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68 CHAPTER 5


be needed to show a significant positive effect when there is attrition (as will be estimated later) compared to when there is no attrition. Therefore, the estimated treatment effects of the program using the data at hand are likely to be underestimated, to the extent that the models are specified and estimated correctly.


Determinants of Participation in the NAADS Program: Overlap in Covariate Distributions


As discussed earlier, to improve overlap in the covariate distributions, or common support, we tried different combinations and transformations of the explanatory variables (x) in estimating the probit participation equation. Details are presented in Table 5.6. First we measured participation using the status observed in 2007, which is essentially measuring participation at any point in time because there were no dropouts. This is also important for the matching to ensure that participants are matched with nonparticipants in all years to avoid duplication or crossover. This way, a treatment household is always a treatment household and cannot switch status, and the same is true for a control household. Based on the different definitions of treatment (i.e., direct participants only, indirect participants only, or both) and controls (i.e., those with no exposure to the program), we estimated Equation 3.3 using the 2004 values of the explanatory variables and specifications shown in Table 5.6.


Table 5.6—Description of NAADS participation models estimated Feature Description Variable


Household Outcomes


Inclusion of the variables measuring the household characteristics: gender, age, education, size, income strategy, and membership in other organizations


Initial value of outcome indicator (i.e., of the dependent variable to be estimated in the second-stage regression) plus inclusion versus exclusion of the variables measuring the 2004 values of potential explanatory endogenous variables (i.e., land owned and productive assets)


Higher level


Inclusion versus exclusion of the variables measuring the higher-level effects: access to services and regional location


NAADS program Inclusion versus exclusion of the variable on the length of time the NAADS program has been in the subcounty (NAADS_years)


Transformation Squared Logs


Interaction


Inclusion versus exclusion of squared values of the continuous variables Inclusion versus exclusion of logarithmic values of the continuous variables


Inclusion versus exclusion of interaction terms among different variables: gender × education and gender × land owned


Source: Authors’ specification of the models to be estimated.


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