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Identification of Marginal Treatment Effects using Subjective Expectations

Author

Listed:
  • Briggs, Joseph
  • Caplin, Andrew
  • Leth-Petersen, Søren
  • Tonetti, Christopher

Abstract

We develop a method to identify the individual latent propensity to select into treat- ment and marginal treatment effects. Identification is achieved with survey data on individuals’ subjective expectations of their treatment propensity and of their treatment-contingent 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 assump- tions 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

  • Briggs, Joseph & Caplin, Andrew & Leth-Petersen, Søren & Tonetti, Christopher, 2024. "Identification of Marginal Treatment Effects using Subjective Expectations," CEPR Discussion Papers 18995, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:18995
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    References listed on IDEAS

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    1. Katja Maria Kaufmann, 2014. "Understanding the income gradient in college attendance in Mexico: The role of heterogeneity in expected returns," Quantitative Economics, Econometric Society, vol. 5(3), pages 583-630, November.
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    3. 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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    More about this item

    Keywords

    Marginal treatment effects;

    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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