FEMALE PARTICIPATION IN THE LABOUR MARKET AND GOVERNMENT POLICY IN KENYA: IMPLICATIONS FOR

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1 FEMALE PARTICIPATION IN THE LABOUR MARKET AND GOVERNMENT POLICY IN KENYA: IMPLICATIONS FOR POVERTY REDUCTION Rosemary Atieno Institute for Development Studies University of Nairobi, P.O. Box 30197, Nairobi Kenya Paper submitted to the Fourth IZA/World Bank Conference on Employment and Development, May , Bonn, Germany

2 1. INTRODUCTION Participation in employment is important because of its direct impact on incomes and livelihoods. In Kenya, female labour force participation is especially important because: women constitute a significant share of household heads and are also over represented among the poor. their participation in wage employment in the modern sector has remained low, having access to less than 30% of wage employment Factors that restrict women s access to formal employment: Traditional roles, occupational segregation by gender, lack of access to technology, productive assets and credit among others.

3 INTRODUCTION Participation in employment important for women because of: Direct impact on incomes and livelihoods The strategic position of women in the Kenyan economy, being significant share among household heads and being overepresented among the poor. Women constitute over 50% of the labour force but participation in wage employment remain low.

4 INTRODUCTION The government policy of focusing on economic growth as a means of realising rapid development failed to ensure sustained improve economic performance. The declining economic performance resulted in declining ability of the economy to generate employment especially in the formal sector. The informal sector has therefore emerged as a major source of employment and incomes, becoming a major employer of the female labour force. Although women own a significant number of informal sector activities, of importance for poverty reduction is the fact that in the informal sector, female owned enterprises employ fewer workers, and less capital compared to the male owned ones.

5 INTRODUCTION Certain characteristics of the sector also raise questions regarding its potential in generating income and employment and hence poverty reduction: Many of them do not grow. They are characterised by small size of activities, few workers often less than six, little growth and limited access to services. A significant proportion of those counted as employed are also underemployed. Generally, there are gender inequalities in employment, with implications for poverty reduction. This paper discusses the factors determining participation in the labour market by women in Kenya, and the implications of women s over representation in the informal sector for poverty reduction in the country.

6 ECONOMIC PERFORMANCE AND EMPLOYMENT IN KENYA The reduction of poverty and unemployment have been major government objectives. Most policy initiatives undertaken to address these the vices emphasised achieving rapid economic growth as a means to reducing poverty and improving the standard of living. But poverty and unemployment have increased, becoming more entrenched. The level of poverty currently stands at 56%, and is highest in the rural areas where the poverty level stands at 59.56%, compared to 51.48% for the urban areas. Rapid economic growth was experienced in the first decade but not sustained with steady declines experienced thereafter.

7 ECONOMIC PERFORMANCE AND EMPLOYMENT IN KENYA Major economic indicators experienced decline: Investments, savings, and capital formation in major sectors Decline in employment due to retrenchment, privatisation of public enterprises and restructuring of private sector Lack of structural transformation necessary for employment creation aggravated the unemployment problem. The result has been decline in formal sector employment, but a steady growth in the informal sector employment, becoming major source of employment and livelihoods for a majority of the population.

8 FEMALE EMPLOYMENT IN KENYA There is general inequality in access to opportunities between men and women spilling over to employment. Such inequalities exist in: Assets, earnings and education. Female employment in modern wage sector is only 30%. Participation rates for women in rural areas is higher than for men. Women are disproportionately concentrated in certain areas of the formal wage employment sector. There are fewer women than men among regular employees and skilled workers, while they outnumber men in unskilled workers and unpaid family workers. Limited opportunities in access to formal employment confines women to informal sector activities.

9 FEMALE EMPLOYMENT IN KENYA Characteristics of women owned enterprises in the informal sector are important for poverty reduction: They are small and grow slower than male owned enterprises They rely more on less skilled and unpaid workers They employ fewer workers

10 FACTORS EXPLAINING LABOUR MARKET PARTICIPATION Different approaches exist that can be used to explain the participation in the labour market and hence access to different occupations. The theory of human capital: Occupational differences a reflection of the differences in human capital endowments. differences in wages and segregation in work is largely due to differences in the human capital content of male and female reflected through differences in productivity. Investments in human capital like education and health contributes to development by raising labour productivity. Neoclassical explanation emphasises the importance of individual and household characteristics labour market participation.

11 FACTORS EXPLAINING PARTICIPATION IN THE LABOUR FORCE Empirically identified factors: Access to factors of production and services, assets, technology, and training. Changes in political and economic landscape. Social attitudes towards women, inadequate skills and lack of gender sensitive employment

12 METHODOLOGY The study uses the multinomial logit model, which allows for the identification of factors determining the participation in various sectors. The dependent variable in the logit model is grouped into six categories based on the realities of the Kenyan Labour market as: public sector, private sector, informal sector, agricultural sector, unemployed and unpaid family worker. The study uses secondary data obtained from the 1997 Welfare Monitoring Survey (WMS) III. This data was collected through a survey conducted by the Central Bureau of Statistics (CBS), using the National Sample Survey Evaluation Programme (NASSEP). From a total population of 50,713 individuals, the total adult population was 27,767 out of which 13,277 were males and 14,490 females. This paper is based on the analysis of the female adult sample.

