Bounds on Treatment Effects in Regression Discontinuity Designs with a Manipulated Running Variable

53 Pages Posted: 5 Dec 2016 Last revised: 21 Jul 2023

See all articles by François Gerard

François Gerard

Center for Operations Research and Econometrics (CORE)

Miikka Rokkanen

Columbia University

Christoph Rothe

Columbia University

Date Written: December 2016

Abstract

The key assumption in regression discontinuity analysis is that the distribution of potential outcomes varies smoothly with the running variable around the cutoff. In many empirical contexts, however, this assumption is not credible; and the running variable is said to be manipulated in this case. In this paper, we show that while causal effects are not point identified under manipulation, they remain partially identified under a general model that covers a wide range of empirical patterns. We derive sharp bounds on causal parameters for both sharp and fuzzy designs under our general model, and show how additional structure can be used to further narrow the bounds. We use our methods to study the disincentive effect of unemployment insurance on (formal) reemployment in Brazil, and show that our bounds remain informative, despite the fact that manipulation has a sizable effect on our estimates of causal parameters.

Suggested Citation

Gerard, François and Rokkanen, Miikka and Rothe, Christoph, Bounds on Treatment Effects in Regression Discontinuity Designs with a Manipulated Running Variable (December 2016). NBER Working Paper No. w22892, Available at SSRN: https://ssrn.com/abstract=2880330

François Gerard (Contact Author)

Center for Operations Research and Econometrics (CORE) ( email )

34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium

Miikka Rokkanen

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Christoph Rothe

Columbia University ( email )

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