IDEAS home Printed from https://ideas.repec.org/p/red/sed017/1644.html
   My bibliography  Save this paper

The Rise of Meritocracy and the Inheritance of Advantage

Author

Listed:
  • Michael Watts

    (University of Edinburgh)

  • Jose V. Rodriguez Mora

    (University of Edinburgh)

  • David Comerford

    (University of Strathclyde)

Abstract

We present a model human capital accumulation with statistical discrimination on the background of agents. Firms do not observe the productivity of workers, they determine their wage after observing two signals. A direct signal on the productivity of the workers and a second signal on the income of the parents of the agent. The signal on background is useful (insofar the direct signal is not perfectly accurate) because parents invest in their children education, increasing their human capital. Knowing that richer parents invest more in education and that their children will be in average more productive, firms rationally discriminate favoring those with perceived good backgrounds. Thus, the income of an agent depends on both the objective information that society has on its human capital and the existing perceptions on her background. Our main result is that the accuracy of both signals have very similar effects in both inequality and intergenerational mobility. More accurate information on the background of individuals facilitates statistical discrimination increasing inequality and its persistence. More accurate information on the actual capabilities of workers leads to exactly the same result via an interesting feed back mechanism. It allows to discern good workers from the able ones, increasing their wage differential and inequality. But more inequality increases the uncertainty that firms have on workers characteristics, which itself increases the weight that firms give to signals, which further increases inequality, and so on. There is, though, a big difference in both types of signals in the manner that they affect investment. Accurate direct information on productivity is directly linked to the return to investment in human capital, as it is what connects human capital with income. The effects of information on parental background on investment are much more indirect, as it implies that your income affects your children independently from the investment you make in them. Using our model to interpret the data suggests that a country like the US (with relatively little intergenerational mobility, large degree of inequality and very large investment in education) might be characterized by having very precise direct signals on ability, resulting in high rewards to merit, and (conditioning on it) little rewards to have a good background. The US might be still a land of opportunities conditional in having the right ability. Of course, those abilities are to a large extent inherited, and that is the reason why the intergenerational persistence of income is so large. But notice that the reason why they are so inheritable might precisely be that merit is highly rewarded; that in a deep sense, the US may still be a very meritocratic society.

Suggested Citation

  • Michael Watts & Jose V. Rodriguez Mora & David Comerford, 2017. "The Rise of Meritocracy and the Inheritance of Advantage," 2017 Meeting Papers 1644, Society for Economic Dynamics.
  • Handle: RePEc:red:sed017:1644
    as

