Abstract Punctuated Equilibrium Theory posits that policy-making is generally characterized by long periods of stability that are interrupted by short periods of fundamental policy change. The literature converged on the measure of kurtosis and L-kurtosis to assess these change patterns. In this letter, we critically discuss these measures and propose the Gini coefficient as a (1) comparable, but (2) more intuitive, and (3) more precise measure of “punctuated” change patterns.
{"title":"Why We Should Use the Gini Coefficient to Assess Punctuated Equilibrium Theory","authors":"Constantin Kaplaner, Yves Steinebach","doi":"10.1017/pan.2021.25","DOIUrl":"https://doi.org/10.1017/pan.2021.25","url":null,"abstract":"Abstract Punctuated Equilibrium Theory posits that policy-making is generally characterized by long periods of stability that are interrupted by short periods of fundamental policy change. The literature converged on the measure of kurtosis and L-kurtosis to assess these change patterns. In this letter, we critically discuss these measures and propose the Gini coefficient as a (1) comparable, but (2) more intuitive, and (3) more precise measure of “punctuated” change patterns.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"450 - 455"},"PeriodicalIF":5.4,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/pan.2021.25","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44413980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the effect modifier and other covariates, and including these interactions can lead to unstable estimates due to overfitting. In this paper, we explore the usefulness of machine learning algorithms for stabilizing these estimates and show how many off-the-shelf adaptive methods lead to two forms of bias: direct and indirect regularization bias. To overcome these issues, we use a post-double selection approach that utilizes several lasso estimators to select the interactions to include in the final model. We extend this approach to estimate uncertainty for both interaction and marginal effects. Simulation evidence shows that this approach has better performance than competing methods, even when the number of covariates is large. We show in two empirical examples that the choice of method leads to dramatically different conclusions about effect heterogeneity.
{"title":"Reducing Model Misspecification and Bias in the Estimation of Interactions","authors":"M. Blackwell, Michael Olson","doi":"10.1017/pan.2021.19","DOIUrl":"https://doi.org/10.1017/pan.2021.19","url":null,"abstract":"Abstract Analyzing variation in treatment effects across subsets of the population is an important way for social scientists to evaluate theoretical arguments. A common strategy in assessing such treatment effect heterogeneity is to include a multiplicative interaction term between the treatment and a hypothesized effect modifier in a regression model. Unfortunately, this approach can result in biased inferences due to unmodeled interactions between the effect modifier and other covariates, and including these interactions can lead to unstable estimates due to overfitting. In this paper, we explore the usefulness of machine learning algorithms for stabilizing these estimates and show how many off-the-shelf adaptive methods lead to two forms of bias: direct and indirect regularization bias. To overcome these issues, we use a post-double selection approach that utilizes several lasso estimators to select the interactions to include in the final model. We extend this approach to estimate uncertainty for both interaction and marginal effects. Simulation evidence shows that this approach has better performance than competing methods, even when the number of covariates is large. We show in two empirical examples that the choice of method leads to dramatically different conclusions about effect heterogeneity.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"495 - 514"},"PeriodicalIF":5.4,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/pan.2021.19","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43572289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Imputing 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.
{"title":"Choosing Imputation Models","authors":"M. Marbach","doi":"10.1017/pan.2021.39","DOIUrl":"https://doi.org/10.1017/pan.2021.39","url":null,"abstract":"Abstract Imputing 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.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"597 - 605"},"PeriodicalIF":5.4,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42350547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We introduce a fine-grained measure of the extent to which electoral districts combine and split local communities of co-partisans in unnatural ways. Our indicator—which we term Partisan Dislocation—is a measure of the difference between the partisan composition of a voter’s geographic nearest neighbors and that of her assigned district. We show that our measure is a good local and global indicator of district manipulation, easily identifying instances in which districts carve up clusters of co-partisans (cracking) or combine them in unnatural ways (packing). We demonstrate that our measure is related to but distinct from other approaches to the measurement of gerrymandering, and has some clear advantages, above all as a complement to simulation-based approaches, and as a way to identify the specific neighborhoods most affected by gerrymandering. It can also be used prospectively by district-drawers who wish to create maps that reflect voter geography, but according to our analysis, that goal will sometimes be in conflict with the goal of partisan fairness.
