arXiv 29 Sep 2021 · Econometrics · publishedThe Review of Economics and Statistics (2022)
arXiv:2109.14343 · PDF · DOI · OpenAlex · Extracted main text
It is widely accepted that women are underrepresented in academia in general and economics in particular. This paper introduces a test to detect an under-researched form of hiring bias: implicit quotas. I derive a test under the Null of random hiring that requires no information about individual hires under some assumptions. I derive the asymptotic distribution of this test statistic and, as an alternative, propose a parametric bootstrap procedure that samples from the exact distribution. This test can be used to analyze a variety of other hiring settings. I analyze the distribution of female professors at German universities across 50 different disciplines. I show that the distribution of women, given the average number of women in the respective field, is highly unlikely to result from a random allocation of women across departments and more likely to stem from an implicit quota of one or two women on the department level. I also show that a large part of the variation in the share of women across STEM and non-STEM disciplines could be explained by a two-women quota on the department level. These findings have important implications for the potential effectiveness of policies aimed at reducing underrepresentation and providing evidence of how stakeholders perceive and evaluate diversity.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Arcidiacono, P., J. Kinsler, and T. Ransom (2020) Asian American Discrimination in Harvard Admissions | 0.644 | 2 | 2 | 100% |
| 2 | Bagues, M. and P. Campa (2021) Can gender quotas in candidate lists empower women? Evidence from a regression discontinuity design | 0.511 | 2 | 1 | 100% |
| 3 | AllBright Stiftung, B (2020) AllBright Bericht: Deutscher Sonderweg: Frauenanteil in DAX-Vorstnden sinkt in der Krise | 0.405 | 1 | 1 | 100% |
| 4 | Balafoutas, L. and M. Sutter (2012) Affirmative action policies promote women and do not harm efficiency in the laboratory | 0.405 | 1 | 1 | 100% |
| 5 | Bayer, A. and C. E. Rouse (2016) Diversity in the economics profession: A new attack on an old problem | 0.405 | 1 | 1 | 100% |
| 6 | Beaman, L., E. Duflo, R. Pande, and P. Topalova (2012) Female leadership raises aspirations and educational attainment for girls: A policy experiment in India | 0.405 | 1 | 1 | 100% |
| 7 | Bertrand, M., S. E. Black, S. Jensen, and A. Lleras-Muney (2018) Breaking the glass ceiling? The effect of board quotas on female labour market outcomes in Norway | 0.405 | 1 | 1 | 100% |
| 8 | Boring, A (2017) Gender biases in student evaluations of teaching | 0.405 | 1 | 1 | 100% |
| 9 | Buser, T., M. Niederle, and H. Oosterbeek (2014) Gender, competitiveness, and career choices | 0.405 | 1 | 1 | 100% |
| 10 | Card, D., S. DellaVigna, P. Funk, and N. Iriberri (2020) Are Referees and Editors in Economics Gender Neutral? | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A missed opportunity? Labor demand and workforce diversity | 0.644 | 2 | 2 |