Benjamin Fifield, K. Imai, J. Kawahara, Christopher T. Kenny
{"title":"The Essential Role of Empirical Validation in Legislative Redistricting Simulation","authors":"Benjamin Fifield, K. Imai, J. Kawahara, Christopher T. Kenny","doi":"10.1080/2330443x.2020.1791773","DOIUrl":"https://doi.org/10.1080/2330443x.2020.1791773","url":null,"abstract":"ABSTRACT As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plans and courts determine their legality. These simulation methods are designed to yield a representative sample of all redistricting plans that satisfy statutory guidelines and requirements such as contiguity, population parity, and compactness. A proposed redistricting plan can be considered gerrymandered if it constitutes an outlier relative to this sample according to partisan fairness metrics. Despite their growing use, an insufficient effort has been made to empirically validate the accuracy of the simulation methods. We apply a recently developed computational method that can efficiently enumerate all possible redistricting plans and yield an independent sample from this population. We show that this algorithm scales to a state with a couple of hundred geographical units. Finally, we empirically examine how existing simulation methods perform on realistic validation datasets.","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.2020.1791773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44089864","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}
{"title":"Statistical Procedures for Assessing the Need for an Affirmative Action Plan: A Reanalysis of Shea v. Kerry","authors":"Qing Pan, W. Miao, J. Gastwirth","doi":"10.1080/2330443x.2019.1693313","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1693313","url":null,"abstract":"Abstract In the 1980s, reports from Congress and the Government Accountability Office (GAO) presented statistical evidence showing that employees in the Foreign Service were overwhelmingly White male, especially in the higher positions. To remedy this historical discrimination, the State Department instituted an affirmative action plan during 1990–1992 that allowed females and race-ethnic minorities to apply directly for mid-level positions. A White male employee claimed that he had been disadvantaged by the plan. The appellate court unanimously held that the manifest statistical imbalance supported the Department’s instituting the plan. One judge identified two statistical issues in the analysis of the data that neither party brought up. This article provides an empirical guideline for sample size and a one-sided Hotelling’s T2 test to answer these problems. First, an approximate rule is developed for the minimum number of expected minority appointments needed for a meaningful statistical analysis of under-representation. To avoid the multiple comparison issue when several protected groups are involved, a modification of Hotelling’s T2 test is developed for testing the null hypothesis of fair representation against a one-sided alternative of under-representation in at least one minority group. The test yields p-values less than 1 in 10,000 indicating that minorities were substantially under-represented. Excluding secretarial and clerical jobs led to even larger disparities. Supplemental materials for this article are available online.","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.2019.1693313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46315284","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}
{"title":"Who Is My Neighbor? The Spatial Efficiency of Partisanship","authors":"Nicholas Eubank, Jonathan Rodden","doi":"10.1080/2330443X.2020.1806762","DOIUrl":"https://doi.org/10.1080/2330443X.2020.1806762","url":null,"abstract":"Abstract Relative to its overall statewide support, the Republican Party has been over-represented in congressional delegations and state legislatures over the last decade in a number of US states. A challenge is to determine the extent to which this can be explained by intentional gerrymandering as opposed to an underlying inefficient distribution of Democrats in cities. We explain the “spatial inefficiency” of support for Democrats, and demonstrate that it varies substantially both across states and also across legislative chambers within states. We introduce a simple method for measuring this inefficiency by assessing the partisanship of the nearest neighbors of each voter in each US state. Our measure of spatial efficiency helps explain cross-state patterns in legislative representation, and allows us to verify that political geography contributes substantially to inequalities in political representation. At the same time, however, we also show that even after controlling for spatial efficiency, partisan control of the redistricting process has had a substantial impact on the parties’ seat shares. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2020.1806762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193194","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}
S. Caldera, Daryl R. DeFord, M. Duchin, Samuel C. Gutekunst, Cara Nix
