same job—struggle to control for all other differences between the actors. They may also be subject to “demand effects” because actors are aware of their treatment status and do not have real incentives to perform well. Correspondence studies—where fictitious applications with different sounding names are sent to a possible discriminator like employers—can only measure coarse outcomes such as job application callback rates rather than actual job hiring (Bertrand and Duflo, 2017). In our setting, the daily name randomization eliminates omitted variable concerns, the name masking alleviates concerns about demand effects, and by studying real workplace interactions we can collect detailed outcome measures—e.g., the likelihood of purchase—and specifics of the interaction—e.g., bargaining behavior.

While customer discrimination may be a global phenomenon, it may be particularly important in the context of this study for a number of reasons. First, the share of women employed in the service industry in Sub-Saharan Africa has increased exponentially in the last decade. Identifying the scale of customer discrimination in these occupations will be increasingly relevant for female labor force participation in the long run. Second, social norms in Sub-Saharan Africa favor men over women as economic agents and in business, which may contribute to higher rates of customer discrimination. For example, data from the World Bank World Development Indicators (WDI) show that households in Sub-Saharan Africa are more likely to agree that men make better business leaders and that women have no say in decisions on large household purchases (Jayachandran, 2015). Finally, it is particularly timely to study these issues as gender inequity in the workplace has become a central policy goal for governments across the continent and international institutions alike (Bank, 2011; O’Donnell et al., 2020).<sup>2</sup> The results are striking: we find that randomly assigned female names reduce the likelihood that customers make any purchase, the number of purchases, and the value of the purchases. Specifically, the likelihood of any purchase decreases by 3.9 percentage points, or 51% relative to the baseline purchase rate (7.6%). We observe similarly large reductions in the total number of purchases and the total value of goods purchased.

These results suggest that productivity differences between workers in our context are not just a reflection of worker attributes but also of differential customer responses. While the literature has identified productivity differences as a key determinant of the gender wage gap (Sin et al., 2020; Gallen et al., 2017; Blau and Kahn, 2017; Caliendo et al., 2017), our results demonstrate that underlying customer-based discrimination can contribute to these productivity differences. This could lead decompositions of the gender wage gap to underappreciate the importance of discrimination and overestimate the role of productivity. This finding has important implications for policies such as “equal pay for equal work,” which if assessed by productivity at work, may not account for the difficulties women face while doing the same job. While governments cannot regulate customers’ discriminatory behaviors, they may regulate hiring and wage practices. For example, incentive-based pay schemes are commonly used in customer-facing roles, despite potentially exacerbating gender (or other

> 2 For example, The African Union Strategy for Gender Equality includes the promotion of laws to achieve pay equality—as the gender wage gap is persistent across most industries where women work (ILO, 2019).

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