30 CHAPTER 3


egy, for example. Regarding the grouping based on age of the household head, for example, we used 40 years as the cutoff point as opposed to the 30 years used by the NAADS program managers to distinguish the young from adults. One acre was used in the case of access to land, one-third and two-thirds of the range of the value of productive assets was used for defining asset terciles, and 0.5, 5.0, and 10.0 kilometers were used as the cutoff points to define low and high access to all-weather roads, markets, and credit, respectively. Details of all the cutoff points used are given in the relevant tables in which the results are presented. The analysis was carried out using agricultural revenue per adult equivalent as the outcome indicator for the alternative definitions of participation in the NAADS program using the PSM and 2SWR methods.


Estimating the distributional effects in this manner means that, for any subsample, change between 2004 and 2007 values of the particular explana- tory variable used in creating the subsample may not also be used in the estimation. This is important for discrete variables such as gender, education, income strategy, and region to avoid the dummy variable trap, especially when the values of the variable have not changed over time. For subsamples based on explanatory continuous variables, it depends on the variation of the vari- able within the subsample, because limited variation could lead to estimation problems when the affected variable becomes severely correlated with other explanatory variables. To generate greater confidence in our findings, we carried out the estimations both including and excluding the concerned explanatory variable. Another issue is that because the estimation was done for the alternative definitions of treatment, which are in turn based on sub- samples of matched direct participants and nonparticipants or indirect par- ticipants and nonparticipants, the number of observations (and degrees of freedom) is further reduced. This is particularly the case for subsamples of direct participants and nonparticipants, which are relatively small, so we could not obtain many of the estimates, especially with the matching method esti- mator. In general, the results regarding the estimations based on smaller sub- samples of the data are less reliable to the extent that they are not repre- sentative of the alternative states of the indicators. Because the subcounties were purposely selected and households were randomly selected within the subcounties, the results associated with subsamples defined on the basis household-level characteristics would tend to be more reliable. The second procedure was performed using the 2SWR method by including interaction factors between the NAADS participation variable and many of the other explanatory variables. Due to multicollinearity problems, however, we included only one interaction term at a time instead of multiple inter- action terms. Therefore, the results from these analyses are suggestive only,


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