IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.08614.html

Estimating the Intensive Margin Effect in Panel Data Settings

Author

Listed:
  • Javier Viviens

Abstract

Many policies operate through two different channels: the extensive margin (e.g., the decision to participate) and the intensive margin (e.g., the intensity of the response among participants). This paper develops a novel identification strategy to estimate the intensive margin effect in panel data settings. I adapt the Horowitz-Manski-Lee bounds to the Changes-in-Changes framework to partially identify both the average and quantile intensive margin treatment effects. Additionally, I explore how to leverage multiple sources of sample selection to relax the monotonicity assumption in the original Horowitz-Manski-Lee bounds, which may be of independent interest. Alongside the identification strategy, I present estimators and inference results. I illustrate the relevance of the proposed methodology by analyzing a job training program in Colombia.

Suggested Citation

  • Javier Viviens, 2025. "Estimating the Intensive Margin Effect in Panel Data Settings," Papers 2502.08614, arXiv.org, revised Feb 2026.
    • Handle: RePEc:arx:papers:2502.08614
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.08614
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    2. 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.
    3. Lin, Julia Y. & Ten Have, Thomas R. & Elliott, Michael R., 2008. "Longitudinal Nested Compliance Class Model in the Presence of Time-Varying Noncompliance," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 462-473, June.
    4. Dmitry Arkhangelsky & Guido Imbens, 2024. "Causal models for longitudinal and panel data: a survey," The Econometrics Journal, Royal Economic Society, vol. 27(3), pages 1-61.
    5. Alyssa Carlson & Anastasia Semykina, 2024. "Addressing Attrition in Nonlinear Dynamic Panel Data Models with an Application to Health," Working Papers 2408, Department of Economics, University of Missouri.
    6. Christopher M. Cornwell & Kyung Hee Lee & David B. Mustard, 2005. "Student Responses to Merit Scholarship Retention Rules," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 895-917.
    7. Dmitry Arkhangelsky & Guido W Imbens, 2022. "Doubly robust identification for causal panel data models [Sufficient statistics for unobserved heterogeneity in structural dynamic logit models]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 649-674.
    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. Lorenzo Testa & Edward H. Kennedy & Matthew Reimherr, 2025. "Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random," Papers 2509.25009, arXiv.org, revised Jan 2026.

    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. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    2. Ben Deaner & Chen-Wei Hsiang & Andrei Zeleneev, 2025. "Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities," Papers 2503.20769, arXiv.org, revised Mar 2025.
    3. Aibo Gong, 2021. "Bounds for Treatment Effects in the Presence of Anticipatory Behavior," Papers 2111.06573, arXiv.org, revised Dec 2022.
    4. Brantly Callaway & Derek Dyal & Pedro H. C. Sant'Anna & Emmanuel S. Tsyawo, 2025. "Beyond Parallel Trends: An Identification-Strategy-Robust Approach to Causal Inference with Panel Data," Papers 2511.21977, arXiv.org.
    5. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    6. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2023. "Optimal Environmental Targeting in the Amazon Rainforest," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1608-1641.
    7. John Mullahy, 2017. "Individual Results May Vary: Elementary Analytics of Inequality-Probability Bounds, with Applications to Health-Outcome Treatment Effects," NBER Working Papers 23603, National Bureau of Economic Research, Inc.
    8. Tetsuya Kaji & Jianfei Cao, 2023. "Assessing Heterogeneity of Treatment Effects," Papers 2306.15048, arXiv.org, revised Aug 2025.
    9. Mullahy, John, 2018. "Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects," Journal of Health Economics, Elsevier, vol. 61(C), pages 151-162.
    10. Daisuke Kurisu & Yuta Okamoto & Taisuke Otsu, 2025. "Difference-in-Differences with Interval Data," Papers 2512.08759, arXiv.org.
    11. Molinari, Francesca, 2020. "Microeconometrics with partial identification," 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 355-486, Elsevier.
    12. 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.
    13. Patrick Bajari & Jeremy Fox & Stephen Ryan, 2008. "Evaluating wireless carrier consolidation using semiparametric demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 6(4), pages 299-338, December.
    14. Foliano, Francesca & Tonei, Valentina & Sevilla, Almudena, 2024. "Social restrictions, leisure and well-being," Labour Economics, Elsevier, vol. 87(C).
    15. Jason M. Lindo & Nicholas J. Sanders & Philip Oreopoulos, 2010. "Ability, Gender, and Performance Standards: Evidence from Academic Probation," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 95-117, April.
    16. Picarelli, Nathalie, 2016. "Who really benefits from export processing zones? Evidence from Nicaraguan municipalities," Labour Economics, Elsevier, vol. 41(C), pages 318-332.
    17. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    18. Molinari, Francesca, 2010. "Missing Treatments," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 82-95.
    19. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    20. Joan Costa-Font & Nilesh Raut & Courtney H. Van Houtven, 2024. "Medicaid Expansion and the Mental Health of Spousal Caregivers," Review of Economics of the Household, Springer, vol. 22(3), pages 1047-1084, September.

    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:2502.08614. 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.