6) (6) (6) (7) (6) (6) (7) (4) (3) (4) (4) (5)

30 32 30 34 38 34 33 32 34 33 33 33 35 29 30 30 29 31 29 32 29 29 32 34 38 39 39 37

(1) (1) (1) (2) (2) (2) (2) (1) (2) (1) (2) (1) (2) (1) (1) (1) (1) (1) (1) (2) (1) (1) (1) (2) (2) (3) (2) (2)

72,299 135,164 78,144 79,438 137,414 141,694 81,493 83,832 51,291 120,246 114,844 88,433 93,363 56,571 108,304 163,327 115,410 100,122 151,027 170,187 111,704 97,400 79,575 118,767 154,645 181,846 100,172 105,715

47 (4) 60 (7) 64 (7) 52 (7) 47 (8) 51 (5) 53 (5) 47 (6) 33 (7) 51 (6) 45 (6) 48 (6) 48 (7) 39 (5) 40 (6) 42 (8) 41 (8) 40 (7) 38 (5) 45 (6) 49 (5) 48 (6) 57 (6) 56 (9) 42 (9) 41 (8) 50 (10) 44 (8) 38 (6) 38 (5) 36 (5) 34 (9) 42 (5) 37 (5) 43 (5) 32 (5) 59 (6) 49 (7) 53 (7) 37 (4) 47 (5) 48 (5)

16 23 26 19 15 17 19 16 9 17 15 16 16 12 12 13 13 12 11 15 16 16 22 20 13 14 17 14 12 12 11 10 13 11 14 9 22 16 18 13 16 16

(2) (4) (5) (4) (4) (2) (3) (3) (3) (3) (3) (3) (3) (2) (2) (4) (4) (3) (2) (3) (2) (3) (4) (5) (4) (3) (5) (4) (3) (2) (2) (4) (2) (2) (2) (2) (4) (4) (4) (2) (3) (3)

35 33 35 36 33 35 33 34 32 33 34 33 34 34 34 34 33 34 33 33 32 33 36 33 31 35 32 33 33 35 37 34 33 36 35 36 33 33 33 42 36 34

(1) (2) (2) (3) (2) (2) (1) (1) (1) (1) (1) (1) (1) (1) (1) (2) (2) (2) (1) (1) (1) (1) (2) (2) (2) (2) (2) (2) (1) (1) (1) (2) (1) (2) (1) (1) (1) (1) (1) (3) (2) (1)

5,738,868 130,906 132,270 68,386 58,991 114,498 114,469 77,234 25,694 147,829 213,743 140,129 169,646 136,709 118,675 63,640 74,188 51,153 84,027 115,748 124,687 145,833 151,002 61,714 78,529 101,287 40,382 72,361 153,644 92,039 181,691 9,305 167,797 170,102 133,551 145,510 158,606 125,975 194,765 131,668 74,130 100,350

Estimated Number of Poor Individuals (Std. error)

28,802 42,281 19,679 25,088 37,693 34,185 21,120 25,660 16,698 53,178 38,995 27,762 29,907 16,518 31,505 43,981 36,260 37,374 55,256 58,942 34,333 36,009 26,933 23,259 25,711 29,307 21,873 32,738

(4,728) (5,937) (2,902) (3,703) (4,220) (4,849) (3,192) (3,845) (2,191) (9,825) (5,999) (3,919) (4,775) (2,557) (4,778) (6,211) (6,044) (5,684) (8,981) (9,869) (5,795) (5,547) (4,511) (2,916) (2,628) (3,201) (2,655) (4,521)

2,691,529 (110,353) 78,093 (5,084) 84,924 (5,716) 35,865 (2,560) 27,466 (2,136) 58,533 (2,870) 61,117 (3,020) 36,067 (2,135) 8,547 (630) 75,507 (4,591) 96,828 (6,125) 67,908 (4,226) 81,552 (5,615) 53,363 (2,701) 47,603 (2,756) 26,483 (2,049) 30,733 (2,592) 20,355 (1,500) 31,646 (1,689) 52,641 (3,173) 60,620 (3,131) 69,335 (4,319) 86,401 (5,119) 34,422 (3,032) 33,333 (3,161) 41,956 (3,228) 20,209 (2,006) 31,516 (2,563) 58,851 (3,765) 35,142 (1,757) 64,695 (3,223) 3,120 (289) 70,853 (3,805) 62,490 (3,361) 57,700 (3,107) 46,711 (2,343) 93,163 (5,792) 62,034 (4,577) 103,040 (7,329) 48,589 (2,139) 34,926 (1,720) 48,407 (2,573)

Table 3 contâ&#x20AC;&#x2122;d Constituency Code

Province/ Constituency Name

Rift Valley Province cont... 147 Bomet 148 Chepalungu 149 Sotik 150 Konoin 151 Buret 152 Belgut 153 Ainamoi 154 Kipkelion

Poverty Incidence Percent of Individuals below Poverty Line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Gini Index Inequality measure (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

52 56 49 52 49 49 51 47

(7) (7) (7) (6) (6) (5) (6) (6)

18 20 17 18 16 17 18 15

(3) (4) (3) (3) (3) (3) (3) (3)

33 32 34 36 34 34 36 33

(1) (1) (1) (2) (1) (1) (2) (2)

170,266 116,757 131,139 127,536 119,220 157,936 100,695 162,456

87,857 65,495 64,511 66,092 58,070 77,511 51,734 76,601

(5,866) (4,825) (4,207) (3,640) (3,467) (3,942) (3,073) (4,974)

155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178

Western Province Malava Lugari Mumias Matungu Lurambi Shinyalu Ikolomani Butere Khwisero Emuhaya Sabatia Vihiga Hamisi Mt Elgon Kimilili Webuye Sirisia Kanduyi Bumula Amagoro Nambale Butula Funyula Budalangi

60 56 63 63 59 63 68 72 62 64 59 58 56 59 54 59 59 57 58 52 49 68 69 69 69

(2) (4) (4) (3) (4) (3) (3) (3) (3) (4) (5) (5) (5) (5) (4) (3) (3) (3) (3) (3) (4) (4) (4) (4) (4)

22 19 24 23 22 23 28 30 23 24 23 23 20 23 17 21 21 19 21 17 15 24 24 24 27

(1) (2) (2) (2) (2) (2) (2) (3) (2) (2) (3) (3) (3) (3) (2) (2) (2) (1) (2) (1) (2) (2) (2) (2) (3)

34 33 36 32 34 36 36 35 33 33 36 39 34 41 29 33 35 31 35 31 30 30 28 30 41

(2) (2) (1) (1) (1) (2) (2) (1) (1) (1) (1) (2) (1) (11) (1) (1) (1) (1) (1) (1) (1) (1) (2) (1) (2)

3,016,119 189,887 158,983 131,728 107,011 164,152 132,177 90,407 101,033 87,361 150,762 104,183 74,089 133,354 128,313 208,837 147,111 170,967 116,623 127,240 157,206 127,478 89,571 71,991 45,655

1,818,720 106,067 100,435 82,616 63,219 103,395 89,658 64,847 62,961 55,692 88,543 60,790 41,201 78,733 69,799 123,260 87,411 98,116 67,700 65,908 76,359 87,241 62,173 49,882 31,634

(32,737) (3,852) (3,547) (2,857) (2,293) (3,197) (2,551) (2,247) (1,785) (2,043) (4,109) (2,966) (2,112) (3,969) (2,603) (4,065) (2,950) (2,958) (2,110) (2,038) (3,330) (3,533) (2,515) (1,800) (1,271)

179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210

Nyanza Province Ugenya Alego Gem Bondo Rarieda Kisumu Town East Kisumu Town West Kisumu Rural Nyando Muhoroni Nyakach Kasipul-Kabondo Karachuonyo Rangwe Ndhiwa Rongo Migori Uriri Nyatike Mbita Gwassi Kuria Bonchari South Mugirango Bomachoge Bobasi Nyaribari Masaba Nyaribari Chache Kitutu Chache Kitutu Masaba West Mugirango N.Mugirango Borabu

65 60 67 68 71 73 61 61 70 64 55 63 72 72 72 72 45 49 49 49 69 65 80 73 61 63 63 57 57 65 66 73 72

(2) (5) (5) (4) (4) (4) (6) (4) (4) (5) (5) (5) (3) (3) (3) (3) (7) (7) (8) (8) (5) (6) (3) (4) (5) (5) (5) (7) (6) (4) (5) (4) (5)

24 21 25 25 27 29 22 23 28 24 19 23 28 28 30 31 14 15 15 16 27 24 34 28 20 22 23 20 20 24 24 28 29

(1) (3) (3) (2) (3) (3) (4) (3) (2) (3) (2) (3) (2) (3) (3) (2) (3) (3) (4) (4) (3) (3) (3) (3) (3) (3) (3) (4) (3) (2) (3) (3) (3)

34 33 33 31 32 32 34 37 33 32 35 32 31 31 37 35 39 37 36 36 32 32 29 30 30 31 32 34 34 32 32 31 33

(1) (1) (1) (1) (1) (1) (2) (3) (1) (2) (2) (2) (1) (1) (3) (2) (4) (3) (3) (3) (1) (2) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1)

3,825,752 167,952 148,883 132,252 106,847 110,220 58,819 61,571 119,144 98,763 102,928 107,502 166,258 122,821 115,959 127,067 144,654 109,252 85,666 103,180 72,831 71,579 136,880 80,483 119,113 162,867 156,233 102,514 98,077 168,471 163,357 120,690 182,919

2,475,262 101,165 99,380 90,082 75,813 81,008 35,887 37,507 83,758 63,482 56,783 68,138 120,521 87,929 83,337 91,990 64,616 53,143 41,779 50,369 50,348 46,880 110,113 59,130 72,176 102,522 98,598 58,936 55,761 110,225 108,200 87,562 131,950

(44,555) (4,920) (4,614) (3,415) (2,893) (3,245) (2,281) (1,617) (3,036) (3,244) (2,727) (3,138) (3,405) (2,990) (2,896) (2,969) (4,406) (3,793) (3,329) (3,840) (2,470) (2,630) (3,787) (2,367) (3,730) (4,858) (4,977) (3,852) (3,153) (4,023) (5,279) (3,585) (5,954)

Table 4:

Constituency Level Urban Poverty and Inequality Estimates

Constituency Province/ Code Constituency Name

Poverty Incidence Percent of Individuals below Poverty Line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Gini Index Inequality measure (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

Nairobi Province 1 Makadara 2 Kamukunji 3 Starehe 4 Langata 5 Dagoretti 6 Westlands 7 Kasarani 8 Embakasi

59 46 44 40 46 31 47 41

(4) (4) (4) (3) (4) (3) (4) (3)

23 15 14 12 14 10 15 12

(3) (2) (2) (2) (2) (2) (2) (2)

39 36 36 40 36 40 34 37

(2) (2) (2) (2) (2) (2) (2) (2)

184,541 183,468 205,225 271,111 229,612 188,107 320,739 408,921

109,001 84,050 90,430 108,617 104,934 58,826 151,592 166,608

(4,567) (3,285) (3,332) (3,483) (3,965) (2,026) (5,735) (5,725)

Coast Province 9 Changamwe 10 Kisauni 11 Likoni 12 Mvita 13 Msambweni 14 Matuga 15 Kinango 16 Bahari 17 Kaloleni 18 Ganze 19 Malindi 20 Magarini 21 Garsen 22 Galole 25 Lamu West 26 Taveta 27 Wundanyi 28 Mwatate 29 Voi

47 45 46 45 35 60 54 65 56 56 58 52 59 66 62 44 57 48 57 50

4 (4) (4) (4) (4) (4) (4) (4) (3) (4) (6) (3) (6) (5) (5) (4) (4) (4) (5) (5)

17 16 16 16 11 24 21 28 23 22 24 20 23 27 24 16 22 18 22 18

(2) (2) (2) (2) (2) (3) (3) (3) (2) (3) (4) (2) (4) (4) (4) (2) (3) (3) (3) (3)

36 35 35 35 35 36 36 36 37 35 38 37 35 34 34 38 35 36 34 35

(2) (2) (2) (2) (2) (2) (2) (3) (2) (2) (3) (2) (3) (2) (2) (2) (2) (3) (2) (2)

840,516 178,184 244,739 99,582 71,108 55,964 3,996 1,626 50,859 13,964 1,307 51,100 3,508 4,885 9,383 16,269 11,219 4,055 3,386 15,382

398,405 81,012 111,688 44,452 24,544 33,490 2,174 1,062 28,642 7,873 752 26,477 2,076 3,220 5,801 7,141 6,375 1,940 1,933 7,673

(13,944) (2,938) (3,968) (1,572) (1,069) (1,199) (84) (47) (964) (347) (43) (892) (127) (173) (309) (280) (270) (84) (95) (348)

North Eastern Province 30 Dujis 31 Lagdera 32 Fafi 33 Ijara 34 Wajir North 35 Wajir West 36 Wajir East 38 Mandera West 39 Mandera Central 40 Mandera East

61 52 69 68 75 66 70 57 67 70 67

(5) (5) (5) (7) (7) (6) (6) (6) (7) (6) (4)

24 20 30 28 33 26 30 22 27 29 28

(3) (3) (4) (4) (6) (4) (4) (4) (5) (4) (3)

36 36 35 32 30 32 33 36 31 33 35

(3) (2) (2) (3) (3) (2) (2) (2) (3) (2) (2)

135,942 46,960 8,646 2,427 1,204 4,349 6,949 19,275 1,034 17,201 27,897

82,653 24,303 5,962 1,650 908 2,850 4,894 11,031 689 12,045 18,702

(3,885 (1,104) (296) (114) (64) (185) (291) (612) (51) (663) (799)

Eastern Province 41 Moyale 42 North Horr 43 Saku 44 Laisamis 45 Isiolo North 46 Isiolo South 47 Igembe 48 Ntonyiri 51 North Imenti 53 South Imenti 54 Nithi 56 Manyatta 57 Runyenjes 59 Siakago 60 Mwingi North 61 Mwingi South 62 Kitui West 63 Kitui Central 65 Kitui South 66 Masinga 67 Yatta 68 Kangundo 69 Kathiani 70 Machakos Town 71 Mwala 72 Mbooni 73 Kilome 74 Kaiti

51 36 92 19 95 38 55 37 63 58 60 39 47 46 49 59 39 57 52 59 50 70 59 72 47 75 72 67 75

(5) (21) (13) (23) (6) (8) (23) (5) (7) (4) (6) (4) (3) (4) (9) (7) (7) (7) (5) (4) (7) (9) (5) (6) (3) (6) (6) (8) (10)

22 17 62 7 62 14 27 12 26 23 24 13 17 16 18 24 13 23 21 24 19 32 24 32 18 37 35 28 36

(3) (13) (20) (11) (14) (5) (15) (2) (5) (3) (4) (2) (2) (2) (5) (4) (4) (5) (3) (3) (4) (7) (3) (5) (2) (5) (5) (5) (8)

44 44 31 36 30 37 40 35 35 37 37 38 37 37 37 37 35 37 39 37 36 37 39 35 40 40 39 33 36

(5) (4) (4) (3) (4) (3) (4) (3) (3) (3) (3) (3) (3) (3) (3) (4) (5) (4) (4) (4) (4) (4) (4) (3) (4) (5) (4) (3) (4)

263,098 12,823 326 10,619 243 21,492 3,248 8,739 1,895 40,659 8,051 7,870 28,482 1,515 2,312 9,281 433 1,478 11,088 1,424 654 4,913 8,520 22,142 26,438 3,191 3,691 1,848 501

133,391 4,566 299 2,061 231 8,250 1,800 3,276 1,203 23,681 4,868 3,102 13,373 695 1,133 5,486 167 842 5,764 844 325 3,448 5,006 15,977 12,506 2,386 2,659 1,247 374

