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Kenya smallholder farmer education and farm productivity

University of British Columbia

This research was undertaken to study the effect of education on small farm revenues and profits in Kenya. Schooling (defined as the number of school standards completed by the farm operator) was used as the most important source of education. It was hypothesized that schooling has a positive effect on farm revenues and profits. The effect of other sources of information viz; extension contact, demonstration attendance and baraza attendance on farm revenues and profits were also investigated. The research was done using regression analysis where these variables and other farm activity relevant variables were fitted in regression equations. The choice of these variables were based on economic theory, Kenya small farm characteristics and the objective of the study. Several factors would qualify as supporting evidence for the argument that educated farmers are more productive. We expect educated farmers to be more informed in terms of use of new production technologies. Education as a source of human capital also enhances the productive abilities of human beings and also enables those who have invested in education to use their resources more efficiently as well as adjusting to new "ways of producing more efficiently". In the current study we find that schooling of the farm operator is positively related to level of expenditure on farm purchased variable inputs. This indicates that education enhances adoption of new technologies and innovativeness. Further it was shown that farmers with more education earned more value added per acre from their farm business compared to their less educated counterparts. On the overall farm activity, farmers with eight or more standards of schooling earned upto 80.2% in value added per acre compared to those who had no schooling. The regression estimates were done on a stepwise procedure where farm specific enterprises were estimated separately and then aggregated and estimated as one farm sector. Thus a crop equation, a livestock equation and a total farm output equation were estimated. This model was then developed into a variable profit function. A simple linear function procedure was used in the regression analysis. In all the estimated value added equations the schooling coefficient was positive and significant at 5% level two tail t-test. As we move from farm specific activities to a farm aggregate output model and lastly to value added model the schooling coefficient increased in size confirming the positive role of education in allocative effect. These results show that schooling plays an important role in allocation of other purchased inputs and also choice of crop mix and input selection. The estimated marginal return to schooling of farm operator in the profit function was Kshs.281. In an earlier function where schooling of the farm operator was fitted into a total farm income equation the estimated marginal return to schooling was Kshs.778.89. When schooling of the farm operator is allowed to interact with extension service the estimated interaction variable coefficient is negative showing the two act as substitute sources of knowledge, and the schooling coefficient increased in size showing that those who had both schooling and extension service earned comparatively more farm revenues. The role of other educative factors like extension service, demonstration attendance, and baraza attendance in influencing agricultural production was investigated. Regression results showed that extension contact had a negative and significant effect on farm revenues and profits. Demonstration and baraza attendance had similar effects on farm revenues and profits. In the value added function hired labour variable was fitted as the cost of hired labour per day. The estimated coefficient for this variable was positive and significant at 5%. The estimated coefficient for this variable shows hired labour is not optimally used, and farmers can increase their farm profits by hiring more labour. When this variable was fitted as the wage rate paid to hired labour per day the estimated coefficient was positive and significant. These results indicate that cost of hired labour depends on its quality. In the sales function hired labour was specified as mandays of hired labour per year and the estimated coefficient which reflects the shadow price of labour was higher than average hired labour wage rate implying that this factor is underemployed. In the sales function the estimated coefficient for the value of purchased inputs variable indicates that there is an element of underutilization of these inputs. This variable is fitted in value terms and in profit maximizing conditions the estimated coefficient is expected to be no different from unit. However, the estimated coefficient for this variable is approximately 2.5 showing a shilling spent on purchased inputs will bring forth 2.5 shillings. Thus an increase in the use of purchased inputs will increase farm revenues. Results show evidence of regional differences in farmer productivity and utilization of purchased inputs in favour of Central province. The study is based on the 1982 CBS-IDS-World Bank Household Survey of Rural Kenya data set.

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2010-10-27

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http://hdl.handle.net/2429/29578

Agricultural Economics

University of British Columbia

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