Documents Cortes, Guido Matias 4 results

"This article reviews the findings from Cortes (2016) and Cortes, Jaimovich, and Siu (2017), which explore the micro-level patterns associated with the decline in middle-wage routine employment in the United States. I show that male workers who remain in routine jobs experience significantly slower long-run wage growth than those who switch to other occupations, even when compared to those who transition to lower-skill non-routine manual jobs. I also show that changes in the employment patterns of men with low levels of education and women with intermediate levels of education account for the majority of the decline in routine employment. Individuals with these demographic characteristics used to predominantly work in routine jobs. In more recent years, they have become increasingly likely to be out of work."

"Gender wage gaps in developed economies have narrowed substantially in past decades: these changes are driven by institutional, cultural, and economic factors. A key economic driver shaping modern labour markets is technological change, yet there is a paucity of evidence on its direct impact on gender wage disparities. We study this question by considering how men and women are differentially exposed to the structural employment and wage changes across occupations associated with advancing technology, and how this has impacted gender wage gaps since the mid-1980s for two countries, Portugal and the United States. Our findings suggest that while women have generally been less exposed to the automation of work, this has not always led to declining gender wage gaps: at times, women have transitioned to jobs where wage levels or wage growth were lower. Non-technological changes appear at least as important in understanding the evolution of the gender wage gap."

"The authors study the distributional consequences of the COVID-19 pandemic's impact on employment, both during the onset of the pandemic and over subsequent months. Using cross-sectional and matched longitudinal data from the Current Population Survey, they show that the pandemic has exacerbated pre-existing inequalities. Although employment losses have been widespread, they have been substantially larger—and more persistent—in lower-paying occupations and industries. Hispanics and non-White workers suffered larger increases in job losses, not only because of their over-representation in lower-paying jobs but also because of a disproportionate increase in their job displacement probability relative to non-Hispanic White workers with the same job background. Gaps in year-on-year job displacement probabilities between Black and White workers have widened over the course of the pandemic recession, both overall and conditional on pre-displacement occupation and industry. These gaps are not explained by state-level differences in the severity of the pandemic nor by the associated response in terms of mitigation policies. In addition, evidence suggests that older workers have been retiring at faster rates."

"We argue that skill-biased technological change not only affects wage gaps between skill groups, but also increases wage inequality within skill groups, across workers in different workplaces. Building on a heterogeneous firm framework with labor market frictions, we show that an industry-wide skill-biased technological change shock will increase between-firm wage inequality within the industry through four main channels: changes in the skill wage premium (as in traditional models of technological change); increased employment concentration in more productive firms; increased wage dispersion between firms for workers of the same skill type; and increased dispersion in the skill mix that firms employ, due to more sorting of skilled workers to more productive firms. Using rich administrative matched employer-employee data from Germany, we provide empirical evidence of establishment-level patterns that are in line with the predictions of the model. We further document that industries with more technological adoption exhibit particularly pronounced patterns along the dimensions highlighted by the model."