Strategy and Diversity: Unpacking the Gender and Racial Gaps in Earning
Bibliographic record
Abstract
Organizational scholars have long focused on earning inequalities across genders and races, yet large earning differences prevail in almost all occupations worldwide. While most research focuses on within-firm processes to explain this phenomenon, such as hiring and promotion, other processes play an important role, such as between-firm segregation and organizational characteristics. This symposium aims to unpack the racial and gender gaps in earnings in a wide range of occupations and organizations. We will focus on employees in both the public and private sectors, along with entrepreneurs. The first paper documents a decrease in the gender gap in the public sector in the US over the past twenty years. Yet, this gap persists, particularly at the very top percentiles of the wage distribution. The second paper examines the role of organizational characteristics on gender disparities in profitability. It shows that with the exact same business characteristics, the gender gap is reduced but persists. The third paper focuses on racial segregation between American workplaces and shows that it is greater today than it was a generation ago. Racial segregation has increased between establishments, but not within establishments. The last paper shows that one way to reduce the earning gap is to use machine-learning algorithms in the hiring process, to overcome the biases of employers. Measuring and Explaining the Gender Wage Gap in the Federal Government Presenter: John M. De Figueiredo; Duke U. Presenter: Alexander Bolton; Emory U. Unpacking the Gender Profit Gap: Evidence from Micro-Businesses in India Presenter: Solene Delecourt; Stanford GSB Presenter: Odyssia Ng; Stanford U. Firm Turnover and the Return of Racial Establishment Segregation Presenter: John-Paul Ferguson; McGill U. Presenter: Rembrand Michael Koning; Harvard Business School Bias and Productivity in Humans and Algorithms: Theory and Evidence from Resumé Screening Presenter: Bo Cowgill; Columbia Business School
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".