American Economic Review 2009. 99: 1. 49-84 http://www.aeaweb.org/articles.phpdoi=10.1257/aer.99.1.49 Liquidity Constraints and Imperfect Information in Subprime Lending By WILLIAM ADAMS LIRAN EINAV AND JONATHAN LEVIN We present new evidence on consumer liquidity constraints and the credit mar- ket conditions that might give rise to them. We analyze unique data from a large auto sales company serving the subprime market. Short-term liquidity appears to be a key driver of consumer behavior. Demand increases sharply during tax rebate season and purchases are highly sensitive to down-payment require ments Lenders also face substantial informational problems. Default rates rise significantly with loan size, providing a rationale for loan caps, and higher-risk borrowers demand larger loans. This adverse selection is mitigated, however, by risk-based pricing (JEL D14, D82, D83, G21) Access to credit markets is generally considered a hallmark of developed economies. In the United States, most households appear to have substantial ability to borrow; indeed, households in the United States have an average of over $23,000 in nonmortgage debt alone. Nevertheless, economists often point to limited borrowing opportunities for lower-income households to explain anomalous findings about consumption behavior, labor supply, and the demand for credit. Despite a sizeable theoretical literature that explains why some borrowers might have trouble obtaining credit even in competitive markets(e.g, Dwight M. Jaffee and Joseph E Stiglitz 1990), there has been relatively little work relating the consumer behavior indicative of liquidity constraints to the actual functioning of the credit market. c In this paper, we use unique data from a large auto sales company to study credit market con- tions for precisely the population that is most likely to have a difficult time obtaining credit, those with low incomes and poor credit histories. These consumers, who typically cannot qualify for regular bank loans, comprise the so-called subprime market. Because the company we study originates subprime loans, its loan applications and transaction records provide an unusual win- dow into the consumption and borrowing behavior of households that are rationed in primary credit markets. Moreover, we track loan repayments, allowing us to analyze the difficulties in lending to the subprime population. le begin by documenting the importance of short-term liquidity constraints for individual in our sample. We present two pieces of evidence. Both are based on purchasing behavior and ndicate a high sensitivity to cash-on-hand. First, we document a dramatic degree of demand seasonality associated with tax rebates. Overall demand is almost 50 percent higher during tax rebate season than during other parts of the year. This seasonal effect varies with household s Adams: Citigr = eenwichStreet.NewYork,NY10013(e-mail:williamadams(@citi.com):Einav University, Stanford, CA 94305-6072, and National Bureau of Economic Research n: Department of Economics, Stanford University, Stanford, CA 94305-6072, and National Bureau of Economic Research(e-mail: jdlevin@stanford. edu). We thank two anonymous referees and the edi and encouragement. Mark Jenkins provided stellar research assistance and Ricky Townser ly assisted our early data analysis. Einav and Levin acknowledge the support of the National Science Foundation and the Stanford Institut for Economic Policy Research, and Levin acknowledges the support of the Alfred P. Sloan Foundation
49 American Economic Review 2009, 99:1, 49–84 http://www.aeaweb.org/articles.php?doi=10.1257/aer.99.1.49 Access to credit markets is generally considered a hallmark of developed economies. In the United States, most households appear to have substantial ability to borrow; indeed, households in the United States have an average of over $23,000 in nonmortgage debt alone. Nevertheless, economists often point to limited borrowing opportunities for lower-income households to explain anomalous findings about consumption behavior, labor supply, and the demand for credit. Despite a sizeable theoretical literature that explains why some borrowers might have trouble obtaining credit even in competitive markets (e.g., Dwight M. Jaffee and Joseph E. Stiglitz 1990), there has been relatively little work relating the consumer behavior indicative of liquidity constraints to the actual functioning of the credit market. In this paper, we use unique data from a large auto sales company to study credit market conditions for precisely the population that is most likely to have a difficult time obtaining credit, those with low incomes and poor credit histories. These consumers, who typically cannot qualify for regular bank loans, comprise the so-called subprime market. Because the company we study originates subprime loans, its loan applications and transaction records provide an unusual window into the consumption and borrowing behavior of households that are rationed in primary credit markets. Moreover, we track loan repayments, allowing us to analyze the difficulties in lending to the subprime population. We begin by documenting the importance of short-term liquidity constraints for individuals in our sample. We present two pieces of evidence. Both are based on purchasing behavior and indicate a high sensitivity to cash-on-hand. First, we document a dramatic degree of demand seasonality associated with tax rebates. Overall demand is almost 50 percent higher during tax rebate season than during other parts of the year. This seasonal effect varies with household Liquidity Constraints and Imperfect Information in Subprime Lending By William Adams, Liran Einav, and Jonathan Levin* We present new evidence on consumer liquidity constraints and the credit market conditions that might give rise to them. We analyze unique data from a large auto sales company serving the subprime market. Short-term liquidity appears to be a key driver of consumer behavior. Demand increases sharply during tax rebate season and purchases are highly sensitive to down-payment requirements. Lenders