Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Public opinion, racial bias and labour market outcomes in the USA


Here we study the role of negative shifts in public opinion in the economic lives of under-represented racial groups by investigating sudden changes in views towards Asian people following the anti-Chinese rhetoric that emerged with the COVID-19 pandemic, and associated changes in employment status and earnings in the US labour market. Using data from the Current Population Survey, we find that, unlike other under-represented groups, Asian workers in occupations or industries with a higher likelihood of face-to-face interactions before the pandemic were more likely to become unemployed afterwards. While widespread along the political spectrum, negative shifts in the perceived favourability of Asian people, and not of other under-represented groups, were much stronger among those who voted for Donald Trump in 2016 and could have been more influenced by the anti-Asian rhetoric.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: US unemployment rate over time by race/ethnicity.
Fig. 2: Unfavourable opinions towards different races/ethnicities over time by Trump voting and Fox News viewership.

Similar content being viewed by others

Data availability

This study uses five sources of data collected by third parties: the publicly available CPS data from IPUMS (, data from Dave Leip’s Atlas of US Elections for the 2016 presidential election (, data from Nationscape (, data on COVID-19 death rates from the New York Times ( and the FRED database ( All data are publicly available at no cost.

Code availability

Our analysis was performed using Stata v.18 and Python v.3.5. All analysis code used in this study has been deposited in the Zenodo repository (


  1. Yanagizawa-Drott, D. Propaganda and conflict: evidence from the Rwandan genocide. Q. J. Econ. 129, 1947–1994 (2014).

    Article  Google Scholar 

  2. Adena, M., Enikolopov, R., Petrova, M., Santarosa, V. & Zhuravskaya, E. Radio and the rise of the Nazis in prewar Germany. Q. J. Econ. 130, 1885–1939 (2015).

    Article  Google Scholar 

  3. Grosjean, P., Masera, F. & Yousaf, H. Inflammatory political campaigns and racial bias in policing. Q. J. Econ. 138, 413–463 (2023).

    Article  Google Scholar 

  4. Müller, K. & Schwarz, C. From hashtag to hate crime: Twitter and anti-minority sentiment. Am. Econ. J. Appl. Econ. 15, 270–312 (2023).

    Article  Google Scholar 

  5. Cao, A., Lindo, J. M. & Zhong, J. Can Social Media Rhetoric Incite Hate Incidents? Evidence from Trump’s “Chinese Virus” Tweets Technical Report (National Bureau of Economic Research, 2022).

  6. Bursztyn, L., Haaland, I. K., Rao, A. & Roth, C. P. Disguising Prejudice: Popular Rationales as Excuses for Intolerant Expression Technical Report (National Bureau of Economic Research, 2020).

  7. Report to the Nation: Anti-Asian Prejudice Hate Crime (Center for the Study of Hate and Extremism at California State Univ., San Bernardino, 2021).

  8. Chinese Immigration and the Chinese Exclusion Acts (Office of the Historian, US Department of State, 2016).

  9. Kashima, T. et al. Personal Justice Denied: Report of the Commission on Wartime Relocation and Internment of Civilians (Univ. Washington Press, 2012).

  10. Rabby, F. & Rodgers, W. M. Post 9-11 US Muslim labor market outcomes. Atl. Econ. J. 39, 273–289 (2011).

    Article  Google Scholar 

  11. Becker, G. S. The Economics of Discrimination (Chicago Univ. Press, 1971).

  12. Guryan, J., & Charles, K. K. Taste-based or statistical discrimination: the economics of discrimination returns to its roots. Econ. J. 123, F417–F432 (2013).

    Article  Google Scholar 

  13. Lang, K. & Kahn-Lang Spitzer, A. Race discrimination: an economic perspective. J. Econ. Perspect. 34, 68–89 (2020).

    Article  Google Scholar 

  14. Holzer, H. J. & Ihlanfeldt, K. R. Customer discrimination and employment outcomes for minority workers. Q. J. Econ. 113, 835–867 (1998).

    Article  Google Scholar 

  15. Leonard, J. S., Levine, D. I. & Giuliano, L. Customer discrimination. Rev. Econ. Stat. 92, 670–678 (2010).

    Article  Google Scholar 

  16. Zussman, A. Ethnic discrimination: lessons from the Israeli online market for used cars. Econ. J. 123, F433–F468 (2013).