{"title":"Partisan Dislocation: A Precinct-Level Measure of Representation and Gerrymandering","authors":"Daryl R. DeFord, Nicholas Eubank, Jonathan Rodden","doi":"10.1017/pan.2021.13","DOIUrl":"https://doi.org/10.1017/pan.2021.13","url":null,"abstract":"Abstract We introduce a fine-grained measure of the extent to which electoral districts combine and split local communities of co-partisans in unnatural ways. Our indicator—which we term Partisan Dislocation—is a measure of the difference between the partisan composition of a voter’s geographic nearest neighbors and that of her assigned district. We show that our measure is a good local and global indicator of district manipulation, easily identifying instances in which districts carve up clusters of co-partisans (cracking) or combine them in unnatural ways (packing). We demonstrate that our measure is related to but distinct from other approaches to the measurement of gerrymandering, and has some clear advantages, above all as a complement to simulation-based approaches, and as a way to identify the specific neighborhoods most affected by gerrymandering. It can also be used prospectively by district-drawers who wish to create maps that reflect voter geography, but according to our analysis, that goal will sometimes be in conflict with the goal of partisan fairness.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"403 - 425"},"PeriodicalIF":5.4,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/pan.2021.13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44581566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This issue began as an exchange between Grant and Lebo (2016) and ourselves (Keele, Linn, and Webb 2016) about the utility of the general error correction model (GECM) in political science. The exchange evolved into a debate about Grant and Lebo's proposed alternative to the GECM and the utility of fractional integration methods (FIM). Esarey (2016) and Helgason (2016) weigh in on this part of the debate. Freeman (2016) offers his views on the exchange as well. In the end, the issue leaves readers with a lot to consider. In his comment, Freeman (2016) argues that the exchange has produced little significant progress because of the contributors' failures to consider a wide array of topics not directly related to the GECM or FIM. We are less pessimistic. In what follows, we distill what we believe are the most important elements of the exchange–the importance of balance, the costs and benefits of FIM, and the vagaries of pre-testing.
{"title":"Concluding Comments","authors":"L. Keele, Suzanna Linn, Clayton Webb","doi":"10.1093/pan/mpv030","DOIUrl":"https://doi.org/10.1093/pan/mpv030","url":null,"abstract":"This issue began as an exchange between Grant and Lebo (2016) and ourselves (Keele, Linn, and Webb 2016) about the utility of the general error correction model (GECM) in political science. The exchange evolved into a debate about Grant and Lebo's proposed alternative to the GECM and the utility of fractional integration methods (FIM). Esarey (2016) and Helgason (2016) weigh in on this part of the debate. Freeman (2016) offers his views on the exchange as well. In the end, the issue leaves readers with a lot to consider. In his comment, Freeman (2016) argues that the exchange has produced little significant progress because of the contributors' failures to consider a wide array of topics not directly related to the GECM or FIM. We are less pessimistic. In what follows, we distill what we believe are the most important elements of the exchange–the importance of balance, the costs and benefits of FIM, and the vagaries of pre-testing.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"24 1","pages":"83 - 86"},"PeriodicalIF":5.4,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/pan/mpv030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48471143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Although placebo conditions are ubiquitous in survey experiments, little evidence guides common practices for their use and selection. How should scholars choose and construct placebos? First, we review the role of placebos in published survey experiments, finding that placebos are used inconsistently. Then, drawing on the medical literature, we clarify the role that placebos play in accounting for nonspecific effects (NSEs), or the effects of ancillary features of experiments. We argue that, in the absence of precise knowledge of NSEs that placebos are adjusting for, researchers should average over a corpus of many placebos. We demonstrate this agnostic approach to placebo construction through the use of GPT-2, a generative language model trained on a database of over 1 million internet news pages. Using GPT-2, we devise 5,000 distinct placebos and administer two experiments (N = 2,975). Our results illustrate how researchers can minimize their role in placebo selection through automated processes. We conclude by offering tools for incorporating computer-generated placebo text vignettes into survey experiments and developing recommendations for best practice.
{"title":"Placebo Selection in Survey Experiments: An Agnostic Approach","authors":"Ethan Porter, Y. Velez","doi":"10.1017/pan.2021.16","DOIUrl":"https://doi.org/10.1017/pan.2021.16","url":null,"abstract":"Abstract Although placebo conditions are ubiquitous in survey experiments, little evidence guides common practices for their use and selection. How should scholars choose and construct placebos? First, we review the role of placebos in published survey experiments, finding that placebos are used inconsistently. Then, drawing on the medical literature, we clarify the role that placebos play in accounting for nonspecific effects (NSEs), or the effects of ancillary features of experiments. We argue that, in the absence of precise knowledge of NSEs that placebos are adjusting for, researchers should average over a corpus of many placebos. We demonstrate this agnostic approach to placebo construction through the use of GPT-2, a generative language model trained on a database of over 1 million internet news pages. Using GPT-2, we devise 5,000 distinct placebos and administer two experiments (N = 2,975). Our results illustrate how researchers can minimize their role in placebo selection through automated processes. We conclude by offering tools for incorporating computer-generated placebo text vignettes into survey experiments and developing recommendations for best practice.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"481 - 494"},"PeriodicalIF":5.4,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/pan.2021.16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45331185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Logit and probit (L/P) models are a mainstay of binary time-series cross-sectional (BTSCS) analyses. Researchers include cubic splines or time polynomials to acknowledge the temporal element inherent in these data. However, L/P models cannot easily accommodate three other aspects of the data’s temporality: whether covariate effects are conditional on time, whether the process of interest is causally complex, and whether our functional form assumption regarding time’s effect is correct. Failing to account for any of these issues amounts to misspecification bias, threatening our inferences’ validity. We argue scholars should consider using Cox duration models when analyzing BTSCS data, as they create fewer opportunities for such misspecification bias, while also having the ability to assess the same hypotheses as L/P. We use Monte Carlo simulations to bring new evidence to light showing Cox models perform just as well—and sometimes better—than logit models in a basic BTSCS setting, and perform considerably better in more complex BTSCS situations. In addition, we highlight a new interpretation technique for Cox models—transition probabilities—to make Cox model results more readily interpretable. We use an application from interstate conflict to demonstrate our points.
