participants (de Mel, McKenzie, and Woodruff 2014; Sonobe, Suzuki, and Otsuka 2011). However, concerns have been raised about the ability of low‐skill participants to implement what they have been taught (McKenzie and Woodruff 2014). Given that noncognitive skills are known to be strong determinants of entrepreneurial ability and that the program had impacts on these outcomes, an analysis of the heterogeneity of effects by this dimension is important.

In terms of skills formation, the program had a greater impact on both cognitive and noncognitive skills among those participants with lower initial skills by nearly all measures of noncognitive skills (appendix A, table A6, columns 6–9). However, these skill gains translated into labor market impacts differently depending on the initial noncognitive ability, possibly because of differences in the ability of beneficiaries to mitigate shocks. Among those participants with higher noncognitive skills (that is, impulse control) at baseline, the program had larger effects on employment, earnings, and the accumulation of capital stock (appendix A, table A6, columns 1–5). The direction of the interaction is similar to the interaction of employment and self‐employment, although the coefficient is not significantly different from zero. Meanwhile, those participants with lower initial ability experienced larger impacts on consumption.

At first glance, this result appears to confirm the second hypothesis above that more highly skilled individuals were better able to utilize the skills acquired through the program for productive ends in the short term. However, because of the Ebola crisis, the story is likely to be more complex. Although, because baseline data on wealth were not collected, the study has not been able to test whether initially poorer individuals achieved larger impacts, statistical analyses suggest that noncognitive ability is strongly associated with wealth: on average, youth in households with a lower proxy means test score have lower noncognitive ability. A 1 point reduction in the proxy means test score is associated with a 4.1 percent, 6.7 percent, and 9.1 percent decrease in impulse control, locus of control, and multitasking scores, respectively. The differential impact by initial skill may therefore reflect differences in wealth. Individuals with less ability—who are also the poorest—may have been less able to withstand the Ebola shock in the short term and, as a result, utilize the modest capital acquired through the program to smooth their consumption rather than making productive investments.

There is also suggestive evidence that, in the longer term, those participants with initially low skills may have benefited more once they had an opportunity to apply their newly acquired skills. The data confirm that there was a strong positive link between noncognitive skills among urban Sierra Leonean youth and their consumption, earnings, and average profits. Together with the finding that the program had a larger effect in terms of noncognitive skills among those participants who had less initial ability, this may indicate that there is a time lag between when skills are acquired and returns materialize among those who start out with low ability. Additional follow‐up surveys are needed to confirm this is the case, which would support previous evidence showing that targeting productive interventions on the poorest may increase the returns (Macours, Premand, and Vakis 2013).

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