(6,269) (965) (40) (465) (15) (671) (409) (150) (81) (977) (294) (126) (432) (27) (106) (368) (12) (61) (286) (37) (23) (318) (248) (1,004) (401) (138) (167) (103) (36)

Table 4 contâ&#x20AC;&#x2122;d Constituency Province/ Code Constituency Name

Poverty Incidence Percent of Individuals below Poverty Line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Gini Index Inequality measure (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

Eastern Province cont... 75 Makueni 76 Kibwezi

58 65

(6) (6)

23 29

(4) (4)

38 39

(3) (3)

7,974 11,248

4,603 7,268

(275) (423)

Central Province 77 Kinangop 78 Kipipiri 79 Ol_Kalou 80 Ndaragwa 82 Kieni 83 Mathira 84 Othaya 85 Mukurwe-Ini 86 Nyeri Town 87 Mwea 88 Gichugu 89 Ndia 90 Kerugoya/Kutus 91 Kangema 93 Kiharu 94 Kigumo 95 Maragwa 96 Kandara 100 Juja 101 Githunguri 102 Kiambaa 103 Kabete 104 Limuru

49 67 84 68 29 44 32 49 68 57 17 52 45 43 85 53 13 9 11 42 81 37 34 33

(6) (13) (25) (16) (14) (13) (6) (5) (10) (4) (9) (5) (5) (5) (20) (6) (8) (8) (5) (5) (15) (7) (5) (6)

21 36 57 42 11 18 10 19 33 25 6 26 20 20 54 21 4 3 4 15 46 15 12 11

(4) (10) (26) (17) (6) (7) (3) (3) (8) (3) (4) (5) (5) (4) (21) (3) (3) (3) (2) (3) (15) (4) (3) (3)

48 51 35 50 41 43 37 38 39 40 44 49 46 47 38 35 37 39 39 39 40 44 36 36

(6) (6) (4) (11) (3) (3) (3) (3) (3) (3) (4) (6) (7) (8) (3) (2) (3) (3) (3) (3) (3) (3) (3) (3)

327,741 8,494 984 8,481 3,604 7,694 6,275 3,846 1,553 40,522 12,124 2,426 3,011 8,496 2,262 10,497 1,568 4,880 1,883 158,134 10,654 22,921 3,724 3,708

158,954 5,664 831 5,762 1,027 3,371 1,985 1,885 1,059 23,004 2,115 1,254 1,362 3,618 1,913 5,605 201 437 210 65,765 8,592 8,510 1,268 1,220

(9,696) (722) (208) (906) (147) (421) (113) (100) (111) (968) (199) (65) (73) (171) (387) (319) (16) (34) (11) (3,024) (1,290) (595) (70) (73)

Rift Valley Province 106 Turkana North 107 Turkana Central 108 Turkana South 110 Kapenguria 112 Samburu West 113 Samburu East 115 Saboti 117 Eldoret North 118 Eldoret East 119 Eldoret South 122 Keiyo North 123 Keiyo South 124 Mosop 126 Emgwen 127 Tinderet 128 Baringo East 129 Baringo North 130 Baringo Central 131 Mogotio 132 Eldama Ravine 133 Laikipia West 134 Laikipia East 135 Naivasha 136 Nakuru Town 137 Kuresoi 138 Molo 139 Rongai 140 Subukia 141 Kilgoris 142 Narok North 144 Kajiado North 145 Kajiado Central 146 Kajiado South 147 Bomet 149 Sotik 151 Buret 152 Belgut 153 Ainamoi 154 Kipkelion

54 (2) 68 (3) 65 (3) 71 (6) 65 (3) 62 (3) 64 (3) 54 (2) 64 (4) 60 (4) 68 (4) 54 (3) 51 (3) 23 (9) 25 (10) 24 (10) 57 (21) 57 (5) 54 (3) 60 (3) 62 (3) 71 (5) 62 (5) 48 (2) 46 (2) 63 (3) 61 (3) 53 (3) 67 (3) 63 (3) 58 (3) 46 (2) 52 (3) 64 (3) 52 (3) 53 (3) 47 (3) 21 (6) 19 (4) 27 (6)

19 24 23 26 23 21 22 18 24 23 25 18 17 6 6 6 17 18 18 20 21 31 28 15 14 21 20 17 24 21 19 14 17 22 17 17 15 5 5 7

(1) (2) (2) (4) (2) (2) (2) (1) (3) (3) (3) (2) (2) (3) (3) (3) (9) (2) (2) (2) (2) (4) (4) (1) (1) (2) (2) (2) (2) (2) (2) (1) (2) (2) (2) (2) (2) (2) (1) (2)

35 28 30 25 29 29 29 33 33 36 32 33 36 31 31 30 11 28 31 29 30 42 46 33 34 28 29 30 28 29 31 34 32 30 30 32 33 29 33 29

(1) (1) (1) (3) (1) (1) (1) (1) (1) (2) (1) (2) (2) (2) (1) (2) (9) (2) (1) (1) (1) (1) (1) (1) (2) (1) (1) (1) (1) (1) (1) (2) (1) (1) (1) (2) (2) (2) (2) (1)

909,420 22,887 15,421 449 13,338 21,131 7,052 42,884 93,238 24,971 55,056 3,831 1,737 1,708 16,942 3,615 29 748 13,319 3,982 17,401 29,353 29,113 54,828 212,162 2,914 60,025 2,163 6,246 7,184 25,223 53,923 10,314 12,998 4,205 2,390 2,297 1,513 26,133 6,697

491,996 15,517 10,004 317 8,699 13,206 4,486 23,342 60,006 14,935 37,206 2,071 892 392 4,185 877 16 425 7,165 2,402 10,810 20,723 18,180 26,294 96,617 1,838 36,588 1,153 4,211 4,520 14,596 24,630 5,315 8,259 2,202 1,268 1,081 325 5,044 1,808

(9,348) (531) (308) (18) (262) (410) (151) (546) (2,538) (617) (1,556) (62) (29) (34) (400) (84) (3) (20) (206) (75) (307) (1,062) (922) (570) (2,134) (61) (1,001) (35) (124) (137) (388) (612) (151) (238) (73) (42) (35) (19) (221) (102)

Western Province 155 Malava

68 76

34 39

(4) (5)

45 40

(3) (3)

260,229 1,551

175,915 1,181

(7,388) (71)

(4) (6)

Table 4 contâ&#x20AC;&#x2122;d Constituency Province/ Code Constituency Name

Western Province cont... 156 Lugari 157 Mumias 158 Matungu 159 Lurambi 162 Butere 164 Emuhaya 165 Sabatia 166 Vihiga 168 Mt Elgon 169 Kimilili 170 Webuye 171 Sirisia 172 Kanduyi 174 Amagoro 175 Nambale 176 Butula 177 Funyula 178 Budalangi Nyanza Province 179 Ugenya 180 Alego 181 Gem 182 Bondo 184 Kisumu Town East 185 Kisumu Town West 186 Kisumu Rural 187 Nyando 188 Muhoroni 189 Nyakach 190 Kasipul-Kabondo 191 Karachuonyo 192 Rangwe 193 Ndhiwa 194 Rongo 195 Migori 197 Nyatike 198 Mbita 199 Gwassi 200 Kuria 201 Bonchari 202 South Mugirango 203 Bomachoge 204 Bobasi 205 Nyaribari Masaba 206 Nyaribari Chache 207 Kitutu Chache 208 Kitutu Masaba 209 West Mugirango 210 N.Mugirango Borabu

Poverty Incidence Percent of Individuals below Poverty Line (Std. error)

73 61 59 62 79 82 77 72 71 74 61 75 68 65 68 78 84 72

(5) (5) (7) (5) (4) (4) (5) (6) (6) (4) (6) (5) (4) (6) (5) (4) (5) (5)

63 (2) 89 (6) 87 (6) 85 (7) 65 (8) 69 (6) 64 (6) 35 (8) 82 (5) 69 (5) 70 (7) 56 (7) 93 (4) 73 (5) 90 (4) 36 (12) 41 (12) 37 (13) 59 (8) 60 (8) 87 (4) 74 (5) 93 (3) 53 (7) 40 (8) 100 (1) 58 (5) 53 (6) 8 (4) 14 (5) 10 (5)

Poverty Gap As a percent of the poverty line (Std. error)

38 28 27 29 43 46 42 38 35 39 28 39 34 32 34 43 46 36

Gini Index Inequality measure (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

(4) (4) (4) (4) (5) (5) (5) (6) (6) (4) (5) (5) (4) (5) (4) (5) (5) (5)

44 42 40 45 44 43 45 45 42 43 43 42 44 44 44 43 38 41

(3) (3) (3) (4) (3) (3) (3) (3) (3) (3) (3) (3) (3) (3) (3) (3) (3) (3)

8,553 33,055 414 51,852 8,749 8,114 10,236 6,959 5,040 15,061 19,373 6,826 38,582 18,671 15,766 4,870 1,120 5,437

6,260 20,265 243 31,909 6,880 6,652 7,920 5,041 3,600 11,190 11,753 5,127 26,355 12,084 10,721 3,818 942 3,923

(296) (977) (16) (1,575) (280) (280) (365) (309) (210) (490) (698) (243) (1,141) (684) (494) (164) (48) (205)

25 (2) 46 (7) 44 (7) 44 (7) 25 (5) 27 (4) 26 (4) 11 (4) 37 (5) 28 (4) 26 (4) 20 (4) 44 (6) 30 (4) 42 (5) 11 (5) 13 (5) 10 (5) 21 (4) 21 (4) 41 (5) 33 (4) 53 (7) 20 (4) 14 (4) 68 (11) 24 (3) 20 (3) 2 (1) 3 (2) 2 (1)

39 30 31 32 32 33 37 38 30 35 27 34 21 33 25 35 32 28 31 29 29 36 29 38 36 14 38 36 38 37 36

(2) (2) (2) (2) (2) (2) (2) (3) (2) (2) (2) (2) (3) (2) (2) (2) (2) (2) (2) (2) (2) (3) (3) (3) (3) (3) (2) (2) (3) (2) (2)

541,607 3,849 12,530 2,108 11,418 136,940 47,416 2,199 7,566 19,811 2,242 8,066 381 29,315 1,395 16,427 29,825 3,322 5,745 1,633 8,370 3,699 3,693 3,322 2,107 658 8,843 12,451 3,395 9,559 3,241

340,671 3,411 10,925 1,797 7,443 94,341 30,151 759 6,226 13,623 1,579 4,554 356 21,316 1,258 5,914 12,245 1,219 3,405 976 7,318 2,745 3,448 1,759 851 656 5,166 6,616 258 1,325 323

(7,835) (201) (655) (126) (576) (5,480) (1,808) (62) (335) (723) (110) (340) (16) (1,074) (50) (714) (1,496) (155) (271) (79) (267) (128) (115) (119) (70) (6) (280) (400) (10) (72) (16)

Chapter Six: Socio-Economic Dimensions of Poverty This chapter presents a preliminary socio-economic profile of poverty for the urban and rural population in each Constituency. It focuses on education and gender of the household head based on the 1999 Population and Housing Census data. The presented estimates constitute preliminary results; a more complete profile touching on other key socio-economic dimensions will be produced and presented in a subsequent report.

compared to households in Githunguri Constituency that are headed by persons that never went to school. This is suggestive of substantial variation in the returns to higher education across rural Constituencies in Kenya.

Background

The majority of households in Kenya are headed by a male. The 1999 Population and Housing Census indicate that only 37 per cent of households are headed by females. However, the percentages differ across Provinces. In Nyanza province, almost 50 per cent of households are headed by women, whereas in Nairobi over 7 out of every 10 households are headed by men. In Kenya, households headed by females are sometimes the result of divorce, separation, or widowhood, all of which generally are thought to contribute to lower levels of economic well being. However, as this chapter shows, this is not necessarily the case. The statistics in Table 5, show that while poverty incidence by gender of the household head differs between Constituencies, there are by and large no significant differences among the two groups of households within Constituencies.

The Census defines a household to include all persons who occupy a housing unit.The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated persons who share living arrangements. In other words, a household is defined as a group of persons who live together in the same compound or dwelling unit(s) and share the same sleeping facilities and/or the same cooking or eating facilities. The members who were temporarily absent but were expected to return to the household were included in the household. Helpers living in the household and sharing the same cooking or eating arrangements are considered members of the household. Household size was defined as the number of people living in the household including usual members who were absent. The head of household was the person of either sex, who generally runs the affairs of the household and is looked upon by the other members as the main decision maker. For the socio-economic profile, educational characteristics of heads of households are classified into three main categories; those with no education, those that have attained primary education, and finally, those with secondary and above. All households captured by the 1999 census are grouped into these three categories and poverty measures where estimated separately for each group of households based on the same underlying regression model. With respect to household headship, all census households are categorised into two: households headed by males and headed by females. Poverty incidence and depth of poverty are then estimated for the two groups separately. While in this preliminary profile the analysis is confined to education and gender characteristics of the head, it is important to point out that well being of the overall household is also in part affected by the distribution of these socio-economic dimensions across and between all household members; these issues will be explored in follow-up research.

Preliminary Profile of Poverty by Educational Attainment Table 5 presents separate rural and urban poverty incidence measures by level of education of household head for each Constituency. The results indicate that households headed by individuals with educational attainment at the secondary level or above are better off than those headed by individuals with primary level of education. Within each province, households headed by individuals with no education depict the highest poverty incidence.This pattern holds among urban and rural residents across all constituencies. Among rural households headed by a member with no education, the poverty incidence varies from a low of 27 per cent in Githuguri Constituency in Central Province to a high of 89 per cent in Ganze Constituency in Kilifi District in Coast Province. Similarly, the incidence of poverty among rural households whose heads have primary education ranges from 20 per cent in Kabete Constituency in Central Province to 82 per cent in Kuria Constituency in Nyanza province. Finally, among rural households headed by persons with post-primary school educational attainment, poverty incidence ranges from a mere 1 per cent in Kabete Constituency in Central Province to a high of 71 per cent in Kuria Constituency in Nyanza Province. Among urban households, poverty incidence for households headed by people with no education varies from a low of 10 per cent among the urban residents of Maragwa Constituency in Central province to almost 100 per cent of the urban residents of Nyaribari Masaba Constituency in Nyanza Province. Overall, the emerging spatial pattern reveals that, controlling for educational attainment of the household head, poverty rates vary substantially among Constituencies. For instance, households in Kuria Constituency whose household heads have primary education are three-times as likely to be poor

Preliminary Profile of Poverty by Gender of the Household Head

In just over a third of the urban population across Constituencies is poverty incidence greater among female headed households. Sixty percent of these are located in Constituencies from three provinces namely Rift Valley, Western and Eastern. In Nairobi only 2 out of eight constituencies depicted a female headed poverty incidence that is greater than that of their counterpart male-headed households.