also face substantial informational problems. Default rates rise significantly with loan size, providing a rationale for loan caps, and higher-risk borrowers demand larger loans. This adverse selection is mitigated, however, by risk-based pricing. (JEL D14, D82, D83, G21) * Adams: Citigroup Inc., 390 Greenwich Street, New York, NY 10013 (e-mail: william.adams@citi.com); Einav: Department of Economics, Stanford University, Stanford, CA 94305-6072, and National Bureau of Economic Research (e-mail: leinav@stanford.edu); Levin: Department of Economics, Stanford University, Stanford, CA 94305-6072, and National Bureau of Economic Research (e-mail: jdlevin@stanford.edu). We thank two anonymous referees and the editor, as well as Raj Chetty, Amy Finkelstein, Robert Hall, Richard Levin, and many seminar participants for suggestions and encouragement. Mark Jenkins provided stellar research assistance and Ricky Townsend greatly assisted our early data analysis. Einav and Levin acknowledge the support of the National Science Foundation and the Stanford Institute for Economic Policy Research, and Levin acknowledges the support of the Alfred P. Sloan Foundation
THEAMERICAN ECONOMIC REVIEW MARCH 2009 income and with the number of dependents, closely mirroring the federal earned income tax credit schedule. Second, we find that demand is highly responsive to changes in minimum down payment requirements. A S100 increase in the required down payment, holding car prices fixed, reduces demand by 9 percent. In contrast, generating the same reduction in demand requires an increase in car prices of almost $3,000. We calculate that in the absence of borrowing con straints, rationalizing these effects requires an annual discount rate of 1, 415 percent. These findings raise the question of whether consumer liquidity constraints can be tied to underlying credit market conditions. One possibility is that high default rates, coupled with legal caps on interest rates, simply rule out some forms of lending. A second possibility is that funda mental features of the consumer credit market are responsible. We focus on the latter, turning to the information economics view of credit markets as developed by Jaffee and Thomas Russell (1976)and Stiglitz and Andrew Weiss(1981) Modern information economics emphasizes that credit constraints can arise in equilibrium even if financing terms can adjust freely and lenders are fully competitive. Its explanation lies in the problems of moral hazard and adverse selection. In the moral hazard version of the story, individual borrowers are more likely to default on larger loans. This leads to problems in the loan market because borrowers do not internalize the full increase in default costs that come with larger loan sizes. As a result, lenders may need to cap loan sizes to prevent overborrowing In contrast, adverse selection problems arise if borrowers at high risk of default also desire large loans, as might be expected given that they view repayment as less likely. As the theoretical literature has pointed out, adverse selection can give rise not only to loan caps, but also to some worthy borrowers being denied credit. 2 The second half of the paper explores these ideas, first from the standpoint of theory and then empirically In Section Ill we present a simple model of consumer demand for credit and competitive lending, along the lines of Jaffee and Russell(1976). We show that such a model can explain many of the institutional features we observe on the lender side of the market, such as the adoption of credit scoring and risk-based pricing, and the use of interest rates that increase with loan size. We also explain why informational problems, compounded by interest rate caps, cre- ate a rationale for lenders to limit access to credit. The model therefore provides a simple credit market-based explanation for why purchasing behavior might reflect liquidity constraints. Having outlined the theoretical framework, we investigate the empirical importance of moral hazard and adverse selection for subprime lending. Separately identifying these two forces is often a challenge because they have similar implications: both moral hazard and adverse selec tion imply a positive correlation between loan size and default. A useful feature of our data is that we can exploit exogenous( to the individual) variation in car price and minimum down pay ment to isolate the moral hazard effect of increased loan size on default. This in turn. allows us to back out a quantitative estimate of self-selection from the cross-sectional correlation between loan size and default. We explain the econometric strategy in detail in Section IV. We find compelling evidence for both moral hazard and adverse selection. We estimate that for a given borrower, a $1,000 increase in loan size increases the rate of default by 16 percent. This alone provides a rationale for limiting loan sizes because the expected revenue from a loan is not monotonically increasing in the size of the loan. Regarding adverse selection, we find that borrowers who are observably at high risk of default are precisely the borrowers who desire the I If s denotes the discount rate, the discount factor is 1/(1 +s). So an annual discount rate of 1, 415 percent implies an annual subjective discount factor of less than 0.07. Such an individual is indifferent between paying $100 today and The fact that imperfect information in the credit market leads to limits on lending is analogous to Michael Rothschild and Stiglitz,s(1976)famous observation that imperfect information in an insurance market may lead to underinsurance relative to the full-information optimum
