Using Outlier Analysis to Detect Partisan Gerrymanders: A Survey of Current Approaches and Future Directions

RamachandranGowri,; GoldDara,

doi:10.1089/elj.2018.0503

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…Using real-world data with simulation methods (McCartan and Imai 2020), however, we can randomly sample from the set of all redistricting plans and obtain the probability of each seat or firm allocation; our inferences use those representative samples as a null distribution for outlier analysis as above. This analysis is rooted in the outlier analysis of Ramachandran and Gold (2018), commonly included in expert reports and used as evidence in redistricting court cases (see Chen and Rodden 2015). We are the first to apply these longstanding Monte Carlo methods for identifying racial and partisan gerrymandering (Cain et al 2018;Chen and Rodden 2013;Chen and Stephanopoulos 2020;Fifield et al 2020;Tam Cho and Liu 2016) to firms.…”

Section: Methods: Simulating Redistricting Plansmentioning

confidence: 99%

Are Firms Gerrymandered?

ARTÉS,

KAUFMAN,

RICHTER

et al. 2024

Am Polit Sci Rev

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We provide the first evidence that firms, not just voters, are gerrymandered. We compare allocations of firms in enacted redistricting plans to counterfactual distributions constructed using simulation methods. We find that firms are over-allocated to districts held by the mapmakers’ party when partisans control the redistricting process; maps drawn by courts and independent commissions allocate firms more proportionately. Our results hold when we account for the gerrymandering of seats: fixing the number of seats the mapmakers’ party wins, they obtain more firms than expected in their districts. Our research reveals that partisan mapmakers target more than just voters, shedding new light on the link between corporate and political power in the United States and opening new pathways for studying how mapmakers actually draw district boundaries.

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Section: Methods: Simulating Redistricting Plansmentioning

confidence: 99%

Are Firms Gerrymandered?

ARTÉS,

KAUFMAN,

RICHTER

et al. 2024

Am Polit Sci Rev

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show abstract

“…Similarly, Mayer notes that automated redistricting methods have captured substantial attention recently (Chen and Rodden 2013; Cho and Liu 2018; Liu, Cho, and Wang 2016; Magleby and Mosesson 2018; Vanneschi, Henriques, and Castelli 2017). One method draws large numbers of maps with different decisions rules and initial conditions, with the resulting maps used to identify outliers that indicate partisan gerrymanders or possible “natural” gerrymanders (Cain et al 2017; Chen 2017; Chen and Cottrell 2016; Chen and Rodden 2013, 2015; Fifield et al 2020; Ramachandran and Gold 2018; Tam Cho and Liu 2016). The network figure shows these studies with self-tying nodes and node connections, including the relationship between geographic partisan distribution and partisan advantage .…”

Section: Application To Redistrictingmentioning

confidence: 99%

Mapping Literature with Networks: An Application to Redistricting

Lo¹,

Judge-Lord

Hudson³

et al. 2023

Polit. Anal.

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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.

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“…They produce 1000 districting plans and calculate their convexity scores, measuring convexity by taking the ratio of area of each district and the area of the convex hull of that district and summing over all districts. They then compare the compactness of the Florida plan in their paper [71] against the distribution of compactness in their sampled plans and find it to be a statistical outlier [73].…”

Section: Related Workmentioning

confidence: 99%

Seed-Fill-Shift-Repair: A redistricting heuristic for civic deliberation

et al. 2020

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Political redistricting is the redrawing of electoral district boundaries. It is normally undertaken to reflect population changes. The process can be abused, in what is called gerrymandering, to favor one party or interest group over another, resulting thereby in broadly undemocratic outcomes that misrepresent the views of the voters. Gerrymandering is especially vexing in the United States. This paper introduces an algorithm, with an implementation, for creating districting plans (whether for political redistricting or for other districting applications). The algorithm, Seed-Fill-Shift-Repair (SFSR), is demonstrated for Congressional redistricting in American states. SFSR is able to create thousands of valid redistricting plans, which may then be used as points of departure for public deliberation regarding how best to redistrict a given polity. The main objectives of this paper are: (i) to present SFSR in a broadly accessible form, including code that implements it and test data, so that it may be used for both civic deliberations by the public and for research purposes. (ii) to make the case for what SFSR essays to do, which is to approach redistricting, and districting generally, from a constraint satisfaction perspective and from the perspective of producing a plurality of feasible solutions that may then serve in subsequent deliberations. To further these goals, we make the code publicly available. The paper presents, for illustration purposes, a corpus of 11,206 valid redistricting plans for the Commonwealth of Pennsylvania (produced by SFSR), using the 2017 American Community Survey, along with descriptive statistics. Also, the paper presents 1,000 plans for each of the states of Arizona, Michigan, North Carolina, Pennsylvania, and Wisconsin, using the 2018 American Community Survey, along with descriptive statistics on these plans and the computations involved in their creation.

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Using Outlier Analysis to Detect Partisan Gerrymanders: A Survey of Current Approaches and Future Directions

Cited by 12 publications

References 17 publications

Are Firms Gerrymandered?

Are Firms Gerrymandered?

Mapping Literature with Networks: An Application to Redistricting

Seed-Fill-Shift-Repair: A redistricting heuristic for civic deliberation

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