However, the analysis of poverty by gender of household head is complicated by the concept and definition of the head of household. There are a number of problems with the concept of female headship and of household headship in general. For instance, it is hard to define what headship means or how to measure it appropriately for purposes of poverty analysis. Headship could be defined by authority, culture, economic contribution or other factors, but headship measured in surveys and censuses is typically self-reported could be driven by considerable subjectivity and biases that are not well understood. For example, there is often a lack of symmetry, in that female headship tends to be identified where there are no adult males present, whereas male heads generally co-reside with adult women and/or other males. Divorced, separated, widowed or never-married women may hold de jure (legal) headship of a household. But, de facto headships arise in situations where men are absent from the household for at least 50 percent of the time, but where they may continue to participate in household decision-making. Holding all other factors constant, the level of household income may vary widely amongst de facto female headed households depending on the degree to which absent males contribute to household finances. Many studies have shown that a large group of de facto female headed households depend on remittances from male kin who have migrated to find work on estates, in urban areas or outside their country of birth. Moreover, the collective economic contribution of male household members (as a proportion of total household income) may be less than that of female members and this is not measured in typical household surveys which do not record these intrahousehold differentials but only measure the sum of these parts (i.e., overall household income or consumption). While female headship arises out of many varied situations not all of the processes through which female headship arises are linked to impoverishment. Female headship may be a temporary phase in a longer term strategy of migrant labour and accumulation. Further complicating gender analysis in Kenya is cultural diversity within the country such as communities with a tradition of polygamy and in which wives in polygamous households have separate residences. However, while female headship in itself does not predispose a household to poverty, it is important to note that there are particular types of female-headed household which can be especially vulnerable to poverty, such as single mothers, widowed women, de facto female-headed households which are not supported by remittances from migrant household members. The erosion in support systems and networks may also be a factor contributing to increasing poverty

incidence among certain female headed households. Compounding these problems are the issues of discrimination in access to jobs, wages and welfare payments. Unequal economic opportunities, especially for female headed households, could contribute to lower level of well being. There is thus need for more in-depth research into the relative importance of these different factors in Kenya in order to engender poverty analysis and subsequently design and target appropriate policy interventions. Overall, there is a need for more systematic analysis, with information on the gender and age composition of households, and flows of income within and among households,

including issues of distribution and control over household income and consumption. Overall, this partial and preliminary socio-economic poverty profile suggests there is a need for more subtle categorizations of households, including femaleheaded households, cross checking of large-scale survey data with more in-depth qualitative work. Most importantly, perhaps, there is a need to go beyond the simple use of gender of head of household as a proxy for examining gender discrimination. A more disaggregated and thorough analysis of all types of household is required.

Table 5: code

Constituency Level Rural Poverty and Education

Name of Constituency

No Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Primary Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Secondary and above Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

COAST RURAL 13 Msambweni 14 Matuga 15 Kinango 16 Bahari 17 Kaloleni 18 Ganze 19 Malindi 20 Magarini 21 Garsen 22 Galole 23 Bura 24 Lamu East 25 Lamu West 26 Taveta 27 Wundanyi 28 Mwatate 29 Voi

70 62 81 76 84 89 77 77 42 35 31 47 59 70 80 75 76

(5) (6) (4) (5) (4) (3) (5) (6) (8) (10) (9) (8) (11) (14) (10) (9) (7)

27 23 35 31 37 43 32 31 13 11 9 15 23 26 35 30 31

(3) (3) (3) (3) (4) (4) (4) (4) (3) (4) (3) (4) (7) (10) (8) (7) (5)

53 46 67 60 69 79 60 63 42 41 28 34 63 54 66 58 63

(7) (5) (6) (6) (5) (6) (6) (6) (9) (8) (10) (10) (5) (7) (6) (6) (5)

18 15 25 21 26 34 21 22 13 13 8 10 24 18 25 20 23

(4) (3) (4) (3) (4) (5) (3) (3) (4) (4) (4) (4) (3) (4) (3) (3) (3)

35 27 52 37 46 65 36 42 31 29 13 15 46 31 47 37 37

(8) (7) (8) (9) (11) (12) (9) (13) (9) (11) (8) (9) (9) (10) (12) (8) (7)

10 8 17 11 15 24 11 13 9 8 3 4 15 8 15 11 11

(3) (3) (4) (4) (5) (8) (4) (5) (4) (5) (2) (3) (5) (4) (5) (3) (3)

NORTH EASTERN RURAL 30 Dujis 31 Lagdera 32 Fafi 33 Ijara 34 Wajir North 35 Wajir West 36 Wajir East 37 Wajir South 38 Mandera West 39 Mandera Central 40 Mandera East

65 66 64 66 68 66 64 68 66 65 62

(6) (7) (6) (7) (5) (8) (6) (6) (6) (7) (7)

23 23 21 23 24 23 22 24 23 22 21

(3) (4) (3) (4) (3) (5) (4) (4) (4) (4) (4)

52 46 46 55 58 51 49 54 50 48 47

(14) (15) (17) (16) (22) (16) (18) (22) (18) (17) (25)

16 14 14 17 19 15 14 17 15 14 14

(7) (7) (8) (7) (11) (8) (8) (11) (8) (8) (10)

30 28 23 38 42 32 29 33 32 35 28

(20) (14) (16) (19) (15) (22) (19) (21) (21) (20) (14)

8 8 6 11 13 9 7 8 8 9 7

(7) (5) (6) (7) (7) (8) (6) (8) (7) (7) (4)

EASTERN RURAL 41 Moyale 42 North Horr 43 Saku 44 Laisamis 45 Isiolo North 46 Isiolo South 47 Igembe 48 Ntonyiri 49 Tigania West 50 Tigania East 51 North Imenti 52 Central Imenti 53 South Imenti 54 Nithi 55 Tharaka 56 Manyatta 57 Runyenjes 58 Gachoka 59 Siakago 60 Mwingi North 61 Mwingi South 62 Kitui West 63 Kitui Central 64 Mutito 65 Kitui South 66 Masinga 67 Yatta 68 Kangundo 69 Kathiani 70 Machakos Town 71 Mwala 72 Mbooni 73 Kilome 74 Kaiti 75 Makueni 76 Kibwezi

72 63 59 40 54 55 63 37 66 64 49 50 49 67 67 65 67 66 75 67 67 73 80 71 80 68 68 67 67 66 68 70 68 72 72 62

(4) (5) (4) (7) (5) (6) (4) (8) (5) (4) (5) (5) (5) (4) (4) (5) (5) (5) (4) (3) (3) (3) (4) (5) (4) (5) (4) (5) (5) (3) (4) (3) (3) (4) (3) (4)

27 22 21 12 19 18 21 10 23 23 16 16 15 25 24 23 25 25 32 24 24 29 34 28 33 25 25 25 24 24 25 27 25 27 28 21

(3) (3) (2) (3) (2) (3) (2) (3) (3) (3) (2) (2) (2) (2) (2) (3) (3) (3) (3) (2) (2) (2) (4) (4) (3) (3) (3) (3) (3) (2) (3) (2) (2) (3) (2) (2)

70 62 51 42 48 49 58 33 63 62 43 47 46 64 65 60 63 63 73 64 65 71 76 70 77 67 66 64 61 60 68 69 65 69 71 57

(7) (9) (8) (9) (5) (9) (4) (8) (4) (4) (4) (4) (4) (3) (4) (3) (3) (5) (3) (4) (4) (3) (4) (6) (4) (2) (3) (3) (3) (4) (3) (3) (3) (3) (2) (3)

26 22 17 13 16 16 19 9 22 21 14 15 14 23 23 21 22 24 31 22 23 28 31 26 30 24 24 23 22 21 25 26 23 26 27 19

(5) (5) (4) (4) (2) (4) (2) (3) (2) (2) (2) (2) (2) (2) (2) (2) (2) (3) (3) (2) (2) (2) (3) (4) (3) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2)

50 52 38 31 27 36 38 22 45 43 27 29 29 41 44 37 42 43 51 44 47 52 54 53 59 45 46 46 38 38 48 50 46 52 50 37

(8) (10) (8) (8) (5) (9) (4) (6) (4) (4) (4) (4) (5) (5) (7) (5) (4) (5) (7) (5) (5) (5) (7) (7) (8) (7) (4) (4) (3) (4) (4) (6) (6) (6) (4) (4)

16 18 12 9 8 11 12 6 14 14 8 8 8 13 14 11 14 15 19 14 15 18 19 18 21 14 15 15 12 11 16 17 15 18 17 11

(4) (5) (3) (3) (2) (4) (2) (2) (2) (2) (2) (2) (2) (2) (3) (2) (2) (2) (4) (2) (2) (3) (4) (4) (4) (3) (2) (2) (1) (2) (2) (3) (3) (3) (2) (2)

Table 5 contâ&#x20AC;&#x2122;d code

Name of Constituency

No Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Primary Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Secondary and above Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

CENTRAL RURAL 77 Kinangop 78 Kipipiri 79 Ol_Kalou 80 Ndaragwa 81 Tetu 82 Kieni 83 Mathira 84 Othaya 85 Mukurwe-Ini 86 Nyeri Town 87 Mwea 88 Gichugu 89 Ndia 90 Kerugoya/Kutus 91 Kangema 92 Mathioya 93 Kiharu 94 Kigumo 95 Maragwa 96 Kandara 97 Gatanga 98 Gatundu South 99 Gatundu North 100 Juja 101 Githunguri 102 Kiambaa 103 Kabete 104 Limuru 105 Lari

43 45 38 35 40 37 30 30 37 42 50 41 38 39 33 36 34 35 45 39 46 35 40 44 27 28 28 32 39

(15) (14) (9) (10) (13) (9) (8) (9) (12) (18) (10) (10) (9) (13) (11) (8) (9) (13) (10) (9) (9) (9) (10) (12) (9) (7) (8) (9) (12)

13 14 10 10 12 11 8 8 11 13 16 12 11 11 9 10 9 9 14 11 14 9 11 13 7 7 7 9 12

(6) (6) (3) (4) (6) (4) (3) (4) (5) (8) (5) (4) (3) (5) (4) (3) (3) (5) (5) (3) (4) (3) (5) (6) (3) (3) (3) (3) (5)

38 41 34 30 38 33 29 28 35 39 45 35 33 36 30 34 31 35 43 38 42 32 39 37 23 23 20 26 35

(7) (9) (6) (6) (7) (5) (6) (7) (8) (9) (6) (6) (6) (7) (6) (5) (5) (7) (5) (5) (5) (5) (5) (7) (6) (5) (4) (4) (5)

11 12 9 8 11 10 8 7 10 12 14 10 9 10 8 9 8 10 13 10 12 8 11 10 6 6 5 7 10

(3) (4) (3) (2) (3) (2) (2) (3) (3) (4) (3) (2) (2) (3) (2) (2) (2) (3) (2) (2) (2) (2) (2) (3) (2) (2) (1) (2) (2)

21 23 16 15 20 20 15 15 22 22 31 23 20 21 17 20 18 22 29 26 27 20 26 25 13 12 11 15 19

(4) (6) (4) (4) (5) (5) (4) (6) (8) (7) (7) (7) (6) (7) (8) (6) (5) (7) (7) (8) (8) (6) (8) (8) (4) (3) (3) (4) (5)

5 6 4 3 5 6 4 4 6 6 9 6 5 5 4 5 4 5 8 7 7 5 6 6 3 3 3 4 5

(1) (2) (1) (1) (2) (2) (1) (2) (3) (3) (3) (2) (2) (2) (2) (2) (1) (2) (3) (3) (3) (2) (2) (3) (1) (1) (1) (1) (2)

RIFT VALLEY RURAL 106 Turkana North 107 Turkana Central 108 Turkana South 109 Kacheliba 110 Kapenguria 111 Sigor 112 Samburu West 113 Samburu East 114 Kwanza 115 Saboti 116 Cherangany 117 Eldoret North 118 Eldoret East 119 Eldoret South 120 Marakwet East 121 Marakwet West 122 Keiyo North 123 Keiyo South 124 Mosop 125 Aldai 126 Emgwen 127 Tinderet 128 Baringo East 129 Baringo North 130 Baringo Central 131 Mogotio 132 Eldama Ravine 133 Laikipia West 134 Laikipia East 135 Naivasha 136 Nakuru Town 137 Kuresoi 138 Molo 139 Rongai 140 Subukia 141 Kilgoris 142 Narok North 143 Narok South 144 Kajiado North

61 65 56 49 58 59 49 36 63 58 59 58 50 50 51 53 50 48 54 58 58 68 58 51 55 58 51 51 51 51 46 52 48 58 45 66 56 57 59

(7) (7) (7) (9) (6) (5) (6) (8) (6) (7) (6) (6) (7) (13) (8) (12) (18) (10) (7) (8) (8) (6) (8) (10) (10) (10) (13) (7) (14) (8) (13) (6) (8) (8) (8) (6) (6) (5) (8)

23 27 20 16 21 21 17 10 23 20 21 21 16 16 17 18 16 16 18 21 21 28 21 17 20 20 17 17 17 18 15 17 16 21 14 26 19 20 22

(4) (6) (4) (4) (3) (3) (3) (3) (4) (4) (3) (4) (3) (6) (4) (6) (9) (5) (4) (4) (4) (5) (5) (5) (6) (6) (6) (3) (7) (4) (6) (3) (4) (5) (4) (4) (4) (3) (5)

45 52 31 28 44 44 30 22 46 43 44 43 35 36 32 36 39 34 43 45 45 54 39 38 36 44 38 31 31 31 26 39 34 40 30 51 35 47 24

(9) (10) (9) (10) (8) (8) (11) (11) (8) (8) (9) (9) (9) (9) (11) (11) (12) (11) (9) (10) (9) (6) (9) (6) (6) (8) (8) (5) (7) (6) (15) (7) (6) (8) (7) (7) (7) (8) (5)

16 20 9 8 14 14 9 6 15 13 14 14 10 11 9 11 12 10 13 14 14 20 12 11 11 14 11 9 9 9 7 12 10 13 8 18 10 15 7

(5) (6) (4) (4) (4) (4) (4) (4) (4) (3) (4) (4) (4) (4) (5) (4) (5) (4) (4) (5) (4) (4) (4) (2) (3) (4) (3) (2) (3) (2) (5) (3) (2) (4) (3) (4) (3) (4) (2)

35 43 31 29 42 46 33 24 51 43 46 46 36 37 37 42 38 36 44 47 45 54 33 42 36 45 41 36 39 33 34 43 35 42 30 54 36 49 22

(12) (17) (17) (10) (10) (13) (15) (15) (8) (6) (7) (6) (10) (8) (9) (8) (10) (7) (10) (7) (6) (9) (14) (12) (7) (11) (10) (8) (9) (6) (12) (5) (4) (5) (5) (7) (7) (7) (4)

12 16 10 9 13 15 10 6 17 14 15 15 11 11 11 13 11 11 14 15 15 20 10 12 11 15 13 10 12 10 10 13 11 13 9 19 11 16 6

(6) (9) (7) (4) (4) (6) (6) (5) (4) (3) (3) (3) (4) (3) (4) (4) (4) (3) (5) (4) (3) (5) (6) (5) (3) (5) (4) (3) (4) (3) (4) (2) (2) (2) (2) (4) (3) (3) (2)

Table 5 contâ&#x20AC;&#x2122;d code

Name of Constituency

No Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Primary Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Secondary and above Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

RIFT VALLEY RURAL CONT... 145 Kajiado Central 146 Kajiado South 147 Bomet 148 Chepalungu 149 Sotik 150 Konoin 151 Buret 152 Belgut 153 Ainamoi 154 Kipkelion

54 56 63 66 58 63 59 60 62 58

(9) (7) (8) (8) (8) (7) (8) (8) (8) (7)

19 19 23 25 21 24 21 22 24 20

(5) (4) (5) (5) (5) (4) (4) (4) (5) (4)

33 36 47 50 45 49 46 46 48 44

(8) (7) (10) (12) (11) (9) (10) (7) (7) (7)

10 11 15 17 15 17 15 15 16 14

(3) (3) (5) (6) (5) (5) (5) (3) (4) (3)

32 34 53 59 48 51 50 49 47 47

(7) (7) (7) (8) (7) (7) (10) (9) (6) (6)

10 10 18 21 16 18 17 17 16 16

(3) (3) (4) (4) (4) (4) (5) (4) (3) (3)

WESTERN RURAL 155 Malava 156 Lugari 157 Mumias 158 Matungu 159 Lurambi 160 Shinyalu 161 Ikolomani 162 Butere 163 Khwisero 164 Emuhaya 165 Sabatia 166 Vihiga 167 Hamisi 168 Mt Elgon 169 Kimilili 170 Webuye 171 Sirisia 172 Kanduyi 173 Bumula 174 Amagoro 175 Nambale 176 Butula 177 Funyula 178 Budalangi

64 75 68 67 71 77 79 70 72 69 70 67 69 59 70 69 65 69 60 54 80 79 79 81

(5) (5) (4) (4) (5) (4) (4) (4) (5) (6) (5) (7) (6) (7) (6) (6) (4) (4) (6) (5) (5) (6) (5) (4)

