# **5.2 Robustness checks**

I implement the nearest-neighbor matching estimator suggested by Abadie et al. (2004) for the exposed and non-exposed comparison.<sup>77</sup> The results in Panel A of Figure 2 confirm the positive and significant effect on women’s involvement in decision-making, with an estimate of 0.24 SD. This is a third smaller than the estimate I obtain using PDS Lasso, of 0.36 SD. I also find additional evidence for the reduction in spending on men’s assignable expenditures, with a highly significant coefficient of -1.17 percentage points that is very similar to the PDS Lasso estimate of -1.06.<sup>78</sup> To investigate whether the results could be driven by operators sorting to the lines of the new female supervisors, I estimate the effects on the main outcomes for the subsample of operators who joined the lines exposed to the new female supervisors before the start of the promotion program, comparing them to the previous sample of non-exposed operators. I use the data from the factory follow-up for Uckat and Woodruff (2020) to determine this condition. Panel B of Figure 2 shows that the results are not due to operator sorting. The impact on the decision-making index of 0.29 SD for this subsample is highly significant and similar to the result for the whole sample of exposed operators of 0.36 SD. The impact on men’s expenditures is again negative, though smaller and insignificant at -0.35 percentage points.<sup>79</sup> The Online Appendix shows several additional robustness checks.<sup>80</sup> The large positive impact on women’s decision-making and the negative impact on men’s expenditures are robust to different OLS specifications, a larger set of potential control variables for the PDS Lasso algorithm and a inverse hyperbolic sine transformation. The negative effect on men’s expenditures is also found when using the husbands’ data (though the coefficient is smaller and insignificant at -0.69 percentage points), and the husbands report a very small increase in women’s involvement in decision-making index of 0.02 SD.<sup>81</sup>

# **5.3 Mechanisms**

The evidence shown in the last subsection makes it unlikely that the increase in exposed women’s decision-making power and the negative effect on men’s expenditures is driven by an income effect.

> 77I follow the strategy described in Section 4.4, and match on all variables for which I test balance in Table 2, in addition to factory, enumerator and month dummy variables.

> 78Table C.33 in the Online Appendix shows the matching results for all main outcomes.

> 79Table C.34 in the Online Appendix presents the results for this subsample for all main outcomes. Note that due to data limitations I am unable to impose the condition of having been on the line before trial start on the comparison group and that the subsample of the exposed group is therefore likely more experienced, which may explain the larger impact on income shown in the table.

> 80See Tables C.35 and C.36.

> 81I do not find a positive impact on women’s decision-making when I use data from the same sample of exposed and non-exposed operators at factory follow-up (about three months after exposure started), when women’s involvement in four different spheres of decision-making was enumerated. The coefficient is negative at -0.12 SD and insignificant.

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