OUTCOMES AND IMPACTS OF NAADS 81


At first glance, the histograms of the estimated propensity scores result- ing from the probits and presented in Figures 5.1–5.3 do not seem very dif- ferent, suggesting that different specifications of the probit model are not likely to yield different outcomes of common support. The general pattern of the histograms, however, is a skewness of the propensity scores toward


one for the treated observations (NAADSDIR) and skewness toward zero for the comparison observations (NAADSNON-1, NAADSNON-2, and NAADSNON-3), with the situation most perverse when the variable measuring the length of the


program (NAADS_years) is included in the case with NAADSNON-1 (see Figure 5.1). The balancing test results presented in Tables 5.11–5.13 also show that


including the length of the program (i.e., Models IV–VI) will likely undermine the results because several treated observations will have to be dropped to achieve adequate common support.


Combining the results just presented, that is, probits, histograms, and balancing tests, suggests that Model III (i.e., inclusion of squared and interac- tion terms and excluding the length of the program) is the preferred model because there is more common support, that is, more control observations in the upper bins of the propensity scores. Nevertheless, we still estimate the ATTs with the other model specifications, particularly Models I and II, to assess the sensitivity of the results.


Impact of NAADS: Second-Stage Model Specification, Estimation, and Results


To assess the impact of the NAADS program on the different outcome vari- ables presented earlier, we used different estimators in the second stage depending on how each outcome variable was measured. For dichotomous discrete variables such as adoption of technologies and practices, we used the random-effects probits estimator rather than the fixed-effects estimator because the conditional fixed-effects estimator is difficult to obtain, whereas the unconditional fixed-effects estimator is biased. For multiple-choice discrete variables such as perception of change in welfare (measured by increase, no change, or decrease in food and nutrition security and wealth), we used multinomial probit regression. For continuous-outcome variables, including number of extension visits received and agricultural (crop, live- stock, and total) marketed output and revenue, we used weighted least squares. For the continuous-outcome variables, the impact of the program was also assessed using the PSM. We also considered different specifications of the second-stage estimator to assess the sensitivity of the results. For each of Models I, II, and III, described in Table 5.7, the different specifications


comprised (1) estimation without and with the covariates, xj; (2) no correc- tion versus correction for stratification, clustering, and weighting of sample;


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