identity) differences in wages and promotions.

To confirm that our treatment effects result from the implied gender of agents’ assigned names, we investigate whether customers are aware of the agents’ names. In 11% of chats, customers mention the agent’s assigned name—indicating names are a salient feature of the interaction.<sup>3</sup> Helpfully, we can also rule out a potential confound—customers responding to a “mismatch” between the gender implied by agents’ assigned name and the agents’ actual gender. It is unlikely that this confound explains our results because every agent in our experimental sample is female, and any mismatch comes from being assigned a male-sounding name.<sup>4</sup> Consequently, this “mismatch effect” would likely attenuate our estimated effect of discrimination against female agents. In sum, receiving a female name reduces productivity via consumers’ gender preferences.

These reductions in worker productivity may be explained by several mechanisms: general customer disinterest in working with female agents, differential bargaining, or overtly negative interactions.<sup>5</sup> Data from agent-customer chat interactions suggest that customer disinterest is the most likely channel. We find that consumers respond more slowly to female agents, only responding after receiving additional messages from the agent.<sup>6</sup> This result suggests some consumers are hesitant to engage with female agents unless the agents persist through additional messaging. We also find that consumers are less likely to express any non-neutral conversational tone, which we interpret as another measure of engagement with the agent.

The data do not support other possible mechanisms. We find no evidence that consumers differentially bargain when agents receive female-sounding names. This result is interesting as bargaining (e.g., asking for discounts) is common in these interactions (occurring in 15% of conversations), and differences in bargaining by gender feature prominently in studies of wage gaps and job-application behavior (e.g., Card et al. (2016); Rousille (2021); Castillo et al. (2013)). We also find no differences in hostile or harassing behavior, although any form of harassment is rare in this context.

Our finding of customer bias does not directly indicate whether female workers would be more or less productive than their male co-workers after correcting for this bias. Discerning between these cases is informative about how the labor market functions. To investigate this, we compare other workers at the company—both female and male agents—outside of the experimental sample to compare how observable productivity differs by worker gender, inclusive of customer bias. We find average productivity is both economically and statistically indistinguishable across worker gender. This implies the female workers are actually _more_ productive when customer bias is factored in. That women work in this industry despite the discrimination they face suggests they have a relative comparative advantage or derive non-pecuniary benefits in this occupation, or they face greater discrimination in the

> 3 This information is not typically observed in studies of discrimination.

> 4 More broadly, roughly two-thirds of the employees working in these roles at the company are female.

> 5 These behaviors are consistent with various theories of discrimination, including statistical and taste-based discrimination. Differentiating between these theories is not the intent of this study.

> 6 Agents always send the first message. The assigned name of the agent is revealed with this first message.

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