A

INEQUALITIES IN KENYA: IMPLICATIONS FOR DEVELOPMENT

AND REVENUE SHARING

by

John Thinguri Mukui

Paper prepared for a workshop on “Financing for a Fairer and Prosperous Kenya”, Naivasha, 27-28

June 2012 The United Nations Millennium Campaign Africa Office, Nairobi,

facilitated the workshop

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INEQUALITIES IN KENYA: IMPLICATIONS FOR DEVELOPMENT AND REVENUE SHARING1 “In God we trust, all others bring data” – William Edwards Deming (1900-1993), cited in Friedman,

Hastie and Tibshirani (2008) 1. INTRODUCTION The Constitution provides a unique opportunity and new impetus for the Government to refocus and reengineer its efforts on equity and poverty reduction. The challenge is to develop the necessary policy and legislative instruments that will put that into practice. A prerequisite for doing so is to build consensus on the meaning of equity in resource allocation and to establish rigorous monitoring processes to demonstrate the extent to which this is achieved. The Constitution established the Equitable Share of national revenue allocated to county governments, which was to be not less than 15% of all revenue collected by the national government. The Constitution also established an Equalization Fund equivalent to 0.5% of the national revenue “to provide basic services including water, roads, health facilities and electricity to marginalized areas to the extent necessary to bring the quality of those services in those areas to the level generally enjoyed by the rest of the nation, so far as possible”. Article 202(2) of the Constitution adds that county governments may be given additional allocations either conditionally or unconditionally. There is also emphasis on use of own-revenue sources in order to create hard budgetary constraints that will result into fiscal responsibility and proper planning (Aden, 2011) – see also Wildasin (2004) and Tanzi (1996). Failure to emphasize own-revenue generation may increase the overall size of Government, which is consistent with theoretical arguments drawn from welfare economics and positive political economy (Rodden, 2003). However, the Constitution is vague as to the nature of sub-county decentralization and the role of existing local authorities (Boex and Kelly, 2011). The Commission on Revenue Allocation (CRA) proposed a simple formula for allocation of the Equitable Share among the 47 Counties based on the following percentage weighting: population (60), basic equal share (20), level of poverty (12), land area (6) and fiscal performance (2)2. The Constitution (section 216) requires CRA to make recommendations concerning the basis for the equitable sharing of revenue raised by the national government, although the level of detail expected in the CRA recommendations is open to varied interpretation. The constitution appears to define equity on the basis of equal opportunity (a person’s life achievements should be determined primarily by his or her talents and efforts, rather than by predetermined circumstances such as race, gender, or social and family background) and avoidance of deprivation in outcomes, particularly in health, education, and consumption levels (see, especially World Bank, 2005).

1 I am grateful to Joanne Bosworth, Godfrey Ndeng’e, Michael Chege, Germano Mwabu, Albert Mwenda, Phyllis N. Makau and Leonard Obidha for comments on an earlier draft of the paper. 2 The final CRA recommendations to Parliament dated 8 August 2012 changed the percentage weighting to population (45), basic equal share (25), poverty gap (20), land area (8) and fiscal responsibility (2), and amended the poverty parameter from poverty headcount index to poverty gap index. CRA also placed minimum and maximum contribution of a county to total land area at 1% and 10%, respectively.

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The distributive theories of justice have two main strands: (a) responsibility based on individual preferences (Rawls, 1971; Dworkin, 1981), and (b) Sen and Roemer (which combines determinism and responsibility, as individuals are not always responsible of their preferences). The “Equality of Opportunity principle” by Roemer (1996, 1998) is an extension (or criticism) of Sen’s capability approach (1985, 1992), as it considers individual responsibility. In particular, in Rawlsian theory, individuals are not considered responsible of the position they have in society, and thus no premium is granted since it is an outcome of natural abilities that are randomly allocated to human beings (Rawls, 1971). Sen’s (1980) capability approach was essentially to alleviate the drawback of Rawls’s theory by considering human diversity. In the methods of compensation, Rawls and Dworkin imply resource equality, while Sen and Roemer would advocate equality of opportunity. In terms of social prescriptions, equalization of opportunity a la Roemer is more focused on the correction of inequalities, while Sen’s capability aims at preventing them at the beginning (see Arneson, 1989; Hausman and McPherson, 1993; Blake and Risse, 2008; and Tungodden, 2008, for a summary of the discussion). The purpose of this brief is to highlight the nature of inequalities in Kenya and their implications for growth strategies, poverty eradication and revenue sharing. The purpose is to stimulate debate but not to offer any specific recommendations on the CRA formula. The paper was expected to also summarize existing knowledge/experience on resource allocation, and how this relates to inequality in development outcomes with reference to devolution. However, the latter was dropped since most of the existing literature on revenue sharing in other countries rarely relates the formula with the allocation of functions between national and devolved governments, which makes it difficult to judge their immediate relevance to Kenya. In addition, Kenya is not moving towards a federal system but the state is only ceding some powers and functions to the counties while retaining primary responsibility for provision of some basic services e.g. education and security. However, there may be need to redefine the land area criterion so that it is based on the square root of a county’s land area as this is a more realistic indicator of relative cost of providing services within geographical domains. 2. ECONOMIC AND SOCIAL RIGHTS UNDER THE CONSTITUTION The Constitution states that every person has the right to (a) the highest attainable standard of health, which includes the right to healthcare services, including reproductive healthcare; (b) accessible and adequate housing and to reasonable standard of sanitation; (c) be free from hunger and to have adequate food of acceptable quality; (d) clean and safe water in adequate quantities; (e) social security; and (f) education. Sections 52 to 57 of the Constitution recognize specific application of rights with respect to children, persons with disabilities, minorities and marginalized groups, and older members of society. It is important to note that high inequality will hinder progress towards meeting constitutional rights and undermine progress on achievement of Millennium Development Goals and economic growth (Alesina and Rodrik, 1994; Barro, 2000; Bénabou, 1996; Galor, 2011; Galor and Zeira, 1993; Perotti, 1996; Persson and Tabellini, 1994; Gituto, 2007), while poverty provides a mechanism for intergenerational inequality through low human capital among the poor. Poverty is also associated with high fertility so that the poor are more represented in the next generation.