13 DESCRIBING THE SAMPLE Table 1: Distribution of the 1997 Sample by Employment Category Employment Category Total Sample Males Females Public Sector 1,425 1, Private Sector Formal 1, Informal Sector 3,628 2,324 1,304 Agriculture 6,623 3,604 3,019 Unpaid Worker Family 9,239 2,433 6,806 Unemployed 1, Missing 3,933 2,158 1,775 Total 27,767 13,277 14,490

14 SUMMARY STATISTICS Table 2: Summary Statistics for the Variables used in the Study by gender Total Sample Male Female Variable N Mean Std dev. N Mean Std dev. N Mean Std dev. Age 27, , , Years school in Household Headship (1=yes) Household size Land size owned Number of Infants In gainful employm ent (1=yes) 27, , , , , , , , , , , , , , , , , ,

15 SUMMARY STATISTICS Varia ble Public sector Private Formal Sector Informal Sector Unemployed Agriculture Unemployed Family Worker Male Female Male Female Males Female Males Female Male Female Male Female Age Years in school Househo ld Headship (1=yes) Househo ld Size Land owned Gainful Employ ment (1=yes) Number of infants

16 PARTICIPATION IN THE LABOUR MARKET Table 4: Multinomial Logit Parameter Estimates for Female Labour Force Participation, 1997 Variable Public sector Private sector Informal Sector Age 0.795*** (3.04) Age *** (-2.64) Years of schooling 0.680*** (3.83) Marital status (0.91) Land owned (0.88) Household head *** (4.01) Household size (0.48) Rural/urban cluster 1.884* (1.72) Constant ** (8.79) Model χ 2 (40) N Log likelihood , *** (3.08) *** (.2.76) 0.533*** (3.01) (0.41) (0.88) *** (4.46) (0.15) 2.117* (1.92) ** (9.23) Note: *** Significant at 1%; ** Significant at 5%; * Significant at 10%. Figures in parentheses are the Z-statistic 0.718*** (3.54) *** (3.47) 0.332** (2.01) (0.54) (0.74) *** (5.69) (0.18) (1.33) ** (10.25) Unpaid family worker 0.495*** (3.27) *** (3.12) (1.56) (1.02) (0.88) *** (8.29) (0.04) (0.39) ** (7.89) Agriculture 0.380*** (2.65) *** (2.60) (1.43) (.73) (0.88) (0.51) * (1.74) *** (9.95)

17 PARTICIPATION IN THE LABOUR MARKET Table 5: Marginal Effects on Probabilities for Labour Market participation Variable Public sector Private sector Informal Sector Age (1.43) (1.35) (0.57) Unpaid family worker (0.38) Agriculture ** (2.55) Age Sqd (1.3) (1.26) (0.57) (0.67) 0.005** (2.30) Marital Status (0.30) (0.71).005 (0.44) (0.84) (0.44) Years of schooling Land owned.029*** (3.21).002** (2.10) 0.018** (2.44) 0.002* (1.84) (0.28) (.001) (1.51) 0.001*** (3.50) ** (1.99) 0.006** (1.98) Household head 0.078*** (2.88) Household size (0.70) 0.081*** (3.05) (0.69) (0.55) (0.05) 0.413*** (5.84) (1.21) 0.358*** (5.23) (1.45) Rural/urban cluster 0.191** (2.49) 0.215*** (2.70) (0.58) (0.19) *** (3.63)

18 PARTICIPATION IN THE LABOUR MARKET Female participation in the public and private sectors is likely to increase with age, years of schooling, being a household head and being in the urban areas. The land owned and household size do not appear to have any effect on participation in both the public and private sectors. being in the rural or urban areas does not seem to affect the participation of females in the informal sector. Observations from results: Education as represented by the years of schooling increases women s chances of being employed in the public and private sectors. The significance of education for informal sector employment may be attributed to the occupational discrimination against women in the labour market or the limited job opportunities in

19 POLICY ISSUES Poverty reduction and employment creation among women requires pro-poor growth policies, targeting those sectors with highest potential returns for women. Reducing poverty and creating decent work opportunities for women will require increasing the opportunities for women to become more productive and earn more through their labour. This requires policies to improve women s access to assets, education, health and other social services that enable them to improve their human capital.

20 POLICY ISSUES These include: improving girls access to higher education and increasing women s access to productive assets. Improving the access to financial services to enable women to exploit their human capital potential like entrepreneurial potential. In the informal economy, measures to improve women s employment need to focus on services that enable them to spend more time on income generating activities. There is also need to build the capacity of local authorities to provide social services and infrastructure that facilitates profitable income earning opportunities especially in the informal sector.