Showing papers in "Political Analysis in 2017"
TL;DR: In this article, the synthetic control method is combined with linear fixed effects models for causal inference in time-series cross-sectional data, and a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients is proposed.
Abstract: Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.
360 citations
TL;DR: It is shown how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects and how pooling models lead to superior performance to individual methods across diverse problems.
Abstract: Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.
134 citations
TL;DR: The authors analyzed the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which MEPs have reacted to external and internal stimuli when making plenary speeches.
Abstract: This study analyzes the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making plenary speeches. To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF). This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999 to 2014. Our findings suggest that two-layer NMF is a valuable alternative to existing dynamic topic modeling approaches found in the literature, and can unveil niche topics and associated vocabularies not captured by existing methods. Substantively, our findings suggest that the political agenda of the EP evolves significantly over time and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro Crisis. MEP contributions to the plenary agenda are also found to be impacted upon by voting behavior and the committee structure of the Parliament.
128 citations
TL;DR: In this paper, the authors provide step-by-step guidelines for explicit Bayesian process tracing, calling attention to technical points that have been overlooked or inadequately addressed and illustrate how to apply this approach with the first systematic application to a case study that draws on multiple pieces of detailed evidence.
Abstract: Bayesian probability holds the potential to serve as an important bridge between qualitative and quantitative methodology. Yet whereas Bayesian statistical techniques have been successfully elaborated for quantitative research, applying Bayesian probability to qualitative research remains an open frontier. This paper advances the burgeoning literature on Bayesian process tracing by drawing on expositions of Bayesian “probability as extended logic” from the physical sciences, where probabilities represent rational degrees of belief in propositions given the inevitably limited information we possess. We provide step-by-step guidelines for explicit Bayesian process tracing, calling attention to technical points that have been overlooked or inadequately addressed, and we illustrate how to apply this approach with the first systematic application to a case study that draws on multiple pieces of detailed evidence. While we caution that efforts to explicitly apply Bayesian learning in qualitative social science will inevitably run up against the difficulty that probabilities cannot be unambiguously specified, we nevertheless envision important roles for explicit Bayesian analysis in pinpointing the locus of contention when scholars disagree on inferences, and in training intuition to follow Bayesian probability more systematically.
81 citations
TL;DR: In this article, the authors use results from an audit study with a large number of aliases and data from detailed public records to empirically test the excludability assumption undergirding the use of racially distinctive names.
Abstract: Researchers studying discrimination and bias frequently conduct experiments that use racially distinctive names to signal race. The ability of these experiments to speak to racial discrimination depends on the excludability assumption that subjects’ responses to these names are driven by their reaction to the individual’s putative race and not some other factor. We use results from an audit study with a large number of aliases and data from detailed public records to empirically test the excludability assumption undergirding the use of racially distinctive names. The detailed public records allow us to measure the signals about socioeconomic status and political resources that each name used in the study possibly could send. We then reanalyze the audit study to see whether these signals predict legislators’ likelihood of responding. We find no evidence that politicians respond to this other information, thus providing empirical support for the excludability assumption.
60 citations
TL;DR: A Bayesian method, LASSOplus, is introduced that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility.
Abstract: We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects while returning estimated confidence intervals for discovered effects. Furthermore, we show how LASSOplus easily extends to modeling repeated observations and permits a simple Bonferroni correction to control coverage on confidence intervals among discovered effects. We situate LASSOplus in the literature on how to estimate subgroup effects, a topic that often leads to a proliferation of estimation parameters. We also offer a simple preprocessing step that draws on recent theoretical work to estimate higher-order effects that can be interpreted independently of their lower-order terms. A simulation study illustrates the method’s performance relative to several existing variable selection methods. In addition, we apply LASSOplus to an existing study on public support for climate treaties to illustrate the method’s ability to discover substantive and relevant effects. Software implementing the method is publicly available in the R package sparsereg.
