QUANTIFYING GERRYMANDERING REVEALING GEOPOLITICAL STRUCTURE THROUGH SAMPLING
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1 QUANTIFYING GERRYMANDERING REVEALING GEOPOLITICAL STRUCTURE THROUGH SAMPLING TURING INSTITUTE LONDON, UK JONATHAN MATTINGLY, GREG HERSCHLAG + THE DUKE MATH
2 Impact of Duke Team s work Common Cause v. Rucho (N.C. Congressional): 3 judge conditional panel. Direct appeal to SCOTUS. Nov 2017 Undergraduate research 2014, 2015, 2016, 2018 Provided expert testimony and report in lawsuit Heavily cited in court judgment As seen in Gill v. Whitford (WI State Assembly) : Oral argument held in Supreme Court (SCOTUS) October, 2017 Provide report supporting Amicus Brief by Eric S. Lander North Carolina v. Covington (N.C. State Assembly): 3 judge panel rule racial gerrymander. Affirmed by SCOTUS in June Provide expert testimony on new maps produces at courts order Preparing for partisan gerrymander present (arxiv: arxiv: ) sites.duke.edu/quantifyinggerrymandering
3 No din THE ROBERT A. RUCHO, et al., Supreme Court of the United States v. Appellants, Last Month submitted to the US Supreme court 14 packed into overwhelmingly Democratic districts at the top of the S or dispersed within safe Republican districts at the bottom of the S. App COMMON CAUSE, et al., Appellees. ON APPEAL FROM THE UNITED STATES DISTRICT COURT FOR THE MIDDLE DISTRICT OF NORTH CAROLINA MOTION TO AFFIRM BY THE COMMON CAUSE APPELLEES GREGORY L. DISKANT JONAH M. KNOBLER PETER A. NELSON ELENA STEIGER REICH PATTERSON BELKNAP WEBB & TYLER LLP 1133 Avenue of the Americas New York, New York (212) gldiskant@pbwt.com EMMET J. BONDURANT Counsel of Record BENJAMIN W. THORPE BONDURANT MIXSON & ELMORE LLP 3900 One Atlantic Center 1201 West Peachtree Street Atlanta, Georgia (404) bondurant@bmelaw.com Counsel for the Common Cause Appellees (Counsel continued on inside cover) On January 9, 2018, the District Court issued an opinion holding unanimously that Appellants had standing to challenge the 2016 Plan on a statewide and district-by-district basis. The court unanimously found that the Plan violates the Equal Protection Clause and Art. I, 2 and 4. A two-judge majority also held that the Plan violates the First Amendment. On January 18, 2018, this Court stayed the judgment pending appeal. Dr. Mattingly, meanwhile, generated over 24,000 alternative maps using only nonpartisan districting criteria. Fewer than 0.7% of them resulted in a Republican advantage as lopsided as App.101. Dr. Mattingly s simulations also confirmed that the 2016 Plan both packed and cracked Democratic voters. As he explained, this can be shown by plotting the Democratic vote share of each district on a graph, with the most Republican districts on the left and the most Democratic on the right. As the diagram below reflects, with no packing or cracking, the median map in Dr. Mattingly s simulation set (shown in yellow) yields a straight line. The actual results for the 2016 Plan (shown in blue) are quite different. They resemble an S curve, with Democratic voters either
4 Gerrymander Manipulate district boundaries to favor one party (partisan) or class (racial) Change the outcome of an election "gerrymander the results Boston Gazette 26 March, 1812 racial vs partisan gerrymander
5 North Carolina 13 Congressional Representatives
6 Gerrymander Manipulate district boundaries to favor one party (partisan) or class (racial) Change the outcome of an election "gerrymander the results Boston Gazette 26 March, 1812 racial vs partisan gerrymander
7 NC 2012 US Congressional Districts Is Gerrymandering Oddly Shape Districts?
8 Which Doesn t Belong?
9 Same NC 2012 Same NC 2016 Different Beyond Gerrymandering Judges
10 Gerrymandering as Startling Election Results NC : US House 2012 WI : Gen Assembly 2014 Vote Seats Vote Seats Democratic 50.65% 4 (31%) Republican 48.80% 9 (69%) MD : US House 2012 Democratic 51.28% 36 (36%) Republican 48.72% 63 (64%) USA : US House 2012 Vote Seats Vote Seats Democratic 63% 7 (87.5%) Republican 33% 1 (12.5%) Democratic 50.65% 4 (31%) Republican 48.80% 9 (69%) The most Democratic district had 79.63% Democratic votes The most Republican district had 63.11% Republican votes.
