IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v123y2014i2p220-223.html
   My bibliography  Save this article

Sensitivity checks for the local average treatment effect

Author

Listed:
  • Huber, Martin

Abstract

The nonparametric identification of the local average treatment effect (LATE) hinges on the satisfaction of three instrumental variable assumptions: (1) unconfounded assignment of the instrument, (2) no average direct effect of the instrument on the outcome within compliance types (exclusion restriction), and (3) weak monotonicity of the treatment in the instrument. While (1) often appears plausible in experiments when using randomization as instrument for actual participation, (2) and (3) may be controversial. For this reason, this paper proposes easily implementable sensitivity checks to assess the robustness of the LATE to deviations from either the exclusion restriction or monotonicity. An empirical illustration based on female labor supply data is also provided.

Suggested Citation

  • Huber, Martin, 2014. "Sensitivity checks for the local average treatment effect," Economics Letters, Elsevier, vol. 123(2), pages 220-223.
    • Handle: RePEc:eee:ecolet:v:123:y:2014:i:2:p:220-223
    DOI: 10.1016/j.econlet.2014.02.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176514000822
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2014.02.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kenneth I. Wolpin & Mark R. Rosenzweig, 2000. "Natural "Natural Experiments" in Economics," Journal of Economic Literature, American Economic Association, vol. 38(4), pages 827-874, December.
    2. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    3. Jungmin Lee, 2008. "Sibling size and investment in children’s education: an asian instrument," Journal of Population Economics, Springer;European Society for Population Economics, vol. 21(4), pages 855-875, October.
    4. Timothy G. Conley & Christian B. Hansen & Peter E. Rossi, 2012. "Plausibly Exogenous," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 260-272, February.
    5. Jin‐young Choi & Myoung‐jae Lee, 2012. "Bounding endogenous regressor coefficients using moment inequalities and generalized instruments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(2), pages 161-182, May.
    6. Fabrizia Mealli & Barbara Pacini, 2013. "Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1120-1131, September.
    7. Carlos A. Flores & Alfonso Flores-Lagunes, 2013. "Partial Identification of Local Average Treatment Effects With an Invalid Instrument," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 534-545, October.
    8. Angrist, Joshua D & Evans, William N, 1998. "Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size," American Economic Review, American Economic Association, vol. 88(3), pages 450-477, June.
    9. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 791-821.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicolas Apfel & Helmut Farbmacher & Rebecca Groh & Martin Huber & Henrika Langen, 2022. "Detecting Grouped Local Average Treatment Effects and Selecting True Instruments," Papers 2207.04481, arXiv.org, revised Oct 2023.
    2. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    3. Martin Huber, 2015. "Testing the Validity of the Sibling Sex Ratio Instrument," LABOUR, CEIS, vol. 29(1), pages 1-14, March.
    4. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    5. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    6. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

    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. Martin Huber, 2015. "Testing the Validity of the Sibling Sex Ratio Instrument," LABOUR, CEIS, vol. 29(1), pages 1-14, March.
    2. Huber, Martin, 2012. "Statistical verification of a natural "natural experiment": Tests and sensitivity checks for the sibling sex ratio instrument," Economics Working Paper Series 1219, University of St. Gallen, School of Economics and Political Science.
    3. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    4. Hans (J.L.W.) van Kippersluis & Niels (C.A.) Rietveld, 2017. "Beyond Plausibly Exogenous," Tinbergen Institute Discussion Papers 17-096/V, Tinbergen Institute.
    5. Emla Fitzsimons & Bansi Malde, 2014. "Empirically probing the quantity–quality model," Journal of Population Economics, Springer;European Society for Population Economics, vol. 27(1), pages 33-68, January.
    6. Rui Wang, 2023. "Point Identification of LATE with Two Imperfect Instruments," Papers 2303.13795, arXiv.org.
    7. Menta, Giorgia & Lepinteur, Anthony, 2021. "Boys don’t cry (or do the dishes): Family size and the housework gender gap," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 164-188.
    8. Kiviet, Jan F., 2020. "Testing the impossible: Identifying exclusion restrictions," Journal of Econometrics, Elsevier, vol. 218(2), pages 294-316.
    9. Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 474-484, October.
    10. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
    11. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2017. "Measuring the Sensitivity of Parameter Estimates to Estimation Moments," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1553-1592.
    12. Damian Clarke & Benjamín Matta, 2018. "Practical considerations for questionable IVs," Stata Journal, StataCorp LP, vol. 18(3), pages 663-691, September.
    13. Joshua D. Angrist & Victor Lavy & Analia Schlosser, 2005. "New Evidence on the Causal Link Between the Quantity and Quality of Children," NBER Working Papers 11835, National Bureau of Economic Research, Inc.
    14. Jan Priebe, 2020. "Quasi-experimental evidence for the causal link between fertility and subjective well-being," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 839-882, July.
    15. Daniel L. Millimet & Le Wang, 2011. "Is the Quantity-Quality Trade-Off a Trade-Off for All, None, or Some?," Economic Development and Cultural Change, University of Chicago Press, vol. 60(1), pages 155-195.
    16. Griffen, Andrew S. & Nakamuro, Makiko & Inui, Tomohiko, 2015. "Fertility and maternal labor supply in Japan: Conflicting policy goals?," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 52-72.
    17. Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso & Parisian, Daniel J., 2016. "The effect of degree attainment on arrests: Evidence from a randomized social experiment," Economics of Education Review, Elsevier, vol. 54(C), pages 259-273.
    18. Baez, Javier E., 2008. "Does More Mean Better? Sibling Sex Composition and the Link between Family Size and Children’s Quality," IZA Discussion Papers 3472, Institute of Labor Economics (IZA).
    19. Eduardo Fé, 2021. "Pension eligibility rules and the local causal effect of retirement on cognitive functioning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 812-841, July.
    20. Schmieder, Julia, 2021. "Fertility as a driver of maternal employment," Labour Economics, Elsevier, vol. 72(C).

    More about this item

    Keywords

    Instrumental variable; Treatment effects; LATE; Sensitivity analysis;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

    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:eee:ecolet:v:123:y:2014:i:2:p:220-223. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.