GENDER AND TAXATION IN

KENYA

Bernadette Wanjala Jane Kiringai

Naomi Mathenge

Prepared for THE INSTITUTE OF ECONOMIC AFFAIRS

NAIROBI, KENYA

May 2006

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ACKNOWLEDGEMENTS

The Institute of Economic Affairs (IEA) would like to thank the following peer reviewers for their comments; Mrs. Lineth Oyugi, Ms. Hulda Ouma, Ms. Debbie Budlender, Dr. Mbui Wagacha, Mr. Jared Osoro, Mrs. Miriam Omolo, Mr. GK Ndungu, Mr. Albert Mwenda, Ms. Thitu J. Mwaniki and Mr. Kwame Owino.

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ABSTRACT

This study seeks to analyse how Kenya’s tax system treats men and women given that they both play different roles in the economy and society. Using micro household data, the study quantifies the personal income tax (PIT) and value added tax (VAT) incidence on different household types classified by gender and per capita expenditure quintiles. The analysis is complemented by descriptive analysis to identify explicit gender biases in income tax. The study finds that reforms to personal income tax on eligibility for relief and the method of filing tax returns for couples, have removed explicit gender biases that formerly existed. In fiscal year 1996/97, Kenya introduced a uniform tax relief that is applicable to all who are in formal employment irrespective of marital or family status. In terms of filing tax returns, couples as of this fiscal year, have the option of joint or single filing. Looking at personal income tax burden there is, however, an implicit gender bias in terms of which sex pays a higher proportion of tax. Males in Kenya have been found to bear a greater burden given that they dominate the formal labour market, especially the high-cadre positions. With VAT, the study finds an implicit gender bias against females. The analysis shows that female-headed households bear a greater VAT burden as compared to male-headed households despite having greater tax exempted proportions. Discrepancies in tax burden also exist according to rural/urban settings and the level of per capita expenditure. Based on the findings we conclude that income tax policy reforms cannot be carried out in isolation from labour market and informal sector developments. While VAT reforms measures will only benefit females if carried out in tandem with labour market reforms (or other reform measures aimed at increasing female’s incomes). These findings pose a policy challenge given that the Kenyan government recognises the need for gender equality for poverty reduction, growth and development within development plans, and also given that tax policy leans more on indirect taxation. These findings indicate a great need to engender taxation policies as part of the fiscal policy framework if the development goals are to be met.

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TABLE OF CONTENTS

1. INTRODUCTION...........................................................................................................................4 2. SOCIO-DEMOGRAPHIC DISTRIBUTION.................................................................................6 3. GENDER MAINSTREAMING IN PUBLIC POLICY AND PLANNING IN KENYA.............7 4. KENYA’S TAX SYSTEM ............................................................................................................... 10

4.1 THE TAX REFORM EXPERIENCE............................................................................................................... 10 4.1.1 Personal Income Tax (PIT).................................................................................................................. 11 4.1.2 Sales/Value Added Taxes ..................................................................................................................... 12

4.2 THE CURRENT TAX SYSTEM: STRUCTURE AND REVENUE PERFORMANCE ....................................... 13 4.2.1 Structure .................................................................................................................................................. 13 4.2.2 Revenue Performance ........................................................................................................................... 15

5. METHODOLOGY......................................................................................................................... 17 5.1 THEORETICAL FRAMEWORK ..................................................................................................................... 17 5.2 ANALYTICAL AND METHODOLOGICAL FRAMEWORK .......................................................................... 18

6. IMPACT OF PERSONAL INCOME TAX AND VALUE ADDED TAX ON GENDER ......... 20 6.1 GENDER BIASES IN PERSONAL INCOME TAX (PIT)............................................................................... 20 6.1.1 PIT FILING............................................................................................................................................ 21

6.1.2 PIT Relief................................................................................................................................................ 21 6.1.3 PIT Burden............................................................................................................................................. 23

6.2 GENDER BIASES IN VALUE ADDED TAX (VAT) ............................................................................. 25 6.2.1 Consumption patterns........................................................................................................................... 26 6.2.2 Computation of VAT burden .............................................................................................................. 27

7. SUMMARY...................................................................................................................................... 31 8. CONCLUSION AND RECOMMENDATIONS…………………………………………………..32

9. STUDY LIMITATIONS…………………………………………………………………………….33

REFERENCES…………………………………………………………………………………………….34

APPENDICES ......................................................................................................................................... 36

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1. INTRODUCTION

Gender refers to the array of society-determined roles, personality traits, attitudes, behaviors, values, relative power and influence that society ascribes to the two sexes on a differential basis. Men and women are placed differently in the economy and society, and hence face different constraints, assume different responsibilities, and are ultimately likely to behave differently in response to policy (Himmelweit, 2002). Engendering the budgeting process and public finance in general have in the recent past received significant attention given the fact that both revenue and expenditure policies have different impacts on men and women, boys and girls. It has been widely acknowledged that macroeconomic policy framework has a bi-directional relationship with gender inequality (Elson, 1998, as quoted by IEA, 2004). Attempts to incorporate gender into the budgetary process were initiated twenty years ago when the Australian government introduced the first gender budget exercise in 1984. Some countries have made progress in engendering their budgets. In Eastern Africa for example, Rwanda and Uganda have national gender strategies though not fully integrated in the budget process, while studies have been conducted in South Africa, assessing the impact of tax policy on South African women. In Kenya, a training manual on engendering the budgetary process in Africa has been developed by ABANTU (a civil society organisation) and the Ministry of Finance (IEA, 2004). The interest in gender and taxation is due to the fact there have been concerns worldwide that tax policy is biased against women because it tends to increase the incidence of taxation on the poorest women while failing to generate enough revenue to fund the programmes needed to improve these women’s lives (Barnett and Grown, 2004). However, emphasis to date has been on the expenditure side of budgets and not taxation despite the fact that taxation impacts on income redistribution, and hence gender inequality. The relationship between gender and taxation still remains unexplored for most economies. Understanding the nature and composition of taxation is important for competitive markets, growth and poverty reduction given that taxes are a major source of revenue for most governments. Kenya, for instance, unlike many Sub-Saharan African countries, is a high tax yield country with a tax to GDP ratio of over 20 percent. Given that tax revenues constitute a considerable percentage of GDP, revenue collection can adversely impact on both social and economic aspects of development, with adverse implications on gender if there is gender inequality. There is also the possibility for positive impact if tax laws are suitably formulated and implemented. Taxation greatly impacts on income distribution, not only between men and women, but also between the rich and the poor, whereby majority of the poor in

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developing countries, are women. This calls for consideration of gender-sensitive fiscal policy in terms of both revenue collection and expenditure allocation. Considerable evidence indicates that the current Kenyan fiscal policy framework is gender-blind. Feeble attempts have been made to mainstream gender in the budgeting process and there is no in-depth analysis of the impact of taxation on gender. This is despite acknowledgement in the 2002-2008 National Development Plan that the pace of development can best be accelerated and sustained if the full creative and productive potential of both men and women is mobilised. A gender analysis of taxation is thus important given the existing gap in the Kenyan fiscal policy framework. This study hopes to fill this gap by informing the formulation process of gender-aware tax policies. This study aims to look at the impact of taxation on gender. The scope will be limited to personal income tax and Value Added Tax (VAT) for two reasons. Firstly, these two taxes are relatively important sources of government revenue given their shares in total revenue. Secondly, they directly affect the majority of taxpayers as compared to excise tax, which is levied on few commodities. The broad objectives of the study are:

To give an overview of the Kenyan tax system; To outline the history of tax reforms; To identify gender biases in Kenya’s tax policies and tax reform efforts; and In light of the findings, give recommendations on the likely impact of tax policies on

gender equity.

The rest of the paper is organised as follows: Section two provides an insight into the socio-demographic distribution in Kenya. Section three outlines the progress made so far in engendering fiscal policy in Kenya. Section four provides an overview of Kenya’s tax structure and revenue performance and also outlines the history of tax reforms, with particular emphasis on income tax and VAT. Section five gives the theoretical framework and methodology of the study. Section six presents the findings. Section seven provides a summary of the study and lastly, section eight concludes and provides recommendations for consideration by policy makers, researchers and lobbyists.

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2. SOCIO-DEMOGRAPHIC DISTRIBUTION

As of 1999, the ratio of males to females was 49.9:50.1 (Were and Kiringai, 2004). The highest number of males was found to be in the 0-14 age group.

Table 1

Population Distribution

Male Female

All ages 14,205,589 14,481,018 0-14 6,327,496 6,209,177 15-34 4,946,114 5,235,407

35-64 2,394,449 2,450,932>64 434,036 498,546 Age NS 103,487 86,956 Source, Economic Survey 2004, GoK

Thirty seven percent of households in Kenya are female-headed, and this proportion differs across regions with Rift Valley province having the highest percentage (50%) of female-headed households (GOK, 2005). Statistics on poverty indicate that poverty is slightly higher (though not significant) in female-headed rural households than male-headed households. (Figure 1 in the Appendix). Given less favourable terms for women in labour markets and a high dependency ratio1, one would expect households headed by female main income earners to be poorer, and more vulnerable to economic shocks (Economic Survey, 2005).

In terms of employment, there are clear gender disparities in earnings, types of occupations and senior positions held. These disparities have been attributed to cultural perceptions of the roles of males and females, skewed levels of skills, inadequate access to productive resources and a lack of responsive gender policies and programmes.

Appendix 1 shows the gender distribution of wage employment by income category. Females account for a much lower percentage of wage employment, with the discrepancy more pronounced in high-income cadres. There is a concentration of females in public administration where they constitute 37% of employees, education 43%, domestic services 39% and other services 38%. It is worth noting that there are more females than males

1 The portion of the population that is economically dependent on women relative to men is higher. Both young and elderly women find themselves bearing responsibility for dependants who include male and female children, young adults and the elderly.

