We examine how 17 behavioural biases relate to each other, to three standard measures of risk and time preferences, to cognitive skills, personality, and demographics, and to outcomes in household finance, well-being, and health. Most consumers in our nationally representative panel data exhibit multiple biases, with substantial cross-person heterogeneity. Biases are positively correlated within person, especially after adjusting for measurement error. From that correlation structure, we reduce our 20 bias and standard preference measures to four behavioural common factors. Each BCF reflects a group of related biases regarding beliefs, decision quality, discounting, or risk/uncertainty attitudes. The first two BCFs also strongly correlate with each other (positively) and cognitive skills (negatively). The first three BCFs and cognitive skills strongly correlate with various outcomes in the expected directions. Our results support processing-based models where basic limitations in cognition and/or attention produce multiple biases, and they have several other implications for theory and practice.

A randomized encouragement design yields null average effects of a credit builder loan (CBL) on consumer credit scores. But machine learning algorithms indicate the nulls are due to stark, offsetting treatment effects depending on baseline installment credit activity. Delinquency on preexisting loan obligations drives the negative effects, suggesting that adding a CBL overextends some consumers and generates negative externalities on other lenders. More favorably for the market, CBL take-up generates positive selection on score improvements. Simple changes to CBL practice, particularly to provider screening and credit bureau reporting, could ameliorate the negative effects for consumers and the market.