80 CHAPTER 5
Gender, age, and education did not have a statistically significant effect on participation. Although they all had a positive effect among households
in comparison with NAADSNON-3 households, they had a mixed effect (mostly positive in the case of age and negative in the case of education) for house- holds in comparison with NAADSNON-1 households.
Although the higher-order (i.e., squared and interaction) terms generally had no statistically significant effect on the likelihood of participation by themselves, their inclusion in the estimation strengthened the effect (i.e., increased the magnitude) and statistical significance of the other factors included in the model.
Interestingly, the probit results show that the longer the NAADS pro- gram has been in the subcounty, the less likely farmers are to participate directly in the program, suggesting that farmers may be giving up direct participation with time. This is not surprising and is likely due to several factors. Generally, it reflects declining net gain from direct participation. It is possible that farmers may not have received grants they expected to receive or that the cost of participation, including membership fees, has exceeded the benefits from any grants and other benefits received. The findings from analysis of the farmer group surveys also reflect the possibil- ity that farmers dropped out when their preferred enterprise(s) was not selected among the three promoted in the subcounty or when the focus of the program graduated from their preferred enterprise(s) to others.
Indirect participation (associated with NAADSNON-1) or exposure to informa- tion (associated with NAADSNON-2) may meet their needs. These results are consistent with those of Benin et al. (2011), although it may not seem so
at first glance, because those researchers found participation positively associated with the length of program implementation. However, Benin et al. (2011) define participation as including both direct and indirect participa-
tion (i.e., NAADSDIR + NAADSNON-1) and nonparticipation irrespective of expo- sure (NAADSNON-2 + NAADSNON-3). Therefore, some of the findings on the determinants of participation in the NAADS program in Benin et al. (2011)
are also different. Because we have seen that different covariates have different types of influence (sign, magnitude, and statistical significance) on the decision to participate or not to participate in the NAADS program across different groups of households by location relative to where the program is implemented, our findings suggest that pooling observations for the different unique groups as was done in Benin et al. (2011) could lead to different policy implications and, as we shall see later, limit any potential gain from matching each direct participant with multiple non- participants that are similar in several attributes but different in many others.
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