51 citations
TL;DR: The authors study a case of catastrophic flooding in the American South in 1927, in which disaster aid was broadly and fairly distributed and Herbert Hoover (the 1928 Republican presidential candidate) was personally responsible for overseeing the relief efforts.
Abstract: Do natural disasters help or hurt politicians’ electoral fortunes? Research on this question has produced conflicting results. Achen and Bartels (2002, 2016) find that voters punish incumbent politicians indiscriminately after such disasters. Other studies find that voters incorporate the quality of relief efforts by elected officials. We argue that results in this literature may be driven, in part, by a focus on contemporary cases of disaster and relief. In contrast, we study a case of catastrophic flooding in the American South in 1927, in which disaster aid was broadly and fairly distributed and Herbert Hoover (the 1928 Republican presidential candidate) was personally responsible for overseeing the relief efforts. Despite the distribution of unprecedented levels of disaster aid, we find that voters punished Hoover at the polls: in affected counties, Hoover’s vote share decreased by more than 10 percentage points. Our results are robust to the use of synthetic control methods and suggest that—even if voters distinguish between low- and high-quality responses—the aggregate effect of this disaster remains broadly negative. Our findings provide some support for Achen and Bartels’ idea of blind retrospection, but also generate questions about the precise mechanisms by which damage and relief affect vote choice.
51 citations
TL;DR: The central benefits of predictive modeling are reviewed from a perspective uncommon in the existing literature: it is focused on how predictive modeling can be used to complement and augment standard associational analyses.
Abstract: The large majority of inferences drawn in empirical political research follow from model-based associations (e.g., regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model’s parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and augment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict.
49 citations
TL;DR: The Earth Mover's Distance (EMD) as mentioned in this paper measures congruence between masses and elites on a single dimension, and has been applied to two debates in research on mass-elite con-gruence.
Abstract: Scholars of representation are increasingly interested in mass–elite congruence—the degree to which the preferences of elected elites mirror those of voters. Yet existing measures of congruence can be misleading because they ignore information in the data, require arbitrary decisions about quantization, and limit researchers to comparing masses and elites on a single dimension. We introduce a new measure of congruence—borrowed from computer science—that addresses all of these problems: the Earth Mover’s Distance (EMD). We demonstrate its conceptual advantages and apply it to two debates in research on mass–elite congruence: ideological congruence in majoritarian and proportional systems and the determinants of congruence across countries in Latin America. We find that improving measurement using the EMD has important implications for inferences regarding both empirical debates. Even beyond studies of congruence, the EMD is a useful and reliable way for political scientists to compare distributions.
47 citations
TL;DR: In this paper, the authors extend the scaling methodology previously used in Bonica (2014) to jointly scale the American federal judiciary and legal profession in a common space with other political actors.
Abstract: We extend the scaling methodology previously used in Bonica (2014) to jointly scale the American federal judiciary and legal profession in a common space with other political actors. The end result is the first dataset of consistently measured ideological scores across all tiers of the federal judiciary and the legal profession, including 840 federal judges and 380,307 attorneys. To illustrate these measures, we present two examples involving the U.S. Supreme Court. These data open up significant areas of scholarly inquiry.
36 citations
TL;DR: The relationships among rivals (RAR) framework as discussed by the authors is a tripartite solution to the problem of dealing with mutually exclusive explanations in social science research, which is rare in qualitative research.
Abstract: Methodologists and substantive scholars alike agree that one of process tracing’s foremost contributions to qualitative research is its capacity to adjudicate among competing explanations of a phenomenon. Existing approaches, however, only provide explicit guidance on dealing with mutually exclusive explanations, which are exceedingly rare in social science research. I develop a tripartite solution to this problem. The Relationships among Rivals (RAR) framework (1) introduces a typology of relationships between alternative hypotheses, (2) develops specific guidelines for identifying which relationship is present between two hypotheses, and (3) maps out the varied implications for evidence collection and inference. I then integrate the RAR framework into each of the main process-tracing approaches and demonstrate how it affects the inferential process. Finally, I illustrate the purchase of the RAR framework by reanalyzing a seminal example of process-tracing research: Schultz’s (2001) analysis of the Fashoda Crisis. I show that the same evidence can yield new and sometimes contradictory inferences once scholars approach comparative hypothesis testing with this more nuanced framework.