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As observed by Gertler (2004), one of the greatest tragedies of extreme poverty is its intergenerational transmission: children who grow up in poor families tend to be in poorer health and have lower levels of education. They thus enter adulthood without the basic capabilities necessary to take advantage of labor market opportunities to pull themselves out of poverty and to enjoy an acceptable quality of life. In particular, child poverty has a permanent impact on children, especially on their mental, physical, emotional and spiritual development (Minujin, Delamonica, Davidziuk and Gonzalez, 2006; UNICEF, 2011). In turn, growth theory posits that human capital accumulation can replace physical capital accumulation as a prime engine of growth along the process of development (Galor and Moav, 2004). There is therefore need for Kenya to generate a programmatic definition of child poverty, and set guiding principles for public policy and targets for the elimination of child poverty (see, for example, Corak, 2005). Section 20 of the Constitution states that “every person shall enjoy the rights and fundamental freedoms in the Bill of Rights to the greatest extent consistent with the nature of the right or fundamental freedom”. A pervasive issue in measuring achievements in economic rights is the denominator problem, or the extent to which the service rendered cover the potential need for the service in a community. The most pervasive denominator problems are caused by errors in population estimates (census) and the differing needs based on, say, epidemiology of a particular disease e.g. malaria ecology. In epidemiological research, the denominator problem also manifests itself through incomplete information or lack of reliable methods of estimating non-attenders3. The denominator problem may have an effect on the perceived achievement of national development goals that are population-based, since the numerator is derived from administrative records (e.g. voter registration and gross enrolment by school cycle). An overestimation of the population is expected to lead to understatement of achievement, and vice versa. 3. SHARING OF ROLES BETWEEN NATIONAL AND COUNTY GOVERNMENTS The main functions of County governments with large budgetary implications are: • Agriculture, including crop and animal husbandry, livestock sale yards, county abattoirs, plant

and animal disease control, and fisheries; • County health services, including county health facilities and pharmacies, promotion of

primary healthcare, veterinary services (excluding regulation of the profession), and solid waste disposal;

• Control of air pollution, noise pollution, other public nuisances and outdoor advertising; • County transport, including county roads, street lighting, traffic and parking; • Trade development and regulation, including markets, local tourism and cooperative societies; • County planning and development, including land survey and mapping, housing, and

electricity and gas reticulation and energy regulation; • Preprimary education, village polytechnics, home craft centers and childcare facilities; • Implementation of specific national government policies on natural resources and

environmental conservation, including soil and water conservation, and forestry;

3 The denominator problem also affects other professions. In law, two issues in the denominator problem are: (a) obscenity cases, which require that the offensiveness of a particular work be measured against some “community standard” (Young, 2005); and (b) compensation by Government for partial use of private property, where the “numerator” is the value or rights “taken” by government action and the “denominator” is the entirety of the owner’s property (Alperin, 2001).

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• County public works and services, including storm water management systems in built-up areas, and water and sanitation services;

• Fire fighting services and disaster management. The county roles are therefore mainly in human and animal health, agriculture, country transport, and public works. The CRA formula needs to be evaluated within the functions enumerated above, and noting that it does not include all funding windows (national budget, Equalization Fund and vertical funds by development partners). In particular, it is important to review per capita allocations from the Equitable Share and Equalization Fund jointly rather than piecemeal, since both funding windows have overlapping functions with respect to narrowing economic disparities within and among counties and affirmative action towards disadvantaged areas and groups. Section 187 of the Constitution states that a function or power of government at one level may be transferred to a government at the other level by agreement between the governments if “the function or power would be more effectively performed or exercised by the receiving government”, but with a rider that “constitutional responsibility for the performance of the function or exercise of the power shall remain with the government to which it is assigned by the Fourth Schedule”4. Section 187 of the Constitution therefore introduces the principle of subsidiarity so that activities are locally relevant and globally coherent (see Gelauff, Grilo and Lejour, 2008; and Føllesdal, 1998). The subsidiarity principle, borrowed from the Catholic social teaching as developed in the papal encyclicals Rerum Novarum (1891) and Quadragesimo Anno (1931), is a principle of organization in social, economic, environmental and political fields that espouses the need to address issues at the most appropriate level (Barrett, Mude and Omiti, 2007). This tenet holds that nothing should be done by a larger and more complex organization if it can be done as well by a smaller and simpler organization. When the need in question cannot be adequately met at the lower level, then it is not only necessary, but also imperative that higher levels of government intervene. The concept of subsidiarity is especially relevant in environmental management because of the linkages between individuals and the global consequences of their actions, and the fact that rules developed at one level (e.g. in international regimes) must be adapted to conditions in a wide variety of regional or local environments (see, for example, Ribot, 2003, on multiple accountability measures; and Marshall, 2008, on community-based approaches to environmental management under nested governance systems). In 1969, Parliament enacted the Local Government (Transfer of Functions) Act 1969 whereby the Government transferred to itself local authority functions in relation to primary education, public health and roads. However, the Act did not affect municipal councils, of which only Nairobi, Mombasa, Nakuru, Kisumu, Kitale and Eldoret were then in existence. The Local Government (Transfer of Functions) Act expired on 31 March 1970; and was replaced by Legal Notice No. 41 of 1970 which amended the relevant enabling legislation specific to health (Public Health Act, Malaria Prevention Act, and Food, Drugs and Chemical Substances Act), education (Education Act), and relevant sections of the Local Government Regulations of 1963. Municipal councils are the only local authorities which provide services in education and health sectors (Colebatch, 1973; Sheffield et al, 1974; Court and Kinyanjui, 1980; World Bank, 1992; Smoke, 2001; Mukui et al, 4 A county can also use the provision to repudiate a function assigned to it by the Constitution if the funding for the function would reduce the quality and coverage of service compared to what is normally provided through direct implementation by the national government.

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2002). There is need for clarity on functions vested in municipalities designated as counties (Nairobi and Mombasa) and municipalities nested within counties (e.g. Kisumu), and its relationship with national and county budgets. 4. THE CRA FORMULA The CRA formula appears to be based on the need to increase access to services under County governments, ensure equity in access to quality services with special attention to underserved populations and areas, avoid interruption to service delivery in any part of the country (“hold harmless”), and ensure that growth objectives are not adversely affected by equity considerations. However, as shown in Table 1 below, the criteria may not be straightforward. It is important to ensure that the allocation criteria do not generate “perverse incentives” (unintended and undesirable results) on performance5. For example, a county that pursues strategies that are not poverty reducing could receive more allocations in the future. Space as defined by a Country boundary and budget may not coincide with preferred (or traditional) healthcare service points for some individuals and communities (partly based on access costs borne by the users), while the bona fide “owner” of transit populations in need of emergency medical care may also be contestable. There may be differences between geographically defined space and conception of space as a dimension of social practice, especially because a social service (school, hospital, church, market centre) is normally immovable while the service seekers are mobile (Raper, 2000; Sanders, 2007). Other perverse incentives include: • The risk to the national statistical system especially on indicators used as criteria for resource

allocation e.g. poverty and population; • Migration (population arbitrage) to neutralize some of the differences in per capita allocations

(Wildasin, 1997; Epple and Romer, 1991; Hercowitz and Pines, 1991); • A county free-riding on the national health budget through underperformance of lower-level

health facilities; and • Recurrent cost problem of infrastructure funded by County governments but maintained by the

national government, and vice versa (Over, 1981; Agbonyitor, 1998; USAID, 1982). For example, Bagaka (2008) observes that the Constituency Development Fund (CDF) has 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 recurrent cost problem is a “prisoner’s dilemma” game where the solution is cooperation between national, county and lower levels of government involved in project selection and implementation. There can be varied interpretation of roles of national and county governments. For example, “disaster management is listed as a function of both levels, which could lead to confusion and poor coordination when people are most in need” (Leonard, 2011). Droughts are also different from rapid-onset disasters and require management that has far more in common with sustainable development than with disaster response, but the resilience component and disaster response may

