Gerrymandering

Cartography as Weapon

5 hours4 topicsPrimary sources included

2.1

Origins: The Original Gerrymander

In 1812, Massachusetts Governor Elbridge Gerry signed a bill creating a district so contorted it resembled a salamander. A political cartoon dubbed it a "Gerrymander," and the practice of drawing districts for political advantage got its name.

1812

The Original Gerrymander

Governor Gerry's Massachusetts redistricting creates salamander-shaped district. Term 'gerrymander' coined.

1962

Baker v. Carr

Supreme Court rules redistricting is justiciable. 'One person, one vote' principle established.

2010s

REDMAP Initiative

Republican State Leadership Committee executes coordinated gerrymandering strategy. $30M investment yields decade of control.

2019

Rucho v. Common Cause

Supreme Court declares partisan gerrymandering non-justiciable. Federal courts cannot intervene.

THE RUCHO DECISION

In 2019, the Supreme Court ruled that federal courts cannot adjudicate partisan gerrymandering claims. Chief Justice Roberts wrote that while partisan gerrymandering may be "incompatible with democratic principles," there are no "judicially manageable standards" for courts to apply.

2.2

The Techniques: Packing and Cracking

Modern gerrymandering uses two primary techniques to dilute opponents' voting power: packing concentrates opposition voters into few districts, while cracking spreads them across many districts where they can't form majorities.

gerrymander_algorithm.pseudo

pseudo

// Core gerrymandering techniques
FUNCTION pack(opposition_voters, districts):
    // Concentrate opponents into minimal districts
    super_majority_threshold = 0.80
    
    FOR district IN controllable_districts:
        IF opposition_concentration > super_majority_threshold:
            MARK as "packed"  // Opponents win by huge margins
            // Their "extra" votes above 50%+1 are wasted
    
    RETURN opposition_wins_few_seats_decisively

FUNCTION crack(opposition_voters, districts):
    // Spread opponents thin across many districts
    target_opposition_share = 0.45  // Below winning threshold
    
    FOR district IN remaining_districts:
        DISTRIBUTE opposition_voters such that:
            opposition_share < 0.50
    
    RETURN opposition_loses_many_seats_narrowly

// Combined strategy
FUNCTION optimal_gerrymander(voter_data):
    pack(strongest_opposition_areas)
    crack(remaining_opposition_voters)
    
    // Result: 55% of votes -> 70% of seats

55%

Vote share

Typical winning party

70%+

Seat share

After effective gerrymander

15%

Representation gap

Votes vs. seats disparity

2.3

Algorithmic Gerrymandering

Modern redistricting has become a data science problem. With precinct-level voting data, demographic information, and powerful computers, map-drawers can optimize districts with surgical precision.

modern_redistricting.pseudo

pseudo

// Modern algorithmic redistricting
IMPORT voter_file_data        // 200M+ voter records
IMPORT precinct_results       // Historical voting patterns
IMPORT census_blocks          // Smallest geographic unit
IMPORT demographic_estimates  // Age, race, income, education

FUNCTION draw_optimal_map(target_party):
    constraints = {
        equal_population: true,      // Must be within 1%
        contiguity: true,            // Districts must connect
        compactness: "ignore",       // No legal requirement
        communities: "ignore"        // No legal requirement
    }
    
    voter_index = calculate_partisan_lean(precinct_data)
    
    // Simulate millions of possible maps
    FOR iteration IN range(10_000_000):
        candidate_map = generate_random_map(constraints)
        score = evaluate_partisan_advantage(candidate_map, target_party)
        IF score > best_score:
            best_map = candidate_map
            
    RETURN best_map  // Statistically optimized for one party

THE DATA ADVANTAGE

Redistricting today uses voter files with hundreds of data points per voter: party registration, voting history, consumer behavior, estimated ideology scores. The party controlling redistricting has access to this data; citizens don't.

2.4

Measuring Gerrymandering: The Efficiency Gap

Political scientists developed the "efficiency gap" metric to quantify gerrymandering. It measures "wasted votes"—votes for losing candidates or votes beyond what's needed to win.

efficiency_gap.pseudo

pseudo

// Efficiency Gap Calculation
FUNCTION calculate_efficiency_gap(districts):
    wasted_A = 0
    wasted_B = 0
    
    FOR district IN districts:
        votes_A = district.party_A_votes
        votes_B = district.party_B_votes
        winning_threshold = (votes_A + votes_B) / 2 + 1
        
        IF votes_A > votes_B:  // Party A wins
            wasted_A += (votes_A - winning_threshold)  // Surplus votes
            wasted_B += votes_B                        // All losing votes
        ELSE:  // Party B wins
            wasted_B += (votes_B - winning_threshold)
            wasted_A += votes_A
    
    efficiency_gap = (wasted_A - wasted_B) / total_votes
    
    // Gap > 7% suggests significant gerrymandering
    // Gap > 10% indicates extreme gerrymandering
    RETURN efficiency_gap

// Example: Wisconsin 2012
// Republicans: 48.6% of votes, 60% of seats
// Efficiency gap: 11.7% favoring Republicans

Warning threshold

Efficiency gap suggesting gerrymander

11.7%

Wisconsin 2012

One of most gerrymandered states

2.5

Racial vs. Partisan Gerrymandering

Federal courts can still strike down racial gerrymanders under the Voting Rights Act, but not partisan ones. This creates a legal paradox: since race and party correlate, map-drawers can claim partisan intent to avoid racial gerrymandering findings.

legal_paradox.pseudo

pseudo

// The Legal Distinction
FUNCTION evaluate_gerrymandering_claim(map, intent):
    
    IF intent == "racial":
        // Subject to strict scrutiny under VRA
        // Courts CAN strike down
        legal_standard = "strict_scrutiny"
        outcome = LIKELY_UNCONSTITUTIONAL
        
    ELIF intent == "partisan":
        // Post-Rucho: non-justiciable
        // Federal courts CANNOT intervene
        legal_standard = "none"
        outcome = LEGAL
    
    // The loophole
    FUNCTION claim_partisan_intent(actual_racial_gerrymander):
        defense = "We weren't targeting Black voters"
        defense += "We were targeting Democrats"
        // Since Black voters are ~90% Democratic...
        
        IF plausible_deniability:
            RECLASSIFY as partisan
            outcome = LEGAL

THE RACIAL-PARTISAN PARADOX

When North Carolina's maps were challenged as racial gerrymanders, the state's defense was that they were merely partisan gerrymanders. After Rucho, this defense became essentially bulletproof in federal court.

2.6

Reform Attempts: Independent Commissions

Some states have moved redistricting to independent commissions, removing it from legislative control. Results have been mixed, and the definition of "independent" varies widely.

States

With some form of commission

States

With truly independent commissions

States

Legislators draw their own districts

COMMISSION TYPES

Independent: California, Arizona, Michigan - Citizen commissions with partisan balance requirements.
Politician: New Jersey, Hawaii - Legislators serve on commission.
Advisory: Maine, Vermont - Commissions recommend, legislators decide.

MODULE_02 // KEY_TAKEAWAYS

→Gerrymandering allows the party drawing maps to convert minority vote shares into majority seat shares.
→Since Rucho v. Common Cause (2019), federal courts cannot hear partisan gerrymandering claims.
→Modern redistricting uses algorithmic optimization on voter data unavailable to ordinary citizens.
→The racial-partisan paradox allows race-based manipulation to hide behind partisan intent claims.