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engaged in private sector agriculture and forestry activities (Economic Survey 2005). On average, females constitute only 29% of the total modern sector employment and earn 33% less than their male counterparts (Were and Kiringai, 2004). A substantial number of women participate in the informal sector as owners and/or employees. Unfortunately, sex disaggregated data on ownership and employment in the informal sector, and data in general on earnings, are unavailable. Culturally, Kenya is large and diverse consisting of various tribes, languages and religious beliefs. However, there are some common perceptions and customs that hold when it comes to adult-child, man-woman, boy-girl, husband-wife roles and relations. Generally speaking, the family as a unit has higher value than does the individual, as indicated for example, by the esteem attached to marrying, and having children, and the assumption that a household consists of a husband, wife and children. To some extent, the tradition of rearing boys to be future heads of homes and breadwinners whilst girls on the other hand are reared to be wives and mothers, still exists. So is the notion that one is incomplete in the absence of a spouse and children. Additionally, due to social constructions of what constitutes masculinity or femininity, behaviour, attitude and career choices are influenced by one’s sex. For example there are more female than male nurses, and males are not expected to show too much emotion unless it is something manly like anger. Regrettably, this works to the detriment of both males and females and economic growth and welfare at large. 3. GENDER MAINSTREAMING IN PUBLIC POLICY AND PLANNING

IN KENYA

3.1 Gender Mainstreaming in Public Policy and Planning

Due to the different socio-economic dimensions of gender, macroeconomic policies have different impacts on women and men. Hence the need for well thought-out policy design is critical. The overall objective of mainstreaming gender in public policy is to enhance the effectiveness of policy and promote gender equity. Gender can be used as an analytical tool at both macro and micro levels and should therefore be integrated into the analysis of economic and social problems at all levels (planning, implementation, evaluation etc). Experience shows that the macroeconomic framework underpinning the budget determines the incentive structure in the economy (consumption, savings, investment etc). Similarly, revenue strategies used to raise taxes and expenditure patterns have different impacts on men, women, boys and girls (IEA, 2004). This necessitates careful assessment of the

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objectives and possible outcomes of any fiscal policy before it is implemented. For the Kenyan case, fiscal policy has been incoherent (with minimal impact analysis), coupled with little understanding of fiscal failures and policy conflicts in the policy process.

3.2 Kenya’s Efforts to Mainstream Gender in Public Policy and Planning

The aim of public policy is to guide planning. In Kenya, there is no explicit policy on gender equality or mainstreaming. What Kenya has instead, are a few ad hoc programmes that respond to gender gaps and needs, and this is due primarily, to civil society and donor influence. The National Development Plan 2002-2008 realises gender equality and the enhancement of women’s participation in economic activities as critical to achieving the plan. As with changes that occur when a new government steps in, when the current government took over in December 2002, it devised a different plan for Kenya’s economy. This is called the Economic Recovery Strategy for Wealth and Employment Creation also known as the ERS. The ERS is based on the premise that for wealth and employment to be created, sound physical infrastructure, healthy and skilled human capital, governance and rule of law and economic growth are essential. These four areas are referred to as the pillars of the strategy. Although the ERS acknowledges that gender mainstreaming is critical for the successful establishment of these four pillars, it is does so casually. It was not until the year 2003, that Kenya saw the creation of institutions to support its recognition of gender. The Womens Bureaus were created in the year 2003 and have been inherited by a much larger body called the Gender Department. In December 2004, the Department of Gender was introduced in the Ministry of Gender, Culture, Sports and Social Services, and assigned the task of providing technical support for promoting the range of mechanisms in gender mainstreaming. This includes aspects of policy, plans, programmes and laws. In the same year, a National Commission Gender and Development was established. At present, there are plans to set up gender desks in every ministry to sensitise ministries on gender and push for gender mainstreaming in policy making, planning, budgeting, implementation, monitoring and evaluation. The main challenge expressed by the ministry is where to start and the inadequacy of resources - financial and technical.

Some line ministries have also made attempts to consider gender in the formulation of their strategic plans and priorities. For instance, the Ministry of Education has consciously tried to address gender disparities in access to services through the provision of free primary

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education implemented in 2004, and provision of affirmative action for secondary school bursaries started in the 1990s. This has not eliminated the gender gap but is a way of trying to provide a solution to disparities that later manifest themselves in higher education and the labour market.

Some ministries are also collecting and publishing sex disaggregated data (SDD), and this indicates some level of interest in gender issues. The data however are minimal. For instance, the most recent Economic Survey (2005) which is the only annual and most authoritative national survey on demographics and the economy, only had SDD on a few issues such as formal wage employment, students and agricultural professions. However, each Economic Survey attempts to gather more SDD which although making time series analysis of gender issues tricky, shows a step in the right direction. This and should be continued for all sectors, and gender disaggregated data should also be collected.

In terms of a national policy orientation towards gender equity, Kenya still has a ways to go. One challenge is that policy statements in regard to enhancing equity continue to focus more on the social dimensions of men and women. Emphasis is given to poor women living in rural areas and mainly engaged in agricultural activities ignoring the care economy, where women’s participation is highest. This emphasis includes boosting women’s capital and skills to engage in profitable agriculture and hence alleviate poverty (GoK, 2006). No mention is made on promoting equity through tax policies. In 2005, the National Commission on Gender and Development developed a national policy on gender and development which was actually birthed in the year 2000. The policy aims to provide a framework for the operations of gender mainstreaming in policy, planning and programming and human relations. As of May 2006, the paper has yet to go before Parliament for approval. For policy analysis, Kenya makes use of the KIPPRA Treasury Macro Model (KTMM), which does not provide for any gender related analysis. KTMM is a macro model that provides forecasts of macro aggregates besides policy analysis by way of simulations. In modelling its agents, the KTMM lumps all individuals and households with no regard to gender differences. It is therefore difficult to pick out any effects (real or otherwise) on the impact of policy on either males or females. While it is possible to model the effects of tax related policy issues using the model, it is not possible to assess the impact of such a policy on gender. Also, the KTMM does not capture the contribution of the reproductive and non-productive sectors and their linkages to the rest of the economy (IEA, 2004). It is therefore critical that this model is revised.

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This fiscal year, members of the Kenya Gender Budget Network (KGBN) formed in 2004, made attempts to join sector working groups that form a part of the Medium Term Expenditure Framework (MTEF). MTEF was introduced in Kenya in 1999/2000, to link policy making to planning and budgeting. The MTEF process compels policy making and planning to come from sectors rather than line ministries. Sectors audit past performance and plan for the next three fiscal years in terms of resources that are primarily financial. These resource requests are made against the budget outlook. Once the sector reports are ready they are tabled before the public for their feedback and then supposed to be revised accordingly in preparation of the Budget Strategy Paper (BSP) which outlines the revenue projections for the coming three fiscal years and resulting overall resource ceilings for sectors. The sector working groups therefore provide a good opportunity for gender issues to be mainstreamed. Unfortunately, of the 5 applications made by the KGBN, only 2 were successful. Therefore these sector working groups although providing great opportunity, remain fairly closed to those outside government.

Subsequently, gender mainstreaming in Kenya’s national policies, budget and planning process is relatively weak. On the plus side, there is more gender awareness in and outside government than there has ever been - thus indicating progress and potential for more.

4. KENYA’S TAX SYSTEM

4.1 The Tax Reform Experience

The current tax structure comprises of two main direct taxes (i.e. individual income tax and corporate tax) and three main indirect taxes (i.e. Value Added Tax - VAT, excise and customs). Since Kenya gained independence in the year 1963, the tax structure has changed a number of times. In some cases, the objectives of tax reforms have been influenced by a national vision, but in others, political ambitions and lobbyists.

An analysis of Kenya’s reform experience reveals two distinct phases; 1960s-early 1980s and 1980s onwards. The period 1963/64 – 1983/84 was characterised by piecemeal changes. Among the significant reforms during this period was the replacement of the existing consumption taxes with a sales tax in 1972/73. This switch was made because sales tax was considered favourable to targeting specific types of goods, not only to raise additional revenues, but because it favoured the inward-looking industrialisation policy pursued at the time. The introduction of the sales tax also marked the beginning of the policy change in early 1980s, i.e. relying more on indirect taxes as a major source of development finance.

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Major tax reforms in Kenya occurred under the Tax Modernisation Programme (TMP), which was initiated in the 1980s. The impetus behind these reforms was mainly the need to restore buoyancy to revenues, reduce complexity in the tax system and address equity [but not necessarily gender equity] in the distribution of tax burden as well as composition of the tax structure. The TMP also aimed to strengthen tax administration, expand the tax base and improve service delivery to taxpayers. Focus was also placed on increasing voluntary compliance and self-assessment, improving collection procedures, and improving tax administration. Implementation of the reforms has over time involved the introduction of new taxes or new rates on existing bases, the introduction of more stringent administrative changes to seal loopholes (including imposition of prohibitive penalties), the widening of tax bases and the reduction of exemptions.

Initial reform efforts under the TMP were outlined in the Sessional paper No 1 of 1986 on Economic Management for Renewed Growth. The major objectives of the TMP: raising revenue from 22% of GDP to 28% of GDP; improving economic efficiency through lowering and rationalisation of tax rates; improving administrative efficiency through computerisation and audit capacity; improving taxpayer education; establishing tax policy analysis capacity to implement organisational reform; and lastly enhancing greater reliance on self-assessment systems supported by selective audit. The specific tax reform measures under TMP on a revenue type by revenue type basis were: 4.1.1 Personal Income Tax (PIT)

There has been ongoing rationalisation under income tax reforms through lowering of rates and also reducing the number of brackets. Regular adjustment of brackets and relief were done to counter inflation creep and make the tax more equitable. The PIT structure has also been used to achieve redistribution objectives. This has mainly been through gradual lowering of the top marginal tax rate from 65% over the period 1974-89 to the current 30% (Appendix 2). Given that high tax rates are a disincentive to savings, the lowering of the top marginal tax rate was used to remove the savings disincentive both for households and enterprises (Budget Statements, GoK). The personal income taxes were considered as an essential instrument that could be used to achieve not only equity objectives but also growth outcomes as under the Tax Modernisation Programme.

