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

Yingying Dong and Arthur Lewbel

No 759, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: Regression discontinuity models, where the probability of treatment jumps discretely when a running variable crosses a threshold, are commonly used to nonparametrically identify and estimate a local average treatment effect. We show that the derivative of this treatment effect with respect to the running variable is nonparametrically identified and easily estimated. Then, given a local policy invariance assumption, we show that this derivative 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 this result to Manacorda (2012), who estimates a treatment effect of grade retention on school outcomes. Our MTTE identifies how this treatment effect would change if the threshold for retention was raised or lowered, 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. (search for similar items in EconPapers)
JEL-codes: C21 C25 (search for similar items in EconPapers)
Date: 2010-08-01, Revised 2012-12-15
New Economics Papers: this item is included in nep-ecm and nep-ias
Note: Previously circulated as "Regression Discontinuity Marginal Threshold Treatment Effects"
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Citations: View citations in EconPapers (12)

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Journal Article: Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models (2015)
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