# **1 Introduction**

Many workers are evaluated on their ability to engage with customers. If customers have explicit or implicit biases against workers of a certain gender or race, then workers of this identity will perform less well. Since worker productivity informs firm hiring, pay, and promotion decisions, quantifying the magnitude of discriminatory customer behavior is important for understanding the persistence of identity-based discrimination in the labor market. It is also a relevant input for firms that may want to identify and promote the most talented workers rather than those least subject to bias, and for regulators who may wish to identify whether facially race-neutral policies like performance-based pay may actually promote discriminatory outcomes. Yet, we know very little about the magnitude of customer discrimination in the workplace.

Identifying the impact and scale of customer discrimination is challenging. First, many factors affect worker productivity—e.g., the worker’s own skills and behavior, customers’ behaviors and preferences, and the workplace environment—which makes it difficult to isolate the impact of any single determinant. Second, identifying discrimination typically involves testing whether wages or hiring outcomes are different across individuals _who are equally productive_ . In doing so, these tests fail to account for discrimination that directly impacts productivity, such as consumer preferences. Consequently, they may erroneously conclude there is no discrimination at all.

This paper directly addresses these challenges to provide new evidence on the magnitude of gender-based discriminatory behavior by customers in the workplace. We do so by running a randomized experiment with an online travel agency whose offices are scattered across Sub-Saharan Africa. The company sells flights and hotels and hires local sales agents to assist customers who are mainly from other parts of the continent. We study workers who chat with customers online to answer questions and increase sales. This context allows us to measure worker productivity through customer purchases and rich patterns of engagement, including bargaining and harassment, through chat transcripts.

We apply a novel framework for estimating the causal effect of customer-based discrimination. First, the names of workers—and implied genders—were randomized daily, providing plausible variation in customer beliefs about the gender of the agent they were chatting with.<sup>1</sup> Customers could only infer agents’ gender from their names, as they did not receive any other information about the agent. Second, workers were unaware of their assigned name due to a web plugin that masked the assigned name from their view. This step ensured that agents’ behavior was not directly affected by their name assignment. Consequently, any change in consumer behavior towards sales agents could only occur if consumers responded to the randomly assigned names.

This research design is unique and overcomes challenges with two common methods to study discrimination: audit and correspondence studies. Audit studies—where actors, who are as similar as possible except on one dimension, engage in a task like applying to the 1 Changing the names of workers appears to be relatively common in online sales settings (e.g., LiveAgent).

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