Identifying racial disparities in policy and politics is a pressing area of research within the United States. Where early work made use of identifying potentially noisy correlations between county or precinct demographics and election outcomes, the advent of Bayesian Improved Surname Geocoding (BISG) vastly improved estimation of race by employing voter lists. Machine Learning (ML)-modified BISG in turn offers accuracy gains over the static – and potentially outdated – surname dictionaries present in traditional BISG. However, the extent to which ML might substantively alter the policy and political implications of redistricting is unclear given its improvements in voter race estimation. Therefore, we ascertain the potential gains of ML-modified BISG in improving the estimation of race for the purpose of redistricting majority-minority districts. We evaluate an ML-modified BISG program against traditional BISG estimates in correctly estimating the race of voters for creating majority-minority congressional districts within North Carolina and Georgia, and in state assembly districts in Wisconsin. Our results demonstrate that ML-modified BISG offers substantive gains over traditional BISG, especially in diverse political geographic units. Further, we find meaningful improvements in accuracy when estimating majority-minority district racial composition. We conclude with recommendations on when and how to use the two methods, in addition how to ensure transparency and confidence in BISG-related research.

Redistricting reformers have proposed many solutions to the problem of partisan gerrymandering, but they all require either bipartisan consensus or the agreement of both parties on the legitimacy of a neutral third party to resolve disputes. In this paper, we propose a new method for drawing district maps, the Define–Combine Procedure, that substantially reduces partisan gerrymandering without requiring a neutral third party or bipartisan agreement. One party defines a map of $2N$ equal-population contiguous districts. Then the second party combines pairs of contiguous districts to create the final map of N districts. Using real-world geographic and electoral data, we employ simulations and map-drawing algorithms to show that this procedure dramatically reduces the advantage conferred to the party controlling the redistricting process and leads to less-biased maps without requiring cooperation or non-partisan actors.

Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on long-standing assumptions and social networks that exclude new research. In both cases, ad hoc literature reviews hinder accumulation of knowledge. Scholars are rarely systematic in selecting relevant prior work or then identifying patterns across their sample. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. In our method, we consider a literature as a network of recurring concepts (nodes) and theorized relationships among them (edges). Network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature. Critically, our approach is systematic and powerful but also low cost; it requires researchers to enter relationships they observe in prior studies into a simple spreadsheet—a task accessible to new and experienced researchers alike. Our open-source R package enables researchers to leverage powerful network analysis while minimizing software-specific knowledge. We demonstrate this approach by reviewing redistricting literature.

Identifying the geographic constituencies of representatives is among the most crucial, yet challenging, aspects of state and local politics research. Regularly changing district lines, incomplete data, and computational obstacles can present barriers to matching individuals to their respective districts. Geocoding residential addresses is the ideal method for matching purposes. However, cost constraints can limit its applicability for many researchers, leading to geographic assignment methods that use polygonal units, such as ZIP codes, to estimate constituency membership. In this study, we quantify the trade-offs between three geographic assignment matching methods – centroid, geographic overlap, and population overlap matching – on the assignment of individual voters to state legislative districts. We confirm that population overlap matching produces the highest accuracy in assigning voters to their state legislative districts when polygonal location data are all that is available. We validate this finding by improving model estimates of lobbying influence through a replication analysis of Bishop and Dudley (2017), “The Role of Constituency, Party, and Industry in Pennsylvania’s Act 13,” State Politics and Policy Quarterly 17 (2): 154–79. Our replication suggests that distinguishing between out-of-district and in-district donations reveals a greater impact for in-district lobbying efforts. We make evident that population overlap assignment can confidently be used to identify constituencies when precise location data is not available.

During the most recent round of redistricting, many states have enacted a number of reforms to their mapmaking practices. One reform that has received increased attention in recent years is a ban on prison gerrymandering—the practice of counting incarcerated individuals in prisons instead of their home addresses. Eleven states drew districts while counting incarcerated persons in their homes after the 2020 Census. Though substantial research has investigated redistricting practices, far less attention has been paid to empirically examining the effect of prison gerrymandering on elections. We seek to fill this void by evaluating the effect of New York’s ban on prison gerrymandering on state legislative elections between 2002 and 2020. We find that altering how the prison population is counted, indeed, altered the electoral dynamics across the state.

Redistricting poses a potential harm to American voters in limiting choice and accountability at the polls. Although voters still retain their right to contact their representatives, research shows that the confusion created when redistricting divides ZIP codes confounds the constituent–representative link. We build on existing research that shows splitting ZIP codes across multiple congressional districts leads to harms in representation. Specifically, we examine the role of splitting ZIP codes on the recognition of the racial group membership of one’s Congressional representative, a foundational component of the descriptive representation of racial minority voters via minority–majority districts in the United States. We find that citizens living in split ZIP codes are significantly less likely to know the race of their member of Congress. This occurs even when controlling for a host of factors including the race and partisanship of the constituent, the tenure of the member, and the amount of time a constituent has lived in their congressional district. Our work provides further evidence of the democratic harms experienced by American citizens living in ZIP codes that are split between multiple congressional districts. This work also points to the representational harms produced by poor district design on the representation of American voters.

Ensuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological inference methods in identifying voter race. In this article, we test the viability of employing BISG to redistricting under two posterior allocation methods for race assignment: plurality versus probabilistic. We validate these methods through 10,000 redistricting simulations of North Carolina and Georgia’s congressional districts and compare BISG estimates to actual voter file racial data. We find that probabilistic summing of the BISG posteriors significantly reduces error rates at the precinct and district level relative to plurality racial assignment, and therefore should be the preferred method when using BISG for redistricting. Our results suggest that BISG can aid in the construction of majority-minority districts during the redistricting process.

