Benefit Incidence Analysis of Constituencies Development Fund Spending on Education Bursaries in Makueni County, Kenya

BENEFIT INCIDENCE ANALYSIS OF CONSTITUENCIES DEVELOPMENT FUND SPENDING ON EDUCATION BURSARIES IN MAKUENI COUNTY, KENYA

MAKALI B. MULU K96/12327/2009

A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY IN ECONOMICS TO THE SCHOOL OF ECONOMICS OF KENYATTA UNIVERSITY

JUNE, 2015

DECLARATION

This thesis is my original work and has not been presented for a degree in any other university or any other award.

Signature ------Date ------

Makali B. Mulu B.A (Hons), M.A (Economics) Reg.No. K96/12327/2009

This thesis has been submitted for examination with our approval as university supervisors.

Signature ------Date ------

Prof. Nelson H Were Wawire Department of Applied Economics Kenyatta University

Signature ------Date ------

Dr Susan O. Okeri Department of Econometrics and Statistics Kenyatta University

Signature ------Date ------

Dr. Dianah Muchai Department of Econometrics and Statistics Kenyatta University

DEDICATION

To my family for prayers, support, encouragement and patience

ACKNOWLEDGEMENTS

Above all, my sincere thanks and praises to our Almighty God who has brought me this far. Special appreciation and gratitude go to my supervisors Prof. Nelson

H.W. Wawire, Dr. Susan Okeri and Dr. Dianah Muchai for immensely providing guidance and suggestions during the Thesis writing as well as showing keen interest in my research. Without their unwavering support and advice, this Thesis would never have become a reality. I also acknowledge the valuable comments and advice received from my lecturers at the School of Economics, Kenyatta

University.

I am equally indebted to my classmates Elphas Ojiambo, Maurice Ogada, James

Karau, Mutuku Mutinda, Kennedy Ocharo, Fredrick Owiti, Herman Mwangi,

Maina Mutuaruhiu, Charles Nyadenge and Maurice Ombok for their encouragement, inspiration and moral support. My dear wife Agnes Muindi

Makali and our children Erastus Mwema, Jason Mulu, Stacy Ndaa and Irene

Kavutha deserve special mention for their patience, prayers and encouragement.

Finally, my special thanks to all who contributed in one way or the other in making this study a success. May God bless you all.

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TABLE OF CONTENTS

DECLARATION ...... i DEDICATION ...... ii ACKNOWLEDGEMENTS ...... iii TABLE OF CONTENTS ...... iv LIST OF TABLES ...... vii LIST OF FIGURES ...... viii ABBREVIATIONS AND ACRONYMS ...... ix OPERATIONAL DEFINITION OF TERMS ...... xi ABSTRACT...... xiv

CHAPTER ONE ...... 1 INTRODUCTION ...... 1 1.1 Background ...... 1 1.1.1 Poverty Alleviation and Income Redistribution Strategies ...... 9 1.1.2 Education as Anti-poverty Sectoral Policy Initiative ...... 13 1.1.3 The Constituencies Development Fund...... 18 1.1.4 Constituencies Development Fund Achievements and Challenges ...... 22 1.2 The Statement of the Problem...... 24 1.3 Research Questions ...... 26 1.4 Objectives of the Study ...... 26 1.5 Significance of the Study ...... 27 1.6 Organisation of the Study ...... 27

CHAPTER TWO ...... 29 LITERATURE REVIEW ...... 29 2.1 Introduction ...... 29 2.2 Theories of Fiscal Federalism ...... 29 2.3 Empirical Literature ...... 33

2.4 Overview of Literature ...... 49

CHAPTER THREE ...... 52 RESEARCH METHODOLGY ...... 52 3.1. Introduction ...... 52 3.2 Research Design ...... 52 3.3 Theoretical Foundations of Benefit Incidence Analysis ...... 53 3.4 Benefit Incidence Estimation Procedure ...... 60 3.5 Definition and Measurement of Variables ...... 65 3.6 Study Area ...... 66 3.7 Target Population ...... 66 3.8 Sampling Technique and Sample Size ...... 67 3.9 Data Type and Source ...... 69 3.10 Research Instruments ...... 70 3.11 Data Collection ...... 70 3.12 Data Cleaning, Coding and Refinement ...... 71 3.13 Data Analysis ...... 71

CHAPTER FOUR ...... 73 EMPIRICAL RESULTS, INTERPRETATION AND DISCUSSIONS ...... 73 4.1 Introduction ...... 73 4.2 Response Rate ...... 73 4.3 Descriptive Statistics ...... 74 4.4 Distribution of CDF Educational Bursaries ...... 80 4.4.1 Distribution of CDF Bursaries by Nature of Engagement of Household Head ...... 80 4.4.2. Distribution of CDF Bursaries by Education Level of Household Head ... 83 4.4.3 Distribution of CDF Bursaries by Constituency ...... 85 4.4.4. Distribution of CDF Bursaries by Quintile ...... 86 4.4.5 Distribution of CDF Bursaries by Sex ...... 88

4.5 Extent of Progressivity, Regressivity or Neutrality of CDF Spending on Education Bursaries ...... 90 4.6 Gender Dimension on Benefit Incidence Analysis of CDF Spending on Educational Bursaries ...... 106

CHAPTER FIVE ...... 111 SUMMARY, CONCLUSIONS AND POLICY IMPLICATIONS ...... 111 5.1 Introduction ...... 111 5.2 Summary of the Study ...... 111 5.3 Conclusion ...... 116 5.4 Policy Implications ...... 118 5.5 Areas for Further Research ...... 122 5.6 Limitations of the Study ...... 123

REFERENCES ...... 124 APPENDICES APPENDIX 1: Data Collection Instruments ...... 136 APPENDIX 11: Study Data ...... 139

LIST OF TABLES

Table 1.1: Absolute Poverty Estimates in Kenya 1981 – 2005 4

Table 1.2: Sectoral Distribution of CDF, 2003/04- 2012/13 20

Table 3.1: Summarised Sampling Process and Sample Sizes 68

Table 4.1: Sample Distribution and Share of CDF Bursary Expenditure by

Type of Institution 75

Table 4.2: Overall Students’ Distribution by Sex and Constituency 79

Table 4.3: Sample Distribution and Average Bursary Allocation by Nature

of Engagement of Household Head 81

Table 4.4: Bursary Distribution and Average Allocations by Highest Level

of Education of Household Head 83

Table 4.5: Average CDF Bursary Allocation by Constituency 85

Table 4.6: Average Bursary Allocation by Quintile 87

Table 4.7: Average CDF Bursary Allocation by Sex 88

Table 4.8: CDF Unit Cost (Bursary) by Education Level 91

Table 4.9: Constituency CDF Unit Cost (Bursary) for Tertiary Institutions 92

Table 4.10: Average Household Expenditure by Quintiles 93

Table 4.11: Unit Bursary Equivalent by Quintile 94

Table 4.12: Benefit Incidence of CDF Expenditure on Education Bursaries

by Level of Education 95

Table 4.13: CDF Spending on Education Bursaries Concentration Indices 102

Table 4.14: Benefit Incidence of CDF Spending on Education Bursaries by

Constituency 104

Table 4.15: Benefit incidence of CDF Spending on Education Bursaries by Sex 106

Table 4.16: CDF Spending on Education Bursaries Concentration Indices 110

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LIST OF FIGURES

Figure 1.1: Income Inequality Estimates for Kenya, 1964 – 2006 8

Figure 1.2: CDF Allocation in Kshs. Billions 2003/04 – 2011/12 19

Figure 1.3: CDF Projects Distribution by Sector 2003 – 2013 21

Figure 3.1: Measuring Benefits – Average Cost versus Total Benefit 54

Figure 4.1: Benefit Incidence of CDF Spending on Secondary and

Tertiary Education Bursaries 96

Figure 4.2: Concentration curves for CDF Spending on Education Bursaries 100

Figure 4.3: Concentration curves for CDF Spending on Education Bursaries

by Gender 108

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ABBREVIATIONS AND ACRONYMS

BA Bachelor of Arts BIA Benefit Incidence Analysis AIDs Acquired Immune Deficiency Syndrome CDF Constituencies Development Fund CDTF Community Development Trust Fund CRC Citizen Report Card CSOs Civil Society Organizations DDC District Development Committees ECOSOC Economic, Social and Cultural Rights EFA Education for All ERB Electricity Regulatory Board FDSE Kenya Education Support Programme FPE Free Primary School Education GDP Gross Domestic Product HIV Human Immuno-deficiency Virus HQs Headquarters IEA Institute of Economic Affairs IMF International Monetary Fund IPAR Institute of Policy Analysis and Research KESSP Kenya Education Support Programme KIPPRA Kenya Institute for Public Policy Research and Analysis KPC Kenya Power Company LAs Local Authorities LATF Local Authorities Transfer Fund MA Masters of Arts MDGs Millennium Development Goals MP Member of Parliament NACC National Aids Control Council NATSEM National Centre for Social and Economic Modelling, Australia NCCK National Council of Churches in Kenya NGOs Non Governmental Organisations PAF Poverty Alleviation Fund PETS Public Expenditure and Tracking Survey PSIA Poverty and Social Impact Analysis RMLF Roads Maintenance Levy Fund RAPLF Rural Electrification Programme Levy Fund

UNICEF United Nations Children’s Fund UPE Universal Primary Education WB World Bank WEF Women Enterprise Fund WCP Constituency’s Contribution to National Poverty WHO World Health Organisation YEDF Youth Enterprise Development Fund

OPERATIONAL DEFINITION OF TERMS

Absolute poverty: a state where an individual cannot raise the income required to

meet the expenditure for purchasing a specified bundle of basic requirements.

Concentration Curves: graphical representation of benefits’ distribution generated

by plotting the cumulative distribution of benefits of public spending on the y-

axis against the cumulative distribution of population sorted by per capita

income or expenditure on the x-axis.

Distribution of benefits: The distribution of benefits lies along the 45- degree

diagonal of concentration curve where the poorest 10 per cent of the

population gets 10 per cent of the benefits from public spending i.e. perfect

Equality.

Gender Dimension: refers to considerations based on female and male students

but does not factor in the roles in this study.

Gini coefficient: the Gini coefficient is commonly used to measure income

inequality in a country and for comparisons across countries or regions. A

Gini coefficient of zero confirms complete equality while Gini coefficient

closer to one indicates greater income inequality.

Hardcore poverty: refers to those households who could not afford to meet basic

minimum food requirement even if they allocate all their spending on food.

Household expenditure: refers to total annual expenditure of a household on

selected food and non food items in line with Kenya National Bureau of

Statistics guidelines. Included food and non food items are clearly shown in

annex one (1) of this Thesis.

Household expenditure: this is the most responsible member of the household

who makes key decisions in the household on day to day basis and whose

authority is honoured by all members of the household.

Income inequality: indicates the extent to which distribution of income in an

economy differs from that of equal shares among the population and is

concerned with variations in standards of living in the whole population.

Nyumba Kumi: National policy where ten households are group together in a

village to form one cell for purposes of national security. Each household is

supposed to monitor each other and report to security agencies any suspicious

characters.

Poverty line: determines an income level at which an individual just meets the cost

of a specified bundle of basic needs.

Progressive distribution of benefits: the poorest of the population gets a larger

share of the benefits from public spending than the richer income groups. For

instance the poorest 10 per cent of the population receives more than 10 per

cent of the benefits.

Regressive distribution of benefits: the poorest of the population gets a smaller

share of the benefits from public spending than the richer income groups.

xii

Relative poverty: a state when one cannot purchase a bundle of basic needs

available to a reference social group, such as people within a median income

level.

Rural: is a large and isolated area of open country (in reference to open fields and

forests), often with low population density (Republic of Kenya, 2009).

Tertiary Level: Combines both post –secondary education level and university

education level.

Urban: is an area with increased density of human-created structures in

comparison to the areas surrounding it and has a population of 2,000 and above

(Republic of Kenya, 2009).

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ABSTRACT

The Constituencies Development Fund is one of the decentralized efforts started by Government of Kenya to tackle poverty and regional imbalances in Kenya since 2003. It has allocated over 40 per cent of the resources annually to education sector including bursaries. However, doubts have been cast over time as to whether the Fund’s expenditure is effectively targeted towards meeting the needs of the poor as anticipated especially in the education bursaries that take substantial resources. This study examined the distribution of the Fund’s education bursaries; ascertained the extent to which the Fund’s spending on education bursaries was progressive, regressive or neutral; and examined the gender dimension in the benefit incidence of CDF spending on education bursaries. The study used primary data for two variables: actual CDF expenditure on education bursaries at different levels and expenditure data for households from which the users of the CDF bursaries belonged. Secondary data was collected for users (by sex) of educational bursaries provided through CDF funding. The Benefit Incidence Analysis was used as the estimation technique to arrive at empirical findings. The study established that distribution of CDF education bursaries depended a lot on the nature of engagement of the head of the household and there was an inverse relationship between level of education attained by household head and the amount of CDF bursary awarded. The study also found that there was inconsistency in average bursary awards to different quintiles demonstrating poor targeting of such bursaries making access to education difficult and costly to poorer Kenyans. Results of the study established that CDF’s spending on education bursaries was progressive for secondary education and regressive for the tertiary level of education where students from the rich households gained undue advantage over the students from poor households. On gender dimensions, the study established that CDF expenditure on education bursaries was biased towards male students. In light of the foregoing, the study recommends that the government through the CDF boards improve dissemination of information on CDF bursaries by providing adequate publicity / communication budgets to enhance inclusiveness in bursary applications; government opens as many opportunities as possible for young Kenyans to achieve higher levels of education; the government should subsidise tertiary education to guarantee access to tertiary education by the poor; and the government could as a matter of urgency address poor targeting of CDF bursaries through effective profiling of needy students. The government through the ministry of education could increase budgetary allocations for secondary school bursaries by better prioritisation and ring fencing resources. On gender dimensions, it is important that the government has the right policy framework to ensure equal opportunities are availed. In conclusion, there should be better targeting and harmonisation of all educational bursaries focusing more on efficiency, equity and effective participation.

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CHAPTER ONE

INTRODUCTION

1.1 Background

The rationale for government expenditure is to provide goods and services that markets do not provide or provide inefficiently. Government expenditure is automatic in the case of pure public goods where marginal cost of additional consumption is zero (Samuelson, 1954) and is fully justified where positive externalities are assured in the provision of goods and services. Shenggen et al.

(1999) argued that government spending can have direct and indirect effects on people’s welfare in three ways; macroeconomic effects (inflation and unemployment); primary income effect (the expenditure incidence); and the transfer effect (the benefit incidence). People also expect governments to perform two additional key functions that include reducing inequality and poverty eradication (Sahn and Younger, 2000). Government spending in principal should therefore promote efficiency through correcting market failures and/or generating positive externalities and promote equity through improving access of the poor to important services or distribution of economic welfare.

The global challenge of high poverty rates obligates most governments to target most public resources to poverty eradication. More than 1.3 billion of the world population live in absolute poverty (less than $1.25 per day) while nearly

a half of the world’s population (more than 3 billion people) live on less than

$2.50 per day (World Bank, 2013). The main causes of the world poverty include; poor people’s lack of resources; an extremely unequal income distribution in the world; and within specific countries internal conflicts and hunger itself (World Bank, 2013).

The World Bank estimated that there were slightly over 1 billion poor people in the developing countries who live on $1.25 or less per day by 2011.

Comparing with 1.91 billion poor people in 1990 and 1.93 billion poor people in 1981 in the world, generally poverty eradication initiatives had positive impact on poverty eradication (World Bank, 2013). East Asia experienced the most dramatic reduction in absolute poverty where poverty rates reduced from

78 per cent in 1981 to 8 per cent in 2011 (UNICEF et al, 2014). In south Asia, the share of the population living in absolute poverty by 2011 was the lowest since 1981, dropping from 61 per cent to 25 per cent in 2011 (UNICEF et al,

2014).

In Africa, about 415 million people lived under absolute poverty by 2011

(World Bank, 2013). By 2013, 75 per cent of the world poorest countries were located in Africa and included countries like, Democratic Republic of Congo,

Zimbabwe, Liberia and Ethiopia (UNICEF et al, 2014). The Democratic

Republic of Congo, second largest county in Africa, was ranked the poorest in the world with Gross Domestic Product of $394.25 in 2013. Anti-Poverty initiatives were less effective in Africa where Sub –Sahara Africa only reduced

absolute poverty from 53 per cent in 1981 to 47 per cent in 2011 (World Bank,

2013).

In the East African context, absolute poverty rates remained high by the year

2012. There were 67 per cent of Ugandans, 65 per cent of Tanzanians, 44.9 per cent of Rwandan people and 45.9 per cent of Kenyans living under absolute poverty (World Bank, 2013). The high absolute poverty rates forced respective

Governments to direct most public resources towards poverty eradication.

After independence in 1963, Kenya pursued economic development through central planning. This was aimed at accelerating economic growth, reducing poverty and promoting equitable distribution of resources across regions

(Republic of Kenya, 1965). However, the centralization of authority and management of resources did not result to equitable distribution of resources across regions. Inequality in provided services, infrastructure and development remained evident across the country (Court and Kinyanjui, 1980; Mapesa and

Kibua, 2006). A number of decentralized programmes were started to mitigate against high poverty rates and growing inequality such as; the District

Development Grant Program (1966); the Special Rural Development Program

(1969/1970); the District Development Planning (1971); the District Focus for

Rural Development (1983 -84); and the Rural Trade and Production Center

(1988-89). Despite the decentralized programmes, not much success was recorded because the programmes became politicized and misallocation of resources persisted (Court and Kinyanjui, 1980). The programmes suffered from lack of funding and excessive bureaucratic capture by the central

government (Khadiagala and Mitullah, 2004). Consequently, poverty levels remained high with the poor increasingly being unable to afford adequate food and nutrition; access to basic services viz. education, health, and safe water; and decent housing (Omiti et al., 2002). Table 1.1 provides poverty estimates in Kenya for selected years between 1981 and 2005.

Table 1.1: Absolute Poverty Estimates in Kenya 1981 - 2005 Province Percentage of the poor

1981/82 1992 1994 1997 2000 2005

Central 25.7 35.9 31.9 31.4 35.32 30.4

Coast 54.6 43.5 55.6 62.1 69.88 69.7

Eastern 47.7 42.2 57.8 58.6 65.9 50.9

Rift Valley 51.1 51.1 42.9 50.1 56.38 49.0

North Eastern N.a N.a 58.0 65.5 73.06 73.9

Nyanza 57.9 47.4 42.2 63.1 70.95 47.6

Western 53.8 54.2 53.8 58.8 66.11 52.2

Nairobi N.a 26.5 25.9 50.2 52.56 21.3

Rural 48.8 46.3 46.8 52.9 59.56 49.1

Urban N.a 29.3 28.9 49.2 51.48 33.7

National 46.8 46.3 46.8 52.3 56.78 45.9 Data Source: Kimalu et al. (2002) and Republic of Kenya (1998 and 2008)

It is noteworthy that the only available official published data on poverty estimates in Kenya was collected in 2005. The Kenya government is in the final stages of preparations to publish updated poverty estimates. The provinces no longer exist after the promulgation of the Kenyan constitution in

2010 and counties are currently the administrative units.

Table 1.1 shows that poverty has remained rampant and afflicts a large proportion of the population especially those in the rural areas. Absolute national poverty estimates remained high peaking in the year 2000 when 56.78 per cent of the population lived below poverty line. Before the year 2000, the lowest national poverty estimates were recorded in 1992 when the population that lived below poverty line was 46.3 per cent. In the year 2005, 45.9 per cent of the total population lived below poverty line with 49.1 per cent of the poor living in the rural areas and 33.7 per cent in the urban areas.

Regional disparities in absolute poverty estimates were evident in the country.

Central province recorded relatively low poverty estimates over the years with

30.4 per cent of the population below poverty line in 2005. North Eastern and

Coast provinces had 73.9 per cent and 69.7 per cent of the population below poverty line respectively. Other provinces that recorded high poverty estimates in 2005 were Western and Eastern provinces. Western province had 52.2 per cent living below poverty line while Eastern had 50.9 per cent.

In the year 1994, the prevalence of absolute poverty was highest in North

Eastern at 58 per cent followed by Eastern province at 57.8 per cent and then

Coast province at 55.6 per cent. However, in terms of absolute hardcore poverty in rural areas, the highest prevalence was found in Eastern province at

39 per cent followed by North Eastern province at 38 per cent, Coast province at 36 per cent and Western province at 35 per cent (Republic of Kenya, 1998b).