TL;DR: In this article, the authors present a comparative validation design that is able to detect false positives without the need for an individual-level validation criterion, which is often unavailable, and show that the most widely used crosswise-model implementation produces false positives to a non-ignorable extent.
Abstract: Validly measuring sensitive issues such as norm violations or stigmatizing traits through self-reports in surveys is often problematic. Special techniques for sensitive questions like the Randomized Response Technique (RRT) and, among its variants, the recent crosswise model should generate more honest answers by providing full response privacy. Different types of validation studies have examined whether these techniques actually improve data validity, with varying results. Yet, most of these studies did not consider the possibility of false positives, i.e. that respondents are misclassified as having a sensitive trait even though they actually do not. Assuming that respondents only falsely deny but never falsely admit possessing a sensitive trait, higher prevalence estimates have typically been interpreted as more valid estimates. If false positives occur, however, conclusions drawn under this assumption might be misleading. We present a comparative validation design that is able to detect false positives without the need for an individual-level validation criterion – which is often unavailable. Results show that the most widely used crosswise-model implementation produced false positives to a non-ignorable extent. This defect was not revealed by several previous validation studies that did not consider false positives - apparently a blind spot in past sensitive question research.
TL;DR: The authors examined the properties of six recent measures of candidates' political orientations in different domains and found that these measures are capturing domain-specific factors rather than just candidates' ideology, and that they do poorly at distinguishing between moderate and extreme roll call voting records within each party.
Abstract: Over the past decade, a number of new measures have been developed that attempt to capture the political orientation of both incumbent and nonincumbent candidates for Congress, as well as other offices, on the same scale. These measures pose the possibility of being able to answer a host of fundamental questions about political accountability and representation. In this paper, we examine the properties of six recent measures of candidates’ political orientations in different domains. While these measures are commonly viewed as proxies for ideology, each involves very different choices, incentives, and contexts. Indeed, we show that there is only a weak relationship between these measures within party. This suggests that these measures are capturing domain-specific factors rather than just candidates’ ideology. Moreover, these measures do poorly at distinguishing between moderate and extreme roll call voting records within each party. As a result, they fall short when it comes to facilitating empirical analysis of theories of accountability and representation in Congress. Overall, our findings suggest that future research should leverage the conceptual and empirical variation across these measures and avoid assuming they are synonymous with candidates’ ideology.
TL;DR: A novel method to detect election fraud from irregular patterns in the distribution of vote-shares is developed and a resampled kernel density method (RKD) is proposed to measure whether the coarse vote-Shares occur too frequently to raise a statistically qualified suspicion of fraud.
Abstract: I develop a novel method to detect election fraud from irregular patterns in the distribution of vote-shares. I build on a widely discussed observation that in some elections where fraud allegations abound, suspiciously many polling stations return coarse vote-shares (e.g., 0.50, 0.60, 0.75) for the ruling party, which seems highly implausible in large electorates. Using analytical results and simulations, I show that sheer frequency of such coarse vote-shares is entirely plausible due to simple numeric laws and does not by itself constitute evidence of fraud. To avoid false positive errors in fraud detection, I propose a resampled kernel density method (RKD) to measure whether the coarse vote-shares occur too frequently to raise a statistically qualified suspicion of fraud. I illustrate the method on election data from Russia and Canada as well as simulated data. A software package is provided for an easy implementation of the method.
TL;DR: In this article, a statistical procedure to carry out empirical research that combines recent insights about preanalysis plans (PAPs) and replication is discussed, where the authors send their datasets to an independent third party who randomly generates training and testing samples.
