sensitivity of the estimated returns to the functional form used for modeling agricultural production. The third concerns data-related problems. All three are, in the final analysis, empirical issues, although the first two follow from well-established theoretical considerations in dealing with cross-sectional data and technology specification. As the following discussion explains, the results are sensitive to omitted regional effects and data considerations, but are seemingly robust with respect to the functional form for the production function.

# **A brief recap of the B&E study**

The estimate by B&E of returns to the Kenyan T&V system was based on estimates from an empirical "meta-production" function. The study was designed to overcome a key limitation of most studies that have attempted to establish the impact of extension in a production-function framework, namely the specification of an appropriately exogenous variable for extension supply (Birkhaeuser, Evenson, and Feder, 1991). B&E use a variable based on front-line extension staff<sup>2</sup> , specified as a ratio of extension workers to the number of farmers in each location (a subdistrict administrative unit).<sup>3</sup> Since the allocation of staff to each location is determined by the central government, the variable is exogenous to the household decision making process and hence is a valid regressor for farm-level production function estimation. The actual data on staffing were, however, available only for 1990. For previous years, B&E had no choice but to construct staff-farm ratios somewhat arbitrarily, based on the number of years each staff member had been in the same location. The weighted staff-farm variable, constructed to capture the lagged impact of extension on productivity and the key variable in calculating the returns to extension, is thus measured with error (B&E,1993, page 26, first and second paragraphs).

The original design of the B&E study was to obtain panel data by revisiting some 700 households that were interviewed in 1982 for the Kenya Rural Household Budget Survey. While the 1990 survey was able to collect the information needed to estimate a production function for about 670 households, comparable information for 1982 was lacking, preventing the estimation of a fixed or random effects model (B&E, 1993, p. 25). Given the circumstances, B&E were forced to estimate a cross-sectional production function using the 1990 data, supplemented by the information available from the 1982 survey in an attempt to control for household- and areaspecific factors affecting production. Household-specific effects were controlled by including total production and area cultivated by the household in 1982, and the 1990 age, education and sex of the head of the household. To control for area-specific characteristics, dummy variables for the "production potential" for the zones in which each cluster was located were included (for medium- and low-potential zones), as were the topography indicators (for hilly and undulating regions).<sup>4</sup> To control for economic and infrastructure conditions characterizing the area, the regression included 1982 cluster-level means for variables representing access to roads, transport facilities, farm size, farm-level cropped area, farm-level livestock capital, value of cash crops produced, value of other crops produced and household non-farm income.

- 2B&E tried other extension variables, including supervisors and subject matter specialists, but the results were not significantly different.

- 3On average, a district has 6 divisions, a division has 6 locations, and a location has 6 sub-locations.

- 4A "cluster" is a census enumeration unit used by the CBS for the 1982 RHBS sampling frame. In the sample, each cluster belongs to a distinct sub-location.

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