Gender imbalances at work are traditionally seen through the lens of equity. The aim of this paper is to show that such imbalances cause a significant loss of efficiency; and that eliminating them would lead to greater productivity for the same wage bill. At the core of the paper is the idea that, to understand gender imbalances within the firm, we need to understand gender imbalances in who selects into the firm and into the workforce more generally. When this insight is taken into account, we find that gender differences in talent arise from who selects in; importantly, eliminating these differences would increase productivity by 32%.
To quantify the loss of productivity arising from gender imbalances requires data on the potential productivity of the women who are not in the labor force and that of the men they could potentially replace. By definition, both of these factors cannot be observed. For this reason, the productivity gains from gender equality have not been quantified to date. We propose a new method to back out an accurate estimate of these unobserved factors, and therefore an accurate estimate of the productivity gains that would accrue if differences in labor force participation (henceforth LFP) were eliminated. Our method relies on four core assumptions. The first is that individuals choose to enter the labor force if the payoff (both financial and non-) from doing so is higher than the payoff from staying at home. The second assumption is that the firm rewards talent in every country it operates, so more talented individuals have higher payoff at work than less talented individuals. The third is that the value of staying at home depends on individual preferences as well as societal norms that can vary across countries. The fourth is that innate talent- that is the raw potential at birth- is equal across genders.
The key implication of these assumptions is that when the cost of working outside the home is high, the payoff at work has to be higher to compensate for that. Because payoff at work is increasing in talent, the cost of working outside the home determines the level of payoff- and hence the talent- needed to enter the labor force. Thus, if the cost of working outside the home is higher for women, their payoff at work and their talent will also need to be higher to enter the labor force. This implies that in countries where the cost of entering the labor force is higher for women the average woman in the labor force will have more talent than the average man in the labor force. But this is not necessarily the case for each individual because additional factors such as financial necessity or preferences for spending time with children will also affect the decision. When estimating our model we allow each individual to have different preferences for staying home/going to work.
This method gives us a relationship that links salary in the MNE to the cost of working outside the home. Both variables have a component which is common to all people of the same gender in the same country, for instance: country-specific social norms about gender, and a component that is specific to the individual: their own talent and preferences. Because we have individual-level data on salary (from anonymised personnel data of thousands of employees of the same gender in the same country), we can separate the common component of salary from the reward to individual-specific performance. Likewise, because we have data on LFP across all countries, we can separate the common component of the cost to women working outside the home from the individual specific preferences.
Our method yields an estimate of the productivity of the individuals that have been hired and, more crucially, of those who could have been hired. Armed with these estimates we can compute the change in productivity that would ensue should the firm eliminate gender imbalances in talent.
We use personnel data from the MNE and construct an employee-level yearly panel covering the universe of employees between 2015 and 2019. We focus our main analysis on regular, full-time, and domestic (non-expats) workers resulting in 100,819 workers in 101 countries. The company is organized into a hierarchy of work levels (WL) that goes from work level 1 to 6. All WLs are included in our analysis, although 80% of workers are in WL1 and 16% are in WL2. Our main outcome variable is total compensation in logs (fixed plus variable pay) and we look at different 10-year age cohorts within the company, 18-29, 30-39, 40-49 and 50-59.
We combine the company administrative records with country-cohort data on labor force participation rates of males and females from the World Bank. In particular, we match the age cohorts in the firm with the average LFP rate in the country in the decade of labor market entry, separately by gender. For example, employees of age 18-29 are associated to the LFP rates of the 2010-2020 decade.