# _Specification_

The main concerns in dealing with cross-sectional data include the handling of household- and region-specific effects. B&E attempt to control for these effects by including locational characteristics and household human-capital variables. These data are from the 1982 survey as well as data collected for the 1990 study.

The specification issue, however, remains because of the possibility that agro-ecological zones (AEZs) in different parts of the country (e.g., in different districts) are likely to have different production patterns and productivity. Further, it is also possible that deployment of extension staff, the key variable used to measure the supply of extension, is correlated with the productivity potential of each area. If the deployment is independent, the regression coefficient on the extension variable can be taken to represent the impact of extension on agricultural productivity, with the variation across locations providing the opportunity to measure an unbiased and efficient estimate of the impact of the marginal extension staff deployed. If, on the other hand, staff deployment is influenced by the regional productivity effects, (for example, if higher potential areas receive priority in staff assignments or receive a relatively larger number of staff, as was the plan for NEP-I), the extension staff variable would implicitly also capture regional productivity differentials in addition to the impact of extension.

Even if the intended initial deployment by the center (Ministry headquarters in Nairobi) was neutral with respect to regional productive potential, the problem may re-emerge if staff are able to transfer out of relatively undesirable locations into more desirable ones. This behavior would be understandable with staff moving to locations with better amenities and access to infrastructure within each district, or moving to more progressive and dynamic districts. Subsequently, if that dynamism translates into higher value-added production, then the staff-farm ratio would again be "spuriously" positively correlated with the value of production.

From an empirical standpoint, the issue reduces to whether or not the cluster-level and other variables included in the B&E regression adequately control for region-specific effects. If not, then the regression will suffer from an "omitted variables" problem and will give biased and inconsistent estimates. In such an event, the extension staff variable would erroneously attribute to extension the effect of "natural" productivity differentials. On the other hand, if staff deployment follows a standard rule of thumb, say a pre-determined staff-farm ratio as is typically prescribed in T&V implementation plans, or if the turnover of staff is not region-specific, the correlation between the regional productivity potential and the staff-farm ratio would probably be about zero and the estimated coefficient would remain an unbiased estimate.

B&E recognized this problem and attempted to control for the "staff deployment effect" by treating it as an "endogenous" variable (page 120). However, the specification bias in this case is one of omitted variables; the extension supply variable is correlated with the error term because it is correlated with omitted regional productivity effects. B&E's instrumenting equation (page 126) validates this concern.'<sup>4</sup> The appropriate solution would thus be to effectively control

> 14 For example, mean locational education level of household heads, the availability of extension under the previous system (which was reportedly biased towards more productive and progressive areas), and hilly terrain have a positive and significant effect in explaining the variation in the extension supply variable, while low and medium potential indicators have a negative sign. The number of farms and the mean household size in a location have an unexpected negative influence on staff allocation.

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