Occupational Tasks and Changes in the Wage Structure

Abstract

This paper argues that changes in the returns to occupational tasks have contributed to changes in the wage distribution over the last three decades. Using Current Population Survey (CPS) data, we first show that the 1990s polarization of wages is explained by changes in wage setting between and within occupations, which are well captured by tasks measures linked to technological change and offshorability. Using a decomposition based on Firpo, Fortin, and Lemieux (2009), we find that technological change and deunionization played a central role in the 1980s and 1990s, while offshorability became an important factor from the 1990s onwards.

Artificial Intelligence and Employment: New Cross-Country Evidence

“…To control for the effect of non-AI technologies, the analysis includes measures of exposure to software and industrial robots developed by Webb ( 2020 ) based on the overlap between the text of job descriptions provided in the O * NET database and the text of patents in the fields corresponding to each of these technologies 47 . Offshoring is proxied by an index of offshorability developed by Firpo et al ( 2011 ) and made available by Autor and Dorn ( 2013 ), which measures the potential offshoring of job tasks using the average between the two variables “Face-to-Face Contact” and “On-Site Job” that Firpo et al ( 2011 ) derive from the O * NET database 48 . This measure captures the extent to which an occupation requires direct interpersonal interaction or proximity to a specific work location 49 .…”

“… 49 Firpo et al ( 2011 ) define “face-to-face contact” as the average value between the O * NET variables “face-to-face discussions,” “establishing and maintaining interpersonal relationships,” “assisting and caring for others,” “performing for or working directly with the public”, and “coaching and developing others.” They define “on-site job” as the average between the O * NET variables “inspecting equipment, structures, or material,” “handling and moving objects,” “operating vehicles, mechanized devices, or equipment,” and the mean of “repairing and maintaining mechanical equipment” and “repairing and maintaining electronic equipment”. …”

Business disruptions from social distancing

“…We use the Occupational Information Network (O*NET) [17] to measure the characteristics of a given occupation, similarly to previous studies [29,30,31]. The O*NET dataset contains detailed standardized descriptions on almost 1,000 occupations along eight dimensions.…”

“…The O*NET dataset contains detailed standardized descriptions on almost 1,000 occupations along eight dimensions. We focus on job characteristics that are related to recent social distancing measures, while prior work focused mainly on measuring offshorability of the given tasks [29,30]. The third indicator takes into consideration the possibility that workers may need to be in physical proximity of one another even if they do not communicate.…”

A generative model for age and income distribution

“…Focus on inequality and upward mobility between quintiles considering gender effects are investigated [7]. The previous models in literature either incorporate numerous external variables, distribution characteristics and functions, such as innovation constants or limiting their scope to the investigation of dependence on a single variable [8] [9]. A more recent article by Guvenen investigated a model for which focal variables are the human capital consisting of education, work experience, and idiosyncratic shocks [10], following research modelled male income for studying the impact of labour income taxation policy on inequality [11] The referred life-cycle model's distribution characteristics of the pre-tax income arise from the differences in the individual's ability to learn new things and idiosyncratic shocks.…”