Eastern province’s high absolute poverty estimates of 50.9 per cent in the year

2005 were distributed across 36 constituencies and contributed about 17.7 per cent to the national poverty estimates (Republic of Kenya, 2008a). Considering the Eastern province’s poverty estimates, 34 per cent of the poor were concentrated in 7 of the 36 constituencies, namely: Makueni (6.1 per cent),

Mbooni (5 per cent), Kangundo (4.9 per cent), Kibwezi (4.8 per cent), Kitui

Central (4.4 per cent), Mwingi North (4.4 per cent), and Mwala (4.3 per cent).

Out of the seven constituencies, 3 constituencies (Makueni, Mbooni and

Kibwezi) are in Makueni County (Republic of Kenya, 2008a) confirming

Makueni County as the lead contributor to absolute poverty in Eastern province.

Based on the Welfare Monitoring Survey of 1997, Makueni and Homabay districts were the poorest, in terms of overall poverty, with over 70 per cent of their population living below the absolute poverty line (Republic of Kenya,

2000a). At the same time, only five districts had over 50 per cent of their population living in hardcore poverty and those included West Pokot with 60 per cent, Makueni with 59 per cent, Homabay with 54 per cent, Nyamira with

50 per cent and Busia with 50 per cent. The 1994 Welfare Monitoring Survey report confirmed six districts that had more than 70 per cent of their population below overall absolute poverty line i.e. Marsabit 88 per cent, Samburu 84 per cent, Isiolo 82 per cent, Makueni 76 per cent, Turkana 74 per cent, and Tana

River 72 per cent (Republic of Kenya, 1998b). Analysis of poverty trends confirms Makueni County as a key contributor to high levels of poverty in

Kenya.

The high level of poverty in the rural areas is largely explained by low access to physical assets, inadequate non-farm employment opportunities, low access to health care and schooling, and low agricultural productivity (Wambugu et al., 2010). Since most of the people are employed in agriculture with women forming the majority, this suggests that majority of the poor in the rural areas are women. Previous studies on poverty in Kenya (Mwabu et al., 2000;

Republic of Kenya 1998; 1999) showed that the poor are clustered into a number of social categories that include the landless, the handicapped, female headed households, households headed by people without formal education, subsistence farmers, pastoralists in drought prone districts, unskilled and semiskilled casual labourers, AIDS orphans, street children and beggars.

Poverty victims could therefore be identified by region of residence and by certain social characteristics.

The poor performance of the decentralized programmes as a result of resource misallocation and excessive bureaucratic capture by central government also contributed to high degrees of income inequality. Figure 1.1 provides estimates of the Gini Coefficient for Kenya for various time periods.

0.8 0.69 0.7 0.63 0.599 0.604 0.599 0.57 0.573 0.6 0.556 0.481 0.452 0.5 0.443 0.419 Gini Coeffient0.4 0.3

0.2

0.1

0 1964 1969 1974 1976 1977 1982 1984 1992 1994 1997 1999 2006 Year

Data Source: Wambugu et al. 2010 Figure 1.1: Income inequality estimates for Kenya, 1964 - 2006

Figure 1.1 shows that income inequality in Kenya was high in the 1960s, 1970s and 1980s but was relatively lower in the 1990s and 2000s. The highest recorded Gini coefficient was 0.69 in 1974 while the lowest registered Gini

Coefficient was 0.419 in 1997. High degrees of inequality in income distribution could have a negative effect on economic growth and increase poverty. A study by Person and Tabellini (1989) found a strong negative relationship between initial income inequality and future growth and poverty reduction in both developing and developed countries hence the justification to address both. It is also widely recognized that economic growth on its own might not bring about reduction in absolute poverty because the growth might increase, decrease or have no effect on income inequality (Wambugu et al.,

2010).

1.1.1 Poverty Alleviation and Income Redistribution Strategies in Kenya

In the war against the persistent high levels of poverty and income inequality, the Government of Kenya employed different strategies over the years. The fight against poverty remained a high government priority where virtually all the Development Plans, Sessional Papers and other government economic policy documents issued in the post-independence period prominently featured poverty alleviation as an area of concern (Manda et al., 2001).

Promotion of rapid economic growth as a means of alleviating poverty and creation of employment opportunities got prominence as a key poverty alleviating strategy. The Kenyan economy achieved a high growth rate of almost 7 per cent per annum in the 1960s and 1970s but the two problems of poverty and unemployment persisted and income inequality widened implying economic growth is necessary but not sufficient for alleviating poverty. The failure of economic growth to solve the problems continued to be observable in the 1980s and 1990s (Manda et al., 2001).

Another poverty alleviation strategy that was used as a complimentary strategy was the basic needs approach to development that focused on the provision of basic services such as food, water, shelter, and health care for the poor (Manda et al., 2001). Since the provision of such basic needs depended on public budgetary outlays which in turn were based on national economic growth, the basic needs approach did not overcome the biases that pervaded all earlier efforts at poverty alleviation. Consequently, the basic needs approach to

poverty alleviation attained the connotation of rural development, where the majority of the population and the poor live. However, the rural dimension of poverty alleviation was not combined with explicit reference to social, political, cultural, and environmental concerns all of which were either mentioned in passing as by-products of mainstream development programmes or completely ignored (Bahemuka et al., 1998).

With agriculture regarded as the backbone of Kenya’s economy, the government provided land for the poor through settlement schemes and land redistribution as a poverty alleviation strategy. In the 1960s and 1970s, the country was able to design and implement some pro-poor programmes which included the settlement schemes in which thousands of landless people and squatters displaced by the colonial settlers were provided with small scale landholdings, especially in the former Rift Valley Province. In total, over a million acres of mixed farm land previously owned by 2000 Europeans was transferred to 47,000 African smallholders by means of land purchase and development loans (Republic of Kenya, 1999).

Another poverty alleviation strategy was the district focus for rural development strategy launched in 1983 with the main objective of allocating resources in a more geographically equitably basis (Republic of Kenya, 1999).

More funds were allocated to the less developed regions to be spent on projects given priority by the local communities. However, due to poor preparation, unfamiliarity of district staff with methods of participatory planning, and the

absence of monitoring and evaluation, a number of decentralised projects were poorly conceived and designed (Republic of Kenya, 1999).

From the mid-1980s, the growth of the informal sector started to receive greater attention as a poverty alleviation strategy. The sector was seen as one with high potential to alleviate poverty, through creation of employment opportunities in the form of off-farm activities in both the rural and urban areas

(Republic of Kenya, 1986; 1996). Unfortunately, the government never really created a truly enabling and supportive environment for the informal sector

(Ikiara, 1998). The government in addition promoted micro financing to meet the credit needs of the poor. Non Governmental Organisations (NGOs) like the

National Council of Churches in Kenya (NCCK), K-REP, Kenya Women Trust

Fund and Faulu - Kenya were encouraged to provide micro finances to the poor to support them get out of poverty. Nevertheless, micro finance faced challenges in addressing the credit needs of the very poor because most of the credit programmes were concentrated in the urban areas and focused on business as compared to agriculture where most of the poor are concentrated.

Despite all the efforts by the Kenya government to combat poverty, poverty has persisted suggesting that the adopted policies have not been effective or adequate in addressing the problem. Poverty reduction remains therefore a big national challenge. Poverty’s persistence and spread is recognised as a major threat to a very significant section of the Kenyan society with worrying consequences for security and economic wellbeing. The poor performance of the earlier poverty alleviation initiatives / strategies did not fully discourage the

government. Since the 1990s, renewed interest has been witnessed to sustain the efforts to reverse regional imbalances and tackle poverty.

To avoid past mistakes in poverty alleviation strategies, the renewed efforts have been targeted at empowering the grassroots through devolved decision making in programmes prioritization, devolved planning, participatory budgeting and costing, consultative implementation and participatory programmes monitoring and evaluation. Since the 1990s, the government introduced various devolved funds aimed at addressing the critical issues of regional imbalances and poverty reduction at the grassroots to cushion the country’s poor and vulnerable groups. The devolved funds include: the Roads

Maintenance Levy Fund (RMLF) established in 1993; Secondary School

Education Bursary Fund started in 1993; the Community Development Trust

Fund (CDTF) established in 1996; the Rural Electrification Programme Levy

Fund (RAPLF) started in 1998; the Local Authorities Transfer Fund (LATF) established in 1999; the Poverty Alleviation Fund (PAF) started in 1999; the

HIV/AIDS Fund established in 1999; the Constituencies Development Fund

(CDF) started in 2003; the Free Primary School Education (FPE) started in

2003; the Youth Enterprise Development Fund (YEDF) started in 2006; and the Women Enterprise Fund (WEF) established in 2006. Each fund is anchored on a specific legislative framework. The education sector has benefited more from the devolved funds with five of the established devolved funds supporting educational initiatives.

1.1.2 Education as Anti-poverty Sectoral Policy Initiative

Education and poverty in Kenya are intimately related with incidence of poverty among the better-educated households being lower than among less- educated households (Wambugu et al., 2010). The importance of education as one of the sectoral anti-poverty policy initiatives is underscored by the many policy initiatives that the Kenya government has initiated to support education and training sector. At independence in 1963, the Government recognized education as a basic human right and a powerful tool for human resource and national development. Education was seen not only as a welfare indicator per se but also a key determinant of earnings providing an important exit route from poverty. Since then, policy documents have reiterated the importance of education in eliminating poverty, disease and ignorance. The government put concerted efforts to streamline the education sector through Commissions,

Committees and Taskforces to achieve the educational goals (Republic of

Kenya, 2005).

The Ominde Education Commission (Republic of Kenya, 1964) proposed a post-independence educational system that would foster national unity and the creation of sufficient human capital for national development. The Gachathi

Report (Republic of Kenya, 1976) of the National Committee on Educational

Objectives and Policies redefined Kenya’s educational policies and objectives giving consideration to national unity, and economic, social and cultural aspirations of the people of Kenya. The Mackay Report of the Presidential

Working Party on the Second University in Kenya (Republic of Kenya, 1981)

led to the removal of the advanced (A) level of secondary education, and the expansion of other post-secondary training institutions. The Kamunge Report of the Presidential Working Party on Education and Manpower Training for the

Next Decade and Beyond (Republic of Kenya, 1988) focused on improving education financing, quality and relevance. This led to the policy of cost sharing between government, parents and communities.

The Commission of Inquiry into the Education System of Kenya commonly referred to as the Koech Commission, recommended Totally Integrated Quality

Education and Training (Republic of Kenya, 2000). In 2005, policy initiatives shifted to focus on the attainment of education for all (EFA) by 2015 and, in particular, Universal Primary Education (UPE). The key concerns were access, retention, equity, quality and relevance, and internal and external efficiencies within the education system. The attainment of the EFA goals is in line with the Government’s commitment to international declarations, protocols and conventions as resolved in world conferences on EFA in 2000.

All the efforts made to reform the education system were a demonstration of the Government’s commitment to the provision of quality education and training as a human right for all Kenyans in accordance with the Kenyan law and the international conventions. Some of the international conventions to which Kenya is a signatory include; the United Nations International

Convention on Social and Economic Rights, article 13; the Convention on the

Rights of the child, articles 28, 29 and 30; the African Charter on Human and

People’s Rights, article 17; and the African Charter on the Rights and Welfare of the Child, article 11.

The overall policy goal for the Government is to achieve EFA in order to give every Kenyan the right to education and training no matter his/her socioeconomic status. The vision is guided by the understanding that quality education and training contribute significantly to economic growth and poverty reduction as well as reduction of income inequalities (Republic of Kenya,

2005). The vision is in tandem with Kenya’s long term development blue print, the Vision 2030 that also acknowledges the central role played by education through the Social Pillar. The Social Pillar seeks to create a just, cohesive and equitable social development in a clean and secure environment through focusing on eight key social sectors, namely education and training; health; water and sanitation; environment; housing and urbanization; gender; youth and vulnerable groups (Republic of Kenya, 2008).

Over the years, the Government has also demonstrated its commitment to the development of education and training through sustained allocation of resources to the education sector. The Central government spending on the

Ministry of Education and Ministry of Higher Education, Science and technology increased from Kshs.75.6 billion in 2003/04 to Kshs.141.6 billion in 2008/09 and to Kshs.216.6 billion 2011 in nominal prices (Republic of

Kenya, 2013). As a share of total public spending, the education sector was about 20 per cent and was 7.2 per cent of the Gross Domestic Product (GDP)

(Republic of Kenya, 2013). Through the continued government financial

support, the country has made considerable progress towards increasing access and participation in primary education with net enrolment rate increasing from

80.4 per cent in 2003 to 95.7 per cent in 2011 (Republic of Kenya, 2013).

However, despite the substantial allocation of resources and notable achievements attained, the education sector still faces major challenges that relate to access, equity, quality, relevance, efficiency in the management of educational resources, cost and financing of education, gender and regional disparities, and teacher quality and teacher utilization (Republic of Kenya,

2005). Out of the myriad challenges, the issue of access to education has major serious consequences and all efforts should be made to address it.

With introduction of free primary education, the issue of access to education is more serious at secondary and tertiary institutions. The national reported transition rate from primary to secondary was 59.9 per cent in 2007 but there was considerable regional variance and gender disparity (Republic of Kenya,

2010). Since 2007, the transition rate from primary to secondary schools rose from 59.9 per cent to 73.3 per cent in 2011 (Republic of Kenya, 2013). The lowest transition rates were registered in Nairobi province (45.1 per cent), followed by Western province (45.7 per cent) and then Eastern province (45.9 per cent). The low access rates were worsened by the high cost, (the average annual unit cost for secondary education was 5 times higher than primary education), high levels of poverty and increased number of orphans in and out of school as a result of HIV/AIDS. It was estimated that 30 per cent dropout rate was due to the high levels of poverty alone (Republic of Kenya, 2005).

Gender disparity is bigger at secondary schools but increases significantly at tertiary level. The gender parity index for secondary level was 0.85 and for tertiary level was 0.60 in 2008 (Republic of Kenya, 2010). The gender parity index for secondary level increased from 0.85 in 2008 to 0.87 in 2011

(Republic of Kenya, 2013).

Notwithstanding the challenges the sector is facing in terms of access to education, the government developed strategies with a view to improving access to secondary education and tertiary institutions. Some of the strategies include: promoting the development of day schools as a means of expanding access and reducing the cost to parents; providing support to poor and disadvantaged students through secondary school bursaries; providing targeted support for the development of infrastructure in areas where parents are not able to provide such support; increasing the provision of bursaries and devising better methods of targeting and disbursing funds to the needy taking into account gender parity; and providing scholarships based on the needs of the economy.

The strategy of supporting the poor and disadvantaged students through provision of bursaries has been given a lot of weight by the Government through Secondary School Education Bursary Fund started in 1993 and increased volumes of specific ―devolved funds‖ allocated to local communities to cater for education bursaries (Republic of Kenya, 2008). One of the key devolved funds that cater for education bursaries is Constituencies

Development Fund discussed in the following section.

1.1.3 The Constituencies Development Fund

The CDF was established in 2003 through the CDF Act in the Kenya Gazette

Supplement No. 107 (Act No. 11) of 9th January 2004. The CDF Act was amended in 2013. Unlike other development funds that filter from the central government through larger and more layers of administrative organs and bureaucracies, funds under this programme go directly to local levels and thus provide people at the grassroots the opportunity to make expenditure decisions that maximize their welfare. The CDF targets all constituency-level development projects and its prime objectives include: funding projects with immediate social and economic impact in order to uplift the lives of the people; and alleviating poverty for purposes of development at the constituency level.

The Fund comprises an annual budgetary allocation of at least 2.5 per cent of the government's ordinary revenue (Republic of Kenya, 2003). 75 per cent of the fund is allocated equally amongst all 290 constituencies. The remaining 25 per cent is allocated as per constituency poverty levels. The ceilings allocated to each constituency are determined using a formula guided by the national and constituency poverty indexes. The formula used as per the CDF Act, 2003 is:

CDF Allocation = [(0.75 x CDF)/290] + {(0.25x CDF) x WCP}

where: CDF Allocation = CDF allocation to each constituency CDF = Total CDF allocation less (3 per cent administration costs + 5 per cent for emergency) WCP = Constituency’s contribution to national poverty.

The net available CDF fund is the total CDF allocation after netting out 3 per cent for an administrative budget and 5 per cent for constituency emergency budget. According to the CDF Act (2003), each constituency was required to keep aside 5 per cent as an emergency reserve and a maximum 10 per cent of each constituency’s annual allocation could be used for an education bursary scheme. The maximum amount for education bursaries was increased to 25 per cent through the amendment of CDF Act in 2013.

Since the year 2003/2004, the total amount of funds allocation through the

CDF is Kshs.86.16 billions up to the 2011/2012 financial year. Figure 1.2 shows the CDF’s annual allocations since inception.

18 16.99

16 K 13.86 s 14 h 11.96 s 12 . 9.797 10.1 B 10 9.737 i 8 l 7.029 l i 6 5.432 o n 4 s 2 1.26

0 2003/4 2004/5 2005/6 2006/7 2007/8 2008/9 2009/10 2010/11 2011/12 Fiscal Years

Source of Data: CDF website (www.cdf.go.ke) and author’s derivation Figure 1.2: CDF Allocation in Kshs. Billions 2003/04 – 2011/12

It is evident that the annual allocations have been increasing in absolute terms from Kshs.1.26 billion in 2003/2004 financial year to Kshs.16.99 billion in the

2011/2012 financial year. The annual increase of the allocations demonstrates government of Kenya commitment to rural development in general through devolved funds.

Table 1.2 presents CDF allocations between 2003/04 and 2012/13 to different sectors.

Table 1.2: Sectoral Distribution of CDF (percentages), 2003/04- 20012/13 2003/ 2004/ 200 2006/ 2007/ 2008/ 2009/ 2010/ 2011/ 2012/ Sector 04 05 5/06 07 08 09 10 11 12 13

Education 41.3 37.7 37.8 37.6 37.7 33.8 35.5 39.8 43.6 44.2

Health 19.2 11.9 9.3 9.1 8.2 7.0 6.8 7.0 7.5 6.3

Water 19.3 15.9 14.2 13.5 11.9 9.5 11.5 7.5 7.2 7.8

Roads/ Bridges 4.1 7.9 6.9 8.5 8.5 6.6 6.5 5.3 6.2 8.6

Agriculture 1.7 2.0 2.4 2.1 1.8 1.9 1.4 1.0 1.0 0.9

Security 1.2 0.7 0.0 1.8 3.1 3.4 4.2 3.1 3.1 3.1

Others 7.5 10.4 13.9 11.1 12.8 11.9 11.0 14.0 7.8 6.1

Bursary 2.5 5.6 7.4 8.4 8.3 11.9 12.8 12.1 13.2 12.3

Emergency 0.6 5.0 5.0 5.0 5.0 5.0 5.3 5.2 6.4 5.3

Administration 2.7 2.8 3.1 2.9 2.8 3.8 5.0 5.2 4.3 5.2

Data Source: Constituency Development Fund budgets and author’s derivation.

As shown in table 1.2, it is evident that CDF spending on the education sector including bursaries accounted for over 40 per cent of the total funds disbursed for all the years since 2003/04. The CDF spending on the education sector including bursaries continued increasing over the years and has since 2010/11 financial year accounted for more than 50 per cent of the funds disbursed.

Table 1.2 shows the growth of the educational bursaries over time hence

confirming the importance of educational bursaries in improving access to education as well as addressing high levels of poverty in the long run.

In terms of sectoral distribution of CDF expenditure, figure 1.3 shows the cumulative expenditure on key sectors for the years 2003/14 -2012/13. CDF funded projects in various sectors are scattered across the country ensuring availability of the social services in different constituencies.

Emergency Bursary 6% 13%

Education Others 41% 11%

Security 3% Agriculture 1% Roads/Bridges 7% Water Health 10% 8%

Source of Data: CDF HQs records and authors derivation. Figure 1.3 CDF Total Expenditure by Sector 2003/04 – 2012/13

Figure 1.3 shows that most of the CDF spending is on basic social services like education infrastructure (41 per cent), education bursaries (13 per cent), water

(10 per cent) and healthcare (8 per cent). Education as a merit good is important for increasing the welfare of the poor through provision of knowledge, skills, values and attitudes that increase productivity and employability. Despite the increased funding of educational bursaries through

CDF, the question of effective targeting of the disbursed funds to the needy taking into account gender parity remains unanswered.

1.1.4 Constituencies Development Fund Achievements and Challenges

CDF has registered key achievements since inception in 2004. The greatest achievement has been genuine shifting of programme / projects formulation from line ministries to communities where local initiative ownership, participatory supervision and accountability have been encouraged. Citizens have an opportunity to align their demands with resource allocation (Mwangi,

2005).