11 How to quantify how gerrymandered or U.S. Not a Proportional Representation System \ unrepresentative a redistricting is? Geographically Localize Representation
12 Geographically Diverse Population Density Presidential Election 2016 Ideal Congressional District (1/13 of population) : 733,499 people Charlotte Area: Charlotte-Gastonia-Salisbury- population 2,402,623 The Triangle: Raleigh-Durham-Cary-Chapel Hill- population 1,749,525 The Piedmont Triad: Greensboro Winston-Salem High Point- population 1,
13 How to quantify gerrymandering? maybe we should understand this as How to reveal a state s natural geopolitical structure?
14 60% Red Red wins 3 Red wins 5 Red wins 2 40% Blue Blue wins 2 Blue wins 0 Blue wins 3 Wikipedia; image by Steven Nass
15 How to quantify how gerrymandered or When is a map fair? unrepresentative a redistricting is? When is a map typical?
16 How to quantify how gerrymandered or What if we drew the districts randomly? unrepresentative a redistricting is? with no regard for party registration or most demographics Look for the likely behavior of an ensemble of districting plans create a null-hypothesis without partisan bias
17 Groups using algorithmic generated maps to benchmark Jowei Chen (Michigan), Jonathan Rodden (Stanford) Wendy Cho (UIUC) Kosuke Imai, Benjamin Fifield (Princeton) Alan Frieze, Wesley Pegden, Maria Chikina (CMU,Pitt) All generating alternative maps. Some sampling a defined distribution. Some using actual surrogate districts. Focus on our group at Duke is based on principled, explicit distribution on redistricting plans
18 The Recipe 1. Determine a compliant random redistricting plan (equal population, compact, VRA compliant, communities of interest kept intact) 2. Count number of Democratic and Republican votes in each of the new districts using actual votes 3. Determine winner in each district of the random redistricting plan 4. Return to step 1 Use Markov Chain Monte Carlo to sample a distribution on redistricting plans
19 Criteria for Sampling
20 non-partisan design criteria (HB 92) 1. districts have equal population 2. the districts are connected and compact, 3. splitting counties is minimized, and 4. African American voters are sufficiently concentrated in 2 districts to affect the winner.
21 N.C. HOUSE BILL 92 REDISTRICTING STANDARDS Districts within 0.1% of equal population Districts shall be reasonably compact Contiguous territory, attempting not to split cities or counties Comply with the Voting Rights Act of 1965 Ignore: Incumbency, party affiliation, demographics
22 N.C. HOUSE BILL 92 REDISTRICTING STANDARDS Districts within 0.1% of equal population (we get close) Districts shall be reasonably compact Contiguous territory, attempting not to split cities or counties Comply with the Voting Rights Act of 1965 Ignore: Incumbency, party affiliation, demographics
23 Use Markov Chain Monte Carlo to sample from redistricting with good scores. Sample: (density) / e (score of redistricting) Know what distribution we are sampling from. Not just generating a large number of alternatives.
24 N.C. Precincts: around 3,000 : {Precincts} 7! {1,..., 13} 1 P ( ) = e Z J( ) (a 13 color Potts Model with an unusual energy)
25 Score function J( ) =w p J pop ( )+w I J compact ( )+w c J county ( )+w m J mino ( ) : {Precincts} 7! {1,...,13} P ( ) = 1 Z e J( ) (a 13 color Potts Model with an unusual energy)
26 Population Score Sum of square deviation from ideal district population 13 X h i 2 Ideal (Pop in district n) n=1 Ideal = Population of N.C , 499
27 Compactness score (Perimeter) 2 Area Minimized for a circle Also considered the ratio of district s area to the smallest circumscribing rectangle
28 Also include score terms for Voting Rights Act and Preserving County Boundaries Soft penalization : for number of split counties of different sizes redistricting plans without two districts meeting minimal voting age black population.
29 Use Markov Chain Monte Carlo to sample Sample: (density) / e (score of redistricting)
30 One Step of MCMC Proposal Then accept/reject according to score function
31 N.C. Precincts around 3,000
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41 Ensemble of ~24,000 NC redistricting plans Fraction of result congressional votes 2016 congressional votes Number of Democrats Elected
42 NC 2012 NC 2016 Panel of Judges
43 Situate maps in ensemble of 24,000 redistricting plans Fraction of result NC2016 NC2012 Judges NC2016 NC2012 Judges congressional votes 2016 congressional votes Number of Democrats Elected
44 Across many elections NC2012 Statewide Democratic Vote Fraction NCSS16 USH12 GOV16 USS14 PRE12 PRE16 USH16 GOV12 NC2016 Judges Democrats Elected
