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Teacher Bias in Assessments by Student Ascribed Status: A Factorial Experiment on Discrimination in Education

Carlos J. Gil-Hernández, Irene Pañeda-Fernández, Leire Salazar, Jonatan Castaño Muñoz

Sociological Science August 27, 2024
10.15195/v11.a27


Teachers are the evaluators of academic merit. Identifying if their assessments are fair or biased by student-ascribed status is critical for equal opportunity but empirically challenging, with mixed previous findings. We test status characteristics beliefs, statistical discrimination, and cultural capital theories with a pre-registered factorial experiment on a large sample of Spanish pre-service teachers (n = 1, 717). This design causally identifies, net of ability, the impact of student-ascribed characteristics on teacher short- and long-term assessments, improving prior studies’ theory testing, confounding, and power. Findings unveil teacher bias in an essay grading task favoring girls and highbrow cultural capital, aligning with status characteristics and cultural capital theories. Results on teachers’ long-term expectations indicate statistical discrimination against boys, migrant origin, and working-class students under uncertain information. Unexpectedly, ethnic discrimination changes from teachers favoring native origin in long-term expectations to migrant origin in short-term evaluations, suggesting compensatory grading. We discuss the complex roots of discrimination in teacher assessments as an educational (in)equality mechanism.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Carlos J. Gil-Hernández∗: Department of Statistics, Computer Science, Applications, University of Florence
∗Corresponding author, E-mail: carlos.gil@unifi.it

Irene Pañeda-Fernández: WZB Berlin Social Science Center
E-mail: irene.paneda@wzb.eu

Leire Salazar: Institute for Public Goods and Policies, Consejo Superior de Investigaciones Científicas
E-mail: leire.salazar@cchs.csic.es

Jonatan Castaño Muñoz: Departamento de Didática y Organización Educativa, Universidad de Sevilla
E-mail: jcastanno@us.es

Acknowledgements: This project has been funded through the JRC Centre for Advanced Studies and the project Social Classes in the Digital Age (DIGCLASS). Jonatan Castaño Muñoz acknowledges the support of a) the ‘Ramón y Cajal’ grant RYC2020-030157 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”; and b) University of Seville “VI University research plan” (VI plan propio de investigación). We thank Lilian Weikert, William Foley, Zbigniew Karpiñski, David Martínez de Lafuente, Alberto López, and Mario Spiezio for their valuable feedback and support. We also thank the participants at the following venues where we presented earlier versions of the article: the ‘Experiments on Social Inequality’ Workshop at Sciences Po-LIEPP, the ‘Colloquium of the Migration and Diversity Department’ at the WZB Berlin Social Science Centre, the ECSR Thematic Conference ‘Effort and Social Inequality’ and ‘2024 IC3JM Conference’ at Carlos III-Juan March Institute of Social Sciences, the FES ‘Inequality and Social Stratification Committee Workshop’ in Oviedo, the ‘Education and Social Inequalities Seminar’ at University of Sevilla, the ‘CLIC Seminar Series’ at the European University Institute, the SISEC conference in Cagliari, and the FES National Congress in Sevilla.

Supplemental Materials

Replication Package: Data and replication code are publicly accessible at the GitHub repository: https://zenodo.org/doi/10.5281/zenodo.12666534. The hypotheses and research design were publicly pre-registered with a pre-analysis plan (PAP) before data collection and analysis at the Open Science Foundation repository: https://doi.org/10.17605/OSF.IO/DZB3S.

  • Citation: J. Gil-Hernández, Carlos, Irene Pañeda-Fernández, Leire Salazar, Jonatan Castaño Muñoz, 2024. “Teacher Bias in Assessments by Student Ascribed Status: A Factorial Experiment on Discrimination in Education” Sociological Science 11: 743-776.
  • Received: January 6, 2024
  • Accepted: July 9, 2024
  • Editors: Ari Adut, Stephen Vaisey
  • DOI: 10.15195/v11.a27


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