White, male-dominated professions in the United States are marked with substantial gender and racial inequality in career advancement, yet they often face pressures to increase diversity. In these contexts, are theories of employer biases based on gender and racial stereotypes sufficient to explain patterns of hiring discrimination during common career transitions in the external labor market? If not, how and why do discrimination patterns deviate from predictions? Through a case study of software engineering, we first draw from a large-scale audit study and demonstrate unexpected patterns of hiring screening discrimination: while employers discriminate in favor of White men among early-career job applicants seeking lateral positions, for both early-career and senior workers applying to senior jobs, Black men and Black women face no discrimination compared to White men, and White women are preferred. Drawing on in-depth interviews, we explain these patterns of discrimination by demonstrating how decision-makers incorporate diversity value—applicants’ perceived worth for their contribution to organizational diversity—into hiring screening decisions, alongside biases. We introduce diversity commodification as the market-based valuative process by which diversity value varies across job level and intersectional groups. This article offers important implications for our understanding of gender, race, and employer decision-making in modern U.S. organizations.

In 2016, JASA Applications and Case Studies (ACS) introduced a reproducibility initiative to address the lack of standardized practices for reproducibility in scientific research. This initiative established minimum criteria for the inclusion of code, data, and workflow at JASA ACS, and piloted a new editorial role, Associate Editor of Reproducibility (AER), to implement these standards. This initiative has since expanded from ACS to all original research manuscripts published at JASA, including those submitted to JASA Theory and Methods (TM). Since the inception of the JASA reproducibility initiative, the team of AERs has grown, the process has become more standardized, the role of an AER in guiding reproducibility in statistical research has been refined (Willis and Stodden Citation2020) and, in 2023, we implemented a reproducibility award1 to recognize papers published in JASA with outstanding reproducibility materials. The goals of this editorial are to (a) explain the type of reproducibility that is being addressed during the review process at JASA, (b) describe the steps of this process and its underlying philosophy, and (c) clarify how authors can streamline the review of their reproducibility materials.