50 THE AMERICAN ECONOMIC REVIEW March 2009 income and with the number of dependents, closely mirroring the federal earned income tax credit schedule. Second, we find that demand is highly responsive to changes in minimum down payment requirements. A $100 increase in the required down payment, holding car prices fixed, reduces demand by 9 percent. In contrast, generating the same reduction in demand requires an increase in car prices of almost $3,000. We calculate that in the absence of borrowing constraints, rationalizing these effects requires an annual discount rate of 1,415 percent.1 These findings raise the question of whether consumer liquidity constraints can be tied to underlying credit market conditions. One possibility is that high default rates, coupled with legal caps on interest rates, simply rule out some forms of lending. A second possibility is that fundamental features of the consumer credit market are responsible. We focus on the latter, turning to the information economics view of credit markets as developed by Jaffee and Thomas Russell (1976) and Stiglitz and Andrew Weiss (1981). Modern information economics emphasizes that credit constraints can arise in equilibrium even if financing terms can adjust freely and lenders are fully competitive. Its explanation lies in the problems of moral hazard and adverse selection. In the moral hazard version of the story, individual borrowers are more likely to default on larger loans. This leads to problems in the loan market because borrowers do not internalize the full increase in default costs that come with larger loan sizes. As a result, lenders may need to cap loan sizes to prevent overborrowing. In contrast, adverse selection problems arise if borrowers at high risk of default also desire large loans, as might be expected given that they view repayment as less likely. As the theoretical literature has pointed out, adverse selection can give rise not only to loan caps, but also to some worthy borrowers being denied credit.2 The second half of the paper explores these ideas, first from the standpoint of theory and then empirically. In Section III we present a simple model of consumer demand for credit and competitive lending, along the lines of Jaffee and Russell (1976). We show that such a model can explain many of the institutional features we observe on the lender side of the market, such as the adoption of credit scoring and risk-based pricing, and the use of interest rates that increase with loan size. We also explain why informational problems, compounded by interest rate caps, create a rationale for lenders to limit access to credit. The model therefore provides a simple credit market–based explanation for why purchasing behavior might reflect liquidity constraints. Having outlined the theoretical framework, we investigate the empirical importance of moral hazard and adverse selection for subprime lending. Separately identifying these two forces is often a challenge because they have similar implications: both moral hazard and adverse selection imply a positive correlation between loan size and default. A useful feature of our data is that we can exploit exogenous (to the individual) variation in car price and minimum down payment to isolate the moral hazard effect of increased loan size on default. This, in turn, allows us to back out a quantitative estimate of self-selection from the cross-sectional correlation between loan size and default. We explain the econometric strategy in detail in Section IV. We find compelling evidence for both moral hazard and adverse selection. We estimate that for a given borrower, a $1,000 increase in loan size increases the rate of default by 16 percent. This alone provides a rationale for limiting loan sizes because the expected revenue from a loan is not monotonically increasing in the size of the loan. Regarding adverse selection, we find that borrowers who are observably at high risk of default are precisely the borrowers who desire the 1 If s denotes the discount rate, the discount factor is 1/11 1 s2. So an annual discount rate of 1,415 percent implies an annual subjective discount factor of less than 0.07. Such an individual is indifferent between paying $100 today and $1,515 in a year. 2 The fact that imperfect information in the credit market leads to limits on lending is analogous to Michael Rothschild and Stiglitz’s (1976) famous observation that imperfect information in an insurance market may lead to underinsurance relative to the full-information optimum
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 sophisticated 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 highrisk 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 application rejected or not applying for a loan for fear of being rejected
THE AMERICAN ECONOMIC REVIEW MARCH 2009 Ekaterini Kyriazidou(2008)use Survey of Consumer Finances data on auto loans to show that for most households, and particularly for low-income ones, the demand for loans is much more sensitive to loan maturity than to interest rate. Their interpretation is that because of their lim- ited access to credit, many consumers will pay a substantial premium to smooth payments over a longer period. The purpose of the studies above is to document that a significant set of households has a limited ability to borrow at desirable rates. There is much less empirical work that addresses the causes of credit constraints. Lawrence M. Ausubel(1991, 1999)argues that the high inter- est rates charged by credit card issuers are a market failure caused by adverse selection, a view that is supported by direct marketing experiments. Wendy Edelberg(2003, 2004)also finds evi- dence for adverse selection in both mortgage lending and automobile loans, and documents increasing trend toward risk -based interest rates. We view it as a virtue of our data that we car tie together demand-side evidence for credit-constrained behavior with evidence on the informa- tional problems that might give rise to these constraints. Our related work(Einav, Jenkins, and evin 2008a, b)explores more deeply how lenders respond to informational problems by looking at the introduction of credit scoring and the problem of optimal loan pricing in the presence of moral hazard and adverse selection I. Data and Environment Our data come from an auto sales company that operates used car dealerships in the United States. Each potential customer fills out a loan application and is assigned a credit category that determines the possible