scholarly journals Estimating slim-majority effects in US state legislatures with a regression discontinuity design under local randomization assumptions

2020 ◽  
pp. 1-10
Author(s):  
Leandro De Magalhães
Keyword(s):  
Finite Number ◽  
Regression Discontinuity ◽  
State Legislatures ◽  
State Level ◽  
Variable Number ◽  
Causal Effects ◽  
Regression Discontinuity Design ◽  
Randomized Experiments ◽  
Majority Party ◽  
Empirical Tests

Abstract Regression discontinuity design could be a valuable tool for identifying causal effects of a given party holding a legislative majority. However, the variable “number of seats” takes a finite number of values rather than a continuum and, hence, it is not suited as a running variable. Recent econometric advances suggest the necessary assumptions and empirical tests that allow us to interpret small intervals around the cut-off as local randomized experiments. These permit us to bypass the assumption that the running variable must be continuous. Herein, we implement these tests for US state legislatures and propose another: whether a slim-majority of one seat had at least one state-level district result that was itself a close race won by the majority party.

Subverting the Organizational Cartel: Explaining Cross-Party Leadership Selection in U.S. State Houses

2019 ◽  
Vol 48 (4) ◽  
pp. 475-483
Author(s):  
Matthew N. Green
Keyword(s):  
House Of Representatives ◽  
State Legislatures ◽  
State Level ◽  
Original Data ◽  
Data Set ◽  
Leadership Selection ◽  
Majority Party ◽  
Party Ideology ◽  
Level I ◽  
Selection Of

In the U.S. House of Representatives, the majority party constitutes an organizational cartel that monopolizes the selection of chamber leaders. But in state legislatures, that cartel power is sometimes circumvented by a bipartisan bloc that outvotes the leadership preferences of a majority of the majority party. Drawing from an original data set of instances of cross-party organizational coalitions at the state level, I use statistical analysis to test various hypotheses for when these coalitions are more likely to form. The analysis reveals that party ideology does not adequately explain the violation of these cartels; rather, violations depend on the costs associated with keeping the party unified and the benefits that come from selecting the chamber’s top leadership post. This finding underscores the potential vulnerability of organizational cartels and suggests that governing parties are strategic when deciding how fiercely to defend their cartel power.

scholarly journals Effects of Peers and Rank on Cognition, Preferences, and Personality

2020 ◽  
pp. 1-47
Author(s):  
Utteeyo Dasgupta ◽  
Subha Mani ◽  
Smriti Sharma ◽  
Saurabh Singhal
Keyword(s):  
Big Five ◽  
Regression Discontinuity ◽  
Big Five Personality Traits ◽  
Causal Effects ◽  
Big Five Personality ◽  
Risk Averse ◽  
Regression Discontinuity Design ◽  
Selective College ◽  
Selective Colleges ◽  
Economic Preferences

We exploit the variation in admission cutoffs across colleges at a leading Indian university to estimate the causal effects of enrolling in a selective college on cognitive attainment, economic preferences, and Big Five personality traits. Using a regression discontinuity design, we find that enrolling in a selective college improves university exam scores of the marginally admitted females, and makes them less overconfident and less risk averse, while males in selective colleges experience a decline in extraversion and conscientiousness. We find differences in peer quality and rank concerns to be driving our findings.

scholarly journals Evaluating Econometric Evaluations of Post-Secondary Aid

2015 ◽  
Vol 105 (5) ◽  
pp. 502-507 ◽  
Author(s):  
Josh Angrist ◽  
David Autor ◽  
Sally Hudson ◽  
Amanda Pallais
Keyword(s):  
Financial Aid ◽  
Regression Discontinuity ◽  
Causal Effect ◽  
Causal Effects ◽  
Regression Discontinuity Design ◽  
Random Assignment ◽  
Post Secondary ◽  
Design Selection ◽  
Ongoing Evaluation

In an ongoing evaluation of post-secondary financial aid, we use random assignment to assess the causal effects of large privately-funded aid awards. Here, we compare the unbiased causal effect estimates from our RCT with two types of non-experimental econometric estimates. The first applies a selection-on-observables assumption in data from an earlier, nonrandomized cohort; the second uses a regression discontinuity design. Selection-on-observables methods generate estimates well below the experimental benchmark. Regression discontinuity estimates are similar to experimental estimates for students near the cutoff, but sensitive to controlling for the running variable, which is unusually coarse.

A Regression Discontinuity Design for Studying Divided Government

2020 ◽  
Vol 20 (3) ◽  
pp. 356-389
Author(s):  
Patricia A. Kirkland ◽  
Justin H. Phillips
Keyword(s):  
Real World ◽  
Regression Discontinuity ◽  
Causal Analysis ◽  
Causal Effects ◽  
Divided Government ◽  
Regression Discontinuity Design ◽  
Budget Deficits ◽  
Regression Techniques ◽  
Two Measures ◽  
Election Results

The regression discontinuity design (RDD) is a valuable tool for identifying causal effects with observational data. However, applying the traditional electoral RDD to the study of divided government is challenging. Because assignment to treatment in this case is the result of elections to multiple institutions, there is no obvious single forcing variable. Here, we use simulations in which we apply shocks to real-world election results in order to generate two measures of the likelihood of divided government, both of which can be used for causal analysis. The first captures the electoral distance to divided government and can easily be utilized in conjunction with the standard sharp RDD toolkit. The second is a simulated probability of divided government. This measure does not easily fit into a sharp RDD framework, so we develop a probability restricted design (PRD) which relies upon the underlying logic of an RDD. This design incorporates common regression techniques but limits the sample to those observations for which assignment to treatment approaches “as-if random.” To illustrate both of our approaches, we reevaluate the link between divided government and the size of budget deficits.

Randomized Controlled Studies and Alternative Designs in Outcome Studies

2011 ◽  
Vol 21 (6) ◽  
pp. 636-643 ◽  
Author(s):  
William R. Shadish
Keyword(s):  
Statistical Power ◽  
Regression Discontinuity ◽  
Good Reason ◽  
Regression Discontinuity Design ◽  
Randomized Experiments ◽  
Control Groups ◽  
Reliable Measurement ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
Alternative Designs

This article reviews several decades of the author’s meta-analytic and experimental research on the conditions under which nonrandomized experiments can approximate the results from randomized experiments (REs). Several studies make clear that we can expect accurate effect estimates from the regression discontinuity design, though its statistical power is lower, it estimates a different parameter than the RE, and its analysis is considerably more complex. For other nonrandomized designs, the picture is more complex. They may yield accurate estimates if they are prospectively designed to include comprehensive and reliable measurement of the process by which participants selected into conditions, if they use large sample sizes, and if they carefully select control groups that are from the same location and with the same substantive characteristics. By contrast, we have little good reason to think that nonrandomized experiments using archival data without comprehensive selection measures are likely to yield accurate effect estimates.

scholarly journals Methods to Estimate Causal Effects - An Overview on IV, DiD and RDD and a Guide on How to Apply them in Practice

2021 ◽  
Author(s):  
Matthias Collischon
Keyword(s):  
Social Sciences ◽  
Instrumental Variable ◽  
Regression Discontinuity ◽  
Estimation Method ◽  
Causal Effects ◽  
Small Scale ◽  
Regression Discontinuity Design ◽  
Difference In Differences ◽  
Estimation Techniques ◽  
Online Appendix

The identification of causal effects has gained increasing attention in social sciences over the last years and this trend also has found its way into sociology, albeit on a relatively small scale. This article provides an overview of three methods to identify causal effects that are rarely used in sociology: instrumental variable (IV) regression, difference-in-differences (DiD), and regression discontinuity design (RDD). I provide intuitive introductions to these methods, discuss identifying assumptions, limitations of the methods, promising extension, and present an exemplary study for each estimation method that can serve as a benchmark when applying these estimation techniques. Furthermore, the online appendix to this article contains Stata syntax that simulates data and shows how to apply these techniques in practice.

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