There is general improvement of physical infrastructure in majority of the constituencies especially social facilities like schools, health and water points hence improved accessibility to such facilities. In particular the poor in the past experienced serious problems accessing basic services but through CDF the situation has changed for the better (Mwangi, 2005).

The programme has created employment for local populace because local artisans are awarded contracts and materials for construction works are normally sourced locally. The involvement of local artisans and local suppliers has improved their capacities and income contributing to poverty alleviation

(Roxana, 2008).

Despite the reported CDF achievements, the fund faces numerous challenges.

Incidences of misuse and inefficient use of funds have been reported (Republic of Kenya, 2007, 2009a, 2010; KIPPRA, 2006; Mapesa and Kibua, 2006). CDF suffers significant degree of duplication in project planning and financing and

different organizations have ended up funding the same projects increasing the opportunity for officials to siphon funds hence adversely impacting on poverty alleviation efforts (Republic of Kenya, 2010). Low project completion rates have also been documented due to inadequate institutional, technical and human resource capacity to oversee the timely implementation of funded projects (Republic of Kenya, 2010).

Despite CDF being a form of decentralization, it is not a pure fiscal decentralization which is characterized by both revenues and expenditures.

CDF is a one sided fiscal decentralization scheme where expenditures are not linked to the local revenue sources (Bagaka, 2008), hence can be associated with fiscal illusion which minimizes the extent to which beneficiaries monitor use of funds. The fiscal illusion might explain findings of studies that found out that CDF experienced performance challenges that included; cases of fund embezzlement prompting the Government to employ managers for each

Constituency to inject professionalism; improper projects prioritization leading to projects with minimal impact being implemented; and poor distribution of the projects.

CDF is also very political in nature due to the way it is designed. The theory of median voter explains why a number of politicians attempt to use the CDF monies as political bait in order to gain political mileage. Mapesa and Kibua

(2006) found overwhelming evidence and acceptance that the CDF was being used to advance the political agenda of the MPs. At the same time the poor lack both economic and political power and thus public policies tend to become

the battleground for various interest groups and rent seekers, and benefits end up with the more vocal and not with the more needy (Conyers, 1990;

Harberger, 1998). This explains the ―elite capture‖ that was also evident in some areas.

Analysis of how CDF funds are spent clearly confirmed that CDF per capita spending is not correlated with poverty levels (Republic of Kenya, 2010). In other words the intended poverty targeting through CDF might not be achieved. Doubts are therefore cast as to whether CDF expenditure on educational bursaries is effectively targeted towards meeting the needs of the poor. The important debate is no longer on the CDF expenditure on education bursaries but who has benefited from the expenditure in order to foster access to education.

1.2 The Statement of the Problem

With increased CDF funding to educational bursaries from 2.5 per cent in

2003/04 to 8.3 per cent in 2007/08, access to secondary and tertiary education for the poor students should have improved. However, reviewed studies (IEA,

2005; IPAR, 2006; KIPPRA, 2006; Republic of Kenya, 2010) provided evidence of myriad CDF challenges that included incidences of misuse and inefficient use of funds, significant degree of duplication in project planning and financing, low project completion rates, fiscal illusion, issues of nepotism and CDF per capita spending not correlated with poverty levels. Such findings

cast doubts as to whether CDF expenditure on educational bursaries is effectively targeted towards meeting the needs of the poor students. Effective targeting of the poor students is quite important because the poor are typically trapped in poverty due to limited access to services that can facilitate them escape from poverty and also due to their limited human capital. Some studies have provided positive results of CDF’s contribution to poverty reduction

(Roxana, 2008; Bagaka, 2008), while others found that CDF is still at risk of failing just like previous government attempts at decentralization (IEA, 2005;

IPAR, 2006; KIPPRA, 2006). Moreover, reviewed studies on CDF (IEA,

2005; IPAR, 2006; KIPPRA, 2006; Roxana, 2008; Bagaka, 2008) focused on efficiency issues and general effects on poverty reduction. None of them specifically focused on which segments of the student population benefits from the CDF spending on education bursaries and how the bursaries are shared between male and females students. This study sought to fill that knowledge gap by empirically determining out which segments of the student population benefitted from the CDF spending on education bursaries.

Furthermore, despite the increased CDF expenditure on bursaries for education, the persistent question is whether the additional spending meets the needs of the poor bearing in mind that the poor are not a homogenous group living in one specific area. It is therefore critical to assess the contribution of

CDF spending on education bursaries to the poor students by empirically finding out who benefits from the bursaries and how they are shared between male and female students. The gender dimension is especially relevant for poverty assessment, since the weak targeting of government spending to the

poor is closely related to gender biases in the use of government services

(Demery, 1996). It was important to explore this issue because the Kenya

Vision 2030 highlights some of the key challenges faced by citizens as; majority of Kenyans still living in poverty; high inequality in income distribution; and gender imbalance.

1.3 Research Questions

The study sought to address the following questions;

i) How are the CDF provided educational bursaries distributed at

secondary and tertiary education levels in Makueni County?

ii) To what extent is CDF’s spending on education bursaries in Makueni

County progressive, regressive or neutral?

iii) How is the gender dimension in the benefit incidence of CDF spending

on education bursaries in Makueni County?

1.4 Objectives of the Study

The general objective of the study was to conduct a benefit incidence analysis of Constituencies Development Fund spending on education bursaries in

Makueni County. The specific study objectives were to;

i) Examine the distribution of CDF provided educational bursaries at

secondary and tertiary education levels in Makueni County.

ii) Ascertain the extent to which CDF’s spending on education bursaries is

progressive, regressive or neutral in Makueni County.

iii) Examine gender dimension in the benefit incidence of CDF spending

on education bursaries in Makueni County.

1.5 Significance of the Study

The results of the study are very useful as information on which sections of the student population benefit from spending on education bursaries informs policy makers in making public spending choices. The study is also important in informing necessary policy adjustments to the CDF as prompted by the study findings. Last but not least, the study makes a major contribution to the non existence of empirical literature on who benefits from the Constituency

Development Fund spending on Education bursaries in Makueni County.

1.6 Organisation of the Study

The study is structured as follows; chapter one is an introduction that provides relevant background information on poverty, education as an anti-poverty initiative, brief on the Constituency Development Fund, statement of the problem and study objectives; chapter two presents theoretical framework, empirical literature and overview of literature; and chapter three provides study methodology, estimation procedure, data collection and data analysis. Chapter

four presents the study findings in terms of descriptive statistics, benefits incidence of CDF spending on education bursaries in Makueni County and gender dimension to the benefit incidence. Chapter five presents summary of the study findings, conclusions and policy implications, areas for further research and study limitations.

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

In this chapter, both the theoretical and empirical literature on the devolved funds and poverty reduction is reviewed. The first section reviews theories and exposes the theoretical foundations of fiscal federalism under which CDF funding is anchored. The second section reviews the empirical studies and the final section provides overview of the literature.

2.2 Theories of Fiscal Federalism

The traditional theory of fiscal federalism provides a framework for the assignment of functions to multiple layers of government and the appropriate fiscal instruments for carrying out the assigned functions (Musgrave, 1959;

Oates, 1972). The theoretical rationale for transfer of responsibility for the provision of public goods and services from the central government to the local level is given by Wallace Oates’ Decentralization Theorem. The theorem stipulates that it will always be more efficient for local levels to provide the

Pareto-efficient levels of output for their respective jurisdictions than for the central government to provide any specified and uniform level of output across all jurisdictions (Oates, 1972). In other words, so long as there are no economies of scale in the provision of public goods whose benefits accrue to

geographically separable populations with differing preferences, the level of welfare of local provision will always be at least as high as the level of welfare that can be achieved from central provision.

The fiscal federalism theories define how best to provide public goods and services and how best to finance such goods and services in such a way as to maximize community welfare often represented by the median voter theorem.

Decentralization is more sensitive to local needs and preferences hence better targeting of public goods while central provision leads to uniform supply over all jurisdictions. Decentralization can be an effective tool for implementing poverty reduction policies because people at the local level have the information and incentives to design and implement policies that respond to local needs and preferences (Litvack et al, 1998, and World Bank, 2001). It leads to bottom up poverty reduction through broader participation of citizens as well as funding projects that fit their tastes and preferences (Turner and

Hume, 1997).

Fiscal federalism theories stipulate that multiple layers of government become responsible for local revenue and expenditure assignments. National public goods should therefore be financed at the national level while the local public goods should be funded at the local level. Musgrave (1983) suggested that the local public sector should be financed basically by user charges and local taxes, especially the property tax and the state by consumption taxes with the income tax being left largely to central government. The division of revenues in most cases lead to greater expenditure responsibilities than can be financed by own

revenues hence justifying another important element of fiscal federalism of intergovernmental grants to close the revenue gap. The theory of fiscal federalism provides insights on the role of grants / transfers and their attendant problems. Theoretical literature suggests that the only clear case for intergovernmental grants is to compensate local governments for benefits spill- overs to ensure optimal provision of the public goods or services (Bird et al.,

1995).

The theories of fiscal federalism conceive the organization of the public sector in a federal way so that different levels of government provide public services and have some scope for de facto decision-making authority irrespective of the formal constitution within a nation state (Oates, 1972; 1999). From a normative perspective, fiscal federalism identifies three roles for the public sector: macroeconomic stabilization, income redistribution and resource allocation in the presence of market failure (Oates, 1999; Burkhead and Miner, 1971).

The macroeconomic stabilization and income redistribution functions are assigned to the central government while resource allocation function is assigned to sub-national governments (World Bank, 2000). The main benefit associated with a federal fiscal structure is economic efficiency (Oates, 1972;

Ebel and Yilmaz, 2002), which rests on two assumptions. First, it assumes that a group of individuals who reside in a community or region possess tastes and preference patterns that are homogenous and that these tastes and preferences differ from those of individuals who live in other communities or regions.

Second, it assumes that individuals within a region have a better knowledge of

the costs and benefits of public services of their region (Burkhead and Miner,

1971).

Another theory of fiscal federalism is based on the Tiebout Model (Tiebout,

1956). In the model, highly mobile households choose as a jurisdiction of residence that locality that provides the fiscal package best suited to their tastes. The households select their preferred supply of public goods by selecting amongst competing local jurisdictions and discretionally moving to that community that most satisfies their set of preferences.

Individual households would be sort into taste homogenous jurisdictions where individuals vote with their feet and locate in the community that offers the bundle of public services and taxes they like best. Individuals will therefore satisfy their demand for public services by the appropriate selection of a community in which to live and pay taxes for the services. In equilibrium, people will distribute themselves across communities on the basis of their demands for public services where each individual receives desired level of public services and cannot be made better off by moving elsewhere.

Tiebout presented a model of local government expenditure that tries to determine the optimal level of public goods through a mechanism of preference revelation of the households. The Tiebout model therefore represents the preferences of the population more adequately than national level models and was said to be a model where people vote with their feet. The model works

under extreme assumptions; Government activities generate no externalities; individuals are completely mobile; each person can travel costlessly to a jurisdiction whose public services are best; people have perfect information with respect to each community’s public services and taxes; there are enough different communities with public services for individual’s choice; public services are financed by a proportional property tax; and communities can enact exclusionary zoning laws.

2.3 Empirical Literature

Benefit incidence studies have a long history with early studies on Canada and the United States (Gillespie, 1964 and 1965), Aaron and McGuire (1970),

Meerman (1979) for Malaysia, and Selowsky (1979) for Colombia. However, the interest in benefit incidence surged as a result of Robert McNamara's optimism about the degree to which government spending can alter the income distribution and living standards of the poor in developing countries (Selden and Wasylenko, 1992). According to McNamara, shifts in the patterns of public expenditure represent one of the most effective techniques a government possesses to improve the conditions of the poor (McNamara, 1972). Reviewed empirical literature comprises of only the recent Benefit Incidence Studies.

Verghis and Demery (1994) examined the poverty focus of public expenditures in the education sector in Kenya. The study considered the extent to which public expenditures had targeted the poor and compensated for their lower

command over resources. The results showed that public expenditure on primary education was well targeted, with the bottom 50 per cent of the population i.e. those identified as falling below the poverty line, obtaining 55 per cent of the total subsidy. On the other hand, the bottom 50 per cent of the population got only 31 per cent of the total subsidy on secondary education and therefore public expenditure was highly regressive mainly benefiting the non- poor. This study by Verghis and Demery (1994) used incidence benefit approach to estimate who was benefiting from the total government spending on education while this specific study uses the same approach to estimate who benefits from CDF spending on education bursaries in Makueni County.

Van de Walle and Nead (1995) employed Benefit Incidence Analysis to assess economic groups that benefited most from the financial subsidies provided by the government in education expenditures. The study was carried out for 13

African countries and on average, only 10 per cent of the subsidies for higher education went to the poorest 40 per cent of the population, while 43 per cent of subsidies for "all education" accrued to the poor income group. The conclusion was that education sector expenditures varied in their incidence according to the level of service where primary and secondary education levels were more pro-poor than university/higher education. The study by Van de

Walle and Nead (1995) was cross country while this study focuses on a programme in a specific country.

Harding (1995) examined the combined impact on income distribution of public outlays on health, education and housing in Australia in the 1990’s. The

methodology used was the National Centre for Social and Economic Modelling

(NATSEM’s) static micro-simulation model. The findings were that the pattern of receipt shows a strong life cycle effect with the value of non-cash benefits peaking in the 30–40 years age group and rising again in retirement. Non-cash benefits were also shown to have an equalizing effect on income distribution.

Major beneficiaries of public outlays on social services were families with children and the aged.

Demery, Dayton and Mehra (1996) used traditional Benefit Incidence Analysis in assessing who benefited from educational expenditures in Côte d'Ivoire. The findings were that there was a marked improvement in the targeting of education spending in Côte d'Ivoire between 1986 and 1995 despite a reduction in overall real spending on education. The study also found out that changes in benefit incidence were not necessarily a result of changes in public spending.

Castro-Leal et al. (1999) examined the comparative benefit incidence of health and education spending in Cote d’Ivoire, Ghana, Kenya, Madagascar, South

Africa and Tanzania. The methodology involved a comparative benefit- incidence examination of government health and education spending for the seven Sub-Saharan countries to establish how benefits from public spending on health and education were distributed. The findings were that on average, the amount of overall government health expenditure benefiting the top 20 per cent of the population was about two and a half times the amount benefiting the bottom 20 per cent.

The public expenditure on primary care was less regressive. The highest 20 per cent of the population received more financial benefit than the lowest 20 per cent in five of the seven countries. Overall the richest 20 per cent benefitted more by about one and a half times from primary care spending than the poorest 20 per cent. The study concluded that public spending was regressive in all the countries and therefore poor were not properly targeted. The study findings confirmed public spending on primary education to be regressive while most of the other reviewed studies found spending to be progressive. The mixed results justify country specific studies to empirically determine who benefits from public spending.

Chu et al. (2000) reviewed 38 benefit incidence studies carried out between

1978 and 1995. The review found that public health expenditures were well targeted in 21 of the 38 studies and were progressive in all 30 of the studies for which data was available. Well targeted public health expenditures implied that the poorest 20 per cent received more public subsidies than the richest 20 per cent hence the distribution of benefits was considered progressive. In terms of the 29 developing countries and countries in transition covered by the review, public health expenditure was generally well targeted in Asia and Latin

America but poorly targeted in sub-Saharan Africa and transition countries.

All types of health expenditure tended to be well targeted, with the exception of hospital-based outpatient services. In sub-Saharan Africa, expenditure at all

levels was found to be poorly targeted. All primary and secondary schools programmes had progressive incidences but only half of the tertiary institutions programmes had progressive incidences. The different findings warrant specific studies targeting different levels.

Sahn and Younger (2000) examined the progressivity of the social sector expenditure in eight Sub-Saharan African countries i.e. Cote d’Ivoire, Ghana,

Guinea, Madagascar, Mauritania, South Africa, Tanzania and Uganda.

Dominance tests complemented by extended Gini / concentration coefficients were used to determine whether health and education expenditures redistribute resources to the poor. The study found that social services were poorly targeted where primary education tended to be most progressive while university education was the least progressive. The benefits associated with hospital care were less progressive than other health facilities.

Use of concentration curves as a way of summarizing information on the distribution of benefits of government expenditures was found to be important but statistical testing of differences in curves is crucial. The use of concentration curves to summarize distributional benefits was crucial to the study because it was one of the earmarked methods to demonstrate benefits from CDF spending on education bursaries.

Foster, Adrian, Naschold and Conway (2002) synthesised the key findings from case studies in five African countries that had examined how public expenditure management was linked to poverty reduction policy goals. The five countries were Ghana, Malawi, Mozambique, Tanzania and Uganda. Key findings from the case studies were that each of the studied five countries entered the 1990s with a pattern of public expenditure in which efficiency and effectiveness of public spending was very low and its benefits went mainly to the non-poor hence the need to institutionalize a poverty focus. This study intended to confirm who benefits from CDF funding on education bursaries to improve poverty targeting.

Ye Xiao and Canagarajah (2002) analysed Ghana’s public expenditure flows from line ministries to the basic service provision facilities, including primary and junior schools and health clinics using the general principles of Public

Expenditure and Tracking Survey (PETS). The study used data collected from a pilot public expenditure tracking survey in 2000. The results from the PETS data indicated that only about 20 per cent of non-salary public health expenditure and 50 per cent of non-salary public education expenditure reached the facilities. In the health sector, evidence suggests that a large proportion of the leakage occurred between the line ministries and the district offices, where public expenditures were turned into materials from cash flows. The study concluded that a consistent and transparent recording system from the line ministries to the service provision facilities may significantly improve the efficiency of public resource distribution by providing easy public access to the resource flow data.

Sabir (2003) employed the benefit incidence analysis in ascertaining to what extent government expenditure on education spending in Pakistan was effective in reaching the poor. The findings confirmed that government subsidies directed towards higher education either general education or professional education were poorly targeted to low-income households and indeed favoured those who were better off i.e. subsidies benefited the rich more than the poor.

Heltberg, Simler and Tarp (2003) assessed whether public expenditures on education and health in particular are successful in reaching the poor segments of the population in Mozambique using the standard non-behavioral social benefit incidence approach. The findings of the studies were that post-primary intermediate education crosses the Lorenz curve at 0.1 on the horizontal axis, and lay well below the Lorenz curve for the rest of the distribution implying that it was regressive. From the concentration curves it was observed that the poorest 50 per cent of school-age children constituted 50 per cent of all students enrolled in lower primary education and 32 per cent of students in upper primary education. At higher levels, participation by the poor dropped drastically with the poorest half accounting for only 19 per cent of students in post-primary education and 11 per cent of students in the intermediate post- primary category.

Public service provision in Mozambique was more equal than in many other

African countries, with the major exceptions of upper secondary and university education. Health services and investments in public infrastructure were distributed progressively. Regional and gender inequalities in access to public services were more pronounced than inequalities by income level. The focus on gender inequalities is relevant to this study because it factored into the analysis gender considerations.

Yuki (2003) employed standard benefit-incidence analysis to examine the distributional effects of public education spending in Yemen. The study found the distribution of total public education spending moderately favouring the poorest households in absolute terms. However, in higher education and vocational training the distribution of public spending did not favour the poor, whereas it moderately favoured the poor in basic education and was almost neutral in secondary education. Yemen’s public education spending was more equitably distributed than its household expenditures but the distribution did not favour the poor in absolute terms or in proportion to the school-aged population, especially in higher education. Moreover, within the poor, females did not receive even half of the benefits received by males from public spending on basic education.

Liberati (2003) extended the consumption dominance curves to subgroups of population in Belarus using 1997 data to examine public subsidies on rents and utilities, healthcare and public transport in six groups of the population. The

study found that the highest decile consumed proportionally more of all the subsidized goods which means that efficiency score of the corresponding subsidies was quite low with a greater degree of leakage to richer households.

The most disproportionate distribution was from public transport. The study concluded that decomposition had useful informational advantages because it allowed policy makers to get detailed information on poverty reduction strategies for population sub groups without being constrained to a specific poverty line. The empirical decomposition of poverty reducing reforms by sub-groups of the population in Belarus revealed that different groups suffered a poverty increase for some conceivable poverty lines even in the presence of clear-cut poverty reducing directions of reforms obtained when considering the total population. This study decomposed the education expenditure to focus on bursaries only to provide useful informational advantages to policy makers in terms of detailed information on that specific component of public education spending.

Ajay (2005) used the method of average benefit incidence analysis to assess how well focused and targeted Karnataka’s (in India) public expenditures were on people identified by economic rank, residence, and gender. The findings confirmed that public health and education subsidies were not particularly well targeted in Karnataka, even though they did benefit the poor on aggregate.

