Can Non-randomized Studies Provide Evidence of Causal Effects? A Case Study Using the Regression Discontinuity Design

2009 ◽  
pp. 115-134
Keyword(s):  
Regression Discontinuity ◽  
Causal Effects ◽  
Regression Discontinuity Design ◽  
Randomized Studies

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.

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.

Does a farmland zoning program impact farm income: empirical evidence from farm households in Taiwan

2020 ◽  
Vol 47 (5) ◽  
pp. 1621-1643
Author(s):  
Hung-Hao Chang ◽  
Tzu-Chin Lin
Keyword(s):  
Income Distribution ◽  
Regression Discontinuity ◽  
Wealth Effect ◽  
Farm Households ◽  
Farm Income ◽  
Regression Discontinuity Design ◽  
Program Impact ◽  
Farm Operators ◽  
On Farm

Abstract Does farmland zoning affect farm income, and why? This study addresses these questions using a case study in Taiwan. We use a unique farmland-level dataset and apply the regression discontinuity design to quantify the effects of zoning on farm income. We find that the zoning program decreases farm income. The programme effects are heterogeneous, as they are more pronounced for farms in the higher percentiles of the farm income distribution. Moreover, a larger effect is found for elderly farm operators. Concerning the mechanism, we argue that the zoning program generated an optional benefit or wealth effect for eligible farms. This wealth effect then reallocates family labour to off-farm jobs. Consequently, the zoning program reduces income from farming.

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.

Maximum Likelihood Analysis for a Generalized Regession-Discontinuity Design

1984 ◽  
Vol 9 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Ronald A. Visser ◽  
Jan De Leeuw
Keyword(s):  
Maximum Likelihood ◽  
Regression Discontinuity ◽  
Bivariate Distribution ◽  
Causal Effects ◽  
Regression Discontinuity Design ◽  
Maximum Likelihood Analysis ◽  
Design Data ◽  
Likelihood Analysis ◽  
Quasi Experimental ◽  
Tests Of Hypotheses

The regression-discontinuity design (RDD) offers the possibility of making inferences about causal effects from observations on selected groups. The quasi-experimental groups are formed by dividing the scores of a premeasurement in two halves. The treatment effect is inferred from the differences between the regression of a postmeasurement on the premeasurement for the two groups. We discuss a generalized form of this design: (a) Apart from parallel shift of the regression lines, differences in variance and covariance are considered; (b) pretest and posttest may be multivariate; and (c) more than two groups may be involved in the design. Data from such a design are considered to have a truncated bivariate distribution. For the RDD, maximum likelihood parameter estimation procedures and tests of hypotheses are presented.

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