1,721,001 research outputs found
Replication Data for: Analyzing Decision Records from Committees
No full textIn the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions
Replication Data for: Choosing Imputation Models
No full textImputing missing values is an important preprocessing step in data analysis, but the literature offers little guidance on how to choose between imputation models. This letter suggests adopting the imputation model that generates a density of imputed values most similar to those of the observed values for an incomplete variable after balancing all other covariates. We recommend stable balancing weights as a practical approach to balance covariates whose distribution is expected to differ if the values are not missing completely at random. After balancing, discrepancy statistics can be used to compare the density of imputed and observed values. We illustrate the application of the suggested approach using simulated and real-world survey data from the American National Election Study, comparing popular imputation approaches including random forests, hot-deck, predictive mean matching, and multivariate normal imputation. An R package implementing the suggested approach accompanies this letter
Replication Data for: Analyzing Decision Records from Committees
No full textIn the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions
Replication Data for: Causal Effects, Migration and Legacy Studies
No full textPolitical scientists have long been interested in the persistent effects of history on contemporary behavior and attitudes. To estimate legacy effects, studies often compare people living in places that were historically exposed to some event and those that were not. Using principal stratification, we provide a formal framework to analyze how migration limits our ability to learn about the persistent effects of history from observed differences between historically exposed and unexposed places. We state the necessary assumptions about movement behavior to causally identify legacy effects. We highlight that these assumptions are strong; therefore, we recommend that legacy studies circumvent bias by collecting data on people's place of residence at the exposure time. Reexamining a study on the persistent effects of US civil-rights protests, we show that observed attitudinal differences between residents and non-residents of historic protest sites are more likely due to migration rather than attitudinal change
Pre-Analysis Plan for a Survey Experiment on Committees, Voting Rules and Voters' Beliefs
No full textMany institutional environments share two common features: Decisions are made by a group of representatives (a committee) and decision-making takes place by secret ballot. Yet, even a secret ballot does not guarantee complete anonymity. As long as the observer has information about the committee’s joined decision and the employed voting rule, some information about representatives’ vote choices is leaked. Probability theory and Bayes’ Theorem in particular suggest that the amount of learning that is possible is a function of the voting rule, the size of the committee and the observer’s prior belief about representatives’ vote choices. Using a survey experiment we evaluate if voters are Bayesian learners and update prior beliefs about representatives' behavior in a committee using knowledge about the committee's joined decision. The experiment is implemented in the German Internet Panel wave 24 (July 2016)
Replication Data for: Profiling Compliers and Non-compliers for Instrumental-Variable Analysis
No full textInstrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by non-compliance or leverage partially exogenous treatment variation in observational studies. The potential outcomes framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE), and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and non-compliers. Yet profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers and never-takers in terms of their covariates, and easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and non-compliers as standard practice accompanying any IV analysis
Replication Data for: Profiling Compliers and Non-compliers for Instrumental-Variable Analysis
No full textInstrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by non-compliance or leverage partially exogenous treatment variation in observational studies. The potential outcomes framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE), and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and non-compliers. Yet profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers and never-takers in terms of their covariates, and easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and non-compliers as standard practice accompanying any IV analysis
Pre-Analysis Plan for a Survey Experiment on Committees, Voting Rules and Voters' Beliefs
No full textMany institutional environments share two common features: Decisions are made by a group of representatives (a committee) and decision-making takes place by secret ballot. Yet, even a secret ballot does not guarantee complete anonymity. As long as the observer has information about the committee’s joined decision and the employed voting rule, some information about representatives’ vote choices is leaked. Probability theory and Bayes’ Theorem in particular suggest that the amount of learning that is possible is a function of the voting rule, the size of the committee and the observer’s prior belief about representatives’ vote choices. Using a survey experiment we evaluate if voters are Bayesian learners and update prior beliefs about representatives' behavior in a committee using knowledge about the committee's joined decision. The experiment is implemented in the German Internet Panel wave 24 (July 2016)
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