Essays on income inequality in the United States

This dissertation studies income inequality in the United States during the last two decades. The connections income inequality has with other topics and its measurement features allow for its exploration from different perspectives, giving origin to the overarching objective of this dissertation. To examine contemporaneous U.S. income inequality under two of the three stands it might take in any research process: a phenomenon itself and a dependent (outcome) variable. Therefore, the chapters in this dissertation position income inequality under a different spotlight, using a wide array of quantitative methodologies. Income inequality is first considered a phenomenon and disaggregated under Liao's (2016) decomposition at an in-vogue geographical level: Commuting Zones. Such decomposition helps identify the within-share element from a commonly shared income range across all local labor markets and the within-differentiation arising from the differences across the income distributions. Then, it is possible to identify the degree to which the within, between, within-share, and within-differentiations inequality dynamics drive its overall increasing pattern. This approach identifies, through the between component, those local labor markets exerting the most influence in the overall measure. The second perspective considers income inequality as a dependent variable throughout the study of income effects at different parts of its distribution and directly on traditional measures. In doing so, the quasi-random staggered implementation of the Secure Communities program (hereon referred to as SC) is exploited. SC is, in a few words, a federal program to strengthen immigration enforcement efforts across different levels of government agencies. Short-term effects of SC on income inequality are obtained using the improved doubly robust difference-in-differences (DiD) estimator weighted for multiple treatment periods (DRIMP) proposed by Callaway and Sant’Anna (2021). Effects in overall wages, by gender and main education groups, by income deciles, and by traditional inequality measures are estimated.