Targeting was less effective at the higher-end facilities than at the lower-level ones while unequal distribution of subsidies was more marked for the rural populations and women.

The Karnataka results highlighted the fact that relative to health, promoting equality in the allocation of higher-education subsidies would require greater government attention to services at the elementary level because most poor students did not progress to higher levels of education and richer groups tended to use proportionately more private health and education services than did the poor at all levels. In conclusion, the findings suggested that policy measures aimed at increasing the attractiveness of the private sector for the rich might enhance equality.

Institute of Economic Affairs (2005) conducted a study on CDF in 2005 focusing on 25 constituencies and interviewing 1,231 citizens and 577 members of CDF management committees. The main objective of the study was to obtain beneficiary and committee assessment of the CDF financed services with regard to access, utilization, and satisfaction. Another objective was to explore ways on how to enhance accountability, transparency and performance. The focus was on assessing the status of the CDF with respect to the planning and implementation of the fund including beneficiary involvement in the process as well as links with existing local development funds, such, as the local authority transfer fund (LATF).

The study also assessed beneficiary satisfaction with regards to implementation of the fund and overall impact on their livelihoods and performance criteria from the perspective of the clients. The tracking study was carried out using the

citizen report card (CRC) methodology, a combination of household survey and qualitative data collection methodologies to assess the beneficiaries’ perception of the performance of the CDF funded projects. The study found extremely low participation among residents in CDF activities, and weak mechanisms in place for the grassroots to have a say in the projects to be implemented. IEA’s survey respondents claimed that the biggest challenge facing the CDF was poor management.

Soares, Medeiros and Osorio (2006) conducted a study in Brazil using a methodology that separated out the income of different cash transfers programmes. The study evaluated the incidence of the programmes using concentration curves indices and decomposing the Gini indices into the contribution of each income source. The study found that both Beneficio de prestacao Continuada (BPC – Continuous Benefit) – the means tested old age pension and disability grant programme and Bolsa Familia were quite well targeted and both jointly contributed to the fall of 28 per cent in the Gini inequality between 1995 and 2004. The study also found pensions equal to the minimum wage whether contributory or not and this better performance was attributed to the fact that they make up of 4.6 per cent of the total family income. The study concluded that analysis of distributive effects of these programmes contributed to the correction of existing deficiencies and to the planning of future expansion because of the programmes importance in eradicating poverty and inequality reduction to tolerable levels within a reasonable time frame.

KIPPRA (2006) conducted a survey on the CDF in 35 constituencies and found that half of the respondents believed that the CDF monies had been widely mismanaged. The survey found that CDF was viewed as the worst managed fund among all the ongoing devolved government funds i.e. Rural

Electrification Programme Levy, Local Authority Transfer Fund, Roads

Maintenance Funds, Secondary School Education Bursary Fund, HIV/AIDS

Fund and the Free Primary Education. The respondents mentioned that the main reason for the CDF mismanagement was the power given to the local

Member of Parliament to appoint and replace members of the CDF committee.

Other reasons mentioned were that political loyalties had led to the unfair sharing of the resources across the constituencies and wards as well as lack of transparency and accountability due to the blending of supervisory and implementing roles.

IPAR (2006) studied five constituencies of Limuru, Kajiado, Machakos,

Kangundo and Makadara. Using both primary and secondary data sources the study found that CDF lacks direction, transparency and had flawed legal foundations. In the constituencies examined, the study confirmed that people were aware of the existence of resources for the constituency but did not have enough knowledge on exactly how CDF operated. The majority of people interviewed disagreed on the way in which the CDF committees were selected and a very small percentage (less than 2 per cent) of the respondents participated in the selection of the committees.

IPAR’s study found overwhelming evidence and acceptance that the CDF was being used to advance the political agenda of the Members of Parliament.

Evidence was found of a ―tug-of-war‖ between Members of Parliament and

Councillors that believed there were enough loopholes that could be exploited for individual financial-political advantage. This was demonstrated by the fact that in four out of five of the constituencies analysed, the appointees to the

CDF committees were composed of MP’s supporters and friends not elected by the local population. With a few exceptions, the members of the CDF committees were found to be technically incompetent, lacked understanding of how the CDF operates, and had limited capacity in project identification, planning, monitoring and evaluation. Committees did not have their own offices and used the premises of the MP’s political party.

There was also lack of a proper mechanism for tracking the funds released to the approved projects. Perhaps these potential sources of mismanagement could be prevented or penalised if there was a proper auditing system of the fund. However the IPAR study found that although the Auditor General was expected to audit constituencies’ expenditures, there was no provision for the committees at constituency level to answer any queries on resources spent.

Mapesa and Kibua (2006) in their study on CDF found that institutions of decision making were weak; mechanisms of transparency and accountability were absent; there were design problems especially lack of technical staff to support communities during implementation; there was lack of adequate community participation in project selection, implementation, selection of

committees, monitoring and evaluation and there existed very low awareness levels among the community / beneficiaries.

The Hanns Seidel Foundation (2006) study argued that the Kenyan decentralization policy was characterised by an umbrella of funds with overlaps of areas and responsibilities. For instance, education funds were given under the LATF, Education Funds and CDF creating overlaps. In addition to this lack of coordination among funds there was lack clarity on the total amount of resources being allocated to each Local Authority and Constituency.

Roxana (2008) conducted a pre-election survey on CDF where the majority of the survey respondents thought that CDF helped to reduce poverty and improve services in their constituency. However they also believed that the CDF had been misused for political purposes. The survey results suggested that the mismanagement reduced the likelihood of many MPs getting re-elected, but undoubtedly more efforts were needed for the CDF to become the hoped panacea for tackling poverty.

The study also found that in 2003 a total of 1,970 CDF projects were run and by 2006-07 this number increased to 20,361 projects. The number of CDF projects increased as the CDF total allocations did. However, in the elections year of 2006-07 there was a particularly notable increase in activity. For instance, the number of CDF projects increased by 119 per cent compared to the previous year whilst the CDF allocations increased by just 38 per cent.

Over the period 2003-07 the majority of projects (55 per cent) were dedicated

to education, followed by water (11 per cent), health (10 per cent) and various other projects such as roads, infrastructure and sports. Constituencies provided scant information about the exact nature of the projects as most information merely referred to dispensary, school building improvement or road in town.

According to the pre-electoral survey, 88 per cent of respondents reported their community was not involved in the CDF project design meetings while 56 per cent of respondents reported CDF was not being used for the purpose intended in their communities. Despite these perceptions, 60 per cent of the respondents reported CDF had helped to reduce poverty in their community and 77 per cent reported CDF had improved services such as education and health in their community.

Bagaka (2008) explored the financial implications of fiscal decentralization policies on the central government's operating budget in Kenya. The study evaluated how devolved funds under the CDF had been utilized to start healthcare capital projects (clinics) at the local level. The study found that fiscal decentralization had promoted allocative efficiency and equity but at a cost of exporting tax burdens (operations and maintenance) to the central government emanating from capital projects implemented at the local level.

The exported tax burdens had policy implications and called for reforms of the

CDF program to reflect a benefit-expenditure structure.

Demery and Gaddis (2009) used benefit incidence approach to estimate who was benefiting from social sector expenditure in Kenya. The study adopted

both average and the margin benefits using data from the Kenya Integrated

Household and Budget Survey (2005/6). The results showed that the education subsidy was better targeted to the poorest groups where 18 per cent of the subsidy benefits the poorest quintile while just 14 per cent of health spending reached the poorest. At the same time the richest got the largest share of the health spending (27 per cent) of the health recurrent budget.

The estimates of the incidence of marginal changes in spending on education and health followed a similar pattern to those observed on average confirming that additional spending on primary education and primary health-care were likely to benefit the poorest groups in Kenya. Gender imbalance in the marginal benefits from education spending was not evident and females were predicted to benefit more than males from an expansion in primary health spending. The results confirmed that the richest females benefited more from an expansion of hospital services but their counterparts in the poorer quintiles gained far less than even the males in the quintile.

The study by Demery and Gaddis (2009) used incidence benefit approach to estimate who was benefiting from the total government spending on education and health while this study uses the same approach to estimate who benefits from CDF spending on education bursaries in Makueni County.

2.4 Overview of Literature

From the foregoing literature review there are various past studies that did examine the benefit incidence of public spending on education in different countries. Twelve studies used standard benefit incidence analysis approach with mixed results. Five studies (Van de Walle and Nead, 1995; Chu et al.,

2000; Sahn and Younger, 2000; Yuki, 2003; Soares et al., 2006) found the public spending at primary education level to be progressive. Public expenditures at secondary and tertiary levels were found to be regressive in six studies (Van de Walle and Nead, 1995; Sahn and Younger, 2000; Heltberg et al., 2003; Sabir, 2003; Yuki, 2003; Ajay, 2005).

Two studies (Foster et al., 2002; Liberati, 2003) found the public expenditures to be regressive for all levels of education and two studies (Van de Walle and

Nead, 1995; Chu et al., 2000) found both primary level and secondary levels to be progressive. A total of six studies (Van de Walle and Nead, 1995; Sahn and

Younger, 2000; Sabir, 2003; Heltberg et al., 2003; Yuki, 2003; Ajay, 2005) found public expenditure on tertiary education was regressive. Despite use of the same methodology (standard benefit incidence analysis), the mixed findings justify the need for country specific empirical studies. Data accuracy and availability remained a major challenge for the two key variables of service users and actual government expenditures on the services.

Three reviewed studies (Yuki, 2003; Heltberg et al., 2003; Ajay, 2003) factored gender considerations in the studies and confirmed gender biasness in benefits distributions with women disadvantaged. These findings were critical in informing this study where distribution of benefits from CDF spending on education bursaries between female and male students was examined as one of the study objectives.

The reviewed literature on Kenya examined the benefit incidence of government expenditure at the national level and household survey data was used for analysis. The results provided the benefit incidence in respect to total government expenditure on the education sector and not a specific government programme to find out what benefits accrue and who benefits most from such public spending. This study addresses that gap by examining the benefit incidence of Constituencies Development Fund spending on education bursaries in Makueni County. The study uses the three key variables used in conducting benefit incidence analysis i.e. the CDF actual expenditure on education bursaries at different levels, the users of the provided education service and the expenditure data for households from which the users belong.

Primary data was used because secondary data on actual expenditure and users of the provided services was not available.

Empirical studies on CDF (see IEA, 2005; KIPPRA, 2006; IPAR, 2006;

Roxana, 2008; and Bagaka, 2008) concentrated on the accountability of the fund, citizen participation, perception on poverty reduction, knowledge on

CDF operations and financial management. The studies used primary data and the findings were mixed where CDF performance in service delivery was reported as poor but CDF effects on poverty alleviation were reported as positive. Nevertheless, none of the empirical studies on CDF examined the benefit incidence of CDF spending in total or on any specific sector. This study purposes to fill that knowledge gap by examining the benefit incidence of CDF spending on education bursaries using standard benefit incidence analysis approach.

CHAPTER THREE

RESEARCH METHODOLGY

3.1. Introduction

This chapter presents the research methodology and covers research design, the theoretical framework, estimation procedure, study area and target population.

Other important sections captured include sampling techniques, data type and sources, and data collection, processing and analysis.

3.2 Research Design

This study aimed at empirically examining the distribution of CDF provided educational bursaries at secondary and tertiary education levels, ascertaining the extent to which CDF’s spending on education bursaries is progressive, regressive or neutral, and examining the gender dimension in the benefit incidence of CDF spending on education bursaries in Makueni County. The research design was non experimental but descriptive survey where a range of variables were examined and their associations established. The study applied the Benefit Incidence Analysis approach to answer the research questions. The study used primary data collected from the five Makueni county constituencies for the following variables: actual CDF expenditure on education bursaries at different levels and expenditure data for households from which the users of

the CDF services belonged. Secondary data was collected for users by sex of educational bursaries provided through CDF funding. A data collection form for students and household questionnaire were used to collect data that was analysed using the Benefit Incidence Analysis method.

3.3 Theoretical Foundations of Benefit Incidence Analysis

Analyses of the incidence of social spending is founded on welfare economics where knowledge of the underlying individuals or households demand functions is important to measure the individual preferences for the goods in question (Demery, 2000). Unlike a market setting where consumers and producers reveal their preferences for private goods through supply and demand interaction, there is no market-type solution to determine the level of expenditure of public goods. The non-excludability and non-rivalry properties of a public good encourages free-riding and assures that the public good is consumed irrespective of complete preference revelation. In the case of public goods, the price paid does not necessarily reflect its value to the consumer and therefore not a good measure of value. The long standing interest in economics in measuring the benefits that are derived from public spending prompted development of two broad approaches.

The welfarist literature, since 1970, was characterized by the two broad approaches in measuring the public spending benefits. The first one emphasizes the need to measure individual preferences for the public goods in

question based on refinements of the Aaron and McGuire (1970) methodology.

The approach uses the consumer surplus and expenditure-based measures of benefits for the case of efficient provision of public good at point Qo in Figure

3.1 where marginal benefits equal marginal cost. Figure 3.1 illustrates the consumer surplus and expenditure benefits.

P A = Total Cost p A + B = Full Consumer Benefit

P0 SS

A DD

Q Q0

Figure 3.1: Measuring Benefits – average cost versus total benefit Source: Selden and Wasylenko, 1992

Point Qo is the equilibrium point where the marginal benefit represented by Po is equal to marginal cost represented by the supply curve (SS). The compensating variation or total benefit measure consists of areas "A" and "B" in the graph (Aaron and McGuire, 1970). Conventional expenditure-based benefit incidence studies typically allocate the expenditure on the public good among households, represented by area "A" on the graph. Benefits are then compared among households in different income groups or other socioeconomic groups.

This typical method of measuring household benefits does not consider a number of important issues that affect benefit incidence. For example, it assumes that all relative prices and real incomes are fixed, benefits are not shifted, marginal benefits are equal to average benefits, and average cost is a good proxy for marginal benefit. Despite the approach being well founded in microeconomic theory, it is quite data demanding where knowledge of the underlying demand functions of individuals or households is required (Selden and Wasylenko, 1992). These limitations expose the approach to numerous criticisms.

The second approach is Benefit Incidence Analysis (BIA) that combines the cost of providing public services with information on their use to generate distributions of the benefit of the government spending. The BIA assumes the cost of providing public goods and or services is a reasonable approximation of how much they are valued because in making allocations across competing goods, a political process establishes priorities based on how each good or service is valued (Demery, 2000). Benefit incidence provides analysis on who is benefiting from public goods and services. It combines information about the unit costs of providing those services (obtained usually from government or service-provider data) with information on the use of these services (usually obtained from the households themselves through a sample survey).

Demery (2000) provides a detailed analysis of how benefit incidence is calculated. Taking the example of government spending on education, this can be formally written as:

X1 = E1p [Sp / Ep ] + E1s [Ss / Es ]+ E1t [St / Es ],

where X1 is the amount of the education subsidy that benefits group 1, S and E refer to the government subsidy and the number of students enrolments respectively, and the subscripts p, s and t denote the level of education

(primary, secondary and tertiary respectively).

The benefit incidence of total education spending accruing to group 1 is given by the number of primary school enrolments from the group (E1p) times the unit cost of a primary school place [Sp /Ep ], plus the number of secondary school enrolments times the secondary unit cost, plus the number of tertiary institutions enrolments times the unit cost of tertiary education. This can be re- written as:

3 3 푆푖 퐸푖푗 푋푗 = 퐸푖푗 = 푆푖 (3.1) 퐸푖 퐸푖 푖=1 푖=1

where:

Xj is the benefit incidence of spending on a service (say education) to group j,

Eij is the number of enrolled students from group j at education level i.

Ei is the total number of enrolled students at level i.

Si is the net spending by the government on education at level i. 56

(Si /Ei ) is the mean unit subsidy of an enrolled student at education level i.

The share of total education spending to group j (xj ) is:

3 3 퐸푖푗 푆푖 푥푗 = = 푒푖푗 푠푖 (3.2) 퐸푖 푆 푖=1 푖=1

Three steps are therefore involved in estimating benefit incidence of public spending. Step one involves calculating the unit costs. The unit cost of providing a service is defined as total government spending on a particular service divided by the number of users of that service. The unit costs must be based on actual expenditures by government and not on budget allocations because the former represents actual costs of the services availed by the users.

The unit costs are flow variables defined for a time period usually a year and any revenue from cost recovery must be netted out of government spending to derive unit costs for benefit incidence. It is important that the cost recovery is netted out because it reduces the in-kind transfers once returned to national exchequer. In a situation where the cost revenue is retained with the facility providing services, it should not be netted out because it adds to the value of the services received. In calculating the unit cost, out of pocket expenditures should be factored in because the burden of these costs (especially to low- income households) can discourage the use of the services.

The second step involves identification of the users of the provided service.

Service users are the beneficiaries of a specific public service. For instance, for schools users will be pupils while for a health services it could be children immunized in the health centre. Usually official data on users (enrolments, visits to health facilities etc.) is obtained from household surveys or from the service providers. In most cases the data from the service providers is more accurate because it reflects the official records. When using household surveys as a basis for benefit incidence, analysts must be aware of potential biases in the data. For instance in the use of health services, the poorer respondents may fail to report those illnesses which are considered commonplace and part of normal life, and which are reported by the better-off. The poor would appear to make less use of services relative to the rich simply because they were less able to identify such use.

The third step involves aggregating the identified users, either households or individuals, into socioeconomic groups in order to describe how the benefits from public spending are distributed across the population using the public services. Ranking households or individuals by welfare indicator is important for benefit incidence, since it indicates whether government spending is well targeted to those that need it most—the poorest in society. The main classifier used to group households is either income or total household expenditure. The procedure requires that the household survey from which estimates of the use of public services are derived also contains information on the welfare measure where individuals are then ranked according to the welfare measure.

Aggregating households or individuals into groups of equal size either quintiles or deciles of the population is critical in benefit incidence analysis as a benefit accruing to a particular group is determined by the number of individuals occupying each decile (or quintile) cell. The bottom decile thus represents the poorest 10 per cent of the population while the top decile would be the richest

10 per cent. It is customary to group individuals into deciles or quintiles, though other groupings are possible like poor/non-poor, region, rural/urban, occupation of household head, gender, and ethnicity.

Since the works by Meerman (1979) on Malaysia and Selowsky (1979) on

Colombia, benefit incidence analysis studies have been widely replicated in developing countries because benefit incidence measures are more comparable with measures of expenditure and income, which do not include the consumer surplus and are easier to calculate. In addition the benefit incidence is not based on individual valuations, and does not take into account the behavioral responses of individuals and households to changes in public spending. The benefit incidence analysis is assessed as the most appropriate method to measure benefits of government spending and was included in the World

Bank’s experimental tool kit for Poverty and Social Impact Analysis (PSIA) of economic policies (Davoodi, 2003). Due to assessed appropriateness of the benefit incidence analysis, this study used the method.

3.4 Benefit Incidence Estimation Procedure

The estimation procedure for this study was adopted from studies by Demery

(1997); Van de Walle and Nead (1995); Selden and Wasylenko (1992). The studies used Benefit Incidence Analysis that shows how the initial ―pre- intervention‖ position of individuals is altered by public spending or how well public spending serves to redistribute resources to the poor (Van de Walle,

1995). Alternatively it estimates how much the income of a household would have to be raised if the household would fully pay for the subsidized public services (Sabir, 2003). This study used a standard Benefits Incidence Analysis

(BIA) that established who indeed benefits from CDF expenditure on education bursaries hence determining how effectively the limited resources were targeted. Two main components were crucial for the study; a measure of the value of the benefit that students receive from CDF spending on education bursaries in Makueni County; and grouping of the students on the basis of their total household expenditure and thereafter aggregated into sub-groups

(quintiles) to get distributional impact of the CDF spending on education bursaries across such sub-groups. Comparison of the students who benefited from CDF bursaries was on sex basis.

The study systematically applied different steps to estimate the benefit incidence of CDF spending on education bursaries at different levels following

Demery (2000). The first step involved obtaining CDF actual expenditure on education bursaries at different levels i.e. secondary education and tertiary education for the five constituencies of Makueni County. For each level of

education, the users of the services were identified for each constituency. In this study, the users are the students in secondary and tertiary institutions who were awarded bursaries. Using the data on actual expenditure and service users, the average unit cost of providing education bursaries at different levels was obtained by dividing CDF actual expenditure on education bursaries by the total number of students who were awarded bursaries at that particular level.

