Publications & Working Papers

This paper investigates whether rating systems generate discriminatory spillovers and act as “discrimination amplifiers.” When platforms display aggregated customer ratings, these “quality metrics” also serve as anchors for future evaluations. Because they embed ratings from biased customers, displayed averages memorialize past discrimination, transmitting it to otherwise unbiased customers and amplifying bias among discriminatory ones. We formalize this mechanism using a stylized analytical model and test it with data from an online labor platform. Allowing for unobserved heterogeneity, we identify three customer segments: a neutral segment, and two biased segments, one that cancels more minority-accepted jobs and another that both cancels more and rates minorities lower. All segments are impacted strongly by displayed ratings. Customer discrimination generates a rating gap of 5.2% and an earnings gap of 36.1% between minority and majority workers. Notably, the unbiased segment produces discriminatory outcomes through spillovers. Adjusting displayed ratings reduces the gaps by mitigating the spillovers to the unbiased segment and amplification of the biased segment, but aggregate rating and earnings disparities persist because biased segments constitute a large share of the market.

Research has consistently found that organizational evaluations produce gendered outcomes. We advance understanding of this inequality by examining how multistage evaluations—a common organizational design feature—shape gendered evaluative disparities. We integrate and extend research on evaluations, status, and inequality to theorize how gendered outcomes can vary between stages within a single, unified evaluation process—what we term “stakes driven gender inequality.” Our theoretical framework centers on a ubiquitous feature of organizational multistage evaluation processes that is missing from prior theoretical accounts: escalating stakes across stages via increasingly binding commitments and rising cost of wrong selections. We conceptualize two stylized stages within the same evaluation process: a shortlisting stage (a preliminary selection of candidates for further consideration) and a winners stage (in which final, binding selections are made). Using data from a large multistage startup competition, we test our theory and find that female-led startups are 18.7 percent more likely than male-led startups to be selected in the shortlisting stage but 30.7 percent less likely to be selected in the winners stage. Mechanism tests in the shortlisting stage are most consistent with female-led startups being assessed as higher quality, whereas in the winners stage, we find evidence consistent with gendered performance expectations and evaluators’ risk aversion.

Organizations are increasingly using large language models (LLMs) to support strategic evaluations. We examine whether and how these systems rely on gender and race. We asked GPT to evaluate identical startup pitches varying only the founder’s name, shaping gender and race perceptions. Across 26,000 evaluations, GPT did not systematically assign lower scores to underrepresented minorities but avoided ranking them last without increasing winning likelihoods. To explain these patterns, we conducted “Second Opinion” experiments where GPT evaluated pitches alongside inputs simulating human bias. GPT more readily corrected explicit, identity-based bias than bias framed as neutral business critiques, with corrections limited in magnitude. We theorize these findings reflect symbolic compliance: LLMs suppress overt discrimination without substantively altering evaluative logic, allowing inequality to persist in AI-supported strategic evaluations.

Employment continuity facilitates access to career opportunities, professional growth, and financial security, making involuntary disruptions—such as employer failures—potentially consequential for individuals’ career trajectories. Although prior research has explored how organizational leaders’ careers fare following employer failure, implications for non-executive employee remain unclear in theoretical terms. Integrating theories of evaluation, evaluative stigma, and careers, we develop a theoretical framework to clarify how employer failure relates to subsequent career outcomes for employees. Using confidential, anonymized data from the United States Census complemented by detailed, identifiable data from the Automatic Speech Recognition industry, we find that employer failure is negatively related to organizational leaders’ wage growth, consistent with internal attribution and evaluative stigma. Conversely, non-executive employees experience wage (and industry retention) outcomes comparable to those of their unaffected peers, suggesting that affiliation with a failed employer does not stigmatize this set of employees. However, non-executive employees are not always unscathed: Career penalties emerge when failure involves a scandal, when industry labor-market conditions become more competitive, and when employees belong to some marginalized demographic groups (gender, race, immigration). By clarifying conditions of evaluative stigma, we extend organizational research on careers and evaluations, highlighting the conditions under which affiliations with failed firms relate to subsequent career outcomes.

Innovation-driven entrepreneurship (IDE) is viewed as a key driver of economic growth. This review provides a structured guide to the key themes of the growing literature on the economics of IDE, which differs substantially in its nature from other types of entrepreneurship. We describe the key themes of the growing literature speaking to the central economic questions raised by IDE, including who enters into IDE activity, the nature of its founding teams and organization, its financing, the strategic choices faced by IDE founders, and policy and programs to support IDE activity. The tendency of much of the empirical economics literature to date to confound IDE with other types of entrepreneurial ventures offers a significant opportunity for research focused specifically on the IDE phenomenon.