45 Atypical NC 2012 Atypical NC 2016 Typical Panel of Judges
46 Gerrymandering can occur in the absence of oddly shaped districts
47 What about Startling results?
48 Order the districts by the Blue vote fraction Percentage of Blue from lowest to highest Most Red Most Blue 10% 10% 60% 60% 60% Red
49 Order the districts by the Blue vote fraction Percentage of Blue from lowest to highest Most Red Most Blue 10% 10% 60% 60% 60% Red Red Red 0% 0% 0% 100% 100% 40% 40% 40% 40% 40% 20% 30% 40% 50% 60%
50 NC Congressional Delegation 0.8 Democratic vote fraction (2012 congressional votes) (2016 congressional votes). Most Republican To Most Democratic Districts
51 Judges Comp/County a 538 plan Compact a 538 plan
52 Are we sampling the space in a reasonable way? 0.8 Democratic vote fraction Judges Comp/Cnty Compact Medians (2012 congressional votes) (2016 congressional votes). Most Republican To Most Democratic Districts
53 Gerrymandering Index Probability distribution Judges 538-Comp 538-Comp/Cnty Judges 538-Comp/Cnty 538-Comp (2012 congressional votes) (2016 congressional votes) Gerrymandering index
54 NC2012 NC2016
55 NC Congressional Delegation Democratic vote fraction Judges NC2012 NC2016 Medians (2012 congressional votes) (2016 congressional votes). Most Republican To Most Democratic Districts
56 Gerrymandering Index Probability distribution Judges NC2016 NC2012 Judges NC2016 NC (2012 congressional votes) (2016 congressional votes) Gerrymandering index Outlier analysis Eric Lander s Amicus Brief in Gill v. Whitford
57 Rep (538) Dem (538)
58 Signature of Gerrymandering Democratic vote fraction GOP Dem NC2012 NC2016 Judges Medians (2012 votes) (2016 votes). Most Republican To Most Democratic Districts
59 Signature of Gerrymandering 49%-51% 53%-47% -6% -8% -7% +15% +14% +11% -6% -8% -10% +10% +6% +5% D3 D6 D5 D11 D2 D10 D13 D8 D9 D7 D4 D1 D12 D3 D11 D10 D7 D6 D8 D5 D9 D2 D13 D12 D4 D1 Two principle plots presented in Common Cause v. Rucho Identify Cracked and Packed districts
60 The Signature of Gerrymandering and its effects
61 Ensemble of around 19,000 districting plans Wisconsin General Assembly WSA Act 43 0 Fraction of result WSA 2014 Act43 WSA Act Act 43 (act.) Elected Republicans
62 Wisconsin historical elections 0.54 WSA16 GOV12 Fraction of Republican vote USH14 USH12 GOV14 WSA14 PRE16 WSA Number of Republican seats
63 Wisconsin historical elections 0.54 WSA16 GOV12 GOV14 Fraction of Republican vote USH14 USH12 WSA12 WSA14 PRE Number of Republican seats
64 Wisconsin historical elections 0.54 WSA16 GOV12 GOV14 Fraction of Republican vote USH14 USH12 SOS14 USS12 PRE12 WSA12 WSA14 PRE16 Firewall Number of Republican seats
65 Stagnating NC election results due to Gerrymandering Statewide Democratic Vote Fraction NCSS16 USH12 GOV16 USS14 PRE12 PRE16 USH16 GOV Democrats Elected NC2012 NC2016 Judges Democratic vote fraction Democratic vote fraction NC2012 NC2016 Judges Medians 5 10 USH 2012 USH 2016
66 Back to WI 100 Structural advantage exists; sampling decouples geopolitical effects from Gerrymandered effects Probability WI Republican vote needed for parity in election (2012) Probability 50 Probability 40 WI 20 WI Republican vote needed for parity in election (2014) Republican vote needed for parity in election (2016)
