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Do Employers Learn from Public, Subjective, Performance Reviews?

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  • Alex Wood-Doughty

    (Department of Economics, University of California, Santa Barbara, CA 93106)

Abstract

Much of the new “gig economy” relies on reputation systems to reduce problems of asymmetric information. In most cases, these reputation systems function well by soliciting unbiased feedback from buyers and sellers. However, certain features of onlinelabor markets create incentives for employers to misreport worker performance. This paper tests whether employers learn about worker productivity from public, subjective, performance reviews using data from a large online labor market. Starting with a simple model of employer learning in the presence of potentially biased reviews, I derive testable hypotheses about the relationship between public information and wages, worker attrition, and contract renewals. I find that these public reviews provide substantial information to the market and that other firms use them to learn about the productivity of workers. I also find evidence that these reviews affect how long workers stay in the labor market. Finally, using data on applications, I provide evidence of a mechanism for honest reviews. I show that workers punish firms that leave negative reviews by refusing to work for them again. Together, this body of evidence suggests that reputation systems in online labor markets provide significant information to both workers and firms and help reduce problems of asymmetric information.

Suggested Citation

  • Alex Wood-Doughty, 2016. "Do Employers Learn from Public, Subjective, Performance Reviews?," Working Papers 16-11, NET Institute.
  • Handle: RePEc:net:wpaper:1611
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    References listed on IDEAS

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    1. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Roy Mill, 2011. "Hiring and Learning in Online Global Labor Markets," Working Papers 11-17, NET Institute, revised Oct 2011.
    3. Ajay Agrawal & John Horton & Nicola Lacetera & Elizabeth Lyons, 2015. "Digitization and the Contract Labor Market: A Research Agenda," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 219-250, National Bureau of Economic Research, Inc.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    5. Lisa B. Kahn & Fabian Lange, 2014. "Employer Learning, Productivity, and the Earnings Distribution: Evidence from Performance Measures," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1575-1613.
    6. Cragg, John G, 1971. "Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods," Econometrica, Econometric Society, vol. 39(5), pages 829-844, September.
    7. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    8. Jed DeVaro & Michael Waldman, 2012. "The Signaling Role of Promotions: Further Theory and Empirical Evidence," Journal of Labor Economics, University of Chicago Press, vol. 30(1), pages 91-147.
    9. Joshua C. Pinkston, 2009. "A Model of Asymmetric Employer Learning with Testable Implications," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(1), pages 367-394.
    10. Lawrence F. Katz & Alan B. Krueger, 2016. "The Rise and Nature of Alternative Work Arrangements in the United States, 1995-2015," Working Papers 603, Princeton University, Department of Economics, Industrial Relations Section..
    11. Chris Nosko & Steven Tadelis, 2015. "The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment," NBER Working Papers 20830, National Bureau of Economic Research, Inc.
    12. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," Harvard Business School Working Papers 17-017, Harvard Business School.
    13. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," NBER Chapters, in: Innovation Policy and the Economy, Volume 17, pages 77-93, National Bureau of Economic Research, Inc.
    14. Apostolos Filippas & John J. Horton & Joseph M. Golden, 2019. "Reputation Inflation," NBER Working Papers 25857, National Bureau of Economic Research, Inc.
    15. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    16. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    17. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," NBER Working Papers 22616, National Bureau of Economic Research, Inc.
    18. Steven Tadelis, 2016. "Reputation and Feedback Systems in Online Platform Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 321-340, October.
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    More about this item

    Keywords

    online labor markets; reputation systems; employer learning;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J49 - Labor and Demographic Economics - - Particular Labor Markets - - - Other

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