5 The term “perverse incentives” is traced to Edgar Allan Poe in his short story “The Imp of the Universe” (1845).

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fall under different budgets. The likelihood and impact of pervasive incentives can be minimized through appropriate but nonintrusive checks and balances. The issue of entitlement to benefits from a natural resource whose benefits are shared nationally or benefit more than one county has no rival in confusion and obscurity. Examples include the oil find in Turkana, and the community sharing of benefits from the Masai Mara Game Reserve. Other contestable issues are water resources, need for urban areas to compensate communities downstream for polluting surface water, and price-based transfers. An example of the latter is adverse long-term changes in terms of trade between livestock and grains, which may have progressively driven pastoralists out of their traditional occupations into cultivating marginal lands and into camps for food aid, with the attendant environmental consequences (see Swift, 2006). There is even less clarity on (a) the appropriate formula for the distribution of devolved revenues among localities in a county (horizontal revenue sharing), and (b) share of nationally collected mineral revenue that should be returned to a mineral-bearing county on the principle of derivation and compensation for ecological risks of mineral production (see, Ojo, 2010, for the Nigerian case). Table 1: Relation between Allocation Criteria and Cost of Service Provision Criteria Factors/Assumptions Influencing Cost of Service Provision Population • Homogeneity of needs for each service provided;

• The denominator problem with respect to day population (the base population for some services) and night population (enumerated in censuses). This is especially the case between Nairobi and neighboring dormitory towns located in other counties e.g. Ongata Rongai, Kitengela, Ngong’, Athi River, Githurai, Wangige, Kikuyu and Kiambu whose economies depend largely on Nairobi city’s influence (Kingoriah, 1980; Mukui, 2002);

• Some county populations being served in neighboring counties due to proximity of services;

• The bona fide “owner” of transit populations in need of emergency medical care (and a county preferentially assigning resources to health facilities that do not serve transit populations);

• Private provision of essential services e.g. healthcare and education Basic Equal Share

• Equal administrative costs in diverse jurisdictions regardless of land area and terrain, and population total and its distribution within the county

Levels of Poverty

• Poverty measurement and its updating over time; • Depth of poverty

Land Area • Terrain and distribution of human settlements; • Uninhabited land area e.g. forests, national parks and game reserves; • Homogeneity of a county between arable and non-arable land; • Whether land area is an appropriate proxy for distance to (or cost of) services

Fiscal Performance

• Robust indicators of fiscal performance; • No service delivery standards especially on gender and specific needs of women e.g.

maternal health General concerns

• Omission on sharing of Equalization Fund among eligible counties, and therefore total resource envelope;

• Clarity on roles between national and county governments; • Lack of clarity on micro Nile-basin issues (petroleum, water catchments, coal) –

conservation and utilization of natural resources, and cost of abatement for pollution

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Criteria Factors/Assumptions Influencing Cost of Service Provision especially to surface water;

• Recurrent cost problem – “the care and feeding of a gift horse” – of infrastructure funded by county governments but maintained by the national government, and vice versa6

5. THE NATURE OF INEQUALITIES IN KENYA 5.1 Analytical Methodology The measures of inequality include money-metric measures, human capital (e.g. education and health), access to amenities (e.g. potable water), and social indicators (e.g. child nutrition). The abovementioned indicators refer to populations that survived. Consequently, the brief includes demographic indicators (e.g. under-five mortality, life expectancy at age five to avoid crossover effects with under-five mortality rate, and neonatal mortality and its share in under-five mortality) in order to highlight those who failed to thrive due to a variety of factors. A proper understanding of gender inequality requires information on position and condition of women in relation to men. ‘Position’ normally refers to leadership at public, community and household levels, while ‘condition’ includes factors that can be compared between men and women (e.g. educational attainment) and those that cannot (e.g. reproductive health). Although most of the measurable factors may have low gender gaps (e.g. primary school enrolment), Kenya’s achievements are generally low in incomparable indicators e.g. reproductive health (antenatal and peri-natal care). A focus on comparable indicators may have led to poor condition of women’s specific needs. The main sources of information on poverty and inequality are the Kenya Integrated Household Budget Survey (KIHBS) 2005/06, Kenya Demographic and Health Survey 2008-09, and the 1999 Population and Housing Census. Due to the poor performance of maternal health indicators nationally and within regions, there will be need for special mention of delivery care (these indicators have not changed for two decades). The use of a county’s indicators implies some level of homogeneity within a county, which in most parts of Kenya is not likely to be the case. For example, Narok imputed county poverty rate (33.8%) is the weighted average of Trans Mara (51.7%) and Narok (27.2%), while that of Siaya County (35.3%) is the average of Bondo (25.0%) and Siaya (40.0%). In the case of counties bordering Nairobi, service delivery may be procured from a neighboring county and vice versa; while the share of private sector provision of essential services (e.g. healthcare and education) is hardly known or uniform across counties. The KIHBS 2005/06 presents data by district, while the Fact Sheet prepared by the Commission on Revenue Allocation gives data by county. A simple approximation of a county’s poverty rate is the constituent districts’ poverty rates weighted by the respective district’s share of county population

6 The reference may have its origins in the expression ‘don’t look a gift horse in the mouth’, a phrase which appears in John Heywood’s “A dialogue conteinyng the nomber in effect of all the prouerbes in the Englishe tongue” (1546) and St. Jerome’s Letter to the Ephesians (circa AD 400) as ‘never inspect the teeth of a given horse’. The recurrent cost problem is analogous to the relatively higher burden of caring and feeding an old gift horse.

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expressed in adult equivalents. As shown in Table 2, there are discrepancies between the imputed KIHBS values and CRA estimates, and the county analysis below is based on the former. The 2009 population figures reported in CRA Fact Sheet and KNBS website for Kericho/Bomet, Bungoma/Busia and Homa Bay/Migori/Kisii counties also differ due to redistribution within each of the three population clusters. The two sources also report different figures on land area for Meru, Homa Bay, Migori, Kisii, Kericho, Bomet, Kakamega, Vihiga, Bungoma and Busia counties. 5.2 Prevalence of Poverty According to KIHBS 2005/06, the proportion of the rural population below the poverty line was 49.1%, with the lowest in Central province (30.4%), followed by Nyanza (47.6%), Rift Valley (49.0%), Eastern (50.9%), Western (52.2%), Coast (69.7%) and North Eastern province (73.9%). The averages for Central and Nyanza provinces lie below the rural national head count ratio while the rest of the provinces lie above it. As shown in Figure 1, the mountain of absolute poverty starts at 11.6 points above sea level (with height normalized to 100) in Kajiado, with a gentle slope in the middle but with a steep slope over the arid and semi-arid districts before reaching its peak at Turkana at 94.9 points. The halfway point (53.3) lies between Bungoma (51.4) and Elgeyo/Marakwet (55.7). By the time the mountaineer is three quarters uphill at 74.1 points, he will be somewhere between Samburu (73.5 points) and Kwale (74.7 points). The last quarter expressed in height above sea level is arid and rather steep. Table 3 also shows the distribution of the population in adult equivalents and the contribution of each regional domain in the poverty measure. Where the contribution of the poverty measure is higher than the region’s contribution to total population, the region has a higher measure of poverty than the national mean. For example, Central province’s share of population is higher than its contribution to the measure of poverty, while those for Coast and North Eastern provinces are all above their respective population shares because Coast and North Eastern provinces are disproportionately represented among the poor.