There were also regular increases in income tax relief, with the aim of exempting low-income earners from the tax net. The effectiveness of such credits however depends on how the relief varies with respect to the level of income i.e. for it to have impact, relief should be

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progressive in nature. This is contrary to the Kenyan case whereby every registered income taxpayer is entitled to the same level of relief irrespective of the level of income. This undermines the effectiveness of the relief in meeting redistribution objectives. Looking at the income tax relief reforms, the level of relief has been increased over time to its current annual level of Kshs 13,9442 with the most significant reform being the unification of the single tax and the married (family) relief into one. A differentiated relief level depending on marital status had a discriminatory element/bias as will be discussed in the next section. In general, fairness and efficiency were central in income tax rationalization in order to ensure that the objectives of TMP were translated to reality, even though their effectiveness in achieving the set objectives was not realised.

4.1.2 Sales/Value Added Taxes

Under the TMP, there were clear moves to make the sales tax an easier tax to administer and comply with. Some of the sales tax rates increased (those on goods considered as luxuries) in order to compensate for revenue lost from lowering taxes on basic commodities. At other times, the sales tax was used to stimulate local production through increased domestic demand by reducing sales tax rates on local products in order to encourage their domestic production. Sales tax was also used as a discretionary tax policy instrument to maximise revenues from temporary economic shocks. A typical example is when the sales tax on oil products was increased and remissions on oil products revoked as part of a decision to increase the share of the windfall gains from low oil prices in 1986.

VAT replaced sales tax with effect from 1st January 1990. The input credit system/invoice method3 was adopted at its introduction. The standard VAT rate was set at 17% and it was to cover not only manufactured goods but all goods and services. The initial phases of the VAT introduction had a complex system, as there were 15 different rates with the highest rate at 210 percent. In the following year after its introduction, the number of rates was reduced to 8, with the top VAT rate being reduced to 100 percent (see Appendix 3). The rationalisation of VAT rates and the lowering of the top VAT rate were aimed at reducing tax evasion and also making local products more competitive. It was also to remove misclassification and ease administration, improve compliance, reduce smuggling and reduce requests for exemptions.

2 Equivalent to US $194. The current exchange rate of Kshs. 72/US$ was used. 3 Under this method, all VAT registrants are obliged to collect and remit VAT on their taxable supplies, with an allowance to recover tax paid on purchases.

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In order to increase voluntary compliance, the standard rate was reduced to 15 percent in 1995/96 and the highest rate from 30 percent to 25 percent. At this time, the structure of VAT was moving towards a single rate, which would simplify its administration significantly. The tax has also been used as part of the industrial strategy to revamp Kenyan industries and stimulate economic activities, and also encourage local production of specific commodities. However, it is only in 2003/2004 that VAT came to be seen as an important instrument that could be used to boost consumption demand in the country (Budget Speech, 2003/04). In general, this tax has been seen by policy makers as the tax for the future for the Kenyan economy given the change in policy direction towards indirect taxes.

4.2 The Current Tax System: Structure and Revenue Performance 4.2.1 Structure The current tax structure in Kenya comprises direct and indirect taxes. There are two main direct taxes. These are personal income tax (PIT) and corporate tax. There are also three main indirect taxes. These are Value Added Tax (VAT), excise and customs duties. The statutory obligation for each of these taxes is explained below.

Direct Taxes

The Kenya Income Tax Act came into operation on 1st January 1974 after the dissolution of the East African Community Management Act. Income taxes existed before independence but very few native Africans were liable to pay taxes at that time. Income tax is a direct tax that is imposed on income derived from business, employment, rent, dividends, interests, and pensions among others. Income from gainful employment (includes all forms of personal income, including income from self employment) is subject to Pay As You Earn (P.A.Y.E.). Personal income tax and PAYE are charged at the same graduated scale. The current income tax brackets are: 10% on the first Kshs. 1,694; 15% on the next Kshs. 1,596; 20% on the next Kshs. 1,596; 25% on the next Kshs. 1,596; and 30% on all income over Kshs. 6,482 (annually).4 Every individual who receives income is granted a tax relief or credit known as Personal Relief which is currently Kshs. 13,944 or US $194. Insurance relief and mortgage

4 10% on the first US $23; 15% on the next US$22; 20% on the next US$22; 25% on the next US$22; and 30% on all income over US$132 (annually). The current exchange rate of Kshs. 72/US$ was used.

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relief are also available for eligible persons5. The total tax credit is spread evenly over the tax year. At the end of the year, an individual will submit his or her self-assessment on total income received from various sources. Should the tax relief be lower than actual tax charged during the year, the balance of tax due will be payable.

Corporate income tax (CIT) is charged on profits of limited liability companies at a flat rate of 30%. Withholding Tax (WHT) is another type of income tax that is charged on interest, dividends, royalties, commission and pension. Withholding taxes are deducted at source from the following sources of income: interest, dividends, royalties, management or professional fees, commissions, pension or retirement annuity, rent, appearance or performance fees for entertaining, sporting or diverting an audience. The person paying out these amounts is required to withhold a certain percentage, as prescribed in the Act, and remit the same to the Commissioner of Income Tax by the 20th of the following month. A withholding tax certificate is issued for the amount withheld. Other Income Taxes include fringe benefit tax, advance tax, taxes under Widows and Pensions Act and Parliamentary Pensions Act and property taxes. Indirect Taxes

Value Added Tax (VAT) is charged on the supply of taxable goods or services made or provided in Kenya and on the importation of taxable goods or services into Kenya. Taxable goods and services are contained in the various schedules to the VAT Act. VAT is a multi-stage consumption tax based on the destination principle. The tax is applied to the sale of goods and services at all stages of the production and distribution chain. Only registered traders are required to charge VAT and they include sole proprietors, partnerships, limited liability companies or corporations. For one to qualify for registration under VAT, one must have an annual sales turnover of Kshs. 3 million, or Kshs. 2.4 million in 9 months; Kshs. 1.8 million in 6 months or Kshs. 1.2 million in 3 months.

The current standard VAT rate is 16% except for hotel and restaurant services where a lower rate of 14% applies. More specifically the 14% is applicable to restaurant services (including bar and beverage services) and all other service provided by hotel/restaurant owner or operator including telecommunications, entertainment, laundry, dry cleaning, storage, safety deposits, conference and business services. However, designated goods such as cigarettes, 5 Insurance and mortgage reliefs are likely to have a gender bias given that most mortgages are in held by males

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matches, gift items, confectionaries and other articles sold over the counter or in mini shops within the hotels and restaurants are taxable at the general rate of 16%.

Several commodities are zero-rated under VAT. The term zero-rating is used in the VAT law to refer to supplies of goods and services that are deemed to be taxable supplies at the rate of zero per cent. The zero-rating concept was introduced in the VAT system to enable exporters, manufacturers and suppliers of zero-rated goods and services to claim refund of tax paid on inputs incurred in dealing with zero-rated supplies. Examples of zero-rated supplies include all exports, agricultural inputs, pharmaceuticals and educational materials. The VAT law provides for zero-rating of exported goods irrespective of the tax status of the goods. The zero rating concept is a common phenomenon among countries where the VAT system is in operation. Some commodities are considered exempt under VAT. Exempt supplies are not taxable and do not form part of the taxable turnover. Persons who deal exclusively in exempt supplies are not liable to register and cannot claim input tax on these supplies. Exempt supplies are divided into financial services, insurance, public education and training services, health (including veterinary) services, sanitary services, agricultural services and social welfare services. Excise tax is levied on particular goods and services. The tax may be applied to either production or sale, domestic output or those which are imported, with ad valorem or specific rates. Kenya’s excisable commodities at present are alcoholic beverages, tobacco, fuel and motor vehicles. Other than motor vehicles, excise tax on beer, cigarettes and petroleum is on a specific basis while duty on motor vehicles is on ad valorem.

Customs duty is currently charged on the Cost, Insurance and Freight (CIF) value of imported goods. The current structure of the tariff bands is: 0%, 5%, 15%, 20%, 25%, 30%, and 35%. Sugar is at 100%. Imports from regional trading blocs like COMESA and East African Community are subject to customs duty at the rate of zero.

4.2.2 Revenue Performance

In terms of revenue performance, Kenya is considered a high tax yield country in Sub-Saharan Africa, with revenues at 20 – 21 % of GDP. Income tax is the biggest contributor to total revenue despite attempts to shift reliance towards indirect taxation under earlier tax reforms.

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The share of income tax in total revenue increased from 35% in 1999/2000 to 37% in 2003/04 (Table 2). The share of Value Added Tax increased from 27% to 31% respectively. Despite this improvement in the VAT revenue, its productivity6 has declined over time, which has been attributed to structural problems in its implementation. The share of excise duty also increased from 18.8% to 21.2% over the period 1999/2000 to 2003/04. Trade taxes (import duties) have continued to decline in importance due to globalisation (adherence to WTO rules) and regional integration. The relative use of these tax instruments has not changed significantly over time despite deliberate efforts to rely more on indirect taxes.

Table 2

Composition of Tax Revenues

Total Revenue 99/00 00/01 01/02 02/03 03/04Import Duty 28,605.2 28,803.7 21,583.7 18,436.2 21,684.0Excise Duty 28,493.1 28,318.0 32,076.9 35,684.1 41,939.0Income Tax 53,317.0 53,428.9 55,862.0 66,744.3 74,143.0VAT 40,944.2 50,220.9 50,871.7 56,135.3 60,405.0(I) VAT local 22,416.6 26,226.0 26,325.5 26,502.3 31,700.0(ii) VAT imports 18,527.6 23,994.9 24,546.2 29,632.9 28,705.0 151,359.4 160,771.5 160,394.2176,999.9 198,171.0Other Revenues Trade Licenses 90.6 86.3 97.5 105.4 229.1Licenses and Fees Under traffic Act 1,562.6 1,050.0 885.4 1,145.0 1,358.0Other Taxes, Licenses and Duties 3,953.1 1,171.0 1,087.3 1,011.5 1,222.1Total 5,606.3 2,307.3 2,070.2 2,261.9 2,809.2Total Tax Revenue 156,965.7 163,078.8 162,464.4 179,261.7 200,980.2Source: Economic Survey 2004 (GoK)

6 VAT productivity is derived by dividing the ratio of VAT to GDP with the VAT standard rate

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5. METHODOLOGY

5.1 Theoretical Framework

Two fundamental concerns in public finance are (i) who bears the final burden of a tax and (ii) the incidence of a tax. Taxation is one of the public policies, which has some directly observable or easily conjectured impact at the household level (Bourguignon and Da Silva, 2003). The impact of taxation on households can be indicated through a tax incidence analysis.

