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 density overlap statistic to compare the sectors' distribution of weekly wages at all wage levels. A simple refinement yields locational information by decile. To counter problematic features of Current Population Survey data--namely, 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 the estimation procedures are evaluated with simulations designed to fit the dataset. Bootstrapped standard errors and confidence intervals are calculated to indicate the statistical significance of the results.
Throughout the period from 1969 to 1993, comparisons of the complete full-time, weekly wage densities in the goods- and service-producing sectors emphasize broad similarities that typical comparison statistics do not identify. The wage densities, which are close in the early 1970s, diverge until around 1980, after which they tend to converge. By the 1990s, the estimated densities are more than 95 percent identical. Furthermore, the wage densities are most comparable in the central deciles, a finding that disputes the bimodal characterization of service-sector wages. Two potential explanations for the time pattern of the overlapping coefficient are considered by forming hypothetical distributions, but neither of these explanations removes the pattern.
Suggested citation: Dupuy, Max, and Mark Scweitzer, 1995. "Sectoral Wage Convergence: A Nonparametric Distributional Analysis,” Federal Reserve Bank of Cleveland, Working Paper no. 95-20.