The fundamental driver is a complex mix of both surplus and shortage in domestic supply of STEM workers. Employers and universities are incented to shout shortage. The federal government does not have the skill to monitor the situation. Employer lobbies have prevailed.

On Sept 19, the Trump administration without warning imposed draconian changes to the H1 B program. This program brings in foreign workers, primarily for STEM work, for up to six years. H 1B visa holders have a good deal of success in obtaining green cards.  The  number of STEM workers currently in the country is not known (due to limitations in the federal data systems); estimates range between 500,000 and 700,000. The policy changes are designed to reduce abuses in the program. The abuse allegations are focused on H-1B workers not only taking STEM jobs from Americans but also paying them less.

The immediate fight between prolific H-1B users, such as Tesla and Google, and critics deals with large scale abuses, such as when Disney in 2014-2015 not only replaced hundreds of American workers, but required these workers on condition of termination benefits to train their replacements. The underlying issue is whether there is a shortage of STEM workers in the country.

H-1B has four levels of expertise of applicants. Only about 10-15% of workers are at the top level, This means, in effect, that the policy justification for H-1B does not rely on recruiting only to best of the best, but main stream workers. the program therefore is implicitly justified due to worker shortages.

Two authors in particular have focused on how there may, or may not, be STEM workers shortages. Their shared conclusion: there can be both surplus and shortage in domestic supply, but those supporting H-1B (typically employers and universities) have repeated sounded the alarm of shortages by oversimplifying the analysis.

In 2014, Michael Teitelbaum argued that U.S. anxieties over STEM worker shortages have unfolded in recurring “boom-and-bust” cycles since World War II. He identifies at least five: the post-Sputnik expansion of the late 1950s and 1960s, the defense and NASA downshift of the early 1970s, the competitiveness scare against Japan in the 1980s, the internet and telecom boom and bust of the late 1990s and early 2000s, and the biomedical surge tied to the NIH budget doubling from 1998 to 2003 that later left many young scientists stranded. Each round began with alarms about falling behind, followed by educational expansion and policy changes, then oversupply and disappointment.

Per Teitelbaum, American universities scaled up STEM training and maintained global excellence, drawing large numbers of international students. Yet they also produced chronic mismatches, often training too many researchers for narrow academic tracks while industry needed applied skills. Graduates entering after the busts encountered glutted markets, and undergraduates were frequently discouraged by overly abstract curricula. For Teitelbaum, higher education has not calibrated to real, evidence-based labor needs.

Falling Behind?: Boom, Bust, and the Global Race for Scientific Talent (Princeton, 2014)

Ron Hira, a persistent critic of H 1B,  in 2022 challenged the narrative of a chronic U.S. STEM worker shortage. Like Teitelbaum, He noted alarms about shortages surfacing repeatedly. Businesses, universities, and government agencies. He critiqued the misuse of labor data: Bureau of Labor Statistics employment projections are treated as precise forecasts, despite a history of major errors; unemployment rates are misinterpreted, with occupational benchmarks ignored; and, most tellingly, wages, the clearest indicator of scarcity, have stagnated or declined in many STEM fields.A distorted policy debate fails to acknowledge labor market heterogeneity across STEM occupations or the impact of global trends like offshoring and guest worker programs. If genuine shortages existed, wages would rise sharply, diversity efforts would improve, and companies would invest heavily in training—none of this is evident. He called for better data collection, more nuanced analysis, and transparency in employment practices.