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Table 2: Prevalence of Absolute Poverty by County, 2005/06 (%) COUNTY Population

(2009) - CRA

Population (2009) -KNBS

Population CRA-KNBS

CRA Poverty rate (%)

KIHBS rate (%)

CRA-KIHBS

(%)

Land area km2

Land area

County share (%)

Diameter County

share (%)

1 2 1 minus 2 3 4 3 minus 4

5 6 7

Mombasa 939,370 939,370 0 37.6 37.6 0.0 219 0.038 0.344Kwale 649,931 649,931 0 74.9 74.7 0.2 8,270 1.423 2.113Kilifi 1,109,735 1,109,735 0 71.4 70.7 0.7 12,610 2.169 2.609Tana River 240,075 240,075 0 76.9 76.9 0.0 38,437 6.612 4.556Lamu 101,539 101,539 0 32.7 32.9 -0.2 6,273 1.079 1.840Taita/Taveta 284,657 284,657 0 54.8 57.2 -2.4 17,084 2.939 3.037Garissa 623,060 623,060 0 49.2 49.7 -0.5 44,175 7.599 4.884Wajir 661,941 661,941 0 84.0 84.3 -0.3 56,686 9.751 5.533Mandera 1,025,756 1,025,756 0 87.8 89.1 -1.3 25,991 4.471 3.746Marsabit 291,166 291,166 0 83.2 83.7 -0.5 70,961 12.207 6.190Isiolo 143,294 143,294 0 72.6 71.3 1.3 25,336 4.358 3.699Meru 1,356,301 1,356,301 0 28.3 27.8 0.5 6,936 1.193 1.935Tharaka-Nithi 365,330 365,330 0 48.7 48.9 -0.2 2,639 0.454 1.194Embu 516,212 516,212 0 42.0 41.8 0.2 2,818 0.485 1.234Kitui 1,012,709 1,012,709 0 63.5 63.1 0.4 30,496 5.246 4.058Machakos 1,098,584 1,098,584 0 59.6 58.8 0.8 6,208 1.068 1.831Makueni 884,527 884,527 0 64.1 64.3 -0.2 8,009 1.378 2.080Nyandarua 596,268 596,268 0 46.3 46.1 0.2 3,245 0.558 1.324Nyeri 693,558 693,558 0 32.7 31.3 1.4 3,337 0.574 1.342Kirinyaga 528,054 528,054 0 25.2 24.9 0.3 1,479 0.254 0.894Murang’a 942,581 942,581 0 29.9 30.4 -0.5 2,559 0.440 1.176Kiambu 1,623,282 1,623,282 0 27.2 26.6 0.6 2,543 0.437 1.172Turkana 855,399 855,399 0 94.3 94.9 -0.6 68,680 11.815 6.090West Pokot 512,690 512,690 0 69.8 68.5 1.3 9,169 1.577 2.225Samburu 223,947 223,947 0 73.0 73.5 -0.5 21,022 3.616 3.369Trans Nzoia 818,757 818,757 0 50.2 49.5 0.7 2,496 0.429 1.161Uasin Gishu 894,179 894,179 0 51.3 49.7 1.6 3,345 0.575 1.344Elgeyo/Marakwet 369,998 369,998 0 55.5 55.7 -0.2 3,030 0.521 1.279Nandi 752,965 752,965 0 47.4 46.9 0.5 2,884 0.496 1.248Baringo 555,561 555,561 0 57.4 57.8 -0.4 11,015 1.895 2.439Laikipia 399,227 399,227 0 50.5 49.3 1.2 9,462 1.628 2.260Nakuru 1,603,325 1,603,325 0 40.1 38.0 2.1 7,495 1.289 2.012Narok 850,920 850,920 0 33.8 34.3 -0.5 17,933 3.085 3.112Kajiado 687,312 687,312 0 11.6 11.6 0.0 21,901 3.768 3.439Kericho 758,339 590,690 167,649 44.2 37.0 7.2 2,479 0.426 1.157Bomet 724,186 891,835 -167,649 46.5 59.0 -12.5 2,471 0.425 1.155Kakamega 1,660,651 1,660,651 0 53.0 51.3 1.7 3,051 0.525 1.284Vihiga 554,622 554,622 0 41.8 40.1 1.7 531 0.091 0.535Bungoma 1,630,934 1,375,063 255,871 52.9 51.4 1.5 3,593 0.618 1.393Busia 488,075 743,946 -255,871 66.7 66.0 0.7 1,134 0.195 0.783Siaya 842,304 842,304 0 35.3 35.3 0.0 2,530 0.435 1.169Kisumu 968,909 968,909 0 47.8 45.0 2.8 2,086 0.359 1.061Homa Bay 958,791 963,794 -5,003 44.1 45.0 -0.9 2,586 0.445 1.182Migori 563,033 917,170 -354,137 46.7 48.4 -1.7 1,969 0.339 1.031Kisii 1,511,422 1,152,282 359,140 60.7 59.4 1.3 2,542 0.437 1.172Nyamira 598,252 598,252 0 48.1 47.2 0.9 899 0.155 0.697Nairobi 3,138,369 3,138,369 0 22.5 21.3 1.2 695 0.120 0.613TOTAL 38,610,097 38,610,097 0

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Figure 1: The Mountain of Absolute Poverty, 2005/06 (%)

Source: Table 2

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Table 3: Rural Overall Poverty by Adult Equivalents, 2005/06 Headcount (%) % of Population Contribution to Poverty (%) Total Rural 49.1 100.0 100.0 Central 30.4 14.5 9.0 Coast 69.7 7.1 10.1 Eastern 50.9 19.6 20.3 North Eastern 73.9 3.1 4.6 Nyanza 47.6 15.2 14.7 Rift Valley 49.0 26.7 26.7 Western 52.3 13.8 14.7 Source: Kenya Integrated Household Budget Survey 2005/06 5.2 Education Indicators The net attendance ratio (NAR) for primary school is the percentage of the primary school age (6-13 years) population attending primary school. The NAR for secondary school is the percentage of secondary school age (14-17 years) population attending secondary school. The gross attendance ratio (GAR) for primary school is the total number of primary school students, expressed as a percentage of the official primary school age population. The GAR for secondary school is the total number of secondary school students, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of overage and underage students at a given level of schooling, the GAR can exceed 100 percent. The Gender Parity Index (GPI) for primary school is the ratio of primary school NAR (GAR) for females to NAR (GAR) for males. GPI for secondary school is the ratio of secondary school NAR (GAR) for females to NAR (GAR) for males. A GPI less than 1 indicates gender disparity in favor of the male population, i.e. a higher proportion of males than females attends that level of schooling. A GPI greater than 1 indicates gender disparity in favor of females. A GPI of one indicates parity or equality between the rates of participation for the sexes. Table 4 shows NAR and GAR for the de facto household population by sex and level of schooling, and GPI based on the Kenya Demographic and Health Survey 2008-09. The data for NAR indicates that 79% of children of primary school age were attending school, and is slightly higher for girls (80%) than for boys (78%). The NAR for primary school is higher in urban (84%) than in rural (78%) areas. The GAR indicates that there are children in primary school who are not of primary school age, with ratios of 113 and 110 for males and females, respectively. As expected, the NAR and GAR are lower at secondary school level than at primary level. In primary school, there is parity between the sexes because GPI is close to one. However, GPI for secondary school drops to 0.75, indicating a bias in favor of males. The lowest GAR was in North Eastern province (21.4%), Coast (32.1%) and Western (33.6%). In particular, the GPI is quite low at secondary school level, especially in Western (0.51), Rift Valley (0.55) and North Eastern province (0.74).