Abstract: We discuss a statistical procedure to carry out empirical research that combines recent insights about preanalysis plans (PAPs) and replication Researchers send their datasets to an independent third party who randomly generates training and testing samples Researchers perform their analysis on the training sample and are able to incorporate feedback from both colleagues, editors, and referees Once the paper is accepted for publication the method is applied to the testing sample and it is those results that are published Simulations indicate that, under empirically relevant settings, the proposed method delivers more power than a PAP The effect mostly operates through a lower likelihood that relevant hypotheses are left untested The method appears better suited for exploratory analyses where there is significant uncertainty about the outcomes of interest We do not recommend using the method in situations where the treatment are very costly and thus the available sample size is limited An interpretation of the method is that it allows researchers to perform direct replication of their work We also discuss a number of practical issues about the method’s feasibility and implementation
TL;DR: In this paper, the authors proposed a novel procedure to sample different parties over time and space by utilizing the advertising option of the social media webpage Facebook, which allows for quotas and the collection of large samples at relatively low cost, and improves the representativeness through poststratification and subsample robustness checks.
Abstract: Parties and social movements play an important role in many theories of political science. Yet, the study of intraparty politics remains underdeveloped as random samples are difficult to conduct among political activists. This paper proposes a novel procedure to sample different parties over time and space by utilizing the advertising option of the social media webpage Facebook. As this method allows for quotas and the collection of large samples at relatively low cost, it becomes possible to improve the representativeness through poststratification and subsample robustness checks. Three examples illustrate these advantages of Facebook sampling: First, a Facebook sample approximated intraparty decisions and the outcome of a leadership contest of the Alternative for Germany. Second, a weighted Facebook sample achieved similar estimates as a representative local leader survey of the Social Democratic Party of Germany. Third, by evaluating subgroups of key demographics for parties with unknown population parameters, two Facebook samples show that the color-coded conflict in Thailand was driven by different concepts of regime type, but not by a left–right divide on economic policy-making. Facebook sampling appears to be the best and cheapest method to conduct time-series cross-sectional studies for political activists.
Journal Article•
TL;DR: Keenovoj knjizi naglasak se stavlja na krizu iz 2008. godine as mentioned in this paper, prvu iznimno razornu krizama i ciklusima inherentno obilježje kapitalističke ekonomije, nije prikladna za objašnjenje nastanka kriza.
Abstract: Knjiga autora Stevea Keena Can we avoid another financial crisis? obrađuje tematiku nastanka kriza u tržišnim ekonomijama. Kako bi objasnio mehanizam nastanka kriza, Keen kritizira temeljne postulate mainstream neoklasične ekonomske teorije, pritom se oslanjajući na teoriju koju je razvio američki ekonomist Hyman Minsky (1919.-1996.). Temeljni Keenov argument na kojem počiva ova knjiga jest da se smislena makroekonomska teorija ne može izgraditi na postavkama neoklasične mikroekonomije koja u fokus stavlja ponašanje pojedinačnog aktera usmjereno prema maksimizaciji koristi ili profita. Navedeno shvaćanje makroekonomije koje je utemeljeno na mikroekonomskim postavkama ne uzima u obzir važnost financijskog sektora i novca, ili tim faktorima ne pridaje mnogo značaja. Upravo zbog takvog simplističkog shvaćanja makroekonomije, neoklasična teorija, prema Keenu, nije prikladna za objašnjenje nastanka kriza. Kao alternativu takvom pristupu, Keen predlaže pomak prema teoriji kompleksnih sustava koja svoju eksplanatornu snagu ne temelji na proučavanju pojedinih entiteta, već na proučavanju interakcija među entitetima. U Keenovoj knjizi naglasak se stavlja na krizu iz 2008. godine – prvu iznimno razornu krizu koja je pogodila nacionalno gospodarstvo Sjedinjenih Američkih Država u razdoblju nakon kraja Drugoga svjetskog rata. Keenovo objašnjenje nastanka kriza temelji se na hipotezi financijske nestabilnosti koju je razvio već spomenuti Minsky. Osnovna ideja njegove teorije jest da je sklonost krizama i ciklusima inherentno obilježje kapitalističke ekonomije. Sama priroda kapitalizma, naime, navodi aktere na optimistično djelovanje te na preuzimanje rizika, što rezultira inovacijama. Pojava inovacija nužno podrazumijeva određenu
TL;DR: In this article, four methodological practices currently uncommon in such experiments have previously undocumented complementarities that can dramatically relax these constraints when at least two are used in combination: online surveys recruited from a defined sampling frame, with at least one baseline wave prior to treatment, with multiple items combined into an index to measure outcomes and, when possible, a placebo control.
