by the factories. The participating women therefore gained experience in a management position whose leadership responsibilities were very different from the work they did before, even if not all of them were officially promoted. Importantly, this empirical set-up allows me to show that the impacts I identify are not primarily due to an income effect that a promotion or exposure to female managers may generate.

To study the impact on the household, I conducted two waves of extensive surveys with the women involved in the program and with their husbands, or another adult household member if the woman was unmarried. Because the vast majority of respondents were married couples, for simplicity I refer to them as “wife” and “husband” in the rest of the paper. The respondents were interviewed simultaneously but separately at their homes.<sup>4</sup> I compare outcomes of women who were selected for the program to the nominated and shortlisted runners-up for an intent-to-treat analysis, thus exploiting the selection process for the program as a quasi-experiment. At the start of this process, we determined the number of new supervisors to be selected in each factory together with the respective management as factory-specific cutoffs. Factories then provided us with a ranked list of female production-line operators as candidates. Our team conducted extensive eligibility checks, which meant that only highly skilled and experienced operators with a supportive family were included in the shortlist for the program. Out of the eligible and shortlisted operators, only those with a rank that was better than the factoryspecific cutoff were “selected” for the promotion program. The comparison group consists of the “nominated” and shortlisted women who passed all diagnostic checks but continued to work as production-line operators.

I argue that the assignment to the selected group is quasi-random because factories have limited ability to predict which women would become successful supervisors among the shortlisted group of women. Because the factories had historically nearly always promoted men, they had large numbers of skilled and experienced female line operators from which to choose new supervisors. The eligibility criteria and diagnostic tests further ensured that the considered line operators were a largely homogenous sample. While it might be easy for factories to identify operators who would **not** make a good supervisor in their factories, it is much harder to decide the ranking among a sample of women who had **all** been judged to be suitable after an extensive screening. This argument is similar in spirit to the literature on microenterprises, recruitment and venture capitalism, which shows that humans are often inept at predicting which firms, workers or investments will be successful among an already select sample (McKenzie and Sansone, 2017; Hoffman et al., 2018; Nanda, 2016). I conduct extensive checks comparing the two groups using variables from the promotion program baseline, which show that the two groups do not differ on observable criteria.

I use three estimation approaches for the analysis: a machine-learning technique to choose

> 4For funding reasons, the home surveys took place after the conclusion of the promotion program.

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