IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2401.04200.html
   My bibliography  Save this paper

Teacher bias or measurement error?

Author

Listed:
  • Thomas van Huizen
  • Madelon Jacobs
  • Matthijs Oosterveen

Abstract

In many countries, teachers' track recommendations are used to allocate students to secondary school tracks. Previous studies have shown that students from families with low socioeconomic status (SES) receive lower track recommendations than their peers from high SES families, conditional on standardized test scores. It is often argued that this indicates teacher bias. However, this claim is invalid in the presence of measurement error in test scores. We discuss how measurement error in test scores generates a biased coefficient of the conditional SES gap, and consider three empirical strategies to address this bias. Using administrative data from the Netherlands, we find that measurement error explains 35 to 43% of the conditional SES gap in track recommendations.

Suggested Citation

  • Thomas van Huizen & Madelon Jacobs & Matthijs Oosterveen, 2024. "Teacher bias or measurement error?," Papers 2401.04200, arXiv.org, revised Feb 2024.
  • Handle: RePEc:arx:papers:2401.04200
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2401.04200
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Louis Guttman, 1945. "A basis for analyzing test-retest reliability," Psychometrika, Springer;The Psychometric Society, vol. 10(4), pages 255-282, December.
    2. Terrier, Camille, 2020. "Boys lag behind: How teachers’ gender biases affect student achievement," Economics of Education Review, Elsevier, vol. 77(C).
    3. Armin Falk & Fabian Kosse & Pia Pinger, 2020. "Mentoring and Schooling Decisions: Causal Evidence," CESifo Working Paper Series 8382, CESifo.
    4. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    5. Jan Feld & Ulf Zölitz, 2017. "Understanding Peer Effects: On the Nature, Estimation, and Channels of Peer Effects," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 387-428.
    6. 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.
    7. Ben Gillen & Erik Snowberg & Leeat Yariv, 2019. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1826-1863.
    8. Lavy, Victor & Sand, Edith, 2018. "On the origins of gender gaps in human capital: Short- and long-term consequences of teachers' biases," Journal of Public Economics, Elsevier, vol. 167(C), pages 263-279.
    9. Nicholas W. Papageorge & Seth Gershenson & Kyung Min Kang, 2020. "Teacher Expectations Matter," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 234-251, May.
    10. Borghans, By Lex & Diris, Ron & Smits, Wendy & de Vries, Jannes, 2020. "Should we sort it out later? The effect of tracking age on long-run outcomes," Economics of Education Review, Elsevier, vol. 75(C).
    11. Christian Dustmann & Patrick A. Puhani & Uta Schönberg, 2017. "The Long‐term Effects of Early Track Choice," Economic Journal, Royal Economic Society, vol. 127(603), pages 1348-1380, August.
    12. Christopher Cornwell & David B. Mustard & Jessica Van Parys, 2013. "Noncognitive Skills and the Gender Disparities in Test Scores and Teacher Assessments: Evidence from Primary School," Journal of Human Resources, University of Wisconsin Press, vol. 48(1), pages 236-264.
    13. Jan Feld & Ulf Zölitz, 2017. "Understanding Peer Effects: On the Nature, Estimation, and Channels of Peer Effects," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 387-428.
    14. Sönke Hendrik Matthewes, 2020. "Better together? Heterogeneous effects of tracking on student achievement," CEP Discussion Papers dp1706.pdf, Centre for Economic Performance, LSE.
    15. Simon Burgess & Ellen Greaves, 2013. "Test Scores, Subjective Assessment, and Stereotyping of Ethnic Minorities," Journal of Labor Economics, University of Chicago Press, vol. 31(3), pages 535-576.
    16. 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).
    17. 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.
    18. Zhuan Pei & Jörn-Steffen Pischke & Hannes Schwandt, 2019. "Poorly Measured Confounders are More Useful on the Left than on the Right," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 205-216, April.
    19. Lex Borghans & Ron Diris & Wendy Smits & Jannes de Vries, 2019. "The long-run effects of secondary school track assignment," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-29, October.
    20. Andrew J. Hill & Daniel B. Jones, 2021. "Self-Fulfilling Prophecies in the Classroom," Journal of Human Capital, University of Chicago Press, vol. 15(3), pages 400-431.
    Full references (including those not matched with items on IDEAS)