24 31 26 26 28 34 36 28 28 29 30 27 29 19 27 27 23 27 21 17 31 30 30 34

(3) (3) (3) (3) (3) (3) (4) (3) (3) (4) (4) (5) (4) (4) (4) (4) (2) (3) (3) (2) (4) (4) (4) (4)

60 68 65 60 65 70 73 64 65 62 63 58 61 56 65 64 60 63 54 49 70 68 69 70

(3) (3) (4) (4) (3) (3) (4) (4) (4) (5) (5) (6) (5) (4) (4) (5) (4) (3) (3) (4) (4) (5) (4) (4)

21 27 24 22 24 28 31 24 24 24 25 21 23 18 24 24 21 23 18 15 24 22 24 27

(2) (2) (2) (2) (2) (2) (3) (2) (2) (3) (4) (3) (3) (2) (2) (3) (2) (2) (2) (2) (2) (3) (2) (3)

43 48 49 46 50 49 59 49 47 39 41 42 43 49 47 50 51 47 41 41 53 55 56 45

(4) (5) (5) (6) (4) (4) (6) (5) (7) (7) (7) (9) (8) (6) (5) (5) (4) (4) (4) (5) (4) (8) (5) (6)

13 16 15 14 17 17 22 15 14 13 13 13 14 14 14 16 16 15 11 11 16 16 17 14

(2) (2) (2) (3) (2) (2) (3) (3) (3) (3) (3) (4) (4) (2) (2) (2) (2) (2) (2) (2) (2) (4) (3) (3)

NYANZA RURAL 179 Ugenya 180 Alego 181 Gem 182 Bondo 183 Rarieda 184 Kisumu Town East 185 Kisumu Town West 186 Kisumu Rural 187 Nyando 188 Muhoroni 189 Nyakach 190 Kasipul-Kabondo 191 Karachuonyo 192 Rangwe 193 Ndhiwa 194 Rongo 195 Migori 196 Uriri 197 Nyatike 198 Mbita 199 Gwassi 200 Kuria 201 Bonchari 202 South Mugirango 203 Bomachoge 204 Bobasi 205 Nyaribari Masaba 206 Nyaribari Chache 207 Kitutu Chache 208 Kitutu Masaba 209 West Mugirango 210 N.Mugirango Borabu

61 68 70 73 73 66 67 73 64 63 65 74 74 73 73 51 54 52 53 73 68 84 75 65 68 67 63 59 69 71 78 78

(6) (6) (7) (8) (8) (9) (8) (6) (10) (12) (7) (6) (5) (5) (6) (9) (10) (11) (10) (7) (9) (5) (8) (9) (8) (9) (10) (11) (7) (7) (6) (6)

22 26 26 28 29 24 26 30 23 22 23 29 29 31 31 16 17 16 17 29 25 36 29 23 24 24 22 20 25 27 32 32

(3) (4) (4) (6) (6) (6) (5) (4) (7) (7) (5) (5) (4) (3) (4) (4) (5) (5) (5) (6) (6) (5) (6) (6) (5) (6) (6) (6) (5) (5) (5) (5)

63 70 72 73 76 65 66 74 67 58 66 75 75 74 74 43 47 48 48 71 68 82 76 64 68 69 63 63 71 71 77 76

(5) (6) (5) (5) (5) (7) (6) (3) (5) (6) (6) (4) (3) (4) (4) (8) (8) (10) (10) (7) (7) (4) (6) (6) (5) (5) (8) (7) (4) (6) (4) (5)

22 27 27 29 30 23 25 30 25 20 24 29 30 31 31 12 14 15 15 28 25 35 29 22 24 25 22 22 26 26 30 31

(3) (4) (3) (4) (4) (4) (3) (2) (3) (3) (3) (3) (3) (3) (3) (3) (4) (4) (4) (5) (4) (4) (4) (3) (3) (3) (4) (4) (3) (4) (3) (4)

48 50 55 61 65 46 46 57 54 42 52 63 62 61 64 42 44 45 48 62 56 71 62 49 51 52 47 43 51 54 61 61

(7) (6) (8) (7) (7) (11) (7) (6) (6) (7) (6) (6) (7) (9) (10) (7) (8) (9) (8) (10) (12) (9) (9) (8) (6) (7) (8) (7) (5) (6) (7) (7)

16 18 19 22 25 15 16 21 19 13 18 23 23 25 27 14 15 15 17 23 20 29 23 16 16 17 16 14 17 18 22 23

(3) (3) (4) (4) (5) (6) (4) (4) (3) (3) (4) (4) (4) (6) (6) (4) (4) (4) (4) (7) (6) (7) (6) (4) (3) (4) (4) (3) (3) (3) (4) (5)

Table 6: code

Constituency Level Urban Poverty and Education

Name of Constituency

No Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Primary Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Secondary and above Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

NAIROBI 1 Makadara 2 Kamukunji 3 Starehe 4 Langata 5 Dagoretti 6 Westlands 7 Kasarani 8 Embakasi

81 74 64 62 70 58 71 68

(5) (7) (7) (6) (6) (6) (6) (7)

37 30 24 23 26 22 27 25

(5) (5) (4) (4) (4) (4) (4) (4)

72 48 48 50 52 47 52 49

(4) (4) (4) (4) (4) (5) (4) (4)

29 15 15 15 16 16 16 15

(3) (2) (2) (2) (2) (3) (2) (2)

52 38 39 32 40 24 42 36

(4) (3) (3) (3) (3) (3) (4) (3)

20 11 12 10 12 8 13 11

(3) (2) (2) (1) (2) (1) (2) (1)

COAST URBAN 9 Changamwe 10 Kisauni 11 Likoni 12 Mvita 13 Msambweni 14 Matuga 15 Kinango 16 Bahari 17 Kaloleni 18 Ganze 19 Malindi 20 Magarini 21 Garsen 22 Galole 25 Lamu West 26 Taveta 27 Wundanyi 28 Mwatate 29 Voi

60 58 58 45 73 69 76 72 65 70 60 59 71 64 50 60 67 63 59

(4) (4) (4) (5) (4) (6) (6) (4) (5) (7) (4) (6) (6) (6) (4) (5) (9) (6) (6)

25 24 24 16 32 29 35 33 28 31 25 23 30 26 19 24 28 24 23

(3) (3) (3) (3) (3) (4) (5) (3) (4) (5) (3) (4) (4) (4) (3) (3) (6) (4) (4)

52 52 50 39 64 61 67 61 60 60 57 62 66 64 44 63 58 61 56

(4) (4) (4) (5) (4) (6) (7) (4) (5) (8) (4) (7) (6) (6) (4) (5) (5) (6) (5)

19 19 18 13 26 24 28 25 24 24 22 25 27 26 16 25 23 24 21

(2) (2) (2) (2) (3) (3) (4) (3) (3) (5) (3) (4) (4) (4) (3) (3) (4) (3) (3)

36 36 34 27 43 45 55 43 47 38 40 49 50 56 29 45 38 46 41

(4) (4) (4) (4) (4) (4) (6) (4) (5) (8) (3) (8) (6) (5) (4) (5) (4) (7) (5)

12 12 11 8 15 16 21 15 17 14 13 18 18 22 9 16 12 16 14

(2) (2) (2) (2) (2) (2) (3) (2) (3) (4) (2) (4) (3) (3) (2) (2) (2) (3) (2)

NORTH EASTERN URBAN 30 Dujis 31 Lagdera 32 Fafi 33 Ijara 34 Wajir North 35 Wajir West 36 Wajir East 38 Mandera West 39 Mandera Central 40 Mandera East

55 71 69 76 66 72 62 68 71 69

(5) (5) (8) (8) (7) (7) (6) (8) (6) (5)

21 30 28 33 26 30 24 27 30 29

(3) (4) (5) (6) (5) (5) (4) (6) (4) (4)

52 67 71 81 66 74 53 69 69 64

(5) (6) (13) (23) (10) (9) (6) (13) (6) (5)

19 27 32 39 26 32 19 28 29 26

(3) (4) (10) (17) (6) (6) (4) (8) (4) (3)

39 49 59 66 61 58 40 57 62 51

(5) (6) (20) (35) (8) (8) (7) (15) (7) (5)

13 20 23 26 24 22 13 21 24 19

(2) (4) (11) (19) (5) (5) (3) (8) (4) (3)

EASTERN URBAN 41 Moyale 42 North Horr 43 Saku 44 Laisamis 45 Isiolo North 46 Isiolo South 47 Igembe 48 Ntonyiri 51 North Imenti 53 South Imenti 54 Nithi 56 Manyatta 57 Runyenjes 59 Siakago 60 Mwingi North 61 Mwingi South 62 Kitui West 63 Kitui Central 65 Kitui South 66 Masinga 67 Yatta 68 Kangundo 69 Kathiani 70 Machakos Town 71 Mwala 72 Mbooni 73 Kilome

52 74 39 91 50 64 46 74 71 71 64 65 55 68 58 39 54 60 66 57 70 65 80 62 83 80 67

(27) (34) (33) (16) (15) (29) (14) (13) (11) (13) (15) (10) (15) (16) (16) (21) (17) (12) (12) (17) (18) (11) (10) (9) (10) (9) (17)

30 47 20 58 24 36 17 36 34 33 29 30 21 30 24 13 21 26 28 24 31 30 42 27 45 45 28

(22) (29) (23) (22) (11) (24) (7) (11) (9) (10) (10) (7) (8) (11) (9) (10) (9) (8) (8) (10) (12) (8) (9) (7) (11) (10) (11)

46 82 35 93 41 64 40 60 62 64 51 52 55 62 58 39 57 54 61 46 67 59 73 55 79 71 64

(27) (32) (31) (14) (12) (28) (9) (12) (8) (11) (8) (8) (10) (14) (14) (12) (16) (13) (13) (16) (18) (12) (12) (7) (11) (11) (14)

26 56 17 63 17 36 14 25 27 28 20 21 22 28 23 12 23 21 24 16 28 26 33 22 41 35 26

(20) (31) (20) (22) (7) (23) (4) (7) (5) (7) (5) (4) (6) (10) (8) (5) (9) (7) (7) (8) (12) (8) (9) (5) (10) (9) (9)

37 81 25 88 30 55 28 47 45 47 30 39 33 37 46 30 44 40 47 36 56 47 61 40 60 59 53

(25) (28) (27) (18) (11) (28) (11) (16) (6) (9) (8) (7) (9) (15) (13) (14) (15) (12) (10) (14) (18) (10) (12) (7) (9) (13) (15)

19 53 12 57 11 29 9 18 18 19 10 14 11 14 17 9 16 14 17 13 22 18 25 15 27 28 20

(17) (28) (16) (23) (6) (21) (5) (8) (3) (5) (4) (3) (4) (7) (7) (6) (8) (6) (5) (7) (11) (5) (7) (4) (6) (8) (7)

Table 6 contâ&#x20AC;&#x2122;d code

Name of Constituency

No Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Primary Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Secondary and above Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

EASTERN URBAN CONT... 74 Kaiti 75 Makueni 76 Kibwezi

80 69 72

(16) (14) (10)

41 32 37

(14) (11) (9)

72 60 65

(18) (11) (11)

33 24 28

(13) (7) (7)

61 44 55

(19) (9) (14)

25 16 22

(12) (5) (8)

CENTRAL URBAN 77 Kinangop 78 Kipipiri 79 Ol_Kalou 80 Ndaragwa 82 Kieni 83 Mathira 84 Othaya 85 Mukurwe-Ini 86 Nyeri Town 87 Mwea 88 Gichugu 89 Ndia 90 Kerugoya/Kutus 91 Kangema 93 Kiharu 94 Kigumo 95 Maragwa 96 Kandara 100 Juja 101 Githunguri 102 Kiambaa 103 Kabete 104 Limuru

67 71 69 27 55 53 78 83 77 27 63 58 80 80 70 18 10 11 61 79 65 62 44

(22) (33) (33) (20) (18) (12) (9) (13) (7) (16) (17) (16) (9) (26) (9) (13) (12) (11) (8) (20) (13) (11) (15)

39 47 44 10 26 24 42 46 39 11 30 20 35 51 33 6 3 4 28 45 31 27 16

(17) (30) (28) (9) (12) (8) (9) (14) (8) (7) (13) (8) (9) (26) (7) (5) (5) (4) (6) (19) (10) (7) (7)

62 70 67 29 46 42 55 73 71 13 65 49 54 77 67 10 8 8 53 75 48 52 46

(18) (33) (30) (17) (16) (11) (9) (12) (6) (10) (13) (18) (11) (27) (9) (8) (8) (7) (5) (20) (11) (9) (11)

35 45 41 11 19 15 23 39 34 5 27 14 19 47 31 3 3 2 22 42 21 20 17

(14) (29) (26) (8) (9) (6) (5) (11) (5) (4) (10) (6) (5) (25) (7) (3) (3) (2) (3) (18) (6) (5) (6)

60 65 61 26 37 38 47 55 55 7 46 44 40 66 57 10 5 14 45 65 29 39 38

(14) (36) (21) (14) (13) (10) (11) (15) (7) (7) (15) (20) (15) (29) (10) (8) (7) (6) (8) (21) (8) (8) (9)

32 41 34 10 14 13 18 23 24 2 17 11 11 38 22 3 2 5 17 34 11 14 13

(10) (30) (18) (6) (7) (5) (6) (9) (4) (2) (8) (7) (6) (23) (6) (3) (2) (2) (4) (16) (4) (4) (4)

RIFT VALLEY URBAN 106 Turkana North 107 Turkana Central 108 Turkana South 110 Kapenguria 112 Samburu West 113 Samburu East 115 Saboti 117 Eldoret North 118 Eldoret East 119 Eldoret South 122 Keiyo North 123 Keiyo South 124 Mosop 126 Emgwen 127 Tinderet 128 Baringo East 129 Baringo North 130 Baringo Central 131 Mogotio 132 Eldama Ravine 133 Laikipia West 134 Laikipia East 135 Naivasha 136 Nakuru Town 137 Kuresoi 138 Molo 139 Rongai 140 Subukia 141 Kilgoris 142 Narok North 144 Kajiado North 145 Kajiado Central 146 Kajiado South 147 Bomet 149 Sotik 151 Buret 152 Belgut 153 Ainamoi 154 Kipkelion

71 69 73 73 67 65 66 78 77 79 73 70 34 31 30 58 63 66 67 70 83 75 62 58 70 68 71 71 70 69 65 65 74 69 65 55 28 27 33

(4) (4) (8) (4) (3) (4) (3) (4) (4) (4) (7) (8) (15) (11) (12) (20) (10) (4) (4) (4) (5) (5) (3) (3) (7) (3) (8) (5) (4) (3) (3) (4) (3) (8) (7) (12) (13) (6) (7)

26 25 27 27 23 22 23 32 31 32 29 25 9 8 8 18 21 22 24 25 39 35 21 20 25 24 26 25 25 24 23 23 28 25 24 17 6 7 8

(3) (2) (4) (2) (2) (2) (2) (3) (3) (3) (5) (5) (6) (4) (4) (9) (5) (2) (2) (2) (5) (5) (2) (2) (4) (2) (4) (3) (2) (2) (2) (2) (2) (4) (4) (5) (4) (2) (2)

70 69 77 69 65 68 63 73 74 76 63 55 27 29 29

(3) (3) (8) (3) (3) (3) (3) (4) (4) (4) (4) (4) (11) (11) (11)

25 25 29 24 22 24 21 28 29 30 22 19 7 7 7

(2) (2) (5) (2) (2) (2) (2) (3) (3) (3) (2) (2) (4) (4) (4)

57 54 45 59 51 54 47 57 51 60 47 40 16 19 18

(3) (3) (12) (4) (3) (4) (2) (4) (4) (5) (4) (4) (8) (8) (8)

19 18 13 20 16 18 15 20 18 22 15 12 4 5 4

(2) (2) (4) (2) (2) (2) (1) (3) (2) (3) (2) (2) (2) (2) (2)