financing terms. Almost all buyers finance a large fraction of their purchase with a loan that extends over a period of several years. What makes the company an unusual window into consumer borrowing is its customer population. Customers are primarily low-income workers and a great majority are subprime borrowers In the United States, Fair Isaac(FICO)scores are the most-used measure of creditworthiness. They range from 350 to 800, with the national median between 700 and 750. Less than half of the company's applicants have a FICO score above 500. This kind of low credit score indicates either a sparse or checkered redit record q The principal characteristics of subprime lending are high interest rates and high default rates. orkpical loan in our data has an annual interest rate on the order of 25-30 percent. The flip side of high interest rates is high default rates. Over half of the companys loans end in default. with such a high probability of default, screening the good risks from the bad, and monitoring loan payments, is extremely important. The company has invested significantly in proprietary credit scoring technology Our specific data consist of all loan applications and sales from June 2001 through December 2004. We combine this with records of loan payments, defaults, and recoveries through April 2006. This gives us information on the characteristics of potential customers, the terms of the consummated transactions, and the resulting loan outcomes. we have additional data on the loan terms being offered at any given time as a function of credit score, and inventory data that allow us to observe the acquisition cost of each car, the amount spent to recondition it, and its list price on the lot The top panel of Table I contains summary statistics on the applicant population. There are well over 50,000 applications in our sample period (to preserve confidentiality, we do not report Dean Karlan and Jonathan Zinman(2008)report a similar finding, that loan demand is more sensitive to maturity than to interest rate, based on a pricing experiment carried out by a South African lender. Their experiment also pro- vides some evidence for moral hazard and adverse selection(Karlan and Zinman 2007)
52 THE AMERICAN ECONOMIC REVIEW March 2009 Ekaterini Kyriazidou (2008) use Survey of Consumer Finances data on auto loans to show that for most households, and particularly for low-income ones, the demand for loans is much more sensitive to loan maturity than to interest rate.5 Their interpretation is that because of their limited access to credit, many consumers will pay a substantial premium to smooth payments over a longer period. The purpose of the studies above is to document that a significant set of households has a limited ability to borrow at desirable rates. There is much less empirical work that addresses the causes of credit constraints. Lawrence M. Ausubel (1991, 1999) argues that the high interest rates charged by credit card issuers are a market failure caused by adverse selection, a view that is supported by direct marketing experiments. Wendy Edelberg (2003, 2004) also finds evidence for adverse selection in both mortgage lending and automobile loans, and documents an increasing trend toward risk-based interest rates. We view it as a virtue of our data that we can tie together demand-side evidence for credit-constrained behavior with evidence on the informational problems that might give rise to these constraints. Our related work (Einav, Jenkins, and Levin 2008a, b) explores more deeply how lenders respond to informational problems by looking at the introduction of credit scoring and the problem of optimal loan pricing in the presence of moral hazard and adverse selection. I. Data and Environment Our data come from an auto sales company that operates used car dealerships in the United States. Each potential customer fills out a loan application and is assigned a credit category that determines the possible financing terms. Almost all buyers finance a large fraction of their purchase with a loan that extends over a period of several years. What makes the company an unusual window into consumer borrowing is its customer population. Customers are primarily low-income workers and a great majority are subprime borrowers. In the United States, Fair Isaac (FICO) scores are the most-used measure of creditworthiness. They range from 350 to 800, with the national median between 700 and 750. Less than half of the company’s applicants have a FICO score above 500. This kind of low credit score indicates either a sparse or checkered credit record. The principal characteristics of subprime lending are high interest rates and high default rates. A typical loan in our data has an annual interest rate on the order of 25–30 percent. The flip side of high interest rates is high default rates. Over half of the company’s loans end in default. With such a high probability of default, screening the good risks from the bad, and monitoring loan payments, is extremely important. The company has invested significantly in proprietary credit scoring technology. Our specific data consist of all loan applications and sales from June 2001 through December 2004. We combine this with records of loan payments, defaults, and recoveries through April 2006. This gives us information on the characteristics of potential customers, the terms of the consummated transactions, and the resulting loan outcomes. We have additional data on the loan terms being offered at any given time as a function of credit score, and inventory data that allow us to observe the acquisition cost of each car, the amount spent to recondition it, and its list price on the lot. The top panel of Table 1 contains summary statistics on the applicant population. There are well over 50,000 applications in our sample period (to preserve confidentiality, we do not report 5 Dean Karlan and Jonathan Zinman (2008) report a similar finding, that loan demand is more sensitive to maturity than to interest rate, based on a pricing experiment carried out by a South African lender. Their experiment also provides some evidence for moral hazard and adverse selection (Karlan and Zinman 2007)
