34 CHAPTER 3


using STATA software (version 11, StataCorp 2008), which calculates the cor- rect standard errors based on the specification used in terms of identifying the strata, cluster, and sampling weights to be used in the estimation. In the data used here, the clusters were the villages and the sampling weights were calculated using parish-level human population data (Uganda, BOS 2003). The sample weights are the inverse of the probability of a household’s being selected in the sample, which was calculated as (the number of selected parishes divided by the total number of parishes in the subcounty) multiplied by (the number of selected households divided by the total number of house- holds in the parish). Because population data were available only at the par- ish (not the village) level, random selection of households at the parish level was assumed in the calculation. Due to the nature of the sampling methods used in the surveys, however, the results are representative only of the selected subcounties, because these were purposely selected. The panel of 719 households reflects the valid number of households with valid observations on the variables of interest in both the 2004 and the 2007 surveys (see Table 3.1). This means that 183 (i.e., 902 – 719) of the initial house- holds were not used: 159 of them were not unobservable in 2007 because the households were unavailable to be interviewed that year due to either reloca- tion or dissolution of the household; 24 were not used due to missing observa- tions on the variables of interest in one year or the other. The former issue presents a potential for attrition bias when the panel data are used. We tested for the potential attrition bias in two ways. First, we used a simple student t-test to analyze differences in the mean values of the relevant variables mea- sured in the initial period (i.e., 2004 data) between the panel observations (719) and the attritor observations (159). This was done separately for the participants and the different subgroups of nonparticipants. Then we applied a commonly used formal test proposed by Becketti, Gould, Lillard, and Welch (1988), the BGLW test, which involves regressing an outcome variable on the explanatory variables (x), an attrition dummy, and the attrition dummy inter- acted with x using the 2004 data. An F-test of the joint significance of the attrition dummy and the interaction variables is then conducted to determine whether the coefficients from the explanatory variables differ between the attritor households and those remaining in the panel. This is done separately for the participants and the different subgroups of nonparticipants. We pre- sent and discuss the test and its results later, in Chapter 5, after details of the variables used in the analysis have been presented.


Limitations and Interpretation of Results


Although our preferred approach for estimating the treatment effect is the 2SWR method given the drawback from using either the DID or the matching


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