The second step involved attribution of the calculated average unit costs to individual students identified as beneficiaries of the education bursary where each student who benefited from CDF bursaries gained a transfer equivalent to the unit cost. The third step involved grouping of users where the total number of users (students) were grouped on the basis of total household expenditure and thereafter aggregated into sub-groups (quintiles) of the population to get distributional impact of the CDF spending on education bursaries across such sub-groups. Using total household expenditure as a welfare indicator, students were ranked from poorest to richest based on their household expenditure.

Since education services are provided to individuals, population quintiles were used instead of household quintiles to avoid giving misleadingly pro-poor impression of the expenditure simply because poorer household quintiles tend to have more individuals than richer quintiles (Demery, 2000). The richest 20 per cent of the student population were found in the top quintile while the poorest 20 per cent were in the bottom quintile.

The fourth step involved distribution of the benefits accruing from CDF spending on education bursaries to each of the ranked groups by multiplying

the average benefit calculated in step one by the number of students in each group per education level. It is assumed that the average benefit from unit cost of the service was the same for all income levels. The four steps on the estimation of benefit incidence were summarized in equation 3.3. Total benefit from CDF spending on education bursaries accrued to different group j is estimated as:

2 2 푆푖 퐸푖푗 푋푗 = 퐸푖푗 = 푆푖 푗 = 1,2,3,4,5 (3.3) 퐸푖 퐸푖 푖=1 푖=1

where 푋푗 is the benefit incidence accrued to expenditure group j from CDF spending on level i (1- Secondary level and 2- Tertiary Education level); Si is

CDF actual expenditure on education bursaries at level i; 퐸푖푗 represents number of student beneficiaries at level i from group j where each group is a quintile; and 푆푖 is the unit cost of providing education bursaries at level i. Groups are 퐸푖 typically ordered from lowest to highest with respect to the household expenditure.

By dividing both sides of expression (3.3) by total CDF educational bursaries spending, S, the share of benefits accrued to quintile j from total CDF spending on education bursaries was obtained:

2 2 퐸푖푗 푆푖 푥푗 = × = 푒푖푗 푠푖 푗 = 1,2,3,4,5 (3.4) 퐸푖 푆 푖=1 푖=1

푋푗 퐸푖푗 Where 푥푗 = ; 푒푖푗 = is the quintile j share of total students awarded 푆 퐸푖 bursaries secondary and tertiary levels; 푠푖 is share of CDF spending for a given level, i , in total education bursaries spending; and 푆 = 2 푠 where 푠 = 푆푖 . 푖=1 푖 푖 푆

Estimate of 푥푗 across quintiles add up to one. Equation (3.4) reveals that the more the CDF expenditure on the education level that is widely utilized by a given quintile, the more that quintile will benefit. In other words, benefit incidence depends on the composition of the users of education services as defined by the users’ expenditure and the composition of educational bursaries spending.

To illustrate the distribution of benefits in a better manner than just the focus on the five discrete points, concentration curves were used for CDF spending on education bursaries. This was because these curves described the entire distribution and not just the five points. A concentration curve for benefits from education bursaries plotted the cumulative proportions of individuals ranked from the poorest to the richest, on the horizontal axis, against the cumulative proportions of benefits received by individuals on the vertical axis.

The concentration curves conveyed some important messages after comparison with the 45 degree diagonal. Where the curve lies above the diagonal, it means that the poorest quintile gains more than 20 per cent of the total CDF expenditure on education bursaries and the richest quintile, less than 20 per cent. Such a distribution is progressive in absolute terms relative to CDF expenditure on education bursaries. Concentration curves lying below the 45

degree diagonal are regressive while concentration curves that are the same as the 45 degree diagonal are neutral.

To examine the gender dimension in the benefit incidence of CDF spending on education bursaries, gender disaggregated data was collected and used to differentiate benefits associated with female students and male students. The gender pattern across the quintiles were determined to assess whether there are any gender biasness in the distribution of the benefits from CDF spending on education bursaries.

3.5 Definition and Measurement of Variables

Variable Definition Measurement Users of the Students in Secondary and Number of education Tertiary institutions who received beneficiaries services education bursaries in Makueni (students). Data was County from CDF in the disaggregated by sex 2010/2011 fiscal year. to cater for gender analysis. Actual CDF Actual amount of money spent on The expenditure was expenditure on education bursaries in Makueni captured in Kenya education County at different education Shilling. services levels in the 2010/2011 fiscal year. Unit cost Obtained by dividing CDF actual The unit cost was expenditure on education captured in Kenya bursaries by the total number of Shillings. students awarded the bursaries. Total CDF Total CDF expenditure on Measured in Kenya education education bursaries at secondary Shillings. bursaries schools and tertiary institutions in spending Makueni County for the 2010/2011 fiscal year. Household Total expenditure per household Measured in Kenya expenditure as collected from sampled shillings and was households from where the used to group the beneficiaries (students) are students to quintiles. members.

3.6 Study Area

The study area was Makueni County and covered all the five constituencies in the county namely, Kibwezi, Makueni, Kaiti, Mbooni and Kilome. In 2005/06,

Eastern province’s high poverty estimates of 50.9 per cent were distributed across 36 constituencies (Republic of Kenya, 2008a). Thirty four per cent of the poor in the province were concentrated in 7 of the 36 constituencies namely, Makueni (6.1 per cent), Mbooni (5 per cent), Kangundo (4.9 per cent),

Kibwezi (4.8 per cent), Kitui Central (4.4 per cent), Mwingi North (4.4 per cent), and Mwala (4.3 per cent). Of the seven constituencies, 3 constituencies

(Makueni, Mbooni and Kibwezi) are in Makueni County hence the choice of study area.

3.7 Target Population

The target population was students from secondary schools and tertiary institutions that benefited from CDF provided education bursaries in the

2010/11 fiscal year. In total 4,898 students were awarded education bursaries for the 2010/11 fiscal year where Makueni constituency awarded 1086 students, Mbooni 783 students, Kaiti 1117 students, Kibwezi 900 students and

Kilome 1012 students. About 90 per cent of the beneficiary students were in tertiary institutions and 10 per cent in secondary schools. Secondary school students were fewer because of the existence of the Ministry of Education

Bursary that benefitted secondary school students only. Students who benefited

from CDF bursaries were spread in different divisions, locations and sub- locations in the five constituencies of Makueni County.

3.8 Sampling Technique and Sample Size

It was not possible to interview all households from where the students who benefited from CDF bursaries belonged due to high cost implications and time constraints hence the need to sample the students. Multi-stage sampling method was used to sample students at different levels and the decision on which households were to be interviewed was informed by the sampled students. The sample size was determined scientifically to ensure fair representation. The sample size was calculated using the following formula:

푧2 × 푝 1 − 푝 2 Sample size = 푒 푧2 × 푝 1 − 푝 1 + 푒2푁

Where N is the Population size, e = margin error, z = z-score and p = percentage of picking a choice expressed as decimal (for example, 3% = 0.03).

With a population of 4,898 students at confidence level of 99 per cent and a confidence interval of 8 per cent, a minimum sample size of 247 students is required. The study used a sample size of 250 students which is slightly above the minimum sample size to ensure proportionate representation of the student population. First step involved stratifying all the students into respective constituencies.

At the constituency level, students were first stratified into tertiary institutions and secondary schools from the list of students who were awarded bursaries in

2010/11. Out of each stratum, students were once more stratified on the basis of sex and location of their rural home to ensure fair representation. Once all the different levels of stratification were finalised, stratified random sampling was used to sample the students for the survey. Proportional stratified random sampling was used to ensure students from both secondary schools and tertiary institutions got a fair share in the sample size of about five per cent of beneficiary students for each constituency. It was only the households where the sampled students belonged were interviewed to collect household’s expenditure data that was used to group the students into quintiles.

The whole sampling process and sample sizes are summarised in table 3.1.

Table 3.1: Summarised Sampling Process and Sizes Constitu Total As % of Sample Tertiary Seconda Sample Sample ency Students total size institutio ry size size students ns Schools tertiary secondary Makueni 1086 22.2 56 1086 0 56 0 Kibwezi 900 18.4 46 660 240 34 12 Kaiti 1117 22.8 57 1117 0 57 0 Mbooni 783 16.0 40 783 0 40 0 Kilome 1012 20.6 51 762 250 38 13 Makueni 4898 100 250 4408 490 225 25 County Data Source: CDF Offices and Author’s computation

Table 3.1 shows the total number of students who benefited from CDF bursaries in Makueni County as well as the beneficiaries for each of the five constituencies. Based on the proportioned representation of students who

benefited from CDF bursaries in each constituency as compared to the total

Makueni County beneficiary students, the sample size for each constituency was calculated. After calculating the total sample size for each constituency, the students who benefited from CDF bursaries for each level of education, secondary schools and tertiary institutions, was determined and sample size for each of education per constituency worked out proportionately. The zeroes in the secondary schools column reflect a situation where all the CDF bursary funds for respective constituencies were allocated to only students in the tertiary institutions after a decision was made to allocate all secondary school students bursary funds from the Ministry of Education Bursary. Constituencies with zero entries for secondary schools decided to separate the two bursary funds while the rest combined the two and awarded to all students both in secondary schools and tertiary institutions. Students who benefited from CDF bursaries were spread across divisions, locations and sub-locations and the sampling process also considered such administrative units in determining the households to be interviewed.

3.9 Data Type and Source

The study used both secondary data and primary data to examine the benefit incidence of CDF spending on education bursaries. Data on students who benefited from CDF spending on education bursaries in 2010/11 and actual

CDF expenditure on education bursaries was obtained from the CDF Managers for all constituencies in Makueni County. Data on household expenditure for

purposes of grouping the users was obtained by conducting a survey in the five constituencies of Makueni County.

3.10 Research Instruments

Students’ data collection forms and household questionnaire were developed to assist in collecting the required secondary and primary data (see appendix 1).

The household questionnaire was pilot tested in Kilome constituency to ascertain its practicability before actual study data was collected. The results of pilot testing were found adequate to ensure validity and reliability of the study data. The piloted study questionnaire was therefore used for the actual collection of the study data.

3.11 Data Collection

The primary data was collected through visitations by trained research assistants to sampled households where the study questionnaire was administered to the household head. All responses were captured in the questionnaires by the research assistants. The secondary data was extracted from the CDF managers’ records in line with the data collection forms.

Concerted efforts were made to ensure relevant, accurate and consistent data was collected for all the variables.

3.12 Data Cleaning, Coding and Refinement

After primary data was collected, the data was subjected to data cleaning, data coding and refinement before data analysis was done. It was important that the primary data was subjected to cleaning, coding and refinement to ensure consistency. For the secondary data, necessary verification was done to guarantee accuracy and comparability.

3.13 Data Analysis

The study sought to respond to three objectives. The first objective was to examine the distribution of CDF provided education bursaries. This objective was achieved through analysis of the data on users of the provided services at different levels of education disaggregated by males and females. The second objective entailed ascertaining whether CDF expenditure on education bursaries has been progressive, regressive or neutral. This was achieved through use of the BIA after grouping the users into quintiles and attributing the expenditure to specific quintiles for each level of education. The third objective was to examine the gender dimension in the benefit incidence of CDF spending on education bursaries in Makueni County. This objective focused on gender considerations and was achieved by ensuring all the collected data was disaggregated by sex and analysis done on sex basis. The last objective was to draw policy implications from the study findings and was achieved by drawing relevant policy recommendations in line with the empirical findings. Data

analysis was done with the help of STATA Statistical Software while for computation of concentration curves both STATA and ADePT software of the

World Bank were used.

CHAPTER FOUR

EMPIRICAL RESULTS, INTERPRETATION AND DISCUSSIONS

4.1 Introduction

This chapter presents the empirical results as well as interpretation and discussion of the same. An examination of the distribution of CDF provided educational bursaries at secondary and tertiary education levels, the extent to which CDF’s spending on education bursaries is progressive, regressive or neutral and gender dimension in the benefit incidence of CDF spending on education bursaries is presented. The chapter starts with a presentation of a response rate and descriptive statistics that are critical for a study that used both primary data and secondary data.

4.2 Response Rate

The study had targeted a sample size of 250 students who benefited from CDF bursaries in Makueni County. Based on the sampled students, it is noteworthy that only the head of households from where the sampled students belonged were interviewed to collect the required household data for the purposes of this study. The actual number of household heads interviewed across the County was 248 out of the targeted 250 household heads representing a response rate of 99.2 per cent. At the constituency level, Makueni Constituency had 51 household heads interviewed out of the targeted 56 household heads

representing 91.1 per cent response rate. Kaiti Constituency had 54 household heads interviewed out of the targeted 57 household heads representing 94.7 per cent response rate. Mbooni Constituency had 39 household heads interviewed out of the targeted 40 household heads representing 97.5 per cent response rate.

Kilome Constituency had 49 household heads interviewed out of the targeted

51 household heads representing 96.1 per cent response rate and lastly Kibwezi

Constituency had 55 household heads interviewed out of the targeted 46 household heads representing 119.6 per cent response rate. The high response rate realised implied that the findings are accurate, reliable and useful for decision making.

4.3 Descriptive Statistics

As shown in table 3.1, in total 4,898 students were awarded education bursaries in Makueni County for the fiscal year 2010/11. The students were drawn from all the five constituencies of Makueni County. Makueni Constituency had

1,086 students who benefited from CDF bursaries and Mbooni Constituency had 783 students who benefited from CDF bursaries. Kaiti Constituency had

1,117 students who benefited from CDF bursaries and Kibwezi Constituency had 900 students who benefited from CDF bursaries while the last

Constituency, Kilome had 1,012 students who benefited from CDF bursaries.

Within individual constituencies, students who benefited from CDF bursaries were spread in different divisions, locations and sub-locations. In addition to

CDF bursaries, the Ministry of Education had also a bursary scheme that benefits only secondary school students. The existence of such bursary scheme

greatly influenced the sharing of CDF funded bursaries between secondary schools and tertiary institutions. Consequently about 90 per cent of the students who benefited from CDF bursaries were drawn from tertiary institutions and only 10 per cent from secondary schools in the year 2010/2011.

In total, CDF expenditure on education bursaries in Makueni County in the

2010/2011 financial year was Kshs. 24,931,900.00. Students in tertiary institutions were awarded a higher share of Kshs.21,480,500.00 (85.2 per cent) and secondary school students were awarded Kshs.3,451,400.00 (14.8 per cent). With a total of 4,408 students who benefited from CDF bursaries in tertiary institutions and 490 students in secondary school, the expenditure translates to kshs.4,873.00 per student in tertiary institution and Kshs.7,044.00 per student in secondary school.

Table 4.1 shows the sample distribution and share of CDF bursary expenditure by type of institution.

Table 4.1: Sample Distribution and Share of CDF Bursary Expenditure by Type of Institution Type of Total Sample Total CDF Percentage Percentage Institution Number Size. Expenditure Sample Share of CDF of Student (No.) 2010/11 Distribution Expenditure on who (Kshs.) (%) Bursary (%) Benefited

Secondary 490 35 3,451,400.00 14.1 14.8

Tertiary 4,408 213 21,480,500.00 85.9 85.2

Data Source: Author’s Computation

Table 4.1 shows that out of a sample of 248 households, 14.1 per cent represented households whose students were in secondary schools while 85.9 per cent represented those students who were in tertiary institutions. In terms of total CDF expenditure share on education bursaries, secondary schools share was 14.8 per cent while tertiary institutions share was 85.2 per cent. These findings reflect biasness in distribution of CDF education bursaries in favour of tertiary institutions. The smaller allocation to secondary schools is explained by the existence of the Ministry of Education Bursary that targeted only secondary school students.

Analysis of average household expenditure of all respondents for Makueni

County showed an overall annual average household expenditure of

Kshs.256,775.00. The average household expenditure figure differed from one constituency to another with Kibwezi Constituency respondents recording the highest average household expenditure of Kshs.326,528.00 and Makueni

County respondents recording the lowest average household expenditure of

Kshs.175,772.00. Kilome Constituency respondents recorded the second highest average household expenditure of Kshs.285,026.00 closely followed by

Kaiti Constituency respondents with average household expenditure of

Kshs.284,579.00. Mbooni Constituency had the second lowest with annual average household expenditure of Kshs.189,268.00. The results showing

Makueni and Mbooni Constituencies to have recorded the lowest average household expenditures seem to support the earlier findings that those constituencies were the poorest in Makueni County. Makueni and Mbooni

Constituencies were found to be the lead contributor to overall poverty in

Makueni County as well as Eastern province explaining the low average household expenditure (Republic of Kenya, 2008a). The average annual household expenditure being the lowest for the two constituencies could be a clear pointer to where proper bursary targeting should be. The results implied that if the CDF funded bursaries were being awarded at County level, proper bursary targeting would demand more students who benefited from CDF bursaries should be from Makueni and Mbooni Constituencies.

Analysing the interviewed household heads by average annual household expenditure against highest level of education of the household head, the household heads with university level of education had the highest average annual expenditure of Kshs.377,007.00 followed by those with primary level of education with average annual expenditure of Kshs.272,592.00. In the third position were those household heads with secondary school level of education who had average annual household expenditure of Kshs.219,464.00 while the lowest average annual household expenditure was recorded for household heads with tertiary level of education with average annual household expenditure of Kshs.197,778.00. The results showing those household heads with primary level of education to have higher average annual expenditure than those with secondary and tertiary education were unexpected.

The inconsistency in the results could be explained by the small number of household heads in the sample who had achieved primary school level of education and were interviewed. Out of a total of 248 household heads interviewed, only 13 household heads (5.3 per cent) indicated to have achieved

primary school level of education. The results showing the household heads with university level of education had the highest average annual expenditure collaborated findings of earlier poverty studies (Republic of Kenya 1998;

1999) that supported earlier findings that households headed by university graduates live above poverty lines and their household expenditure is generally high.

Comparing the average annual household expenditure by nature of engagement of the household head, the teachers registered the highest average household expenditure of Kshs.365,549.00 followed by business people with

Kshs.265,506.00 and the third position were the public officers with

Kshs.239,680.00. The farmers had average annual household expenditure of

Kshs.207,563.00 while the pastors had the lowest average household expenditure of Kshs.183,300.00. These results reflect findings of earlier study

(Mwabu et al., 2000) that showed the poor to be clustered into a number of social categories including subsistence farmers, handicapped and landless.

Such social categories normally have very low levels of income hence their average annual household expenditure should be low.

Analysing the sampled students by sex provided a different perspective to the

CDF funded bursary distribution. Table 4.2 shows the students’ distribution by sex and constituency.

Table 4.2: Overall Students’ Distribution by Sex and Constituency Constituency Male (%) Female (%)

Makueni 66.67 33.33

Kaiti 58.49 41.51

Mbooni 56.41 43.59

Kilome 55.10 44.90

Kibwezi 50.00 50.00

All 57.26 42.74

Data Source: Author’s Computation

The overall distribution of students by sex shows more male students were sampled as compared to female students i.e. 57.3 per cent of male students and

42.7 per cent for the female students. The relative biasness to the male students was as a result of the original awarding of CDF funded bursaries where proportionately more male students were awarded bursaries as compared to female students. It was important that the sampling process factored in proportionate representation of the students who benefited from CDF bursaries to avoid inbuilt biasness of the results. At the constituency level, only Kibwezi

Constituency achieved gender parity while Makueni Constituency had the worst gender disparity. Kaiti, Mbooni and Kilome Constituencies were not very far from their proportional share in achieving gender parity.

When students who benefited from CDF bursaries were analysed on the basis of the average annual household expenditure of the households they belonged, the households from where the male students belonged registered higher average annual household expenditure of Kshs.262,362.00 while those from

where the female students belonged recorded average annual household expenditure of Kshs.249,291.00. These findings indicated that there was poor targeting of CDF bursaries because based on the average annual household expenditure, more girls should have been awarded. This implies that while the

CDF bursaries are by design targeted at students from poor background, allocations of such bursaries is not purely informed by household poverty levels since it appears that other factors might be at play.

4.4 Distribution of CDF Educational Bursaries

The first objective of the study was to examine the distribution of CDF provided educational bursaries at secondary and tertiary education levels in

Makueni County. A number of variables were considered to describe the distribution of CDF education bursaries to students. The following is a presentation and discussion of the findings.

4.4.1 Distribution of CDF Bursaries by Nature of Engagement of Household Head

Analysis of CDF bursaries’ distribution by nature of engagement of the household was important for this study because the nature of engagement of the household head determines the level of expenditure of the household. At the same time, the nature of engagement of household head determines whether a household is categorised as being below or above poverty line. Average annual household expenditure was a key variable in this study hence the need

to consider the nature of engagement of all sampled households. Table 4.3 summaries sample distribution and average bursary allocation by nature of engagement of the head of household.

