IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v5y2019i1d10.1057_s41599-019-0256-3.html
   My bibliography  Save this article

Gender bias in Nobel prizes

Author

Listed:
  • Per Lunnemann

    (Blackwood Seven)

  • Mogens H. Jensen

    (University of Copenhagen)

  • Liselotte Jauffred

    (University of Copenhagen)

Abstract

Strikingly few Nobel laureates within medicine, natural and social sciences are women. It is obvious that there are fewer women researchers within these fields, but does this still fully account for the low number of female Nobel laureates? We examine whether women are awarded the Nobel Prizes less often than the gender ratio suggests. Based on historical data across four scientific fields and a Bayesian hierarchical model, we quantify any possible bias. The model reveals, with exceedingly large confidence, that indeed women are strongly under-represented among Nobel laureates across all disciplines examined.

Suggested Citation

  • Per Lunnemann & Mogens H. Jensen & Liselotte Jauffred, 2019. "Gender bias in Nobel prizes," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-4, December.
    • Handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0256-3
    DOI: 10.1057/s41599-019-0256-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-019-0256-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-019-0256-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Carol V. Robinson, 2011. "In pursuit of female chemists," Nature, Nature, vol. 476(7360), pages 273-275, August.
    3. Helen Shen, 2013. "Inequality quantified: Mind the gender gap," Nature, Nature, vol. 495(7439), pages 22-24, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ho Fai Chan & Benno Torgler, 2020. "Gender differences in performance of top cited scientists by field and country," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2421-2447, December.
    2. Antonio De Nicola & Gregorio D’Agostino, 2021. "Assessment of gender divide in scientific communities," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3807-3840, May.
    3. Julián D. Cortés & Daniel A. Andrade, 2022. "Winners and runners-up alike?—a comparison between awardees and special mention recipients of the most reputable science award in Colombia via a composite citation indicator," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    4. Pandelis Mitsis, 2022. "The Nobel Prize time gap," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    5. Wang, Yukai & Yang, Zhongkai & Liu, Lanjian & Wang, Xianwen, 2020. "Gender bias in patenting process," Journal of Informetrics, Elsevier, vol. 14(3).
    6. Nakajima, Kazuki & Liu, Ruodan & Shudo, Kazuyuki & Masuda, Naoki, 2023. "Quantifying gender imbalance in East Asian academia: Research career and citation practice," Journal of Informetrics, Elsevier, vol. 17(4).
    7. Alexander Krauss, 2024. "Science’s greatest discoverers: a shift towards greater interdisciplinarity, top universities and older age," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    8. Hoffmann, Robert & Coate, Bronwyn, 2022. "Fame, What’s your name? quasi and statistical gender discrimination in an art valuation experimentc," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 184-197.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    2. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    3. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    4. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    5. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    6. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    7. Meijun Liu & Sijie Yang & Yi Bu & Ning Zhang, 2023. "Female early-career scientists have conducted less interdisciplinary research in the past six decades: evidence from doctoral theses," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    8. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    9. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    10. Una Tellhed & Anna Jansson, 2018. "Communicating Gender-Equality Progress, Reduces Social Identity Threats for Women Considering a Research Career," Social Sciences, MDPI, vol. 7(2), pages 1-11, January.
    11. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    12. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    13. D. Fouskakis & G. Petrakos & I. Rotous, 2020. "A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 255-270, August.
    14. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    15. Jonas Moss & Riccardo De Bin, 2023. "Modelling publication bias and p‐hacking," Biometrics, The International Biometric Society, vol. 79(1), pages 319-331, March.
    16. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    17. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    18. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    19. Loke Schmalensee & Pauline Caillault & Katrín Hulda Gunnarsdóttir & Karl Gotthard & Philipp Lehmann, 2023. "Seasonal specialization drives divergent population dynamics in two closely related butterflies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    20. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0256-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.