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Table 4: School Attendance Ratios, 2008-09 Characteristic Net attendance ratio Gross attendance ratio Male

(%) Female

(%) Total (%)

Gender-parity index

Male (%)

Female (%)

Total (%)

Gender-parity index

PRIMARY SCHOOL

RESIDENCE Urban 85.4 82.5 83.9 0.97 106.8 100.4 103.5 0.94 Rural 76.4 79.6 77.9 1.04 114.3 111.2 112.8 0.97 PROVINCE Nairobi 89.8 92.1 91.1 1.03 102.7 100.2 101.3 0.98 Central 89.4 90.5 89.9 1.01 117.5 117.1 117.3 1 Coast 69.4 73.1 71.4 1.05 106.3 95.3 100.6 0.9 Eastern 80.3 84.7 82.5 1.06 116.9 114.6 115.8 0.98 Nyanza 85.3 87.5 86.3 1.03 128.7 121.5 125.3 0.94 Rift Valley 70.5 73.2 71.9 1.04 100.8 101 100.9 1 Western 79 82 80.5 1.04 127.8 128.5 128.1 1.01 North Eastern 55.7 50.5 53.4 0.91 88 66.6 78.3 0.76 KENYA 77.6 80.0 78.8 1.03 113.3 109.6 111.5 0.97 SECONDARY SCHOOL

RESIDENCE Urban 43.7 32 37.5 0.73 79.4 61.1 69.8 0.77 Rural 13.1 16 14.5 1.23 46.6 34.3 40.5 0.73 PROVINCE Nairobi 55 51.1 53 0.93 102 84.7 92.9 0.83 Central 18.6 31.2 25.3 1.68 56.8 57.5 57.2 1.01 Coast 22.1 14.6 18.5 0.66 34.2 29.9 32.1 0.87 Eastern 15.8 17.8 16.7 1.13 50.9 43.2 47.4 0.85 Nyanza 16.1 23.6 19.6 1.46 47.8 42.5 45.3 0.89 Rift Valley 14.4 14.4 14.4 1 58.3 31.8 43.8 0.55 Western 14 6.2 10.3 0.44 43.9 22.2 33.6 0.51 North Eastern 10.6 10 10.4 0.95 24.1 17.9 21.4 0.74 KENYA 17.0 18.4 17.7 1.08 50.8 38.2 44.6 0.75 Source: Kenya Demographic and Health Survey 2008-09 5.3 Nutritional Status among Young Children The height-for-age index (stunting) is an indicator of linear growth retardation and cumulative growth deficits. Stunting reflects failure to receive adequate nutrition over a long period and is affected by recurrent and chronic illness. Stunting therefore represents the long-term effects of malnutrition in a population and is not sensitive to recent, short-term changes in dietary intake. As shown in Table 5, 35% of children under five were stunted, while the proportion severely stunted was 14%. Stunting is highest in children age 18-23 months (46%) and lowest in children age less than 6 months (11%). Severe stunting shows a similar trend, where children age 18-23 months have the highest proportion of severely stunted children (22%) and those less than 6 months have the lowest proportion (4%). Children living in rural areas are moderately and severely stunted to a greater extent (37%) when compared with urban children (26%). At the provincial

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level, Eastern province (42%) has the highest proportion of stunted children, while Nairobi province had the lowest (29%). Table 5: Nutritional Status of Children, 2008-09 (height-for-age) Characteristic Percentage below -3 standard

deviations Percentage below -2 standard

deviations Mean Z-score (standard

deviations) RESIDENCE Urban 8.7 26.4 -1 Rural 15.3 37.1 -1.5 PROVINCE Nairobi 8.7 28.5 -1.1 Central 9.4 32.4 -1.3 Coast 14.3 39 -1.4 Eastern 17.1 41.9 -1.7 Nyanza 13 30.9 -1.2 Rift Valley 14.9 35.7 -1.5 Western 14.8 34.2 -1.5 North Eastern 17.7 35.2 -1.1 KENYA 14.2 35.3 -1.4 Source: Kenya Demographic and Health Survey 2008-09 5.4 Demographic Indicators Child mortality indicators are categorized into neonatal mortality (probability of dying within the first month of life), infant mortality (probability of dying before the first birthday) and under-five mortality (probability of dying before the fifth birthday) expressed per 1,000 live births. The under-five mortality was 74, i.e. one in every 14 children born in Kenya dies before its fifth birthday, compared with neonatal mortality of 31 (or one in every 32 children). As shown in Table 6, the regions with the highest neonatal mortality rates were Nairobi (48) and Coast (44), while the highest under-five mortality rates were in Nyanza (149) and Western province (121). In particular, the ratio of neonatal mortality to under-five morality was high in Nairobi (75%), Central (61%) and Eastern (60%), and lowest in Western (20%). Table 6: Early Childhood Mortality Rates by Region and Residence, 2008-09

Neonatal Infant Under-five Neonatal/Under-five Mortality (%) RESIDENCE Urban 32 63 74 43 Rural 33 58 86 38 PROVINCE Nairobi 48 60 64 75 Central 31 42 51 61 Coast 44 71 87 51 Eastern 31 39 52 60 Nyanza 39 95 149 26 Rift Valley 30 48 59 51 Western 24 65 121 20 North Eastern 33 57 80 41 KENYA 31 52 74 42 Source: Kenya Demographic and Health Survey 2008-09

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Figure 2: Neonatal, Infant and Under-five Mortality Rates, 2008-09

Source: Kenya Demographic and Health Survey 2008-09 In most human populations, more males are born than females, probably as an evolutionary adaptation to the fact that females have higher survival probabilities than males, such that their numbers are almost even by the time they reach marriageable ages (Poston, 2005). However, these patterns can be affected by extreme forms of human intervention and disturbance e.g. war (which would reduce significantly the numbers of young males), international migration, and female-specific abortion. In Kenya, mortality is relatively high at infancy and early childhood, with a trough around age 10-14, and then steadily climbs to the older ages with a second peak at age 70+ years. Sex differences in mortality are affected by biological survival probabilities that tend to favor women; child bearing (which penalizes women); differences in socioeconomic status which in most cases favor males; and differences in behavioral patterns (e.g. alcohol, tobacco, drugs and accidents) that tend to penalize men. Based on the 1999 Population and Housing Census, there were more female than male survivors at each specified age, while life expectancy for females was also higher. However, at age groups 20-24 and 25-29 years, female mortality is higher than that of males (Kenya, 2002), probably because the period coincides with the peak of childbearing. Table 7 shows life expectancy at ages 5 and 15 years. The result show that the lowest male life expectancy at age 5 was in Nyanza and the highest in North Eastern, Central and Rift Valley; while the lowest male life expectancy at age 15 was in Nyanza and the highest in North Eastern, Rift Valley and Central provinces. This shows that the main spatial differences in survivorship occur below the age of 5, hence the need to understand the causal factors of child mortality in various spatial, environmental and cultural jurisdictions/domains. In North Eastern province, life expectancy for males is higher than that of females despite the expected biological advantage in favor of women – globally at about 5 years (Waldron, 1976; Waldron, 1983; Waldron, 1993; Waldron, 1998; Sen, 1992; Kalben, 2000; Lemaire, 2002; Pampel, 2005; Elo and Drevenstedt, 2005; UNDP, 2007; Abdulraheem, Jimoh and Oladipo, 2011; Hosseinpoor et al, 2012). The higher life expectancy of males over females in North Eastern province may be a reflection of lower male