61 61 63 68 79 72 57 56 66 66 59 70 67 62 59 58 66 62 57 59 25 24 30

(6) (3) (4) (3) (5) (5) (2) (3) (4) (3) (4) (3) (4) (3) (3) (3) (3) (3) (5) (4) (7) (5) (6)

20 21 21 24 36 33 19 18 23 23 19 25 24 21 20 19 23 20 19 19 6 6 7

(3) (2) (2) (2) (5) (5) (1) (1) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2) (2)

51 46 54 50 62 54 39 38 56 52 41 59 56 45 36 43 52 44 48 39 18 15 21

(8) (3) (4) (3) (6) (5) (2) (2) (4) (3) (4) (4) (4) (3) (3) (3) (3) (4) (4) (4) (5) (4) (5)

16 14 17 16 26 24 11 12 18 16 12 19 18 14 11 13 17 13 15 11 4 4 5

(4) (1) (2) (2) (4) (4) (1) (1) (2) (2) (2) (2) (2) (1) (1) (2) (2) (2) (2) (2) (2) (1) (2)

Table 6 contâ&#x20AC;&#x2122;d code

Name of Constituency

WESTERN URBAN 155 Malava 156 Lugari 157 Mumias 158 Matungu 159 Lurambi 162 Butere 164 Emuhaya 165 Sabatia 166 Vihiga 168 Mt Elgon 169 Kimilili 170 Webuye 171 Sirisia 172 Kanduyi 174 Amagoro 175 Nambale 176 Butula 177 Funyula 178 Budalangi NYANZA URBAN 179 Ugenya 180 Alego 181 Gem 182 Bondo 184 Kisumu Town East 185 Kisumu Town West 186 Kisumu Rural 187 Nyando 188 Muhoroni 189 Nyakach 190 Kasipul-Kabondo 191 Karachuonyo 192 Rangwe 193 Ndhiwa 194 Rongo 195 Migori 197 Nyatike 198 Mbita 199 Gwassi 200 Kuria 201 Bonchari 202 South Mugirango 203 Bomachoge 204 Bobasi 205 Nyaribari Masaba 206 Nyaribari Chache 207 Kitutu Chache 208 Kitutu Masaba 209 West Mugirango 210 N.Mugirango Borabu

No Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Primary Education Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Secondary and above Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

82 83 77 68 78 86 86 87 85 82 81 69 83 77 75 75 84 87 80

(9) (6) (5) (10) (5) (4) (5) (4) (5) (6) (4) (6) (5) (5) (5) (5) (4) (5) (6)

42 46 40 33 41 49 49 49 49 44 46 34 46 41 39 39 47 49 41

(7) (6) (5) (7) (5) (6) (6) (6) (6) (7) (5) (5) (6) (5) (5) (5) (5) (7) (6)

78 75 67 59 69 81 84 82 82 73 76 67 77 73 71 71 78 85 73

(5) (5) (5) (9) (5) (5) (4) (4) (5) (6) (5) (5) (5) (4) (5) (5) (5) (5) (6)

41 39 32 26 34 44 48 46 46 36 40 32 41 37 36 35 42 47 36

(5) (5) (4) (5) (4) (5) (6) (5) (6) (6) (5) (4) (5) (4) (4) (4) (5) (6) (6)

74 69 53 57 55 70 77 68 63 67 71 57 71 64 58 62 75 80 67

(5) (5) (7) (8) (5) (5) (5) (5) (7) (6) (5) (5) (6) (6) (6) (5) (5) (8) (6)

38 35 23 26 24 37 42 34 31 32 37 26 35 31 27 30 41 41 32

(5) (5) (4) (5) (4) (5) (6) (4) (6) (5) (4) (4) (5) (5) (4) (4) (5) (8) (5)

94 95 95 74 87 82 30 91 85 85 73 97 84 94 51 55 51 70 63 92 87 99 78 44 100 90 77 11 29 15

(6) (4) (5) (7) (5) (3) (24) (4) (4) (7) (8) (3) (5) (4) (13) (13) (13) (9) (12) (4) (6) (2) (8) (21) (1) (4) (6) (8) (9) (10)

52 53 53 29 40 37 8 45 39 37 29 50 38 47 17 19 20 27 22 47 45 63 36 15 71 48 34 3 8 4

(8) (7) (8) (5) (5) (3) (9) (5) (5) (5) (5) (6) (4) (6) (7) (7) (6) (5) (6) (5) (5) (7) (6) (9) (11) (5) (5) (2) (4) (3)

92 92 93 70 87 79 49 88 77 78 67 94 78 94 44 47 37 64 65 91 82 98 67 45 100 74 66 10 23 12

(5) (5) (4) (8) (4) (3) (10) (4) (4) (6) (6) (4) (5) (4) (13) (14) (14) (8) (9) (3) (4) (2) (7) (9) (1) (4) (5) (5) (8) (6)

49 49 54 27 40 33 18 42 34 30 26 44 33 46 14 15 10 23 24 44 38 58 27 15 69 34 27 2 6 3

(8) (8) (7) (5) (4) (3) (5) (5) (4) (5) (4) (7) (4) (6) (6) (6) (5) (5) (5) (5) (4) (7) (5) (5) (11) (4) (4) (1) (3) (2)

84 83 79 57 65 59 31 77 63 64 47 90 65 85 28 33 36 56 54 82 67 88 45 38 99 49 47 6 8 8

(7) (7) (9) (7) (7) (4) (7) (5) (4) (8) (7) (6) (4) (7) (10) (10) (13) (7) (10) (4) (5) (5) (6) (7) (3) (5) (6) (4) (4) (4)

41 40 36 20 24 22 10 32 25 22 16 41 25 36 8 10 10 20 19 36 28 46 15 12 66 17 16 2 2 2

(8) (7) (8) (4) (4) (2) (3) (4) (3) (4) (3) (6) (3) (6) (4) (4) (5) (4) (5) (4) (4) (8) (3) (3) (12) (2) (3) (1) (1) (1)

Table 7: Code

Constituency Level Rural Poverty and Gender Name of Constituency

Male Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Female Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

COAST RURAL 13 Msambweni 14 Matuga 15 Kinango 16 Bahari 17 Kaloleni 18 Ganze 19 Malindi 20 Magarini 21 Garsen 22 Galole 23 Bura 24 Lamu East 25 Lamu West 26 Taveta 27 Wundanyi 28 Mwatate 29 Voi

59 50 74 63 74 83 64 69 40 38 30 40 57 53 64 56 57

(4) (4) (4) (4) (4) (5) (5) (7) (9) (7) (9) (7) (5) (6) (7) (6) (5)

22 17 30 24 31 38 24 26 12 12 8 12 21 17 24 20 21

(3) (2) (3) (3) (3) (5) (3) (5) (4) (3) (3) (3) (3) (3) (4) (3) (3)

65 58 77 69 77 86 69 71 42 35 32 46 62 59 68 63 65

(5) (6) (4) (5) (4) (4) (5) (5) (8) (8) (11) (10) (8) (9) (6) (7) (5)

24 21 32 27 33 40 27 28 13 11 9 15 24 21 26 23 24

(3) (3) (3) (3) (3) (4) (4) (4) (3) (3) (4) (4) (6) (5) (4) (4) (3)

NORTH EASTERN RURAL 30 Dujis 31 Lagdera 32 Fafi 33 Ijara 34 Wajir North 35 Wajir West 36 Wajir East 37 Wajir South 38 Mandera West 39 Mandera Central 40 Mandera East

65 66 63 65 67 65 63 68 65 65 63

(7) (8) (7) (8) (7) (6) (8) (7) (6) (8) (8)

23 23 21 22 24 22 21 23 22 22 21

(4) (4) (4) (5) (4) (3) (4) (4) (4) (4) (4)

63 66 63 63 68 63 61 66 63 63 61

(13) (12) (13) (10) (10) (11) (10) (10) (10) (9) (10)

22 23 21 22 24 22 20 23 21 21 20

(7) (7) (7) (6) (6) (6) (5) (6) (6) (5) (5)

73 65 59 44 52 56 58 34 63 61 40 43 42 59 63 54 58 60 70 63 64 68 73 69 76 63 63 59 54 53 64 66 62 66 67 53

(5) (5) (6) (7) (5) (9) (3) (7) (3) (3) (4) (4) (4) (3) (3) (3) (3) (4) (5) (4) (4) (3) (4) (5) (4) (3) (3) (3) (3) (4) (3) (3) (4) (4) (3) (3)

27 23 21 13 18 19 19 9 22 21 12 13 13 21 22 19 20 22 29 22 23 26 30 27 31 23 22 21 19 18 23 24 22 24 25 18

(3) (3) (3) (3) (3) (5) (2) (3) (2) (2) (1) (2) (2) (1) (2) (2) (2) (3) (4) (2) (2) (2) (3) (3) (3) (2) (2) (2) (2) (2) (2) (2) (2) (3) (2) (2)

68 57 53 35 49 50 58 35 59 58 42 44 43 59 62 56 59 58 70 62 61 67 74 67 76 64 62 59 59 56 63 64 60 65 66 55

(6) (5) (6) (6) (4) (9) (4) (8) (5) (6) (4) (5) (4) (3) (4) (4) (4) (5) (3) (4) (3) (3) (4) (6) (5) (5) (4) (4) (4) (4) (3) (3) (4) (4) (4) (3)

25 19 19 10 17 16 20 9 20 20 14 14 13 21 22 20 21 22 29 22 22 26 30 25 30 23 22 21 21 19 23 23 21 24 25 18

(4) (2) (3) (2) (2) (4) (2) (3) (3) (3) (2) (2) (2) (2) (2) (2) (2) (3) (3) (2) (2) (2) (3) (4) (4) (3) (2) (2) (2) (2) (2) (2) (3) (3) (2) (2)

CENTRAL RURAL 77 Kinangop

(6)

(2)

(6)

(2)

Table 7 contâ&#x20AC;&#x2122;d Code

Name of Constituency

Male Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Female Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

CENTRAL RURAL CONT... 78 Kipipiri 79 Ol_Kalou 80 Ndaragwa 81 Tetu 82 Kieni 83 Mathira 84 Othaya 85 Mukurwe-Ini 86 Nyeri Town 87 Mwea 88 Gichugu 89 Ndia 90 Kerugoya/Kutus 91 Kangema 92 Mathioya 93 Kiharu 94 Kigumo 95 Maragwa 96 Kandara 97 Gatanga 98 Gatundu South 99 Gatundu North 100 Juja 101 Githunguri 102 Kiambaa 103 Kabete 104 Limuru 105 Lari

38 31 26 33 28 24 24 34 33 42 33 30 33 29 32 28 34 40 36 38 29 34 32 20 17 16 22 30

(7) (6) (6) (7) (4) (5) (7) (8) (8) (5) (5) (5) (5) (7) (5) (5) (5) (6) (4) (4) (6) (6) (6) (4) (3) (3) (3) (4)

11 9 7 10 8 7 6 10 10 12 9 8 9 7 9 7 9 12 10 11 7 9 9 5 4 4 6 8

(3) (2) (2) (3) (2) (2) (2) (3) (3) (2) (2) (2) (2) (3) (2) (2) (2) (3) (2) (2) (2) (2) (2) (1) (1) (1) (1) (2)

36 31 27 32 32 25 22 30 34 45 35 30 33 28 29 28 32 40 34 40 28 35 38 22 21 19 26 32

(7) (5) (4) (6) (4) (6) (6) (8) (10) (8) (7) (8) (7) (10) (7) (5) (6) (11) (7) (7) (7) (7) (8) (7) (5) (5) (6) (7)

10 8 7 9 10 7 6 8 10 14 10 8 9 7 8 7 9 12 9 12 7 10 11 5 6 5 7 9

(3) (2) (2) (2) (2) (2) (2) (3) (4) (4) (3) (3) (3) (3) (3) (2) (2) (5) (3) (3) (3) (3) (3) (2) (2) (2) (2) (3)

60 66 54 48 53 54 48 36 52 45 49 48 39 41 42 43 41 40 46 49 48 57 57 44 42 48 41 39 40 36 34 42 37 44 32 58 48 53 34 48

(7) (7) (7) (9) (5) (5) (7) (11) (6) (5) (6) (7) (5) (6) (7) (6) (8) (9) (7) (7) (6) (5) (7) (11) (8) (10) (7) (5) (5) (5) (12) (7) (7) (8) (7) (8) (8) (8) (5) (5)

23 27 19 16 18 19 16 10 18 15 16 16 12 13 13 13 13 12 15 16 16 22 20 14 14 16 13 12 12 11 10 13 11 14 9 21 16 18 11 17

(4) (5) (4) (4) (3) (3) (4) (4) (3) (3) (3) (4) (2) (2) (3) (3) (3) (4) (3) (3) (3) (3) (4) (5) (4) (5) (3) (2) (2) (2) (5) (3) (3) (4) (3) (4) (4) (4) (2) (3)

60 62 49 44 50 50 45 32 52 44 49 48 36 38 40 41 39 36 44 47 48 57 53 40 41 46 42 38 36 37 36 44 37 43 31 59 49 52 44 48

(6) (7) (10) (8) (5) (8) (8) (14) (11) (9) (11) (8) (12) (10) (9) (12) (11) (9) (11) (7) (7) (6) (8) (8) (8) (11) (9) (5) (6) (8) (18) (8) (6) (8) (6) (7) (5) (6) (5) (6)

23 25 17 14 17 17 15 9 18 14 16 16 10 12 12 13 12 11 14 15 16 21 19 12 14 15 13 12 10 11 10 14 12 14 9 21 16 18 16 16

(4) (5) (5) (4) (3) (4) (4) (5) (6) (4) (5) (4) (5) (5) (4) (5) (5) (4) (5) (3) (3) (4) (5) (3) (3) (5) (4) (2) (2) (3) (7) (3) (3) (4) (2) (4) (3) (3) (3) (3)

Table 7 contâ&#x20AC;&#x2122;d Code

Name of Constituency

Male Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Female Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

RIFT VALLEY RURAL CONT... 146 Kajiado South 147 Bomet 148 Chepalungu 149 Sotik 150 Konoin 151 Buret 152 Belgut 153 Ainamoi 154 Kipkelion

48 53 58 50 52 48 49 50 48

(5) (6) (7) (6) (6) (7) (5) (6) (6)

16 18 21 17 19 16 16 18 16

(2) (3) (4) (3) (3) (4) (3) (4) (3)

48 51 55 48 51 46 50 50 47

(6) (7) (8) (8) (8) (12) (14) (8) (7)

16 17 19 16 18 15 17 18 15

(3) (4) (4) (4) (4) (6) (7) (5) (3)

57 64 64 60 64 68 73 63 64 59 59 56 60 55 60 60 58 58 53 50 70 70 70 68

(4) (4) (4) (4) (4) (3) (4) (4) (4) (5) (5) (6) (5) (4) (3) (4) (3) (3) (3) (4) (4) (5) (4) (4)

19 25 23 22 24 28 31 24 24 23 23 20 23 17 21 22 19 21 17 15 24 24 25 27

(2) (2) (2) (2) (2) (2) (3) (2) (3) (3) (3) (3) (3) (2) (2) (2) (2) (2) (2) (2) (2) (3) (3) (3)

54 63 61 57 61 67 71 61 63 58 58 54 58 53 59 58 56 57 50 47 67 68 69 72

(3) (4) (4) (4) (3) (3) (4) (4) (4) (5) (5) (5) (5) (5) (5) (4) (4) (5) (5) (4) (4) (5) (4) (4)

18 24 22 21 22 27 29 22 23 22 22 19 22 16 21 21 19 20 16 14 23 23 24 29

(2) (2) (2) (2) (2) (2) (3) (2) (2) (3) (3) (3) (3) (2) (2) (2) (2) (3) (2) (2) (2) (3) (3) (3)

