Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1919260
Ruoqi Yu, Dylan S. Small, David J. Harding, J. Aveldanes, P. Rosenbaum
Abstract A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative research contends that open-ended, narrative descriptions of a subset of people can be informative. Here, we discuss and apply a form of optimal matching that supports such an integrated, quantitative-plus-qualitative study. The optimal match provides many closely matched pairs plus a subset of exceptionally close pairs suitable for narrative interpretation. We illustrate the matching technique using data from a recent study of police responses to domestic violence in Philadelphia, where the police report includes both quantitative and narrative information.
{"title":"Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research","authors":"Ruoqi Yu, Dylan S. Small, David J. Harding, J. Aveldanes, P. Rosenbaum","doi":"10.1080/2330443X.2021.1919260","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1919260","url":null,"abstract":"Abstract A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative research contends that open-ended, narrative descriptions of a subset of people can be informative. Here, we discuss and apply a form of optimal matching that supports such an integrated, quantitative-plus-qualitative study. The optimal match provides many closely matched pairs plus a subset of exceptionally close pairs suitable for narrative interpretation. We illustrate the matching technique using data from a recent study of police responses to domestic violence in Philadelphia, where the police report includes both quantitative and narrative information.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1919260","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49042689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1906806
Jonathan Auerbach, Steve Pierson
Abstract We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail.
{"title":"Does Voting by Mail Increase Fraud? Estimating the Change in Reported Voter Fraud When States Switch to Elections By Mail","authors":"Jonathan Auerbach, Steve Pierson","doi":"10.1080/2330443X.2021.1906806","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1906806","url":null,"abstract":"Abstract We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1906806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45385684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1900762
K. Klauenberg, Cord A. Müller, C. Elster
Abstract Millions of measuring instruments are verified each year before being placed on the markets worldwide. In the EU, such initial conformity assessments are regulated by the Measuring Instruments Directive (MID). The MID modules F and F1 on product verification allow for statistical acceptance sampling, whereby only random subsets of instruments need to be inspected. This article re-interprets the acceptance sampling conditions formulated by the MID. The new interpretation is contrasted with the one advanced in WELMEC guide 8.10, and three advantages have become apparent. First, an economic advantage of the new interpretation is a producers’ risk bounded from above, such that measuring instruments with sufficient quality are accepted with a guaranteed probability of no less than 95%. Second, a conceptual advantage is that the new MID interpretation fits into the well known, formal framework of statistical hypothesis testing. Thirdly, the new interpretation applies unambiguously to finite-sized lots, even very small ones. We conclude that the new interpretation is to be preferred and suggest re-formulating the statistical sampling conditions in the MID. Re-interpreting the MID conditions implies that currently available sampling plans are either not admissible or not optimal. We derive a new acceptance sampling scheme and recommend its application. Supplementary materials for this article are available online.
{"title":"Hypothesis-based Acceptance Sampling for Modules F and F1 of the European Measuring Instruments Directive","authors":"K. Klauenberg, Cord A. Müller, C. Elster","doi":"10.1080/2330443X.2021.1900762","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1900762","url":null,"abstract":"Abstract Millions of measuring instruments are verified each year before being placed on the markets worldwide. In the EU, such initial conformity assessments are regulated by the Measuring Instruments Directive (MID). The MID modules F and F1 on product verification allow for statistical acceptance sampling, whereby only random subsets of instruments need to be inspected. This article re-interprets the acceptance sampling conditions formulated by the MID. The new interpretation is contrasted with the one advanced in WELMEC guide 8.10, and three advantages have become apparent. First, an economic advantage of the new interpretation is a producers’ risk bounded from above, such that measuring instruments with sufficient quality are accepted with a guaranteed probability of no less than 95%. Second, a conceptual advantage is that the new MID interpretation fits into the well known, formal framework of statistical hypothesis testing. Thirdly, the new interpretation applies unambiguously to finite-sized lots, even very small ones. We conclude that the new interpretation is to be preferred and suggest re-formulating the statistical sampling conditions in the MID. Re-interpreting the MID conditions implies that currently available sampling plans are either not admissible or not optimal. We derive a new acceptance sampling scheme and recommend its application. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1900762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46190994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1971126
A. Gelman
Abstract The recent successes and failures of political polling invite several questions: Why did the polls get it wrong in some high-profile races? Conversely, how is it that polls can perform so well, even given all the evident challenges of conducting and interpreting them?