Tax incidence refers to impact of a tax on the welfare of an individual/society (Fullerton and Metcalf, 2002). It consists of identifying those economic agents that actually bear the cost of a particular tax, those who gain from it, and the amount that each group will gain or lose (Bourguignon and Da Silva, 2003).

There are alternative concepts of incidence that can be analysed. These concepts include absolute tax incidence, differential tax incidence and budget incidence. Absolute tax incidence examines the distributional effects of imposing a particular tax while holding public expenditure constant. Differential tax incidence analysis examines the distributional changes, which result if one tax is substituted with another while total revenue and expenditure are held constant. Budget incidence considers changes in household positions, which result if the combined effects of tax and expenditure changes are considered. Following from Sahn and Younger (2003), the basic duality theory is used to measure the incidence of a tax. From this theory, it is postulated that a household’s expenditure function, y = e(p,u), is the minimum amount of money that it must spend to generate utility level u, given prices p for all goods and services consumed. A household’s compensating variation (CV) for a tax increase is the amount of income that it would need to keep its utility constant given the price increases resulting from a tax increase.

( ) ( )uu pepeCV 00

01 ,, −= …………………………….(1)

Where zero (0) indicates the initial state while one (1) indicates the state after the tax change.

Given that estimating a household expenditure function is not easy, lets assume that the tax change only affects one price, pi. By Shephard’s lemma the derivative of the expenditure function with respect to pi is the compensated demand function for good i. Taylor’s expansion of equation 1 is given as:

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( ) ......,

21,

2

00

00 pupx

x ii

c

i

ic

i ppupCV ∆∗

∂

⎟⎠⎞⎜

⎝⎛∂

∗+∆∗≈ ………..(2)

Where ( )00 ,upxci is the compensated demand function and ∆Pi is the change in price of Pi

caused by a change in the tax. The first term gives the change (marginal) in expenditure that the household would have to undertake to keep utility constant without changing its demand for good i (it is the initial quantity multiplied by the price).

The only part that is used in incidence analysis is the first term in the Taylor’s expansion, which only observes the existing pattern of demand, multiplied by a hypothesised price change, and then uses the result as an estimate for each household’s loss in real income. This implies that behavioral changes are not taken into account.

5.2 Analytical and Methodological Framework

Gender analysis of tax policy involves identification of the differential impact of tax alternatives on men and women given their different social and economic responsibilities. Tastes and preferences with respect to tax also differ between men and women. Alvarez (1998) posits that “while there is reason to believe that men and women may indeed share similar primary or ‘first order’ attitudes toward matters of tax, the weighting or ‘second order’ preferences that men and women put on the importance of tax issues seem to have marked differences”. The most immediate effects while assessing a tax policy are on incomes, both at the individual and household level, the bargaining power and the distribution of resources within households, labour market behaviour and the long term futures of men and women. The relationship between gender and taxation can be analysed by looking at how the tax system treats men and women differently. These differences constitute gender biases (discrimination), which can be implicit or explicit. Explicit biases are those that are stated in laws and regulations. Implicit gender biases mainly emanate from the different impacts on men and women because of different social arrangements and economic behaviour. To be able to analyse the impact of explicit biases of direct taxes on women and men in this study, a descriptive analysis is adopted. This requires a historical evaluation of the tax reform process and identification of possible gender biases (Valodia and Smith 2004).

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There exist various methodologies for identifying implicit biases in taxation (Barnett and Grown, 2004). These methodologies include: tax burden analysis, incidence analysis, micro-simulation modelling and macro-simulation modelling. Tax burden refers to proportion of tax borne. Tax burden analysis involves the direct computation of tax burden of different taxpayers based on gender and the level of income. If information on tax filers, tax payments and net income is available, then the tax burden can be computed. A tax incidence analysis on the other hand aims to evaluate how individuals and society are affected by a change in the tax system. A sex-disaggregated tax incidence analysis therefore looks at the distributional effects of a tax burden on men and women. This research technique involves looking at the revenue component of taxation and examines both direct and indirect taxes in order to calculate how much taxation different individuals or households pay. It however assumes that income is shared equally within households, which is not the case as many studies have shown (Budlender, Sharp and Allen, 1998). A major difficulty in applying this technique is the fact that households are heterogeneous (in terms of income sharing, consumption behaviour etc) despite belonging to the same socio-demographic group. Micro-simulation modelling requires use of micro data on taxpayers, preferably data on tax returns. It is mainly used for income and corporate taxes. Simulations can then be done to analyze the impact of tax policy on issues such as the distribution of the tax burden, compliance etc. Data on income tax can be disaggregated using sex to assess the gender impacts, but such an analysis might not be possible for corporate tax. Macro-simulation modelling involves use of longer-term forecasting macro-models with an aim of producing a gender aware medium-term macro-economic framework. Common models are financial programming models, growth accounting models, macro-econometric simulations, and computable general equilibrium models. Most of these models are gender-blind, and the gender perspective can be introduced by adding variables that capture the gender dimension. However, application of any of these methodologies depends on the availability of data. For this particular analysis, we propose to directly estimate the individual tax burden using micro data by estimating the distribution across individuals (Tax burden analysis). The choice of this methodology is mainly guided by availability of data given that micro data on taxpayers is not available. To compute the share of taxes paid by different groups, households are grouped according to welfare level (using expenditure quintiles), gender of the household head and region (rural or urban). It is assumed that indirect taxes on goods are shifted entirely to consumers if markets are competitive and taxes apply to final sales. Taxes can be computed by multiplying the base (which can be income or consumption of a particular good or service) with the

19

statutory rate or by using the effective rate (computed as tax revenue as a proportion of the base). For ad valorem rates, the tax paid is given as

jij

jjijijji e

tt

xptT ,,,, 1+== ……………………… (4)

Where Ti,j is household i’s total loss in purchasing power for a tax on good j; pi,jxi,j is household i’s pre-tax amount of expenditure on good j; tj is the tax rate; and ei,j is the post-tax amount of expenditure on good j. The tax burden is then computed as the ratio of the tax payable (Ti,j) to total income. A tax is said to be progressive if the tax burden (ratio of tax payable to income) rises with income, regressive if it falls with income, and proportional if constant. A tax is said to be progressive if the tax burden (ratio of tax payable to income) rises with income, regressive if it falls with income, and proportional if constant. Use is made of household data, which is obtained from the Welfare Monitoring Survey (WMS). Given that there is no up-to-date survey, the WMS 1997 is used7.

6. THE IMPACT OF PERSONAL INCOME TAX AND VALUE ADDED

TAX ON GENDER

6.1 Gender Biases in Personal Income Tax (PIT)

Explicit biases are those which are expressed in provisions of the laws and regulations. The personal income tax may exhibit explicit gender discrimination because it is a tax on individuals or family units and can therefore easily show the different treatment between men and women This could be displayed for example in the rules governing the allocation of shared income, allocation of exemptions, deductions and other tax preferences, in tax rates, who files tax returns and who pays the tax. It is also in the personal income tax that tax liability is established with respect to the income of an individual or household (other taxes e.g. commodity taxes, trade taxes and corporate taxes may lead to implicit gender bias from their effect on household consumption choices, their income or industrial development patterns.

7 These results will be updated with data from an ongoing household survey when it is completed.

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6.1.1 PIT Filing

Under PIT, biases can exist both in cases of individual and joint filing. For individual filing, explicit biases might exist whereby exemptions are differentiated on the basis of gender and also marital status e.g. married men are entitled to family/married relief, but single mothers are not. On the other hand, joint filing may lead to the taxpayers entering a higher marginal tax bracket given that their joint income will be higher. The practice previously in Kenya was that a wife’s income was filed jointly with the husband’s. As of June 2005, married men and women now have the option of filing separately. Tax payable is computed separately (even with joint filing), which implies that a couple does not enter into a higher marginal tax bracket when their incomes are jointly assessed. There is therefore no implicit or explicit gender bias.

6.1.2 PIT Relief

Income tax credits are major instruments that governments all over the world use to achieve redistribution objectives (Karingi and Wanjala, 2005). One of the tax credits that are applied in Kenya is the income tax relief. The income tax system has been such that tax relief is provided to every registered income taxpayer irrespective of the level of income. The relief cannot therefore be said to be playing any significant role in terms of income redistribution even though it has been a useful instrument in providing income tax exemption for low-income earners. For instance, it was indicated that increases in tax relief resulted in the following number of taxpayers being removed from the tax net: 140,000 in 1987/88; 60,000 in 1989/90; 50,000 in 1992/93; 150,000 in 1993/94; 230,000 in 1994/95; 152,000 in 1997/98 and; 200,000 in 2000/01 (Table 3).

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Table 3

Personal Relief Reforms in Kenya

Personal Relief Approximate Number of

Individuals out of Tax Net1981/82 Married relief of Kshs. 1800 p.a., single relief of

Kshs.600 and a special single relief of Kshs.820 for singles with children.

1987/88 Single relief Kshs.960, family relief Kshs2400 and special single Kshs.1200.

140,000 employees

1989/90 Married relief Kshs.1980, single relief Kshs.660 and special single relief Kshs.920.

60,000 employees

1991/92 Single relief Kshs. 1,320, family relief 2,640 1992/93 Single relief Kshs.1,452, family relief Kshs. 2,904. 50,000 employees 1993/94 Single relief Kshs. 2,424, family relief Kshs. 3,636. 150,000 employees 1994/95 Single relief Kshs. 3,636, family relief Kshs. 5,460. 230,000 employees 1995/96 Single relief Kshs. 4,368, family relief Kshs. 6,552. 130,000 employees

1996/97 Personal relief combined into uniform personal relief of Kshs. 7,200 p.a.