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investment in own offspring and higher overall parental investment in male children (Kruger and Nesse, 2004; Kruger and Nesse, 2006; Kruger, 2008). Table 7: Life Expectancy at Ages 5 and 15 years, 1999

At 5 years At 15 years Male Female Male Female

Nairobi 54.6 60.6 46.2 51.9 Central 59.6 67.6 50.8 58.5 Coast 55.4 62.2 47.4 53.7 Eastern 57.5 65.8 48.8 56.8 Nyanza 46.7 53.7 39.4 45.8 Rift Valley 59.6 67.2 51.0 58.3 Western 54.3 60.9 46.4 60.9 North Eastern 64.7 62.9 56.0 54.1 KENYA 54.8 63.0 46.6 54.3 Source: 1999 Population and Housing Census: Analytical Report on Mortality 5.5 Assistance during Delivery The 2008-09 KDHS showed that 43% of births are delivered in a health facility, while 56% are delivered at home. As shown in Table 8, 44% of births are delivered under the supervision of a health professional, usually a nurse or midwife. Traditional birth attendants continue to play a vital role in delivery, assisting with 28% of births (the same percentage as are assisted by nurses and midwives). Relatives and friends assist in 21% of births, and for 7% of births, mothers do not receive any form of assistance. A reported 53% of women did not receive a postnatal check up. The percentage of live births assisted at delivery by a skilled provider is way below those of Zimbabwe (66.2%), Namibia (81.4%) and Democratic Republic of Congo (74%) as shown by their respective Demographic and Health Surveys. Kenya’s maternal mortality ratio was estimated at 488 maternal deaths per 100,000 live births. Indeed the IMF (2012) observes that Millennium Development Goal Goal 5 on maternal health is “one of the most challenging goals given that maternal mortality ratio has actually risen from 414 in 2003 to 488 in 2009. There is need for more attention to address challenges in this area in order to reverse this trend”. Table 8: Assistance during Delivery, 2008-09 (%)

Doctor Nurse/ midwife

Other-health-worker

Traditional-birth-attendant

Relative/ other

No one

Don’t know/ missing

Total Skilled provider

RESIDENCE Urban 28.3 46.5 0.1 15.2 7.8 1.6 0.5 100.0 74.8Rural 13.3 23.5 0.6 30.4 24.2 8.0 0.1 100.0 36.8PROVINCE Nairobi 33.7 55.2 0.1 5.6 3.7 1.2 0.5 100.0 88.9Central 45.0 28.8 0.1 1.7 17.8 6.6 0.0 100.0 73.8Coast 21.3 24.3 0.2 21.0 27.5 5.4 0.3 100.0 45.6Eastern 16.9 26.2 0.0 27.8 26.0 2.9 0.2 100.0 43.1Nyanza 13.5 32.0 1.5 26.2 20.5 6.3 0.0 100.0 45.5Rift Valley 10.0 23.7 0.4 30.7 26.7 8.3 0.1 100.0 33.7Western 5.5 20.3 0.1 45.0 14.2 14.6 0.3 100.0 25.8North Eastern 1.0 30.6 0.7 64.2 1.9 0.0 1.6 100.0 31.6KENYA 16.0 27.8 0.5 27.6 21.2 6.8 0.2 100.0 43.8Skilled provider includes doctor, nurse or midwife Source: Kenya Demographic and Health Survey 2008-09

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5.6 Distance to Nearest Health Facility As shown in Table 9, nationally, only 11.3% of Kenyans travel one kilometer or less to reach a health facility, while about a half (47.7%) travel for 5 kilometers or more. This national average conceals huge urban and rural differences. For instance, while only 7.4% of rural dwellers travel for one kilometer or less to reach a health facility, this proportion is 48.5% for urban residents. In addition, more than a half of rural dwellers travel 5 or more kilometers to reach a health facility, while only 11.9% of urban dwellers travel similar distances. Table 9: Percentage Distribution of Communities by Distance to nearest Health Facility, 2005/06 Region 500 metres or

less 500 metres - 1 Km 1.1 - 2.9 Kms 3 - 4.9 Kms 5 or more Kms Total Count

KENYA 4.9 6.4 12.4 28.5 47.7 356,576 Rural 3.0 4.4 12.0 29.0 51.5 322,352 Urban 23.3 25.2 15.9 23.7 11.9 34,224 Nairobi 10.9 8.2 60.7 20.2 5,171 Central 5.7 6.2 16.0 43.4 28.6 57,150 Kiambu 31.2 68.8 3,864 Kirinyaga 19.2 43.2 37.6 9,295 Murang’a 22.0 10.3 16.0 51.8 5,558 Nyandarua 7.7 14.7 40.8 36.9 9,819 Nyeri 12.5 14.3 23.8 39.3 10.0 16,493 Thika 3.3 96.7 6,101 Maragua 7.7 58.4 33.9 6,020 Coast 3.7 6.2 13.0 16.9 60.3 24,032 Kilifi 8.0 26.4 65.5 8,866 Kwale 49.1 50.9 2,302 Lamu 19.1 80.9 654 Taita Taveta 3.1 11.7 2.4 46.6 36.1 5,676 Tana River 21.2 15.1 5.6 58.0 2,865 Malindi 5.6 5.6 88.8 3,669 Eastern 1.5 5.2 2.3 27.2 63.8 60,548 Embu 5.2 6.6 50.4 37.8 2,620 Isiolo 48.8 51.2 1,002 Kitui 2.1 8.2 10.1 79.6 7,601 Makueni 1.9 10.8 87.3 8,745 Machakos 2.2 1.0 35.3 61.5 14,555 Marsabit 100.0 1,620 Mbeere 7.0 11.9 10.3 70.8 2,724 Meru Central 3.5 16.8 47.4 32.2 6,524 Moyale 21.9 78.1 285 Mwingi 29.3 70.7 2,629 Meru North 9.3 8.3 32.2 50.3 10,147 Tharaka 100.0 1,110 Meru South 42.5 57.5 990 North Eastern 3.2 3.5 7.7 85.7 8,754 Garissa 11.8 88.2 1,866 Mandera 6.8 22.8 70.4 2,947 Wajir 2.0 2.1 95.9 3,941 Nyanza 0.1 7.8 11.6 36.8 43.7 61,272 Gucha 10.2 38.3 51.5 5,732 Homa Bay 1.8 3.6 44.7 49.9 4,857 Kisii 1.1 25.5 14.3 27.1 32.0 7,470