VOL 99 NO. I ADAMS ETAL.: SUBPRIME LENDING TABLE 1-SUMMARY STATISTICS Number of Standard Ninety-fifth observations" Mean deviation perce percentile 1,074 4.500 House owner Lives with parents ank account Risk category N Medium Buyer characteristics 0.34N 347 onthly income 0.3 se owner 0.34N 二二08- Lives with parents Bank account 0.34N Risk category 0.3 0.35 Medium 0.34N 0.34N Car characteristics Acquisition cost 0.34N 5,213 Total cost 0.34N 0.34N Odometer 0.34N 102.300 Lot age(days) 0.34N 0.34N 10.777 13,595 Transaction characteristics Minimum down payment(applicants) N 750 00 Minimum down payment(buyers) 0.34N 276 terest rate(APR) 0.34N 447 term(months) 0.34N Down paymen 0.34N 10 7,982 13,560 Monthly 0.34N 49 314 471 efault(uncensored observations only) 0.13N Recovery amount(uncensored defaults) 1,382 0 To preserve the confidentiality of the company that provided the data, we do not report the exact number of the exact number of applications). The median applicant is 31 years old and has a monthly house hold income of $2.101. We do not have a direct measure of household assets or debt. but we bserve a variety of indirect measures. A small fraction of applicants are homeowners, but the majority are renters and more live with their parents than own their own home. Nearly a third report having neither a savings nor a checking account. The typical credit history is spotty: more than half of the applicants have had a delinquent balance within six months prior to their loan application. In short, these applicants represent a segment of the population for whom access to credit is potentially probler Just over one-third of the applicants purchase a car. As shown in the second panel of Table 1 the average buyer has a somewhat higher income and somewhat better credit characteristics than
VOL. 99 NO. 1 Adams Et al.: Subprime Lending 53 the exact number of applications). The median applicant is 31 years old and has a monthly household income of $2,101. We do not have a direct measure of household assets or debt, but we observe a variety of indirect measures. A small fraction of applicants are homeowners, but the majority are renters and more live with their parents than own their own home. Nearly a third report having neither a savings nor a checking account. The typical credit history is spotty: more than half of the applicants have had a delinquent balance within six months prior to their loan application. In short, these applicants represent a segment of the population for whom access to credit is potentially problematic. Just over one-third of the applicants purchase a car. As shown in the second panel of Table 1, the average buyer has a somewhat higher income and somewhat better credit characteristics than Table 1—Summary Statistics Number of Standard Fifth Ninety-fifth observationsa Mean deviation percentile percentile Applicant characteristics Age N 32.8 10.7 19 53 Monthly income N 2,414 1,074 1,299 4,500 House owner N 0.15 — — — Lives with parents N 0.18 — — — Bank account N 0.72 — — — Risk category Low N 0.27 — — — Medium N 0.45 — — — High N 0.29 — — — Car purchased N 0.34 — — — Buyer characteristics Age 0.34N 34.7 10.8 20 55 Monthly income 0.34N 2,557 1,089 1,385 4,677 House owner 0.34N 0.17 — — — Lives with parents 0.34N 0.16 — — — Bank account 0.34N 0.76 — — — Risk category Low 0.34N 0.35 — — — Medium 0.34N 0.47 — — — High 0.34N 0.17 — — — Car characteristics Acquisition cost 0.34N 5,213 1,358 3,205 7,240 Total cost 0.34N 6,096 1,372 4,096 8,213 Car age (years) 0.34N 4.3 1.9 2 8 Odometer 0.34N 68,776 22,091 31,184 102,300 Lot age (days) 0.34N 33 44 1 122 Car price 0.34N 10,777 1,797 8,095 13,595 Transaction characteristics Minimum down payment (applicants) N 750 335 400 1,400 Minimum down payment (buyers) 0.34N 648 276 400 1,200 Interest rate (APR) 0.34N 26.2 4.4 17.7 29.9 Loan term (months) 0.34N 40.5 3.7 35 45 Down payment 0.34N 963 602 400 2,000 Loan amount 0.34N 10,740 1,802 7,982 13,560 Monthly payment 0.34N 395 49 314 471 Default (uncensored observations only) 0.13N 0.61 — — — Recovery amount (uncensored defaults) 0.08N 1,382 1,386 0 3,784 a To preserve the confidentiality of the company that provided the data, we do not report the exact number of observations, N W 50,000
THEAMERICAN ECONOMIC REVIEW MARCH 2009 the average applicant. In particular, the company assigns each applicant a credit category, which we partition into"high, ""medium, " and"low"risk. The applicant pool is 27 percent low risk and 29 percent high risk, while the corresponding percentages for the pool of buyers are 35 and 17. The sales terms, summarized in the third panel of Table l, reflect the presumably limited options of this population. A typical car, and most are around three to five years old, costs around $6,000 to bring to the lot. The average sale price is just under $11,000. The average down payment is a bit less than $1,000, so after taxes and fees, the average loan size is similar to the sales priee. ee Despite the large loans and small down payments, it appears that many buyers would prefer to put down even less money. Forty-three percent make exactly the minimum down payment, which varies with the buyer's credit category but is typically between $400 and S1, 000. Some buyers do make down payments that are substantially above the required minimum, but the number is small. Less than 10 percent of buyers make down payments that exceed the required down pay ment by $1,000. In a financed purchase, the monthly payment depends on the loan size, the loan term, and the nterest rate. Much of the relevant variation in our data is due to the former rather than the lat ter. Over 85 percent of the loans have an annual interest rate over 20 percent, and around half the loans appear to be at the state-mandated maximum annual interest rate. Most states in our data have a uniform 30 percent cap. These rates mean that finance charges are significant. For instance, a borrower who takes an $11,000 loan at a 30 percent APR and repays it over 42 