61 67 70 71 73 60 59 71 64 54 63 74 72 73 73 46 50 51 50 69 66 82 74 60 64 64 59 58 65 66 73 72

(5) (5) (5) (6) (5) (6) (5) (5) (5) (6) (5) (4) (4) (4) (5) (7) (7) (8) (8) (4) (5) (3) (5) (6) (5) (6) (6) (6) (5) (6) (5) (5)

22 26 26 27 29 22 22 29 24 18 23 29 29 31 31 14 16 16 16 27 24 35 28 20 22 23 21 20 24 25 29 29

(3) (4) (3) (4) (4) (3) (3) (4) (3) (3) (3) (3) (3) (3) (4) (3) (3) (4) (4) (3) (3) (3) (4) (3) (3) (4) (4) (4) (3) (4) (4) (4)

58 66 68 71 73 62 63 72 63 58 63 71 71 70 70 43 47 48 48 71 66 79 72 60 63 62 57 54 64 66 71 71

(5) (5) (5) (4) (4) (7) (7) (4) (7) (7) (6) (5) (5) (5) (5) (7) (7) (8) (8) (6) (8) (5) (7) (7) (7) (6) (7) (8) (5) (6) (6) (7)

20 25 25 27 29 22 24 29 23 20 22 27 27 29 29 13 14 15 15 28 24 32 27 20 22 22 20 18 23 24 27 28

(2) (3) (3) (3) (3) (4) (5) (3) (4) (4) (3) (4) (3) (3) (3) (3) (3) (4) (4) (5) (5) (4) (5) (4) (4) (4) (4) (4) (3) (4) (4) (5)

Table 8: Code

NAIROBI 1 2 3 4 5 6 7 8

Constituency Level Urban Poverty and Gender Name of Constituency

Makadara Kamukunji Starehe Langata Dagoretti Westlands Kasarani Embakasi

Male Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Female Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

60 44 43 40 46 30 47 40

(4) (4) (4) (3) (4) (3) (4) (3)

23 14 13 12 14 10 14 12

(3) (2) (2) (2) (2) (2) (2) (2)

57 51 47 41 47 36 49 42

(4) (4) (4) (3) (4) (4) (4) (4)

22 17 15 13 15 12 15 13

(3) (3) (2) (2) (2) (2) (2) (2)

44 45 43 34 58 52 62 53 55 57 51 59 66 62 43 56 46 53 47

(4) (4) (4) (4) (4) (4) (5) (3) (4) (6) (3) (6) (5) (5) (4) (4) (4) (5) (5)

16 16 15 11 23 20 26 21 22 25 19 24 28 25 16 21 17 20 17

(2) (2) (2) (2) (3) (2) (4) (2) (3) (4) (2) (4) (4) (4) (2) (3) (3) (3) (3)

48 49 50 38 63 59 70 62 59 59 55 59 65 61 46 59 54 62 54

(4) (4) (4) (5) (4) (6) (5) (4) (5) (8) (4) (7) (6) (6) (4) (5) (5) (6) (5)

17 18 18 12 26 23 30 26 24 24 21 23 27 24 17 23 20 24 20

(2) (2) (2) (2) (3) (3) (4) (3) (3) (5) (3) (4) (4) (4) (3) (3) (3) (4) (3)

NORTH EASTERN URBAN 30 Dujis 31 Lagdera 32 Fafi 33 Ijara 34 Wajir North 35 Wajir West 36 Wajir East 38 Mandera West 39 Mandera Central 40 Mandera East

65 66 63 65 67 65 63 68 65 65

(7) (8) (7) (8) (7) (6) (8) (7) (6) (8)

23 23 21 22 24 22 21 23 22 22

(4) (4) (4) (5) (4) (3) (4) (4) (4) (4)

63 66 63 63 68 63 61 66 63 63

(13) (12) (13) (10) (10) (11) (10) (10) (10) (9)

22 23 21 22 24 22 20 23 21 21

(7) (7) (7) (6) (6) (6) (5) (6) (6) (5)

48 79 35 89 39 61 35 54 53 55 38 43 42 50 53 33 51 46 54 41 63 51 65 44 71 66 56

(27) (31) (30) (18) (11) (28) (7) (14) (6) (9) (8) (6) (10) (14) (12) (14) (16) (10) (9) (13) (16) (8) (15) (6) (10) (10) (15)

27 53 17 59 17 34 12 22 23 24 14 16 16 22 21 10 20 17 21 14 26 21 28 17 36 33 22

(20) (29) (20) (23) (7) (23) (3) (8) (4) (5) (4) (3) (5) (8) (6) (6) (9) (6) (5) (6) (9) (5) (10) (4) (8) (7) (8)

49 75 35 89 42 64 37 62 57 59 39 48 45 55 56 36 52 49 60 49 67 57 71 47 71 70 63

(27) (32) (32) (19) (13) (29) (9) (13) (8) (11) (9) (7) (13) (16) (16) (17) (18) (14) (13) (16) (17) (9) (13) (7) (12) (10) (15)

28 47 17 58 18 36 12 27 24 24 14 19 17 23 22 12 20 19 24 18 28 24 32 18 35 35 25

(21) (29) (21) (24) (8) (24) (4) (8) (5) (7) (4) (4) (7) (10) (9) (8) (10) (8) (8) (8) (12) (6) (10) (4) (10) (8) (9)

Table 8 contâ&#x20AC;&#x2122;d Code

Name of Constituency

Male Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Female Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

EASTERN URBAN CONT... 74 Kaiti 75 Makueni 76 Kibwezi

65 50 58

(19) (10) (10)

28 19 24

(12) (6) (6)

72 57 65

(18) (13) (12)

34 23 29

(13) (8) (9)

64 70 64 28 42 40 50 58 61 11 52 48 46 72 58 10 7 12 47 71 37 43 40

(17) (34) (27) (14) (15) (11) (9) (14) (5) (9) (14) (15) (10) (28) (7) (8) (8) (6) (6) (20) (9) (9) (9)

36 45 38 11 18 14 20 26 27 4 20 13 14 44 23 3 2 4 19 39 15 16 14

(14) (29) (23) (7) (9) (6) (5) (9) (4) (3) (8) (5) (4) (24) (4) (3) (3) (2) (3) (17) (5) (5) (5)

59 70 65 24 44 37 55 68 61 12 54 48 40 75 63 12 7 10 50 70 39 45 42

(18) (32) (28) (16) (15) (11) (11) (13) (9) (9) (13) (22) (14) (27) (11) (9) (7) (6) (8) (21) (8) (8) (10)

32 45 39 9 18 13 23 35 28 4 23 12 13 45 27 4 3 3 20 37 17 17 15

(13) (29) (24) (7) (8) (5) (6) (11) (6) (3) (10) (7) (5) (24) (7) (3) (3) (2) (5) (17) (5) (4) (5)

68 64 68 65 61 62 54 64 59 65 52 50 23 25 25 62 53 52 58 61 69 61 47 45 61 60 50 67 62 56 45 49 62 50 51 45 22 19 27

(3) (3) (8) (3) (3) (3) (2) (4) (4) (4) (3) (3) (9) (9) (10) (27) (5) (3) (3) (3) (5) (5) (2) (2) (4) (3) (4) (3) (3) (3) (2) (3) (3) (3) (4) (4) (6) (4) (6)

24 23 25 23 21 22 18 24 22 24 17 17 6 6 6 19 17 17 20 21 30 27 15 14 20 20 16 24 21 19 14 16 21 16 17 14 5 4 7

(2) (2) (5) (2) (2) (2) (1) (3) (3) (3) (2) (2) (3) (3) (3) (13) (2) (1) (2) (2) (4) (4) (1) (1) (2) (2) (2) (2) (2) (1) (1) (1) (2) (1) (2) (2) (2) (1) (2)

68 66 72 66 64 65 54 66 61 71 58 55 24 25 24 53 64 57 63 65 74 65 51 46 66 62 59 69 65 61 49 56 67 60 58 54 22 20 27

(4) (3) (8) (3) (3) (3) (2) (4) (4) (4) (4) (6) (9) (10) (10) (36) (7) (3) (4) (3) (5) (5) (2) (2) (4) (3) (4) (4) (4) (3) (2) (3) (3) (4) (5) (5) (7) (5) (6)

24 23 26 23 21 22 18 25 23 27 19 18 6 6 6 15 21 18 21 22 33 30 16 15 23 21 20 24 22 21 16 18 24 20 19 17 5 5 7

(2) (2) (5) (2) (2) (2) (1) (3) (3) (3) (2) (3) (3) (3) (3) (14) (3) (2) (2) (2) (5) (4) (1) (1) (2) (2) (2) (2) (2) (2) (1) (2) (2) (2) (2) (2) (2) (1) (2)

Table 8 contâ&#x20AC;&#x2122;d Code

Name of Constituency

Male Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

Female Headed Households Poverty Poverty Incidence Gap Percent of individuals As a percent of below poverty line the poverty line (Std. error) (Std. error)

74 72 59 55 60 77 80 76 70 69 73 59 73 66 63 66 78 85 68

(6) (5) (5) (7) (5) (4) (5) (5) (6) (5) (5) (6) (5) (5) (5) (4) (5) (5) (6)

38 37 27 24 28 42 44 41 37 33 38 27 37 32 30 32 42 48 32

(5) (4) (4) (4) (4) (5) (6) (5) (5) (5) (4) (4) (5) (4) (4) (4) (5) (6) (5)

82 74 70 69 67 82 85 80 77 78 77 67 81 74 70 71 80 84 80

(6) (5) (5) (10) (5) (5) (4) (5) (6) (6) (5) (6) (5) (5) (5) (5) (5) (6) (5)

43 39 35 32 32 46 49 43 42 40 41 32 43 38 36 36 43 45 41

(6) (5) (4) (7) (4) (6) (6) (5) (6) (6) (5) (5) (5) (5) (5) (5) (5) (6) (6)

NYANZA URBAN 179 Ugenya 180 Alego 181 Gem 182 Bondo 184 Kisumu Town East 185 Kisumu Town West 186 Kisumu Rural 187 Nyando 188 Muhoroni 189 Nyakach 190 Kasipul-Kabondo 191 Karachuonyo 192 Rangwe 193 Ndhiwa 194 Rongo 195 Migori 197 Nyatike 198 Mbita 199 Gwassi 200 Kuria 201 Bonchari 202 South Mugirango 203 Bomachoge 204 Bobasi 205 Nyaribari Masaba 206 Nyaribari Chache 207 Kitutu Chache 208 Kitutu Masaba 209 West Mugirango 210 N.Mugirango Borabu

87 86 83 64 75 67 35 82 69 71 54 93 70 87 36 41 36 59 58 87 74 93 52 40 99 59 52 7 15 9

(6) (6) (8) (8) (6) (3) (7) (5) (3) (7) (8) (5) (4) (5) (12) (12) (13) (6) (6) (4) (5) (4) (6) (8) (2) (4) (5) (4) (6) (4)

44 43 42 24 31 27 12 37 28 26 19 45 29 40 11 13 10 21 20 42 33 53 20 13 67 24 20 2 4 2

(7) (7) (8) (4) (4) (2) (4) (5) (2) (5) (4) (6) (3) (5) (5) (5) (5) (3) (3) (5) (4) (9) (3) (4) (12) (3) (3) (1) (2) (1)

90 89 87 65 74 69 34 85 71 72 59 94 73 94 33 42 37 62 62 88 75 93 55 43 100 59 54 9 12 9

(5) (5) (6) (6) (6) (3) (8) (5) (4) (6) (7) (5) (4) (4) (11) (13) (13) (7) (7) (5) (6) (4) (7) (9) (1) (4) (6) (5) (5) (5)

47 46 46 25 32 28 11 39 29 27 21 43 30 45 9 12 11 22 22 41 32 52 19 14 68 24 19 2 3 2

(7) (7) (7) (4) (5) (2) (4) (5) (3) (4) (4) (7) (3) (6) (4) (5) (5) (4) (4) (6) (5) (9) (4) (4) (12) (3) (3) (1) (1) (1)

Chapter Seven:

North Eastern Province Poverty Profile - District to Locations

This Chapter presents previously unavailable poverty estimates for North Eastern Province at the District, Division and Location level in rural areas and down to the SubLocation level in the urban areas. The estimates in this Chapter complement those published in Volume I (Geographic Dimensions of Poverty – From Districts to Location) to complete a comprehensive database of urban and rural poverty estimates for all administrative areas in Kenya. The chapter summarizes the rural and urban poverty estimates obtained for North Eastern Province (the complete set of estimates is provided in Table 11 and Table 12) and concludes with some remarks on the methodology used to compute these estimates.

Rural Poverty Estimates Approximately 428 thousand rural poor people inhabit North Eastern Province, which has a provincial mean headcount index of 64 per cent. Poverty incidences at the Divisional level (across 46 Divisions) ranges from 58 per cent to 70 per cent.Across the 214 Locations within the Province, the poverty incidence ranges from 55 per cent to 76 per cent. The Province’s rural poor population is concentrated in Wajir District, which contributes 42.2 per cent of the rural poor individuals. Divisional analysis of headcount poverty reveals that Central Division in Mandera district has least poverty incidence of 58 per cent followed by Central–Wajir (60 per cent) and Sankuri-Garissa (60 per cent). The poorest Divisions, which are all in Wajir district, include Buna, Habaswein, Bute (all with a poverty headcount of 68 per cent), while Sebule has 70 per cent of its population falling below the absolute rural poverty line of 1,239 per person per month (in 1997 KSh). At the Location level, two Locations are inhabited by over 6,000 poor people; Kaliwaheri Location in Banisa Division has 6,676 poor individuals followed by Korodile Location in Buna Division with 6,478 poor individuals. All Locations in Banisa Division have over 3,000 individuals who are poor.

Urban Poverty Estimates Poverty varies from 55 per cent in urban areas of Garissa to 68 per cent in Mandera urban. There are about 139 thousand poor urban residents in the province, majority of whom reside in Garissa and Mandera.At the Location level, urban poverty incidence in Northeastern Province varies from 44 per cent in Wajir to 78 per cent in Mandera.

Methodological Notes Sub-District level poverty estimates for North Eastern Province could not be derived using the standard small area statistical regression approach. This is because the 1997 WMS-III did not survey any rural households in the NorthEastern province. Therefore, the research team had to develop an additional extrapolation model. Typically, in the regional (Provincial) regression models, the survey’s direct estimate of household consumption (or income) for the reference year (1997) is the dependent variable and the predictor variables are those variables that are both common and comparable in both census and survey.

In the absence of household survey data from rural areas in North Eastern Province, the Sub-District level poverty estimates were generated using regression coefficients and error structures calibrated on 1997 WMS data from the Coast Province combined with Population and Housing Census data from the North-Eastern Province. Hence, the estimates should be interpreted with more caution and due attention must be paid to the computed standard errors and corresponding statistical bounds of confidence. This is work in progress and the research team will further test the sensitivity of these estimates by developing alternative estimation and calibration approaches.

