100 CHAPTER 5


different model specifications shows that using the different measures for the livestock productivity indicator had very little effect on the results. Whether the estimates were corrected for the sampling characteristics or not did not seem to matter much in terms of statistical significance with the 2SWR with- out the covariates. Regarding the 2SWR with the covariates, however, the correction gave rise to more statistically significant estimates. Here, too, we observe the U-shaped or inverted U-shaped nature of the results in relation to the different subsamples of nearest-neighbor matches. In general, the esti- mates associated with the subsample based on matching with a single nearest neighbor or 10 nearest neighbors (results not shown) tended to be very large or very small and sometimes different in sign and statistical significance, for reasons given earlier.


Given all this, in addition to the fact that the earlier results of Model III (i.e., inclusion of squared and interactions terms and excluding the length of the program) provided more common support, we place more confidence in the Model III results associated with the 2SWR with covariates corrected for the sampling characteristics and based on the subsample using the three and


five nearest-neighbor matches. Thus the direct effect (i.e., ATTNON-3) of the NAADS program on change in crop and livestock productivity between 2004 and 2007 could be up to 68.0–140.5 and 166.0–251.0 percent, respectively.6 As we cautioned earlier, because of our inability to capture the separate effect of access to other or non-NAADS extension services, these may overestimate the impacts of the NAADS program to the extent that NAADS participants also benefited from other extension services. The same applies to the indirect impacts. This issue and inconsistencies across results for different impact indicators make it difficult to arrive at definitive conclusions regarding the direct and, particularly, the indirect impacts of the NAADS program.


Other Factors Affecting Change in Crop Productivity Changes in other factors that significantly influenced change in crop produc- tivity between 2004 and 2007 include changes in age of household head and in education and size of household (see Table A.4).7 The negative and significant effect of the initial value of crop productivity on the change in productiv-


6 The predicted impact associated with the NAADS indicator (as well as other discrete variables) on the productivity indicators can be calculated by taking the exponential of the relevant co- efficient reported in the respective table in Appendix A, because the dependent variable is the logarithm of the value of the indicator. In particular, if the estimated coefficient is represented


by δ, then the ATT in percentage terms is given by (exp(δ) – 1) × 100. 7 In the appendix tables for these and other results, we report details of only the second- stage regression results based on the subsample using the three nearest-neighbor matches and excluding the potential endogenous explanatory variables (see Table 5.7 for the different model specifications).


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