applied propensity score weighting and matching techniques to achieve a higher degree of baseline comparability across groups.<sup>40</sup>

# **b. Survey Response Rates and Attrition**

One potential problem with any study such as ours is sample attrition. Approximately one year after each baseline survey, we conducted a follow-up survey.<sup>41,42</sup> The response rates were quite high for both followup surveys (see Table 5). Overall, the survey firm was able to track and successfully interview 88 percent of the baseline survey respondents, yielding a final sample for analysis of 4,101 individuals.<sup>43</sup> High response rates such as these limit the degree to which any differences between respondents and non-respondents can affect our estimates.

Particularly worrisome is the possibility of so-called “ _differential attrition_ ”, a situation in which we reinterview trainees and non-trainees at statistically different rates. Even with low attrition rates for a panel survey, differential attrition may influence the scientific validity of results. Suppose, hypothetically, that very motivated individuals in the control group ( _i.e.,_ non-trainees) are more likely than trainees to migrate to a district where they find employment. As a result of this migration, these individuals will not show up in our analyses and this scenario could compromise the scientific validity of this study’s estimates.

In Table 19, we explore this possibility and show no evidence to support it. Table 19 shows a series of regressions on the correlates of survey attrition. We regress the panel status of respondents on their treatment status, depending on how the treatment indicator is defined. This specification provides information on whether treatment or control individuals are more likely to be lost to follow-up. Next, we add a series of covariates, such as gender, age, marital status, parental status, ethnicity, and employment, a set of district and T&E provider-specific control dummy variables. The results indicate that attrition is not correlated with treatment status, and hence differential attrition between treatment and control groups is not an issue in this evaluation.

# **c. Uptake of EF-Sponsored Training Courses**

Another threat to our study is imperfect compliance, which occurs when those offered treatment choose not to participate, or when people from the control group gain access to the program. These deviations from treatment assignment may bias our estimates of program impact, because the factors that determine one’s actual participation, such as motivation and tenacity, cannot be observed and are likely correlated with the outcomes of interest.

> 40 We have redone the baseline balance tests after matching, and the results indicate that though the similarity of treatment and control groups has improved, significant differences remain. The post-matching balance tests are available upon request.

> 41 Because the EF-sponsored training courses vary in length from 1 to 3 months, the follow-up survey examines outcomes 9 to 11 months after the end of the training.

> 42 The EF itself conducts follow-up with a sample of participants up to 6 months after the training to verify employment and earnings. Hence, the impact evaluation follow up survey occurs 3-5 months after the treatment group’s last contact with the program.

> 43 The reasons given for loss to follow-up for the 2010 and 2011 cohorts include: inability to track the household (11%), no one in the household during multiple visits (15%), refusal (8%), respondent migrated for work within Nepal or abroad (8%), respondent migrated after marriage (10%), or other (40%).

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