120 CHAPTER 5
associated with estimated coefficients. We now discuss key findings associ- ated with each of the factors considered, focusing mostly on the estimates that are statistically significant.
Gender
Although inclusion of the term of interaction between the NAADS indicator and change in gender had no significant effect on the individual separate effects, the grouping approach shows that the estimated program impact was greater for male-headed households in the case of livestock productiv-
ity when NAADSDIR households were compared with NAADSNON-1 households but lower in the case of crop productivity when they were compared with
NAADSNON-1 households. Looking at total agricultural output, however, the estimated impact was greater among female-headed households when direct
participants were compared with NAADSNON-1 households. Thus it seems that the NAADS program has so far been effective for men and women alike, although in different ways. That the majority of the extension providers in Uganda are men (about 88 percent) does not seem to have biased delivery of advisory services, although research shows that gender similarity between provider and receiver plays a major role in its effectiveness (Lahai, Goldey, and Jones 2000). The gender imbalance in service providers in Uganda is consistent with the general knowledge that women experience limited access to agricultural extension (e.g., see Staudt 1982; Alawy 1998; Adesina et al. 2000; Doss 2001). In SSA it has been estimated that only 7 percent of exten- sion resources are spent on women; globally it is estimated that women receive only 2–10 percent of extension contacts and 5 percent of services (Swanson, Bentz, and Sofranko 1997).
Age, Education, and Household Size Inclusion in the estimation of terms of interaction between the NAADS indicator and these variables, particularly changes in age and education, is important for assessing the overall impact of the program, especially on crop productivity.
The estimated average impact associated with the subsample with NAADSNON-1 households was lower for both younger and older household heads regard- ing crop and livestock productivity, respectively. Regarding income, it was
larger when compared with NAADSNON-1 households and smaller for younger participants when compared with NAADSNON-2 households. For education we see greater impacts across all three categories (no formal schooling, com-
pleted primary education, and completed post-primary education) on crop and livestock productivity and agricultural revenue among the subsample with NAADSNON-2 and NAADSNON-3 households.
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