or production managers. In this process, the most important consideration is to match operators to the work stations that they are skilled at (e.g. overlock machines are only operated by workers skilled in using this machine).

The management of the production floor typically prefers to keep the composition of wellfunctioning lines unchanged. However, there are some situations when it can be necessary to move operators for production reasons, for example to temporarily cover absences of operators on other lines, or when the style produced changes and with it the production requirements. Operators might also be moved to lines with a lower target if they struggle to meet targets or increase alteration rates. Line operators and supervisors are sometimes able to express their preferences for the assignment of operators to lines, but — barring any serious allegations of misconduct — the production requirements always take precedence in dealing with any potential requests. According to the industrial engineer, it would be especially unlikely to observe sorting of line operators towards lines of new supervisors in their trial period. During this trial period, supervisors are expected to prove themselves with the team they have been given, and any requests by themselves or operators to move to their lines would be unlikely to be granted.

I provide three pieces of evidence that support this strategy.<sup>41</sup> First, I show that the exposed and non-exposed operators are balanced on a number of observable characteristics in Table 2. Since there is no baseline data for the random operators, these characteristics are from the household surveys and more limited than the data available for the promotion analysis. Out of the 15 meanequality tests in Table 2, one test — for assets brought into the marriage — rejects at the 10% significance level using the conventional p-values in column (5), and one additional test — for age — rejects when I use the randomization inference p-values in column (6). This is in line with what one would expect to happen by chance. However, the normalized differences in column (7) are small to moderate. None of them is greater than 0.16 in absolute value, much smaller than the cutoff used by Imbens (2015). Second, I show that all 15 variables do not jointly predict exposure to a female supervisor by estimating a linear regression of _exposed_ on all 15 variables. The p-value of the overall F test of the regression is 0.41.<sup>42</sup> Lastly, I investigate whether the lines of the operators who were exposed to the female su-

> 41As in Section 3.5.1, to take attrition into account, I only include women in these checks whose data is also used in the analysis, i.e. who were interviewed in the household survey and had another adult decision-maker in the household. In the ideal scenario, I would be able to use data of line operator movements to exclude sorting towards lines exposed to new female supervisors based on characteristics that determine the operators’ household outcomes of interest. Unfortunately, the factories in this sample do not keep a reliable account of operator movements.

> 42See Table C.7 in the Online Appendix. As in Section 3.5.1, to include the variables only defined for married women, 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. As Table C.7 in the appendix shows, 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 0.28 and 0.32, respectively.

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