Online platforms are rife with racial discrimination, but current interventions focus on employers rather than customers. We propose a customer-facing solution: changing to a two-point rating scale (dichotomization). Compared with the ubiquitous five-star scale, we argue that dichotomization reduces modern racial discrimination by focusing evaluators on the distinction between ‘good’ and ‘bad’ performance, thereby reducing how personal beliefs shape customer assessments. Study 1 is a quasi-natural experiment on a home-services labour platform (n = 69,971) in which the company exogenously changed from a five-star scale to a dichotomous scale (thumbs up or thumbs down). Dichotomization eliminated customers’ racial discrimination whereby non-white workers received lower ratings and earned 91 cents for each US dollar paid to white workers for the same work. A pre-registered experiment (study 2, n = 652) found that the equalizing effect of dichotomization is most prevalent among evaluators holding modern racist beliefs. Further experiments (study 3, n = 1,435; study 4, n = 528) provide evidence of the proposed mechanism, and eight supplementary studies support measurement and design choices. Our research offers a promising intervention for reducing customers’ subtle racial discrimination in a large section of the economy and contributes to the interdisciplinary literature on evaluation processes and racial inequality.

In most evaluation systems—such as those governing the allocation of prestigious awards—the evaluator’s primary task is to reward the highest quality candidates. However, these systems are imperfect; top performers may not be acknowledged and thus be underrecognized, and low performers may receive unwarranted recognition and thus be overrecognized. An important feature of many evaluation systems is that people alternate between being candidates and being evaluators. How does experiencing misrecognition as a candidate affect how people subsequently evaluate others? We develop novel theory that underrecognition and overrecognition lead people to reproduce those experiences when they are evaluators. Across three studies—a quasi-natural experiment and two preregistered, multistage experiments—we find that underrecognized evaluators are less likely to grant recognition to others—even to the highest-performing candidates. Conversely, overrecognized evaluators are more likely to grant rewards to others—even to the lowest-performing candidates.

Evaluations play a critical role in the allocation of resources and opportunities. Although evaluation systems are a cornerstone of organizational and market processes, they often reinforce social and economic inequalities. The body of organizational research on inequality and evaluations is extensive, but it is also fragmented, siloed within specific contexts and types of evaluations (e.g., hiring, performance). As a result, we currently lack a systemic understanding of the conditions under which inequalities emerge. This paper provides a unifying framework to identify how gender and racial inequality is produced and reproduced in evaluations across professional contexts (e.g., digital platforms, entrepreneurship, traditional employment). Our framework categorizes the drivers of inequality into three main areas: prevailing beliefs in evaluative contexts, the design and structure of evaluation processes, and the characteristics of evaluators. Our approach not only sheds light on the common processes that exacerbate inequality but also underscores why an integrative framework is critical for both theoretical advancement and enacting effective reforms.

Over the last two decades, the sociology of entrepreneurship has exploded as an area of academic inquiry. Most of this research has been focused on understanding the environmental conditions that promote entrepreneurship and processes related to the initial formation of an organization. Despite this surge in activity, many important questions remain open. Only more recently have scholars begun to turn their attention to what happens to organizations, and the people connected to them, as they mature and move through the life cycle of entrepreneurship. These open questions, moreover, connect to many classic themes in the literature on careers, organizational sociology, stratification, and work and occupations. Using a framework that focuses on three phases of the entrepreneurial life cycle—pre-entry, entry, and post-entry—we summarize sociological research on entrepreneurship and highlight opportunities for future research.

Collective evaluation processes, which offer individuals an opportunity to assess quality, have transcended mainstream sectors (e.g., books, restaurants) to permeate professional contexts from within and across organizations to the gig economy. This paper introduces a theoretical framework to understand how evaluators’ visibility into prior evaluations influences the subsequent evaluation process: the likelihood of evaluating at all and the value of the evaluations that end up being submitted. Central to this discussion are the conditions under which evaluations converge—are more similar to prior evaluations—or diverge—are less similar—as well as the mechanisms driving observed outcomes. Using a quasi-natural experiment on a platform where investment professionals submit and evaluate investment recommendations, I compare evaluations that are made with and without the possibility of prior ratings influencing the subsequent evaluation process. I find that when prior ratings are visible, convergence occurs. The visibility of prior evaluations decreases the likelihood that a subsequent evaluation occurs by about 50%, and subsequent evaluations become 54%–63% closer to the visible rating. Further analysis suggests that peer deference is a dominant mechanism driving convergence, and only professionals with specialized expertise resist peer deference. Notably, there is no evidence that initial ratings are related to long-term performance. Thus, in this context, convergence distorts the available quality signal for a recommendation. These findings underscore how the structure of evaluation processes can perpetuate initial stratification, even among professionals with baseline levels of expertise.

