Political Redlining by Algorithm

Abstract

Modern political campaigns decide who to contact using turnout-propensity scores. Practitioners have long argued that these scores “redline” low-scored communities, which campaigns rarely attempt to reach. I examine this claim using person-level records of Democratic campaigns and community-based organizations’ 414 million contact attempts (aimed at 14 million voters in ten battleground states in 2024), matched to validated turnout. Three of every five attempts—and two of every three successful conversations—went to voters already scored likely to vote. Nearly a third of the registered voters in this universe (3.8 million) did not cast a ballot, and 1.43 million of them were never reached by any program. These patterns suggest a self-fulfilling prophecy: low-scored voters go uncontacted, and the abstention that follows becomes the record from which their next score is estimated. Local membership-based community organizations offer one promising response to this dynamic. In 2024, these groups’ contact attempts succeeded at roughly twice Democratic campaigns’ rate among Black, Hispanic, Native American, young, new-registrant, and low-income voters. Each additional conversation is associated with a turnout gain, and the association is larger among voters scored less likely to vote.