140,000 employees

1997/98 Relief increased to Kshs. 7,920 p.a. 152,000 employees 1998/99 Relief increased to Kshs. 8,712 p.a. 1999/2000 Relief increased to Kshs. 9,600 p.a. 2000/2001 Relief increased to 11,520 p.a. 200,000 employees 2001/2002 Relief increased to 12,672 p.a. 2004/2005 Relief was increased to 13,944 p.a. Source: Budget Statements

Looking at the level of relief over time, a differentiated relief level depending on marital status was applied from independence until 1996/97 (Table 3). Three different types of relief were applied: married/family relief, single, and special single for singles with children. This meant that those single and single with children, bore a greater tax burden than married men. In addition, the relief was only available to married males but was not given to married women. The reasoning behind this was equal sharing within the household, so the higher relief for married men was assumed to benefit both the man and his wife. The single and special single were combined in 1990, which meant that being a single parent did not guarantee an individual a lesser tax burden as compared to those who were married. This reflected a gender bias as women are more likely to be single parents than men. This differentiated type of relief had a discriminatory element. While the level of relief was

22

increased over time to its current annual level of Kshs 13,944, the most significant reform was the unification of the single tax and the married (family) relief into one, which removed the discriminatory element.

6.1.3 PIT Burden

To estimate the tax payable and tax burden, the top margin of every tax bracket was used given that the breakdown of the income categories was not available. The implication of this approach is that the tax payable between Kshs. 2,000 and Kshs. 29,999 is over-estimated given the fact that several individuals earn less than the top margin. But this is counter-acted by the fact that the tax payable by the top bracket of 30% is not captured (therefore underestimated) because no information on income distribution above Kshs.30,000 is available. Despite these limitations, this approach provides an important insight into the size and distribution of the tax burden across income brackets and gender.

Tax payable per individual was computed using the available monthly tax brackets8 and information on tax relief9. For example, tax payable by an individual earning a monthly income of Kshs. 29,999 in 2003 is Kshs. 3,623.8, which is computed as [(0.1*9680)+((18800-9680)*0.15)+((27920-18800)*0.2)+((29999-27920)*0.25))-1056] (Table 5). Given the widening of the tax brackets and an increase in the tax relief, the tax payable on Kshs. 29,999 in 2004 is Kshs. 3,377, which is computed as [((0.1*10164)+((19740-10164)*0.15)+((29316-19740)*0.2)+((29999-29316)*0.25))-1162].

Two important points emerge from this analysis. First, the widening of tax brackets and at the same time increasing the level of tax relief reduces the amount of tax payable and consequently tax burden. This approach has been termed pro-poor given the approximate number of individual low-income taxpayers getting out of the tax net as a result of widening the brackets and also increasing the tax relief. However, the people benefiting from the relief are only those who pay the tax given that tax compliance is a problem in Kenya. Secondly, income tax in Kenya is progressive, as indicated by the trends in the tax burden. As earlier indicated, a tax is progressive if the relative tax burden increases with income. The tax burden in this case increases gradually from 4.7% for the 8,000-14,999 tax bracket to 12.1% for the 25,000-14,999 tax bracket in 2004. (Table 4).

8 Monthly tax brackets are: For 2003 (Kshs): 1-9680 @10%; 9681-18800 @ 15%; 18801-27920 @20%; 27921-37040 @ 25%; and >37040 @ 30%. For 2004 (Kshs): 1-10164 @10%; 10165-19740 @ 15%; 19741-29316 @20%; 29317-38892 @ 25%; and >38892 @ 30%. 9 Monthly tax relief is Kshs. 1056 for 2003 and Kshs. 1162 for 2004.

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Table 4

Approximate monthly income tax burden per income category

Monthly income tax payable per income category per individual (Kshs.)

Tax payable as a percentage of total income (Tax Burden)

2003 2004 2003 2004

<2,000 0.0 0.0 0.0 0.02,000 - 3,999 0.0 0.0 0.0 0.04,000 - 5,999 0.0 0.0 0.0 0.06,000 - 7,999 0.0 0.0 0.0 0.08,000 - 14,999 709.9 579.7 4.7 3.915,000 - 19,999 1,519.8 1,342.6 7.6 6.720,000 - 24,999 2,519.8 2,342.6 10.1 9.425,000 - 29,999 3,623.8 3,376.8 12.1 11.3>30,000 3,624.0 3,377.0 12.1 11.3Source: Authors Computation using data from Statistical Abstract

How does the income tax burden vary with gender in Kenya? It is an experience in most developing nations that the formal employment sector is male-dominated, Kenya being no exception. It therefore follows that men are expected to bear a greater direct income tax burden as compared to women (Smith (2000), Esim (2000)). Using the approximate tax payable per income category computed above, tax payable per sex category is computed as tax payable per individual per income category, multiplied by the number of employees per category.

The results show that males pay twice as much income tax as females in terms of income tax in Kenya, which is consistent with the literature (Table 5). This lends to an implicit gender bias and may have implications for intra-household resource allocations where the male is the main or only breadwinner. The discrepancy in the top bracket, if captured, would be greater given that top high paying jobs in Kenya are male-dominated. This is mainly attributed to the fact that males are also twice as much as the females in the formal sector employment.

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Table 5

Total monthly tax payable per gender category (Kshs.)

2003 2004

Male Female Male Female

<2,000 0.0 0.0 0.0 0.02,000 - 3,999 0.0 0.0 0.0 0.04,000 - 5,999 0.0 0.0 0.0 0.06,000 - 7,999 0.0 0.0 0.0 0.08,000 - 14,999 195,646,017.6 80,739,758.7 159,283,762.5 65,316,121.315,000 - 19,999 426,840,389.4 161,189,988.0 376,008,556.0 140,864,249.420,000 - 24,999 542,356,712.4 344,093,808.8 507,421,215.6 322,159,037.225,000 - 29,999 652,260,505.0 332,062,331.3 677,045,128.5 343,722,759.3>30,000 149,044,248.0 58,491,360.0 154,230,967.0 60,083,584.0Total 1,966,147,872.4 976,577,246.8 1,873,989,629.6 932,145,751.2Source: Authors Computation

There also exist discrepancies between the different sources of income by gender. Using the Welfare Monitoring Survey, households were categorised according to the gender of household head, region (rural or urban) and also per capita expenditure, which is classified into four quintiles. The sources of income as per household category are shown in Appendix 4. The results indicate that urban male-headed households’ major source of income is wage income, while urban female-headed households have a lower reliance on wage income. Agriculture on the other hand is the major source of income for rural male-headed households’ first and second expenditure quintiles, and also for the rural female-headed households first, second and third expenditure quintiles. The informal sector, capital and profits are more dominant sources of income in rural areas than urban areas. The domination of males in wage income in urban areas reflects the implicit gender bias in income tax, whereby males bear a greater income tax burden. The informal sector, agriculture and also majority of the rural areas’ activities fall in the tax net, but compliance has been very low (almost nil). This implies that households whose main source of income falls in these categories do not bear a greater tax burden than those in the formal sector.

6.2 Gender Biases in Value Added Tax (VAT)

While incidence analyses reflect a concern with how well the tax or expenditure system meets the ability to pay principle, most studies encounter complications due to the difficulties of isolating a change in one tax without taking into account the effect of other

25

taxes or expenditures. It is possible to measure the partial incidence of a particular but consideration has to be made on the difficulty of accurate income measurements. However, accurate tax incidence analysis is an important building block for an informed debate on issues of tax fairness (ITEP 2005). Implicit biases are likely to be found in indirect taxes, such as consumption taxes. Consumption taxes in Kenya have over time been widely referred to as taxes of the future given the fact that they are considered to have a less impact on investment, savings and paid income (Budget Statements, GoK). Looking at Kenya’s reform experience, there has been an objective of more reliance on indirect taxes, which are mainly consumption based. The only concern from a public finance perspective is that consumption-based taxes are considered to be highly regressive and therefore place a greater burden on the poor (Barnett and Grown, 2004).

6.2.1 Consumption Patterns

Gender biases in commodity taxes depend on consumption patterns. There is a general belief that women spend more of their income on basic services as opposed to men who spend most of their income on personal items such as alcoholic beverages and tobacco products. To analyse consumption patterns for Kenyan households, the Welfare Monitoring Survey (1997) is used. National consumption patterns are arrived at by weighting household data with the household weights. The results from the household survey indicate that while urban male-headed first expenditure quintile households spend about 42% of their income on agricultural food commodities, their female counterparts spend 43% (Appendix 6). In general, female-headed households spend a greater proportion of their incomes on basic commodities than male-headed households. For instance, female-headed households in the urban fourth quintile spend 32% of their income on health and their rural counterparts 8%, while male-headed households spend 4% and 0.5% of their income respectively. It is also observed that the proportion of income spent on agricultural commodities declines with the level of per capita expenditure. Also, rural households spend larger proportions of their income on education than their urban counterparts, with female-headed households spending a larger proportion than male-headed households.

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6.2.2 Computation of VAT burden

To be able to compute the VAT tax burden, the VAT schedule of exempt, zero-rated and taxable goods and services is used. According to the VAT schedule, the following commodities10 are exempt: grains, flours; roots, tubers; seeds, fruits, vegetables, meat, poultry, fish, livestock products; bread, newspapers, gas, kerosene, charcoal, financial services etc. Exempt supplies are divided into services including financial, insurance, public education and training, health (including veterinary), sanitary, agricultural and social welfare. Several goods are also zero-rated such as coffee, tea, cocoa; books, stationary and medicine. The analysis of VAT tax burden based on expenditure quintile shows that overall the VAT is progressive as a result of exemptions and zero rating of basic commodities. In the expenditure of the 1st quintile, 84% of the value of consumption is VAT exempt and another 3% is zero rated. Only 14% of the value of consumption is VATable. The share of VAT exempt consumption gradually declines to 76% for the second quintile, 70% for the third quintile and 54% for the fourth quintile. Additionally, the VATable share increases by expenditure quintile, from 14% for the first quintile to 22% for the second quintile, 26% for the third quintile and 42 % of the 4th quintile. The results are presented in chart 1.

Chart 1

VAT Tax Burden by expenditure Quintiles

83.52575.5

70.35

54.775

2.625 3 2.85 3.075

13.82521.525

26.85

42.15

0

10

20

30

40

50

60

70

80

90

1 2 3 4

Exenditure Quintiles

Exempt Zero-rated Vatable

Source: Authors computation from WMS 1997 10 These commodities only include those specified in the consumption basket under the WMS as purchased consumption, and not own consumption.

