METHODOLOGICAL FRAMEWORK 17
The two crop years, 2004–05 and 2005–06, were comparable in terms of rainfall. Both years were reported to be normal in terms of agricultural produc- tion, even though production of cereals and tubers for 2004–05 was 8 percent lower than for 2005–06—mainly because of delayed delivery of inputs (FEWSNET 2006). Fortunately, however, the unfavorable production conditions affected both treatment and control groups—thereby netting out the effect on the 2004–05 season.
Data Analysis
Impact assessment studies face three interrelated challenges: establishing a viable counterfactual (the predicted outcome in the absence of the intervention —that is, what would have happened to the beneficiaries had they not partici- pated in the project); attributing the impact to an intervention; and coping with long and unpredictable lag times (Alston and Pardey 2001; Salter and Martin 2001). If a project’s outcome indicator is household income, the average impact of the project on its beneficiaries (referred to in the impact assess- ment literature as the average effect of the treatment on the treated [ATT]) is defined as the difference between the expected income earned by project beneficiaries while participating in the project and the expected income they would have received if they had not participated in the project:
ATT = E(Y1|p = 1) – E(Y0|p = 1), (1) where ATT is ATT, p indicates participation in the project (p = 1 if the subject
participated in the project, and p = 0 if the subject did not participate); Y1 is the outcome (household income, in this example) of the project beneficiary
after participation in the project; and Y0 is the outcome of the same benefi- ciary if he or she had not participated in the project.
Unfortunately, we cannot observe the counterfactual income of the ben-
eficiaries had they not participated in the project, that is, E(Y0|p = 1). Simply comparing incomes of households participating in the project with those not participating could result in serious biases, because the two groups may be quite different and thus likely to have different incomes regardless of their
participation. For example, adding and subtracting E(Y0|p = 0) on the right side of equation (1) results in: ATT = [E(Y1|p = 1) – E(Y0|p = 0)] – [E(Y0|p = 1) – E(Y0|p = 0)]. (2)
2003). We believe that similar principles apply to the results of PSM (the quasi-experimental approach used in this study), although we have not seen specific articles on this issue in the relatively recent literature on this approach.
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