David Card, David S. Lee, Zhuan Pei and Andrea Weber
A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In…
Abstract
A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.
Otávio Bartalotti and Quentin Brummet
Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions…
Abstract
Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To fill this gap, we derive the properties of traditional local polynomial estimators in a fixed-
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The Personal Responsibility Work Opportunity and Reconciliation Act of 1996, better known as Welfare Reform, implemented, in addition to many other features, a 60-month lifetime…
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The Personal Responsibility Work Opportunity and Reconciliation Act of 1996, better known as Welfare Reform, implemented, in addition to many other features, a 60-month lifetime limit for welfare receipt. Research to date primarily documents individual-level barriers, characteristics, and outcomes of those who time out. Very little scholarly work considers experiences of mothering or carework after timing out. In this chapter, I ask, what kinds of carework strategies are used by women who have met their lifetime limits to welfare? What do the ways mothers talk about these strategies tell us about the discursive forces they are resisting and/or engaging? Using in-depth interviews at two points in time with women who have timed out of welfare (n = 32 and 23), this analysis shows how mothers’ strategies and the ways they discuss them reveal covert material and symbolic resistance to key discourses – negative assumptions about welfare mothers and a culture of work enforcement – and the conditions shaping their lives (Hollander & Einwohner, 2004). Mothers use carework strategies very similar to those identified in many other studies (e.g., London, Scott, Edin, & Hunter, 2004; Morgen, Acker, & Weigt, 2010; Scott, Edin, London, & Mazelis, 2001), but they provide us with an understanding of carework in a new context. The three groups of strategies explored here – structuring employment and non-employment, protecting children, and securing resources – reveal raced, classed, and gendered labor in which women engage to care for children in circumstances marked by limited employment opportunities and limited state support. The policy implications of mothers’ strategies are also discussed.
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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…
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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.
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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…
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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.
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Reidar J. Mykletun and Krista Himanen
The purpose of this paper is to examine the antecedents of volunteer commitment and intention to remain volunteering for the same event in the future, in the context of two…
Abstract
Purpose
The purpose of this paper is to examine the antecedents of volunteer commitment and intention to remain volunteering for the same event in the future, in the context of two annually held Norwegian cycling race events.
Design/methodology/approach
A cross-sectional design was used, applying a questionnaire that was developed and distributed to the cycling events volunteers both in hard copies and as online format by QuestBack.
Findings
The volunteers were motivated by egoistic, altruistic, connection to the sport, and external factors. They were highly committed and intended to remain as a volunteers in the future events. Older age; satisfaction from their own contribution and type of work, from recognition; and motivation as personal connections to the sport predicted commitment. Higher levels of education, commitment, and motivation by personal connections to the sport predicted intention to remain as a volunteer for future events. A factor-based structure of sport event volunteer satisfaction was presented, which, to the best of the knowledge is the first of its kind.
Research limitations/implications
The study should be replicated across several events to test the external validation of the models.
Practical implications
This understanding of motivation and satisfaction can be beneficial for the management of volunteers in order to retain the experienced and motivated volunteers and to ensure the continuation of the event in the future.
Originality/value
The study adds new knowledge to the research field by presenting an extensive, updated literature review, development of a fist factor-analysed scale for volunteer satisfaction, and the first application of the model including volunteer demographics, satisfaction, motivation, and commitment in predicting intention to remain volunteers for the biking event in the future.
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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.
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Matias D. Cattaneo and Max H. Farrell
This chapter studies the large sample properties of a subclassification-based estimator of the dose–response function under ignorability. Employing standard regularity conditions…
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This chapter studies the large sample properties of a subclassification-based estimator of the dose–response function under ignorability. Employing standard regularity conditions, it is shown that the estimator is root-n consistent, asymptotically linear, and semiparametric efficient in large samples. A consistent estimator of the standard-error is also developed under the same assumptions. In a Monte Carlo experiment, we investigate the finite sample performance of this simple and intuitive estimator and compare it to others commonly employed in the literature.