IDEAS home Printed from https://ideas.repec.org/p/kud/kucebi/2406.html
   My bibliography  Save this paper

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

    Download full text from publisher

    File URL: https://www.econ.ku.dk/cebi/publikationer/working-papers/CEBI_WP_06-24.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. Pamela Giustinelli & Matthew D. Shapiro, 2024. "SeaTE: Subjective Ex Ante Treatment Effect of Health on Retirement," American Economic Journal: Applied Economics, American Economic Association, vol. 16(2), pages 278-317, April.
    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. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gizem Koşar & Cormac O'Dea, 2022. "Expectations Data in Structural Microeconomic Models," Staff Reports 1018, Federal Reserve Bank of New York.
    2. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    3. Rojas, Eugenio & Sánchez, Rafael & Villena, Mauricio G., 2016. "Credit constraints in higher education in a context of unobserved heterogeneity," Economics of Education Review, Elsevier, vol. 52(C), pages 225-250.
    4. Yifan Gong & Todd Stinebrickner & Ralph Stinebrickner, 2020. "Perceived and actual option values of college enrollment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 940-959, November.
    5. Pamela Giustinelli, 2022. "Expectations in Education: Framework, Elicitation, and Evidence," Working Papers 2022-026, Human Capital and Economic Opportunity Working Group.
    6. Stephane Bonhomme & Angela Denis, 2023. "Estimating Individual Responses when Tomorrow Matters," Papers 2310.09105, arXiv.org, revised May 2024.
    7. Gong, Yifan & Stinebrickner, Ralph & Stinebrickner, Todd, 2022. "Marriage, children, and labor supply: Beliefs and outcomes," Journal of Econometrics, Elsevier, vol. 231(1), pages 148-164.
    8. Madeira, Carlos, 2020. "Learning your own ability," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    9. Arun Advani & William Elming & Jonathan Shaw, 2023. "The Dynamic Effects of Tax Audits," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 545-561, May.
    10. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    11. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    12. Nobuhiko Fuwa & Asa Jose U. Sajise, 2009. "Exploring Environmental Services Incentive Policies for the Philippines Rice Sector: The Case of Intra-Species Agrobiodiversity Conservation," Natural Resource Management and Policy, in: Leslie Lipper & Takumi Sakuyama & Randy Stringer & David Zilberman (ed.), Payment for Environmental Services in Agricultural Landscapes, chapter 10, pages 221-238, Springer.
    13. Hoffman, Mitchell & Burks, Stephen V., 2017. "Worker Overconfidence: Field Evidence and Implications for Employee Turnover and Returns from Training," IZA Discussion Papers 10794, Institute of Labor Economics (IZA).
    14. Ralph Stinebrickner & Todd R. Stinebrickner, 2014. "A Major in Science? Initial Beliefs and Final Outcomes for College Major and Dropout," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(1), pages 426-472.
    15. European Commission, 2013. "Tax reforms in EU Member States - Tax policy challenges for economic growth and fiscal sustainability – 2013 Report," Taxation Papers 38, Directorate General Taxation and Customs Union, European Commission.
    16. Simon Halphen Boserup & Wojciech Kopczuk & Claus Thustrup Kreiner, 2018. "Born with a Silver Spoon? Danish Evidence on Wealth Inequality in Childhood," Economic Journal, Royal Economic Society, vol. 128(612), pages 514-544, July.
    17. Annette Alstadsæter & Wojciech Kopczuk & Kjetil Telle, 2019. "Social networks and tax avoidance: evidence from a well-defined Norwegian tax shelter," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(6), pages 1291-1328, December.
    18. Paul Ellickson & Sanjog Misra, 2012. "Enriching interactions: Incorporating outcome data into static discrete games," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 1-26, March.
    19. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    20. Bachas, Pierre & Fattal Jaef, Roberto N. & Jensen, Anders, 2019. "Size-dependent tax enforcement and compliance: Global evidence and aggregate implications," Journal of Development Economics, Elsevier, vol. 140(C), pages 203-222.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kud:kucebi:2406. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Hoffmann (email available below). General contact details of provider: https://edirc.repec.org/data/cebkudk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.