The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market

Abstract

A vast literature documents a pronounced rise in wage inequality in the United States and numerous other advanced nations commencing in the 1980s and proposes skill-biased technological change as its primary cause. The intellectual foundation of this literature is what Acemoglu and Autor (2011) refer to as the canonical model, which features two distinct skill groups-typically, college and high school workers-performing two distinct and imperfectly substitutable occupations or producing two imperfectly substitutable goods.1 Technology in the canonical model is assumed to take a factor-augmenting form, meaning that it complements either high-or low-skill workers and thus induces either a monotone increase or decrease in wage inequality between skill groups. The canonical model is not only tractable and conceptually attractive but has also proved empirically quite successful in accounting for the evolution of skill premia in the United States throughout the twentieth century, as well as capturing major cross-country differences in skill premia among advanced nations. 2 1 In many cases, this model is extended to more than two skill groups (see, e.g

Does Automation Drive the Labor Market?

“…An implication is that occupations that cannot be easily automated are predicted to perform relatively well. In support, Autor and Dorn (2013) highlight the rise in low-skill services for explaining relatively large wage growth amongst occupations with relatively low wages in 1980. Consistent with this hypothesis we find that low-skill service occupations had wage growth of 21 percent from 1980-2015, compared to 11 percent growth amongst Craft & Repair occupations and 9 percent seen by Fabricators & Laborers over the same period.…”

“…One might reasonably ask whether Truck Drivers and Auto Body Repairers, both middle wage occupations in 1980, are necessarily subject to the same aggregate forces on wages given the different skill sets they require. Figure 2 plots the change in wages and the change 5 We follow Autor and Dorn (2013) and take the sample of noninstitutionalized males in non-farm employment in the US mainland in the 1980 5% Sample of the US Census and the 2005 American Community Survey (ACS). Hourly wages constructed from total wage and salary data (adjusted using PCE deflator), number of weeks worked per year, and usual number of hours worked per year.…”

“…Their approach of measuring routineness from the Dictionary of Occupational Titles (DOT) has been widely replicated. More recent studies by Autor et al (2006) and Autor and Dorn (2013) have classified the routineness of occupations from dimensions measured by Autor et al (2003) in the DOT. Here we take examine the routine task intensity index developed in Autor and Dorn (2013).…”

“…More recent studies by Autor et al (2006) and Autor and Dorn (2013) have classified the routineness of occupations from dimensions measured by Autor et al (2003) in the DOT. Here we take examine the routine task intensity index developed in Autor and Dorn (2013). Hourly wages constructed from total wage and salary data (adjusted using PCE deflator), number of weeks worked per year, and usual number of hours worked per year.…”

“…We further separate all simple occupations into two groups: routine and non-routine. Following Autor and Dorn (2013), routine occupations are those with routine task intensity ranked in the top third amongst all occupations. The table shows mean wages as well as average wage and employment growth for the three occupational categories − simple routine, simple non-routine, and complex − and yields the following insights: 11 1.…”