months will make interest payments totalling $6,000 The main reason for the high finance charges is evident in the fourth panel of Table 1. Most loans end in default. Our data end before the last payments are due on some loans, but of the loans with uncensored payment periods, only 39 percent are repaid in full. o Moreover, loans that do default tend to default quickly. Figure lA plots a kernel density of the fraction of payments made by borrowers who defaulted. Nearly half the defaults occur before a quarter of the pay- ments have been made, that is, within ten months. This leads to a highly bimodal distribution of per-sale profits. To capture this, we calculated the present value of payments received for each uncensored loan in our data, including both the down payment and the amount recovered in the event of default, using an annual interest rate of 10 percent to value the payment stream. We then divide this by the firms reported costs of purchasing and reconditioning the car to obtain a rate of return on capital for each transaction Figure 1B plots the distribution of returns, showing the clear bimodal pattern. It is also interesting to isolate the value of each stream of loan repayments and compare it to the size of each loan. when we do this for each uncensored loan in our data(and use annual discount rates of0 to 10 percent), we find an average repayment-to-loan ratio of 0.79-0.88. Moreover,a substantial majority of loans in the data, 54-57 percent, have a repayment-to-loan ratio below one. This calculation helps to explain why buyers who are going to finance heavily in any event 6 Car prices are subject to some degree of negotiation, which we discuss in Section Il. The price we report here is the negoLatid gandencte n he car parie. the dewnspamieetwh the lisan gehm n ghbenths. and R=1 + r the monthlv interes Co s The company offers lower rates to some buyers who have either particularly good credit records or make down pay- the monthly payment is given by m=(P-D)(R-1(I-R) ments above the minimum. Although we do not have direct data on the offers of competing lenders, it seems unlikely hat this population has access to better rates. Fair lsaac's Web page indicates that borrowers with FICO scores in th 500-600 range(that is, better than the majority of the applicants in our sample) should expect to pay close to 20 percent annual interest for standard used car loans in most states, and in some states will not qualify at all for"standard"loans. oJe.W states have lower caps that depend on characteristics of the car. A t kins(2008) provides more details on defaults and recoveries
54 THE AMERICAN ECONOMIC REVIEW March 2009 the average applicant. In particular, the company assigns each applicant a credit category, which we partition into “high,” “medium,” and “low” risk. The applicant pool is 27 percent low risk and 29 percent high risk, while the corresponding percentages for the pool of buyers are 35 and 17. The sales terms, summarized in the third panel of Table 1, reflect the presumably limited options of this population. A typical car, and most are around three to five years old, costs around $6,000 to bring to the lot. The average sale price is just under $11,000.6 The average down payment is a bit less than $1,000, so after taxes and fees, the average loan size is similar to the sales price. Despite the large loans and small down payments, it appears that many buyers would prefer to put down even less money. Forty-three percent make exactly the minimum down payment, which varies with the buyer’s credit category but is typically between $400 and $1,000. Some buyers do make down payments that are substantially above the required minimum, but the number is small. Less than 10 percent of buyers make down payments that exceed the required down payment by $1,000. In a financed purchase, the monthly payment depends on the loan size, the loan term, and the interest rate.7 Much of the relevant variation in our data is due to the former rather than the latter. Over 85 percent of the loans have an annual interest rate over 20 percent, and around half the loans appear to be at the state-mandated maximum annual interest rate.8 Most states in our data have a uniform 30 percent cap.9 These rates mean that finance charges are significant. For instance, a borrower who takes an $11,000 loan at a 30 percent APR and repays it over 42 months will make interest payments totalling $6,000. The main reason for the high finance charges is evident in the fourth panel of Table 1. Most loans end in default. Our data end before the last payments are due on some loans, but of the loans with uncensored payment periods, only 39 percent are repaid in full.10 Moreover, loans that do default tend to default quickly. Figure 1A plots a kernel density of the fraction of payments made by borrowers who defaulted. Nearly half the defaults occur before a quarter of the payments have been made, that is, within ten months. This leads to a highly bimodal distribution of per-sale profits. To capture this, we calculated the present value of payments received for each uncensored loan in our data, including both the down payment and the amount recovered in the event of default, using an annual interest rate of 10 percent to value the payment stream. We then divide this by the firm’s reported costs of purchasing and reconditioning the car to obtain a rate of return on capital for each transaction. Figure 1B plots the distribution of returns, showing the clear bimodal pattern. It is also interesting to isolate the value of each stream of loan repayments and compare it to the size of each loan. When we do this for each uncensored loan in our data (and use annual discount rates of 0 to 10 percent), we find an average repayment-to-loan ratio of 0.79–0.88. Moreover, a substantial majority of loans in the data, 54–57 percent, have a repayment-to-loan ratio below one. This calculation helps to explain why buyers who are going to finance heavily in any event 6 Car prices are subject to some degree of negotiation, which we discuss in Section II. The price we report here is the negotiated transaction price rather than the “list” price, which is slightly higher. 