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Shunto J. Kobayashi

Assistant Professor of Marketing
Boston University Questrom
sjkobaya@bu.edu


I am an Assistant Professor of Marketing at Boston University’s Questrom School of Business and an Affiliated Faculty member in the Economics Department.

My research interests lie in Empirical Industrial Organization and Quantitative Marketing, with a focus on economic and privacy issues in online advertising and digital platforms. I use a combination of structural and causal inference methods to study these topics.

Education

Publications

  1. The Impact of Privacy Protection on Online Advertising Markets
    with Miguel Alcobendas (Yahoo), Ke Shi (Caltech), Matthew Shum (Caltech)
    Accepted at the Review of Economic Studies (February, 2026) and the ACM EC'23
    Online privacy protection has gained momentum in recent years and spurred both government regulations and private-sector initiatives. A centerpiece of this movement is the removal of third-party cookies, which are widely employed to track online user behavior and implement targeted ads, from web browsers. Using banner ad auction data from Yahoo, we study the effect of a third-party cookie ban on the online advertising market. We first document stylized facts about the value of third-party cookies to advertisers. Adopting a structural approach to recover advertisers' valuations from their bids in these auctions, we simulate a few counterfactual scenarios to quantify the impact of Google's plan to phase out third-party cookies from Chrome, its market-leading browser. Our counterfactual analysis suggests that an outright ban would reduce publisher revenue by 54% and advertiser surplus by 40%. The introduction of alternative tracking technologies under Google's Privacy Sandbox initiative would recoup part of the loss. In either case, we find that big tech firms can leverage their informational advantage over their competitors and gain a larger surplus from the ban.

  2. Can Privacy Technologies Replace Cookies? Ad Revenue in a Field Experiment
    with Garrett A. Johnson (BU Questrom) and Zhengrong Gu (BU Questrom)
    Proceedings of the National Academy of Sciences (May, 2026)

    Coverage: AdExchanger, The Brink

    Digital advertising finances much of the open web, yet relies on tracking technologies that regulators increasingly seek to restrict. In response, industry has developed privacy-enhancing technologies intended to preserve advertising performance while limiting data collection, but their economic effects remain largely untested. We study this question using an open, industry-wide field experiment jointly overseen by Google and the UK Competition and Markets Authority, in which Chrome users were randomly assigned to browse with third-party cookies enabled, with cookies disabled, or with Google’s Privacy Sandbox replacing cookies. Combining this experimental variation with proprietary data from a major ad management firm, we analyze more than 200 million ad impressions across over 5,000 publishers worldwide. Removing third-party cookies reduces publisher advertising revenue by 29.1%. Privacy Sandbox recovers only 4.2% of this lost revenue; this estimate reflects observed adoption and performance during the study period and may reflect modest industry adoption. Privacy-preserving auctions also increase ad latency, reducing impression delivery and further limiting revenue performance. Together, these findings provide a large-scale experimental benchmark for evaluating privacy-preserving reforms and demonstrate the difficulty of reconciling privacy protection with the economics of online content provision.

Working Papers

  1. Dynamic Auctions with Budget-Constrained Bidders: Evidence from the Online Advertising Market
    with Miguel Alcobendas (Yahoo)
    When price discovery is necessary for time-sensitive goods, a common practice is to conduct an auction for each item sequentially, but dynamic incentives can lead to behavior distinct from static settings. We provide a novel empirical analysis of a large-scale sequential market that employs auctions to allocate objects to firms with budget constraints, leveraging a unique proprietary dataset of the online advertising market. In this market, because of their short-run budget constraints, participants face a tradeoff between winning auctions immediately or holding out for later opportunities. This dynamic incentive prompts them to adjust their entry rates and bidding strategies accordingly. We develop and estimate a finite-horizon dynamic game between bidders with heterogeneous budgets facing a sequence of simultaneous auctions to quantify this incentive and analyze its implication in competition and auction design. We find that a substantial markdown occurs due to the dynamic incentives arising from budget constraints, and this markdown varies significantly among bidders with different budgets. Using the estimated structural model, we provide a counterfactual simulation comparing the first-price and second-price formats. Unlike the standard environment, we find that dynamics and heterogeneous budgets lead to a significant disparity in the welfare distributions under them. This highlights that even a seemingly simple mechanism choice can have competitive implications in such a dynamic environment.

  2. Robust Estimation of Risk Preferences
    with Aldo Lucia (Caltech)

    Reject and Resubmit at Econometrica

    Economic models have been successful at rationalizing specific empirical findings, but their ability to concurrently explain multiple behaviors remains limited. In this paper, we illustrate this issue by conducting an experiment with 500 participants that studies two classical behaviors inconsistent with Expected Utility: the common ratio effect and preferences for randomization. The lack of generalizability of leading economic models across these two behaviors calls for the development of new empirical strategies to make predictions. Motivated by this observation, we introduce a novel empirical approach that enables us to predict behavior under risk without leaning on specific decision models. We further demonstrate that this method offers more accurate out-of-sample predictions about behaviors under risk, both inside and outside laboratory settings, than leading economic models and machine learning algorithms.

  3. Privacy-Enhanced versus Traditional Retargeting: Ad Effectiveness in an Industry-Wide Field Experiment
    with Garrett A. Johnson (BU Questrom) and Zhengrong Gu (BU Questrom)

    Coverage: Mobile Dev Memo podcast, The Monopoly Report, AdTechRadar, AdExchanger

    An advertiser can use retargeting to target its site visitors with ads offsite, often to push users to complete a purchase. Retargeting is controversial because it raises both user privacy concerns and questions about its effectiveness for advertisers. In this study, we partner with an advertiser intermediary to measure retargeting effectiveness across more than 2,000 advertisers, leveraging an industry-wide experiment to evaluate both traditional and privacyenhanced retargeting approaches. Google's Privacy Sandbox allows advertisers to retarget users without tracking cross-site browsing by moving ad selling onto the user's device. We provide broad-based evidence that retargeting lifts advertisers' baseline conversions by 4.6%. While removing third-party cookies significantly reduces ad clicks and click-through conversions, implementing Privacy Sandbox recovers 46.3% of lost ad clicks, and 43.5% of lost clickthrough conversions. Importantly, when adjusting for ad expenditure, the performance gap between privacy-enhanced and traditional retargeting narrows: Sandbox's click per dollar and click-through conversion per dollar achieve 86.4% and 81.8% of traditional counterparts, respectively. We provide additional evidence exploring time heterogeneity and advertiser heterogeneity in treatment effects, suggesting that the limited overall performance of Privacy Sandbox may be due to the lack of supply-side adoption of Privacy Sandbox.

Conference Presentations

Teaching