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

Yingying Dong () and Arthur Lewbel
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Yingying Dong: University of California Irvine

The Review of Economics and Statistics, 2015, vol. 97, issue 5, 1081-1092

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.

Keywords: regression discontinuity; sharp design; fuzzy design; treatment effects; program evaluation; threshold; running variable; forcing variable; marginal effects; external validity (search for similar items in EconPapers)
JEL-codes: C21 C25 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (100)

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Working Paper: Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models (2012)
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