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An Elephant in the Classroom: Teacher Bias by Student SES or Ability Measurement Bias?

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Abstract

Teachers are academic merit gatekeepers. Yet their potential role in reproducing inequality via assessments was overlooked or not correctly identified, being an elephant in the classroom. This article teases if teacher grades and track recommendations are biased by student SES or unobserved ability, leading to overestimation in prior research. Using the German NEPS panel across elementary education, we identify student ability with multiple cognitive and noncognitive composite measures and an instrumental variable design. We further assess heterogeneity along the ability distribution to test whether, according to the compensatory hypothesis, teacher bias is largest among low-performers. First, accounting for measurement error, teacher bias declines by 40%, indicating substantial overestimation in previous studies. Second, it concentrates on underperformers, suggesting high-SES parental compensatory strategies to boost teacher assessments. Thus, families and teachers might influence each other in the evaluation process. We discuss the findings’ theoretical and methodological implications for teacher bias as an educational reproduction mechanism.

Suggested Citation

  • Carlos J. Gil-Hernández & Mar C. Espadafor, 2024. "An Elephant in the Classroom: Teacher Bias by Student SES or Ability Measurement Bias?," Econometrics Working Papers Archive 2024_05, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  • Handle: RePEc:fir:econom:wp2024_05
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    File URL: https://labdisia.disia.unifi.it/wp_disia/2024/wp_disia_2024_05.pdf
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    References listed on IDEAS

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    1. Alberto Alesina & Michela Carlana & Eliana La Ferrara & Paolo Pinotti, 2024. "Revealing Stereotypes: Evidence from Immigrants in Schools," American Economic Review, American Economic Association, vol. 114(7), pages 1916-1948, July.
    2. Michela Carlana, 2019. "Implicit Stereotypes: Evidence from Teachers’ Gender Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1163-1224.
    3. Calsamiglia, Caterina & Loviglio, Annalisa, 2019. "Grading on a curve: When having good peers is not good," Economics of Education Review, Elsevier, vol. 73(C).
    4. Fernando Botelho & Ricardo Madeira, Marcos A. Rangel, 2015. "Racial Discrimination in Grading: Evidence from Brazil," Working Papers, Department of Economics 2015_04, University of São Paulo (FEA-USP).
    5. Fernando Botelho & Ricardo A. Madeira & Marcos A. Rangel, 2015. "Racial Discrimination in Grading: Evidence from Brazil," American Economic Journal: Applied Economics, American Economic Association, vol. 7(4), pages 37-52, October.
    6. Apascaritei, Paula & Demel, Simona & Radl, Jonas, 2021. "The Difference Between Saying and Doing: Comparing Subjective and Objective Measures of Effort Among Fifth Graders," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 65(11), pages 1457-1479.
    7. Lisa G. Smithers & Alyssa C. P. Sawyer & Catherine R. Chittleborough & Neil M. Davies & George Davey Smith & John W. Lynch, 2018. "A systematic review and meta-analysis of effects of early life non-cognitive skills on academic, psychosocial, cognitive and health outcomes," Nature Human Behaviour, Nature, vol. 2(11), pages 867-880, November.
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    More about this item

    Keywords

    Teacher assessments; teacher bias and discrimination; class inequality; educational transitions; tracking recommendations; standardized testing; grades; longitudinal studies of education;
    All these keywords.

    JEL classification:

    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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