What redistricting reform models (commission types, formulas) best reduce partisan bias, according to comparative studies?
Executive summary
Comparative studies point to three reforms that most consistently reduce partisan bias: independent or mixed commissions, procedural constraints that limit partisan leeway (including algorithmic or formulaic approaches), and institutional designs that produce divided control or countervailing steps such as the Define-Combine Procedure (DCP); each approach has strengths and documented limits in the literature [1] [2] [3]. Simulation-based evaluations show these reforms correlate with measurably fairer maps in many states, although geography and legal constraints remain important caveats [4] [2].
1. Independent commissions: evidence of improvement, not perfection
Multiple comparative analyses find that independent redistricting commissions tend to produce less biased and more competitive plans than single-party legislative control, with post-2010 commission states showing reduced asymmetry versus sole-legislature control in several studies [1]; broader reviews conclude commissions can improve fairness but also face constraints and variable performance across states [5]. At the same time, empirical work and case studies warn commissions are not a panacea: commissions sometimes deadlock, reflect local political geography, or implement trade-offs among compactness, communities of interest, and partisan fairness that limit uniform gains [6] [7].
2. Procedural constraints and formulaic rules constrain partisan leeway
Research that models the “game” of redistricting shows that institutional features that reduce actors’ leeway — whether by legal constraints, transparency rules, or a priori formulas — correlate with smaller partisan biases in enacted plans [2]. Empirical simulation work similarly demonstrates that applying algorithmic, rule-based constraints (for example, limits on partisan symmetry or efficiency gap thresholds) produces plans that cluster closer to nonpartisan baselines than unconstrained partisan maps [4] [8].
3. The Define‑Combine Procedure: a novel two‑step check on self‑interest
A recent proposal, the Define‑Combine Procedure (DCP), lets each party separately draw subunits and then combines them, which simulations using 2020 election data found “substantially reduced partisan advantage” relative to unilateral party control while preserving flexibility on other criteria [3]. Advocates argue DCP harnesses partisan incentives against each other without requiring an independent third party, but this evidence comes from simulation experiments and scholars note practical and legal adoption questions remain untested in broad real‑world use [3] [7].
4. Divided government and shared control often produce fairer outcomes
Longstanding studies report that when redistricting authority is split across branches or parties, maps tend to be more balanced; comparative primers and historical analyses find divided control at redistricting correlates with lower partisan skew than unified single‑party control [8] [9]. This pattern aligns with game-theoretic treatments showing that constraining any single actor’s leeway reduces the equilibrium ability to entrench a partisan advantage [2], though scholars caution that divided control can also produce stalemate and court intervention.
5. Simulation and measurement: how researchers judge “best”
The comparative literature relies heavily on simulation ensembles and formal measures (partisan symmetry, efficiency gap, seats-votes curves) to separate effects of political geography from institutional bias; studies using these tools find reforms that constrain partisan actors — commissions, formulas, DCP, divided control — generally yield plans closer to simulated nonpartisan baselines [4] [10] [2]. Yet scholars also warn measurement choices matter: different metrics emphasize different tradeoffs (responsiveness vs. symmetry) and no single metric fully captures democratic goals, so conclusions depend on the outcome chosen [7] [10].
6. Limits, tradeoffs and the politics of implementation
Comparative work consistently stresses limits: geography can make “perfect” proportionality impossible and reforms can produce uneven results across states depending on local law, enforcement, and political context [4] [7]. Independent commissions and algorithmic rules have improved maps in several high‑profile cases but face political resistance and legal obstacles, and novel mechanisms like DCP show promise in simulation without yet having broad empirical tests in real legislatures [5] [3].