    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. Thomas van Huizen, 2021. "Teacher bias or measurement error bias? Evidence from track recommendations," Working Papers 2113, Utrecht School of Economics.
    2. Maria Zumbuehl & Nihal Chehber & Rik Dillingh, 2022. "Can skill differences explain the gap in the track recommendation by socio-economic status?," CPB Discussion Paper 439, CPB Netherlands Bureau for Economic Policy Analysis.
    3. Cattan, Sarah & Salvanes, Kjell G. & Tominey, Emma, 2022. "First Generation Elite: The Role of School Networks," IZA Discussion Papers 15560, Institute of Labor Economics (IZA).
    4. Shi, Ying & Zhu, Maria, 2023. "“Model minorities” in the classroom? Positive evaluation bias towards Asian students and its consequences," Journal of Public Economics, Elsevier, vol. 220(C).
    5. Victor Lavy & Rigissa Megalokonomou, 2019. "Persistency in Teachers’ Grading Bias and Effects on Longer-Term Outcomes: University Admissions Exams and Choice of Field of Study," NBER Working Papers 26021, National Bureau of Economic Research, Inc.
    6. Ferman, Bruno & Fontes, Luiz Felipe, 2020. "Discriminating Behavior: Evidence from teachers’ grading bias," MPRA Paper 100400, University Library of Munich, Germany.
    7. Ferman, Bruno & Fontes, Luiz Felipe, 2022. "Assessing knowledge or classroom behavior? Evidence of teachers’ grading bias," Journal of Public Economics, Elsevier, vol. 216(C).
    8. Terrier, Camille, 2020. "Boys lag behind: How teachers’ gender biases affect student achievement," Economics of Education Review, Elsevier, vol. 77(C).
    9. Nicole Black & Sonja C. de New, 2020. "Short, Heavy and Underrated? Teacher Assessment Biases by Children's Body Size," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(5), pages 961-987, October.
    10. Alexandra de Gendre & Nicolás Salamanca, 2020. "On the Mechanisms of Ability Peer Effects," Melbourne Institute Working Paper Series wp2020n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Delaney, Judith M. & Devereux, Paul J., 2023. "Gender Differences in Teacher Judgement of Comparative Advantage," IZA Discussion Papers 16635, Institute of Labor Economics (IZA).
    12. Bach, Maximilian & Fischer, Mira, 2020. "Understanding the response to high-stakes incentives in primary education," ZEW Discussion Papers 20-066, ZEW - Leibniz Centre for European Economic Research.
    13. Lavy, Victor & Sand, Edith, 2018. "On the origins of gender gaps in human capital: Short- and long-term consequences of teachers' biases," Journal of Public Economics, Elsevier, vol. 167(C), pages 263-279.
    14. Alberto Alesina & Michela Carlana & Eliana La Ferrara & Paolo Pinotti, 2018. "Revealing Stereotypes: Evidence from Immigrants in Schools," RF Berlin - CReAM Discussion Paper Series 1817, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    15. Marcenaro-Gutierrez, O.D. & Lopez-Agudo, L.A. & Henriques, C.O., 2021. "Are soft skills conditioned by conflicting factors? A multiobjective programming approach to explore the trade-offs," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 18-40.
    16. Lavy, Victor & Sand, Edith, 2015. "On The Origins of Gender Human Capital Gaps: Short and Long Term Consequences of Teachers’ Stereotypical Biases," The Warwick Economics Research Paper Series (TWERPS) 1085, University of Warwick, Department of Economics.
    17. Rangel, Marcos & Marotta, Luana & van der Werf, Cynthia & Duryea, Suzanne & Drouet Arias, Marcelo & Rodríguez Guillén, Lucina, 2024. "Barriers to Immigrant Assimilation: Evidence on Grading Bias in Ecuadorian High Schools," IDB Publications (Working Papers) 13434, Inter-American Development Bank.
    18. Nicoletti, Cheti & Sevilla, Almudena & Tonei, Valentina, 2022. "Gender Stereotypes in the Family," IZA Discussion Papers 15773, Institute of Labor Economics (IZA).
    19. Delaney, Judith M. & Devereux, Paul J., 2021. "Gender and Educational Achievement: Stylized Facts and Causal Evidence," IZA Discussion Papers 14074, Institute of Labor Economics (IZA).
    20. Sahlström, Ellen & Silliman, Mikko, 2024. "The Extent and Consequences of Teacher Biases against Immigrants," IZA Discussion Papers 16899, Institute of Labor Economics (IZA).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2401.04200. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.