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Ian Schmutte

University of Georgia, USA

I am proud to contribute to IZA World of Labor. Academics have a responsibility to make our research findings accessible and available to policymakers and the general public. The IZA has long been a leader in the dissemination of labor market research and this series continues and expands that tradition

IZA World of Labor role

Author

Current position

Assistant Professor, Department of Economics, University of Georgia, USA

Research interest

Labor economics, social networks, applied econometrics

Website

https://www.terry.uga.edu/directory/ian-schmutte/

Past positions

Economist, US Census Bureau Center for Economic Studies, 2008–2010

Qualifications

PhD Economics, Cornell University, 2010

Selected publications

  • “Job referral networks and the determination of earnings in local labor markets.” Journal of Labor Economics 33:1 (2015): 1–32.

  • “Labor markets with endogenous job referral networks: Theory and empirical evidence.” Labour Economics 42 (2016): 30–42.

  • “Free to move? A network approach to the analysis of job mobility.” Labour Economics 29 (2014): 49–61.

  • “Economic analysis and statistical disclosure limitation.” Brookings Papers on Economic Activity 50:1 (2015): 221–267 (with J. M. Abowd).

  • “Getting handcuffs on an octopus: Minimum wages, turnover, and employment.” Industrial and Labor Relations Review 69:5 (2016): 1133–1170 (with K. Gittings).

article(s)

  • How do social networks affect labor markets?

    Job-referral networks can make labor markets more productive and efficient but may increase the importance of luck in job matches

    Ian Schmutte, October 2016
    Social networks, or “job-referral” networks, can help make labor markets become more efficient. Outside the firm, they help workers obtain employment after displacement and secure higher-paying jobs. They can also match highly-skilled workers to more productive employment. Inside the firm, referrals facilitate employment relationships that are more stable, productive, and profitable. In aggregate, referral networks help “grease the wheels” of a labor market that can be beset by a range of information problems. However, such networks can also be segmented along racial, ethnic, and socio-economic lines, which brings into question the effect they may have on inequality between and within different groups of workers.
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