size and livestock values. Also, the cluster-level infrastructure variables can be replaced by the corresponding means for distance to all-weather road (which includes tarmac and all-weather gravel roads); distance to dirt roads and distance to nearest bus/matatu routes.

The results of including these modifications are presented in table 2 models I-III, corresponding to those of table 1. In model I, even the specification without district effects shows that with updated variables, the extension variable is now no longer significant at any conventional level, and in fact has a negative coefficient (-0.08).20 The traditional input variables have coefficients similar to those for the base model (model I) of table 1. The test for districtspecific effects, model II, shows that district effects continue to be jointly significant. The extension coefficient continues to be statistically insignificant and negative.<sup>2</sup> 1 Finally, the district-specific extension efficiency is tested in model III, where now only the coefficient for Machakos is positive and significant, while the coefficient for Kericho is negative and significant; the others are all negative but insignificant. The test results for model III also show that the simple sum of district-specific extension effects is negative (-0.9), although it is insignificant. The weighted sum of the impact of extension across districts is also negative (- _0.15)_ and insignificant.

The marginal rate of return to extension implied by the estimated coefficients from all models in table 2 is negative (for none of the models in table 2 does the extension coefficient have a positive sign).

# **Summary and Conclusions**

This note is part of a larger exploration by OED of the effects of the investment in agricultural extension in Kenya. The investment was supported by two World Bank projects starting in 1982, which introduced the T&V system of management for public extension services. The impact of the projects has been the subject of much debate. The particular purpose of this note is to revisit the earlier widely disseminated results on the impact of T&V extension in Kenya by Bindlish and Evenson (1993, 1997).

Using household data from 1990, B&E found the returns to extension to be very high. The findings presented in this note, however, show that the returns estimated by B&E suffer from data errors and limitations imposed by cross-sectional data. Correcting for several data processing and measurement errors, the results are shown to be less robust than reported by B&E. The second problem with the B&E results is that they are highly sensitive to regional effects. For the same data set and cross-sectional model specification as used by B&E, with the inclusion of region-specific effects, a positive return to extension cannot be established. The sensitivity to

- 20 Given the significant impact of the new variables on the extension coefficient, a sensitivity analysis was undertaken to determine which variables had the biggest impact. Results (not reported) show that the new RPP indicators have the largest impact (reducing the extension coefficien **t** o 0.04 and insignificant), followed by the 1982 cluster-means for infrastructure variables, area, production, livestock assets and non-farm income (reducing the extension coefficien **t** o 0.11 and insignificant).

- 21 The sensitivity tests show that the extension coefficient fails to attain significance or substantively change in magnitude even when the AEZ and RPP indicators are excluded in the presence of district fixed effects.

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