in the factories that participated in the selection experiment – is orthogonal to the results of their diagnostic tests. Only once the factories are presented with these screening results as part of the selection experiment do they re-rank the candidates on their skills, and especially their soft skills. I explain below how I take this potential selection effect in the half of the factories that were randomized into the selection experiment into account in the estimation.

Second, I find strong support for this empirical strategy when comparing the selected and the nominated group on observable characteristics.<sup>35</sup> The balance tests in Table 1 demonstrate that the selected and nominated groups are statistically indistinguishable on a wide range of observable characteristics.<sup>36</sup> Only two tests out of 34 reject equality at the 10% significance level using the conventional t-tests in column (5), and only one test rejects when using the randomization inference p-values in column (6).<sup>37</sup> This is fewer than one would expect to reject by chance if the null hypothesis is true. The selected women have about a year **less** experience in the garment industry, but they score about seven percentage points higher on the diagnostic literacy test than the nominated group. In addition, the normalized differences between the means of the nominated and selected groups in column (7) are all weakly smaller in absolute value than 0.30 and can therefore be considered well-balanced according to this sample-size free way of investigating balance in covariates, following Imbens (2015).<sup>38</sup> Lastly, I conduct a test of joint orthogonality by investigating whether these 34 observable characteristics jointly predict selection in a linear regression. The p-value of the F-test is 0.87, which shows that selection is not determined by a wide range of baseline observable characteristics.<sup>39</sup> I employ three different estimation approaches. I use OLS with control variables chosen by PDS Lasso in the main analysis, and employ a regression discontinuity design and a matching method in Section 4.4. The following is the main specification for outcome _Y_ of each selected or nominated operator _i_ in factory _f_ , while pooling both household survey waves _w_ = _{_ 1 _,_ 2 _}_ :

> 35Note that, for all checks in this subsection, I only include women whose data is also used in the analysis, i.e. who were interviewed in at least one household survey wave and had another adult decision-maker in the household. This is to ensure that attrition is not driving an imbalance.

> 36See Table C.4 in the Online Appendix for variable definitions.

> 37I use the “ritest” Stata package to implement the randomization inference tests, see Heß (2017).

> 38The normalized difference is calculated as the difference in means between the two groups, divided by the square root of the average of the sample variances of the two groups.

> 39See Table C.6 in the Online Appendix. In this paper, to include the variables only defined for married respondents, I follow two steps. First, I set these variables to zero if they are missing and, second, I include dummy variables that indicate whether the variables are missing in the regression. I also do not find that covariates predict selection if I include all covariates but limit the sample to married respondents, or if I only include covariates which are defined for all respondents in the regression. The p-values for the F-statistic of the regression in these cases are 0.96 and 0.92, respectively.

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