    Download full text from publisher

    File URL: https://economicdynamics.org/meetpapers/2017/paper_1644.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Miles Corak & Patrizio Piraino, 2011. "The Intergenerational Transmission of Employers," Journal of Labor Economics, University of Chicago Press, vol. 29(1), pages 37-68, January.
    2. Gueorgui Kambourov & Iourii Manovskii, 2009. "Occupational Specificity Of Human Capital," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(1), pages 63-115, February.
    3. Guido Matias Cortes & Giovanni Gallipoli, 2014. "The Costs of Occupational Mobility: An Aggregate Analysis," Working Papers 2014-015, Human Capital and Economic Opportunity Working Group.
    4. John Hassler & José Rodríguez Mora & Joseph Zeira, 2007. "Inequality and mobility," Journal of Economic Growth, Springer, vol. 12(3), pages 235-259, September.
    5. Moro, Andrea & Norman, Peter, 2004. "A general equilibrium model of statistical discrimination," Journal of Economic Theory, Elsevier, vol. 114(1), pages 1-30, January.
    6. Ronni Pavan, 2011. "Career Choice and Wage Growth," Journal of Labor Economics, University of Chicago Press, vol. 29(3), pages 549-587.
    7. Oded Galor & Joseph Zeira, 1993. "Income Distribution and Macroeconomics," Review of Economic Studies, Oxford University Press, vol. 60(1), pages 35-52.
    8. Maia Güell & Michele Pellizzari & Giovanni Pica & José V. Rodríguez Mora, 2018. "Correlating Social Mobility and Economic Outcomes," Economic Journal, Royal Economic Society, vol. 128(612), pages 353-403, July.
    9. George Baker & Michael Gibbs & Bengt Holmstrom, 1994. "The Wage Policy of a Firm," The Quarterly Journal of Economics, Oxford University Press, vol. 109(4), pages 921-955.
    10. Kahn, Lisa B., 2010. "The long-term labor market consequences of graduating from college in a bad economy," Labour Economics, Elsevier, vol. 17(2), pages 303-316, April.
    11. Gary S. Becker & Nigel Tomes, 1994. "Human Capital and the Rise and Fall of Families," NBER Chapters, in: Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, Third Edition, pages 257-298, National Bureau of Economic Research, Inc.
    12. Checchi, Daniele & Ichino, Andrea & Rustichini, Aldo, 1999. "More equal but less mobile?: Education financing and intergenerational mobility in Italy and in the US," Journal of Public Economics, Elsevier, vol. 74(3), pages 351-393, December.
    13. Guido Matias Cortes & Giovanni Gallipoli, 2018. "The Costs of Occupational Mobility: An Aggregate Analysis," Journal of the European Economic Association, European Economic Association, vol. 16(2), pages 275-315.
    14. Sullivan, Paul, 2010. "Empirical evidence on occupation and industry specific human capital," Labour Economics, Elsevier, vol. 17(3), pages 567-580, June.
    15. Maxim Poletaev & Chris Robinson, 2008. "Human Capital Specificity: Evidence from the Dictionary of Occupational Titles and Displaced Worker Surveys, 1984-2000," Journal of Labor Economics, University of Chicago Press, vol. 26(3), pages 387-420, July.
    16. Paul Oyer, 2006. "Initial Labor Market Conditions and Long-Term Outcomes for Economists," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 143-160, Summer.
    17. Philip Oreopoulos & Till von Wachter & Andrew Heisz, 2012. "The Short- and Long-Term Career Effects of Graduating in a Recession," American Economic Journal: Applied Economics, American Economic Association, vol. 4(1), pages 1-29, January.
    18. Becker, Gary S & Tomes, Nigel, 1979. "An Equilibrium Theory of the Distribution of Income and Intergenerational Mobility," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1153-1189, December.
    19. Bingley, Paul & Corak, Miles & Westergård-Nielsen, Niels C., 2011. "The Intergenerational Transmission of Employers in Canada and Denmark," IZA Discussion Papers 5593, Institute of Labor Economics (IZA).
    20. George Baker & Michael Gibbs & Bengt Holmstrom, 1994. "The Internal Economics of the Firm: Evidence from Personnel Data," The Quarterly Journal of Economics, Oxford University Press, vol. 109(4), pages 881-919.
    21. Christina Gathmann & Uta Schönberg, 2010. "How General Is Human Capital? A Task-Based Approach," Journal of Labor Economics, University of Chicago Press, vol. 28(1), pages 1-49, January.
    22. Fatih Guvenen & Greg Kaplan & Jae Song & Justin Weidner, 2017. "Lifetime Incomes in the United States over Six Decades," NBER Working Papers 23371, National Bureau of Economic Research, Inc.
    23. Raj Chetty & Nathaniel Hendren & Patrick Kline & Emmanuel Saez, 2014. "Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States," The Quarterly Journal of Economics, Oxford University Press, vol. 129(4), pages 1553-1623.
    24. Coate, Stephen & Loury, Glenn C, 1993. "Will Affirmative-Action Policies Eliminate Negative Stereotypes?," American Economic Review, American Economic Association, vol. 83(5), pages 1220-1240, December.
    25. Roland Benabou, 1993. "Workings of a City: Location, Education, and Production," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 619-652.
    26. repec:hrv:faseco:30750027 is not listed on IDEAS
    27. Simon, Curtis J & Warner, John T, 1992. "Matchmaker, Matchmaker: The Effect of Old Boy Networks on Job Match Quality, Earnings, and Tenure," Journal of Labor Economics, University of Chicago Press, vol. 10(3), pages 306-330, July.
    28. Peter Norman, 2003. "Statistical Discrimination and Efficiency," Review of Economic Studies, Oxford University Press, vol. 70(3), pages 615-627.
    29. David Neumark, 2002. "Youth Labor Markets In The United States: Shopping Around Vs. Staying Put," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 462-482, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lutz Hendricks & Christopher Herrington & Todd Schoellman, 2018. "College Access and Attendance Patterns: A Long-Run View," Opportunity and Inclusive Growth Institute Working Papers 10, Federal Reserve Bank of Minneapolis.
    2. Lutz Hendricks & Christopher Herrington & Todd Schoellman, 2016. "The Changing Roles of Family Income and Academic Ability for US College Attendance," Working Papers 1602, VCU School of Business, Department of Economics, revised Apr 2017.
    3. Chris Bidner & John Knowles, 2018. "Matching for Social Mobility with Unobserved Heritable Characteristics," Discussion Papers dp18-05, Department of Economics, Simon Fraser University.

