Search results

1 – 10 of over 1000
Content available
Book part
Publication date: 13 May 2017

Abstract

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Article
Publication date: 6 September 2016

Silvana Chambers

Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to…

Abstract

Purpose

Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about training interventions.

Design/methodology/approach

To demonstrate the key features of RD designs, a simulated data set was generated from actual pre-test and post-test diversity training scores of 276 participants from three organizations in the USA. Parametric and non-parametric analyses were conducted, and graphical presentations were produced.

Findings

This study found that RD design can be used for evaluating training interventions. The results of the simulated data set yielded statistically significant results for the treatment effects, showing a positive causal effect of the training intervention. The analyses found support for the use of RD models with retrospective training intervention data, eliminating ethical concerns from random group assignment. The results of the non-parametric model provided evidence of the plausibility of finding the right balance between precision of estimates and generalizable results, making it an alternative to experimental designs.

Practical implications

This study contributes to the HRD field by explicating the implementation of a sophisticated, statistical tool to strengthen causal claims, contributing to an evidence-based HRD approach to practice and providing the R syntax for replicating the analyses contained herein.

Originality/value

Despite the growing number of scholarly articles being published in HRD journals, very few have used experimental or quasi-experimental design approaches. Therefore, a very limited amount of research has been devoted to uncovering causal relationships.

Details

European Journal of Training and Development, vol. 40 no. 8/9
Type: Research Article
ISSN: 2046-9012

Keywords

Book part
Publication date: 13 May 2017

Zhuan Pei and Yi Shen

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known…

Abstract

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Keywords

Book part
Publication date: 13 May 2017

Jasjeet S. Sekhon and Rocío Titiunik

We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuity-based framework, where the…

Abstract

We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuity-based framework, where the conditional expectations of the potential outcomes are assumed to be continuous functions of the score at the cutoff, and the local randomization framework, where the treatment assignment is assumed to be as good as randomized in a neighborhood around the cutoff. Using various examples, we show that (i) assuming random assignment of the RD running variable in a neighborhood of the cutoff implies neither that the potential outcomes and the treatment are statistically independent, nor that the potential outcomes are unrelated to the running variable in this neighborhood; and (ii) assuming local independence between the potential outcomes and the treatment does not imply the exclusion restriction that the score affects the outcomes only through the treatment indicator. Our discussion highlights key distinctions between “locally randomized” RD designs and real experiments, including that statistical independence and random assignment are conceptually different in RD contexts, and that the RD treatment assignment rule places no restrictions on how the score and potential outcomes are related. Our findings imply that the methods for RD estimation, inference, and falsification used in practice will necessarily be different (both in formal properties and in interpretation) according to which of the two frameworks is invoked.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Keywords

Article
Publication date: 4 June 2021

Haiya Cai, Yongqing Nan, Yongliang Zhao and Haoran Xiao

The purpose of this study is to regard winter heating as a quasi-natural experiment to identify the possible causal effects of winter heating on population mobility. However…

Abstract

Purpose

The purpose of this study is to regard winter heating as a quasi-natural experiment to identify the possible causal effects of winter heating on population mobility. However, there are scant research studies examining the effect of atmospheric quality on population mobility. There also exists some relevant research studies on the relationship between population mobility and environmental degradation (Lu et al., 2018; Reis et al., 2018; Shen et al., 2018), and these studies exist still some deficiencies.

Design/methodology/approach

The notorious atmospheric quality problems caused by coal-fired heating in winter of northern China have an aroused widespread concern. However, the quantitative study on the effects on population mobility of winter heating is still rare. In this study, the authors regard the winter heating as a quasi-natural experiment, based on the of daily panel data of 58 cities of Tencent location Big Data in China from August 13 to December 30 in 2016 and August 16 to December 30 in 2017, and examine the impacts of winter heating on population mobility by utilizing a regression discontinuity method.

Findings

The findings are as follows, in general, winter heating significantly aggravates regional population mobility, but the impacts on population mobility among different cities are heterogeneous. Specifically, the effects of winter heating on population mobility is greater for cities with relatively good air quality, and the effects is also more obvious for big and medium-sized cities than that in small cities. In addition, different robustness tests, including continuity test, different bandwidth tests and alternative empirical model, are adopted to ensure the reliability of the conclusion. Finally, the authors put forward corresponding policy suggestions from the three dimensions of government, enterprises and residents.

Originality/value

First, regarding winter heating as a quasi-natural experiment, a regression discontinuity design method is introduced to investigate the relationship between winter heating and population mobility, which is helpful to avoid the estimation error caused by endogeneity. Second, the authors use the passenger travel “big data” based on the website of Tencent Location Big Data, which can effectively capture the daily characteristics of China's population mobility. Third, this study discusses the population mobility from the perspective of winter heating and researches population mobility before and after winter heating, which is helpful in enriching the research on population mobility.

Details

Kybernetes, vol. 51 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

Book part
Publication date: 13 May 2017

Luke Keele, Scott Lorch, Molly Passarella, Dylan Small and Rocío Titiunik

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a…

Abstract

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Keywords

Book part
Publication date: 13 May 2017

Otávio Bartalotti, Gray Calhoun and Yang He

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild…

Abstract

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Keywords

Book part
Publication date: 13 May 2017

Giovanni Cerulli, Yingying Dong, Arthur Lewbel and Alexander Poulsen

Regression discontinuity (RD) models are commonly used to nonparametrically identify and estimate a local average treatment effect. Dong and Lewbel (2015) show how a derivative of…

Abstract

Regression discontinuity (RD) models are commonly used to nonparametrically identify and estimate a local average treatment effect. Dong and Lewbel (2015) show how a derivative of this effect, called treatment effect derivative (TED) can be estimated. We argue here that TED should be employed in most RD applications, as a way to assess the stability and hence external validity of RD estimates. Closely related to TED, we define the complier probability derivative (CPD). Just as TED measures stability of the treatment effect, the CPD measures stability of the complier population in fuzzy designs. TED and CPD are numerically trivial to estimate. We provide relevant Stata code, and apply it to some real datasets.

Book part
Publication date: 13 May 2017

Brigham R. Frandsen

Conventional tests of the regression discontinuity design’s identifying restrictions can perform poorly when the running variable is discrete. This paper proposes a test for…

Abstract

Conventional tests of the regression discontinuity design’s identifying restrictions can perform poorly when the running variable is discrete. This paper proposes a test for manipulation of the running variable that is consistent when the running variable is discrete. The test exploits the fact that if the discrete running variable’s probability mass function satisfies a certain smoothness condition, then the observed frequency at the threshold has a known conditional distribution. The proposed test is applied to vote tally distributions in union representation elections and reveals evidence of manipulation in close elections that is in favor of employers when Republicans control the NLRB and in favor of unions otherwise.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Keywords

Book part
Publication date: 13 May 2017

Hugo Jales and Zhengfei Yu

This chapter reviews recent developments in the density discontinuity approach. It is well known that agents having perfect control of the forcing variable will invalidate the…

Abstract

This chapter reviews recent developments in the density discontinuity approach. It is well known that agents having perfect control of the forcing variable will invalidate the popular regression discontinuity designs (RDDs). To detect the manipulation of the forcing variable, McCrary (2008) developed a test based on the discontinuity in the density around the threshold. Recent papers have noted that the sorting patterns around the threshold are often either the researcher’s object of interest or may relate to structural parameters such as tax elasticities through known functions. This, in turn, implies that the behavior of the distribution around the threshold is not only informative of the validity of a standard RDD; it can also be used to recover policy-relevant parameters and perform counterfactual exercises.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

Keywords

1 – 10 of over 1000