Table 4.3: Sample Distribution and Average Bursary Allocation by Nature of Engagement of Household Head Nature of Percentage Distribution by Average Bursary Engagement of Nature of Engagement of Allocation (Kshs.) Household Head Household Head (%)

Farmer 28.57 6,986.00

Business 18.37 6,867.00

Public officer 13.47 6,333.00

Teacher 26.12 6,984.00

Pastor 2.86 9,429.00

Other 10.61 7,385.00

No. of Observations 248 248

Data Source: Author’s Computation

Analysis of the interviewed household heads by nature of engagement of the household head revealed that; farmers were 28.6 per cent; teachers 26.1 per cent; business people 18.4 per cent; public officers 13.5 per cent; pastors 2.9 per cent; and others 10.6 per cent. The low percentage of pastors was reflective of the reality on the ground where the number of pastors as presiding officials depends on the number of churches. The category of others included casual labourers, supermarket attendants, drivers and artisans like masons, carpenters, electricians, plumbers and mechanics. It is noteworthy that the farmers and teachers share was more than 54 per cent of the total interviewed household heads while the share of both the business people and public officers was about

32 per cent of the interviewed households. These findings imply that households headed by farmers produced highest number of students who benefited from CDF bursaries followed by households headed by teachers.

Such results give a positive signal in bursary targeting because farmers have been identified as one of the clustered groups that suffer high levels of poverty

(Mwabu et al., 2000 and Republic of Kenya 1998; 1999). At the same time, the findings reflect the reality in Makueni County where most of the residents are subsistence farmers and should therefore contribute majority of the students who benefited from CDF bursaries.

With households headed by farmers and teachers producing the highest number of students who benefited from CDF bursaries, a Kolmogorov – Smirnov test was conducted on the two datasets to determine if the distribution of the two datasets differs significantly. The maximum difference between the cumulative distributions (D) was 0.1312 with a corresponding p-value of

0.582. The p-value provides the estimated probability of rejecting the null hypothesis when the null hypothesis is correct and lies between zero (0) and 1

(Rumsey, 2011). With the p-value greater than 0.05, the null hypothesis should be accepted and the distribution of the two datasets was therefore not significantly different.

In terms of average bursary allocation by nature of engagement of the head of household, students whose household heads were pastors were awarded the highest average bursary allocation at Kshs.9,429.00 followed by students from households whose heads were farmers who were awarded Kshs.6,986.00. In

the third position were students whose household heads were teachers who were awarded Kshs. 6,984.00 and closely followed in the fourth position by students whose household heads were business people with allocation of

Kshs.6,867.00. The findings on average bursary allocation based on the nature of engagement of the head of household seem to be supportive of the poor clusters as identified in earlier poverty studies (Mwabu et al., 2000).

4.4.2. Distribution of CDF Bursaries by Education Level of Household Head

The education level of the household head is important because various studies have found that the higher the level of education of household head, the lower the poverty levels of such household (Wambugu et al., 2010). With proper targeting, CDF funded bursaries ought to be awarded to those with low education levels. Table 4.4 summaries the bursary distribution and average bursary allocations by the highest level of education of the head of the household.

Table 4.4: Bursary Distribution and Average Allocations by Highest Level of Education of Household Head Highest level of Percentage share per Average Bursary Allocation education level of education (Kshs.)

Primary 5.31 6,731.00

Secondary 36.73 6,417.00

Post Secondary 30.61 7,907.00

University 27.35 6,701.00

Data Source: Author’s Computation

Analysing the distribution of CDF bursaries by the highest level of education of the household head, most (36.7 per cent) of the funds were awarded to students whose head of household had achieved secondary school level of education, followed by those students whose head of household had achieved post-secondary level of education (30.6 per cent). It is noteworthy that in the third position (27.3 per cent) were students whose household heads had achieved university level of education and the last (5.3 per cent) were students whose head of household had achieved primary school level of education. The distribution of the bursaries was aligned to the education level of household head where the percentage of total students awarded bursaries decreased as the level of education of the household head increased save for the household heads with primary school level of education. The inconsistency in the results of the household heads with primary school level of education was explained by the small number of household heads who had achieved primary school level of education and were interviewed. This finding might reflect a situation where not many household heads with primary school level of education were able to take their children to tertiary institutions since most of the sampled students were from tertiary institutions.

Kolmogorov – Smirnov test was conducted on three different datasets i.e.

Secondary Schools dataset and Post Secondary dataset, Secondary Schools dataset and University dataset, and Post Secondary dataset with University education dataset to determine if the distribution of the datasets differs significantly. For the secondary schools and post secondary datasets, the

maximum difference between the cumulative distributions (D) was 0.2600 with a corresponding p-value of 0.006. For the secondary schools and university datasets, the maximum difference between the cumulative distributions (D) was 0.1345 with a corresponding p-value of 0.462. Lastly for the post secondary and university datasets, the maximum difference between the cumulative distributions (D) was 0.1751 with a corresponding p-value of

0.206. For all these datasets, the p-value was greater than 0.05 and the null hypothesis was accepted implying the distribution of various datasets was not significantly different.

4.4.3 Distribution of CDF Bursaries by Constituency

CDF funds are allocated per constituency and it was important to consider how the bursaries are distributed per constituency. Table 4.5 shows average CDF bursary allocations by constituency.

Table 4.5: Average CDF Bursary Allocation by Constituency Constituency Tertiary Institutions Secondary Schools Kshs. Kshs. Makueni 4,569.00 -

Kaiti 5,870.00 4,714.00

Mbooni 6,364.00 5,000.00

Kilome 9,722.00 6,846.00

Kibwezi 9,659.00 6,111.00

All 7,127.00 5,914.00

Data Source: Author’s Computation

Analysis of average CDF bursary distribution by constituency as shown in table 4.5 revealed that Kilome Constituency students were awarded the highest average CDF bursaries with students in tertiary institutions getting highest average amount of Kshs.9,722.00 and secondary school students getting

Kshs.6,846.00. They were followed by students from Kibwezi Constituency who got average allocation of Kshs.9,659.00 for tertiary institutions and

Kshs.6,111.00 for secondary schools. The third position was Mbooni

Constituency with average allocations of Kshs.6,364.00 for tertiary institutions and kshs.5,000.00 for secondary school. It should be noted that Makueni

Constituency awarded bursary to tertiary institutions students only and none from the secondary schools. The data provided by the CDF manager Makueni confirmed students from secondary schools were awarded bursaries provided by the Ministry of Education, Science and Technology. The average bursary allocation by constituency is not consistent with reported constituency poverty levels in Makueni county (Republic of Kenya, 2008a). It is therefore observed that constituency poverty levels did not determine the average bursary allocation for each constituency as would be expected.

4.4.4. Distribution of CDF Bursaries by Quintile

Aggregating households into quintiles was critical in benefit incidence analysis as benefit accruing to a specific quintile was determined by the number of households occupying that quintile. It should be noted that quintiles were generated only for sampled and interviewed households. Average Bursary allocation by quintile was calculated by summing all the individual awarded

bursaries and dividing by the number of students in the quintile. Table 4.6 summarises average bursary allocations by quintile.

Table 4.6: Average Bursary Allocation by Quintile Quintile Tertiary Kshs. Secondary Kshs. Both Tertiary and Secondary Kshs.

Poorest quintile 8,758.00 5,824.00 7,760.00

2nd quintile 7,250.00 5,000.00 7,122.00

3rd quintile 5,446.00 8,000.00 5,552.00

4th quintile 7,131.00 6,250.00 6,990.00

Richest quintile 7,333.00 5,500.00 7,184.00

Data Source: Author’s Computation

Analysing the average distribution of CDF bursaries by household groups

(quintiles), overall students from the poorest quintile were awarded the highest average bursary allocation of Kshs.7,760.00 followed by students from the richest quintile with allocation of Kshs.7,184.00. Students from the second quintile were the third with average allocation of Kshs.7,122.00 while those students from the 3rd quintile had the lowest average bursary allocation of

Kshs.5,552.00. Results of analysis based on average distribution by tertiary and secondary education levels revealed that the tertiary students got on average higher CDF bursary allocations than the secondary students save for the 3rd quintile. Students from the poorest quintile for the tertiary institutions got highest average bursary allocation of Kshs.8,758.00 followed by students from the richest quintile with Kshs.7,333.00 while for the secondary schools, students from the third quintile got the highest average bursary allocation of

Kshs.8,000.00 followed by those students from the fourth quintile with

Kshs.6,250.00. It is evident that the distribution of average bursary allocations by quintile was not consistent as should be expected reflecting poor distribution strategies.

A Kolmogorov – smirnov test was conducted for the poorest quintile and richest quintile datasets to determine if distribution of the two datasets differs significantly. The maximum difference between the cumulative distributions

(D) was 0.1913 with a corresponding p-value of 0.288. For these datasets, the p-value was greater than 0.05 implying the distribution of the datasets was not significantly different. The null hypothesis of no significant differences in the distribution of the datasets was accepted.

4.4.5 Distribution of CDF Bursaries by Sex

The gender dimension is relevant for this study since weak targeting of government spending to the poor was closely related to gender biases in the use of government services (Demery, 1996). Expenditure on CDF bursaries is part of government spending and could suffer gender biases hence the need for analysis of data by sex. Table 4.7 presents the average CDF bursary allocation by sex.

Table 4.7: Average CDF Bursary Allocation by Sex Sex Tertiary Kshs. Secondary Kshs. All Kshs.

Male 7,414 5,286.00 7,204.00

Female 6,694 6,333.00 6,622.00

Data Source: Author’s Computation

Distribution of average CDF bursaries by sex demonstrates interesting results where the male students were awarded higher average amounts of bursary than female students for combined average for tertiary institutions and secondary schools. In terms of bursary allocations by sex, the male students were awarded an average Kshs.7,414.00 and female students Kshs.6,694.00 for tertiary institutions while the male students were awarded Kshs.5,286.00 and females students Kshs.6,333.00 for secondary schools. The results reflect biasness in awarding bursaries to male students at tertiary level while at the secondary schools the awarding favourably considered the female students. When both tertiary institutions and secondary schools were considered, the male students were awarded an average of kshs.7,204.00 against female students who were awarded Kshs.6,622.00 portraying a clear biasness towards the male students.

The biasness towards the male students was evident despite the data indicating that the average expenditure for households with female student beneficiaries was kshs.249,291.00 as opposed to Kshs.262,362.00 for those households with the male students. These results reflect the inherent gender biasness in targeting government services and goods as shown in the earlier study by Demery

(1996). Proper targeting of the bursaries would have led to female students being awarded higher average bursary because their households were assessed as poorer.

To determine if the distribution of bursaries between the male students and female students differs significantly, Kolmogorov – Smirnov test was conducted for the male bursary dataset and the female bursary dataset. The

maximum difference between the cumulative distributions (D) was 0.0622 with a corresponding p-value of 0.968. For these datasets, the p-value was greater than 0.05 and the null hypothesis could not be rejected hence the distribution of the datasets was not significantly different.

4.5 Extent of Progressivity, Regressivity or Neutrality of CDF

Spending on Education Bursaries

The second objective of the study was to ascertain the extent to which CDF’s spending on education bursaries is progressive, regressive or neutral in

Makueni County. In achieving this objective the study measured the value of the benefits that students received from CDF spending on education bursaries.

This was done by grouping households from which the students belonged on the basis of their total household expenditure and thereafter aggregating into sub-groups (quintiles) to get distributional impact of the CDF spending on education bursaries across such sub-groups. The actual CDF spending on education bursaries at different levels, secondary education and tertiary education, was obtained for the year 2010/2011 for the five constituencies of

Makueni County. The students in secondary and tertiary institutions who were awarded bursaries were identified and thereafter average unit cost of providing education bursaries at different levels obtained by dividing CDF actual spending on education bursaries by the total number of students who were awarded bursaries at that particular level. The unit cost (bursary) to individual students reflects attribution of benefits where each beneficiary student gained a

transfer equivalent to unit cost of the awarded bursary. Table 4.8 presents the

Makueni County unit cost (bursary) as calculated by level of education.

Table 4.8: CDF Unit Cost (Bursary) by Education Level Level of Total CDF Beneficiary Unit Cost Ratio of Percent Education spending on Students (Bursary) Bursary (share) Bursary (Kshs.) (Kshs.)

Secondary 3,451,400.00 490 7,044.00 1 14.84

Tertiary 21,480,500.00 4408 4,873.00 0.69 85.16

Data Source: Author’s Computation

It is evident that the unit cost (bursary) for secondary school students was higher where students in tertiary institutions managed to get only 69 per cent of the unit cost (bursary) awarded to secondary school students. It is noteworthy that the secondary school students also benefited from Secondary School

Education Bursary that was available to all those who applied. However, the few secondary school students who applied for CDF bursaries ended up getting higher average compared to those in tertiary institutions due to the low number of applicants. The ratio of bursaries shows how bursaries are distributed across different groups and is calculated by using the highest average as denominator and expressing all other allocations as percentage of the denominator that is given unit 1 or 100 per cent (Verghis and Demery, 1994).

The constituency unit cost (bursary) for tertiary level of education was obtained and students from the tertiary institutions were awarded the highest share of CDF education bursary in the year 2010/2011. Table 4.9 presents the

results for tertiary institutions for each of the five constituencies in Makueni

County.

Table 4.9: Constituency CDF Unit Cost (Bursary) for Tertiary Institutions Constituency Number of Total bursary Unit Bursary (Kshs.) beneficiaries (Kshs.)

Makueni 1,086 5,000,000.00 4,604.00

Kaiti 1,117 5,000,000.00 4,476.00

Mbooni 783 5,407,500.00 6,906.00

Kilome 762 3,024,400.00 3,969.00

Kibwezi 660 3,048,600.00 4,619.00

Total 4,408 21,480,500.00 4,873.00

Data Source: Author’s Computation.

At the constituency level, the results show that students from Mbooni

Constituency were awarded the highest unit bursary followed by students from

Kibwezi Constituency while students from Kilome Constituency were awarded the lowest unit bursary followed by students from Kaiti Constituency. The awarded amount per constituency was determined by the total amount allocated for bursaries for that particular year and the number of needy applicants for the same year.

Using total household expenditure as a welfare indicator, sampled households of students who benefited from CDF bursaries were ranked from the poorest to richest and thereafter aggregated into quintiles. Population quintiles were used instead of household quintiles to avoid giving misleadingly pro-poor impression of the bursary expenditure. This was also to ensure that appropriate

distributional impact of the CDF spending on education bursaries across the quintiles was achieved (Demery, 2000).

Table 4.10 presents the results of the aggregation into quintiles of the sampled students. The average household expenditure was for the households where the students belonged.

Table 4.10: Average Household Expenditure by Quintiles Quintile Household Expenditure (Kshs.)

Poorest quintile 88,295.00

2nd quintile 132,002.00

3rd quintile 176,693.00

4th quintile 265,292.00

Richest quintile 628,299.00

Data Source: Author’s Computation

Out of the 248 households sampled, the poorest quintile average household expenditure of Kshs.88,295.00 was only about a seventh of the richest quintile reflecting a huge household expenditure gap between the poorest and richest quintile. This scenario was also supported by the difference between the richest quintile and the fourth quintile where the richest quintile average household expenditure was two point four times the fourth quintile average household expenditure. The poorest quintile, second poorest quintile and the third quintile had relatively small average household expenditure differences of about kshs.50,000.00.

Using the average household expenditure per quintile figures and the total bursary allocations per quintile, the unit bursary equivalent per quintile was calculated and is presented in table 4.11.

Table 4.11: Unit Bursary Equivalent by Quintile Quintile Tertiary Secondary

Poorest quintile 1.76 0.99

2nd quintile 1.52 0.74

3rd quintile 1.20 1.33

4th quintile 1.63 1.00

Richest quintile 1.57 0.88

Data Source: Author’s Computation.

The unit bursary equivalent per quintile reflects the number of unit bursary for each quintile at different levels of education. For instance the poorest quintile was awarded one point seven six (1.76) equivalent of unit bursary at tertiary level while the same quintile only managed zero point nine nine (0.99) unit bursary equivalent for secondary schools. The richest quintile was awarded one point five seven (1.57) unit bursary equivalent at tertiary level and only zero point eight eight (0.88) unit bursary equivalent at secondary schools. The interpretation of these results is that secondary school students were awarded lower average bursaries than students in tertiary institutions.

After computing the unit CDF bursary for the two levels of education and grouping the households into quintiles, the distribution of the benefits accruing from CDF spending on education bursaries to each of the quintiles was calculated by multiplying the unit bursary by the number of students in each

group per education level. This was done in line with studies by Demery

(1997); Van de Walle and Nead (1995); Selden and Wasylenko (1992). The assumption was that the average benefit from unit bursary is the same for all expenditure levels. The results of the computation provide the Benefit

Incidence Analysis of the CDF expenditure on education bursaries for Makueni

County for the year 2010/2011. Table 4.12 presents the benefit incidence of

CDF expenditure on education bursaries by level of education.

Table 4.12: Benefit Incidence of CDF Expenditure on Education Bursaries by Level of Education Quintile Tertiary (%) Secondary (%)

Poorest quintile 19.70 47.83

2nd quintile 21.67 9.66

3rd quintile 17.16 7.73

4th quintile 19.73 24.15

Richest quintile 21.74 10.63

Data Source: Author’s Computation

Table 4.12 demonstrates that benefit incidence of CDF spending on education bursaries was progressive for secondary education and regressive for tertiary level of education. These results indicate that students from poorer households benefitted more from CDF spending on education bursaries in Makueni County at secondary school level than at tertiary level. The same results in table 4.1 could be graphically presented (see figure 4.1).

50 47.83

30 24.15 25 21.67 21.74 19.7 19.73 20 17.16

10.63 15 9.66 7.73 10

0 Poorest quintile 2nd quintile 3rd quintile 4th quintile Richest quintile

Tertiary (%) Secondary (%)

Figure 4.1: Benefit Incidence of CDF Spending on Secondary and Tertiary Education Bursaries

Both table 4.12 and figure 4.1 show that students from the secondary schools benefited a lot by taking almost fifty per cent of the benefits accruing from

CDF spending on bursaries i.e. benefit incidence for secondary school level was progressive. The poorest quintile representing twenty per cent of the students received 47.8 per cent of the total bursaries while the richest quintile only got 10.6 per cent of the secondary school bursaries. The students from poorer households were therefore awarded more than their proportionate share of CDF bursaries implying that the CDF secondary school bursaries were well targeted in Makueni County in the year 2010/2011. These results are collaborated by the findings of two (2) reviewed studies (Van de Walle and

Nead, 1995; Chu et al., 2000) that found public expenditure on both primary level and secondary levels to be progressive in a number of African countries.

Analysing specific quintiles for secondary school level, benefit incidence would have been absolutely progressive if bq1>bq2>bq3>bq4>bq5 where bq1 is the benefits to first quintile, bq2 is the benefits to second quintile, bq3 is the benefits to third quintile, bq4 is the benefits to fourth quintile and bq5 is the benefits to firth quintile. However, this was not the case and the benefit incidence for secondary school level was progressive.

For the tertiary level of education, students from the poor households did not get their expected proportionate share of the bursaries hence students from the rich households gained undue advantage in CDF bursary allocations. The poorest quintile representing twenty per cent of the students received 19.7 per cent of the total bursaries while the richest quintile was awarded 21.7 per cent of the tertiary educational level bursaries. In other words, the CDF funded educational bursaries benefited students from the rich households more in

Makueni County in the year 2010/2011. The finding of regressivity or not well targeted agrees with earlier findings of other six reviewed studies (Van de

Walle and Nead, 1995; Sahn and Younger, 2000; Sabir, 2003; Heltberg et al.,

2003; Yuki, 2003; Ajay, 2005) that found public expenditure on tertiary education to have been regressive. As was the case with secondary school level, the benefit incidence for tertiary education level was not absolute regressive because bq1

Analysing the 2nd and 3rd quintiles, the students in the tertiary institutions were awarded proportionately high percentages than those students in secondary schools demonstrating the biasness in targeting students from well-do households at tertiary level i.e. middle class capture of the benefits. When the richest quintile is analysed, the percentage allocation to students at tertiary level is more than twice the percentage allocation to students in secondary schools. The findings once more provide a clear demonstration of poor targeting at tertiary level of education as compared to secondary level education. The results generally indicate progressive CDF spending on secondary education bursary where students from poorer households clearly benefitted greatly from government spending on education bursaries and poor targeting for students in tertiary institutions. The results could be interpreted to mean that most student enrolment at the tertiary institutions came from the better off households leading to majority of those who applied for bursary coming from the same households.