Table 9: Summary of Rural Poverty Estimates - North Eastern Province District

Number of Rural Divisions

Number of Rural Locations

Poverty Incidence(%) (Std. error)

Poverty Population Estimated Gap (%) (in ‘000) No. of Poor (Std. error) (Std. error)

Poverty Incidence Poverty Incidence Range (%): Range (%): Division - Level Location - Level

Garissa 15 Wajir 13 Mandera 18

60 73 81

64 (5) 65 (4) 64 (5)

22 (3) 22 (3) 22 (3)

193 276 195

123 (6) 181 (8) 124 (6)

60 to 67 60 to 70 58 to 67

56 to 70 58 to 74 55 to 76

Province North Eastern 46

214

64 (4)

22 (2)

665

428 (18)

58 to 70

55 to 76

The details of the poverty statistics for the rural areas of North Eastern Province are found in Table 11

Table 10: Summary of Urban Poverty Estimates - North Eastern Province District

Number of Urban Divisions

Number of Urban Locations

Poverty Incidence(%) (Std. error)

Poverty Population Estimated Gap (%) (in ‘000) No. of Poor (Std. error) (Std. error)

Poverty Incidence Poverty Incidence Range (%): Range (%): Division - Level Location - Level

Garissa 7 Wajir 5 Mandera 5

9 8 10

55 (5) 62 (6) 68 (5)

21 (3) 24 (4) 28 (4)

59 31 49

32 (1) 19 (1) 33 (2)

51 to 76 57 to 73 66 to 77

49 to 76 44 to 73 60 to 78

Province North Eastern 17

61 (5)

24 (3)

139

85 (4)

51 to 77

44 to 78

The details of the poverty statistics for the urban areas of North Eastern Province are found in Table 12

Map 24: Division and Location Level Poverty Incidence - North Eastern Province

Map 25: Division and Location Level Contribution to Poverty - North Eastern Province

Table 11:

North Eastern Province Rural Poverty Estimates - From Districts to Locations

District/ Division/ Location Name

Garissa District Central Division Township Location Iftin Location Waberi Location Bour-Algi Location Korakora Location Sankuri Division Saka Location Shimbir Location Sankuri Location Raya Location Balambala Division Jarajara Location Balambala Location Dujis Location Danyere Division Libahilow Location Danyere Location Sikley Location Benane Division Benane Location Tokojo Location Eldere Location Modogashe Division Maalimin Location Modogashe Location Ilan Location Shant-Abak Division Garufa Location Baraki Location Goreale Location Dadaab Division Alango Arba Location Dertu Location Kumahumato Location Labisigale Location Dagahaley Location Dadaab Location Abakaile Location Liboi Division Kulan Location Liboi Location Damajale Location Jarajila Division Fafi Location Yumbis Location Welmerer Location Jarajila Location Hulugho Division Galmagala Location Gubis Location Bulla Golol Location Hulugho Location Hadi Location Sangailu Division Handaro Location Sangailu Location Ijara Division Ruqa Location Sangole Location Ijara Location Bodhai Location Jalish Location Bulla Golol Location Gerille Location Masalani Division Masalani Location Hara Location Korisa Location Kotile Location Bura Location

Poverty Incidence Percent of individuals below the poverty line (Std. error) 64 61 65 59 59 61 63 60 62 65 58 56 67 67 67 66 64 65 66 61 64 63 67 64 64 58 65 67 64 59 67 64 66 69 67 70 65 62 63 67 66 64 68 67 63 61 66 63 66 64 64 60 62 64 67 66 70 64 64 61 62 66 69 64 61 61 63 63 65 61 59 61

(5) (12) (22) (23) (21) (24) (22) (10) (17) (23) (14) (15) (11) (18) (13) (17) (11) (15) (15) (21) (14) (21) (22) (23) (12) (15) (22) (23) (12) (15) (21) (20) (10) (20) (21) (24) (20) (24) (22) (21) (11) (23) (14) (17) (13) (20) (22) (24) (22) (9) (17) (26) (21) (13) (23) (11) (16) (16) (9) (15) (16) (21) (15) (24) (24) (23) (9) (13) (18) (21) (16) (12)

Poverty Gap As a percent of the poverty line (Std. error) 22 21 24 20 19 21 22 20 21 22 19 19 23 23 24 22 22 22 24 20 22 21 23 22 22 19 23 23 21 20 23 22 23 24 23 25 23 21 21 23 23 22 24 23 22 21 23 22 23 22 22 20 20 22 24 23 25 21 22 20 21 25 24 22 21 20 21 21 22 20 19 21

(3) (7) (14) (13) (11) (13) (12) (6) (9) (13) (7) (9) (6) (10) (9) (9) (6) (9) (9) (11) (8) (13) (12) (12) (7) (8) (13) (13) (6) (8) (12) (10) (6) (12) (12) (14) (12) (12) (12) (13) (6) (13) (9) (10) (7) (11) (13) (13) (13) (5) (10) (14) (11) (7) (13) (6) (10) (8) (5) (8) (8) (13) (9) (14) (13) (12) (5) (7) (10) (11) (8) (6)

Estimated Population From 1999 census

193,359 17,206 4,679 4,435 4,019 1,939 2,134 11,192 4,868 1,231 4,025 1,068 12,715 5,502 4,597 2,616 8,367 1,750 3,269 3,348 13,136 6,404 1,523 5,209 11,367 2,639 4,494 4,234 12,377 3,305 3,384 5,688 14,523 739 3,427 2,860 1,772 1,982 1,401 2,342 14,033 3,906 4,224 5,903 7,207 3,173 1,179 2,109 746 20,203 2,664 2,491 3,460 7,838 3,750 10,767 4,006 6,761 15,280 1,938 2,219 3,878 1,699 3,156 889 1,501 12,976 4,493 4,248 1,849 2,386 12,010

Estimated Number of Poor Individuals (Std. error) 123,322 10,567 3,048 2,614 2,380 1,175 1,350 6,759 3,019 798 2,347 596 8,474 3,688 3,069 1,718 5,354 1,138 2,166 2,051 8,389 4,027 1,020 3,342 7,259 1,522 2,906 2,831 7,872 1,955 2,273 3,645 9,628 512 2,287 1,990 1,159 1,233 876 1,571 9,285 2,481 2,860 3,944 4,534 1,938 780 1,322 495 12,866 1,694 1,491 2,130 5,024 2,526 7,126 2,822 4,305 9,743 1,177 1,379 2,550 1,170 2,004 541 921 8,116 2,828 2,745 1,136 1,407 7,348

(6,238) (1255) (685) (591) (511) (283) (297) (688) (519) (184) (327) (92) (918) (658) (407) (300) (586) (175) (327) (426) (1,206) (848) (224) (768) (881) (221) (634) (646) (916) (302) (480) (714) (929) (103) (476) (482) (236) (297) (190) (335) (990) (576) (414) (686) (599) (383) (172) (316) (109) -1,124.0 (285) (385) (456) (633) (586) (814) (450) (675) (887) (180) (221) (541) (173) (479) (129) (208) (750) (355) (504) (238) (222) (853)

Table 11 contâ&#x20AC;&#x2122;d District/ Division/ Location Name

Poverty Incidence Percent of individuals below the poverty line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

Kamuthe Location Nanighi Location Bura Location Mansabubu Location

64 61 60 60

(25) (21) (24) (16)

22 20 20 20

(14) (11) (13) (8)

2,349 2,536 3,584 3,541

1,503 1,553 2,166 2,127

(378) (327) (522) (339)

Mandera District Khalalio Division Khalalio Location Bur Abor Location Bulla Haji Location Garba-Qoley Location Hareri-Hosle Location Karow Location Gedudiya Location Gingo Location Bella Location Hareri Division Hareri Location Aresa Location Sala Location Qumbiso Location Libehia Division Libehia Location Sarohindi Location Oda Location Farey Location Fino Division Fino Location Arabia Location Omar-Jillow Location Lafey Division Lafey Location Kamora Liban Location Alango Location Kabo Location Damasa Location Rhamu Division Rhamu Location Girisa Location Rhamu Dimtu Division Rhamu Dimtu Location Mado Location Gersey Location Yabicho Location Ashabito Division Ashabito Location Olla Location Guticha Location Chir Chir Location Ogarwein Location Kubuonile Location Sarman Location Marothile Location Banisa Division Banisa Location Lulis Location Derkale Location Eymole Location Kiliwaheri Location Malkamari Division Malkamari Location Guba Location Hullow Location Malkaruka Location Takaba Division Takaba Location Didkuro Location Wangai Dahan Location Dudubele Location Darwed Location Dandu Division Gither Location

64 64 55 65 63 68 65 65 71 62 61 62 57 63 63 64 62 61 61 61 69 64 69 58 67 64 60 64 66 68 67 67 69 67 61 59 61 59 64 65 67 64 64 61 65 63 76 64 64 62 65 65 62 67 64 66 62 64 65 62 64 64 63 62 59 67 66

(5) (7) (17) (19) (16) (22) (15) (19) (22) (17) (15) (13) (24) (22) (24) (22) (10) (17) (18) (16) (22) (11) (15) (21) (20) (12) (22) (23) (23) (22) (24) (15) (23) (17) (12) (23) (18) (21) (24) (9) (21) (24) (22) (24) (23) (23) (19) (21) (10) (22) (21) (21) (24) (16) (11) (23) (16) (22) (21) (11) (23) (22) (24) (21) (22) (9) (16)

22 22 18 23 21 23 22 22 26 21 20 21 19 21 22 22 21 20 20 20 25 21 24 18 22 22 19 22 23 24 24 24 25 24 21 20 20 19 22 22 24 22 22 20 22 21 28 22 22 20 22 22 21 23 21 23 20 21 22 20 21 21 22 20 19 23 23

(3) (4) (9) (12) (9) (12) (8) (10) (14) (9) (8) (7) (12) (11) (13) (12) (6) (9) (9) (8) (14) (6) (9) (11) (12) (7) (11) (14) (13) (14) (15) (9) (13) (10) (6) (12) (9) (11) (14) (6) (13) (14) (12) (13) (13) (12) (13) (12) (6) (12) (13) (12) (14) (9) (6) (13) (9) (13) (12) (6) (12) (12) (13) (12) (11) (6) (10)

194,866 7,906 1,042 1,410 485 665 1,205 973 673 840 613 5,442 902 919 2,335 1,286 2,991 1,632 252 734 373 7,763 2,734 3,028 2,001 7,815 2,428 635 2,767 1,130 855 3,305 468 2,837 7,243 1,874 1,739 1,335 2,295 32,477 4,574 6,434 4,523 4,789 3,239 3,052 3,326 2,540 37,158 6,995 5,318 6,831 8,046 9,968 12,325 3,089 3,965 4,240 1,031 6,724 865 1,661 1,392 441 2,365 16,080 5,502

124,286 5,052 578 921 308 451 785 633 480 519 376 3,389 517 575 1,474 823 1,857 997 153 451 256 4,965 1,888 1,744 1,332 5,017 1,456 403 1,815 769 574 2,215 322 1,893 4,435 1,109 1,060 794 1,472 21,173 3,070 4,121 2,889 2,917 2,109 1,911 2,519 1,638 23,871 4,321 3,441 4,462 4,971 6,676 7,900 2,043 2,462 2,720 675 4,163 552 1,065 881 274 1,391 10,728 3,641

(6,242) (331) (97) (172) (49) (99) (116) (120) (106) (88) (57) (428) (123) (129) (358) (185) (195) (174) (27) (73) (56) (548) (284) (370) (264) (600) (315) (94) (425) (169) (137) (343) (74) (324) (547) (259) (190) (168) (347) (1961) (656) (1,000) (641) (704) (492) (439) (474) (346) (2371) (947) (734) (951) (1,195) (1,094) (890) (465) (397) (605) (145) (456) (125) (234) (214) (58) (307) (961) (586)

Table 11 contâ&#x20AC;&#x2122;d District/ Division/ Location Name

Poverty Incidence Percent of individuals below the poverty line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

Dandu Location Eresteno Location Elwak Division Elwak Location Elwak South Location Wante Location Dasheg Alungo Location Bulla Afya Location Shimbir Fatuma Division Shimbir Fatuma Location Burmayo South Location Burmayo North Location Fincharo Location Wargadud Division Wargadud Location Quramadow Location Warankara Division Warankara Location Gari Location Bambo Location Kotulo Division Kotulo Location Kutayo Location Garsesala Location Bohe Hole Ii Location Dabacity Location El-Ramu Location Central Division Neboi Location Border Point I Location Shaf Shafey Location Bulla Barwako Location Kamor Location

67 67 64 66 64 63 59 59 63 64 65 64 60 61 60 64 65 64 65 68 64 67 61 63 63 66 67 58 61 56 58 60 57

(12) (21) (11) (17) (22) (23) (25) (23) (12) (21) (22) (22) (22) (15) (18) (19) (11) (17) (22) (17) (10) (19) (17) (24) (21) (23) (22) (12) (24) (20) (24) (18) (23)

23 23 22 23 22 21 19 19 21 21 21 22 20 20 20 22 23 22 22 23 22 23 20 22 21 23 23 19 20 18 19 20 18

(7) (12) (6) (10) (12) (13) (13) (12) (7) (11) (12) (12) (12) (8) (9) (10) (6) (9) (12) (9) (6) (11) (8) (13) (12) (12) (13) (6) (12) (10) (12) (9) (12)

8,499 2,079 5,246 2,077 926 1,303 723 217 5,139 2,198 770 1,012 1,159 8,298 6,229 2,069 3,035 1,543 647 845 12,778 2,785 1,685 2,499 2,923 1,318 1,568 13,141 2,860 1,590 4,573 1,029 3,089

5,692 1,395 3,342 1,373 597 819 426 128 3,240 1,401 497 647 694 5,064 3,747 1,316 1,979 988 420 571 8,233 1,862 1,029 1,582 1,840 876 1,043 7,664 1,735 891 2,660 622 1,755

(693) (287) (375) (235) (132) (187) (105) (29) (398) (294) (108) (143) (155) (748) (676) (251) (226) (168) (92) (95) (843) (351) (173) (383) (394) (199) (233) (908) (414) (180) (627) (109) (402)

Wajir District Central Division Township Location Wagberi Location Hodhan Location Jogbaru Location Barwako Location Kulaaley Location Habaswein Division Habaswein Location Tesorie Location Dilmanyaley Location Abakore Location Lagbogol South Location Kiwanja Ndege Location Buna Division Buna Location Mulka Gulfu Location Batalu Location Ingirir Location Korodille Location Leisanyu Location Lakoley North Location Tarbaj Division Tarbaj Location Manza Location Elben Location Dambas Location Dunto Location Sarban Location Wajir-Bor Division Wajir-Bor Location Khorof-Harar Location Riba Location Kotulo Division Kotulo Location El-Kotulo Location

65 60 63 59 61 60 59 62 68 66 69 67 66 68 71 68 61 67 61 66 73 71 73 63 60 62 72 64 63 61 63 64 61 67 65 65 69

(4) (10) (23) (22) (22) (21) (23) (13) (8) (20) (23) (18) (18) (23) (16) (10) (23) (23) (18) (22) (20) (22) (21) (9) (21) (18) (19) (21) (21) (23) (13) (24) (20) (23) (10) (22) (22)

22 20 21 19 20 20 19 20 24 23 24 23 23 24 26 24 20 23 21 23 26 26 26 21 19 21 26 21 21 20 21 22 20 23 22 22 24

(3) (5) (12) (11) (12) (11) (12) (7) (5) (12) (13) (11) (10) (14) (10) (6) (12) (12) (10) (13) (12) (15) (13) (5) (11) (10) (12) (11) (11) (12) (7) (13) (10) (13) (5) (12) (12)

276,532 29,428 2,576 7,275 3,182 5,340 4,593 6,462 26,054 4,102 1,179 5,532 7,172 2,412 5,657 26,791 2,762 1,488 5,532 1,185 8,827 4,715 2,282 22,039 7,059 2,754 3,084 4,644 2,706 1,792 16,610 3,140 9,678 3,792 22,100 3,596 3,024

180,821 17,737 1,634 4,256 1,954 3,188 2,709 3,996 17,645 2,714 813 3,689 4,759 1,648 4,022 18,303 1,672 994 3,385 784 6,478 3,332 1,658 13,934 4,244 1,714 2,210 2,973 1,698 1,096 10,480 2,022 5,922 2,536 14,465 2,328 2,085

(8,114) (1,687) (379) (933) (431) (679) (613) (517) (1,442) (554) (185) (656) (857) (380) (639) (1,786) (386) (225) (620) (176) (1,285) (744) (342) (1,266) (892) (300) (427) (619) (354) (249) (1,373) (484) (1,156) (575) (1404) (507) (461)

