Occupational skill mismatch and the consequences to employment outcomes
Massachusetts Institute of Technology. Institute for Data, Systems, and Society.
Technology and Policy Program.
Donna Rhodes and George Westerman.
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An increase in the stock of high skilled workers boosts labor productivity, though economic theory suggests some of the effect may be attenuated by skill mismatch. This research begins to identify and quantify the mechanisms through which skill mismatch affects employment outcomes. Several unique characteristics of personnel management in the US military, particularly in the Air Force, make it an attractive object of study for estimating the magnitude and range of such costs. Unlike most civilian employers, the Department of Defense directly observes the skills of incoming recruits through their achievement on the Armed Forces Qualification Test. Skill requirements for all specialties are expressed in terms of minimum qualifying scores. The matching process produces a differential between a worker's skills and the skills required by her job. Using historical Air Force recruitment and career data, it is possible to identify the relationship between skill mismatch and employment outcomes such as retention and tenure. Using a probit model specification and an instrumental variables approach, this research finds that poorly-matched workers are 20 percent less likely to be retained relative to well-matched workers. Effects of mismatch are most pronounced among high-aptitude workers. The difference in response to skill mismatch between men and women is statistically indistinguishable. These estimates offer a means to quantify the benefits an employer can expect from more thorough evaluation and better job matching of prospective workers. Technological innovations in online learning platforms and skill evaluations offer opportunities to improve matching outcomes. Strategic partnerships between online learning content providers, businesses and workers are important to make these improvements a reality.
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 93-96).
DepartmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society; Massachusetts Institute of Technology. Engineering Systems Division; Massachusetts Institute of Technology. Technology and Policy Program; Massachusetts Institute of Technology. Technology and Policy Program
Massachusetts Institute of Technology
Institute for Data, Systems, and Society., Technology and Policy Program.