Panel Paper: The Changing Face of Technological Unemployment

Saturday, April 8, 2017 : 10:15 AM
Founders Hall Room 478 (George Mason University Schar School of Policy)

*Names in bold indicate Presenter

Monica Maria Rondon, University of Pennsylvania

The Changing Face of Technological Unemployment

Technological unemployment, or job loss prompted by automation, has historically failed to result in widespread, long-term unemployment in the United States (U.S.).  However, increasing evidence exists demonstrating that Artificial Intelligence (A.I.) and concomitant technologies are changing the U.S.’s labor market. The result is a skill-biased technological change, where a smaller, highly skilled portion of the labor market commands a greater share of the market and associated income at the expense of the market participation and income of low-skilled workers. This research situates the existing data on technological unemployment in the U.S. within emerging evidence that the nature of technological unemployment is shifting and creating new forms of employment risk and financial instability for demographic groups locked out of skill sets required in a new employment landscape.

More specifically, this project addresses both historical and contemporary evidence as well as existing theory surrounding technological unemployment. Emphasis is placed on how recent advancements in A.I., particularly machine learning, challenge our understanding of technological unemployment as a policy issue. Drawing from evolving evidence of changes in the labor market, coupled with a review of major achievements in A.I., this project argues the U.S. may be facing unprecedented challenges regarding technological unemployment.

As a new area of research, this project captures the changing demographics of those impacted by technological unemployment. Traditionally, concerns about the loss of manufacturing jobs have dominated the conversation on technological unemployment. The typical construction of the population associated with technological unemployment is white males with little to no post-secondary education. However, this project argues that because of the changing population demographics of the U.S., combined with advancements in A.I., technological unemployment will come to increasingly impact women and people of color. In turn, this will exacerbate existing challenges for these populations, including already higher rates of poverty. The changing face of technological unemployment will require innovative and evidence-based policy solutions that look beyond a return of manufacturing jobs, towards approaches that re-conceptualize a range of institutions like education, employment, and social assistance.