It is worth noting that the progressivity of CDF spending on education bursaries for secondary education as portrayed by the results say nothing about the quality of the process of awarding the bursaries neither do the results reflect individual household choices to or not to apply for bursaries. It is possible that some richer households’ might not have seen the need to apply for the secondary school bursaries because the school fee is normally low as compared to tertiary institutions. However, the implied well targeting at the secondary school level would go a long way in helping to solve poverty and inequality

problems as envisaged in the CDF programme objectives (Republic of Kenya,

2003).

To illustrate the distribution of benefits in a continuous manner than just the five discrete points, concentration curves were plotted for CDF spending on education bursaries in Makueni County for the year 2010/2011. The concentration curves provided results in a different form because the curves describe the entire distribution or the cumulative proportions of individuals ranked from the poorest to the richest, on the horizontal axis, against the cumulative proportions of benefits received by individuals on the vertical axis.

The following figure 4.2 shows the concentration curves for CDF spending on education bursaries.

1.00

0.90

0.80

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0.50 Cumulative % of subsidy of % Cumulative

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0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Line Cumulativeof equality % of population, ranked from poorest to richest bursary allocation sec - actual bursary allocation tertiary - actual Bursary all

Figure 4.2: Concentration curves for CDF Spending on Education Bursaries

A visual analysis of figure 4.2 for CDF spending on education bursaries reveals an absolute progressivity for secondary education and regressivity for tertiary education. The benefit incidence for both levels of education is also regressive.

Comparison with the 45 degree diagonal (line of equality) indicates that the concentration curve for secondary education lies above the diagonal line implying that students from the poorest quintile gained more than 20 per cent of the total CDF expenditure on education bursaries in the year 2010/2011. The

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fact that the concentration curve for CDF spending on secondary school bursaries lies above the line of equality is a clear pointer to the fact that the distribution of benefits is progressive implying CDF funded bursaries for secondary school students were well targeted for the year 2010/2011. In general, curves that are more convex indicate greater concentration among the poor and vice versa.

The concentration curve for the CDF spending on tertiary education bursaries lies below the 45 degree diagonal line implying regressive distribution of benefits where students from the richest quintile benefitted by more than their expected proportionate share of 20 per cent. The concentration curve for CDF spending on tertiary education bursaries demonstrates a clear case of poor targeting of the bursaries where students from rich households achieved undue advantage over students from poor households. When both levels of education are combined, the concentration curve for CDF bursary spending on both levels of education lies below the 45 degree diagonal line portraying a regressive distribution of CDF expenditure on education bursaries in Makueni County in the year 2010/2011. The results reflect that CDF expenditure on education bursaries was not well targeted and achievement of earmarked objectives of reduction of poverty and income inequalities will be constrained if the status quo remains.

In order to ascertain the extent of progressiveness and regressiveness of benefit incidence of the CDF spending on education bursaries benefit incidence,

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concentration indices were calculated. Concentration indices are directly related to the concentration curves and have an advantage of quantifying the degree of the related inequalities or lack of inequalities. Where there is no inequality, the concentration index takes the value of zero and highest value could either be negative or positive one reflecting total inequality. The sign of the concentration index indicates the relationship while its magnitude reflects both the strength and degree of variability of the variable. Negative index reflects progressiveness of the distribution while positive sign reflects regressiveness of the distribution. Table 4.13 summarises the CDF spending on education bursaries concentration indices.

4.13: CDF Spending on Education Bursaries Concentration Indices Concentration index Secondary Education Bursary -0.1900 Allocation (0.09)

0.1377 Tertiary Education Bursary Allocation (0.03)

0.0981 All Students (0.02) Note. Standard errors are in parenthesis

Data Source: Author’s Computation.

The standard error measures the accuracy with which a sample represents a population. The smaller the standard error, the greater the precision of the estimator and the more representative the sample will be of the overall population (Johnson, 1984). Table 4.13 demonstrates that the concentration index for secondary school bursaries was – 0.19. The negative sign supports the finding that the distribution was progressive. The figure of 0.19 shows the extent of progressiveness as compared to neutrality status where the figure

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would be zero. The extent of progressiveness is not very high (about 20 per cent) bearing in mind the highest possible figure should be one (1). The standard deviation for secondary school index is 0.09 reflecting better representation of the overall population.

The regressiveness of the tertiary institutions education bursary allocation is reflected by the positive sign of the concentration index. The extent of the regressiveness is shown by the figure of 0.137. The extent of regressiveness is not absolute bearing in mind the highest possible figure should be one (1). The standard deviation for tertiary level of education is 0.03 reflecting once better representation of the overall population.

When both levels of education are considered together, the concentration index has a positive sign explaining the poor targeting of the CDF expenditure on education bursaries in Makueni county. However, the extent of the regressiveness is low with a figure of 0.098 compared to neutrality situation where the index would be zero. The standard deviation for both tertiary and secondary education is 0.02 reflecting high precision of the results.

Examining the benefit incidence of CDF spending on education bursaries at constituency level provides mixed results for specific constituencies in

Makueni County. Table 4.14 summarises the benefit incidence of CDF spending on education bursaries for all the five constituencies.

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Table 4.14: Benefit Incidence of CDF Spending on Education Bursaries by Constituency Quintile Benefit Incidence Kaiti Kilome Mbooni Makueni Kibwezi Poorest Quintile 19.14 20.50 28.75 0 35.49 Quintile 2 30.36 2.51 14.58 20.60 31.96 Quintile 3 7.43 8.20 5.83 59.66 12.75 Quintile 4 16.01 42.14 21.67 19.74 3.53 Richest Quintile 27.06 26.65 29.17 0 16.27 Data Source: Author’s Computation

For Kaiti Constituency, students from the poorest quintile were awarded almost the proportioned share with benefit incidence of 19.1 per cent while students’ from the richest quintile’s share was 27.1 per cent. The second poorest quintile’s share was 30.4 per cent while the third and the fourth quintiles students were awarded proportionately lower shares of 7.4 per cent and 16.1 per cent respectively. These results portray a regressive benefit incidence for

Kaiti Constituency for CDF spending on educational bursaries in the year

2010/2011.

Kilome Constituency depicts a different picture with students from the poorest quintile getting a share of 20.5 per cent while students from the richest quintile got a share 26.7 per cent. Students from the fourth quintile which is the second richest were awarded 42.1 per cent while those from the second quintile representing the second poorest group were awarded a mere 2.51 per cent. In total, students from quintiles one and two were awarded 23 per cent while those from quintiles five and four were awarded a total of 68.7 per cent reflecting a regressive benefit incidence. In other words Kilome constituency

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registered poor bursary targeting with the poor students being disadvantaged in the year 2010/2011.

Mbooni Constituency was different because students from the poorest quintile and second poorest quintile were awarded 28.8 per cent and 14.6 per cent respectively representing about 43 per cent of the total share. Students from the richest quintile were awarded 29.2 per cent and those from the second richest were awarded 21.2 per cent totalling to a share of about 51 per cent. Once more, the distribution favours students from the well-do households implying regressive distribution of the CDF spending on education bursaries in the year

2010/2011.

Makueni Constituency results show that students from the poorest quintile and the richest quintile were not awarded any bursaries. This implies that there was no representation in these two quintiles from the sampled households. Students from the second poorest quintile were almost awarded their proportioned share as well as the fourth quintile. It is noteworthy that students from the third quintile for Makueni constituency were awarded a share of 59.7 per cent representing more than half the total share of CDF bursaries awarded in the constituency.

Kibwezi Constituency was the only constituency in the county that registered absolute progressive distribution of CDF educational bursaries. Students from the poorest quintile were awarded 35.5 per cent while those from the second poorest quintile were awarded about 32 per cent giving a total of 67.5 per cent

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share. Students from the richest and second richest quintiles only managed a total share of 19.8 per cent of the awarded bursaries in Kibwezi Constituency.

These results reflect progressivity for the constituency and distribution of CDF educational bursaries was well targeted.

4.6 Gender Dimension on Benefit Incidence Analysis of CDF Spending

on Educational Bursaries

The third objective of the study was to examine the gender dimension in the benefit incidence of CDF spending on education bursaries in Makueni County.

To examine the gender dimension in the benefit incidence of CDF spending on education bursaries, sex disaggregated data was collected and used to differentiate benefits associated with female students and male students. Table

4.15 presents the benefit incidence of CDF spending on education bursaries by sex.

Table 4.15: Benefit incidence of CDF Spending on Education Bursaries by Sex Quintile Benefit Incidence Male (%) Female (%) Poorest Quintile 19.94 27.62 Quintile 2 20.92 19.22 Quintile 3 15.64 16.59 Quintile 4 21.70 18.21 Richest Quintile 21.80 18.37 Data Source: Author’s Computation.

The male students from the poorest quintile were almost awarded the proportionate share after getting 19.9 per cent while male students from the

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richest quintile were awarded 21.8 per cent. Male students from the second poorest quintile were awarded 20.9 per cent while those from the second richest quintile were awarded 21.7 per cent.

The results for male students portray a distribution which is almost neutral implying that all the quintiles were almost awarded the proportionate share of the CDF educational bursaries. The case for the female students was different where those from the poorest quintile were awarded 27.6 per cent and those from the second poorest quintile 19.2 per cent totalling to 48.6 per cent share of the CDF bursaries. Female students from the richest quintile were awarded

18.4 per cent while those from the second richest quintile were awarded 18.2 per cent representing a total of 36.4 per cent of CDF educational bursaries to female students.

It is evident that the distribution of CDF bursaries to female students was progressive reflecting better targeting of the CDF bursaries. When both sexes are considered there was biasness towards the male students and such results reflected gender biasness. These results of gender biasness in the distribution of

CDF educational bursaries collaborates the results of the three reviewed studies

(Yuki, 2003; Heltberg et al., 2003; Ajay, 2003) that factored gender considerations in their studies and their findings indicated gender biasness in benefits distributions with women being disadvantaged.

Such findings send clear a signal that users will only benefit from a public expenditure once deliberate efforts are made to ensure their usage of the funded

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goods and services. Generally, issues of gender biasness with women being disadvantaged are well acknowledged in Kenya with the Constitution of Kenya

2010 having clear constitutional provisions for empowerment and mainstreaming of women in nation building activities.

The same results as found in table 4.15 are better illustrated by the concentration curves for CDF spending on education bursaries by sex in figure

4.3.

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0.40 Cumulative % ofpayments % Cumulative

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0.00 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Cumulative % of population, ranked from poorest to richest Line of equality Male Female Figure 4.3: Concentration curves for CDF Spending on Education Bursaries by Gender

The concentration curves in figure 4.3 describe the entire distribution or the cumulative proportions of households ranked from the poorest to the richest, on the horizontal axis, against the cumulative proportions of benefits received by

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individual students on the vertical axis. Figure 4.3 shows the distribution of benefits to female students from CDF bursary spending to be progressive.

It is noteworthy that the concentration curve for CDF bursary expenditure on female students lies above the 45 degree diagonal implying that the poorest quintile gained more than 20 per cent of the total CDF expenditure on education bursaries in the year 2010/2011. The fact that the concentration curve for CDF bursary spending on female students lies above the 45 degree diagonal reveals the distribution of benefits to be progressive implying CDF funded bursaries for female students were well targeted for the year 2010/2011.

The concentration curve for the CDF spending on the male students lies below the 45 degree diagonal line but very close to the diagonal line. The finding portrays a slightly regressive distribution of benefits where the richest quintile of male students benefitted by more than their expected proportionate share of

20 per cent. The concentration curve for CDF spending on the male students demonstrates a clear case of poor targeting of the bursaries where male students from rich households achieved undue advantage over male students from poor households in Makueni County in the year 2010/2011. The results indicate that CDF bursary expenditure on male students should be better targeted for the poor male students to realise progressive distribution.

In order to ascertain the extent of progressiveness and regressiveness of benefit incidence of the CDF spending on education bursaries by sex, concentration

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indices were calculated. The focus was on male and female students separately to determine the degree of inequality or lack of it. Table 4.16 summarises the

CDF spending on Education Bursaries concentration indices.

4.16: CDF Spending on Education Bursaries Concentration Indices Concentration index 0.0293 Male students allocation (0.05)

-0.0768 Female Students Allocation (0.05)

0.0981 All Students (0.02) Note. Standard errors are in parenthesis Data Source: Author’s Computation.

The results in table 4.16 support the idea that the allocations to male students were regressive because the sign of the index is positive. The figure of 0.03 shows the extent of regressiveness as compared to neutrality status where the figure would be zero. The extent of regressiveness is low bearing in mind the highest possible figure should be one (1). The standard deviation for male students allocation index is 0.05 reflecting better representation of the overall population.

The allocations to female students were progressive as reflected by the negative sign of the index. The figure of 0.077 shows the extent of progressiveness as compared to neutrality status where the figure would be zero. The extent of progressiveness is not very high since the highest possible figure should be one (1). The standard deviation for female students allocation index is 0.05 reflecting high precision of the results.

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CHAPTER FIVE

SUMMARY, CONCLUSIONS AND POLICY IMPLICATIONS

5.1 Introduction

This chapter provides a summary of the study findings and conclusions. Policy implications of the study findings on benefit incidence analysis of constituencies’ development fund spending on education bursaries in Makueni

County in Kenya are discussed and policy recommendations made. In addition, limitations of the thesis and the areas for further research are discussed.

5.2 Summary of the Study

The Constituencies Development Fund is one of the renewed decentralized efforts started by Kenya government to tackle poverty and regional imbalances in Kenya since 2003. Over time, the fight against poverty remained a high government priority where virtually all the Development Plans, Sessional

Papers and other government economic policy documents prominently featured poverty alleviation as an area of concern. One sector that has received much attention is the education sector because education and poverty in Kenya are intimately related with incidence of poverty among the better educated households being lower than among the less educated households. Education is seen not only as a welfare indicator per se but also a key determinant of

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earnings providing important exit route from poverty. Subsequently, CDF has allocated over 40 per cent of the resources annually to education sector including bursaries.

Despite substantial allocation of resources to the education sector, notable major challenges continue being faced. Out of the myriad challenges, the issue of access to education has major serious consequences and needs continuous attention. With the introduction of free primary education, the issue of access to education is more serious at secondary and tertiary institutions. The government developed rigorous strategies with a view to improving access to secondary and tertiary education that included increasing the provision of bursaries through Secondary School Education Bursary Fund and CDF bursaries.

With increased funding of education bursaries through CDF, the question of effective targeting of the disbursed funds to the needy students taking into account gender parity remains unanswered. The important debate is no longer on the CDF expenditure on education bursaries but who has benefited from the expenditure in order to foster access to education. The debate remains important because a number of studies have suggested that the Fund is at risk of failing just like previous government attempts at decentralization. The persistent question was therefore whether the additional spending meets the needs of the poor bearing in mind that the poor are not a homogenous group living in one specific area.

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It was therefore critical to assess the contribution of CDF spending on education bursaries to the poor students by empirically finding out who benefits from the bursaries. The general objective of the study was to conduct a benefit incidence analysis of Constituencies Development Fund spending on education bursaries in Makueni County in Kenya. The specific study objectives were; to examine the distribution of CDF provided educational bursaries at secondary and tertiary education levels in Makueni County, to ascertain the extent to which CDF’s spending on education bursaries is progressive, regressive or neutral and examine the gender dimension in the benefit incidence of CDF spending on education bursaries.

Multi-stage sampling method was used to sample students at different levels.

At the constituency level, students were first stratified into tertiary institutions and secondary schools from the list of students who were awarded bursaries in

2010/11. Out of each stratum, students were once more stratified on the basis of sex and location of their rural homestead to ensure fair representation.

Proportional stratified random sampling was used to ensure students from both secondary schools and tertiary institutions got a fair share in the sample size for each constituency. The list of sampled students determined which household head was to be interviewed. The study used both primary and secondary sex disaggregated data collected from the five Makueni County constituencies.

The study analysed distribution of benefits and gender dimensions using the standard Benefit Incidence Analysis approach. The study also used concentration curves that show the cumulative distribution of benefits plotted

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against cumulative participation in CDF education bursaries to test the progressivity, regressivity or neutrality of CDF spending on education bursaries.

The first objective of the study was to examine the distribution of CDF provided education bursaries at secondary and tertiary education levels in

Makueni County. This objective was achieved through analysis of the data on household heads on the basis of various variables that included nature of engagement, level of education, quintiles and sex. The study established that distribution of CDF education bursaries depended a lot on the nature of engagement of the head of the household. At the same time the study demonstrated that the higher the level of education achieved by the household head, the lower the education bursary was likely to be awarded to students from such households. In terms of average distribution of CDF education bursaries by household groups (quintiles), the study found that students from the poorest quintile were awarded the highest average bursary allocation but there was inconsistency where students from the richest quintile took the second position providing a clear pointer to poor targeting of such bursaries.

The study also demonstrated that there was biasness towards the male students in the average distribution of CDF education bursaries where the male students were awarded higher average amounts of bursary than female students.

The second objective of the study was to ascertain the extent to which CDF’s spending on education bursaries is progressive, regressive or neutral in

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Makueni County. This was achieved through use of the BIA after grouping the users into quintiles and attributing the expenditure to specific quintiles for each level of education. The results showed that benefit incidence of CDF spending on education bursaries was progressive for secondary education and regressive for tertiary level of education. These results indicated that students from poorer households benefited more from CDF spending on education bursaries in

Makueni County at secondary school level reflecting better targeting. For the tertiary education level, students from the poor households did not get their expected proportionate share of the bursaries hence students from the rich households gained undue advantage in CDF bursary allocations in the year

2010/2011. The extent of progressiveness or regressiveness was measured using the concentration indices. Use of concentration curves for CDF bursary spending on education bursaries reflected the same findings of progressivity for secondary education and regressivity for tertiary education. When both levels of education are combined, the concentration curve for CDF bursary spending on both levels of education lies below the 45 degree diagonal line portraying a regressive distribution of CDF expenditure on education bursaries in Makueni

County.

The third objective of the study was to examine the gender dimension in the benefit incidence of CDF spending on education bursaries in Makueni County.

The objective focused on gender considerations and was achieved through disaggregation of collected data by sex before analysis. The findings revealed that the distribution of CDF bursaries to female students was progressive reflecting better targeting of the bursaries. The concentration curve for CDF

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spending on the male students demonstrated a clear case of poor targeting of the bursaries where male students from rich households achieved undue advantage over female students from poor households in Makueni County in the year 2010/2011.

5.3 Conclusion

The study found that households whose heads were farmers and teachers benefited more than others implying that nature of engagement of the household head was important in the distribution of CDF education bursaries.

Farmers and teachers work and reside within the constituencies and normally have better accessibility to information on availability of CDF bursaries hence improving their chances of applying for the bursaries. There is need therefore for rigorous strategies to effectively disseminate information on CDF bursaries.

The study finding that the higher the level of education achieved by the household head, the lower the education bursary was likely to be awarded to students from such households was expected. Earlier studies (Republic of

Kenya 1998; 1999) had confirmed the inverse relationship between levels of poverty and level of education. The study finding re-emphasis the need for concrete strategies to ensure majority of Kenyans achieve higher levels of education to minimize demand on CDF bursaries.

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The study finding that students from the poorest quintile were awarded the highest average bursary allocation but there was inconsistency where students from the richest quintile took the second position was contrary to expectations.

Since CDF bursaries by design should benefit the disadvantaged students, effective targeting of the bursaries would have required consistent allocation where poorest quintile was awarded highest average bursaries followed by second quintile, third quintile, fourth quintile and lastly fifth quintile. The evident poor targeting of such bursaries demonstrates the need for proper profiling of the poor students for effective bursary allocations.

The study finding that CDF’s spending on education bursaries was progressive for secondary education implied that students from poorer households benefited more and secondary school bursaries were well targeted.

Subsequently, more bursary resources should be channelled to secondary schools for enhanced access to education by the poor. For the tertiary education level, students from the poor households did not get the expected proportionate share of the bursaries hence students from the rich households gained undue advantage in the year 2010/2011 in Makueni county. For effective targeting at tertiary level, strategies that promote better targeting of the poor students must be developed.

The study finding of biasness towards the male students in the average distribution of CDF education bursaries agrees with earlier studies that found

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gender biasness in benefits distributions with women disadvantaged. Concerted efforts should therefore be made to mitigate the situation.

5.4 Policy Implications

The findings of the study have critical policy implications that could be useful to policy makers in making public spending choices. Such choices could be made in formulating new policies or making necessary adjustments to existing policies on public spending on education bursaries. It is therefore very important for the results of this study to inform the most appropriate way to target such resources for optimal impact, acknowledging the fact that pro-poor initiatives must be accompanied by pro-poor policies.