    Article  Google Scholar 

  17. Combes, P.-P., Decreuse, B., Laouenan, M. & Trannoy, A. Customer discrimination and employment outcomes: theory and evidence from the French labor market. J. Labor Econ. 34, 107–160 (2016).

    Article  Google Scholar 

  18. Huang, J. T., Krupenkin, M., Rothschild, D. & Lee Cunningham, J. The cost of anti-Asian racism during the COVID-19 pandemic. Nat. Hum. Behav. 7, 682–695 (2023).

    Article  PubMed  Google Scholar 

  19. Luca, M., Pronkina, E. & Rossi, M. Scapegoating and Discrimination in Times of Crisis: Evidence from Airbnb Technical Report (National Bureau of Economic Research, 2022).

  20. Altonji, J. G. & Blank, R. M. Race and gender in the labor market. Handb. Labor Econ. 3, 3143–3259 (1999).

    Article  Google Scholar 

  21. Bertrand, M. & Mullainathan, S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am. Econ. Rev. 94, 991–1013 (2004).

    Article  Google Scholar 

  22. Fix, M. & Turner, M. A. (eds) A National Report Card on Discrimination in America: The Role of Testing (Urban Institute, 1998).

  23. Heckman, J. J. Detecting discrimination. J. Econ. Perspect. 12, 101–116 (1998).

    Article  Google Scholar 

  24. Heckman, J. J. & Siegelman, P. in Clear and Convincing Evidence: Measurement of Discrimination in America (eds Fix, M. & Struyk, R. J.) 187–258 (Urban Institute Press, 1993).

  25. Kline, P., Rose, E. K. & Walters, C. R. Systemic discrimination among large US employers. Q. J. Econ. 137, 1963–2036 (2022).

    Article  Google Scholar 

  26. Elsby, M. W., Hobijn, B. & Sahin, A. The Labor Market in the Great Recession Technical Report (National Bureau of Economic Research, 2010).

  27. Le Espiritu, Y. Asian American Panethnicity: Bridging Institutions and Identities Vol. 231 (Temple Univ. Press, 1992).

  28. Kim, C. J. The racial triangulation of Asian Americans. Polit. Soc. 27, 105–138 (1999).

    Article  Google Scholar 

  29. Junn, J. & Masuoka, N. Asian American identity: shared racial status and political context. Perspect. Polit. 6, 729–740 (2008).

    Article  Google Scholar 

  30. Hswen, Y. et al. Association of “#covid19” versus “#chinesevirus” with anti-Asian sentiments on Twitter: March 9–23, 2020. Am. J. Public Health 111, 956–964 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lajevardi, N. & Abrajano, M. How negative sentiment toward Muslim Americans predicts support for Trump in the 2016 presidential election. J. Polit. 81, 296–302 (2019).

    Article  Google Scholar 

  32. Lajevardi, N. & Oskooii, K. A. R. Old-fashioned racism, contemporary Islamophobia, and the isolation of Muslim Americans in the age of Trump. J. Race Ethn. Polit. 3, 112–152 (2018).

    Article  Google Scholar 

  33. BLS Statement on Effects of COVID-19 on Employment Data (US Bureau of Labor Statistics, 2020).

  34. Majlesi, K., Prina, S. & Sullivan, P. Replication material for 'Public opinion, racial bias, and labor barket outcomes in the United States'. Zenodo (2024).

Download references


We thank A. Agan, S. Black, J. Dana, E. M. Girsberger, G. Hanson, J. Hunt, M. Kuhn, K. Lang, N. Prakash, R. Triest and numerous seminar and conference participants at El Colegio de Mexico, RWI—Leibniz Institute for Economic Research, Melbourne History Workshop, NOVA University Lisbon, Rutgers University, University of Technology Sydney and the Australian Political Economy Network for helpful comments and discussions. A special thanks to J. Cao for serving as our statistical expert. All errors are our own. The authors received no specific funding for this work. The data providers had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The findings and conclusions expressed in this Article are those of the authors. The authors extend their acknowledgement to all individuals affected by racial bias, whose experiences served as the motivation for the analyses performed here.

Author information

Authors and Affiliations



All of the authors developed the study concept, designed the study, wrote the paper and approved the final version of the paper for submission.

Corresponding author

Correspondence to Kaveh Majlesi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Pauline Grosjean, Justin Huang and Morgane Laouenan for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–10 and additional robustness checks.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majlesi, K., Prina, S. & Sullivan, P. Public opinion, racial bias and labour market outcomes in the USA. Nat Hum Behav (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing