Arbeitspapier
Identification and Inference in Regression Discontinuity Designs with a Manipulated Running Variable
A key assumption in regression discontinuity analysis is that units cannot manipulate the value of their running variable in a way that guarantees or avoids assignment to the treatment. Standard identification arguments break down if this condition is violated. This paper shows that treatment effects remain partially identified in this case. We derive sharp bounds on the treatment effects, show how to estimate them, and propose ways to construct valid confidence intervals. Our results apply to both sharp and fuzzy regression discontinuity designs. We illustrate our methods by studying the effect of unemployment insurance on unemployment duration in Brazil, where we find strong evidence of manipulation at eligibility cutoffs.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 9604
Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
Model Construction and Estimation
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
Model Construction and Estimation
Thema
regression discontinuity
treatment effects
manipulation
partial identification
treatment effects
manipulation
partial identification
Ereignis
Geistige Schöpfung
(wer)
Gerard, Francois
Rokkanen, Miikka
Rothe, Christoph
Rokkanen, Miikka
Rothe, Christoph
Ereignis
Veröffentlichung
(wer)
Institute for the Study of Labor (IZA)
(wo)
Bonn
(wann)
2015
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:21 MESZ
Objekttyp
- Arbeitspapier
Beteiligte
- Gerard, Francois
- Rokkanen, Miikka
- Rothe, Christoph
- Institute for the Study of Labor (IZA)
Entstanden
- 2015
Ähnliche Objekte (12)
Regression Discontinuity Design with Continuous Measurement Error in the Running Variable
Inference in Regression Discontinuity Designs with a Discrete Running Variable
Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals
Partial identification of population average and quantile treatment effects in observational data under sample selection
Methods in empirical economics: A selective review with applications
Bounds on treatment effects in regression discontinuity designs with a manipulated running variable
Including Covariates in the Regression Discontinuity Design
Local quantile treatment effects
Treatment Evaluation with Multiple Outcome Periods under Endogeneity and Attrition
Endogeneity and Non-Response Bias in Treatment Evaluation: Nonparametric Identification of Causal Effects by Instruments
Semiparametric estimation of quantile treatment effects with endogeneity
Heterogeneous treatment effects with mismeasured endogenous treatment
Regression Discontinuity Design with Continuous Measurement Error in the Running Variable
Inference in Regression Discontinuity Designs with a Discrete Running Variable
Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals
Partial identification of population average and quantile treatment effects in observational data under sample selection
Methods in empirical economics: A selective review with applications
Bounds on treatment effects in regression discontinuity designs with a manipulated running variable
Including Covariates in the Regression Discontinuity Design
Local quantile treatment effects
Treatment Evaluation with Multiple Outcome Periods under Endogeneity and Attrition
Endogeneity and Non-Response Bias in Treatment Evaluation: Nonparametric Identification of Causal Effects by Instruments
Semiparametric estimation of quantile treatment effects with endogeneity
Heterogeneous treatment effects with mismeasured endogenous treatment
Regression Discontinuity Design with Continuous Measurement Error in the Running Variable
Inference in Regression Discontinuity Designs with a Discrete Running Variable
Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals
Partial identification of population average and quantile treatment effects in observational data under sample selection
Methods in empirical economics: A selective review with applications
Bounds on treatment effects in regression discontinuity designs with a manipulated running variable
Including Covariates in the Regression Discontinuity Design
Local quantile treatment effects
Treatment Evaluation with Multiple Outcome Periods under Endogeneity and Attrition
Endogeneity and Non-Response Bias in Treatment Evaluation: Nonparametric Identification of Causal Effects by Instruments
Semiparametric estimation of quantile treatment effects with endogeneity