27

In addition, analysis of VATable commodities indicates that other manufactured food accounts for the largest share of VATable consumption, with male headed urban 1st expenditure quintile households having the largest share (37.5%), followed by female headed rural 1st expenditure quintile households (36%) (Appendix 5). Textiles also account for relatively larger shares as compared to commodities like beer, wines, other beverages, electricity etc.

Chart 2

VAT Taxable Proportions within Quintiles

Source Authors Computation from WMS 1997

1st Quintile Analysis

83.3 82.6 84.9 83.3

2.9 2.9 2.1 2.6

13.8 14.4 13 14.1

0

10

20

30

40

50

60

70

80

90

male rural fem rural male urban fem urban

Exempt Zero-rated Vatable

2nd Quintile Analysis

74.979.7

67

80.4

2.7 2.8 3.9 2.6

22.417.5

29.2

17

0

10

20

30

40

50

60

70

80

90

male rural fem rural male urban fem urban

Exempt Zero-rated Vatable

3rd Quintile Analysis

7268.5

75.9

65

2.6 2.4 3.4 3

25.529.2

20.7

32

0

10

20

30

40

50

60

70

80

male rural fem rural male urban fem urban

Exempt Zero-rated Vatable

4th Quintile Analysis

50.3

55.6

45.1

68.1

3.4 3.6 1.9 3.4

46.3

40.9

53

28.4

0

10

20

30

40

50

60

70

80

male rural fem rural male urban fem urban

Exempt Zero-rated Vatable

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These results are not surprising. The results demonstrate that indeed the VAT design has achieved one of the desirable characteristics of a tax system—progressivity. However, this overall assessment masks some important differences within each quintile which we now turn to. The analysis shows that within the first quintile male headed urban households enjoy the highest share of exempt goods at 84.9% while female headed rural households have the lowest share of exempt goods at 82.6%. Within this quintile, female headed rural households have the highest share of VATable consumption at 14.4 % while male headed urban households have the lowest VATable share at 13%. We find that although the quintile has a lowest tax incidence, there are differences within the quintile skewed in favour of male headed households. The tax incidence for the second quintile has some interesting results. Within this quintile female headed urban households have the highest share of VAT exempt consumption at 80.4%, followed by female rural counterparts with a 79.7% VAT exempt share (though this difference is not significant). Male headed urban households have the lowest share of VAT exempt consumption (67%) and therefore the highest share of VATable consumption (29.2%). The VAT incidence for the third expenditure quintile closely mimics the results for the first quintile, in that male headed households (urban and rural) enjoy the highest share of VAT exempt consumption at 75.9 and 72% respectively. The female headed urban households have the least share of exempt consumption and by extension the highest share of VATable consumption at 32% compared to 20.7% VATable for male headed households within this quintile. The fourth quintile shows some surprising results; the urban female headed households enjoy the highest share of exempt consumption even higher than the female headed households in the third quintile. Male headed households have the largest share of VATable consumption 53% and 46.3% for urban and rural respectively. Despite female-headed households having a greater proportion of their expenditure being exempt, they bear a higher final burden. This is due to the fact that females in general earn lower incomes than males (as earlier explained), which implies that the proportion of tax in their total incomes is likely to be higher than males.

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Chart 3

Share of VATable Consumption by Expenditure Quintiles

13 13.8 14.1 14.417 17.5

20.7 22.425.5

28.4 29.2 29.232

40.9

46.3

53

0

10

20

30

40

50

60

MU1 MR1 FU1 FR1 FU2 FR2 MU3 MR2 MR3 FU4 MU2 FR3 FU3 FR4 MR4 MU4

share of vatable consumption

Source: Authors’ computation from WMS 1997 Note: MU1 is Male-headed urban 1st quintile; MR1 is Male-headed rural 1st quintile; FU1 is Female-headed urban 1st quintile; FR1 is Female-headed rural 1st quintile; MU2 is Male-headed urban 2nd quintile; MR2 is Male-headed rural 2nd quintile; FU2 is Female-headed urban 2nd quintile, FR2 is female-headed rural 2nd quintile; MU3 is Male-headed urban 3rd quintile; MU3 is Male-headed rural 3rd quintile; FU3 is female-headed urban 3rd quintile; FR3 is female-headed rural 3rd quintile; MU4 is Male-headed urban 4th quintile; MR4 is Male-headed rural 4th quintile; FU4 is Female-headed urban 4th quintile; and FR4 is Female-headed rural 4th quintile.

Chart 3 shows the progressive nature of VAT. Gender differences in tax burden reflect the differences in spending patterns between male and female headed households. They also reflect gender differences in income levels. However, it should be noted that some of the unexplained discrepancies could be due to errors in the database. The tax burden is computed using the statutory tax rate. In 1997/98, the standard VAT rate was 17% and restaurants at 10%. The estimates of tax burden are shown in the Table 5. The results show that female-headed households in the fourth quintile, both rural and urban bear the greatest VAT burden as compared with their male-headed counterparts (Table 6). The statutory burden of female-headed households rural 4th expenditure quintile is 5.95% as compared to 1.77% for their male-headed counterparts.

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Table 6

VAT Tax Burden

Vatable

VAT burden using statutory rate (%)

Male rural 1st Exp quintile 0.11 Male rural 2nd Exp quintile 0.20

Male rural 3rd Exp quintile 0.66 Male rural 4th Exp quintile 1.77 Male urban 1st Exp quintile 0.04 Male urban 2nd Exp quintile 0.55 Male urban 3rd Exp quintile 0.26 Male urban 4th Exp quintile 0.39 Female rural 1st Exp quintile 0.17 Female rural 2nd Exp quintile 0.64 Female rural 3rd Exp quintile 2.26 Female rural 4th Exp quintile 5.95 Female urban 1st Exp quintile 0.27 Female urban 2nd Exp quintile 0.54 Female urban 3rd Exp quintile 2.12 Female urban 4th Exp quintile 4.47 Source: Authors Computation from WMS 1997

It can be noted that for all expenditure quintiles, female-headed households bear a greater VAT burden as compared to male-headed households. Despite female-headed households mainly having a greater proportion of their expenditure being exempt, they bear a higher final burden. This is due to the fact that females in general earn lower incomes than males (as earlier explained), which implies that the proportion of tax in their total incomes is likely to be higher than males. This represents an implicit gender bias. This corroborates the results discussed above that there exist a gender bias in VAT, with female-headed households bearing a greater VAT burden, implying that taxation is not gender neutral. 7. SUMMARY

Gender and taxation continues to form part of public finance policy debates worldwide, with growing concern that taxation is gender blind. This study has sought to contribute to the

31

debate by looking at the gendered impact of taxation in Kenya. There exists a gap in fiscal policy framework in Kenya whereby it is assumed that taxation is gender neutral. From the analysis, it has been shown that, despite numerous attempts to reform the tax system, implicit and explicit gender biases still persist. Significant progress has been made in eliminating explicit gender biases in tax relief through the unification of the single, special single and married relief, but explicit biases still exist in the form of joint filing of tax returns by couples (even though it is not mandatory). The fact that males dominate the formal labour market, especially the high-cadre positions, makes them to bear a greater personal income tax burden as compared to females. In terms of implicit taxation, analysis of the VAT case shows that despite the assumption that VAT is a tax of the future given its minimal impact on investment and savings, the tax has implicit biases. Female-headed households have been shown to bear a greater VAT burden than male-headed households in all expenditure cadres despite having higher exemption ratios. The results also indicate that overall VAT is progressive in nature. 8. CONCLUSION AND RECOMMENDATIONS

Given that the Kenyan government recognises the need for gender equality for poverty reduction, growth and development as envisaged in the development plans, these findings pose a policy challenge as the country works towards revamping the economy. Considerable progress has been made in reforming the tax system in terms of lowering marginal tax rates and exempting basic commodities. The income tax system focuses on income from formal employment. This means that income tax reforms must be made in tandem with developments in the labour market, as well as the informal sector in order to be gender equitable. For VAT exemptions to have a greater impact on gender equity, there is need to minimise the differences between levels of income between males and females. Therefore, a tax policy geared towards exemption of basic commodities, combined with a policy to improve female participation in the labour market would have a positive impact on gender equity in taxation as compared to implementation of tax policy alone. An attempt to achieve gender tax equity within each expenditure group might result in a very complex tax system with increasing administrative difficulties. Therefore an alternative is to identify target groups and compensate them for the higher tax burden through expenditure targeting.

32

It can also be noted that in any economy, there exists both the paid and the unpaid economy. While women devote most of their working time to the unpaid economy, men devote most of their time to the paid economy. With this disparity, the overall distributional effect of government policies that improve the conditions of the paid work over unpaid work can worsen gender inequality. This can be cited as a limitation of the study, and also as a weakness of the current fiscal policy framework given that unpaid work by women is not recognised in national accounting. Having said this, these recommendations are made somewhat cautiously, in light of the fact that information on critical areas such as tax payments, the labour market and consumption behaviour is minimal and/or out dated. This is a preliminary study on the impact of personal income and value added tax on gender. These research findings can be built upon by incorporating consumer behaviour in the analysis of incidence. There is certainly ample room and pressing need for gender analysis in the area of taxation. To this end, relevant data should be collected and research carried out. Some areas in need of information include sources of income, consumption and economic behaviour, employment trends and practices, time use and time series incidence VAT analysis. 10. STUDY LIMITATIONS

From the study it is evident that sex and gender disaggregated, and tax data are very limited. Lack of adequate databases on taxpayers and up-to-date Welfare Monitoring Survey, limited the comprehensive analysis of the subject matter. Availability of this data would enable sufficient tax policy analysis to advice formulation of tax policy, rather than implementation of ad hoc policies. There is great need to improve on existing databases for better policy formulation, implementation and analysis.

