VOL 99 NO. I ADAMS ETAL.: SUBPRIME LENDING largest loans. The company we study assigns buyers to a small number of credit categories. w estimate that all else equal, a buyer in the worst category wants to borrow around $180 more than a buyer in the best category, and is more than twice as likely to default given equally sized loans This strong force toward adverse selection is mitigated by the use of risk-based pricing. In practice, observably risky buyers end up with smaller rather than larger loans because they face higher down payment requirements. The finding is notable because the development of sophist cated credit scoring is widely perceived to have had a major impact on consumer credit markets Einav, Mark Jenkins, and Levin 2008b). Here we document its marked effect in matching high risk borrowers with smaller loans. Of course, risk-based pricing mitigates selection only across observably different risk groups. We also look for, and find, evidence of adverse selection within risk groups, driven by unobservable characteristics. Specifically, we estimate that a buyer who borrows an extra $1, 000 for unobservable reasons will have an 18 percent higher hazard rate of default than one who does not, controlling for car characteristics and loan liability We view these findings as broadly supportive of the information economics view of consumer lending and its explanation for the presence of credit constraints. Overall our evidence supports: ()the underlying forces of informational models of lending, namely moral hazard and adverse election;(ii) the supply-side responses these models predict, specifically loan caps, variable interest rates, and risk-based pricing: and(iii) the predicted consequences, specifically liquidity effects in purchasing behar Our paper ties into a large empirical literature documenting liquidity-constrained consumer behavior and a much smaller literature on its causes. Much of the accumulated evidence on the former comes from consumption studies that document relatively high propensities to consum out of transitory income, particularly for households with low wealth. Some of the sharpest evidence in this regard comes from analyzing consumption following predictable tax rebates. For instance, David S. Johnson, Jonathan A. Parker, and Nicholas S Souleles(2006) find that households immediately consumed 20-40 percent of the 2001 tax rebate, with the effect biggest for low-wealth households(see also Souleles 1999: Parker 1999). A common explanation for these findings is that households with low wealth are unable to effectively access credit(Angus Deaton 1991; Stephen P. Zeldes 1989). Further evidence on credit constraints comes from David B. Gross and Souleles(2002), whe use detailed data from a credit card company to look at what happens when credit limits are raised. They find that a $100 increase in a card holder's limit raises spending by $10-14. Based on this, they argue that a substantial fraction of borrowers in their sample appear to be credit constrained. As will be apparent below, the population in our data is most likely in a substantially worse position to access credit than the typical credit card holder a distinct set of evidence on credit constraints comes from studying household preferences over different types of loan contracts. An early survey by F. Thomas Juster and Robert P Shay (1964) found striking differences between households in their willingness to pay higher interest rates for a longer loan with lower monthly payments. In particular, households likely to be credit onstrained, e. g, those with lower incomes, were much more willing to pay higher interest rates to reduce their monthly payment. More recently, Orazio P Attanasio, Pinelopi K. Goldberg, and Studies of the effects of unemployment insurance also provide evidence for credit constraints (e.g, David Card, Raj Chetty, and Andrea Weber 2007; Chetty 2008). There is no clear consensus, however, on the exact story. For instance, Christopher Carroll(2001)argues that much the evidence on consumption behavior can be explained by a buffer stock model where all agents can borrow freely io Jappelli( 1990) provides some limited evidence supporting credit rationing, based on the fact that 19 percent of the households in the 1983 Survey of Consumer Finances report having had a credit applica tion rejected or not applying for a loan for fear of being rejected.VOL. 99 NO. 1 Adams Et al.: Subprime Lending 51 largest loans. The company we study assigns buyers to a small number of credit categories. We estimate that all else equal, a buyer in the worst category wants to borrow around $180 more than a buyer in the best category, and is more than twice as likely to default given equally sized loans. This strong force toward adverse selection is mitigated by the use of risk-based pricing. In practice, observably risky buyers end up with smaller rather than larger loans because they face higher down payment requirements. The finding is notable because the development of sophisti￾cated credit scoring is widely perceived to have had a major impact on consumer credit markets (Einav, Mark Jenkins, and Levin 2008b). Here we document its marked effect in matching high￾risk borrowers with smaller loans. Of course, risk-based pricing mitigates selection only across observably different risk groups. We also look for, and find, evidence of adverse selection within risk groups, driven by unobservable characteristics. Specifically, we estimate that a buyer who borrows an extra $1,000 for unobservable reasons will have an 18 percent higher hazard rate of default than one who does not, controlling for car characteristics and loan liability. We view these findings as broadly supportive of the information economics view of consumer lending and its explanation for the presence of credit constraints. Overall our evidence supports: (i) the underlying forces of informational models of lending, namely moral hazard and adverse selection; (ii) the supply-side responses these models predict, specifically loan caps, variable interest rates, and risk-based pricing; and (iii) the predicted consequences, specifically liquidity effects in purchasing behavior. Our paper ties into a large empirical literature documenting liquidity-constrained consumer behavior and a much smaller literature on its causes. Much of the accumulated evidence on the former comes from consumption studies that document relatively high propensities to consume out of transitory income, particularly for households with low wealth.3 Some of the sharpest evidence in this regard comes from analyzing consumption following predictable tax rebates. For instance, David S. Johnson, Jonathan A. Parker, and Nicholas S. Souleles (2006) find that households immediately consumed 20–40 percent of the 2001 tax rebate, with the effect biggest for low-wealth households (see also Souleles 1999; Parker 1999). A common explanation for these findings is that households with low wealth are unable to effectively access credit (Angus Deaton 1991; Stephen P. Zeldes 1989).4 Further evidence on credit constraints comes from David B. Gross and Souleles (2002), who use detailed data from a credit card company to look at what happens when credit limits are raised. They find that a $100 increase in a card holder’s limit raises spending by $10–14. Based on this, they argue that a substantial fraction of borrowers in their sample appear to be credit constrained. As will be apparent below, the population in our data is most likely in a substantially worse position to access credit than the typical credit card holder. A distinct set of evidence on credit constraints comes from studying household preferences over different types of loan contracts. An early survey by F. Thomas Juster and Robert P. Shay (1964) found striking differences between households in their willingness to pay higher interest rates for a longer loan with lower monthly payments. In particular, households likely to be credit constrained, e.g., those with lower incomes, were much more willing to pay higher interest rates to reduce their monthly payment. More recently, Orazio P. Attanasio, Pinelopi K. Goldberg, and 3 Studies of the effects of unemployment insurance also provide evidence for credit constraints (e.g., David Card, Raj Chetty, and Andrea Weber 2007; Chetty 2008). 4 There is no clear consensus, however, on the exact story. For instance, Christopher Carroll (2001) argues that much of the evidence on consumption behavior can be explained by a buffer stock model where all agents can borrow freely at relatively low interest rates. Tullio Jappelli (1990) provides some limited evidence supporting credit rationing, based on the fact that 19 percent of the households in the 1983 Survey of Consumer Finances report having had a credit applica￾tion rejected or not applying for a loan for fear of being rejected