Abstract: There is increasing interest in experiments where outcomes are measured by surveys and treatments are delivered by a separate mechanism in the real world, such as by mailers, door-to-door canvasses, phone calls, or online ads. However, common designs for such experiments are often prohibitively expensive, vulnerable to bias, and raise ethical concerns. We show how four methodological practices currently uncommon in such experiments have previously undocumented complementarities that can dramatically relax these constraints when at least two are used in combination: (1) online surveys recruited from a defined sampling frame (2) with at least one baseline wave prior to treatment (3) with multiple items combined into an index to measure outcomes and, (4) when possible, a placebo control. We provide a general and extensible framework that allows researchers to determine the most efficient mix of these practices in diverse applications. Two studies then examine how these practices perform empirically. First, we examine the representativeness of online panel respondents recruited from a defined sampling frame and find that their representativeness compares favorably to phone panel respondents. Second, an original experiment successfully implements all four practices in the context of a door-to-door canvassing experiment. We conclude discussing potential extensions.
TL;DR: The authors developed a method to model whether respondents provide one response to a sensitive item in a list experiment, but answer otherwise when asked to reveal that belief openly in response to the direct question.
Abstract: What explains why some survey respondents answer truthfully to a sensitive survey question, while others do not? This question is central to our understanding of a wide variety of attitudes, beliefs, and behaviors, but has remained difficult to investigate empirically due to the inherent problem of distinguishing those who are telling the truth from those who are misreporting. This article proposes a solution to this problem. It develops a method to model, within a multivariate regression context, whether survey respondents provide one response to a sensitive item in a list experiment, but answer otherwise when asked to reveal that belief openly in response to a direct question. As an empirical application, the method is applied to an original large-scale list experiment to investigate whether those on the ideological left are more likely to misreport their responses to questions about prejudice than those on the right. The method is implemented for researchers as open-source software.
TL;DR: In this article, two policy and office motivated parties compete in an infinite sequence of elections and propose two novel measures to describe the degree of conflict among agents: antagonism is the disagreement between parties; extremism is the disagreements between each party and the representative voter.
Abstract: How does political polarization affect the welfare of the electorate? We analyze this question using a framework in which two policy and office motivated parties compete in an infinite sequence of elections. We propose two novel measures to describe the degree of conflict among agents: antagonism is the disagreement between parties; extremism is the disagreement between each party and the representative voter. These two measures do not coincide when parties care about multiple issues. We show that forward-looking parties have an incentive to implement policies favored by the representative voter, in an attempt to constrain future challengers. This incentive grows as antagonism increases. On the other hand, extremism decreases the electorate’s welfare. We discuss the methodological and empirical implications for the existing measures of political actors’ ideal points and for the debate on elite polarization.
TL;DR: It is shown that a small set of syntactic patterns can extract clauses and identify quotes with good accuracy, significantly outperforming a baseline system based on word order.
Abstract: This article presents a new method and open source R package that uses syntactic information to automatically extract source–subject–predicate clauses. This improves on frequency-based text analysis methods by dividing text into predicates with an identified subject and optional source, extracting the statements and actions of (political) actors as mentioned in the text. The content of these predicates can be analyzed using existing frequency-based methods, allowing for the analysis of actions, issue positions and framing by different actors within a single text. We show that a small set of syntactic patterns can extract clauses and identify quotes with good accuracy, significantly outperforming a baseline system based on word order. Taking the 2008–2009 Gaza war as an example, we further show how corpus comparison and semantic network analysis applied to the results of the clause analysis can show differences in citation and framing patterns between U.S. and English-language Chinese coverage of this war.