7 Letting p denote the car price, D the down payment, T the loan term in months, and R 5 1 1 r the monthly interest rate, the monthly payment is given by m 5 1p 2 D2 1R 2 12/11 2 R2T 2. 8 The company offers lower rates to some buyers who have either particularly good credit records or make down payments above the minimum. Although we do not have direct data on the offers of competing lenders, it seems unlikely that this population has access to better rates. Fair Isaac’s Web page indicates that borrowers with FICO scores in the 500–600 range (that is, better than the majority of the applicants in our sample) should expect to pay close to 20 percent annual interest for standard used car loans in most states, and in some states will not qualify at all for “standard” loans. 9 A few states have lower caps that depend on characteristics of the car. 10 Jenkins (2008) provides more details on defaults and recoveries
VOL 99 NO. I ADAMS ETAL.: SUBPRIME LENDING E 810 0 0.000.100.200.300.400.500.600.700800.901.00 Fraction of loan paid FIGURE IA KERNEL DENSITY OF FRACTION OF LOAN PAID CONDITIONAL ON DEFAULT Note: Figure based on data from uncensored loans that ended in default. 0.06 0.04 0.03 0.0 0.00具 1.0-0.5 1.5 (Revenue-cost)cost FIGURE 1B. RATE OF RETURN HISTOGRAM Notes: Figure based on data from uncensored loans. Revenue is calculated as down payment t present value of loan payments present value of recovery, assuming an internal firm discount rate of 10 percent. might maximize their loan size. In the majority of cases, the present value of payments on an extra dollar borrowed is significantly less than a dollar paid up front The point applies most clearly for small changes in loan size. As we show below, smaller loans decrease th ability of default, which generates a nonconvexity in loan demand. This effect is not reflected in our calculation, which takes the default process as fixed. It is also worth noting that the incentive to borrow on the margin increases with
VOL. 99 NO. 1 Adams Et al.: Subprime Lending 55 might maximize their loan size. In the majority of cases, the present value of payments on an extra dollar borrowed is significantly less than a dollar paid up front.11 11 The point applies most clearly for small changes in loan size. As we show below, smaller loans decrease the probability of default, which generates a nonconvexity in loan demand. This effect is not reflected in our calculation, which takes the default process as fixed. It is also worth noting that the incentive to borrow on the margin increases with 0.0 0.5 1.0 1.5 2.0 2.5 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Low risk Medium risk High risk Fraction of loan paid Density Figure 1A. Kernel Density of Fraction of Loan Paid Conditional on Default Note: Figure based on data from uncensored loans that ended in default. 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 21.0 20.5 0.0 0.5 1.0 1.5 2.0 2.5 Paid loans Defaulted loans (Revenue – cost ) / cost Frequency Figure 1B. Rate of Return Histogram Notes: Figure based on data from uncensored loans. Revenue is calculated as down payment 1 present value of loan payments 1 present value of recovery, assuming an internal firm discount rate of 10 percent
THEAMERICAN ECONOMIC REVIEW MARCH 2009 I. Evidence of Liquidity Constraints: Purchasing Behavior A consumer is liquidity constrained if he cannot finance present purchases using resources that will accrue to him in the future. Subprime borrowers are obvious candidates to find them selves in this position. While we cannot directly observe individual household balance sheets and credit options, our data do permit us to investigate the behavioral implications of liquidity constraints. We consider two such implications in this section The first concerns purchasing sensitivity with respect to current and predictable future cash flow. For an individual who can borrow freely against future resources, the response should be equal. In contrast, a high purchase response to a predictable temporary spike in cash flow, such as a tax rebate, suggests an inability to shift resources over time. The first piece of evidence we present is a striking seasonal increase in applications and sales at precisely tax rebate time Moreover, we show that there is a remarkably clear correlation between the seasonal effects we observe and the amount of the earned income tax credit, which is likely to be a significant por- tion of the tax rebate for many households in our data. The second empirical implication is the mirror image of the first. An individual who is not liquidity constrained should evaluate the cost of a given payment schedule based on its present value. In contrast, a liquidity constrained individual values the opportunity to defer payments to the future, and therefore views current payments as more costly than the present value of future payments. This is consistent with the second piece of evidence we present: individual purchase elasticity with respect to current payment(down payment)is an order of magnitude higher than with respect to future payments These findings are what one might expect from a population that is living check-to-check But are there alternative explanations? Explaining the seasonality finding without reference a cash-on-hand story is difficult. It seems unlikely that this population has a particular need for cars in the month of February. One also might ask whether the car purchase is a form of savings, rather than consumption. But given the price margins and very low down payments, the immedi ate post-purchase equity is negligible. 