One of the most important and high stakes economic debates of recent decades has revolved around the relative importance of skill-biased technological changes vis-à-vis globalization (and the ‘China Shock’) in driving the polarization of the job market and the displacement of many middle-class workers in the U.S.
In the late-1990s and early 2000s, the dominant narrative centered around the notion that breakthroughs in information and communication technology and factory automation augmented the performance of the high-skilled even as they simultaneously diminished the prospects for the mid-skilled workers. Proponents of this view held that skill-biased technological changes led to job polarization and the hollowing out of the middle-class.
Modern economists categorize work tasks as being either cognitive or non-cognitive (manual) on the one hand and as being either routine or non-routine on the other. This allows them to evaluate the degree to which certain tasks are threatened by automation.
While non-cognitive and routine-type tasks (historically, middle-skill jobs) faced significant automation risk, it was widely believed that high-skill jobs involving cognitive and non-routine tasks were secure. Manual and non-routine tasks (often low-paying jobs) were also assumed to be relatively safe — it was either uneconomical or unfeasible to automate such jobs.
The assumption that future high-paying jobs will involve cognitive and non-routine tasks led many public officials and economists to emphasize college education as the primary pathway to success. If technology was skill-biased, then the obvious solution, according to some influential academics, was to boost the overall skillset by raising the formal educational attainment of the American workforce.
Recent trends, however, raise questions about the continuing validity of the hypothesis that there is a race between educational attainment and technological progress and that a sharp increase in the supply of college graduates would help address labor market concerns. Demand for some cognitive tasks and skills may have already peaked. Research suggests that the stagnation in the college wage premium since the early 2000s was likely associated with easing demand for college graduates (resulting from a slowdown in the pace of skill-biased technological changes).
Starting in the early 2010s, a group of economists led by MIT’s David Autor pushed an alternate narrative (the “China Shock” hypothesis) that focused on the rise of China as a global manufacturing superpower and the resultant adverse socio-economic impact on specific sectors and regions in the U.S. that were directly exposed to import competition. This viewpoint also conveniently provided intellectual cover for those who feared the rise of China and the consequences of hyper-globalization.
Globalization’s impact on blue-collar workers (especially in manufacturing) was significant, and the affected regions were hit hard and left behind. Workers displaced from factories often failed to find jobs that offered similar remuneration elsewhere and ended up experiencing a material decline in their quality of life. Recent research indicates that lingering effects of import competition on local labor markets led to a surge in support for nationalistic policies that emphasize trade protectionism and tighter immigration controls.
Politicians, however, may be fighting the last war as the reality on the ground may have already shifted. David Autor himself admitted: “That particular battle has ended. You can say we lost it, or it’s a truce, or that they gained some territory and we held the line, but that battle is over. The nature of competition between [the] U.S. and China has changed since that time. This is now a great power competition around military power, semiconductors, electric cars, energy generation, aircraft and helicopters, telecommunications equipment, and that’s not about jobs…”
Looking ahead, the rise of generative artificial intelligence poses a much bigger challenge for policymakers. Generative AI appears to truly upend prior assumptions regarding the stability of high-skill positions as it can easily and rapidly perform many cognitive and non-routine tasks. Suddenly, white-collar jobs appear vulnerable. Entry-level positions in information technology, law, finance, accounting, marketing and other professional services are already experiencing cutbacks.
Ever since the Luddites smashed textile machinery in early 19th-century England, concerns surrounding technological unemployment have resurfaced with each new wave of disruptive innovation. Technophobes often fall prey to the “lump of labor” fallacy. Thus far, such concerns have proven to be largely unfounded. Neo-luddites often failed to recognize the fact that while new technologies may act as a substitute for certain types of human labor, they also augment the skillset of many workers in ways that enable the creation of new products and services or even entire new industries (which in turn generate vast new array of jobs).
Until now, while some sectors and professions inevitably faced obsolescence (and some workers suffered from labor market displacements) due to technological shifts, there were usually net job gains associated with tech innovations. However, given the nature and scope of generative AI, it is not unreasonable to wonder if this time might indeed be different. While some profess optimism about AI’s potential to help the middle-class, many fear that AI will do to white-collar jobs what automation/globalization did to blue-collar ones.
Several key issues remain unresolved as even experts disagree about the potential nature and scope of the AI revolution. From a policy standpoint, we need to consider what type of skills (the much-maligned humanities may make a comeback) will prove to be valuable in the age of AI. We may need to encourage college students to focus more on attaining foundational training and, as a society, be wary of cognitive offloading and a decline in critical thinking.
Vivekanand Jayakumar, Ph.D., is an associate professor of economics at the University of Tampa.