{"title":"Mathematics of Nested Districts: The Case of Alaska","authors":"S. Caldera, Daryl R. DeFord, M. Duchin, Samuel C. Gutekunst, Cara Nix","doi":"10.1080/2330443x.2020.1774452","DOIUrl":"https://doi.org/10.1080/2330443x.2020.1774452","url":null,"abstract":"ABSTRACT In eight states, a “nesting rule” requires that each state Senate district be exactly composed of two adjacent state House districts. In this article, we investigate the potential impacts of these nesting rules with a focus on Alaska, where Republicans have a 2/3 majority in the Senate while a Democratic-led coalition controls the House. Treating the current House plan as fixed and considering all possible pairings, we find that the choice of pairings alone can create a swing of 4–5 seats out of 20 against recent voting patterns, which is similar to the range observed when using a Markov chain procedure to generate plans without the nesting constraint. The analysis enables other insights into Alaska districting, including the partisan latitude available to districters with and without strong rules about nesting and contiguity. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2020.1774452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45247124","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}
{"title":"On Racial Disparities in Recent Fatal Police Shootings","authors":"L. Mentch","doi":"10.1080/2330443x.2019.1704330","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1704330","url":null,"abstract":"Abstract Fatal police shootings in the United States continue to be a polarizing social and political issue. Clear disagreement between racial proportions of victims and nationwide racial demographics together with graphic video footage has created fertile ground for controversy. However, simple population level summary statistics fail to take into account fundamental local characteristics such as county-level racial demography, local arrest demography, and law enforcement density. Using data on fatal police shootings between January 2015 and July 2016, I implement a number of straightforward resampling procedures designed to carefully examine how unlikely the victim totals from each race are with respect to these local population characteristics if no racial bias were present in the decision to shoot by police. I present several approaches considering the shooting locations both as fixed and also as a random sample. In both cases, I find overwhelming evidence of a racial disparity in shooting victims with respect to local population demographics but substantially less disparity after accounting for local arrest demographics. I conclude the analyses by examining the effect of police-worn body cameras and find no evidence that the presence of such cameras impacts the racial distribution of victims. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2019.1704330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42104168","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}
Daniel Carter, Zach Hunter, Dan Teague, G. Herschlag, Jonathan C. Mattingly
{"title":"Optimal Legislative County Clustering in North Carolina","authors":"Daniel Carter, Zach Hunter, Dan Teague, G. Herschlag, Jonathan C. Mattingly","doi":"10.1080/2330443x.2020.1748552","DOIUrl":"https://doi.org/10.1080/2330443x.2020.1748552","url":null,"abstract":"Abstract North Carolina’s constitution requires that state legislative districts should not split counties. However, counties must be split to comply with the “one person, one vote” mandate of the U.S. Supreme Court. Given that counties must be split, the North Carolina legislature and the courts have provided guidelines that seek to reduce counties split across districts while also complying with the “one person, one vote” criterion. Under these guidelines, the counties are separated into clusters; each cluster contains a specified number of districts and that are drawn independent from other clusters. The primary goal of this work is to develop, present, and publicly release an algorithm to optimally cluster counties according to the guidelines set by the court in 2015. We use this tool to investigate the optimality and uniqueness of the enacted clusters under the 2017 redistricting process. We verify that the enacted clusters are optimal, but find other optimal choices. We emphasize that the tool we provide lists all possible optimal county clusterings. We also explore the stability of clustering under changing statewide populations and project what the county clusters may look like in the next redistricting cycle beginning in 2020/2021. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2020.1748552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44573636","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}
{"title":"Separating Effect From Significance in Markov Chain Tests","authors":"M. Chikina, A. Frieze, J. Mattingly, W. Pegden","doi":"10.1080/2330443x.2020.1806763","DOIUrl":"https://doi.org/10.1080/2330443x.2020.1806763","url":null,"abstract":"Abstract We give qualitative and quantitative improvements to theorems which enable significance testing in Markov chains, with a particular eye toward the goal of enabling strong, interpretable, and statistically rigorous claims of political gerrymandering. Our results can be used to demonstrate at a desired significance level that a given Markov chain state (e.g., a districting) is extremely unusual (rather than just atypical) with respect to the fragility of its characteristics in the chain. We also provide theorems specialized to leverage quantitative improvements when there is a product structure in the underlying probability space, as can occur due to geographical constraints on districtings.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2020.1806763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46921817","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}
