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A nonparametric analysis of the U.S. earnings distribution

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  • D. K. Ginther

Abstract

This paper examines the change in the earnings distribution and in the earnings distribution conditional on years of schooling and experience for white male full-time, year-round workers in the United States from 1967 to 1992. Ginther uses nonparametric kernel estimators to examine changes in the unconditional and conditional earnings distributions and to estimate measures of conditional earnings inequality. Ginther compares estimates from parametric wage equations to nonparametric estimates and finds that parametric estimates are biased: earnings inequality did not change in equal proportions within cohorts and experience groups. Instead, inequality increased the most among workers with 10 and 12 years of schooling at all experience levels and among workers with both 16 years of schooling and less than 15 years experience. Inequality decreased among people with graduate levels of schooling. Controlled for levels of schooling and experience, real wages have declined drastically for all workers except those with more than 16 years of schooling or more than 25 years experience. Groups experiencing the largest increase in earnings inequality are also those with the largest decline in real wages.

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  • D. K. Ginther, "undated". "A nonparametric analysis of the U.S. earnings distribution," Institute for Research on Poverty Discussion Papers 1067-95, University of Wisconsin Institute for Research on Poverty.
    • Handle: RePEc:wop:wispod:1067-95
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    References listed on IDEAS

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    Cited by:

    1. Richard V. Burkhauser & Amy D. Crews & Mary C. Daly, 1997. "A new look at the distributional effects of economic growth during the 1980s: a comparative study of the United States and Germany," Economic Review, Federal Reserve Bank of San Francisco, pages 18-31.
    2. T. Kämpke & R. Pestel & F.J. Radermacher, 2003. "A Computational Concept for Normative Equity," European Journal of Law and Economics, Springer, vol. 15(2), pages 129-163, March.

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