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METHODOLOGY 29


NAADSNON-1, ATTNON-2 for the subsample of NAADSDIR and NAADSNON-2, and ATTNON-3 for the subsample of NAADSDIR and NAADSNON-3. Given that direct participation in the program with access to grants con-


ferred greater benefits than occasional contact with NAADS service providers or information, and assuming that the models were correctly specified and


estimated, we expect ATTNON-3 > ATTNON-2 > ATTNON-1. The average direct effect of the program can be measured by ATT NON-3, while the average indirect effect can be measured by the differences, that is, ATTNON-3–ATTNON-2 and ATTNON-3–ATTNON-1. The aggregate overall effect of the program is obtained by summing these over their respective populations and then adding them up.


Enhancing and Mitigating Factors and Distributional Effects The NAADS program is one of several institutional reforms and investment interventions undertaken in Uganda, especially under the PMA agenda, for overall agricultural and rural development in the country. These include rural financial services, infrastructure, marketing, and education, among others. Thus, besides controlling for the effect of these factors on the outcomes in the estimation framework, it is equally important to assess how and the extent to which they may have enhanced or mitigated the impacts of the NAADS program. By design, the NAADS program also expects to target specific populations, for example, women and economically active poor farmers (i.e., those with limited physical and financial assets, skills, and knowledge) rather than the destitute or large-scale farmers. Thus we can expect the impact of the program to be different in different places or on different socioeconomic groups. For example, it is suggested that the NAADS program’s implementa- tion procedures and practices made it vulnerable to elite capture, benefiting the more educated and influential farmers in the community (Parkinson 2008; Bukenya 2010). Examining these effects as well as other distributional effects of the program can be done in two ways: (1) estimating the ATTs across several categories of variables representing different socioeconomic and demographic groups (including gender, age, education, income, assets) and incidence of or access to different types of other public services (i.e., credit, roads, markets) and (2) estimating the ATTs associated with interaction terms between the stated variables and the NAADS indicators. Regarding the first procedure, we estimated the ATTs using different sub- samples of the data based on categories of the explanatory variables using cutoff points commonly used in measuring related development indicators or those affiliated with the NAADS program, with some adjustments upward or downward to ensure that there were enough observations in each subsample for us to carry out the estimations reliably. These were unnecessary for the discrete explanatory variables such as gender, education, and income strat-


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