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Theories of the Distribution of Labor Earnings

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  • Derek Neal
  • Sherwin Rosen

Abstract

Several empirical regularities motivate most theories of the distribution of labor earnings. Earnings distributions tend to be skewed to the right and display a long right tail. The are leptokurtic (positive fourth cumulant) and have a fat tail. Mean earnings always exceed median earnings and the top percentiles of earners account for a disproportionate share of total earnings. Mean earnings also differ greatly across groups defined by occupation, education, experience, and other observed traits. With respect to the evolution of the distribution of earnings for a given cohort, initial earnings dispersion is smaller than the dispersion observed in prime working years. We explore several classes of models that address these stylized facts. Stochastic theories begin with distributional assumptions about worker endowments and then examine the stochastic structures that might generate observed features of the aggregate distribution of earnings. Selection models describe how workers allocate their skills to tasks. Because workers choose their best option from a menu of careers, these allocation decisions generate earnings distributions which tend to be skewed. Sorting models provide dynamic versions of selection models and illustrate how gradual learning about endowments leads to sorting patterns that amplify dispersiion and generate a skewed distribution of earnings within a cohort of experienced workers. Human capital theory demonstrates that earnings dispersion is a prerequisite for significant skill investments. Without earnings dispersion, workers would not willingly make the investments necessary for high-skill jobs. Human capital model illustrate how endowments of wealth and talent influence the investment decisions that generate observed distributions of earnings.

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  • Derek Neal & Sherwin Rosen, 1998. "Theories of the Distribution of Labor Earnings," NBER Working Papers 6378, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:6378
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    Cited by:

    1. Peter Dolton & Gerald Makepeace & Oscar Marcenaro-Gutierrez, 2005. "Career progression: Getting-on, getting-by and going nowhere," Education Economics, Taylor & Francis Journals, vol. 13(2), pages 237-255.
    2. Pablo Acosta, 2001. "Los determinantes de la desigualdad en los ingresos laborales: El rol de las nuevas tecnologías y la apertura comercial," Department of Economics, Working Papers 034, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
    3. Daniele Checchi, 2000. "Does educational achievement help to explain income inequality?," Departmental Working Papers 2000-11, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    4. Denis Cogneau & Anne-Sophie Robilliard, 2004. "Poverty Alleviation Policies in Madagascar: a Micro-Macro Simulation Model," Working Papers DT/2004/11, DIAL (Développement, Institutions et Mondialisation), revised Nov 2004.
    5. Checchi, Daniele, 2001. "Education, inequality and income inequality," LSE Research Online Documents on Economics 6566, London School of Economics and Political Science, LSE Library.

    More about this item

    JEL classification:

    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs

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