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Gender stereotypes: The case of MisProfesores.com in Mexico

Author

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  • Arceo-Gomez, Eva O.
  • Campos-Vazquez, Raymundo M.

Abstract

In this study we analyze the extent of gender stereotypes in student evaluations of college professors on the internet site MisProfesores.com in Mexico. We downloaded more than 600,000 evaluations for the period 2008–18. The evaluations include three scores on a scale of 0 to 10: how easy it is to obtain a good grade, how much the professor helps his or her students obtain good grades, and how clearly the professor presents the concepts of the course. The site also allows students to comment on the professor and the class, and we performed a quantitative text analysis of these comments. We found that women receive lower scores than their male counterparts, although the difference is relatively small: 1–2% of a standard deviation. Students refer more to the appearance and personality of female professors, and describe them more often as “bad” or “strict.” They also refer to women in less respectful terms, calling them “maestra” (“teacher”), but calling men “profesor” or “licenciado” (the title corresponding to their academic degree), and they use less positive language for women (“good” vs. “great” or “excellent” for men). Finally, words associated with qualities of service (traditionally stereotyped as feminine) favor women more than men; whereas, words with traditionally masculine associations have a negative impact on women's evaluations.

Suggested Citation

  • Arceo-Gomez, Eva O. & Campos-Vazquez, Raymundo M., 2019. "Gender stereotypes: The case of MisProfesores.com in Mexico," Economics of Education Review, Elsevier, vol. 72(C), pages 55-65.
  • Handle: RePEc:eee:ecoedu:v:72:y:2019:i:c:p:55-65
    DOI: 10.1016/j.econedurev.2019.05.007
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    References listed on IDEAS

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    1. Eva O. Arceo-Gomez & Raymundo M. Campos-Vazquez, 2014. "Race and Marriage in the Labor Market: A Discrimination Correspondence Study in a Developing Country," American Economic Review, American Economic Association, vol. 104(5), pages 376-380, May.
    2. repec:hrv:faseco:30703974 is not listed on IDEAS
    3. Uri Gneezy & Kenneth L. Leonard & John A. List, 2009. "Gender Differences in Competition: Evidence From a Matrilineal and a Patriarchal Society," Econometrica, Econometric Society, vol. 77(5), pages 1637-1664, September.
    4. Steffen Andersen & Seda Ertac & Uri Gneezy & John A. List & Sandra Maximiano, 2013. "Gender, Competitiveness, and Socialization at a Young Age: Evidence From a Matrilineal and a Patriarchal Society," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1438-1443, October.
    5. Boring, Anne, 2017. "Gender biases in student evaluations of teaching," Journal of Public Economics, Elsevier, vol. 145(C), pages 27-41.
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    7. Matthias Sutter & Daniela Glätzle-Rützler, 2015. "Gender Differences in the Willingness to Compete Emerge Early in Life and Persist," Management Science, INFORMS, vol. 61(10), pages 2339-23354, October.
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    10. Friederike Mengel & Jan Sauermann & Ulf Zölitz, 2019. "Gender Bias in Teaching Evaluations," Journal of the European Economic Association, European Economic Association, vol. 17(2), pages 535-566.
    11. Cassandra M. Guarino & Victor M. H. Borden, 2017. "Faculty Service Loads and Gender: Are Women Taking Care of the Academic Family?," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(6), pages 672-694, September.
    12. Amanda Bayer & Cecilia Elena Rouse, 2016. "Diversity in the Economics Profession: A New Attack on an Old Problem," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 221-242, Fall.
    13. Marianne Bertrand & Dolly Chugh & Sendhil Mullainathan, 2005. "Implicit Discrimination," American Economic Review, American Economic Association, vol. 95(2), pages 94-98, May.
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    Cited by:

    1. Kuhn, Andreas, 2022. "The Times Have Changed: Tracking the Evolution of Gender Norms over Time," IZA Discussion Papers 15621, Institute of Labor Economics (IZA).
    2. Martin Flegl & Robert Hlavatý, 2022. "Understanding transitions in professors’ evaluation: the application of Markov chain," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 304-323, March.

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    More about this item

    Keywords

    Gender; Stereotypes; Big data; Teaching evaluations; Mexico;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I20 - Health, Education, and Welfare - - Education - - - General
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • O54 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Latin America; Caribbean

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