Decomposition of differences in distribution using quantile regression
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abstract
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Over the last twenty years, many researchers have attempted to
explain the determinants of wage inequality. I propose a flexible,
intuitive and semiparametric estimator of distribution functions in
the presence of covariates. The conditional wage distribution is
estimated by quantile regressions. Then, the conditional
distribution is integrated over the range of the covariates to
obtain an estimate of the unconditional distribution. Counterfactual
distributions can be estimated, allowing the decomposition of
changes in distribution into three factors: changes in regression
coefficients, changes in the distribution of covariates and changes
in residuals. I use the proposed approach to re-assess the sources
of changes in the distribution of wages in the United States between
1973 and 2001. Unlike most others, I find that residuals plays only
a minor role in the overall growth in wage inequality. This suggests
that there was no or only a small increase in the price of
unmeasured skills. The reason of this difference between my results
and those obtained with others methodologies is that quantile
regressions account for heteroscedasticity. Indeed, the variance of
the residuals expands with education and experience. Therefore, the
fact that the population is getting older and more educated put more
weight on groups with higher residual variances.
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type
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journal paper
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keywords
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Quantile regression; wage inequality; Oaxaca decomposition
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language
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English
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kind of paper
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journal article
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date of appearance
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1-8-2005
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journal
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Labour Economics
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publisher
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North-Holland (Amsterdam)
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ISSN
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0927-5371
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volume of journal
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12
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number of issue
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4
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page(s)
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577-590
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review
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external review
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citation
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Melly, B. (2005). Decomposition of differences in distribution using
quantile regression. Labour Economics, 12(4), 577-590.
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