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OUTCOMES AND IMPACTS OF NAADS 119


(about 9.5 percent) in the per capita consumption expenditure of Ugandans between 2002–03 and 2005–06 (Uganda, BOS 2006).


The detailed selected second-stage results presented in Tables A.10–A.12 show that different factors have contributed differently to changes in these outcomes, with differences across the different matched subsamples. Regard- ing change in access to food, for example, changes in gender of household head, size of household, income sources, and access to credit, roads, and markets were statistically significant factors. Regarding perception of change in nutritional quality of food, only changes in gender, household size, and access to credit and markets mattered. Perception of change in wealth was significantly influenced by changes in education and income sources.


Distribution of Program Effects and Factors Enhancing or Mitigating These Effects


In this section we present the results of the estimations done to examine the distributional effects of the NAADS program as well as assess the factors that have enhanced or mitigated the effects of the program. As presented in the methodology chapter, this was done by re-estimating the ATTs associated with the value of crop output per acre, the value of livestock output per TLU, and agricultural revenue per AE using both the grouping and interaction approaches presented earlier. For the grouping approach, the regression is estimated separately for subsamples of categories of the explanatory vari- ables, for example, male-headed and female-headed households (the results are presented in Table 5.28). The second approach includes in the regres- sion a term of interaction between the NAADS participation variable and each of the other explanatory variables (the results are presented in Tables 5.29–5.31). For the grouping approach (Table 5.28), a positive (or negative) coefficient means that the impact of program is greater (or lower) within the relevant subcategory of the factor. Similarly, the coefficients can be com- pared across the subcategories, particularly where they are of the same sign. For the interaction approach (Tables 5.29–5.31), the interpretation has to be done in conjunction with the initial estimates vis-à-vis how the initial effects without the interactions have changed. However, basically, a positive (or neg- ative) coefficient means that the impact of the program has been enhanced (or mitigated) by the change in the relevant factor. For both we used the Model III specification and matched subsamples based on three nearest- neighbor matches. For the interaction approach, however, we report only the


detailed results associated with the subsample of NAADSDIR and NAADSNON-3 households. As expected, different factors have contributed differently to enhancing or mitigating the effects of the program, which is shown by the differences in the signs, magnitudes, and levels of statistical significance


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