The government through the CDF boards could improve dissemination of information on CDF bursaries to enhance inclusiveness of the majority of the needy students. CDF boards could provide adequate publicity / communication budgets to ensure inclusiveness through implementation of well thought out communication strategy. For instance, such funds could be used to inform all potential bursary beneficiaries through local FM radio stations when applications are due. At the same time, such funds could be used to organise awareness creation events within constituencies for all needy students. This policy recommendation is important because the study established that distribution of CDF education bursaries depended a lot on the nature of engagement of the head of the household and students whose household heads

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worked and resided within the constituencies benefited more from CDF bursaries than those whose household heads worked away from the constituencies. It is noteworthy that parents or guardians of the students normally process the applications for CDF bursaries since most students are in schools/colleges at the time of applying for bursaries.

The government needs to open as many opportunities as possible for young

Kenyans to achieve higher levels of education. For instance, the government can enforce the constitutional requirement that basic education is a fundamental human right where every child is guaranteed free and compulsory basic education by making secondary education free or fully sponsored by government as is the case with primary education. In addition the government should subsidise tertiary education to ensure quality tertiary education is accessible by all Kenyans and more specifically by students from poorer households. The government could start an educational levy to subsidise tertiary education. As more resources are made available, the government should ensure that there is attendant transparency, consistency and efficiency in the use of resources to optimize on number of students completing tertiary education. These policy recommendations are important because the results of the study found that there is an inverse relationship between level of education attained by household head and the amount of CDF bursary awarded to students. The higher the level of education attained by the household head, the lower the CDF education bursary awarded to students from such households.

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The government could as a matter of urgency address poor targeting of CDF bursaries through effective profiling of needy students. For instance, the government could fund an annual one-off forum for education stakeholders, church ministers, village elders and the “Nyumba Kumi” initiative teams to profile all needy students per constituency for purposes of bursary awards.

Such profiling of needy students would ensure CDF bursaries are awarded in a consistent manner. This is very important because the study found that there were inconsistencies in bursary awards where students from the poorest quintile despite being awarded the highest average bursaries were very closely followed by students from the richest quintile.

The government could increase budgetary allocations for secondary school bursaries. The national government bursary allocations through Secondary

School Bursary Fund and CDF Bursary Fund should be enhanced by prioritisation and ring fencing resources for secondary school bursaries. The county governments could also supplement national government efforts through provision of secondary school bursary funds. In addition the government could develop a legal framework to float educational bonds or use all funds retained in various suspense accounts for a long time to fund secondary education. This is critical because the results of the study indicated that CDF’s spending on secondary education bursaries was progressive. The results indicate that students from poorer households benefited more from CDF spending on secondary education bursaries in Makueni County implying that the bursaries were well targeted. Enhancement of secondary school bursaries will therefore go a long way in improving access to education by the poor.

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Access to tertiary education by the poor students must be guaranteed by government. Poor students who qualify and are admitted for tertiary education should automatically qualify for the higher education financing from government unless the concerned student officially communicates to the government that such financing is not required. The Government through the

Ministry of Education should also have a detailed data bank of all students who graduate from form four for purposes of intensive monitoring of progress. This is because the study results for the tertiary education level were regressive where students from the rich households gained undue advantage over the students from poor households in the year 2010/2011. The regressive result was quite discouraging reflecting an education system where higher education is a preserve for the rich and where the poor will remain poor because attainment of higher education has high positive impact on poverty reduction.

The government through the CDF Board should encourage all needy students to apply for bursaries. The requirement for institution’s admission numbers before applying for bursary should be done away with and immediately replaced with letters of admission to educational institutions as evidence. The fact that CDF bursaries are only provided to those who have been enrolled in schools or tertiary institutions disadvantages poor students who may never have the resources to get them admitted in the first place to pave way for the application of the bursary. Use of admission letters to educational institutions will make it possible for all deserving students to apply for bursaries including those who belong to poor households. Enforcement of constitutional provision

121

where basic education is a fundamental human right with every child guaranteed free and compulsory basic education is also critical. This is important because the study findings showing biasness to male students must be addressed to correct the situation otherwise households will continue on investing more in male students contrary to Kenyan constitution 2010 provisions.

5.5 Areas for Further Research

This study provides a rich foundation for future research on benefit incidence analysis of CDF expenditure on education bursaries in Kenya. The study was only able to conduct BIA for one county and the same could be done for all 46 counties. At the same time, the study only focused on education bursaries while there so many other educational services that could have been analysed i.e. provision of school physical infrastructure, provision of desks, purchase of buses and funding of school mocks.

Studies on marginal benefit analysis of CDF expenditure on education bursaries in Makueni county could be carried out after this study provided the prerequisite base information for the year 2010/2011. Important questions like what changes occur when bursary allocation is changed by certain percentage could be answered through another study. A study on marginal benefits of CDF

122

spending on education bursaries could not have been possible without the findings of this study.

More studies could be conducted that factor in behavioural Benefit Incidence

Analysis and quality issues. This is because this study has not analysed the quality and behavioural patterns since the estimation procedure could not accommodate such issues.

5.6 Limitations of the Study

Some of the study limitations included availability and reliability of secondary data given the limited coverage of household surveys and indeed problems with official CDF expenditure data. This limitation was mitigated through the use of primary data for variables like household expenditure. The study did not take into account any long-run effects of CDF spending on education bursaries due to the choice of estimation procedure and the results must be interpreted accordingly.

123

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APPENDIX 1: DATA COLLECTION INSTRUMENTS

A: BENEFICIARY STUDENT DATA FORM

Constituency Name: ______

Name of CDF Manager ______

Date of Interview ______

Name of Research Assistant ______

Student Sex School/ Location of the beneficiary student Allocated

Name institution Home Amount

Village Sub- Location Ward

location

B: THE QUESTIONAIRRE TO BE ADMINISTERED TO THE

HOUSEHOLDS

1. Constituency Name: ------

2. Ward Name: ------

3. Sub-location Name:------

4. Village Name:------

5. Total Number of Children belonging to the household: ------

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6. Highest education level completed by household head: ------

7. Employment status of the household head:------

8. How much is the household’s expenditure on?

Food Item Expenditure in Kshs.

Bread

Maize

Cereals

Meat

Fish / sea foods

Poultry (Chicken)

Dairy Products &Eggs

Oils and fats

Fruits

Vegetables

Beans

Roots

Sugar

Tea and Coffee

Beverages

Baby Food

Other food

9. How much is the household’s expenditure on?

Non -Food Item Expenditure in Kshs.

137

Education

Health

Cloth and Footwear

Lighting and Cooking fuel

Transport

Communication

House Rent and furnishing

Non Durables

Durables

Water

Recreation and Personal care

Transfers Out

Insurance

Tobbacco

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APPENDIX 11: STUDY DATA Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 1 4 2 2 10,000 8 3 1 89,980 2 4 1 2 10,000 5 5 6 200,750 3 4 1 2 10,000 5 5 2 190,650 4 4 2 2 10,000 11 5 1 35,500 5 4 1 2 10,000 11 3 1 35,500 6 4 2 2 10,000 2 5 4 253,700 7 4 1 2 10,000 6 5 4 522,880 8 4 1 2 10,000 10 5 1 295,500 9 4 2 2 10,000 7 5 1 224,200 10 4 2 2 10,000 8 4 1 224,000 11 4 1 2 10,000 6 5 4 326,500 12 4 2 1 8,000 8 3 1 89,980 13 4 2 1 8,000 9 3 1 58,550 14 4 2 1 8,000 5 3 6 196,500 15 4 2 1 8,000 8 3 4 385,680 16 4 2 1 3,000 5 4 4 438,000 17 4 2 1 8,000 7 3 1 183,500

139

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 18 4 1 1 8,000 6 3 1 312,000 19 4 2 1 8,000 5 3 2 262,000 20 4 2 1 8,000 3 3 2 387,000 21 4 1 1 3,000 4 4 4 271,000 22 4 1 1 3,000 5 3 2 134,600 23 4 1 1 8,000 8 3 1 101,100 24 4 2 2 10,000 6 3 1 32,800 25 4 2 2 10,000 9 5 2 271,000 26 4 1 2 10,000 6 5 1 67,700 27 4 1 2 10,000 7 4 3 496,304 28 4 1 2 10,000 9 5 1 193,200 29 4 2 2 8,000 4 4 4 326,400 30 4 2 2 10,000 6 5 4 408,100 31 4 2 2 10,000 4 3 2 229,500 32 4 1 2 10,000 4 4 4 356,500 33 4 1 2 8,000 5 3 1 144,000 34 4 1 2 10,000 5 4 2 705,500 35 4 2 2 8,000 6 3 1 339,500

140

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 36 4 1 2 10,000 6 3 4 751,000 37 4 2 2 10,000 5 3 4 542,000 38 4 1 2 10,000 4 4 4 341,000 39 4 1 2 10,000 8 3 2 616,000 40 4 1 2 10,000 3 4 4 358,000 41 4 2 2 10,000 5 3 4 227,000 42 4 1 2 10,000 4 4 4 321,500 43 4 1 2 8,000 6 3 4 380,000 44 4 1 2 10,000 3 4 2 257,500 45 4 1 2 10,000 4 3 2 466,500 46 4 1 2 10,000 4 4 4 416,000 47 4 2 2 8,000 7 3 1 109,500 48 4 1 2 10,000 6 3 4 279,680 49 4 1 1 8,000 3 3 1 111,000 50 1 2 2 4,000 3 3 5 172,700 51 1 2 2 4,000 3 3 6 156,700 52 1 1 2 5,000 4 3 3 196,300 53 1 1 2 5,000 5 4 6 154,200

141

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 54 1 2 2 5,000 3 4 3 176,900 55 1 2 2 5,000 4 5 6 192,000 56 1 1 2 5,000 6 4 2 131,400 57 1 1 2 5,000 4 2 6 168,100 58 1 1 2 5,000 3 3 2 185,200 59 1 2 2 5,000 4 3 6 180,800 60 1 1 2 4,000 6 5 2 212,000 61 1 2 2 5,000 4 3 1 165,900 62 1 2 2 4,000 7 5 4 116,300 63 1 1 2 5,000 4 4 2 177,500 64 1 1 2 4,000 3 4 2 179,800 65 1 1 2 4,000 6 5 4 231,300 66 1 2 2 4,000 4 3 3 125,200 67 1 1 2 4,000 3 3 2 161,600 68 1 2 2 4,000 5 4 2 168,250 69 1 1 2 5,000 6 5 3 240,500 70 1 1 2 4,000 3 4 3 133,000 71 1 2 2 4,000 5 3 1 180,000

142

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 72 1 2 2 4,000 4 3 4 184,000 73 1 1 2 4,000 3 4 3 192,000 74 1 2 2 5,000 3 4 3 224,300 75 1 2 2 5,000 7 5 3 213,500 76 1 1 2 5,000 3 3 3 178,000 77 1 2 2 5,000 5 4 3 152,500 78 1 2 2 5,000 4 4 3 155,200 79 1 1 2 4,000 6 3 1 141,300 80 1 1 2 4,000 3 4 3 139,000 81 1 1 2 4,000 7 5 3 197,900 82 1 1 2 5,000 4 5 4 191,500 83 1 1 2 4,000 5 3 2 135,200 84 1 1 2 5,000 3 5 2 180,500 85 1 1 2 5,000 4 5 4 123,400 86 1 1 2 5,000 5 3 4 196,000 87 1 1 2 4,000 5 5 4 178,000 88 1 1 2 4,000 5 5 2 129,200 89 1 2 2 4,000 3 3 4 160,000

143

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 90 1 1 2 4,000 3 3 4 196,400 91 1 1 2 5,000 5 3 4 197,800 92 1 1 2 5,000 5 3 3 238,800 93 1 2 2 5,000 5 3 6 172,600 94 1 1 2 5,000 5 3 2 164,500 95 1 1 2 5,000 3 3 2 168,700 96 1 1 2 5,000 2 5 4 189,000 97 1 1 2 5,000 3 3 4 152,000 98 1 1 2 5,000 3 5 2 236,000 99 1 1 2 4,000 3 3 4 199,300 100 1 1 2 5,000 4 3 1 172,100 101 3 2 2 5,000 4 4 2 129,000 102 3 1 2 5,000 7 4 2 130,500 103 3 1 2 5,000 6 4 4 109,100 104 3 2 2 4,000 5 5 3 152,300 105 3 2 2 5,000 4 4 2 139,200 106 3 2 2 5,000 6 5 3 164,000 107 3 2 2 4,000 5 4 6 102,500

144

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 108 3 1 2 4,000 7 3 1 100,600 109 3 2 2 4,000 6 4 6 116,900 110 3 1 2 4,000 5 3 2 124,000 111 3 1 2 4,000 6 4 6 102,000 112 3 2 2 4,000 7 3 3 111,500 113 3 1 2 2,000 5 4 110,700 114 3 1 2 5,000 3 5 2 161,800 115 3 1 2 5,000 5 5 4 136,000 116 3 1 2 10,000 6 2 1 221,230 117 3 1 2 20,000 4 5 3 450,720 118 3 1 2 10,000 4 5 1 545,280 119 3 2 2 10,000 6 4 6 625,420 120 3 2 2 4,000 6 5 5 378,820 121 3 2 2 10,000 4 3 1 334,854 122 3 1 2 20,000 2 2 6 456,180 123 3 1 2 10,000 4 2 1 582,200 124 3 1 2 10,000 8 2 1 206,889 125 3 2 1 5,000 5 3 3 204,740

145

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 126 3 1 1 8,000 7 3 5 235,080 127 3 1 2 5,000 6 3 1 224,630 128 3 1 2 4,000 4 3 1 153,600 129 3 2 1 4,000 2 3 1 83,400 130 3 2 2 4,000 3 4 6 76,000 131 3 1 1 4,000 2 3 3 59,000 132 3 2 2 5,000 2 3 6 74,000 133 3 1 1 4,000 3 3 3 70,000 134 3 1 2 4,000 3 4 4 82,000 135 3 2 1 5,000 3 3 1 66,000 136 3 2 2 6,000 2 4 6 78,300 137 3 1 2 4,000 4 6 76,000 138 3 2 2 3,000 2 4 2 117,000 139 3 1 2 6,000 3 4 6 90,000 140 2 1 1 5,000 4 4 4 119,000 141 2 1 2 5,000 3 5 3 130,000 142 2 2 1 5,000 3 4 2 113,000 143 2 2 2 4,000 4 4 6 107,000

146

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 144 2 2 2 10,000 2 3 1 84,200 145 2 1 2 20,000 3 4 2 115,500 146 2 2 2 10,000 3 4 5 127,000 147 2 1 2 10,000 4 4 6 114,000 148 2 2 1 5,000 2 4 6 114,000 149 2 1 2 4,000 4 4 4 121,000 150 2 1 2 5,000 3 4 6 123,000 151 2 1 2 5,000 2 5 2 129,000 152 2 2 2 4,000 116,000 153 2 1 2 4,000 4 5 1 285,000 154 2 1 2 5,000 4 5 1 343,800 155 2 1 2 7,000 9 3 1 369,600 156 2 1 2 5,000 3 5 4 597,800 157 2 1 2 5,000 2 4 4 601,600 158 2 2 2 1,500 9 3 1 279,700 159 2 1 2 7,000 6 5 4 618,200 160 2 2 2 4,000 6 3 2 581,700 161 2 1 2 3,000 6 5 4 553,300

147

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 162 2 2 2 1,500 7 2 1 233,400 163 2 2 2 1,500 7 2 1 206,900 164 2 1 2 5,000 8 3 4 484,300 165 2 1 2 3,000 4 5 4 491,800 166 2 1 2 7,000 8 3 2 310,900 167 2 1 2 7,000 8 3 1 192,700 168 2 2 2 7,000 6 5 4 566,300 169 2 2 2 7,000 6 5 4 532,900 170 2 1 2 7,000 8 3 1 197,600 171 2 1 2 7,000 8 3 1 230,700 172 2 1 2 7,000 8 3 1 184,300 173 2 1 2 7,000 5 3 1 251,000 174 2 2 2 1,500 8 2 1 185,600 175 5 2 2 5,000 5 5 4 841,840 176 2 2 2 4,000 4 4 4 145,000 177 2 2 2 10,000 3 4 6 131,000 178 2 2 1 4,000 2 3 3 128,000 179 2 2 2 6,000 2 4 4 118,000

148

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 180 2 1 1 6,000 2 4 2 84,000 181 2 1 1 4,000 1 1 68,500 182 2 2 2 4,000 2 3 1 57,000 183 2 1 1 4,000 4 3 6 110,000 184 2 1 2 4,000 2 4 3 134,000 185 2 1 2 6,000 3 4 3 31,000 186 2 1 2 6,000 2 5 3 149,000 187 2 1 2 5,000 6 3 1 438,300 188 2 2 2 7,000 5 3 2 746,680 189 2 1 2 7,000 5 3 225,800 190 2 2 2 7,000 5 2 1 597,110 191 2 1 2 7,000 9 4 4 832,068 192 2 2 2 7,000 9 4 4 842,968 193 2 2 2 3,000 7 3 1 432,440 194 5 2 2 20,000 2 5 4 126,500 195 5 2 2 10,000 6 4 4 154,500 196 5 1 2 20,000 3 4 5 123,500 197 5 1 2 20,000 4 4 6 109,000

149

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 198 5 1 2 10,000 2 3 2 91,000 199 5 2 2 15,000 1 4 4 108,500 200 5 1 2 10,000 3 5 4 146,000 201 5 2 2 20,000 2 3 1 110,000 202 5 1 2 30,000 4 4 3 108,000 203 5 2 10,000 3 4 5 109,000 204 5 2 10,000 2 5 4 127,000 205 5 1 2 15,000 6 4 2 123,800 206 5 1 2 20,000 3 4 6 136,000 207 5 1 2 10,000 3 5 4 168,500 208 5 1 2 10,000 4 4 2 125,000 209 5 1 2 10,000 3 4 2 99,500 210 5 1 2 10,000 4 4 1 123,000 211 5 2 2 10,000 4 4 4 136,000 212 5 1 2 10,000 5 1 107,500 213 5 2 2 10,000 4 4 2 114,000 214 5 1 2 10,000 2 4 4 142,000 215 5 2 2 10,000 5 4 3 157,000

150

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 216 5 2 2 10,000 2 4 5 137,000 217 5 1 2 10,000 4 4 3 110,500 218 5 2 2 10,000 6 4 2 163,500 219 5 1 2 10,000 3 3 1 80,100 220 5 1 2 5,000 4 5 4 893,000 221 5 1 2 5,000 6 5 3 1,804,800 222 5 2 2 5,000 6 5 4 643,400 223 5 1 2 5,000 3 5 3 504,380 224 5 2 1 2,000 6 3 1 255,700 225 5 2 2 5,000 6 5 1 171,900 226 5 1 2 5,000 4 5 4 734,800 227 5 2 1 8,000 8 3 1 265,900 228 5 2 1 3,000 6 2 3 381,100 229 5 2 1 8,000 6 2 1 149,600 230 5 2 2 5,000 4 5 2 768,940 231 5 1 2 5,000 4 5 2 1,222,440 232 5 2 2 5,000 4 5 4 1,386,500 233 5 2 2 5,000 5 5 1 646,500

151

Questio Constituency (1 = Sex (1= Type Institution Bursary No_ Highest_Educ (1= Employment ( 1= Farmer 2 Total Annual nnnaire_No. Makueni 2 = Kaiti 3= Male 2= (1 = Secondary Allocation Children None 2= Primary 3= = Business 3= Public Household Mbooni 4 = Kilome 5 = Female ) 2 =Tertiary) Secondary 4=Tertiary officer 4 = Teacher 5= Expenditure Kibwezi ) 4 = University) Pastor 6 = Others) 234 5 2 2 5,000 5 5 1 197,200 235 5 1 2 5,000 8 3 1 151,300 236 5 2 2 5,000 2 5 1 133,900 237 5 2 2 5,000 9 5 2 607,200 238 5 2 2 5,000 9 5 1 644,200 239 5 1 1 8,000 8 3 1 331,300 240 5 1 1 1,000 5 2 1 73,630 241 5 2 1 8,000 8 3 1 90,280 242 5 2 1 9,000 7 2 1 81,800 243 5 1 2 10,000 5 5 4 548,100 244 5 1 2 5,000 8 5 1 153,900 245 5 2 1 8,000 6 3 1 104,800 246 5 1 2 5,000 8 3 4 479,100 247 5 1 2 10,000 5 5 1 193,540 248 5 1 2 5,000 8 5 4 588,100

152

153

154