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Instrument-based estimation with binarized treatments: Issues and tests for the exclusion restriction

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  • Eckhoff Andresen, Martin
  • Huber, Martin

Abstract

When estimating local average and marginal treatment effects using instrumental variables (IV), multivalued endogenous treatments are frequently binarized based on a specific threshold in treatment support. However, such binarization introduces a violation of the IV exclusion if (i) the IV affects the multivalued treatment within support areas below and/or above the threshold and (ii) such IV-induced changes in the multivalued treatment affect the outcome. We discuss assumptions that satisfy the IV exclusion restriction with the binarized treatment and permit identifying the average effect of (i) the binarized treatment and (ii) unit-level increases in the original multivalued treatment among specific compliers. We derive testable implications of these assumptions and propose tests, which we apply to the estimation of the returns to (binary) college graduation instrumented by college proximity.

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  • Eckhoff Andresen, Martin & Huber, Martin, 2018. "Instrument-based estimation with binarized treatments: Issues and tests for the exclusion restriction," FSES Working Papers 492, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    • Handle: RePEc:fri:fribow:fribow00492
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    2. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Nonbinary, Ordered Treatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org.
    3. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    4. Ban, Kyunghoon & Kedagni, Desire, 2020. "Nonparametric Bounds on Treatment Effects with Imperfect Instruments," ISU General Staff Papers 202010120700001113, Iowa State University, Department of Economics.
    5. Baltagi, Badi H. & Flores-Lagunes, Alfonso & Karatas, Haci M., 2023. "The effect of higher education on Women's obesity and smoking: Evidence from college openings in Turkey," Economic Modelling, Elsevier, vol. 123(C).
    6. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    7. Gerten, Elisa & Beckmann, Michael & Kräkel, Matthias, 2022. "Information and Communication Technology, Hierarchy, and Job Design," IZA Discussion Papers 15491, Institute of Labor Economics (IZA).
    8. Tymon Sloczynski & S. Derya Uysal & Jeffrey M. Wooldridge & Derya Uysal, 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," CESifo Working Paper Series 9715, CESifo.
    9. Nibbering, Didier & Oosterveen, Matthijs & Silva, Pedro Luís, 2022. "Clustered Local Average Treatment Effects: Fields of Study and Academic Student Progress," IZA Discussion Papers 15159, Institute of Labor Economics (IZA).
    10. Didier Nibbering & Matthijs Oosterveen, 2023. "Instrument-based estimation of full treatment effects with movers," Papers 2306.07018, arXiv.org.
    11. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    12. 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.
    13. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    14. Martin Eckhoff Andresen & Sturla A. Løkken, 2019. "High school dropout for marginal students. Evidence from randomized exam form," Discussion Papers 894, Statistics Norway, Research Department.
    15. Christophe Bruneel-Zupanc, 2023. "Don't (fully) exclude me, it's not necessary! Identification with semi-IVs," Papers 2303.12667, arXiv.org, revised Jul 2023.
    16. Joppe de Ree & Matthijs Oosterveen & Dinand Webbink, 2023. "The quality of school track assignment decisions by teachers," Papers 2304.10636, arXiv.org.

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    More about this item

    Keywords

    Instrumental variable; LATE; binarized treatment; test; exclusion restriction; MTE;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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

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