33

REFERENCES

Alvarez R. M., (1998), Gender and Tax, California Institute of Technology, Social Science Working Paper 1046 Barnett K., Grown C., (2004), Gender Impacts of Government Revenue Collection: The case for Taxation, Commonwealth Secretariat Bourguignon and Da Silva, 2003, ‘Evaluating Poverty and Distributional Impact of Economic Policies: A Compendium of Existing Techniques’, in ‘The Impact of Economic Policies on Poverty and Income Distribution, Evaluation Techniques and Tools’, Edited by Bourguignon F. and Da Silva, L.A.P. (2003), World Bank, Washington D.C. Budlender D., Sharp R., Allen K., (1998) How to do a Gender Sensitive Budget Analysis: Contemporary Research and Analysis, Australian Agency for International Development, Canberra and the Commonwealth Secretariat, London Esim S., (2000), Impact of Revenues on Poverty and Gender Equality: A Gender Analysis of Budgetary Process, ICRW. Freiler C., Stairs F., Kitchen B., with Cerny J., (2001), Mothers as Earners, Mothers as Carers: Responsibility for Children, Social Policy and the Tax System, Status of Women Canada Fullerton D. and G.E. Metcalf (2002), ‘Tax Incidence’, Handbook of Public Economics, Volume 4, Edited by A.J. Auerbach and M. Feldstein, Elsevier Science B.V. Government of Kenya, Budget Speeches, various issues.

Government of Kenya, Economic Survey, various issues. Government of Kenya, Kenya Gazette Finance Bill, various issues. Government of Kenya, Statistical Abstracts, various issues. GoK (2005), “Geographic Dimensions of Well-Being in Kenya: Who and Where are the Poor? A Constituency Level profile, Volume II., The Regal Press Kenya Ltd

34

________, (1996), Economic Reforms for 1996 - 1998, The Policy Framework Paper ________, (2002) Medium Term Expenditure Framework, Fiscal Strategy Paper, 2002/2003 - 2004/2005 _______ (2006), Post Mortem Analysis of the 2004/05 Budget Himmelweit S., (2002) Making Visible the Hidden Economy: The Case for Gender Impact Analysis of Economic Policy, Feminist Economics 8(1), 2002, 49-70 _______(Undated) Towards Gender Responsive Budgeting: The Tools for Budget Impact Analysis – Taxes and Benefits, Faculty of Social Science, Open University Institute of Economic Affairs (2004), Mainstreaming Gender in National Budgets – The Gaps in the Kenya Budget Process’, IEA, Nairobi, Kenya Institute on Taxation and Economic Policy (ITEP), (2005), Talking Taxes, Policy brief #23, 1311 L Street NW, Washington, DC 2005 Karingi S., and B. Wanjala, (2005), The Tax Reform Experience in Kenya, Research paper No. 2005/67, UNU-WIDER Karingi, S.N., B. Wanjala, A. Kamau, A. Mwangi, E. Nyakang’o and M. Muhoro (2004), ‘Fiscal Architecture and Revenue Capacity in Kenya’, KIPPRA Discussion Paper Series, DP/45/2004. Sahn D.E. and S.D. Younger (2003), ‘Estimating the Incidence of Indirect Taxes in Developing Countries’, in ‘The Impact of Economic Policies on Poverty and Income Distribution, Evaluation Techniques and Tools’, Edited by Bourguignon F. and Da Silva, L.A.P. (2003), World Bank, Washington D.C. Smith, T (2000), ‘Women and Tax in South Africa’, http://www.worldbank.org/wbi/publicfinance/documents/gender/smith.pdf Stotsky J. G., (1997) How Tax Systems Treat Men and Women Differently, Finance and Development,IMF Valodia I. and T. Smith (2004), ‘Gender and Taxation in South Africa’, conference paper, Engendering Macroeconomics and International Economics, June 20-22, 2004, University of Utah, Salt Lake City.

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Wagacha M., and R. Ngugi, (1999) Macroeconomic Structure and Outlook, in Kimuyu P., et al (eds) Kenya's Strategic Policies for the 21st Century, Institute of Policy Analysis and Research, Nairobi, Kenya. Were, M.; Kiringai, J., (2004) in Wanyeki, L.M, Patel, A., (Eds) in Gender Mainstreaming in Macroeconomic Policies and Poverty Reduction Strategy in Kenya; GTZ / African Women's Development & Communication Network (FEMNET), 2004

36

APPENDICES

Figure 1

Highest Pover ty Incidence in Urban Kenya

70 688 9

75 728 5

100

5766 68

8 972 6 9

8 599

6 0

0

20

40

60

80

100

120

Coast

North E

astern

Eastern

Central

Rift Valle

y

Western

Nyanza

Nairobi

Fe ma le he a de d house holds Ma le he a de d house holds

Highest Poverty Incidence in Rural Kenya

86

6876

45

6272

798368

76

42

6673

82

0102030405060708090

100

Coast

North E

astern

Eastern

Centra

l

Rift Valle

y

Western

Nyanza

Fe ma le he a de d house holds Ma le he a de d house holds

Lowest Poverty Incidence in Urban Kenya

38

61

36

720

67

9

3643

63

33

719

55

7

30

01020304050607080

Coast

North East

ernEast

ern

Central

Rift Valle

y

Western

Nyanza

Nairobi

Female headed households Male headed households

Lowest Poverty Incidence in Rural Kenya

32

61

35

19

31

4743

30

63

34

16

32

5346

0

20

40

60

80

Coas t No rthEas tern

Eas tern Central RiftValley

Western Nyanza

Female headed households Male headed households Source: GoK 2005

37

Appendix 1

Distribution of Wage Employment by Sex and monthly income groups

2002 2003 2004 Income Group Male Female Male Female Male Female<2,000 3,310 547 3,324 572 3,415 5622,000 - 3,999 13,531 5,749 13,611 5,780 13,918 5,8774,000 - 5,999 42,141 11,470 43,561 11,955 44,609 12,0646,000 - 7,999 159,001 27,600 163,023 28,855 162,826 28,0528,000 - 14,999 268,834 110,921 275,616 113,742 274,793 112,68215,000 - 19,999 277,721 104,687 280,853 106,060 280,060 104,91920,000 - 24,999 211,778 135,313 215,238 136,556 216,606 137,52225,000 - 29,999 179,135 91,508 179,996 91,635 200,502 101,791>30,000 40,527 15,885 41,127 16,140 45,671 17,792Total 1,195,978 503,680 1,216,349 511,295 1,242,400 521,261

*Excludes casual employees, unpaid family workers and unpaid directors

Source: Statistical Abstract 2004

Appendix 2

Personal Income Tax Brackets, 1974 - 2006

Year Annual Taxable Income (Kshs.)

Rate (%) Year

Annual Taxable Income (Kshs.)

Rate (%)

1974-78 1-24,000 10 1982 1-30,000 10 24,001-48,000 15 30,001-60,000 15 48,001-72,000 25 60,001-90,000 25 72,001-960,000 35 90,001-120,000 35 96,001-120,000 45 120,001-150,000 45 120,001-144,000 50 150,001-180,000 50 144,001-192,000 60 180,001-240,000 60 Over 192,000 65 Over 240,000 65 1986 - 1987 1 - 36,000 10 1988 - 1989 1 - 39,600 10 36,001 - 72,000 15 39,601 - 79,200 15 72,001 - 180,000 25 79,201 - 118,800 25 108,001 - 144,000 35 118,801 - 158,400 35 144,001 - 180,000 45 158,401 - 198,000 45 180,001 - 216,000 50 Over 198,000 65

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216,001 - 252,000 60 Over 252,000 65

Appendix 2 Personal Income Tax Brackets, 1974 – 2006 (continued)

Year

Annual Taxable Income (Kshs.)

Rate (%) Year

Annual Taxable Income (Kshs.)

Rate (%)

1990 - 1991 1 - 42,000 10 1992 1 - 46,000 10 42,001 - 84,000 15 46,001 - 92,000 15 84,001 - 126,000 25 92,001 - 138,000 25 126,001 - 168,000 35 138,001 - 184,000 35 Over 168,000 45 Over 184,000 45 1993 1 - 52,800 10 1994 1 - 60,000 10 52,801 - 105,600 15 60,001 - 120,000 15 105,601 - 158,400 20 120,001 - 180,000 20 158,401 - 211,200 25 180,001 - 240,000 25 211,201 - 264,000 35 240,001 - 300,000 35 Over 264,000 40 Over 300,000 40 1995 1 - 78,000 10 1996 1 - 78,000 10 78,001 - 156,000 15 78,001 - 156,000 15 156,001 - 234,000 20 156,001 - 234,000 20 234,001 - 312,000 25 234,001 - 312,000 25 312,001 - 390,000 35 Over 312,000 35 Over 390,000 37.5 1997 1 - 82,080 10 1998 1 - 90,240 10 82,081 - 164,160 15 90,241 - 180,480 15 164,161 - 246,240 20 180,481 - 270,720 20 246,241 - 328,320 25 270,721 - 360,960 25 328,321 - 410,400 30 360,961 - 451,200 30 Over 410,400 35 Over 451,200 32.5 1999 1 - 94,800 10 2000 1 - 104,400 10 94,801 - 189,600 15 104,401 - 208,800 15 189,601 - 284,400 20 208,801 - 313,200 20 284,401 - 379,200 25 313,201 - 417,600 25 379,201 - 474,000 30 Over 417,600 30 Over 474,000 32.5 2001 1 - 109,440 10 2002-2003 1 - 116,160 10 109,441 - 218,880 15 116,161 - 225,600 15 218,881 - 328,320 20 225,601 - 335,040 20 328,321 - 437,760 25 335,041 - 444,480 25 Over 437,760 30 Over 444,480 30 2004-2006 1-121,960 10 121,961-236,880 15 236,881-351790 20 351,791-466,700 25 Over 466,700 30 Source: Budget Statements and Finance Bills

39

Appendix 3

VAT Rates Rationalisation Process in Kenya

Year Number of Rates

Rates (%) Standard Rate (%)

