IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/14002.html
   My bibliography  Save this paper

Identification of Treatment Effects Using Control Functions in Models with Continuous, Endogenous Treatment and Heterogeneous Effects

Author

Listed:
  • Jean-Pierre Florens
  • James J. Heckman
  • Costas Meghir
  • Edward J. Vytlacil

Abstract

We use the control function approach to identify the average treatment effect and the effect of treatment on the treated in models with a continuous endogenous regressor whose impact is heterogeneous. We assume a stochastic polynomial restriction on the form of the heterogeneity but, unlike alternative nonparametric control function approaches, our approach does not require large support assumptions.

Suggested Citation

  • Jean-Pierre Florens & James J. Heckman & Costas Meghir & Edward J. Vytlacil, 2008. "Identification of Treatment Effects Using Control Functions in Models with Continuous, Endogenous Treatment and Heterogeneous Effects," NBER Working Papers 14002, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:14002
    Note: TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w14002.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    2. Wooldridge, Jeffrey M., 2003. "Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model," Economics Letters, Elsevier, vol. 79(2), pages 185-191, May.
    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. Klein, T.J., 2010. "Heterogeneous treatment effects : Instrumental variables without monotonicity?," Other publications TiSEM 0ec85b01-ab6a-4c2a-9e23-1, Tilburg University, School of Economics and Management.
    2. Klein, Tobias J., 2010. "Heterogeneous treatment effects: Instrumental variables without monotonicity?," Journal of Econometrics, Elsevier, vol. 155(2), pages 99-116, April.
    3. John K. Dagsvik & TorbjØrn HÆgeland & Arvid Raknerud, 2011. "Estimating the returns to schooling: a likelihood approach based on normal mixtures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(4), pages 613-640, June.
    4. Ferreira, Maria & de Grip, Andries & van der Velden, Rolf, 2018. "Does informal learning at work differ between temporary and permanent workers? Evidence from 20 OECD countries," Labour Economics, Elsevier, vol. 55(C), pages 18-40.
    5. Ferreira Sequeda, M.T. & de Grip, A. & van der Velden, R.K.W., 2015. "Does on-the-job informal learning in OECD countries differ by contract duration," Research Memorandum 021, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Juan Carlos Escanciano, 2020. "Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity," Papers 2005.08611, arXiv.org.
    7. Belzil, Christian & Hansen, Jörgen, 2005. "A Structural Analysis of the Correlated Random Coefficient Wage Regression Model with an Application to the OLS-IV Puzzle," IZA Discussion Papers 1585, Institute of Labor Economics (IZA).
    8. DePaula, Guilherme, 2023. "Bundled contracts and technological diffusion: Evidence from the Brazilian soybean boom," Journal of Development Economics, Elsevier, vol. 165(C).
    9. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.
    10. Li, Mingliang & Tobias, Justin L., 2011. "Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling," Journal of Econometrics, Elsevier, vol. 162(2), pages 345-361, June.
    11. Brendan Kline & Justin L. Tobias, 2008. "The wages of BMI: Bayesian analysis of a skewed treatment-response model with nonparametric endogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(6), pages 767-793.
    12. Murtazashvili, Irina & Wooldridge, Jeffrey M., 2008. "Fixed effects instrumental variables estimation in correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 539-552, January.
    13. P.A.V.B. Swamy & I-Lok Chang & Jatinder S. Mehta & William H. Greene & Stephen G. Hall & George S. Tavlas, 2016. "Removing Specification Errors from the Usual Formulation of Binary Choice Models," Econometrics, MDPI, vol. 4(2), pages 1-21, June.
    14. González-Uribe, Juanita & Reyes, Santiago, 2021. "Identifying and boosting “Gazelles”: Evidence from business accelerators," Journal of Financial Economics, Elsevier, vol. 139(1), pages 260-287.
    15. Wong, Maisy, 2010. "The Relationship between Marginal Willingness-to-Pay in the Hedonic and Discrete Choice Models," MPRA Paper 51218, University Library of Munich, Germany.
    16. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    17. Manuel Arellano & Stéphane Bonhomme, 2017. "Quantile Selection Models With an Application to Understanding Changes in Wage Inequality," Econometrica, Econometric Society, vol. 85, pages 1-28, January.
    18. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
    19. Hoderlein, Stefan & Su, Liangjun & White, Halbert & Yang, Thomas Tao, 2016. "Testing for monotonicity in unobservables under unconfoundedness," Journal of Econometrics, Elsevier, vol. 193(1), pages 183-202.
    20. Dillon, Andrew & Friedman, Jed & Serneels, Pieter, 2014. "Health Information, Treatment, and Worker Productivity: Experimental Evidence from Malaria Testing and Treatment among Nigerian Sugarcane Cutters," IZA Discussion Papers 8074, Institute of Labor Economics (IZA).

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

    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:nbr:nberwo:14002. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.