Seats, Votes, and Gerrymandering: Estimating Representation and Bias in State Legislative Redistricting

Browning, Robert X.; King, Gary

doi:10.1111/j.1467-9930.1987.tb00413.x

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…A related line of research studies representational bias (sometimes referred to as partisan bias; e.g., Besley and Preston ; Browning and King ; Calvo ; Linzer ). A recent study by Rodden () is particularly relevant for our analysis.…”

Section: Unequal Voters? a New Look At Parliamentary Representationmentioning

confidence: 99%

Are Voters Equal under Proportional Representation?

Kedar

Harsgor

Sheinerman

2015

American J Political Sci

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We develop and apply a new conceptual framework and measure for evaluating electoral systems, focusing on (in)equality in parliamentary representation. Our main arena of interest is proportional representation with districts, an electoral system employed by more than half of democratic states, and we draw on an almost entirely overlooked fact: Electoral regimes vary substantially within countries, with some voters casting their ballot in semi-majoritarian districts of few representatives and others in large and proportional ones. This within-country institutional variation, we contend, affects representational (in)equality. Evaluating equality in parliamentary representation, we demonstrate that districted proportional representation often leads to overrepresentation of voters supporting right-leaning parties. Utilizing district-level data from 20 Western parliamentary democracies and complementing our within-country approach with a cross-country analysis, we further show that where parliaments are elected by large and small districts, representational inequality among voters is greater compared with countries in which parliament is elected by even-magnitude districts.

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Section: Unequal Voters? a New Look At Parliamentary Representationmentioning

confidence: 99%

Are Voters Equal under Proportional Representation?

Kedar

Harsgor

Sheinerman

2015

American J Political Sci

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“…Consequently, it is more and more important for participants and outside observers to be able to compare the partisan effects of suggested schemes. Unlike other measures of partisan bias (Grofman, 1983;Niemi, 1985;King and Browning, 1987;King, 1989;Gelman and King, 1990), the index of party strength presented here may be computed before an election has been held and it offers strong insights into the intentions of the redistricters and into just how those intentions were put into effect. The test is not only more intuitively meaningful and easier to explain to judges and the public than sophisticated variants of seats-votes ratios (Gelman and King, 1990, 1994a, and 1994b), but the simulations that it suggests are more clearly tied to the specific electoral history in a jurisdiction than are those in more general, abstract schemes.…”

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confidence: 99%

Estimating the Partisan Consequences of Redistricting Plans-Simply

Kousser¹

1996

Legislative Studies Quarterly

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“…Fine granularity: For this case, for every cell or node y we have 0 < Pop(y) ≤ c for some fixed constant c > 0. This kind of data is obtained, for example, when one uses data at the "Voting Tabulation District" (VTD) level 4 or at the "census block" level.…”

Section: Input Data and Its Granularity Levelsmentioning

confidence: 99%

“…Although there is no dearth of legal briefs filed in courts involving gerrymandering over many years in the past, it is only more recently that mathematicians and applied computational researchers have started to investigate this topic, perhaps due to the tremendous progress in high-speed computation in the last two decades. For example, researchers in [1,4,5,15,16,22,23,31,32] have made conceptual or empirical attempts at quantifying gerrymandering and devising redistricting methods to optimize such quantifications using well-known notions such as compactness and symmetry, whereas researchers in [1, 6-8, 19, 31] have investigated designing efficient heuristic approach and other computer simulation approaches for this purpose. Two recent research directions deserve specific mentions here.…”

Section: Introductionmentioning

confidence: 99%

On partisan bias in redistricting: computational complexity meets the science of gerrymandering

Chatterjee,

DasGupta

2019

Preprint

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The main topic of this paper is "gerrymandering", namely the curse of deliberate creations of district maps with highly asymmetric electoral outcomes to disenfranchise voters, and it has a long legal history going back as early as 1812. Measuring and eliminating gerrymandering has enormous far-reaching implications to sustain the backbone of democratic principles of a country or society.Although there is no dearth of legal briefs filed in courts involving many aspects of gerrymandering over many years in the past, it is only more recently that mathematicians and applied computational researchers have started to investigate this topic. However, it has received relatively little attention so far from the computational complexity researchers (where by "computational complexity researchers" we mean researchers dealing with theoretical analysis of computational complexity issues of these problems, such as polynomial-time solvabilities, approximability issues, etc.). There could be several reasons for this, such as descriptions of these problem non-CS non-math (often legal or political) journals that are not very easy for theoretical CS (TCS) people to follow, or the lack of effective collaboration between TCS researchers and other (perhaps non-CS) researchers that work on these problems accentuated by the lack of coverage of these topics in TCS publication venues. One of our modest goals in writing this article is to improve upon this situation by stimulating further interactions between the science of gerrymandering and the TCS researchers. To this effect, our main contributions in this article are twofold:We provide formalization of several models, related concepts, and corresponding problem statements using TCS frameworks from the descriptions of these problems as available in existing non-CS-theory (perhaps legal) venues.We also provide computational complexity analysis of some versions of these problems, leaving other versions for future research.The goal of writing article is not to have the final word on gerrymandering, but to introduce a series of concepts, models and problems to the TCS community and to show that science of gerrymandering involves an intriguing set of partitioning problems involving geometric and combinatorial optimization.

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Seats, Votes, and Gerrymandering: Estimating Representation and Bias in State Legislative Redistricting

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Cited by 17 publications

References 7 publications

Are Voters Equal under Proportional Representation?

Are Voters Equal under Proportional Representation?

Estimating the Partisan Consequences of Redistricting Plans-Simply

On partisan bias in redistricting: computational complexity meets the science of gerrymandering

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