Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models

By Yingying Dong, Arthur Lewbel. 2015

143 citations recorded by the source corpus.

Abstract Regression discontinuity models are commonly used to nonparametrically identify and estimate a local average treatment effect (LATE).We show that the derivative of the treatment effect with respect to the running variable at the cutoff, referred to as the treatment effect derivative (TED), is nonparametrically identified, easily estimated, and has implications for testing external validity and extrapolating the estimated LATE away from the cutoff. Given a local policy invariance assumption, we further show this TED equals the change in the treatment effect that would result from a marginal change in the threshold, which we call the marginal threshold treatment effect (MTTE). We apply these results to Goodman (2008), who estimates the effect of a scholarship program on college choice. MTTE in this case identifies how this treatment effect would change if the test score threshold to qualify for a scholarship were changed, even though no such change in threshold is actually observed.

Subfields: Education, Statistics and Probability

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Citations in the corpus

Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff (2015, methodology): “The second is a version of the Hirano, Imbens, and Ridder (2003) propensity score estimator based on Horvitz and Thompson 365 (1952). We also use the estimated propensity score to document common support, as in Dehejia and Wahba’s (1999) pioneering propensity score study of the effect of a training ”
Interpreting Regression Discontinuity Designs with Multiple Cutoffs (2016, background): “assumption in Hotz et al. (2005), and to the “local policy invariance” condition in Dong and Lewbel (2015). Assumption 5b formalizes the idea of an exclusion restriction, requiring that the cutoff level does not affect the potential outcomes directly.”
External Validity in Fuzzy Regression Discontinuity Designs (2019, unknown): “As shown by HTV, and discussed in more detail below, the traditional regression discontinuity assumptions are sufficient for identification of τ late.”
Impossible inference in econometrics: Theory and applications (2016, unknown): “These include estimation of derivatives of the treatment eﬀect at the cutoﬀ by Dong (2016) and Dong and Lewbel (2015); tests for homogeneity of treatment eﬀects in fuzzy RDD by Bertanha and Imbens (2019); and estimation of average treatment eﬀects in RDD with variation in cutoﬀ values by Bertanha…”
Econometric Methods for Program Evaluation (2018, background): “While there is some very recent work that addresses the issue of extrapolation of RD treatment effects (e.g., Angrist and Rokkanen, 2015; Dong and Lewbel, 2015; Bertanha and Imbens, 2017; Cattaneo et al., 2017f), more work is certainly needed, covering both methodological and empirical issues.”
A Practical Introduction to Regression Discontinuity Designs (2019, unknown): “…example, extrapolation of RD treatment effects can be done by imposing additional assumptions about 18 (i) the regression functions near the cutoff (Dong and Lewbel, 2015; Wing and Cook, 2013), (ii) local independence assumptions (Angrist and Rokkanen, 2015) (iii) exploiting specific features of…”
Regression Discontinuity Designs Using Covariates (2018, background): “Dong and Lewbel (2015) also study derivative estimation in RD designs, without additional covariates.”
Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs (2018, background): “Dong, Y. and A. Lewbel (2015).”