We study how individuals repay their debt using linked data on multiple credit cards from five major issuers. We find that individuals do not allocate repayments to the higher interest rate card, which would minimize the cost of borrowing. Instead, individuals allocate repayments using a balance-matching heuristic under which the share of repayments on each card is matched to the share of balances on each card. We show that balance matching captures more than half of the predictable variation in repayments, performs substantially better than other models, and is highly persistent within individuals over time. Consistent with these findings, we show that machine learning algorithms attribute the greatest variable importance to balances and the least variable importance to interest rates in predicting repayment behavior.

More on this topic

BFI Working Paper·Jan 7, 2026

The Impacts of Parole Supervision

Luke Brinkman, Andrew Jordan, and Derek Neal
Topics: Economic Mobility & Poverty, Fiscal Studies
BFI Working Paper·Jan 7, 2026

A World Trading System For Whom? Evidence from Global Tariffs

Rodrigo Adão, John Sturm Becko, Arnaud Costinot, and Dave Donaldson
Topics: Economic Mobility & Poverty, Employment & Wages
BFI Working Paper·Jan 5, 2026

The Labor Market Return to Permanent Residency

Kory Kroft, Isaac Norwich, Matthew Notowidigdo, and Stephen Tino
Topics: Economic Mobility & Poverty, Employment & Wages