    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. Guido Neidhöfer, 2019. "Intergenerational mobility and the rise and fall of inequality: Lessons from Latin America," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 499-520, December.
    2. Christopher Rauh, 2015. "The Political Economy of Early and College Education - Can Voting Bend the Great Gatsby Curve?," 2015 Meeting Papers 82, Society for Economic Dynamics.
    3. Kondo, Ayako & Naganuma, Saori, 2015. "Inter-industry labor reallocation and task distance," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 127-147.
    4. Brunner, Beatrice & Kuhn, Andreas, 2009. "To Shape the Future: How Labor Market Entry Conditions Affect Individuals' Long-Run Wage Profiles," IZA Discussion Papers 4601, Institute of Labor Economics (IZA).
    5. Beatrice Brunner & Andreas Kuhn, 2014. "The impact of labor market entry conditions on initial job assignment and wages," Journal of Population Economics, Springer;European Society for Population Economics, vol. 27(3), pages 705-738, July.
    6. Beatrice Brunner & Andreas Kuhn, 2010. "The Impact of Labor Market Entry Condition on Initial Job Assignment, Human Capital Accumulation, and Wages," NRN working papers 2010-15, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    7. Beartice Brunner & Andreas Kuhn, 2009. "To Shape the Future: How Labor Market Entry Conditions Affect Individuals’s Long-Run Wage Profiles," NRN working papers 2009-29, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    8. Beatrice Brunner & Andreas Kuhn, 2009. "To shape the future: How labor market entry conditions affect individuals' long-run wage profiles," IEW - Working Papers 457, Institute for Empirical Research in Economics - University of Zurich.
    9. Rauh, Christopher, 2017. "Voting, education, and the Great Gatsby Curve," Journal of Public Economics, Elsevier, vol. 146(C), pages 1-14.
    10. Zsolt Csáfordi & László Lőrincz & Balázs Lengyel & Károly Miklós Kiss, 2020. "Productivity spillovers through labor flows: productivity gap, multinational experience and industry relatedness," The Journal of Technology Transfer, Springer, vol. 45(1), pages 86-121, February.
    11. Raitano, Michele & Vona, Francesco, 2021. "Nepotism vs. Specific Skills: The effect of professional liberalization on returns to parental background of Italian lawyers," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 489-505.
    12. Samuel Bowles & Glenn C. Loury & Rajiv Sethi, 2014. "Group Inequality," Journal of the European Economic Association, European Economic Association, vol. 12(1), pages 129-152, February.
    13. Michele Raitano & Francesco Vona, 2015. "Measuring the link between intergenerational occupational mobility and earnings: evidence from eight European countries," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(1), pages 83-102, March.
    14. Piketty, Thomas, 2000. "Theories of persistent inequality and intergenerational mobility," Handbook of Income Distribution, in: A.B. Atkinson & F. Bourguignon (ed.), Handbook of Income Distribution, edition 1, volume 1, chapter 8, pages 429-476, Elsevier.
    15. Tharcisio Leone, 2019. "The Geography of Intergenerational Mobility: Evidence of Educational Persistence and the “Great Gatsby Curve" in Brazil," Documentos de Trabajo LACEA 017526, The Latin American and Caribbean Economic Association - LACEA.
    16. Leone, Tharcisio, 2019. "The geography of intergenerational mobility: Evidence of educational persistence and the "Great Gatsby Curve" in Brazil," GIGA Working Papers 318, GIGA German Institute of Global and Area Studies.
    17. Raitano Michele & Vona Francesco, 2018. "From the Cradle to the Grave: The Influence of Family Background on the Career Path of Italian Men," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(6), pages 1062-1088, December.
    18. Bernasconi, Michele & Profeta, Paola, 2012. "Public education and redistribution when talents are mismatched," European Economic Review, Elsevier, vol. 56(1), pages 84-96.
    19. Michele Raitano & Francesco Vona, 2015. "Direct and Indirect Influences of Parental Background on Children's Earnings: a Comparison across Countries and Genders," Manchester School, University of Manchester, vol. 83(4), pages 423-450, July.
    20. Hellier, Joël, 2017. "Stratified higher education,social mobility at the top and efficiency: The case of the French ‘Grandes écoles’," MPRA Paper 76724, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion

    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:red:sed017:1644. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/sedddea.html .

    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: Christian Zimmermann (email available below). General contact details of provider: https://edirc.repec.org/data/sedddea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.