About

Ginger Zhe Jin is the Neil Moskowitz Professor of Economics at the University of Maryland, College Park. Her research primarily focuses on information asymmetry among economic agents and how to provide information to overcome this problem. Her work has explored various applications including retail food safety, health insurance, prescription drugs, e-commerce, regulatory inspection, scientific innovation, air quality, blood donation, vaccination, intrafamilial interaction, data regulation, and consumer protection. Jin's research has been published in leading economics, management, and marketing journals, supported by organizations such as the National Science Foundation, the Sloan Foundation, the Net Institute, and the Washington Center for Equitable Growth. She has also been recognized in major media outlets including the Wall Street Journal, New York Times, Forbes, Bloomberg, and the Los Angeles Times. Jin has served as the Director of the FTC Bureau of Economics, Amazon Scholar, and Senior Principal Economist at Amazon.com. She is currently a managing editor of the International Journal of Industrial Organization, an advisory council member of the Journal of Industrial Economics, and a board member of the Industrial Organization Society. She has been a Research Associate of the NBER since 2012 and co-founded Hazel Analytics, an analytics company promoting open government data. Jin earned her PhD in Economics from UCLA in 2000.

Research topics

• Computer Science
• Computer Security
• Microeconomics
• Economics
• Business
• Internet privacy
• Psychology
• Finance
• Commerce
• Industrial organization
• Social psychology

Selected publications

• Adaptive Enforcement with AI-Augmented Monitoring

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Flagship Entry in Online Marketplaces

  Journal of Industrial Economics · 2025-11-02

  article1st author

  ABSTRACT In this paper, we empirically study how flagship entry in an online marketplace affects consumers, the platform, and various sellers on the platform. We find flagship entry may benefit consumers by expanding the choice set, by intensifying price competition within the entry brand, and by improving consumer perception for parts of the platform. In the meantime, flagship entry cannibalizes the sales of same‐brand sellers, while other brands may gain as the buyer base expands on the platform. Counterfactual simulation suggests that flagship entry improves the gross merchandise value (GMV) of the platform and overall consumer welfare in most cases.

  PublisherDOI

• Does a Human-Algorithm Feedback Loop Lead to Error Propagation? Evidence from Zillow's Zestimate

  2025-07-02

  articleOpen access

  We study how home sellers and buyers interact with Zillow's Zestimate algorithm throughout the sales cycle of residential properties, with an emphasis on the implications of such interactions. In particular, leveraging Zestimate's algorithm updates as exogenous shocks, we find evidence for a human-algorithm feedback loop: listing and selling outcomes respond significantly to Zestimate, and Zestimate is quickly updated for the focal and comparable homes after a property is listed or sold. This raises a concern that housing market disturbances may propagate and persist because of the feedback loop. However, simulations suggest that disturbances are short-lived and diminish eventually, mainly because all marginal effects in the selling process - though sizable and significant - are less than one. To further validate this insight in the real data, we leverage the COVID-19 pandemic as a natural experiment. We find consistent evidence that the initial disturbances created by the March 2020 declaration of national emergency faded away in a few months. Overall, our results identify the human-algorithm feedback loop in an important real-world setting but dismiss the concern that such a feedback loop generates persistent error propagation.

  PublisherOA PDFDOI

• Serial Acquisitions in Tech

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Safety Reviews on Airbnb: An Information Tale

  Marketing Science · 2025-09-03

  article

  This paper studies a platform’s incentive to disclose and disseminate consumer reviews about the vicinity safety of short-term rental listings.

  PublisherDOI

• Serial Acquisitions in Tech

  National Bureau of Economic Research · 2025-08-01

  reportOpen access1st authorCorresponding

  We examine serial acquisitions in the technology sector from 2010 to 2023.Defining serial acquisitions based on a granular S&P industry taxonomy, we find that they account for 24-37% of majority-control tech M&A, with over half completed by public firms.Followon targets in a series are generally larger and older than the initial acquisition, and among public acquirers, starting a series is associated with higher market value and greater innovation value, but not with significant changes in market competitiveness.Among deals with valid transaction values, over half of serial deals exceed the reporting threshold of the U.S. Hart-Scott-Rodino (HSR) Act.However, in below-threshold acquisitions, acquirers primarily target their core business category.Accounting for the cumulative value of a series would, in most cases, keep the timing of HSR review unchanged or modestly accelerate it, but when it does accelerate it, review could occur several deals or years earlier, potentially yielding important benefits in markets with long acquisition sequences.Finally, while Google/Alphabet, Amazon, Facebook/Meta, Apple and Microsoft (GAFAM) stand out from the rest of the sample for more frequent serial acquisitions, some other large acquirers display similar patterns.

  PublisherDOI

• Serial Acquisitions in Tech 

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Algorithmic pricing: Implications for marketing strategy and regulation

  International Journal of Research in Marketing · 2025-05-01 · 11 citations

  articleOpen access

  Over the past decade, a growing number of firms have delegated pricing decisions to algorithms in consumer and business markets such as travel, entertainment, and retail, as well as in platform markets such as ride-sharing. We define algorithmic pricing as “the use of programs to automate the setting of prices.” Firms adopt algorithmic pricing to optimize their prices in response to changing market conditions and to leverage the efficiency gains from automation. Advances in information technology and the increased availability of digital data have further facilitated the use of algorithm-driven pricing strategies. Yet adopting algorithmic pricing is not merely a technical upgrade — it is a strategic decision that must align with a company’s existing and future marketing strategies. Moreover, algorithmic pricing can raise various regulatory concerns regarding potential threats to competition and the legality of price discrimination. This paper discusses the implementation of algorithmic pricing in the context of firms’ marketing strategies and regulatory frameworks, while outlining an agenda for future research in this increasingly important area

  PublisherDOI

• Top-up design and health care expenditure: Evidence from cardiac stents

  China Economic Review · 2025-07-04

  article1st author
  PublisherDOI

• The Signaling Value of Technology Venture Acquisitions

  Academy of Management Proceedings · 2025-07-01

  article

  We study the effects of technology venture acquisitions on investment in the acquired firm’s business area. Using data on acquisitions and venture capital funding in the U.S. from Crunchbase, we consider the ventures acquired between 2014 and 2016 alongside a set of comparable control ventures that remained independent as of 2016. By modeling each venture as a point in the technology space, we leverage textual analysis to track investments in business areas similar to acquired or control ventures. Our difference-in-differences analysis shows that acquisitions stimulate venture capital investment, particularly in areas with fewer ventures and more intense past M&A or VC investment activity. Contrary to antitrust concerns, we find that acquisitions by big tech platforms and other large acquirers have a similar positive effect, whereas private equity buyouts lead to an even greater increase in venture capital activity.

  PublisherDOI

Frequent coauthors

• Zhentong Lu

  Bank of Canada

  66 shared

• Xiaolu Zhou

  58 shared

• Lu Fang

  53 shared

• Li‐An Zhou

  Peking University

  41 shared

• Guangyu Cao

  41 shared

• Michael Luca

  William Carey University

  40 shared

• Renna Jiang

  University of Chicago

  40 shared

• Liad Wagman

  Rensselaer Polytechnic Institute

  40 shared

Education

• Ph.D.

  University of Maryland, College Park

Awards & honors

• Research Associate of NBER (2012)