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A Causal Interpretation of Extensive and Intensive Margin Effects in Generalized Tobit Models

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  • Kevin E. Staub

    (University of Melbourne)

Abstract

This note proposes a new decomposition of average treatment effects on nonnegative outcomes. It represents the total effect as a population-weighted sum of the effects for two groups: those induced to participate by the treatment and those participating regardless of it. The usual decomposition into extensive and intensive margins used in the literature is generally incompatible with such a causal interpretation. The difference between decompositions can be substantial and yield diametrically opposed results. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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  • Kevin E. Staub, 2014. "A Causal Interpretation of Extensive and Intensive Margin Effects in Generalized Tobit Models," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 371-375, May.
    • Handle: RePEc:tpr:restat:v:96:y:2014:i:2:p:371-375
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    2. Elisabeth Christen & Michael Pfaffermayr & Yvonne Wolfmayr, 2019. "Decomposing service exports adjustments along the intensive and extensive margin at the firm‐level," Review of International Economics, Wiley Blackwell, vol. 27(1), pages 155-183, February.
    3. Hersche Markus & Moor Elias, 2020. "Identification and Estimation of Intensive Margin Effects by Difference-in-Difference Methods," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 272-285, January.
    4. Castro, Juan Francisco & Velásquez, Daniel & Beltrán, Arlette & Yamada, Gustavo, 2022. "The direct and indirect effects of messages on tax compliance: Experimental evidence from Peru," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 483-518.
    5. Fu Chen & Xiaoxiao Li & Jing Ma & Yongjun Yang & Gang-Jun Liu, 2018. "An Exploration of the Impacts of Compulsory Source-Separated Policy in Improving Household Solid Waste-Sorting in Pilot Megacities, China: A Case Study of Nanjing," Sustainability, MDPI, vol. 10(5), pages 1-14, April.
    6. Michael Pfaffermayr, 2014. "A GMM-Based Test for Normal Disturbances of the Heckman Sample Selection Model," Econometrics, MDPI, vol. 2(4), pages 1-18, October.
    7. Kristina M. Bott & Alexander W. Cappelen & Erik Ø. Sørensen & Bertil Tungodden, 2020. "You’ve Got Mail: A Randomized Field Experiment on Tax Evasion," Management Science, INFORMS, vol. 66(7), pages 2801-2819, July.
    8. Jing Cheng & Dylan S. Small, 2021. "Semiparametric models and inference for the effect of a treatment when the outcome is nonnegative with clumping at zero," Biometrics, The International Biometric Society, vol. 77(4), pages 1187-1201, December.
    9. Myoung-jae Lee, 2017. "Extensive and intensive margin effects in sample selection models: racial effects on wages," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 817-839, June.
    10. Bettendorf, Leon J.H. & Jongen, Egbert L.W. & Muller, Paul, 2015. "Childcare subsidies and labour supply — Evidence from a large Dutch reform," Labour Economics, Elsevier, vol. 36(C), pages 112-123.

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    More about this item

    Keywords

    limited dependent variables; potential outcomes; causality; conditionalon-positives effect; Tobit; selection model. two-part model;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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