TL;DR: A novel maximum likelihood estimator is proposed that corrects for misclassification in data arising from multiple sources of media-based event data and regularly outperforms current strategies that either neglect mis classification, the unique features of the data-generating process, or both.
Abstract: Media-based event data-i.e., data comprised from reporting by media outlets-are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreported by these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly out performs current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.
TL;DR: The authors used randomization inference with historical weather patterns from 73 years as potential randomizations to estimate the variance of the effect of rainfall on voter turnout in presidential elections in the United States, and compared the estimated average treatment effect to a sampling distribution of estimates under the sharp null hypothesis of no effect.
Abstract: Many recent papers in political science and economics use rainfall as a strategy to facilitate causal inference. Rainfall shocks are as-if randomly assigned, but the assignment of rainfall by county is highly correlated across space. Since clustered assignment does not occur within well-defined boundaries, it is challenging to estimate the variance of the effect of rainfall on political outcomes. I propose using randomization inference with historical weather patterns from 73 years as potential randomizations. I replicate the influential work on rainfall and voter turnout in presidential elections in the United States by Gomez, Hansford, and Krause (2007) and compare the estimated average treatment effect (ATE) to a sampling distribution of estimates under the sharp null hypothesis of no effect. The alternate randomizations are random draws from national rainfall patterns on election and would-be election days, which preserve the clustering in treatment assignment and eliminate the need to simulate weather patterns or make assumptions about unit boundaries for clustering. I find that the effect of rainfall on turnout is subject to greater sampling variability than previously estimated using conventional standard errors.
TL;DR: The authors show that while incumbent party candidates are more likely to win close House elections, those who win are no different on observable characteristics from those who lose, and all differences in observable characteristics between barely winning Democrats and barely winning Republicans vanish conditional on which party is the incumbent.
Abstract: An influential paper by Caughey and Sekhon (2011a) suggests that the outcomes of very close US House elections in the postwar era may not be as-if random, thus calling into question this application of regression discontinuity for causal inference. We show that while incumbent party candidates are more likely to win close House elections, those who win are no different on observable characteristics from those who lose. Further, all differences in observable characteristics between barely winning Democrats and barely winning Republicans vanish conditional on which party is the incumbent. Any source of a special incumbent party advantage in close elections must be due to variables that cannot be observed. This finding supports the conclusion of Eggers et al. (2015) that Caughey and Sekhon’s discovery of lopsided wins by incumbents in close races is a mere statistical fluke.
TL;DR: In this article, the authors investigate the effect of covariates on the hazard rate and find that the majority of studies that begin with time-invariant covariates and correct for nonproportional hazards suffer from incorrect model specification.
Abstract: Parametric and nonparametric duration models assume proportional hazards: The effect of a covariate on the hazard rate stays constant over time. Researchers have developed techniques to test and correct nonproportional hazards, including interacting the covariates with some function of time. Including this interaction term means that the specification now involves time-varying covariates, and the model specification should reflect this feature. However, in situations with no time-varying covariates initially, researchers often continue to model the duration with only time-invariant covariates. This error results in biased estimates, particularly for the covariates interacted with time. We investigate this issue in over forty political science articles and find that of those studies that begin with time-invariant covariates and correct for nonproportional hazards the majority suffer from incorrect model specification. Proper estimation usually produces substantively or statistically different results.
TL;DR: In this article, the authors present a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them, including set-theoretic and large-N approaches.
