initial messages. We interpret this as a relatively high share of customer awareness as many chats are brief. Thus, agent names are indeed salient in chat interactions and could affect customers’ behavior.

Table 1 presents the estimated effects of female-name assignment on outcomes related to customer purchases. We measure purchases within 24 or 48 hours of the chat to capture behavior plausibly related to the chat interactions rather than unrelated interactions that happen later.<sup>19</sup> We measure purchases in three ways: the probability of making any purchase, the number of distinct purchases, and the total price of purchases. As discussed in subsection 2.3, our measures of _any purchase_ and _total number of purchases_ include purchases made by customers and by agents on customers’ behalf. In contrast, the total-price measure only includes purchases by customers.<sup>20</sup> We find that consumers assigned to agents with female names are less likely to purchase products on the website. Column (1) shows that female-agent assignment decreases the probability that any purchase occurs (within 48 hours) by 3.9 percentage points ( _p_ = 0.002). The likelihood that a chat results in any purchase in the control group (male-sounding names) is only 7.6%.<sup>21</sup> Thus, the point estimate implies a 51% reduction in the likelihood of making a sale. Column (2) shows that consumers also purchase 0.039 fewer total products = ( _p_ 0.004) when interacting with female-sounding names; column (3) shows that the total value of purchases falls by 3.5 euros. Columns (4-6) repeat the same outcomes but use a 24-hour window after the chat. The results are very similar.<sup>22</sup> These results highlight the importance of customer-side discrimination in productivity differences between women and men in the workplace (for consumer-facing roles). Prior research on the gender wage gap suggests women receive lower pay partly because they are less productive (Sin et al., 2020; Gallen et al., 2017; Blau and Kahn, 2017; Caliendo et al., 2017). We show that discriminatory behavior—on the part of consumers—can drive these productivity differences. In our context, for women and men to have similar productivity levels, women would need to overcome significant barriers created by consumers’ behavior. These results also suggest that piece-rate wage structures—i.e., rewarding employees for their output levels—could further workplace inequality. While we cannot speak to optimal policy responses, regulation that prevents employer discrimination could limit some consequences of customer discrimination.

An advantage of our study design, which assigns each agent to both treatment sta-

> 19 Statistical power is also likely to be higher in the period directly after these focal events.

> 20 Interpretation of the total-price measures, based on only administrative customer-purchase records, is more challenging due to the mode of purchase potentially being endogenous to treatment status. In practice, we find treatment status affects purchases by agents on behalf of customers and purchases made directly by customers in the same magnitude and direction (see Table A5). This means we very likely _underestimate_ the coefficient on total-price, although the effect relative to the baseline mean is not clear.

> 21 We calculate control group means accounting for agent-month fixed effect cells _c ∈ C_ as ∑ _c∈C_ ( _E_ [ _Y|C_ = _c_ , _D_ = 0) _wc_ for weights _wc_ , cell _C_ , and treatment status _D_ . We do so to correspond to the OLS estimand, _β_<sup>_OLS_</sup> = ∑ _c∈C_ ( _E_ [ _Y|C_ = _c_ , _D_ = 1] _− E_ [ _Y|C_ = _c_ , _D_ = 0) _wc_ . The reweighting does not account for additional customer controls.

> 22 We also test for dynamic effects of female name assignment. We do not find evidence for this; the _p_ -value of the joint test of the assignment to a female name in the previous two working days does not reject the null hypothesis of no effect either individually or jointly ( _p_ = .377).

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