Organizations tout the importance of innovation and entrepreneurship. Yet, when hiring it remains unclear how they evaluate entrepreneurial human capital—namely, job candidates with founder experience. How hiring firms evaluate this experience—and especially how this evaluation varies by entrepreneurial success and failure—reveals insights into the structures and processes within organizations. Organizations research points to two perspectives related to the evaluation of founder experience: Former founders may be advantaged, due to founder experience signaling high-quality capabilities and human capital, or disadvantaged, due to concerns related to fit and commitment. To identify the dominant class of mechanisms driving the evaluation of founder experience, it is important to consider how these evaluations differ, depending on whether the founder’s venture failed or succeeded. To isolate demand-side mechanisms and hold supply-side factors constant, we conducted a field experiment. We sent applications varying the candidate’s founder experience to 2,400 software engineering positions in the United States at random. We find that former founders received 43% fewer callbacks than nonfounders and that this difference is driven by older hiring firms. Further, this founder penalty is greatest for former successful founders, who received 33% fewer callbacks than former failed founders. Our results highlight that mechanisms related to concerns about fit and commitment, rather than information asymmetry about quality, are most influential when hiring firms evaluate former founders in our context.

We theorize that status awards will have a disciplining effect on evaluators, changing how they evaluate. Specifically, status awards will lead evaluators to place less weight on unreliable indicators of candidate quality, such as gender. We test this theory using data from restaurant evaluations on Yelp, focusing on the relationship between an evaluator’s restaurant rating and their reporting of being served by a man or a woman in their review text. We use Yelp’s evaluator status award (“Elite”) to analyze whether observed gender bias in the star ratings given to restaurants decreases after an evaluator receives this status award. We find that evaluators rate restaurants more similarly after receiving the award, regardless of whether they report being served by a man or a woman. Status awards in our context close the gender gap in restaurant ratings by 56.5% (a 0.07 stars improvement out of an initial rating gap of −0.13 stars). This reduction in gender bias is mostly due to a decrease in the number of extremely low (1 star) ratings in reviews that reference female servers. Research on status and evaluations has mostly focused on how evaluators react to increases in candidate status. We demonstrate the importance of evaluator status as a mechanism for decreasing observed gender differences in evaluations.

Although knowledge sharing among competitors is seemingly counterintuitive, scholars have found that competitors share knowledge under certain conditions: among actors who have a preexisting relationship and who expect direct reciprocity. However, there are examples of knowledge sharing among competitors that cannot fully be explained using these relational mechanisms. In this study, I propose that in markets where competitors are a set of key stakeholders, knowledge sharing is a strategic response to high levels of buy-in uncertainty related to a potential opportunity, namely, the likelihood that stakeholders will come to realize the value of a potential opportunity in a timely fashion. Using a unique data set of knowledge sharing among investment professionals on a digital platform, this study leverages variation in the platform’s knowledge-sharing structure to test this theory. I find that knowledge sharing among these competitors is most likely when buy-in uncertainty for a given opportunity is high and that this knowledge sharing does lead to subsequent buy-in.

Despite lab-based evidence supporting the argument that double standards—by which one group is unfairly held to stricter standards than another—explain observed gender differences in evaluations, it remains unclear whether double standards also affect evaluations in organization and market contexts, where competitive pressures create a disincentive to discriminate. Using data from a field study of investment professionals sharing recommendations on an online platform, and drawing on status theory, we identify the conditions under which double standards in multistage evaluations contribute to unequal outcomes for men and women. We find that double standards disadvantaging women are most likely when evaluators face heightened search costs related to the number of candidates being compared or higher levels of uncertainty stemming from variation in the amount of pertinent information available. We rule out that systematic gender differences in the actions or characteristics of the investment professionals being evaluated are driving these results. By more carefully isolating the role of this status-based mechanism of discrimination for perpetuating gender inequality, this study identifies not only whether but also the conditions under which gender-based double standards lead to a female disadvantage, even when relevant and objective information about performance is readily available.