{"title":"Failure and Success in Political Polling and Election Forecasting","authors":"A. Gelman","doi":"10.1080/2330443X.2021.1971126","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1971126","url":null,"abstract":"Abstract The recent successes and failures of political polling invite several questions: Why did the polls get it wrong in some high-profile races? Conversely, how is it that polls can perform so well, even given all the evident challenges of conducting and interpreting them?","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49048962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1932645
J. Fox, Nathan Sanders, Emma E. Fridel, G. Duwe, M. Rocque
ABSTRACT Mass public shootings have generated significant levels of fear in the recent years, with many observers criticizing the media for fostering a moral panic, if not an actual rise in the frequency of such attacks. Scholarly research suggests that the media can potentially impact the prevalence of mass shootings in two respects: (i) some individuals may be inspired to mimic the actions of highly publicized offenders; and (ii) a more general contagion process may manifest as a temporary increase in the likelihood of shootings associated with a triggering event. In this study of mass shootings since 2000, we focus on short-term contagion, rather than imitation that can traverse years. Specifically, after highlighting the sequencing of news coverage prior and subsequent to mass shootings, we apply multivariate point process models to disentangle the correlated incidence of mass public shootings and news coverage of such events. The findings suggest that mass public shootings have a strong effect on the level of news reporting, but that news reporting on the topic has little impact, at least in the relative short-term, on the subsequent prevalence of mass shootings. Finally, the results appear to rule out the presence of strong self-excitation of mass shootings, placing clear limits on generalized short-term contagion effects. Supplementary files for this article are available online.
{"title":"The Contagion of Mass Shootings: The Interdependence of Large-Scale Massacres and Mass Media Coverage","authors":"J. Fox, Nathan Sanders, Emma E. Fridel, G. Duwe, M. Rocque","doi":"10.1080/2330443X.2021.1932645","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1932645","url":null,"abstract":"ABSTRACT Mass public shootings have generated significant levels of fear in the recent years, with many observers criticizing the media for fostering a moral panic, if not an actual rise in the frequency of such attacks. Scholarly research suggests that the media can potentially impact the prevalence of mass shootings in two respects: (i) some individuals may be inspired to mimic the actions of highly publicized offenders; and (ii) a more general contagion process may manifest as a temporary increase in the likelihood of shootings associated with a triggering event. In this study of mass shootings since 2000, we focus on short-term contagion, rather than imitation that can traverse years. Specifically, after highlighting the sequencing of news coverage prior and subsequent to mass shootings, we apply multivariate point process models to disentangle the correlated incidence of mass public shootings and news coverage of such events. The findings suggest that mass public shootings have a strong effect on the level of news reporting, but that news reporting on the topic has little impact, at least in the relative short-term, on the subsequent prevalence of mass shootings. Finally, the results appear to rule out the presence of strong self-excitation of mass shootings, placing clear limits on generalized short-term contagion effects. Supplementary files for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2021.1932645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44339845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/2330443X.2021.1978354
James Rosenberger, G. Ridgeway, Lingzhou Xue
Abstract Government reports document more than 14,000 homicides and more than 195,000 aggravated assaults with firearms in 2017. In addition, there were 346 mass shootings, with 4 or more victims, including over 2000 people shot. These statistics do not include suicides (two-thirds of gun deaths) or accidents (5% of gun deaths). This article describes statistical issues discussed at a national forum to stimulate collaboration between statisticians and criminologists. Topics include: (i) available data sources and their shortcomings and efforts to improve the quality, and alternative new data registers of shootings; (ii) gun violence patterns and trends, with statistical models and clustering effects in urban areas; (iii) research for understanding effective strategies for gun violence prevention and the role of the police in solving gun homicides; (iv) the role of reliable forensic science in solving cases involving shootings; and (v) the topic of police shootings, where they are more prevalent and the characteristics of the officers involved. The final section calls the statistical community to engage in collaborations with social scientists to provide the most effective methodological tools for understanding and mitigating the societal problem of gun violence.
{"title":"Statisticians Engage in Gun Violence Research","authors":"James Rosenberger, G. Ridgeway, Lingzhou Xue","doi":"10.1080/2330443X.2021.1978354","DOIUrl":"https://doi.org/10.1080/2330443X.2021.1978354","url":null,"abstract":"Abstract Government reports document more than 14,000 homicides and more than 195,000 aggravated assaults with firearms in 2017. In addition, there were 346 mass shootings, with 4 or more victims, including over 2000 people shot. These statistics do not include suicides (two-thirds of gun deaths) or accidents (5% of gun deaths). This article describes statistical issues discussed at a national forum to stimulate collaboration between statisticians and criminologists. Topics include: (i) available data sources and their shortcomings and efforts to improve the quality, and alternative new data registers of shootings; (ii) gun violence patterns and trends, with statistical models and clustering effects in urban areas; (iii) research for understanding effective strategies for gun violence prevention and the role of the police in solving gun homicides; (iv) the role of reliable forensic science in solving cases involving shootings; and (v) the topic of police shootings, where they are more prevalent and the characteristics of the officers involved. The final section calls the statistical community to engage in collaborations with social scientists to provide the most effective methodological tools for understanding and mitigating the societal problem of gun violence.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46355685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-14DOI: 10.1080/2330443X.2020.1859030
G. Mohler, M. Short, F. Schoenberg, Daniel Sledge
ABSTRACT Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt , falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of Rt , and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of Rt values from 0.47 to 1.20 as we vary the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings.
