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Working Paper

Sectoral Wage Convergence: A Nonparametric Distributional Analysis

The large shift of U.S. employment from goods producers to service producers has generated concern over future income distribution, because of perceived large relative pay differences. This paper applies a nonparametric density overlap statistic to compare the sectors? distribution of full-time, weekly wages at all wage levels. To counter problematic features of Current Population Survey data--sampling variation at infrequent wage rates and extensive rounding at common wage rates--we employ nonparametric density-estimation procedures to isolate the underlying shapes of the densities. The validity and accuracy of these two approaches when combined is supported by Monte Carlo simulations. Standard errors and confidence intervals indicate that our results are statistically significant.

Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.


Suggested Citation

Schweitzer, Mark E., and Max Dupuy. 1996. “Sectoral Wage Convergence: A Nonparametric Distributional Analysis.” Federal Reserve Bank of Cleveland, Working Paper No. 96-11. https://doi.org/10.26509/frbc-wp-199611