Statistical Discrimination and Committees

  1. J. Ignacio Conde-Ruiz 1
  2. Juan José Ganuza 2
  3. Paola Profeta 3
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Department of Economics. University Pompeu Fabra.
  3. 3 Bocconi University and Dondena.
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Revista:
Documentos de trabajo ( FEDEA )

ISSN: 1696-7496

Año de publicación: 2021

Número: 6

Páginas: 1-49

Tipo: Documento de Trabajo

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Otras publicaciones en: Documentos de trabajo ( FEDEA )

Resumen

We develop a statistical discrimination model where groups of workers (males-females) differ in the observability of their productivity signals by the evaluation committee. We assume that the informativeness of the productivity signals depends on the match between the potential worker and the interviewer: when both parties have similar backgrounds, the signal is likely to be more informative. Under this “homo-accuracy” bias, the group that is most represented in the evaluation committee generates more accurate signals, and, consequently, has a greater incentive to invest in human capital. This generates a discrimination trap. If, for some exogenous reason, one group is initially poorly evaluated (less represented into the evaluation committee), this translates into lower investment in human capital of individuals of such group, which leads to lower representation in the evaluation committee in the future, generating a persistent discrimination process. We explore this dynamic process and show that quotas may be effective to deal with this discrimination trap. In particular, we show that introducing a “temporary” quota allows to reach a steady state equilibrium with a higher welfare than the one obtained in the decentralized equilibrium in which talented workers of the discriminated group decide not to invest in human capital. Finally, if the discriminated group is underrepresented in the worker population (race), restoring e¢ciency requires to implement a “permanent” system of quotas.