{"title":"Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana","authors":"G. Mohler, M. Short, F. Schoenberg, Daniel Sledge","doi":"10.1080/2330443X.2020.1859030","DOIUrl":"https://doi.org/10.1080/2330443X.2020.1859030","url":null,"abstract":"ABSTRACT Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt , falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of Rt , and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of Rt values from 0.47 to 1.20 as we vary the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2020.1859030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48013695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-25DOI: 10.1080/2330443X.2022.2050328
A. Suzuki
Abstract How should we evaluate the effect of a policy on the likelihood of an undesirable event, such as conflict? The significance test has three limitations. First, relying on statistical significance misses the fact that uncertainty is a continuous scale. Second, focusing on a standard point estimate overlooks the variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. A new Bayesian decision-theoretic model, “causal binary loss function model,” overcomes these issues. It compares the expected loss under a policy intervention with the one under no intervention. These losses are computed based on a particular range of the effect sizes of a policy, the probability mass of this effect size range, the cost of the policy, and the cost of the undesirable event the policy intends to address. The model is more applicable than common statistical decision-theoretic models using the standard loss functions or capturing costs in terms of false positives and false negatives. I exemplify the model’s use through three applications and provide an R package. Supplementary materials for this article are available online.
{"title":"Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes","authors":"A. Suzuki","doi":"10.1080/2330443X.2022.2050328","DOIUrl":"https://doi.org/10.1080/2330443X.2022.2050328","url":null,"abstract":"Abstract How should we evaluate the effect of a policy on the likelihood of an undesirable event, such as conflict? The significance test has three limitations. First, relying on statistical significance misses the fact that uncertainty is a continuous scale. Second, focusing on a standard point estimate overlooks the variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. A new Bayesian decision-theoretic model, “causal binary loss function model,” overcomes these issues. It compares the expected loss under a policy intervention with the one under no intervention. These losses are computed based on a particular range of the effect sizes of a policy, the probability mass of this effect size range, the cost of the policy, and the cost of the undesirable event the policy intends to address. The model is more applicable than common statistical decision-theoretic models using the standard loss functions or capturing costs in terms of false positives and false negatives. I exemplify the model’s use through three applications and provide an R package. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48273528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-22DOI: 10.1080/2330443X.2021.2024778
Johann D. Gaebler, William Cai, Guillaume W. Basse, Ravi Shroff, Sharad Goel, J. Hill
Abstract In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes. For example, in the criminal justice context, one might ask whether arrested individuals would have been subsequently charged or convicted had they been a different race. It has long been known that such counterfactual questions face measurement challenges related to omitted-variable bias, and conceptual challenges related to the definition of causal estimands for largely immutable characteristics. Another concern, which has been the subject of recent debates, is post-treatment bias: many studies of discrimination condition on apparently intermediate outcomes, like being arrested, that themselves may be the product of discrimination, potentially corrupting statistical estimates. There is, however, reason to be optimistic. By carefully defining the estimand—and by considering the precise timing of events—we show that a primary causal quantity of interest in discrimination studies can be estimated under an ignorability condition that may hold approximately in some observational settings. We illustrate these ideas by analyzing both simulated data and the charging decisions of a prosecutor’s office in a large county in the United States.
{"title":"A Causal Framework for Observational Studies of Discrimination","authors":"Johann D. Gaebler, William Cai, Guillaume W. Basse, Ravi Shroff, Sharad Goel, J. Hill","doi":"10.1080/2330443X.2021.2024778","DOIUrl":"https://doi.org/10.1080/2330443X.2021.2024778","url":null,"abstract":"Abstract In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes. For example, in the criminal justice context, one might ask whether arrested individuals would have been subsequently charged or convicted had they been a different race. It has long been known that such counterfactual questions face measurement challenges related to omitted-variable bias, and conceptual challenges related to the definition of causal estimands for largely immutable characteristics. Another concern, which has been the subject of recent debates, is post-treatment bias: many studies of discrimination condition on apparently intermediate outcomes, like being arrested, that themselves may be the product of discrimination, potentially corrupting statistical estimates. There is, however, reason to be optimistic. By carefully defining the estimand—and by considering the precise timing of events—we show that a primary causal quantity of interest in discrimination studies can be estimated under an ignorability condition that may hold approximately in some observational settings. We illustrate these ideas by analyzing both simulated data and the charging decisions of a prosecutor’s office in a large county in the United States.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41351478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1080/2330443x.2020.1777915
Daryl R. DeFord, M. Duchin, J. Solomon
ABSTRACT The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states’ reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested. In this article, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states—Utah, Georgia, Wisconsin, Virginia, and Massachusetts—chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.
{"title":"A Computational Approach to Measuring Vote Elasticity and Competitiveness","authors":"Daryl R. DeFord, M. Duchin, J. Solomon","doi":"10.1080/2330443x.2020.1777915","DOIUrl":"https://doi.org/10.1080/2330443x.2020.1777915","url":null,"abstract":"ABSTRACT The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states’ reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested. In this article, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states—Utah, Georgia, Wisconsin, Virginia, and Massachusetts—chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2020.1777915","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44071177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：