This article was downloaded by: [8.9.237.33] On: 06 March 2020, At: 11:24 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Management Science Publication details, including instructions for authors and subscription information: http://pubsonline.informs.org Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market Alan Benson, Aaron Sojourner, Akhmed Umyarov To cite this article: Alan Benson, Aaron Sojourner, Akhmed Umyarov (2019) Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market. Management Science Published online in Articles in Advance 12 Sep 2019 . https://doi.org/10.1287/mnsc.2019.3303 Full terms and conditions of use: https://pubsonline.informs.org/Publications/Librarians-Portal/PubsOnLine-Terms-and- Conditions This article may be used only for the purposes of research, teaching, and/or private study. 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INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org MANAGEMENT SCIENCE Articles in Advance, pp. 1–24 http://pubsonline.informs.org/journal/mnsc ISSN 0025-1909 (print), ISSN 1526-5501 (online) Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market Alan Benson,a Aaron Sojourner,a Akhmed Umyarova a Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455 Contact: [email protected], http://orcid.org/0000-0003-4256-3357 (AB); [email protected], http://orcid.org/0000-0001-6839-2512 (AS); [email protected], http://orcid.org/0000-0003-3731-9093 (AU) Received: July 28, 2017 Abstract. Just as employers face uncertainty when hiring workers, workers also face Revised: July 3, 2018; December 17, 2018 uncertainty when accepting employment, and bad employers may opportunistically Accepted: December 28, 2018 departfromexpectations,norms,andlaws.However,priorresearchineconomicsand Published Online in Articles in Advance: information sciences has focused sharply on the employer’s problem of identifying good September 12, 2019 workers rather than vice versa. This issue is especially pronounced in markets for gig https://doi.org/10.1287/mnsc.2019.3303 work, including online labor markets, in which platforms are developing strategies to help workers identify good employers. We build a theoretical model for the value of Copyright: © 2019 The Author(s) such reputation systems and test its predictions on Amazon Mechanical Turk, on which employers may decline to pay workers while keeping their work product and workers protect themselves using third-party reputation systems, such as Turkopticon. We find that(1)inanexperimentonworkerarrival, a good reputation allows employers to operate more quickly and on a larger scale without loss to quality; (2) in an experimental audit of employers, working for good-reputation employers pays 40% higher effective wages because of faster completion times and lower likelihoods of rejection; and (3) exploiting reputation system crashes, the reputation system is particularly important to small, good-reputation employers, which rely on the reputation system to compete for workers against more established employers. This is the first clean field evidence of the effectsofemployerreputationinanylabormarket and is suggestive of the special role that reputation-diffusing technologies can play in promoting gig work, in which con- ventional labor and contract laws are weak. Open Access Statement: This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License. You are free to download this work and share with others commercially or noncommercially, but cannot change in any way, and you must attribute this work as “Management Science. Copyright © 2019 The Author(s). https://doi.org/10.1287/mnsc.2019.3303, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by-nd/4.0/.” History: Accepted by Chris Forman, information science. Funding: The authors thank the University of Minnesota Social Media and Business Analytics Col- laborative for funding. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2019.3303. Keywords: online labor markets • online ratings • employer reputation • labor markets • economics of information systems • job search • electronic markets and auctions • IT policy and management • contracts and reputation • information and market efficiency 1. Introduction and norms. To what extent can the threat of a bad Amazon Mechanical Turk (M-Turk), TaskRabbit, public reputation prevent employer opportunism and Upwork, Uber, Instacart, DoorDash, and other online the promise of a good reputation encourage respon- platforms have drastically reduced the cost of seek- sible employer behavior? To what extent can workers ing, forming, and terminating work arrangements. voluntarily aggregate their private experiences into This development has raised the concern that plat- shared memory to discipline opportunistic employers? forms circumvent regulations that protect workers. These questions are especially important to gig jobs and A U.S. Government Accountability Office (2015,p.22) online labor platforms, which are characterized by high- report notes that such “online clearinghouses for frequency matching of workers and employers, and on obtaining ad hoc jobs” are attempting “to obscure or which platform owners have moved fast and often eliminate the link between the worker and the busi- sought to break out of traditional regulatory regimes. ness . which can lead to violations of worker pro- Our setting focuses on M-Turk, which possesses tection laws.” Developers of these online platforms two useful and rare features for this purpose. First, no argue that ratings systems can help discipline em- authority (neither the government nor the plat- ployers and other trading partners that break rules form) disciplines opportunistic employers. In M-Turk, 1 Benson, Sojourner, and Umyarov: Can Reputation Discipline the Gig Economy? 2 Management Science, Articles in Advance, pp. 1–24, © 2019 The Author(s) after workers put forth effort, employers may keep the indeed, that Yelp penetration in an area is associated with work product but refuse payment for any reason or the decline in chains that presumably rely on other forms no reason. Workers have no contractual recourse or of reputation. Nagaraj (2016) finds that digitization of appeal process. Because M-Turk also has no native magazine articles has a greater effect on Wikipedia entries tool for rating employers, many workers use third- for less-known individuals than for well-known in- party platforms to help them find the best em- dividuals for whom other information is readily available. ployers. Among the most popular of these platforms In three tests, we provide, to our knowledge, the is Turkopticon, which allows workers to share in- first clean field evidence of employer reputation ef- formation about employers. Second, such oppor- fects in a labor market. These tests follow the model’s tunism is observable to researchers with some effort. three predictions. Specifically, the first experiment In traditional labor markets, workers would pre- measures the effect of employer reputation on the sumablyrelyonanemployer’s reputation to protect ability to recruit workers. We create 36 employers on themselves against events not protected by a contract, M-Turk. Using a third-party employer rating site, such as promises for promotion or paying for over- Turkopticon, we endow each with (i) 8–12 good time. Unfortunately, these outcomes are especially dif- ratings, (ii) 8–12 bad ratings, or (iii) no ratings. We ficult to observe for both courts and researchers alike. then examine the rate at which they can recruit The literature on both traditional and online labor mar- workers to posted jobs. We find that employers with kets has instead focused on learning about opportun- good reputations recruit workers about 50% more ism by workers, not by employers (e.g., List 2006,Oyer quickly than our otherwise-identical employers with and Schaefer 2011, Moreno and Terwiesch 2014, Pallais no ratings and 100% more quickly than those with 2014, Stanton and Thomas 2015, Farronato et al. 2018, very bad reputations. Using M-Turk wage elastici- and Filippas et al. 2018). These two contextual fea- ties estimated by Horton and Chilton (2010), we es- tures enable our empirical