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Identification of Marginal Treatment Effects using Subjective Expectations

Author

Listed:
  • Joseph Briggs

    (Goldman Sachs)

  • Andrew Chaplin

    (New York University, NBER)

  • Soeren Leth-Petersen

    (Department of Economics, University of Copenhagen)

  • Christopher Tonetti

    (Stanford Graduate School of Business, NBER)

Abstract

We develop a method to identify the individual latent propensity to select into treatment and marginal treatment effects. Identification is achieved with survey data on individuals’ subjective expectations of their treatment propensity and of their treatmentcontingent outcomes. We use the method to study how child birth affects female labor supply in Denmark. We find limited latent heterogeneity and large short-term effects that vanish by 18 months after birth. We support the validity of the identifying assumptions in this context by using administrative data to show that the average treatment effect on the treated computed using our method and traditional event-study methods are nearly equal. Finally, we study the effects of counterfactual changes to child care cost and quality on female labor supply.

Suggested Citation

  • Joseph Briggs & Andrew Chaplin & Soeren Leth-Petersen & Christopher Tonetti, 2024. "Identification of Marginal Treatment Effects using Subjective Expectations," CEBI working paper series 24-06, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    • Handle: RePEc:kud:kucebi:2406
    as

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    File URL: https://www.econ.ku.dk/cebi/publikationer/working-papers/CEBI_WP_06-24.pdf
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    References listed on IDEAS

    as
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    4. Henrik Jacobsen Kleven & Martin B. Knudsen & Claus Thustrup Kreiner & Søren Pedersen & Emmanuel Saez, 2011. "Unwilling or Unable to Cheat? Evidence From a Tax Audit Experiment in Denmark," Econometrica, Econometric Society, vol. 79(3), pages 651-692, May.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    marginal treatment effects; survey data; expectations;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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