_selected_ is the covariate of interest for this intent-to-treat analysis and is defined as a dummy variable indicating whether the individual is an operator selected for the promotion program, which is zero for the nominated group. _X_ is a vector of potential control variables. The vector consists of all 34 variables for which I tested balance on in Table 1 and their squared terms, in addition to factory, enumerator, and month dummy variables. By including all diagnostic scores as well as factory fixed effects, I account for a potential re-ranking on these scores by the factories participating in the selection experiment. The set of control variables to be included are selected using the PDS Lasso algorithm, using the data-driven penalty loadings for clustered standard errors suggested in Belloni et al. (2013, 2014a,b, 2016). If available, the baseline outcome is in the amelioration set and hence always included in the regression. Standard errors are clustered at the individual level.

The PDS Lasso algorithm achieves a parsimonious selection of control variables in two steps. It first implements the Lasso algorithm twice to select two sets of covariates from the _X_ vector: one set which is predictive for the outcome variable _Y_ and one set which is predictive for _selected_ , the covariate of interest. In the second step, _β_ is estimated using an ordinary least squares regression of _Y_ on _selected_ and the union of the two sets of covariates selected in the first step. As Belloni et al. (2013) show, this method performs well across a variety of methods, enhances efficiency, and leads to valid inference even with mistakes in variable selection.<sup>40</sup> Since I am interested in intra-household decision-making, I limit the sample to those women who report having another adult decision-maker in the household. As shown in Section 3, this is the husband in the vast majority of cases. I compute sharpened q-values to correct for multiple hypotheses testing for all main outcome variables defined in Section 3 following Benjamini and Hochberg (1995) and Benjamini et al. (2006).

# **3.5.2 Role model analysis**

I follow a similar empirical approach to analyze the potential role model effects of female supervisors. I compare outcomes of production line operators who have worked with the new female supervisors from the promotion program to outcomes of production line operators on other, malesupervised lines. I argue that the assignment of workers to production lines is largely determined by production requirements and can be considered quasi-random. As discussed in Section 2, production lines typically consist of 20-80 operators, depending on the type of garment produced. Each operator is responsible for one step in the production process at a work station (e.g. attaching the sleeve to a T-shirt), which differ in the skills required. According to discussions with an industrial engineer with substantial experience in several garment factories, the assignment of line operators to sewing lines is done by the management of the sewing department, typically line chiefs 40I use the Stata package “pdslasso” to implement the algorithm, see Ahrens et al. (2018).

17