{"title":"Financial Literacy and Perceived Economic Outcomes","authors":"David Puelz, R. Puelz","doi":"10.1080/2330443X.2022.2086191","DOIUrl":"https://doi.org/10.1080/2330443X.2022.2086191","url":null,"abstract":"Abstract We explore the relationship between financial literacy and self-reported, reflective economic outcomes from respondents using survey data from the United States. Our dataset includes a large number of covariates from the National Financial Capability Study (NFCS), widely used by literacy researchers, and we use a new econometric technique developed by Hahn et al., designed specifically for causal inference from observational data, to test whether changes in financial literacy infer meaningful changes in self-perceived economic outcomes. We find a negative treatment parameter on financial literacy consistent with the recent work of Netemeyer et al. and contrary to the presumption in many empirical studies that associate standard financial outcome measures with financial literacy. We conclude with a discussion of heterogeneity of the financial literacy treatment effect on household income, gender, and education level sub-populations. Our findings on the relationship between financial literacy and reflective economic outcomes also raise questions about its importance to an individual’s financial well-being.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49584551","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}
{"title":"Discretionary Wars, Cost-Benefit Analysis, and the Rashomon Effect: Searching for an Analytical Engine for Avoiding War","authors":"J. Ratner","doi":"10.1080/2330443x.2019.1688742","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1688742","url":null,"abstract":"Those of us who value analytic thinking about public policy and, in particular, about war, can learn a great deal from reading “Cost Benefit Analysis of Discretionary Wars” by Diane Hu and her coauthors.1 The article also raises many questions, and considering them spurs learning too. Their article contributes to the literature by formulating and implementing an approach to the cost-benefit analysis (CBA) of war that is tractable and amenable to empirical use. Notably, the authors add value by operationalizing several dimensions of war’s benefits, by introducing certain simplified methods of estimating the costs of war, and by applying their framework of measuring costs and benefits to five case-studies of discretionary war. As the authors note, they build on the work of Nordhaus (2002), Stiglitz and Bilmes (2008), and others regarding the costs to the United States of the Afghanistan and Iraq Wars, as well as on Hausken’s important theoretical framework for conducting a CBA of war (Hausken 2016). By abstracting from many complexities articulated by Hausken, the authors create an empirically oriented framework that can be populated with data from their case-studies of U.S. discretionary war.2 By examining a war’s benefits and assigning monetary values to them, the authors are able to juxtapose these monetized benefits to their estimates of these wars’ costs, thereby answering the question: Did the costs of these wars outweigh their benefits? The authors’ extensive attention to war’s benefits is distinctive, especially in estimating these benefits for five wars. (Other studies of a U.S. war’s monetized benefits focus on one war.3) Furthermore, they obtain a striking result: costs exceed benefits for all five wars. None, not even the First Gulf War or Korea, escapes the article’s grim verdict: negative net benefits should have ruled out these wars.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2019.1688742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44637485","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}
{"title":"Understanding Significance Tests From a Non-Mixing Markov Chain for Partisan Gerrymandering Claims","authors":"Wendy K. Tam Cho, Simon Rubinstein-Salzedo","doi":"10.1080/2330443X.2019.1574687","DOIUrl":"https://doi.org/10.1080/2330443X.2019.1574687","url":null,"abstract":"ABSTRACT Recently, Chikina, Frieze, and Pegden proposed a way to assess significance in a Markov chain without requiring that Markov chain to mix. They presented their theorem as a rigorous test for partisan gerrymandering. We clarify that their ε-outlier test is distinct from a traditional global outlier test and does not indicate, as they imply, that a particular electoral map is associated with an extreme level of “partisan unfairness.” In fact, a map could simultaneously be an ε-outlier and have a typical partisan fairness value. That is, their test identifies local outliers but has no power for assessing whether that local outlier is a global outlier. How their specific definition of local outlier is related to a legal gerrymandering claim is unclear given Supreme Court precedent.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2019.1574687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45832676","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}