Referencias bibliográficas

  • Aigner, D. J. and Cain, G. (1977). “Statistical theories of discrimination in labor markets”, Industrial and Labor relations review 30(2), 175-187.
  • Altonji, J.G. and Pierret, C.R. (2001). “Employer Learning and Statistical Discrimination”, The Quarterly Journal of Economics, 116(1), 313-350.
  • Arrow, K. J. (1973). “The Theory of Discrimination”, in Ashenfelter, O. and Rees, A. (eds), Discrimination in Labor Markets, Princeton University Press 3-33.
  • Athey, S., Avery, C., and Zemsky, P. (2000). “Mentoring and Diversity”, American Economic Review, 90, 4, 765-786.
  • Austen-Smith, D. and Feddersen, T.J. (2009). “Information aggregation and communication in committees”, Philosophical Transactions of the Royal Society B: Biological Sciences, 364, 763—769.
  • Austen-Smith, D. and Banks, J.S. (1996). “Information aggregation, rationality, and the Condorcet Jury Theorem.” American Political Science Review, 90, 34—45.
  • Azmat, G. and Ferrer, R. (2017). “Gender Gaps in Performance: Evidence from Young Lawyers”, Journal of Political Economy, 125, 5.
  • Becker, G. (1975). Human Capital: A Theoretical and Empirical Analysis with special reference to Education, University of Chicago Press.
  • Bertrand, M., Goldin, C. and Katz, L. (2010). “Dynamics of the Gender Gap for Young Professionals in the Financial and Corporate Sectors.”, American Economic Journal: Applied Economics, 2(3), 228-55.
  • Besley, T., Folke, O., Persson, T. and Rickne, J. (2017). “Gender Quotas and the Crisis of the Mediocre Men: Theory and evidence from Sweden”, American Economic Review, 107 (8), 2204-42.
  • Booth, A., M. Francesconi, and Frank, J. (2003). “A Sticky Floors Model of Promotion, Gender and Pay”. European Economic Review 47 (2), 295- 322.
  • Bohren, J. A, A. Imas, and Rosenberg, M. (2019). “The Dynamics of Discrimination, Theory and Evidence,” American Economic Review, 109, 3395—3436.
  • Bordalo, P., K. Co§man, and Gennaioli, N. (2019) “Beliefs about Gender,” American Economic Review, 109, 739—773.
  • Coate, S. and Loury, G. C. (1993). “Will A¢rmative-Action Policies Eliminate Negative Stereotypes?”, American Economic Review, 83(5), 1220-1240.
  • Cornelll, B. and Welch, I. (1996). “Culture Information and Screening Discrimination”, Journal of Political Economy 104 (3): 542-571.
  • Coughlan, P.J. (2000). “In Defense of Unanimous Jury Verdicts: Mistrials, Communication, and Strategic Voting”, American Political Science Review, 94(2), 375—93.
  • Diamond, D. W. (1989). “Reputation Acquisition in Debt Markets”, The Journal of Political Economy, 97(4), 828-862.
  • Feddersen, T.J. and Pesendorfer, W. (1996). “The Swing Voter’s Curse”, The American Economic Review, 86 (3), 408-424.
  • Feddersen, T.J. and Pesendorfer, W. (1997). “Voting Behavior and Information Aggregation in Elections with Private Information”, Econometrica, 65(5), 1029—58.
  • Ferrari, G., Mac Millan, R. and Profeta, P. (2015) “Is There Gender Bias in Candidate Selection? Experimental Evidence from Italy”, Università Bocconi mimeo.
  • Flabbi, L. ; Macis, M; Moro, A. and F. Schivardi (2019)"Do Female Executives Make a Di§erence? The Impact of Female Leadership on Gender Gaps and Firm Performance", Economic Journal, 129(622): 2390—2423, 2019.
  • Foster, D.P. and Vohra, R.V. (1992). “An Economic Argument for A¢rmative Action.”, Rationality and Society, 4(2), 176-88.
  • Goldin, C. and Rouse. C. (2000). “Orchestrating Impartiality: the Impact of ‘Blind’Auditions on Female Musicians”, American Economic Review 90(4), 715-741.
  • Kahn, L. and Lange, F. (2014). “Employer Learning, Productivity, and the Earnings Distribution: Evidence from Performance Measures ”, The Review of Economic Studies 81(4), Pages 1575—1613.
  • Kevin, L. (1986) “A Language Theory of Discrimination” The Quarterly Journal of Economics, Volume 101, Issue 2, May 1986, pp: 363-382.
  • Lang, K and Spitzer, AKL (2020) “Race Discrimination: An Economic Perspective”, Journal of Economic Perspectives, 34 (2), 68-89.
  • Lesner,R. (2018) “Testing for Statistical Discrimination Based on Gender”,Labour 32 (2), 141-181.
  • Lundberg, S. J. and Startz, R. (1983) “Private Discrimination and Social Intervention in Competitive Labor Markets”, American Economic Review, 73(3), 340-347.
  • Mailath, G., L. Samuelson and A. Shaked (2000), “Endogenous Inequality in Integrated Labor Markets with Two-Sided Search”, American Economic Review, 90 (1), 46-72.
  • Matsa, D. A. and Miller, A. (2011) “Chipping Away at the Glass Ceiling: Gender Spillovers in Corporate Leadership”, American Economic Review. P&P. 101(3), 635-639.
  • Mengel, F. (2019) “Gender Biases in Opinion Aggregation”, working paper, University of Essex.
  • Morgan, J and Várdy, F. (2009). “Diversity in the Workplace.” American Economic Review, 99(1), 472-85.
  • Moro, A. and Norman, P. (2004). “A General Equilibrium Model of Statistical Discrimination”, Journal of Economic Theory 114(1), 1-30.
  • Fang, H and Moro, A. (2011) “Theories of statistical discrimination and a¢rmative action: A survey.” Handbook of social economics, North-Holland, 133-200
  • Niederle, M., and Vesterlund, L. (2010). “Explaining the Gender Gap in Math Test Scores: The Role of Competition”, The Journal of Economic Perspectives, 24 (2), 129-44.
  • Osborne, M., Rosenthal, J. and Stewart, C. (2020) “Information Aggregation with Costly Reporting”, The Economic Journal, 130, 208—232.
  • Pallais, A. (2014). “Ine¢cient Hiring in Entry-Level Labor Markets.” American Economic Review, 104, (11), 3565-99.
  • Phelps, E. (1972). “The Statistical Theory of Racism and Sexism”, American Economic Review, 62(4), 659-661.
  • Pinkston, J. (2003). “Screening Discrimination and the Determinants of Wages”, Labour Economics 10(6), 643-658.
  • Pinkston, J. (2009). “A Model of Asymmetric Employer Learning with Testable Implications”, The Review of Economic Studies 76(1), 367—394.
  • Reuben, E, P. Sapienza, and Zingales, L. (2014) “How Stereotypes Impair Women’s Careers in Science,” Proceedings of theNational Academy of Sciences, 111, 4403—4408.
  • Roland G. F., Jr., D. Pager, and Spenkuch, J. (2013). “Racial Disparities in Job Finding and O§ered Wages”. Journal of Law and Economics, 56, 633-689.
  • Roux, N and Sobel, J. (2015) “Group Polarization in a Model of Information Aggregation”, American Economic Journal: Micreeconomics, 7(4), 202-32.
  • Schonberg, U. (2007) “Testing for Asymmetric Employer Learning”, Journal of Labor Economics 25, 651-691.
  • Siniscalchi, M and Veronesi, P. (2020) “Self-Image Bias and Lost Talent”, CEPR Discussion Paper Series DP15621.
  • Small, M. and Pager, D. (2020) “Sociological Perspectives on Racial Discrimination”, Journal of Economic Perspectives, 34 (2), 49-67.
  • World Economic Forum, Global Gender Gap Report 2020, available at: http://www3.weforum.org/docs/WEF_GGGR_2020.pdf
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