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Estimating the Intensive Margin Effect in Panel Data Settings

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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.

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