scholarly journals Bounds on treatment effects in regression discontinuity designs with a manipulated running variable

2020 ◽  
Vol 11 (3) ◽  
pp. 839-870 ◽  
Author(s):  
François Gerard ◽  
Miikka Rokkanen ◽  
Christoph Rothe
Keyword(s):  
Unemployment Insurance ◽  
General Model ◽  
Regression Discontinuity ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Binary Decision ◽  
Sharp Bounds ◽  
Wide Range ◽  
Regression Discontinuity Designs ◽  
Disincentive Effect

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, one can derive sharp bounds under a general model that covers a wide range of empirical patterns. The extent of manipulation, which determines the width of the bounds, is inferred from the data in our setup. Our approach therefore does not require making a binary decision regarding whether manipulation occurs or not, and can be used to deliver manipulation‐robust inference in settings where manipulation is conceivable, but not obvious from the data. 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.

scholarly journals Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds

2018 ◽  
Vol 43 (5) ◽  
pp. 540-567 ◽  
Author(s):  
Jiannan Lu ◽  
Peng Ding ◽  
Tirthankar Dasgupta
Keyword(s):  
Causal Effect ◽  
Real Life ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Randomized Experiments ◽  
Sharp Bounds ◽  
Average Causal Effect ◽  
Behavioral Studies ◽  
And Control ◽  
Potential Outcomes Framework

Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the parameter of interest, is difficult to interpret for ordinal outcomes. To address this challenge, we propose to use two causal parameters, which are defined as the probabilities that the treatment is beneficial and strictly beneficial for the experimental units. However, although well-defined for any outcomes and of particular interest for ordinal outcomes, the two aforementioned parameters depend on the association between the potential outcomes and are therefore not identifiable from the observed data without additional assumptions. Echoing recent advances in the econometrics and biostatistics literature, we present the sharp bounds of the aforementioned causal parameters for ordinal outcomes, under fixed marginal distributions of the potential outcomes. Because the causal estimands and their corresponding sharp bounds are based on the potential outcomes themselves, the proposed framework can be flexibly incorporated into any chosen models of the potential outcomes and is directly applicable to randomized experiments, unconfounded observational studies, and randomized experiments with noncompliance. We illustrate our methodology via numerical examples and three real-life applications related to educational and behavioral research.

scholarly journals Universal Mail Ballot Delivery Boosts Turnout: The Causal Effects of Sending Mail Ballots to All Registered Voters

2021 ◽  
Author(s):  
R. Michael Alvarez ◽  
Yimeng Li
Keyword(s):  
Los Angeles ◽  
Voter Turnout ◽  
Los Angeles County ◽  
Regression Discontinuity ◽  
Causal Effects ◽  
Party Affiliation ◽  
American States ◽  
Regression Discontinuity Designs ◽  
Registered Voters ◽  
Legislative Districts

Some American states have transitioned to universal voting-by-mail, where all registered voters receive a ballot in the mail. While this practice was growing in popularity prior to the 2020 general election, universal voting-by-mail was suddenly used in a larger number of states due to the COVID-19 pandemic. In this paper, we utilize a unique situation in which registered voters in some legislative districts in Los Angeles County were subjected to universal voting-by-mail in the March 2020 primary, while most of the rest of the Los Angeles County electorate was not. Using difference-in-difference and regression discontinuity designs, we estimate the causal effects of universal voting-by-mail on voter turnout and on who votes. Our results indicate that voter turnout increased by around 3\% for voters who do not automatically receive a ballot in the mail otherwise, and the increase is larger for registered partisan voters than those without a party affiliation.

Regression discontinuity designs: a hands-on guide for practice

2021 ◽  
pp. 1-19
Author(s):  
Vicente Valentim ◽  
Ana Ruipérez Núñez ◽  
Elias Dinas
Keyword(s):  
Political Science ◽  
Applied Research ◽  
Regression Discontinuity ◽  
Causal Effects ◽  
Stepping Stone ◽  
Hands On ◽  
Regression Discontinuity Designs

Abstract Regression discontinuity (RD) designs have become increasingly popular in political science, due to their ability to showcase causal effects under weak assumptions. This paper provides an intuition-based guide for the use of the RD in applied research. After an intuitive explanation of how the method works, we provide a checklist that can help researchers understand the main robustness checks they should run, and a quick introduction to software implementing the design. We also provide a list of classic designs and examples of their application in political science. We hope this article can constitute a stepping stone from which researchers interested in RD can jump to more advanced literature; and which makes researchers not interested in implementing RDs better consumers of research employing this design.

scholarly journals Equivalence Testing for Regression Discontinuity Designs

2020 ◽  
pp. 1-17
Author(s):  
Erin Hartman
Keyword(s):  
Political Science ◽  
Current Practice ◽  
Regression Discontinuity ◽  
Regression Function ◽  
Superior Performance ◽  
Equivalence Testing ◽  
Simulation Studies ◽  
Equivalence Tests ◽  
Regression Discontinuity Designs ◽  
Testing Approach

Abstract Regression discontinuity (RD) designs are increasingly common in political science. They have many advantages, including a known and observable treatment assignment mechanism. The literature has emphasized the need for “falsification tests” and ways to assess the validity of the design. When implementing RD designs, researchers typically rely on two falsification tests, based on empirically testable implications of the identifying assumptions, to argue the design is credible. These tests, one for continuity in the regression function for a pretreatment covariate, and one for continuity in the density of the forcing variable, use a null of no difference in the parameter of interest at the discontinuity. Common practice can, incorrectly, conflate a failure to reject evidence of a flawed design with evidence that the design is credible. The well-known equivalence testing approach addresses these problems, but how to implement equivalence tests in the RD framework is not straightforward. This paper develops two equivalence tests tailored for RD designs that allow researchers to provide statistical evidence that the design is credible. Simulation studies show the superior performance of equivalence-based tests over tests-of-difference, as used in current practice. The tests are applied to the close elections RD data presented in Eggers et al. (2015b).

scholarly journals The Choice of Neighborhood in Regression Discontinuity Designs

2017 ◽  
Vol 3 (2) ◽  
pp. 134-146
Author(s):  
Matias D. Cattaneo ◽  
Gonzalo Vazquez-Bare
Keyword(s):  
Regression Discontinuity ◽  
Regression Discontinuity Designs

scholarly journals Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs

2020 ◽  
Vol 8 (1) ◽  
pp. 164-181
Author(s):  
Cristian Crespo
Keyword(s):  
Regression Discontinuity ◽  
Internal Validity ◽  
Treatment Assignment ◽  
Research Designs ◽  
Regression Discontinuity Designs ◽  
Administrative Procedures

Abstract This paper elaborates on administrative sorting, a threat to internal validity that has been overlooked in the regression discontinuity (RD) literature. Variation in treatment assignment near the threshold may still not be as good as random even when individuals are unable to precisely manipulate the running variable. This can be the case when administrative procedures, beyond individuals’ control and knowledge, affect their position near the threshold non-randomly. If administrative sorting is not recognized it can be mistaken as manipulation, preventing fixing the running variable and leading to discarding viable RD research designs.

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