67 Where is Gerrymandering occurring? Localized analysis
68 Precinct Level Analysis vote fraction at predict level is this precinct gerrymandered?
69 Precinct Level Analysis vote fraction at predict level pick a districting plan
70 Precinct Level Analysis vote fraction at predict level the district has a partisan vote fraction
71 Precinct Level Analysis vote fraction at predict level 10 Example District Map Probability Democratic vote fraction
72 NC 2012 Red = more Republican than expected Blue = more Democratic than expected vote fraction at predict level
73 NC 2016 average (signed) log likelihood of NC2016 district level results relative to ensemble vote fraction at predict level
74 NC Triangle average (signed) log likelihood of NC2016 district level results relative to ensemble vote fraction at predict level
75 NC Beyond Gerrymandering Judges average (signed) log likelihood of Judges district level results relative to ensemble vote fraction at predict level
76 Local analysis can detect which districts have been Gerrymandered
77 Stability of Conclusions
78 Fraction of result samples samples Democratic vote fraction samples samples Fraction of result Reported S.A. parameters Doubled S.A. parameters Democratic vote fraction Reported S.A. parameters Doubled S.A. parameters Number of Democrats Elected (2012 votes) Most Republican To Most Democratic Districs (2012 votes) Number of Democrats Elected (2012 votes) Most Republican To Most Democratic Districs (2016 votes) Fraction of result Judges (initial) NC2012 (initial) NC2016 (initial) Democratic vote fraction Judges (initial) NC2012 (initial) NC2016 (initial) Fraction of result Population threshold at 1% Population threshold at 0.75% Population threshold at 0.5% Democratic vote fraction Population Threshold 1% Population Threshold 0.5% Number of Democrats Elected (2012 votes) Most Republican To Most Democratic Districs (2016 votes) Number of Democrats Elected (2012 votes) Most Republican To Most Democratic Districs (2012 votes) Fraction of result Main results Dispersion ratio for compactness Democratic vote fraction No change β=0.8 β=1.2 w I =2 w I =3 w m =700 w m =900 w p =2500 w p = Number of Democrats Elected (2012 votes) Most Republican To Most Democratic Districs (2012 votes)
79 Common Metrics
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partisan swing assumption).
81 Efficiency Gap 120 More Wasted R Votes More Wasted D Votes More Wasted R Votes More Wasted D Votes Probability distribution Judges NC2012 NC2016 Judges NC2012 NC (2012 votes) (2016 votes) Efficiency gap
82 Efficiency Gap 0.58 Statewide Republican Vote Fraction GOV12 USH16 USS16 PRE16 PRE12 USS14 GOV16 ATT16 USH12 NCSS Efficiency Gap
83 Partisan Bias 0.4 Dem. Bias Rep. Bias Dem. Bias Rep. Bias Fraction w/ result Judges NC2016 NC2012 Judges NC2012 NC (2012 votes) (2016 votes) Partisan bias
84 0.58 Statewide Republican Vote Fraction GOV12 USH16 USS16 PRE16 PRE12 USS14 GOV16 ATT16 USH12 NCSS Partisan Bias Percent of result Partisan Bias Partisan bias over all elections and plans
85 The Team PRUV 2014 Jonathan Mattingly Christy Graves Sachet Bangia Sophie Guo Bridget Dou Data Data Data Bass Connection Justin Luo Hansung Kang Robert Ravier Greg Herschlag Michael Bell MATH
86 Conclusions Compact districts do not preclude gerrymandering Sampling techniques can detect gerrymandering Provide a Null model against which to compare Local analyses indicate which districts have been gerrymandered
87 Math Questions?
88 Assume the population is uniform model a random distribution of political parties Q: Find null distribution of order statistics for district make up
89 Q: Give some form of stability of plots over a class of energy functions which have certain marginal statistics. Democratic vote fraction No change β=0.8 β=1.2 w I =2 w I =3 w m =700 w m =900 w p =2500 w p = Most Republican To Most Democratic Districs (2012 votes)
90 Q: Characterize the structure of the energy landscape Even with just population and compactness some evidence of phase transitions, and shattering of phase space
91 Accelerate the sampling parallel tempering accelerated sampling hierarchical sampling parallel algorithms
92 One Step of MCMC Proposal Then accept/reject according to score function
93 More details on Wisconsin
94 Shift the global percentages 0.56 Majority Super Majority Majority Super Majority Under Uniform Partisan Swing Assumption 0.54 WSA16 Fraction of Republican vote WSA Republicans elected
95 90 seats vs global vote (Wisconsin) Number of Republican seats Expected seats WI (contested) Standard Deviation 90% of ensemble Bound WSA12 Super Majority Majority % Vote to the Republicans Number of Republican seats Expected seats WI (contested) Standard Deviation 90% of ensemble Bound WSA14 % Vote to the Republicans Super Majority Majority Number of Republican seats Expected seats WI (contested) Standard Deviation 90% of ensemble Bound WSA16 Super Majority % Vote to the Republicans Majority
96 Measuring Representativeness WSA16 WI (int) 0.60 Fraction of result WI (act) Elected Republicans Frac Republican Vote `(map) = log Prob(outcome map produces) Average `(map) over shift Republicans Elected
97 2.0 WSA12 Probability Measuring Representativeness 0.5 WI H 2.0 WSA The Wisconsin plans are clearly an outlier for the average log likelihood over shifts 45%-55% Probability WI H WSA Probability WI
98 Engineered? results should be stable under small changes to districts sample near by districts and observe changes
99 NC 2012 NC 2016 Judges NC2012 NC2016 Judges NC2012 NC2016 Judges Judge s districts resemble near by districts NC 2012 and NC 2016 do not
100 Gerrymander Index Local Perturbations Fraction w/ worse index Judges NC2016 NC Gerrymandering index (2012 votes)
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