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Identification of Marginal Treatment Effects using Subjective Expectations

Author

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

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 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 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 S. Briggs & Andrew Caplin & Søren Leth-Petersen & Christopher Tonetti, 2024. "Identification of Marginal Treatment Effects using Subjective Expectations," NBER Working Papers 32309, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:32309
    Note: CH EFG LS PE
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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.
    2. Adeline Delavande & Susann Rohwedder, 2011. "Individuals' uncertainty about future social security benefits and portfolio choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(3), pages 498-519, April.
    3. Wilbert van der Klaauw, 2012. "On the Use of Expectations Data in Estimating Structural Dynamic Choice Models," Journal of Labor Economics, University of Chicago Press, vol. 30(3), pages 521-554.
    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.
    5. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
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    More about this item

    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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