The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
How Costly Is Diversity? Affirmative Action in Light of Gender Differences in Competitiveness
Open accessAbstract
Affirmative action is often criticized for causing reverse discrimination and lowering the qualifications of those hired under the policy. However, the magnitude of such adverse effects depends on whether the best suited candidate is hired absent the policy. Indeed affirmative action may compensate for the distortion discrimination imposes on the selection of candidates. This paper asks whether affirmative action can have a similar corrective impact when qualified individuals fail to apply for a job. We evaluate the effect of introducing a gender quota in an environment where high-performing women fail to enter competitions they can win. We show that guaranteeing women equal representation among winners increases their entry. The response exceeds that predicted by the change in probability of winning and is in part driven by women being more willing to compete against other women. The consequences are substantial as the boost in supply essentially eliminates the anticipated costs of the policy. This paper was accepted by Uri Gneezy, behavioral economics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
z-Tree: Zurich toolbox for ready-made economic experiments
Fischbacher, U. (2007) · Experimental Economics · doi:10.1007/s10683-006-9159-4 · Cited by 9,510
A threat in the air: How stereotypes shape intellectual identity and performance
Steele, C. M. (1997) · American Psychologist · doi:10.1037/0003-066x.52.6.613 · Cited by 6,770
(1999) · Cited by 6,308
Gender Differences in Preferences
Croson, R., & Gneezy, U. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.2.448 · Cited by 5,694
(2007) · Wiley eBooks · doi:10.1002/9780470147658 · Cited by 3,845
Gender differences in risk taking: a meta-analysis
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999) · Psychological Bulletin · doi:10.1037/0033-2909.125.3.367 · Cited by 3,723
Do Women Shy Away From Competition? Do Men Compete Too Much?
Niederle, M., & Vesterlund, L. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.122.3.1067 · Cited by 3,186
Self-citationHarrison, A., Robinson, C., Ashenfelter, O., & Layard, R. (1989) · The Canadian Journal of Economics · doi:10.2307/135687 · Cited by 3,153
The difference: how the power of diversity creates better groups, firms, schools, and societies
(2007) · Choice Reviews Online · doi:10.5860/choice.45-1534 · Cited by 2,280
Performance in competitive environments: gender differences
Gneezy, U., Niederle, M., & Rustichini, A. (2003) · The Quarterly Journal of Economics · doi:10.1162/00335530360698496 · Cited by 1,997
Self-citation
Cited by
Gender Differences in Preferences
Croson, R., & Gneezy, U. (2009) · Journal of Economic Literature · doi:10.1257/jel.47.2.448 · Cited by 5,694
A survey of experimental research on contests, all-pay auctions and tournaments
Dechenaux, E., Kovenock, D., & Sheremeta, R. M. (2015) · Experimental Economics · doi:10.1007/s10683-014-9421-0 · Cited by 706
Explaining the Gender Gap in Math Test Scores: The Role of Competition
Niederle, M., & Vesterlund, L. (2010) · Journal of Economic Perspectives · doi:10.1257/jep.24.2.129 · Cited by 527
Self-citationGender bias in academia: A lifetime problem that needs solutions
Llorens, A., Tzovara, A., Bellier, L., Bhaya-Grossman, I., Bidet-Caulet, A., Chang, W. K., Cross, Z. R., Dominguez-Faus, R., Flinker, A., Fonken, Y., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.002 · Cited by 361
Do Women Avoid Salary Negotiations? Evidence from a Large-Scale Natural Field Experiment
Leibbrandt, A., & List, J. A. (2015) · Management Science · doi:10.1287/mnsc.2014.1994 · Cited by 328
Metadata
- Authors
- Niederle, M., Segal, C., & Vesterlund, L.
- Venue
- Management Science
- Published
- 2013-01
- Volume
- 59
- Issue
- 1
- Pages
- 1-16
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2147509270
- Cited by
- 417
- References
- 62
- FWCI
- 61.91
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: How Costly Is Diversity? Affirmative Action in Light of Gender Differences in Competitiveness. https://app.cochat.ai/papers/10.1287/mnsc.1120.1602
- The paper
- Niederle, M., Segal, C., & Vesterlund, L. (2013). How Costly Is Diversity? Affirmative Action in Light of Gender Differences in Competitiveness. Management Science. https://doi.org/10.1287/mnsc.1120.1602
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.