IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2601.08281.html

Estimating Treatment Effects in Panel Data Without Parallel Trends

Author

Listed:
  • Shoya Ishimaru

Abstract

This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework's ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups.

Suggested Citation

  • Shoya Ishimaru, 2026. "Estimating Treatment Effects in Panel Data Without Parallel Trends," Papers 2601.08281, arXiv.org.
    • Handle: RePEc:arx:papers:2601.08281
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2601.08281
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    2. Wolfgang Dauth & Johann Eppelsheimer, 2020. "Preparing the sample of integrated labour market biographies (SIAB) for scientific analysis: a guide," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-14, December.
    3. repec:iab:iabjlr:v:54:p:art.10 is not listed on IDEAS
    4. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    5. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    6. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    7. Guido Imbens & Nathan Kallus & Xiaojie Mao, 2021. "Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models," Papers 2108.03849, arXiv.org.
    8. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    9. Jacobson, Louis S & LaLonde, Robert J & Sullivan, Daniel G, 1993. "Earnings Losses of Displaced Workers," American Economic Review, American Economic Association, vol. 83(4), pages 685-709, September.
    10. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    11. Kenneth A. Couch & Dana W. Placzek, 2010. "Earnings Losses of Displaced Workers Revisited," American Economic Review, American Economic Association, vol. 100(1), pages 572-589, March.
    12. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    13. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    14. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2017. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Econometrica, Econometric Society, vol. 85, pages 693-734, May.
    15. Ashenfelter, Orley C, 1978. "Estimating the Effect of Training Programs on Earnings," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 47-57, February.
    16. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, September.
    17. Louis S. Jacobson & Robert J. LaLonde & Daniel G. Sullivan, 1993. "Long-term earnings losses of high-seniority displaced workers," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 17(Nov), pages 2-20.
    18. Douglas L. Miller, 2023. "An Introductory Guide to Event Study Models," Journal of Economic Perspectives, American Economic Association, vol. 37(2), pages 203-230, Spring.
    19. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    20. Ashesh Rambachan & Jonathan Roth, 2023. "A More Credible Approach to Parallel Trends," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2555-2591.
    21. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    22. Kenichi Nagasawa, 2018. "Treatment Effect Estimation with Noisy Conditioning Variables," Papers 1811.00667, arXiv.org, revised Sep 2022.
    23. Ben Deaner, 2022. "Controlling for Latent Confounding with Triple Proxies," Papers 2204.13815, arXiv.org, revised May 2023.
    24. Fatih Guvenen, 2007. "Learning Your Earning: Are Labor Income Shocks Really Very Persistent?," American Economic Review, American Economic Association, vol. 97(3), pages 687-712, June.
    25. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2012. "A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 481-498, May.
    26. Hu, Yingyao & Shiu, Ji-Liang, 2018. "Nonparametric Identification Using Instrumental Variables: Sufficient Conditions For Completeness," Econometric Theory, Cambridge University Press, vol. 34(3), pages 659-693, June.
    27. Gregor Jarosch, 2023. "Searching for Job Security and the Consequences of Job Loss," Econometrica, Econometric Society, vol. 91(3), pages 903-942, May.
    28. Brantly Callaway & Emmanuel Selorm Tsyawo, 2023. "Treatment Effects in Staggered Adoption Designs with Non-Parallel Trends," Papers 2308.02899, arXiv.org.
    29. Joachim Freyberger, 2018. "Non-parametric Panel Data Models with Interactive Fixed Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(3), pages 1824-1851.
    30. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
    31. repec:iab:iabjlr:v:54:i:1:p:art.10 is not listed on IDEAS
    32. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    33. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    34. Jonathan Roth & Pedro H. C. Sant'Anna, 2023. "When Is Parallel Trends Sensitive to Functional Form?," Econometrica, Econometric Society, vol. 91(2), pages 737-747, March.
    35. Andrews, Donald W.K., 2017. "Examples of L2-complete and boundedly-complete distributions," Journal of Econometrics, Elsevier, vol. 199(2), pages 213-220.
    Full references (including those not matched with items on IDEAS)

    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. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    2. Kenichi Nagasawa, 2018. "Treatment Effect Estimation with Noisy Conditioning Variables," Papers 1811.00667, arXiv.org, revised Sep 2022.
    3. Nagasawa, Kenichi, 2020. "Identification and Estimation of Group-Level Partial Effects," The Warwick Economics Research Paper Series (TWERPS) 1243, University of Warwick, Department of Economics.
    4. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    5. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    6. Callaway, Brantly, 2021. "Bounds on distributional treatment effect parameters using panel data with an application on job displacement," Journal of Econometrics, Elsevier, vol. 222(2), pages 861-881.
    7. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    8. Coulibaly, Yacouba, 2024. "Resource-backed loans and ecological efficiency of human development: Evidence from African countries," Ecological Economics, Elsevier, vol. 224(C).
    9. Elena Kotyrlo, 2024. "Simple and complex difference-in-differences approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 73, pages 119-142.
    10. Jeffrey M Wooldridge, 2023. "Simple approaches to nonlinear difference-in-differences with panel data," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 31-66.
    11. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    12. Chabé-Ferret, Sylvain, 2015. "Analysis of the bias of Matching and Difference-in-Difference under alternative earnings and selection processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 110-123.
    13. Brown, Nicholas L. & Butts, Kyle, 2025. "Dynamic treatment effect estimation with interactive fixed effects and short panels," Journal of Econometrics, Elsevier, vol. 250(C).
    14. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    15. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    16. Leombruni, Roberto & Razzolini, Tiziano & Serti, Francesco, 2013. "The pecuniary and non-pecuniary costs of job displacement—The risky job of being back to work," European Economic Review, Elsevier, vol. 61(C), pages 205-216.
    17. Jinghao Sun & Eric J. Tchetgen Tchetgen, 2025. "On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator," Papers 2507.07228, arXiv.org, revised Aug 2025.
    18. Cocco, Valentin & Chakir, Raja & Mouysset, Lauriane, 2025. "Guilty or scapegoat? Land consolidation and hedgerow decline," Journal of Environmental Economics and Management, Elsevier, vol. 133(C).
    19. Nayoung Lee & Hyungsik Roger Moon, 2021. "Heterogeneous Income Profiles Model with Fixed Effects: Incorporating Labour Income Shocks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1377-1407, December.
    20. Kent Eliasson & Pär Hansson, 2016. "Are workers more vulnerable in tradable industries?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(2), pages 283-320, May.

    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:2601.08281. 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: http://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.