published in: Applied Economics, 2016, 49(12): 1147-1163
This paper analyzes the microeconomic sources of wage inequality in the United States from 1967-2012. Decomposing inequality into factors categorized by degree of personal responsibility, we find that education is able to explain more than twice as much of inequality today as 45 years ago. However, neither hours worked nor education, industry, marital status, or geographical location is able to explain the observed general rise in inequality. In fact, "unfair" inequality has risen faster than "fair" inequality, regardless of the set of variables chosen as fair sources of inequality (resulting from responsibility factors).
We also examine inequalities within gender and racial groups and across U.S. states. Several noteworthy findings emerge. Wage inequality among males used to be lower than among females until 1990, but today the opposite is true. We also find several occasions where the distinction between the raw Gini and the Gini adjusted for certain characteristics produces different conclusions. For instance, raw inequality among black females decreased since 1969, but if we acknowledge differences resulting from hours worked and educational outcomes as "fair" inequalities, the remaining inequality measure has increased. We also find that there is substantial geographic heterogeneity in trends of unfair and overall inequality.
We use cookies to provide you with an optimal website experience. This includes cookies that are necessary for the operation of the site as well as cookies that are only used for anonymous statistical purposes, for comfort settings or to display personalized content. You can decide for yourself which categories you want to allow. Please note that based on your settings, you may not be able to use all of the site's functions.
Cookie settings
These necessary cookies are required to activate the core functionality of the website. An opt-out from these technologies is not available.
In order to further improve our offer and our website, we collect anonymous data for statistics and analyses. With the help of these cookies we can, for example, determine the number of visitors and the effect of certain pages on our website and optimize our content.