Individual and differential harm in redistricting

Area of Science:

• Political Science
• Computational Social Science
• Public Policy

Background:

• Existing measures for partisan bias in redistricting are limited.
• Current methods are not easily adaptable to diverse groups (race, class, geography) or contexts (single-party, local).

Purpose of the Study:

• To propose a unified framework of
• harm
• for evaluating districting plan impacts on individual voters and groups.
• To overcome limitations of existing bias measures by focusing on individual voter choices and counterfactual plans.

Main Methods:

• Developed a novel framework defining voter harm based on candidate election outcomes under different districting plans.
• Utilized redistricting simulations to estimate harm.
• Applied the framework to three US redistricting case studies.

Main Results:

• The proposed "harm" framework offers a flexible and precise method for quantifying redistricting impacts.
• Demonstrated the framework's utility across different redistricting scenarios.
• Highlighted the importance of individual voter choice in evaluating districting fairness.

Conclusions:

• The "harm" framework provides a unified and adaptable approach to assessing districting fairness.
• This method precisely quantifies individual voter impacts, enhancing current bias evaluation tools.
• The framework is applicable beyond partisan bias to other group definitions and redistricting contexts.