Occupational skill mismatch and the consequences to employment outcomes

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Mihaylova, Alexandra.

Advisor dc:contributor.advisor

• Donna Rhodes and George Westerman.

• Institute for Data, Systems, and Society.
• Technology and Policy Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122209

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122209