1989/90 15 17 1990/91 9 0, 5, 18, 30, 45, 50, 80, 100, 150 18 1991/92 8 0, 5, 18, 25, 35, 50, 75, 100 18 1992/93 6 0, 3, 5, 18, 30, 50. 18 1993/94 4 0, 5, 18, 40. 18 1994/95 4 0, 5, 18, 30. 18 1995/96 4 0, 6, 15, 25. 15 1996/97 3 0, 8, 15 15 1997/98 3 0, 10, 17 17 1998/99 4 0, 10, 12, 16 16 1999/00 4 0, 10, 13, 15 15 2000/01 4 0, 10, 16, 18 18 2001/02 4 0, 10, 16, 18 18 2002/03 4 0, 10, 16, 18 18 2003/04 3 0, 14, 16 16

Source: Budget Statements

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Appendix 4

Sources of Income by gender, region and per capita expenditure

Household Category Wages Land Capital Profit Informal Agriculture Other Total

Male rural 1st Exp quintile 30.4 2.6 0.3 4.0 25.0 32.3 5.5 100.0Male rural 2nd Exp quintile 21.2 0.5 6.7 0.6 32.5 35.9 2.6 100.0Male rural 3rd Exp quintile 56.6 0.2 0.1 0.1 18.7 22.5 1.8 100.0

Male rural 4th Exp quintile 66.2 2.3 1.8 1.7 7.7 18.5 1.8 100.0Male urban 1st Exp quintile 77.5 0.0 0.0 0.0 0.0 0.0 22.5 100.0Male urban 2nd Exp quintile 77.5 22.5 0.0 0.0 0.0 0.0 0.0 100.0Male urban 3rd Exp quintile 27.9 0.0 0.0 0.0 44.5 3.2 24.4 100.0Male urban 4th Exp quintile 75.5 0.0 0.0 0.0 0.0 3.4 21.1 100.0Female rural 1st Exp quintile 23.4 1.3 0.0 0.4 10.2 63.2 1.5 100.0Female rural 2nd Exp quintile 24.0 1.3 0.0 2.9 13.4 57.1 1.3 100.0Female rural 3rd Exp quintile 23.8 4.0 0.0 0.2 23.5 46.7 1.7 100.0Female rural 4th Exp quintile 51.7 2.5 0.2 0.5 6.0 38.8 0.3 100.0Female urban 1st Exp quintile 23.0 0.0 0.0 0.7 2.3 16.6 57.4 100.0Female urban 2nd Exp quintile 18.9 0.0 0.0 0.0 4.9 64.5 11.6 100.0Female urban 3rd Exp quintile 40.8 0.0 0.0 0.0 4.0 14.9 40.2 100.0Female urban 4th Exp quintile 53.9 0.0 0.0 0.3 3.2 28.1 14.4 100.0

Source: Authors computation from WMS 1997

41

Appendix 5

VATable consumption proportions by comm

VATable

Male rural 1st Exp quintile

Male rural 2nd Exp quintile

Male rural 3rd Exp quintile

Male rural 4th Exp quintile

Male urban 1st Exp quintile

Male urban 2nd Exp quintile

Male urban 3rd Exp quintile

Male urban 4th Exp quintile

Beer 0.0 0.1 2.3 4.7 0.0 0.0 0.0 3.5Wines

0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0Other Beverages 0.0 0.1 0.1 0.2 0.0 0.0 0.0 0.0Cigarettes 3.1 2.1 19.2 1.3 0.8 1.9 0.3 0.0 Other manufactured foods 32.7 20.5 15.8 5.1 37.5 15.1 14.8 8.4Textiles 17.3 19.3 11.5 9.4 31.2 13.8 15.6 11.0Footwear 1.0 8.3 2.9 3.9 0.0 26.6 0.1 20.8Chemicals 14.3 8.0 7.3 3.7 20.6 7.0 7.0 3.5Machinery 4.4 2.3 2.0 19.8 0.1 3.1 2.4 2.4Other manufactures

6.0 3.7 4.7 3.7 2.1 5.2 2.5 4.1

Water 1.2 1.5 2.5 1.3 0.8 3.8 3.8 1.0Electricity 0.2 0.1 1.3 3.7 0.0 0.0 0.0 14.2Trade 1.9 1.3 1.2 7.8 0.2 2.7 0.2 4.7Hotels 2.7 2.9 2.6 4.4 3.8 3.9 6.1 5.4Transport 9.0 20.3 10.4 6.0 2.5 11.0 31.2 5.4Restaurants 1.4 4.1 12.6 16.0 0.0 4.1 10.8 3.6Other services 4.7 5.4 3.7 8.8 0.5 1.7 5.1 12.1Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Authors Computation from WMS 1997

odities (%)

Female rural 1st Exp quintile

Female rural 2nd Exp quintile

Female rural 3rd Exp quintile

Female rural 4th Exp quintile

Female urban 1st Exp quintile

Female urban 2nd Exp quintile

Female urban 3rd Exp quintile

Female urban 4th Exp quintile

1.1 0.8 2.5 2.2 0.0 0.7 0.4 5.3

0.0 0.1 0.0 1.8 0.0 0.0 0.0 0.00.2 0.1 0.2 0.0 0.1 0.2 0.1 0.01.8 1.9 1.3 1.3 1.8 1.6 1.0 1.1

36.0 23.0 13.9 7.2 30.5 22.1 12.4 10.314.6 19.4 14.3 19.3 13.1 15.7 10.3 15.72.7 5.2 5.3 9.8 4.2 16.1 2.8 16.2

13.8 10.2 6.5 3.0 13.8 11.2 5.6 3.52.9 2.7 2.2 7.2 0.8 3.8 3.0 3.5

6.2 5.1 6.6 3.3 2.8 4.7 2.7 2.40.6 1.1 2.9 2.4 0.6 4.4 2.4 1.30.0 0.1 0.2 3.3 0.2 1.2 1.1 0.71.8 1.3 4.6 2.5 4.9 1.7 6.9 4.52.7 7.7 3.5 5.7 18.5 4.1 6.7 5.69.7 13.3 16.8 3.9 5.8 6.7 15.3 6.81.7 3.3 12.0 19.7 1.8 2.8 25.2 15.54.2 4.8 7.2 7.4 1.3 3.1 4.0 7.6

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

42

Appendix 6 Consumption Patterns by Gender

Household Category

Male rural 1st Exp quintile

Male rural 2nd Exp quintile

Male rural 3rd Exp quintile

Male rural 4th Exp quintile

Male urban 1st Exp quintile

Male urban 2nd Exp quintile

Male urban 3rd Exp quintile

Male urban 4th Exp quintile

Female rural 1st Exp quintile

Female rural 2nd Exp quintile

Female rural 3rd Exp quintile

Female rural 4th Exp quintile

Female urban 1st Exp quintile

Female urban 2nd Exp quintile

Female urban 3rd Exp quintile

Female urban 4th Exp quintile

Agricultural products 41.75 39.13 38.77 25.44 54.57 28.61 28.21 26.49 42.69 39.94 34.90 25.39 42.99 40.03 36.26 18.38 Fishing, forestry &wood 3.67 3.15 1.62 1.17 3.25 4.29 2.42 1.70

3.92 3.43 1.73 1.46 2.22 2.40 1.60 1.06 Grain milling 12.94 8.62 10.84 4.21 12.44 4.55 1.19 5.30 10.82 11.75 10.35 5.06 15.45 14.59 6.66 5.16 Bakery 10.44 8.37 8.25 4.09 11.54 8.70 6.17 4.33 10.33 8.42 7.90 4.77 9.08 8.43 7.49 4.19 Beverages 4.29 4.03 8.99 6.27 2.10 3.97 2.29 4.68 4.30 4.32 3.30 5.20 3.93 3.86 3.80 4.87 Other food manufactures 6.62 6.01 4.75 2.74 5.92 7.53 4.20 4.63 7.50 5.73 4.65 3.20 6.32 5.60 5.04 3.21 Petroleum 2.08 2.85 3.01 5.47 0.92 2.12 1.44 11.10 2.04 1.89 2.68 2.94 2.57 1.66 2.15 1.81 Textiles & foot wear 3.15 6.25 3.86 6.92 3.51 17.27 4.06 23.03 3.73 4.59 7.24 10.39 3.02 6.73 4.01 7.85 Printing 1.29 0.78 0.93 1.40 0.46 2.81 1.89 0.33 1.19 1.28 0.83 1.30 1.35 0.85 0.97 1.64 Chemicals 2.73 2.14 2.13 1.52 2.94 2.78 1.95 0.74 2.70 2.42 2.09 1.38 3.65 2.53 2.18 1.06 Machinery 0.74 0.56 0.59 5.09 0.02 0.82 0.71 1.36 0.52 0.67 0.78 3.36 0.17 0.76 1.29 1.88 Other manufactures 1.10 0.98 1.51 1.95 0.56 2.36 0.56 0.83 1.15 1.19 2.01 1.35 0.49 1.04 1.11 0.74 Water 0.26 0.55 0.66 0.59 0.11 1.56 0.70 0.23 0.11 0.21 0.96 0.91 0.22 0.78 0.75 0.43 Electricity 0.04 0.01 0.22 1.53 0.00 0.00 0.00 2.44 0.00 0.02 0.05 1.22 0.04 0.21 0.27 0.16 Trade & hotels 0.83 1.02 1.25 5.19 0.54 2.20 1.62 5.25 0.84 2.11 2.56 4.05 3.59 1.47 3.17 2.42 Transport 1.57 6.99 2.73 3.83 0.32 4.81 8.19 1.39 1.73 2.82 5.51 2.03 1.10 1.65 6.91 2.61 Financial services 0.05 0.09 0.16 2.78 0.00 0.00 0.22 1.07 0.06 0.46 0.24 1.28 0.04 0.27 0.11 2.44 Housing 0.38 1.73 3.98 6.98 0.00 1.72 2.71 1.29 0.35 1.16 4.53 8.27 0.84 0.54 9.86 4.90 Other services 0.82 1.12 1.09 4.37 0.07 0.61 0.98 1.92 0.80 1.04 2.07 3.11 0.25 0.65 1.39 2.16 Health 2.59 1.98 2.39 4.01 0.22 2.36 19.85 0.54 2.36 2.49 1.63 8.18 0.73 3.46 1.17 31.60 Education 2.64 3.64 2.27 4.46 0.53 0.91 10.63 1.33 2.87 4.03 3.99 5.15 1.94 2.53 3.80 1.44

Total 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Source: Authors computation from WMS 1997

43