Declination as a Metric to Detect Partisan Gerrymandering

Campisi, Marion; Padilla, Andrea; Ratliff, Thomas; Veomett, Ellen

doi:10.1089/elj.2019.0562

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…43 Equating the angles of the two lines results in the ideal δ = 0. When the statewide vote for party A is V A , straightforward algebra derives the following ideal δ = 0 relations (Campisi, et al 2019,375, Katz, et al 2020)…”

Section: Declination Measure δmentioning

confidence: 99%

On Measuring Two-Party Partisan Bias in Unbalanced States

Nagle

Ramsay

2021

Election Law Journal: Rules, Politics, and Policy

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Assuming that partisan fairness and responsiveness are important aspects of redistricting, it is important to measure them. Many measures of partisan bias are satisfactory for states that are balanced with roughly equal proportions of voters for the two major parties. It has been less clear which metrics measure fairness robustly when the proportion of the vote is unbalanced. We have addressed this by analyzing past election results for four states with Democratic preferences (CA, IL, MA, and MD), three states with Republican preferences (SC, TN, and TX) and comparing those to results for four nearly balanced states (CO, NC, OH, and PA). We used many past statewide elections in each state to build statistically precise seats for votes and rank for votes graphs to which many measures of partisan bias were applied. In addition to providing values of responsiveness, we find that five of the measures of bias provide mutually consistent values in all states, thereby providing a core of usable measures for unbalanced states. Although all five measures focus on different aspects of partisan bias, normalization of the values across the eleven states provides a suitable way to compare them, and we propose that their average provides a superior measure which we call composite bias. Regarding other measures, we find that the most seemingly plausible symmetry measure fails for unbalanced states. We also consider deviations from the proportionality ideal, but using it is difficult because the political geography of a state can entangle responsiveness with total partisan bias. We do not attempt to separate intentional partisan bias from the implicit bias that results from the interaction of the map drawing rules of a state and its political geography, on the grounds that redistricting should attempt to minimize total partisan bias whatever its provenance.

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Section: Declination Measure δmentioning

confidence: 99%

On Measuring Two-Party Partisan Bias in Unbalanced States

Nagle

Ramsay

2021

Election Law Journal: Rules, Politics, and Policy

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“…The last metric we test, the declination, is more involved to define. Although the metric was coined by Warrington in [War18], we base our definition off the one given in [Cam+19]. Let V = {V 1 , V 2 , .…”

Section: Other Metricsmentioning

confidence: 99%

A Comparison of Metrics for the Identification of Partisan Gerrymandering

Seetharaman¹

2021

Preprint

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Only 2% of Americans believe that the elections are completely fair, and this is mainly due to partisan gerrymandering. However, there is currently no effective, standardized way to identify the presence of partisan gerrymandering. A relatively newly proposed method of identification is ensemble analysis. This is done by generating a large neutral ensemble of voting plans, then comparing the given voting plan against the ensemble through the use of a mathematical metric. In this paper, we survey seven of the most common mathematical metrics used for this identification process. These metrics are the efficiency gap, weighted efficiency gap with weight 2, relative efficiency gap with weight 1, relative efficiency gap with weight 2, partisan bias, mean-median score, and declination, all of which we define and discuss. This is done by performing ensemble analysis on ten elections with each metric to determine whether each metric determines each election as gerrymandered. All ten of these elections have been studied in the literature, so we compare the results in the literature to the results found by the metrics to the results found here to assess accuracy of metrics. We show that, while the mean-median score and partisan bias are the most applicable across all seven metrics, they are also the most inaccurate. No significant difference was detected between the remaining five metrics, so our final recommendation is to use one of the four variants of the efficiency gap or the declination if applicable, and the mean-median score if not. The partisan bias was the least accurate of all metrics tested.

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“…This would allow for observation of results with different degrees of swing in the vote. The EG is sensitive to small changes in vote in highly competitive districts because it relies on them to distinguish seats won (Campisi et al, 2019), but the mean-median is more robust to the choice of election used (Krasno et al 2019).…”

Section: Concerns About the Measuresmentioning

confidence: 99%

Evaluating Legislative Districts Using Measures of Partisan Bias and Simulations

Burden

Smidt

2020

Sage Open

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A well-developed body of research offers ways to measure the partisan advantages that result from legislative districting. Although useful to researchers and legal practitioners, those studies also suffer from theoretical, empirical, and legal limitations. In this essay, we review measures of partisan bias and methods for both simulating election results under existing maps and simulating hypothetical maps. We start by describing the concept of partisan bias and how it has been measured. Then, we turn to new simulation methods that generate hypothetical election results or districts to judge the fairness of a map. While both kinds of evaluation are useful, we point to some unresolved questions and areas for future research.

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Declination as a Metric to Detect Partisan Gerrymandering

Cited by 7 publications

References 6 publications

On Measuring Two-Party Partisan Bias in Unbalanced States

On Measuring Two-Party Partisan Bias in Unbalanced States

A Comparison of Metrics for the Identification of Partisan Gerrymandering

Evaluating Legislative Districts Using Measures of Partisan Bias and Simulations

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