Results from the propensity score estimation (shown in Table 7) exhibit only three variables correlated with an individual being offered training ( _i.e.,_ treatment status).<sup>50</sup> This finding reflects the high degree of similarity among short-listed candidates. The only characteristics correlated with an individual being assigned to a training program are being Muslim, the likelihood of having children, and the number of children one has. No other variables are correlated with training assignment, meaning that within the pool of shortlisted candidates, there are few observable differences between those selected for training and those who are not.

Based on the estimated propensity score, we match each treated individual ( _i.e.,_ a person assigned to a training program) to a group of control observations ( _i.e.,_ individuals who are non-trainees), as described in section IV.

# **b. Impacts on Employment and Earnings for the Full Sample**

In this section we present impacts of the EF program as estimated with the three identification strategies described in Section IV. Table 8 shows the ITT results on employment and earnings for the pooled 2010, 2011 and 2012 cohorts. We find (results in the first row of Table 8) strong evidence of consistent impact on the employment rate across all specifications.<sup>51</sup> All three models indicate a positive and significant effect, despite the high employment rate ( _i.e.,_ 61 percent) at baseline. Restricting the employment to nonfarm activities, we also find a significant increase: the rate of participation in non-farm income-generating activities increases by 15 to 16 percentage points (from a base of 29.6 percent). Translating the results in percentage change terms, we find that the program increased non-farm employments by 50 to 54 percent. These impacts are not only statistically significant but also economically meaningful.

We also examine the trade-specific income generating activity (IGA) rate – the percent of individuals who find employment in the same trade as the training that they applied for – and we find impacts ranging from 18 to 19 percentage points. The trade-specific IGA impacts are larger than the non-farm employment impacts, suggesting that members of the control group, even when able to find employment, were less able than the treatment group to find employment in the trade in which they sought training.

Our results are considerably lower than estimates obtained by a simple before-and-after comparison within the group of trainees. For the 2010 cohort, for example, the treatment group had a non-farm employment rate of 30 percent at baseline and 55 percent at midline. A simple difference in employment rates would have suggested that the program increased employment rate by 25 percentage points (or 83 percent), substantially larger than our quasi-experimental estimate of 16 percentage points (or 50 percent).

The EF program also leads to persistent improvements in the underemployment rate ( _i.e.,_ cases in which people are working fewer hours than they wish). Table 8 shows that EF-sponsored training courses increased hours worked in IGAs for the pooled cohorts by 19-21 hours per month ( _i.e.,_ 28-30 percent). All three model specifications exhibit a statistically significant and positive impact.

> 50 We report the ITT results, for both all three cohorts. For the ITT first-stage probit, treatment is defined as having a score that qualifies the respondent for admission to the EF training course to which s/he applied. 51 We measure employment by whether the respondent reported any income-generating activities in the past month or not.

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