Table 11 contâ&#x20AC;&#x2122;d District/ Division/ Location Name

Wargadud Location Lafaley Location Dasheg Location Diff Division Diff Location Burder Location Ibrahim Ure Location Gerille Location Gurar Division Gurar Location Ajawa Location Danaba Location Quadama Location Griftu Division Griftu Location Tula Tula Location Wagalla Location Elnur Location Ganyure Location Bojiheri Location Arbajahan Location Kukala Location Basir Location Bute Division Bute Location Ogorji Location Godoma Location Dugow Location Eldas Division Dela Location Eldas Location Lakoley South Location Kilkiley Location Hadado Division Hadado South Location Hadado North Location Ademsajida Location Lolkuta North Location Lolkuta South Location Adhi-Bogol Location Lag-Bogol-North Location Sebule Division Sebule Location Dagahaley Location Banane Location Kursin Location Shimbir Location Sarif Location Macheza Location Dadaja Bura Location

Poverty Incidence Percent of individuals below the poverty line (Std. error) 63 67 67 63 62 64 58 69 65 64 65 70 67 66 66 58 67 67 68 63 68 61 69 68 72 63 62 74 65 64 65 69 63 64 62 64 64 68 61 65 62 70 70 70 73 66 68 65 68 71

(13) (16) (22) (10) (14) (17) (24) (16) (12) (17) (20) (21) (23) (7) (13) (23) (22) (16) (16) (23) (15) (22) (21) (11) (16) (15) (25) (20) (13) (22) (21) (22) (21) (10) (21) (21) (21) (22) (23) (21) (22) (8) (16) (18) (13) (15) (21) (23) (22) (20)

Poverty Gap As a percent of the poverty line (Std. error) 21 23 23 21 20 21 19 24 23 22 22 25 24 23 22 18 23 23 24 21 23 20 24 24 27 21 20 26 22 21 22 24 21 21 20 22 22 24 20 23 20 25 26 23 26 23 23 22 23 25

(7) (9) (11) (5) (8) (9) (13) (9) (7) (10) (12) (13) (14) (4) (8) (10) (13) (10) (10) (13) (9) (11) (12) (6) (10) (8) (12) (12) (7) (12) (11) (14) (12) (5) (11) (12) (12) (13) (13) (13) (11) (5) (10) (10) (9) (9) (12) (13) (13) (14)

Estimated Population From 1999 census

7,671 5,703 2,106 18,546 9,451 4,127 2,684 2,284 17,469 8,798 3,580 1,136 3,955 36,105 8,633 2,081 3,818 5,916 5,348 928 6,773 1,364 1,244 11,225 2,459 1,821 3,561 3,384 6,440 2,687 921 1,578 1,254 18,972 2,337 3,756 1,684 3,416 1,592 1,448 4,739 24,753 5,124 2,036 5,928 1,693 1,528 3,093 1,147 4,204

Estimated Number of Poor Individuals (Std. error) 4,827 3,805 1,419 11,640 5,862 2,649 1,559 1,571 11,414 5,637 2318 794 2,665 24,003 5,717 1,203 2,563 3,969 3,652 580 4,618 837 864 7,597 1,767 1,143 2,195 2,492 4,209 1,729 599 1,088 793 12,122 1,451 2,423 1,083 2,325 968 948 2,924 17,271 3,590 1,417 4,344 1,122 1,039 2,009 783 2,967

(646) (606) (306) (1,111) (815) (444) (368) (244) (1,355) (963) (468) (167) (602) (1782) (738) (276) (555) (649) (596) (131) (709) (184) (180) (832) (279) (170) (550) (487) (534) (379) (126) (238) (168) (1,161) (312) (516) (228) (520) (222) (200) (639) (1,342) (585) (258) (580) (174) (220) (458) (173) (596)

Table 12:

North Eastern Province Urban Poverty Estimates - From Districts to Sub-Locations

District/ Division/ Location Name

Poverty Incidence Percent of individuals below the poverty line (Std. error)

Poverty Gap As a percent of the poverty line (Std. error)

Estimated Population From 1999 census

Estimated Number of Poor Individuals (Std. error)

Garissa District Central Division Township Location Galbet Sub-Location Township Sub-Location Iftin Location Waberi Location Modogashe Division Dadaab Division Liboi Division Jarajila Division Ijara Division Bura Division

55 51 49 55 43 54 69 66 68 72 70 76 66

(4) (5) (5) (5) (5) (5) (6) (7) (5) (5) (8) (8) (9)

21 19 18 21 15 20 29 27 29 31 29 33 26

(3) (3) (3) (3) (3) (3) (5) (5) (4) (4) (6) (6) (5)

59,237 46,960 38,123 19,715 18,408 6,387 2,450 2,712 3,699 2,235 1,910 1,204 517

32,482 23,965 18,841 10,896 7,945 3,427 1,698 1,785 2,515 1,618 1,344 916 339

(1,456) (1,116) (907) (511) (422) (163) (99) (121) (129) (84) (102) (69) (30)

Mandera District Rhamu Division Rhamu Location Shantoley Location Banisa Division Takaba Division Elwak Division Elwak Location Elwak Township Sub-Location El-Adi Sub-Location Elwak South Location Central Division Central Location Bulla Jamhuria Location Bulla Jamhuria Sub-Location Bulla Nguvu Sub-Location Bulla Power Sub-Location Bulla Mpya Location Township Location

68 77 78 76 68 67 66 66 65 68 66 66 64 69 66 75 70 68 60

(5) (5) (5) (5) (7) (7) (6) (7) (7) (7) (7) (5) (5) (5) (5) (6) (6) (5) (5)

28 34 35 34 27 27 26 26 25 27 26 27 26 29 27 33 29 28 23

(4) (4) (4) (4) (5) (5) (4) (4) (4) (5) (5) (4) (3) (4) (4) (5) (5) (4) (3)

49,033 6,738 3,811 2,927 1,034 2,901 10,463 4,844 3,512 1,332 5,619 27,897 8,249 10,759 4,480 1,571 4,708 5,828 3,061

33,213 5,190 2,970 2,220 699 1,937 6,876 3,173 2,270 903 3,703 18,511 5,299 7,439 2,954 1,185 3,300 3,944 1,828

(1,626) (255) (154) (108) (48) (132) (442) (207) (153) (62) (246) (855) (246) (377) (143) (68) (197) (196) (100)

Wajir District Central Division Township Location Wagberi Location Hodhan Location Jogbaru Location Buna Division Griftu Division Bute Division Eldas Division

62 57 44 62 49 65 66 70 66 73

(6) (6) (7) (7) (7) (6) (8) (7) (7) (6)

24 22 15 25 17 26 26 29 26 32

(4) (4) (4) (5) (4) (4) (5) (5) (5) (4)

30,573 19,275 5,928 2,218 1,702 9,427 1,676 4,545 2,673 2,404

18,816 10,984 2,614 1,377 838 6,156 1,102 3,194 1,774 1,762

(1,069) (645) (182) (92) (56) (381) (84) (218) (117) (100)

References

Alderman, Harold Miriam Babita, Gabriel Demombynes, Ntabiseng Makhatha and Berk Özler (2002). How Low can you go? Combining census and survey data for poverty mapping in South Africa. Journal of African Economies 11(2):169–200. Demombynes, Gabriel, Chris Elbers, Jean O. Lanjouw, Peter Lanjouw, Johan A. Mistiaen and Berk Özler (2004). Producing an improved Geographic Profile of Poverty: Evidence from Three developing Countries. In Growth, Inequality and Poverty: Prospects for Pro-Poor Economic Development, in Anthony Shorrocks and Rolph van der Hoeven (eds.) Oxford University Press. Elbers Chris, Lanjouw Jean O. and Lanjouw Peter (2003). Micro-level estimation of poverty and inequality, Econometrica 71(1):355–364. Elbers Chris, Lanjouw Peter, Mistiaen Johan A., Özler Berk and Simler Kenneth (2004). On the Unequal Inequality of Poor Communities. World Bank Economic Review 18(3):401-421. GoK (Government of Kenya) (1998a). First report on poverty in Kenya. Incidence and Depth of Poverty.Volume 1. Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (1998b). First report on poverty in Kenya. Poverty and Social Indicators. Volume 2. Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (2000a). Second report on poverty in Kenya. Incidence and Depth of Poverty.Volume 1. Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (2000b). Second report on poverty in Kenya. Poverty and Social indicators.Volume 2. Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (2000c). Second report on poverty in Kenya. Welfare Indicators Atlas.Volume 3. Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (2001a). 1999 Population and Housing Census. Population Distribution by Administrative Areas and Urban Centers. Volume 1. Central Bureau of Statistics, Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (2001b). 1999 Population and Housing Census. Social-Economic Profile of the Population.Volume 2. Central Bureau of Statistics, Ministry of Finance and Planning, Nairobi, Kenya. GoK (Government of Kenya) (2003), Geographic Dimensions of Well-Being in Kenya, Where are the Poor? From Districts to Locations. Hentschel, Jesko and Peter Lanjouw (1996). Poverty profile. In Ecuador Poverty Report.The World Bank,Washington, D.C., USA. (53–91) Hentschel, Jesko, Jean O. Lanjouw, Peter Lanjouw and Javier Poggi (2000). Combining census and survey data to trace the spatial dimensions of poverty:A case study of Ecuador. The World Bank Economic Review 14(1):147–165. Mansuri, Ghazala and Vijayendra Rao (2004). Community Based (and Driven) Development:A Review. World Bank Research Observer 19(1):1-39. Mistiaen Johan A., Özler Berk, Razafimanantena Tiaray and Razafindravonona Jean (2002). Putting welfare on the map in Madagascar. Africa Region Working Paper Series 34.The World Bank,Washington, D.C., USA. (http://www.worldbank.org/afr/wps/index.htm) Ravallion, Martin (1994). Poverty Comparisons. Hardwood Academic Publishers,Amsterdam, Switzerland. SSA (Statistics South Africa) 2000. Measuring Poverty in South Africa. SSA, Pretoria, South Africa. World Bank - World Development Indicators (2005),Washington D.C

Appendix 1

Expenditure-based small area estimation The poverty mapping analysis undertaken was based on a statistical technique, sometimes referred to as small area estimation, that combines household welfare survey and census data (both collected at approximately the same time) to estimate welfare or other indicators for disaggregated geographic units such as communities. Researchers at the World Bank initiated this approach in 1996 (Hentschel and Lanjouw, 1996) and the key methodological paper is Elbers, Lanjouw and Lanjouw (2003).The techniques continue to be refined with many collaborators and there is now considerable reference material, some available on the internet. For readers interested in the details of this methodology refer to Hentschel et al., 1998; Hentschel et al., 2000; SSA 2000;Alderman et al., 2002; Elbers et al., 2002; Demombynes et al., 2002; Mistiaen et al., 2002; Elbers et al., 2003; and Demombynes et al., 2003. Here, we give a relatively brief and nontechnical summary of the approach. The approach begins with the nationally representative household welfare monitoring survey to acquire a reliable estimate of household expenditure (y). To calculate more specific poverty measures linked to a poverty line log-linear regressions are estimated to model per capita expenditure using a set of explanatory variables (x) that are common to both the household welfare monitoring survey and the census (e.g. household size, education, housing and infrastructure characteristics and demographic variables). These first-stage regression models are modelled at the lowest geographical level for which the household welfare monitoring survey data is representative (District), and a different first-stage model is estimated for each stratum (e.g. Province, urban, rural). Next, the estimated coefficients from these regressions (including the estimated error terms associated with those coefficients) are used to predict log per capita expenditure for every household in the census.These household-unit data are then aggregated to small statistical areas, such as Locations, to obtain robust estimates of the percentage of households living below the poverty line. These poverty rates are used to produce a poverty map showing the spatial distribution of poverty at the Location level, in the case of Kenya, which represents a significantly higher level of resolution than the District-level measures obtainable from using the household welfare monitoring survey alone. In the first stage of the Kenya analysis, variables within the census and welfare monitoring surveys were examined in detail. The objective of this stage was to determine whether the variables were statistically similarly distributed over households in the population census and in the household sample survey. For example, there are questions in both the population census and in the welfare monitoring survey about household size, level of education of the household head, and type of housing. However, the exact questions and manner in which the answers are recorded differ in some cases, e.g. the exact number of years of schooling for the household head was asked and recorded in the survey, while whether they have an education at a primary, secondary, or higher level is what was recorded in the census. In many cases, there were also discrepancies between identically defined variables due to regional variation in interpretation, rendering certain variables comparable in some provinces and not in others. The next step was to investigate whether these common variables were statistically similarly distributed over households in the population and those sampled by the survey.This assessment was based on the following statistics for each variable obtained from both the survey and the census for each stratum: (i) the mean, (ii) the standard error, (iii) and the values for the 1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th and 99th percentiles. First, the census mean for a particular variable was tested to see if it lay within the 95 per cent confidence interval around the household survey mean for the same variable. Second, for dummy variables, means were checked to ensure they were not smaller than three per cent and not larger than 97 per cent, so that the variables constructed contain some variation across households. The results of the comparison of variable means for the census and survey, by Province and for urban and rural areas, are available from the authors on request.

Another challenge to overcome in the analysis concerned harmonising the different sampling frames used for the WMS III and the 1999 census. The 1997 Welfare Monitoring Survey was designed to be representative at the District level.The 1997 WMS III survey was based on a frame that was designed using the 1989 census.As of 1999, more Districts had been created and the clusters used in the survey in 1997 no longer belonged to the same Districts, Divisions or Locations.This problem was compounded by the fact that the 1997 survey had no identifiers at the Division, Location and sub-Location level.Thus, considerable effort was made by the research team to rematch the clusters by identifying their current Districts, Divisions, Locations and sub-Locations. In addition, the WMS survey had only collected data by rural or urban strata, while the census had two additional more categories (i.e. peri-urban, and forests or national parks). A comparison of the census means indicated the peri-urban areas were much more like rural areas than urban, and thus they were merged with rural areas for the analysis. The modelling step of the analysis involved developing many models, for each stratum, using the household welfare monitoring survey data in a regression analysis. The variable we were trying to explain in each model was per capita household expenditure for a household in a particular location. The independent or explanatory variables for the model were those observable household characteristics found as comparable variables in both the survey and the census, as described above. We then combined the estimated first stage parameters with the observable characteristics of each household in the census to generate predicted per capita household expenditures (including an error estimate) for every household in the census. For each model estimated, a stepwise regression procedure in SAS was used to select the subset of variables from the set of â&#x20AC;&#x2DC;comparableâ&#x20AC;&#x2122; variables that provided the best explanatory power for log per capita expenditure.We chose a significance level criterion with no ceiling on the number of variables to be selected.All household survey variables that were significant at the five per cent level were selected for the regression.These regressions and relevant diagnostics for the urban and rural strata are available upon request from the authors. The results of the regression analyses show that the models were quite successful at explaining the variation in household expenditures in both urban and rural areas. The adjusted R2 ranged from 0.32 to 0.49 in urban areas, and from 0.31 to 0.49 in rural areas (with location means included).The explanatory power was highest for Nairobi and Nyanza for the urban strata and Coast for the rural strata. In general, household size, education of household members, the marriage status of the household head and some variables concerning housing characteristics (such as roof and wall materials and type of toilet) and access to services (such as principal source of energy and water) were key variables chosen in most regressions.We note that, on average, household size had a negative correlation with per capita household expenditure. Education was positively associated with household expenditures. A grass roof, mud walls and wood as the primary source of cooking fuel were all negatively associated with per capita household expenditures. Access to a good water source was generally found to be positively related to expenditures. Since these regressions are association models, the parameter estimates of the independent variables cannot be interpreted as causal effects, but rather provide us with evidence of the direction of the relationship. With a regression model for explaining household expenditures for each strata and information on the approximate parametric distributions of both error terms, the final stage of the welfare mapping exercise was to impute per capita expenditures for each household in the census and aggregate these to construct poverty and inequality measures for various administrative units. In addition, we calculate bootstrapped standard errors for these welfare estimates, taking into account the complex error structure (spatial effects and heteroskedasticity).