IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2411.11589.html

The Impact of the General Data Protection Regulation (GDPR) on Online Usage Behavior

Author

Listed:
  • Klaus M. Miller
  • Bernd Skiera
  • Julia Schmitt

Abstract

Privacy regulations aim to safeguard consumers, but can have unintended consequences on how users interact with websites. This article estimates the causal effect of the EU's General Data Protection Regulation (GDPR) on online usage behavior and decomposes it into usage frequency (unique visitors) and usage intensity (visits per unique visitor). Using a generalized synthetic control estimator across trillions of visits to 6,387 websites in 24 industries and 13 countries-11 months before and 19 months after enforcement-it compares observations subject to the GDPR (EU users or EU websites) with unaffected observations (non-EU users on non-EU websites). Weekly visits decline by 4.88% within 3 months and by 10.02%% after 18 months, with the decline increasingly driven by usage frequency: by 18 months, unique visitors decline by 6.61%, whereas visits per unique visitor decline by only 0.59%. The average conceals offsetting effects consistent with a reallocation of online activity: about one quarter of websites gain significantly, and among websites losing users, the remaining users engage more intensively, whereas intensity falls where user numbers grow. Losses concentrate among hedonic and smaller websites and continue to deepen during the first wave of data-protection enforcement actions rather than being concentrated around the GDPR compliance deadline. Taken together, these patterns are more consistent with restricted data-driven user acquisition and privacy salience, than with consent friction and degraded retention as persistent explanations. Long-horizon, decompositional evaluation thus reveals GDPR's differential effect on usage frequency/intensity and heterogeneous effects not distinguished by the aggregate traffic effects reported in prior work.

Suggested Citation

  • Klaus M. Miller & Bernd Skiera & Julia Schmitt, 2024. "The Impact of the General Data Protection Regulation (GDPR) on Online Usage Behavior," Papers 2411.11589, arXiv.org, revised Aug 2026.
    • Handle: RePEc:arx:papers:2411.11589
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2411.11589
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Garrett A. Johnson & Scott K. Shriver & Shaoyin Du, 2020. "Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?," Marketing Science, INFORMS, vol. 39(1), pages 33-51, January.
    2. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    3. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    4. Joan Calzada & Ricard Gil, 2020. "What Do News Aggregators Do? Evidence from Google News in Spain and Germany," Marketing Science, INFORMS, vol. 39(1), pages 134-167, January.
    5. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2011. "Synth: An R Package for Synthetic Control Methods in Comparative Case Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i13).
    6. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    7. Congiu, Raffaele & Sabatino, Lorien & Sapi, Geza, 2022. "The Impact of Privacy Regulation on Web Traffic: Evidence From the GDPR," Information Economics and Policy, Elsevier, vol. 61(C).
    8. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    9. Christian Peukert & Stefan Bechtold & Michail Batikas & Tobias Kretschmer, 2022. "Regulatory Spillovers and Data Governance: Evidence from the GDPR," Marketing Science, INFORMS, vol. 41(4), pages 746-768, July.
    10. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    11. Pang, Xun, 2014. "Varying Responses to Common Shocks and Complex Cross-Sectional Dependence: Dynamic Multilevel Modeling with Multifactor Error Structures for Time-Series Cross-Sectional Data," Political Analysis, Cambridge University Press, vol. 22(4), pages 464-496.
    12. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nestor Duch-Brown & Christos Genakos & Blair Yuan Lyu, 2026. "Is data privacy a losing game? Evidence from Apple's App Tracking Transparency (ATT) policy," CEP Discussion Papers dp2207, Centre for Economic Performance, LSE.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michał Marcin Kobierecki & Michał Pierzgalski, 2022. "Sports Mega-Events and Economic Growth: A Synthetic Control Approach," Journal of Sports Economics, , vol. 23(5), pages 567-597, June.
    2. Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.
    3. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    4. Bai, Jushan & Wang, Peng, 2024. "Causal inference using factor models," MPRA Paper 120585, University Library of Munich, Germany.
    5. Bruno Ferman, 2021. "On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1764-1772, October.
    6. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    7. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2021. "Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 549-584, December.
    8. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    9. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    10. Taylor K. Odle, 2022. "Free to Spend? Institutional Autonomy and Expenditures on Executive Compensation, Faculty Salaries, and Research Activities," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(1), pages 1-32, February.
    11. Yi‐Ting Chen, 2020. "A distributional synthetic control method for policy evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 505-525, August.
    12. Kathleen T. Li, 2024. "Frontiers: A Simple Forward Difference-in-Differences Method," Marketing Science, INFORMS, vol. 43(2), pages 267-279, March.
    13. Nyborg, Kjell G. & Woschitz, Jiri, 2025. "Robust difference-in-differences analysis when there is a term structure," Journal of Financial Economics, Elsevier, vol. 170(C).
    14. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    15. Fry, Joseph, 2024. "A method of moments approach to asymptotically unbiased Synthetic Controls," Journal of Econometrics, Elsevier, vol. 244(1).
    16. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    17. Joseph Fry, 2023. "A Method of Moments Approach to Asymptotically Unbiased Synthetic Controls," Papers 2312.01209, arXiv.org, revised Mar 2024.
    18. Aderonke Osikominu & Gregor Pfeifer & Kristina Strohmaier & Gregor-Gabriel Pfeifer, 2021. "The Effects of Free Secondary School Track Choice: A Disaggregated Synthetic Control Approach," CESifo Working Paper Series 8879, CESifo.
    19. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "Debiasing and $t$-tests for synthetic control inference on average causal effects," Papers 1812.10820, arXiv.org, revised May 2025.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2411.11589. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.