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Region 500 metres or less

500 metres - 1 Km 1.1 - 2.9 Kms 3 - 4.9 Kms 5 or more Kms Total Count

Kisumu 48.4 51.6 4,389 Kuria 7.7 92.3 2,211 Migori 6.8 7.6 41.2 44.4 8,078 Nyamira 12.6 15.5 24.9 46.9 8,105 Rachuonyo 0.9 49.1 50.0 4,555 Siaya 22.5 49.6 27.9 7,622 Suba 35.7 64.3 2,435 Bondo 23.7 51.6 10.8 13.9 3,308 Nyando 15.7 49.7 34.6 2,511 Rift Valley 10.6 6.7 17.0 17.7 48.0 85,022 Baringo 9.2 25.2 65.5 1,867 Bomet 0.6 2.4 0.7 96.4 3,175 Keiyo 56.5 43.5 1,459 Kajiado 9.9 28.8 61.3 3,035 Kericho 22.1 77.9 1,699 Koibatek 7.1 30.3 15.5 47.1 2,451 Laikipia 11.3 22.2 40.2 26.3 3,718 Marakwet 13.6 25.8 33.7 27.0 2,807 Nakuru 3.0 28.2 2.5 66.3 16,101 Nandi 9.2 6.3 31.3 30.7 22.6 10,982 Narok 9.8 14.6 75.7 4,150 Samburu 27.8 72.2 941 Trans Mara 23.7 76.3 947 Trans Nzoia 3.9 31.7 15.9 48.6 6,018 Turkana 18.7 81.3 3,633 Uasin Gishu 46.8 11.4 1.7 20.7 19.5 12,434 West Pokot 35.0 65.0 4,132 Buret 44.9 35.0 20.0 5,475 Western 5.7 6.3 14.4 28.6 45.0 54,627 Bungoma 6.9 1.7 19.7 23.2 48.4 14,945 Busia 3.7 1.9 12.6 44.9 36.9 9,142 Mt. Elgon 10.2 36.0 53.8 2,186 Kakamega 4.9 10.0 2.3 82.7 5,989 Lugari 27.1 17.3 34.2 21.4 1,562 Teso 12.7 22.9 9.0 9.5 45.9 3,577 Vihiga 11.0 1.0 29.4 38.0 20.6 9,102 Butere/Mumias 12.9 1.9 35.9 49.2 8,124 Source: Kenya Integrated Household Budget Survey 2005/06 5.7 Summary of Main Findings Sex-disaggregated data is normally reported on indicators that are comparable between the sexes (e.g. school enrolment), but indicators of women’s specific needs (e.g. maternal care) have stagnated and are even lower than for some Sub-Saharan countries with lower GDP per capita. The age groups 20-24 and 25-29 years is the only period along the human lifecycle where there is higher male than female survivorship as measured by age-specific mortality rates, probably because the period coincides with the peak of childbearing. The demographic indicators show a disproportionately high share of neonatal mortality to under-five mortality. The main spatial differences in survivorship occur below the age of 5, hence the need to understand the causal factors of child mortality in various spatial, environmental and cultural jurisdictions/domains.

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The analysis shows multiple deprivations by region e.g. high poverty rates go hand in hand with poor child outcomes (nutrition), low school enrolments and longer distances to health facilities. The regions with low enrolment rates also tend to have higher gender disparities, pointing to some cultural dynamics that discourage or are not supportive of female education. However, there are some discrepancies between poverty estimates derived from the KIHBS 2005/06 and the Fact Sheet prepared by the Commission on Revenue Allocation that need to be harmonized. Kenya’s achievements in maternal and reproductive health indicators are quite low, even in comparison with countries with lower human development index (UNDP, 2011). The indicators include relatively high adolescent fertility rate (100.2 live births per 1,000 women aged 15-19) and maternal mortality ratio (488 maternal deaths per 100,000 live births), low proportion of births attended by skilled health personnel (44%), low contraceptive prevalence rate (46%), and high unmet need for family planning7 at about 43% (Kenya National Bureau of Statistics and ICF Macro, 2010; UNDP, 2011). There is need for special mention of movement towards gender equality in access to services (especially education) in county planning and annual reporting, in addition to maternal and reproductive health indicators. 6. OTHER CONSIDERATIONS IN REVENUE ALLOCATION Apart from population and poverty indicators and the need to provide minimum resource needs to avoid interruption in service delivery, a prime consideration is the cost of providing services in different jurisdictions (land area, dispersal of human settlements, road network, cultural barriers to access, etc). There are enablers (basic physical and social infrastructure required by service providers) e.g. roads, communication, security, and cultural enablers/inhibitors supportive/obstructive to women and children. As an illustration, health-seeking behavior in case of child delivery depends on:

a) Education and knowledge of the mother, and access to reproductive health information; b) Decision making processes at family level on care; c) Transport and logistics and in-transit care (distance, road infrastructure, timely availability

of transport, cost, security in transit); d) Health facility (personnel, facilities) – if need for further referral: transport and logistics.

The prime movers in resource needs are therefore social indicators/poverty levels, enablers, cost of providing each service, and capacity of community personnel to provide services (e.g. technical knowledge to service an automatic transmission vehicle). There is therefore need for information/focus on:

• Resource requirements for achieving targets in selected social indicators (effect of population, poverty and terrain variables);

• The need for statistics used as a basis for sharing revenue among counties to pass the credibility test in terms of quality and timeliness e.g. population and levels of poverty;

• Develop criteria for identifying marginalized areas/counties for purposes of the Equalization Fund, and initiate collection/culling of data for the criteria;

7 Unmet need for family planning refers to the desire of women to delay or postpone their next birth for at least two years, or to not have any more births, while not using a method of family planning. According to the Kenya Demographic and Health Survey 2008-2009, nearly 43% of recent births among women aged 15-49 years were unintended as a result of this unfulfilled need.

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• Criteria for allocation of Equalization Fund between eligible counties; • Functional relevance of existing devolved funds (e.g. CDF and Local Authority Transfer

Fund) to determine their continued existence8; • Mapping a county’s specific needs with each funding window to get a holistic picture on

funding from national, county and equalization fund; • Sequencing in county budget making for achievement of long-term goals (and its relation

with national budget), i.e. use of Medium-Term Expenditure Framework (MTEF) at local level;

• Heterogeneity of needs within a county, and allocations within counties (including urban/rural);

• County’s supervisory role of national government at the local level e.g. in dam construction;

• Specific concern on maternal health indicators (antenatal, delivery care, postpartum) within the health sector indicator framework and goals;

• Coordination between national and county budget priorities and availability of funding, including for enablers;

• The CRA formula cannot capture everything, hence the need for performance criteria to encourage consideration of specific gender inequalities in access to basic services (primary healthcare and education, performance in education), and child health (e.g. immunization effort) and nutrition;

• While natural resource conservation and utilization does not fall under the mandate of the CRA, it is necessary to prepare policy and legislation to addresses sharing of resources arising from the exploitation of natural resources, as this affects a county’s resource envelope and conservation/abatement costs.

