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Male–female achievement variance comparisons are not robust

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  • Domicolo, Carly
  • Nielsen, Eric

Abstract

Males have greater variability in achievement than females in almost all countries using internationally comparable test scores. However, these variance comparisons can frequently be reversed using economically plausible, order-preserving rescalings of the psychometrically derived test scores. The oft-cited “Greater Male Variability Hypothesis” for achievement may be a psychometric artifact.

Suggested Citation

  • Domicolo, Carly & Nielsen, Eric, 2022. "Male–female achievement variance comparisons are not robust," Economics Letters, Elsevier, vol. 220(C).
  • Handle: RePEc:eee:ecolet:v:220:y:2022:i:c:s0165176522003275
    DOI: 10.1016/j.econlet.2022.110853
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    References listed on IDEAS

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    1. Timothy N. Bond & Kevin Lang, 2013. "The Evolution of the Black-White Test Score Gap in Grades K–3: The Fragility of Results," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1468-1479, December.
    2. Thomas S. Dee & Brian Jacob, 2011. "The impact of no Child Left Behind on student achievement," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 30(3), pages 418-446, June.
    3. Schröder, Carsten & Yitzhaki, Shlomo, 2017. "Revisiting the evidence for cardinal treatment of ordinal variables," European Economic Review, Elsevier, vol. 92(C), pages 337-358.
    4. Brian Jacob & Jesse Rothstein, 2016. "The Measurement of Student Ability in Modern Assessment Systems," Journal of Economic Perspectives, American Economic Association, vol. 30(3), pages 85-108, Summer.
    5. Flavio Cunha & Eric Nielsen & Benjamin Williams, 2021. "The Econometrics of Early Childhood Human Capital and Investments," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 487-513, August.
    6. Neal, Derek A & Johnson, William R, 1996. "The Role of Premarket Factors in Black-White Wage Differences," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 869-895, October.
    7. Figlio, David & Loeb, Susanna, 2011. "School Accountability," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 8, pages 383-421, Elsevier.
    8. John Cawley & James Heckman & Edward Vytlacil, 1999. "On Policies To Reward The Value Added By Educators," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 720-727, November.
    9. Eric R. Nielsen, 2015. "The Income-Achievement Gap and Adult Outcome Inequality," Finance and Economics Discussion Series 2015-41, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Human capital; Variance hypothesis; Gender differences; Achievement; Inequality;
    All these keywords.

    JEL classification:

    • J - Labor and Demographic Economics
    • J - Labor and Demographic Economics
    • I - Health, Education, and Welfare
    • I - Health, Education, and Welfare

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