The High Stakes of
Forecasting Future Jobs
How many computer scientists will the United States need over the next decade? How many engineers, mathematicians, or programmers? Answers to questions like these have huge consequences for higher education, private industry, and public policies.
For example, a glut of computer scientists in an economy that doesn’t have enough jobs for them can depress wages in the field, leaving graduates to search for other careers. On the other hand, training too few computer scientists could reduce the country’s competitiveness in high-tech industries. And of course, projections of surplus or shortfall play a role in setting policies that enable the migration of talent from abroad.
Despite the significance of these estimates, reliable projections have been notoriously difficult to determine, as Ron Hira details in his Real Numbers essay. In the highly complex science and engineering labor market, data are limited, which means that policy discussions on the issue sometimes lack crucial nuances. “Better data and more rigorous norms for talking about those data,” Hira notes, “can move the conversation to a higher level.”
How can a better understanding of future STEM jobs improve policymaking?