1. THE ROLE OF RURAL FACTOR MARKETS IN REDUCING POVERTY, RISKS AND VULNERABILITY IN RURAL KENYA: Evidence from Kakamega and Vihiga Districts. BY Joseph Karugia W. Oluoch-Kosura Rose Nyikal Michael Odumbe Paswel Marenya.

2. Study objective Overall objective was to determine the role of factor markets in reducing poverty, risks and vulnerability in study areas.

3. Study areas Shirugu location in Kakamega District- medium agricultural potential, good market access, relatively abundant landholding, recent resettlement patterns. Central Maragoli location in Vihiga District- poor market access, small land parcels, good agricultural potential. The two Districts provided unique data sets: poverty counts of 57.71% and 61.97% respectively compared to national average of 56%.

4. Study methodology Examines all aspects of earning a living in the rural areas. Focus was on rural factor markets (land, capital and labour markets). Considered assets owned (human capital, physical and natural capital, and financial assets), activities engaged in and outcomes in terms of contributing to income.

5. Households visited in one-round survey conducted in months of May and June 2004. Collected both quantitative and qualitative data. Quantitative data: semi- structured questionnaire. Qualitative data: interviews with key informants and groups.

6. Data of interest Farm sizes, Family size, Income sources and levels, main occupation of household head, gender of household head, assets, household members with skilled employment, land tenure, credit situation, labour markets and causes of poverty. Also collected- indicators of vulnerability status at the household level.

7. Findings Figure 1. Distribution of selected household assets by study location.

8. Marked differences in land holdings and livestock ownership(CEUs) between the two locations: Shirugu better placed. Mean Land ownership was 1.97 ha and 0.37 ha, respectively. Mean livestock units were 3.24 and 1.51, respectively. However, comparison of human capital revealed that a higher % of households with above secondary education in Central Maragoli as compared to Shirugu location (20%, 13.4).

9. Partly explained by the small farm sizes that free labour and also limits expected returns to agricultural livelihoods and consequently increase the returns to investment in human capital/non-farm livelihoods activities. Examination of different income sources share in total incomes indicates how different asset endowments affect livelihood strategies that households engage in. These are illustrated in tables 1 and 2 below;

10. Table 1. Share of income sources by income quintiles (Shirugu) Per capita Incomes Salary incomes Busines s TransfersInformal incomes Total off- farm income Crop incomes Livestock incomes 34, 88536177060355 10, 9518.4187.61246 8 5, 825014813354718 4, 58401081331618 1, 640051214315316 11, 27523157750437

11. Table 2. Share of income sources by income quintiles (Maragoli) Per capita Incomes Salary incomes Busines s TransfersInformal incomes Total off- farm income Crop incomes Livestock incomes 24, 455349209721612 9, 20722101214582022 5, 8954.42.19.3414305020 3, 65902.94.821295813 1, 8832191123716 9, 4192381514602614

12. Share of off-farm incomes are highest for the high income groups in both locations. Largest share of off-farm income for the top quintiles accrue salaried income Low income groups receive their off-farm incomes predominately from participation in informal wage opportunities. Poorer households in these regions rely on farming and seasonal labour activities as their main source of income Suggests high levels of vulnerability

13. Additionally off-farm income particularly from the formal wage and salaried sector offers higher returns in both areas. Underscores the need to equip poor households with skills necessary to tap into off-farm opportunities. Shows that with low landholdings and increase in population, farming activities can only offer a very modest basis to secure livelihoods. Becoming less reliant on farming is part of the process of climbing out of poverty. Non-farm income opportunities (formal labor markets) seem to offer a pathway out of poverty.

14. Figure 2: Income distribution across land ownership categories (pooled sample)

15. Households who own (37.5%) above 2 acres of land have higher values of crop, livestock and off-farm incomes as compared to those who own 2 acres and below. Off-farm income has the largest share in total incomes in both land ownership categories. Shows that as far sizes continue to shrink, there is need for equal policy focus on facilitating access to off-farm income opportunities. 66.2% of female respondents fall in the lower land ownership category as compared 60% of their male counterparts. Off-farm income is used to finance on-farm investments in both locations.

16. Figure 3:Distribution of incomes across education level categories (pooled sample).

17. About 61% of respondents had upto primary level of education, 39% had secondary and above. The figure above shows marked differences in incomes among the two categories, with those having secondary education being better placed. Suggest a strong education-non-farm and on-farm association in study areas. However, poor households face financial entry barriers posed by high cost of education beyond the primary level. The result is that they remain trapped in lower category.

18. Exploring correlates of incomes Semi-log analysis of household per capita incomes revealed that the co-efficients of land holding size, education level of household head, non-land based assets,value of livestock holding are positive and significant in inflencing household incomes. On the other hand only education attainment of household head positively influenced amount of off-farm income received by households in study areas.

19. Credit access Respondents in both locations noted lack of financial capital as one of the major bottlenecks hindering improved productivity. Using observed borrowing, the study estimated a logit model for access to credit in each location. Education level of household head and land size influenced credit access positively in Maragoli while amount of off-farm income influenced it negatively. In Shirugu, the education level of household head and amount of off-farm income influenced access positively.

20. Better educated farm household heads are likely to be more aware and can take advantage of existing credit resources. In Maragoli, average land holding size is 0.37 ha, which means that those with relatively smaller land parcels for whom increase in productivity may be necessary do not have access to credit. Duplication of credit programs in different regions is likely to be ineffective in reaching poor households. Need to adapt these to local conditions.

21. Challenges Only 15% of households sampled completed secondary school. The rest have no prospects for being absorbed into remunerative off-farm activities. Employment opportunities are limited to teaching in primary and secondary schools, other jobs in local government offices. Private sector opportunities are also limited e.g. matatu drivers.

22. In Maragoli, the small farm sector cannot generate sufficient income despite farming being the main source of livelihood. Unskilled labour is the only alternative beyond farming that is available to these households yet opportunities are also limited 70% of sampled households stated farming as their main occupation.

23. The poor in both locations seem to be locked in a trap characterized by low education, subsistence farming, and unskilled informal labour activities. Investment in natural capital to improve its productivity is evidently lacking in study sites. Only 50% of total sample used fertilizers and hybrid seeds.

24. Conclusions Access to land is still an important source of livelihoods even where land is scarce. Immediate course of action must lie in improving the productivity of the limited natural capital base. Nevertheless, a burgeoning population and diminishing land sizes imply that access to land alone may not guarantee households sufficient incomes to escape poverty.

25. Conclusions Cont’d More of the rural population must necessarily be absorbed in the off-farm sector. Skill acquisition as well as rural industrialization programs will be key.

26. Policy Implications The study advocates for a more integrated approach to rural development by: expanding educational services especially secondary education to build human capital Investments to provide skills for off-farm activities

27. Policy Implications Cont’d Creation of remunerative employment in the rural areas through rural industrialization. Improving farm productivity by adequate provision of inputs such as fertilizers and high yielding varieties and developing product markets through investments in infrastructure.

28. Acknowledgements USAID for providing the funds for the study Cornell & Clark-Universities for the SAGA initiative SAGA Kenya collaborators Farmers & key informants for sharing data and their knowledge

29. THANK YOU ALL