We consider the measures of partisan symmetry proposed for practical use in the political science literature, as clarified and developed in Katz, King, and Rosenblatt (2020, American Political Science Review 114, 164–178). Elementary mathematical manipulation shows the symmetry metrics to have surprising properties that call their meaningfulness into question. To accompany the general analysis, we study measures of partisan symmetry with respect to recent voting patterns in Utah, Texas, and North Carolina, flagging problems in each case. Taken together, these observations should raise major concerns about the available techniques for quantitative scores of partisan symmetry—including the mean–median score, the partisan bias score, and the more general “partisan symmetry standard”—with the decennial redistricting underway.

Katz, King, and Rosenblatt (2020, American Political Science Review 114, 164–178) introduces a theoretical framework for understanding redistricting and electoral systems, built on basic statistical and social science principles of inference. DeFord et al. (2021, Political Analysis, this issue) instead focuses solely on descriptive measures, which lead to the problems identified in our article. In this article, we illustrate the essential role of these basic principles and then offer statistical, mathematical, and substantive corrections required to apply DeFord et al.’s calculations to social science questions of interest, while also showing how to easily resolve all claimed paradoxes and problems. We are grateful to the authors for their interest in our work and for this opportunity to clarify these principles and our theoretical framework.

Katz, King, and Rosenblatt recently wrote a broad survey developing and extending the theory of partisan symmetry. Our paper reviewed the implementability of the theory, focusing on simplified scores of symmetry—seemingly compatible with their formulation—that are in wide use. We analyzed these simplified scores and concluded that they are not suited for redistricting reform. By our reading of their response, Katz, King, and Rosenblatt agree.

After seeking a “manageable standard” to apply to claims of partisan gerrymandering for over three decades, the Supreme Court has finally given up the chase, ruling that such claims are nonjusticiable. What is to be done? An extended history of successful congressional action suggests that the legislative pathway is more practical than often believed. Statutory requirements also make it possible to consider a broader suite of districting objectives. This paper presents a flexible new software and a framework for evaluating the practical implications of explicit objectives. I apply this approach to the conditions last required by Congress, generating equipopulous, contiguous, and compact districts. Among these conditions, the formal definition of compactness has proven contentious. Does it matter? I contrast the representation of the political parties and of racial and ethnic minorities under plans optimized according to 18 different definitions of compactness. On these grounds, the definitions are markedly consistent. These methods may be extended to alternative districting objectives and criteria.

Legislators might rely on their partisan base for electoral support—what scholars call their normal vote—or they may cultivate support among nonpartisans through casework or constituency service—what scholars call a personal vote. Previous research frequently argues that legislators face a tradeoff between pursuing the normal vote and a personal vote as traditionally defined, often focusing on resources used by incumbents to build their personal vote. In contrast, we argue that securing the support of partisans and nonpartisans alike should be evaluated based on how a legislator performs in office, and that the so-called normal and personal vote need not be viewed as in conflict. We evaluate our claims using data from state legislative elections following redistricting, focusing on legislative professionalism to measure the resources available to incumbents that they might use to cultivate a personal note.

To authoritarian rulers, holding somewhat competitive elections enhances legitimacy, but entails political risks. Committing electoral fraud can secure victory, but may jeopardize regime legitimacy. However, there is a tool of electoral manipulation that allows authoritarian rulers to reduce electoral risk while preserving legitimacy: gerrymandering. This article undertakes a systematic study of gerrymandering in Hong Kong, using a dataset that documents boundary changes at the level of residential buildings. The empirical findings show a significant partisan bias in electoral redistricting: opposition constituencies are more likely to be redistricted. Redistricting, however, fails to deter opposition incumbents from seeking re-election. No significant negative relationship is found between redistricting and opposition incumbents’ vote share, although redistricting does reduce their overall chances of re-election. The results suggest that gerrymandering, which involves the use of packing and cracking strategies in different districts, can be employed to undermine the aggregate electoral performance of the opposition parties.

Why are fewer congressional elections competitive at the district level when the national electoral environment is at its most competitive? This article explores this “pseudoparadox,” and argues that the answer can be found in partisan redistricting. Through an analysis of 40 years of congressional elections, I find that partisan gerrymanders induce greater competitiveness as national tides increase, largely due to unanticipated consequences of waves adverse to the map-drawing party, particularly in seats held by that party. The phenomenon anecdotally coined by Grofman and Brunell as the “dummymander” is thus actually quite common and has significant effects on rates of congressional competition nationally. In contrast, bipartisan maps are shown to induce lower competition, while nonpartisan maps induce higher competition, under all electoral conditions and competitiveness measures. But the effects of partisan gerrymanders on competition, though strong, can only be seen in interaction with short-term national forces.

State legislative elections are increasingly shaped by two factors that influence the prospects of winning a majority: the redistricting cycle and partisan tide elections. Winning control of the redistricting process offers the prospect of shaping elections for the next decade, making majority status significantly more valuable than it otherwise might be. Partisan tides, on the contrary, can dramatically alter perceptions of which seats are safe or vulnerable and of whether majority status is obtainable or not. In this article, I examine how the proximity of redistricting and the presence of partisan tides are reflected in the strategies of the party organizations that contest state legislative elections. Using party finance data from 29 states during the period from 1996 to 2010, I find that parties' majority-seeking behavior is more intense in states with legislative redistricting, when redistricting is imminent, and when partisan tides favor the minority party.