ment. The rate in the female-assigned sample is practically identical to the male-assigned sample and does not differ statistically.

Finally, column (3) of Table 3 tests whether customers bargain more often with female sales agents. While 15% of chats exhibit some bargaining behavior—for example, asking for discounts on the listed price—we find no significant effect of female-name assignment on the likelihood of bargaining. This null result is fairly precise relative to the baseline level and rules out changes to bargaining that exceed 20%. Therefore, in this context, differential bargaining does not appear to drive the observed productivity differences.

Together, our results suggest that customers interact differently with women and men in ways that can meaningfully reduce productivity. This result is especially consequential for the service industry, where customer-facing roles abound. Our investigation of the mechanisms behind this behavior suggests that consumers engage less with female agents—along extensive (any engagement) and intensive (tone used) margins. We find no evidence that customers differentially harass or bargain with women in our setting.<sup>26</sup>

# **4.3 Comparison to non-experimental estimates**

We compare the results from our experimental research design to a simpler non-experimental comparison of male and female agents. The non-experimental results measure correlations between chat purchases and agents’ actual gender using chats _outside_ the experimental sample.<sup>27</sup> We include similar controls in both specifications but cannot include agent fixed effects in the non-experimental comparison as they are collinear with gender. Table 4 shows correlations between agents’ actual gender and sales in Panel A and experimental estimates of female name assignment in Panel B. Only administrative sales records are available for the non-experimental sample, so we compare across samples using these outcomes. In Panel A, we find no statistically significant differences between male and female agents across any of the three purchase outcomes. In Panel B, we find female name assignment leads to statistically significant reductions in sales across all of the administrativerecord purchase outcomes.<sup>28</sup> To compare the effect of female-name assignment in the experimental sample to the effect of being female in the correlational sample, we use seemingly unrelated regression. A test of equality across the two ‘female’ coefficients rejects the null hypothesis at the 5% level for any purchase and number of purchases within 48 hours and at the 10% level for outcomes within 24 hours.

The difference between the experiment and correlational estimates sheds light on genderbased selection into this occupation. In particular, it suggests women in these jobs may be

> 26 We are unable to test for other mechanisms—e.g., homophily (a customer’s preference to interact with an agent of the same gender)—because we lack sufficient customer information. That means we can identify the absolute customer bias workers face in their actual job, which is the key parameter of interest when considering the impact of customer discrimination. We cannot test for relative customer bias depending on customer characteristics which has been a focus of other papers (Combes et al., 2016; Leonard et al., 2010; Bar and Zussman, 2017).

> 27 The experimental sample is not representative of all sales at the company. Therefore, this exercise is only suggestive.

> 28 The coefficients are smaller than in Table 1 since they do not also include agent-initiated purchases.

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