Abstract: This article presents a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them. Despite the existence of a litany of theories that posit asymmetric hypotheses, most empirical studies fail to capture their core insight: boundaries separating zones of data from areas that lack data are substantively interesting. We discuss existing set-theoretic and large-N approaches to the study of asymmetric hypotheses, introduce new ones from the literatures on stochastic frontier and data envelopment analysis, evaluate their relative merits, and give three examples of how asymmetric hypotheses can be studied with this suite of tools.
TL;DR: A method to measure influence over time accurately from sampled network data by ranking individuals by the sum of their connections’ connections—neighbor cumulative indegree centrality—preserves the rank influence ordering that would be achieved in the presence of complete network data, lowering the barrier to measuring influence accurately.
Abstract: How do individuals’ influence in a large social network change? Social scientists have difficulty answering this question because measuring influence requires frequent observations of a population of individuals’ connections to each other, while sampling that social network removes information in a way that can bias inferences. This paper introduces a method to measure influence over time accurately from sampled network data. Ranking individuals by the sum of their connections’ connections—neighbor cumulative indegree centrality—preserves the rank influence ordering that would be achieved in the presence of complete network data, lowering the barrier to measuring influence accurately. The paper then shows how to measure that variable changes each day, making it possible to analyze when and why an individual’s influence in a network changes. This method is demonstrated and validated on 21 Twitter accounts in Bahrain and Egypt from early 2011. The paper then discusses how to use the method in domains such as voter mobilization and marketing.
TL;DR: Geo-nested analysis as discussed by the authors is a framework for mixed-methods research with spatially dependent data, where insights gleaned at each step of the research process set the agenda for the next phase and where case selection for SNA is based on diagnostics of a spatial-econometric analysis.
Abstract: Mixed-methods designs, especially those where cases selected for small-N analysis (SNA) are nested within a large-N analysis (LNA), have become increasingly popular Yet, since the LNA in this approach assumes that units are independently distributed, such designs are unable to account for spatial dependence, and dependence becomes a threat to inference, rather than an issue for empirical or theoretical investigation This is unfortunate, since research in political science has recently drawn attention to diffusion and interconnectedness more broadly In this paper we develop a framework for mixed-methods research with spatially dependent data—a framework we label “geo-nested analysis”—where insights gleaned at each step of the research process set the agenda for the next phase and where case selection for SNA is based on diagnostics of a spatial-econometric analysis We illustrate our framework using data from a seminal study of homicides in the United States
TL;DR: The authors characterize the transformation-induced bias and calculate an approximation, illustrate its importance with two simulation studies, and discuss its relevance to methodological research, concluding that unbiased coefficient estimates are neither necessary nor sufficient for unbiased estimates of the quantities of interest.
Abstract: Political scientists commonly focus on quantities of interest computed from model coefficients rather than on the coefficients themselves. However, the quantities of interest, such as predicted probabilities, first differences, and marginal effects, do not necessarily inherit the small-sample properties of the coefficient estimates. Indeed, unbiased coefficient estimates are neither necessary nor sufficient for unbiased estimates of the quantities of interest. I characterize this transformation-induced bias, calculate an approximation, illustrate its importance with two simulation studies, and discuss its relevance to methodological research.
TL;DR: This paper proposed a method for confronting missing outcome data that makes fairly weak assumptions but can still yield informative bounds on the average treatment effect, based on a combination of the double sampling design and nonparametric worst-case bounds.
Abstract: Missing outcome data plague many randomized experiments. Common solutions rely on ignorability assumptions that may not be credible in all applications. We propose a method for confronting missing outcome data that makes fairly weak assumptions but can still yield informative bounds on the average treatment effect. Our approach is based on a combination of the double sampling design and nonparametric worst-case bounds. We derive a worst-case bounds estimator under double sampling and provide analytic expressions for variance estimators and confidence intervals. We also propose a method for covariate adjustment using poststratification and a sensitivity analysis for nonignorable missingness. Finally, we illustrate the utility of our approach using Monte Carlo simulations and a placebo-controlled randomized field experiment on the effects of persuasion on social attitudes with survey-based outcome measures.