The credibility of indicators used in the CRA allocation formula also needs scrutiny. When releasing preliminary reports of the 2009 Population and Housing Census, the Minister for Planning, National Development and Vision 2030 announced that some districts had reported inconsistent figures. In eight constituencies, the rate of population increase was higher than birth and death rates (population dynamics) would support; age and sex profiles deviated from the norm; and significant growth in household size without accompanying growth in number of households. For example, the statistics on enumerated population and growth rates between 1999 and 2009 shows that North Eastern is the only province where the enumerated population in 2009 was more than double the 1999 population, with a compounded annual growth rate of 9.16%. However, the High Court reversed the Minister’s nullification of the census count for Lagdera, Mandera East, Mandera Central, Mandera West, Wajir East, Turkana North, Turkana South and Turkana Central constituencies, and CRA is therefore obliged to use the enumerated population. CRA applied poverty gap indices from KIHBS 2005/2006 to the 2009 enumerated population, which implies that errors in population estimates affect allocation through the population parameter and the poverty parameter. The enumerated population was also used in allocating 80 more constituencies in accordance with the Constitution (Independent Electoral and Boundaries Commission, 2012), which confers further exorbitant privilege to areas that may have interfered with the integrity of the 2009 census enumeration process. 8 The Taskforce on Devolved Government (2011) stated that, “the place of CDF and its operation as an aspect of Parliamentary business can no longer be justified. The role of service delivery is purely a matter for the National and County governments”.

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Table 10: Enumerated Population by Province, 1999-2009 Province Enumerated population 1999-2009

1999 2009 % Change Compounded Annual Growth Rate (%) Kenya 28,686,607 38,610,097 34.59 3.02Nairobi 2,143,254 3,138,369 46.43 3.89Central 3,724,159 4,383,743 17.71 1.64Coast 2,487,264 3,324,805 33.67 2.94Eastern 4,631,779 5,668,123 22.37 2.04N. Eastern 962,143 2,311,259 140.22 9.16Nyanza 4,392,196 5,442,711 23.92 2.17Rift Valley 6,987,036 10,006,805 43.22 3.66Western 3,358,776 4,334,282 29.04 2.58 7. LAND AREA AS PROXY FOR COST OF SERVICE DELIVERY The CRA formula uses the total land area of a county to distribute the share of revenue allocated to land area (8 percent). However, the cost of providing services in geographical domains of different sizes is more appropriately modeled by transect through the region to a hypothetical central point. If the region resembles a circle, transect from a central point can be estimated as radius of a circle, or simply the square root of the area (see Table 2 for county shares of land area based on radius/diameter of a circle). For other shapes, centroid methods can be used to estimate distances to various service points e.g. the k-means Llyod clustering algorithm (Friedman et al, 2008; S. P. Lloyd, 1982; C. D Lloyd, 2007; Sanders, 2007). The estimated mean distance would be a more appropriate basis for comparison of cost of providing services and sharing revenue among Counties along the land area criterion, unless other factors (e.g. terrain and soil types) are also considered. Once distances are estimated, it would be unrealistic to assume that transport cost per unit to various service points is directly proportional to distance, due to “economies of scale” and “economies of distance” (the tapering principle) – see Lederer (1994), Gabre-Madhin (1991), Keskin (2007), McCann (2005), Fingleton and McCann (2007), and Fingleton (2008). For example, freight and passenger transport costs (C) are normally concave quadratic functions of distance (L) to reflect the fact that total costs are not linear in distance but increase in distance with a decreasing variation rate, i.e. C = aL – bL2 (d’Aspremont, Gabszewicz and Thisse, 1979; Economides, 1986; Anderson, 1988; Carr and Mendelsohn, 2003; Frutosa, Hamoudi and Jarque, 1999)9. However, the transport cost function with respect distance would be of concern to country planners, and does not therefore necessarily influence the revenue allocation among counties along the land area criterion.

9 Hotelling (1929) was the starting point of an intense discussion on optimal location for two firms competing to sell a homogeneous product to customers spread evenly along a liner market. Hotelling claimed that firms will locate in the middle. It was not until 1979 that d’Aspremont, Gabszewicz and Thisse (1979) showed that Hotelling’s argument was invalid, because it assumed linear (rather than quadratic) transportation costs. Similarly, human capital earnings are modeled as a function of education and on-the-job training (Mincer, 1958; 1974). At any point (t) in an individual’s lifetime, log-earnings is depicted as a function of the schooling phase of investment in years (S) and concave quadratic of labour market experience (t): lneYi(t) = a0 + a1Si + a2ti + a3ti2 + ε1. Although the second-order polynomial specification of labour market experience is relatively standard in the literature, Murphy and Welch (1990) have shown that higher-order polynomials may provide better fit to data (see Trostel, 2005, on nonlinearity in returns to education).

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8. EPILOGUE Willie Sutton, the immaculately dressed bank robber, is credited with Sutton’s Law: when asked why he robbed banks, he responded, “Because that’s where the money is” (cited in Bradford, 2011). The heightened political competition in governorate (county) seats is probably because that is where the money will be. In A.A. Milne’s story, Winnie the Pooh (1926), we are told that Pooh always liked a little something at eleven o’clock in the morning, and he was very glad to see Rabbit getting out the plates and mugs; and when Rabbit said, ‘Honey or condensed milk with your bread?’ he was so excited that he said, ‘Both’ and then, so as not to seem greedy, he added, ‘But don’t bother about the bread, please’. When Pooh had finished his meal with rabbit and started to leave, he got stuck in the entrance to the rabbit hole as he had eaten too much condensed milk and honey. Pooh had to be starved for a week and eventually slims up enough to get out of the hole. The policy-makers need to take time to think through, as greed is likely to be an unnecessary obstruction. Just like the use of mobile money transfer in cash transfers to vulnerable populations e.g. orphans and the elderly (Barca et al, 2010), devolution is part of the mechanics of giving people voice and promoting their enjoyment of economic and social rights, and need to be implemented with minimum administrative costs and safeguards to minimize perverse incentives. While the paper is politically supportive of devolution and equality, it is critical of the credibility of some indicators used in revenue allocation (especially population) and the concepts and technical aspects underlying the implementation process. For example, the constitution aims at individual and regional equality even though the two can be at odds (World Bank, 2009). It might be useful to distinguish social equity (across individuals) and spatial equity (across counties), since spatial disparities in economic activity are not necessarily synonymous with living standards and social inequality e.g. along the rural-urban divide (World Bank, 2009). There is therefore need for interdisciplinary discourse that could include:

• Domesticating egalitarian ideas of responsibility with a concern for efficiency; • A common understanding of dimensions of individual (dis)advantage, and a weighting

scheme of dimensions of deficits in entitlements and rights (Tungodden, 2008); • The fallacy of equating average potential and/or outcomes in a geographical unit as

generally representative of the population in the spatial domain; and • Locally relevant reasons behind household movements into and out of poverty and how

they differ by livelihood zones (Kenya, 2007; Kristjanson et al, 2009)

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Figure 3: Contribution of Counties to Kenya’s Land Area and Square Root of Land Area (%)

Source: Table 2

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