{"title":"Getting Time Right: Using Cox Models and Probabilities to Interpret Binary Panel Data","authors":"Shawna K. Metzger, Benjamin T. Jones","doi":"10.1017/pan.2021.14","DOIUrl":"https://doi.org/10.1017/pan.2021.14","url":null,"abstract":"Abstract Logit and probit (L/P) models are a mainstay of binary time-series cross-sectional (BTSCS) analyses. Researchers include cubic splines or time polynomials to acknowledge the temporal element inherent in these data. However, L/P models cannot easily accommodate three other aspects of the data’s temporality: whether covariate effects are conditional on time, whether the process of interest is causally complex, and whether our functional form assumption regarding time’s effect is correct. Failing to account for any of these issues amounts to misspecification bias, threatening our inferences’ validity. We argue scholars should consider using Cox duration models when analyzing BTSCS data, as they create fewer opportunities for such misspecification bias, while also having the ability to assess the same hypotheses as L/P. We use Monte Carlo simulations to bring new evidence to light showing Cox models perform just as well—and sometimes better—than logit models in a basic BTSCS setting, and perform considerably better in more complex BTSCS situations. In addition, we highlight a new interpretation technique for Cox models—transition probabilities—to make Cox model results more readily interpretable. We use an application from interstate conflict to demonstrate our points.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"151 - 166"},"PeriodicalIF":5.4,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42824222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aaron Erlich, S. G. Dantas, Benjamin E. Bagozzi, Daniel Berliner, Brian Palmer-Rubin
Abstract Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current “best practice” of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations among one’s multiple labels are low.
{"title":"Multi-Label Prediction for Political Text-as-Data","authors":"Aaron Erlich, S. G. Dantas, Benjamin E. Bagozzi, Daniel Berliner, Brian Palmer-Rubin","doi":"10.1017/pan.2021.15","DOIUrl":"https://doi.org/10.1017/pan.2021.15","url":null,"abstract":"Abstract Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current “best practice” of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations among one’s multiple labels are low.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"30 1","pages":"463 - 480"},"PeriodicalIF":5.4,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/pan.2021.15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45045420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel M. Smith, Jordi Muñoz, Emily M. Farris, Enrique Hernández, Thomas J. Leeper, S. Hobolt, Denise Laroze, Thomas Robinson, Pablo Beramendi
V o lu m e 28• N u m b er • A p rl20 20 V o l u m e 2 8 •Number2•April2020 V o lu m e 28• N u m b er • A p rl20 20 ARICLES Dcrete Coice Data w ith U noerved H etereneity:A CondnalBinary Q untile M odel Xiao Lu M eauring he om petienessof Eltions Gary W .Cx,Jon H .iva nd DnielM Sm ith U neected Eveduring Srvey Dign: Prom se nd PitfallsfusalInference JrdiM uñoz,rtFalcó-Gim no nd Erique H erndez M eauring Sugroup Preencesin ConjtExperim ents Thom asJ.per,Sara .H obltand am esilley
•28 V或lu m和m b帮助•p rl20 20号V或m和l 2•8 Number2••28 April2020 V或lu m和N u m b帮助•p rl20 20 ARICLES Dcrete noerved H etereneity Coice w ith日期:CondnalBinary Q untile米肖或lu m eauring he om petienessof Eltions Gary w . ex乔恩·H .增值税nd DnielM Sm ith u neected Eveduring Srvey Dign: Prom如果nd PitfallsfusalInference JrdiM uñ奥芝、rtFalcó-Gim不nd Erique H erndez m eauring Sugroup Preencesin ConjtExperim ents汤姆·asJ。谢谢你，莎拉
{"title":"PAN volume 29 issue 3 Cover and Front matter","authors":"Daniel M. Smith, Jordi Muñoz, Emily M. Farris, Enrique Hernández, Thomas J. Leeper, S. Hobolt, Denise Laroze, Thomas Robinson, Pablo Beramendi","doi":"10.1017/pan.2021.17","DOIUrl":"https://doi.org/10.1017/pan.2021.17","url":null,"abstract":"V o lu m e 28• N u m b er • A p rl20 20 V o l u m e 2 8 •Number2•April2020 V o lu m e 28• N u m b er • A p rl20 20 ARICLES Dcrete Coice Data w ith U noerved H etereneity:A CondnalBinary Q untile M odel Xiao Lu M eauring he om petienessof Eltions Gary W .Cx,Jon H .iva nd DnielM Sm ith U neected Eveduring Srvey Dign: Prom se nd PitfallsfusalInference JrdiM uñoz,rtFalcó-Gim no nd Erique H erndez M eauring Sugroup Preencesin ConjtExperim ents Thom asJ.per,Sara .H obltand am esilley","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":" ","pages":"f1 - f4"},"PeriodicalIF":5.4,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/pan.2021.17","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43851438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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