METHODOLOGY 19
light concern over data quality, cause-and-effect methodological issues, and quantification of all related costs and benefits. The case of evaluation of the training and visit extension system in Kenya in the 1980s is a typical example; the previously estimated large positive returns (e.g., Bindlish and Evenson 1997) were found to be grossly overestimated with careful modeling of con- founding factors, whether static or changing over time, among others (Gau- tam and Anderson 1999).
In this study we used data from the two rounds of surveys, various impact assessment methods, and different model specifications within the same general method to deal with the concerns just stated and to generate greater confidence in the policy implications of the resulting estimates. First we examine the underlying attribution problem and other estimation issues asso- ciated with evaluating the NAADS program. Then we present the methods used to address these issues and to estimate the impact of the program. If we let y represent the set of outcomes of interest to the study (e.g., agricultural productivity (Q) and income (INC) as shown in conceptual frame- work), then the impact of the NAADS program can be measured by the dif- ference between the expected value of y earned by each farm household j participating in the program and the expected value of y the farm household would have received if the farm household had not participated in the pro- gram. This difference, which is the impact of the program or simply the impact of treatment, can be measured as the average treatment effect on the treated (ATTj):
ATTj = E[y1j|NAADSj = 1] – E[y0j|NAADSj = 1], (3.1)
where y1j is the value of the outcome of farm household j after participation in the NAADS program and y0j is the value of the outcome of the same farm household j if it had not participated in the NAADS program. Unfortunately,
we cannot observe the counterfactual, i.e., the value of the outcome of the farm household if the farm household had not participated in the program. In addition, because individual farm households may choose to participate in the program or not participate, those that choose to participate are likely to be different from and benefit more than those that choose not to participate. For example, those that choose not to participate may not have the resources to do so, because participation in the program requires some initial cofunding by participants and adoption of more costly technologies and practices (more will be said on the process of participation later). These differences in behav- ior, if they influence the outcome, may invalidate the results from simply comparing outcomes by treatment status, possibly even after adjusting for
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