Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment

Goeun Lee; Myoung Jae Lee

doi:10.1177/0193841X221105968

Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment

Goeun Lee
, Myoung Jae Lee

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

Regression discontinuity is popular in finding treatment/policy effects when the treatment is determined by a continuous variable crossing a cutoff. Typically, a local linear regression (LLR) estimator is used to find the effects. For binary response, however, LLR is not suitable in extrapolating the treatment, as in doubling/tripling the treatment dose/intensity. The reason is that doubling/tripling the LLR estimate can give a number out of the bound (Formula presented.), despite that the effect should be a change in probability. We propose local maximum likelihood estimators which overcome these shortcomings, while giving almost the same estimates as the LLR estimator does for the original treatment. A simulation study and an empirical analysis for effects of an income subsidy program on religion demonstrate these points.

Original language	English
Pages (from-to)	182-208
Number of pages	27
Journal	Evaluation Review
Volume	47
Issue number	2
DOIs	https://doi.org/10.1177/0193841X221105968
Publication status	Published - 2023 Apr

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research of Myoung-jae Lee has been supported by a Korea University research grant.

Publisher Copyright:
© The Author(s) 2022.

Keywords

binary response
control function
extrapolation
local maximum likelihood estimator
regression discontinuity

ASJC Scopus subject areas

Arts and Humanities (miscellaneous)
General Social Sciences

Access to Document

10.1177/0193841X221105968

Cite this

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title = "Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment",

abstract = "Regression discontinuity is popular in finding treatment/policy effects when the treatment is determined by a continuous variable crossing a cutoff. Typically, a local linear regression (LLR) estimator is used to find the effects. For binary response, however, LLR is not suitable in extrapolating the treatment, as in doubling/tripling the treatment dose/intensity. The reason is that doubling/tripling the LLR estimate can give a number out of the bound (Formula presented.), despite that the effect should be a change in probability. We propose local maximum likelihood estimators which overcome these shortcomings, while giving almost the same estimates as the LLR estimator does for the original treatment. A simulation study and an empirical analysis for effects of an income subsidy program on religion demonstrate these points.",

keywords = "binary response, control function, extrapolation, local maximum likelihood estimator, regression discontinuity",

author = "Goeun Lee and Lee, \{Myoung Jae\}",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research of Myoung-jae Lee has been supported by a Korea University research grant. Publisher Copyright: {\textcopyright} The Author(s) 2022.",

year = "2023",

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T1 - Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment

AU - Lee, Goeun

AU - Lee, Myoung Jae

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research of Myoung-jae Lee has been supported by a Korea University research grant. Publisher Copyright: © The Author(s) 2022.

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N2 - Regression discontinuity is popular in finding treatment/policy effects when the treatment is determined by a continuous variable crossing a cutoff. Typically, a local linear regression (LLR) estimator is used to find the effects. For binary response, however, LLR is not suitable in extrapolating the treatment, as in doubling/tripling the treatment dose/intensity. The reason is that doubling/tripling the LLR estimate can give a number out of the bound (Formula presented.), despite that the effect should be a change in probability. We propose local maximum likelihood estimators which overcome these shortcomings, while giving almost the same estimates as the LLR estimator does for the original treatment. A simulation study and an empirical analysis for effects of an income subsidy program on religion demonstrate these points.

AB - Regression discontinuity is popular in finding treatment/policy effects when the treatment is determined by a continuous variable crossing a cutoff. Typically, a local linear regression (LLR) estimator is used to find the effects. For binary response, however, LLR is not suitable in extrapolating the treatment, as in doubling/tripling the treatment dose/intensity. The reason is that doubling/tripling the LLR estimate can give a number out of the bound (Formula presented.), despite that the effect should be a change in probability. We propose local maximum likelihood estimators which overcome these shortcomings, while giving almost the same estimates as the LLR estimator does for the original treatment. A simulation study and an empirical analysis for effects of an income subsidy program on religion demonstrate these points.

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KW - extrapolation

KW - local maximum likelihood estimator

KW - regression discontinuity

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Regression Discontinuity for Binary Response and Local Maximum Likelihood Estimator to Extrapolate Treatment

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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