# 4 Empirical Strategy and Threats to Validity

The analysis relies on the intent‐to‐treat (ITT) estimate through an analysis of covariance regression, as follows:<sup>11</sup> where ��,�� are the outcomes of interest for an individual � in center � at follow‐up ��� . ���� is the outcome for individual � at baseline; �� captures time‐invariant center effects; and ��,�� is the error term. The estimated coefficient �� captures the effect of the random assignment, that is, the offer to participate in the program or the lack of such an offer. The equation includes fixed individual characteristics, ����,��, captured at baseline to increase the precision of the estimates. An analysis of covariance regression specification was used because it is more efficient than the postestimator or the difference‐in‐difference estimator; in particular, it improves power, especially in cases where baseline and follow‐up outcomes have low autocorrelation, which is the case with a number of the outcomes of interest in this study (McKenzie 2012).

Beyond the overall treatment effect, the study sought to test differential impacts by gender. For ease in the interpretation of the results, equation (1) was reestimated for two subsamples (women and men), instead of multiple interaction terms. To test the relative effectiveness of the various treatments, the following specification was used, and an equality of treatment hypothesis was then tested: The ITT coefficients compare the outcomes among individuals who were originally assigned to receive the treatments with the outcomes among the individuals who were assigned to the control group, regardless of whether the individuals actually took part in the program. These estimates are therefore conservative if the impacts observed are positive; they are therefore a lower bound of the effect of the program among those who attended training sessions and participated in program activities. Thus, if the program increased employability and achieved other desired outcomes, the effect would be larger among those who actually participated in the program than among those reported in this paper.

# a. Attrition

At endline, Statistics Sierra Leone was able to track 2,782 of the 3,319 individuals surveyed at baseline, putting the overall survey attrition rate at 16 percent (Appendix C).<sup>12</sup> Over 80 percent of individuals were successfully interviewed at both baseline and endline. To check for bias resulting from attrition, Appendix C presents t‐tests of differences in means between individuals who left the sample (attritors) and individuals who stayed (nonattritors). Most of the differences between attritors and nonattritors arise from variables with zero or a low number of observations among attritors or nonattritors and thus constitute a small share of the sample.

> 11 Conditional regressions do not control for outcomes at baseline for two main reasons: (a) the loss in sample size beyond the loss from conditioning based on employment or on engagement in a household enterprise, particularly because of a labor market weakened by Ebola, and (b) key measurement changes to the endline survey after the first national labor force survey was conducted in 2014. Regressions using consumption and business practices as outcome variables do not control for outcomes at baseline because the relevant data were not collected at baseline.

12 McKenzie and Woodruff (2014) examine survey attrition rates in studies on business training. They find a range of between 5.3 percent and 28.0 percent; the rate in about half the studies is higher than the rate in this evaluation.

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