Despite lab-based evidence supporting the argument that double standardsby 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.

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.

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.

Hilaire provided excellent research assistance. Daniel Lee provided valuable input to early proposal drafts. Opinions and conclusions expressed herein are solely those of the authors and do not represent the opinions or policy of the institutions with which the authors are affiliated. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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