2 Moreover, given the high default rate, most consumers would have to be highly overoptimistic about their repayment ability to view the transaction as a form of savings One might ask in addition if our estimated demand sensitivities could be explained by con sumer impatience. We calculate that to rationalize the relative importance of the down payment with empirically correct expectations of default, consumers would have to equate a $100 cost today with a $1, 515 cost in one year. This number would be still higher if consumers were over- ptimistic about repayment. Moreover, even if consumers were this myopic, discounting alone cannot explain the seasonal pattern. This suggests that consumer purchasing behavior may be best explained by check-to-check existence A. The Effect of Tax Rebate Season We start by examining seasonal patterns in demand. Figure 2A displays the average number of applications and sales, by calendar week, over the 2002-2005 period. Both are markedly higher from late January to early March. Applications are 23 percent higher in February than in the other months, and the close rate(sales to applications ratio) is 40 percent compared to 33 percent buyers'subjective discount rates. Some researchers(e. g, David Laibson, Andrea Repetto, and Jeremy Tobacman 2003) have argued that borrowing behavior reflects a much higher degree of impatience than we assume here out of tax rebates focu
56 THE AMERICAN ECONOMIC REVIEW March 2009 II. Evidence of Liquidity Constraints: Purchasing Behavior A consumer is liquidity constrained if he cannot finance present purchases using resources that will accrue to him in the future. Subprime borrowers are obvious candidates to find themselves in this position. While we cannot directly observe individual household balance sheets and credit options, our data do permit us to investigate the behavioral implications of liquidity constraints. We consider two such implications in this section. The first concerns purchasing sensitivity with respect to current and predictable future cash flow. For an individual who can borrow freely against future resources, the response should be equal. In contrast, a high purchase response to a predictable temporary spike in cash flow, such as a tax rebate, suggests an inability to shift resources over time. The first piece of evidence we present is a striking seasonal increase in applications and sales at precisely tax rebate time. Moreover, we show that there is a remarkably clear correlation between the seasonal effects we observe and the amount of the earned income tax credit, which is likely to be a significant portion of the tax rebate for many households in our data. The second empirical implication is the mirror image of the first. An individual who is not liquidity constrained should evaluate the cost of a given payment schedule based on its present value. In contrast, a liquidity constrained individual values the opportunity to defer payments to the future, and therefore views current payments as more costly than the present value of future payments. This is consistent with the second piece of evidence we present: individual purchase elasticity with respect to current payment (down payment) is an order of magnitude higher than with respect to future payments. These findings are what one might expect from a population that is living check-to-check. But are there alternative explanations? Explaining the seasonality finding without reference to a cash-on-hand story is difficult. It seems unlikely that this population has a particular need for cars in the month of February. One also might ask whether the car purchase is a form of savings, rather than consumption. But given the price margins and very low down payments, the immediate post-purchase equity is negligible.12 Moreover, given the high default rate, most consumers would have to be highly overoptimistic about their repayment ability to view the transaction as a form of savings. One might ask in addition if our estimated demand sensitivities could be explained by consumer impatience. We calculate that to rationalize the relative importance of the down payment with empirically correct expectations of default, consumers would have to equate a $100 cost today with a $1,515 cost in one year. This number would be still higher if consumers were overoptimistic about repayment. Moreover, even if consumers were this myopic, discounting alone cannot explain the seasonal pattern. This suggests that consumer purchasing behavior may be best explained by check-to-check existence. A. The Effect of Tax Rebate Season We start by examining seasonal patterns in demand. Figure 2A displays the average number of applications and sales, by calendar week, over the 2002–2005 period. Both are markedly higher from late January to early March. Applications are 23 percent higher in February than in the other months, and the close rate (sales to applications ratio) is 40 percent compared to 33 percent buyers’ subjective discount rates. Some researchers (e.g., David Laibson, Andrea Repetto, and Jeremy Tobacman 2003) have argued that borrowing behavior reflects a much higher degree of impatience than we assume here. 12 This would not be the case for a nonfinanced car purchase, which is a reason that studies of the marginal propensity to consume out of tax rebates focus on expenditure on nondurables
VOL 99 NO. I ADAMS ETAL.: SUBPRIME LENDING Applications 38品NE Sales Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Calendar week FIGURE 2A. SEASONALITY IN APPLICATIONS AND SALES Notes: Figure based on data from all applications. Both number of applications and sales are normalized by the aver- over the rest of the year. These seasonal patterns cannot be attributed to sales or other changes in the firms offers. In fact, required down payments are almost $150 higher in February, averaging across applicants in our data, than in the other months of the year. Indeed we initially thought these patterns indicated a data problem, until the company pointed out that prospective buyers receive their tax rebates at precisely this time of year. But can tax rebates be large enough to explain such a dramatic spike in demand? All loan pplicants must hold a job to be eligible for a loan, and most are relatively low earners, making them eligible for the earned income tax credit(EltC). The associated rebate, which varies with income and the number of dependents, can be as high as $4, 500. To assess whether purchasing patterns might reflect EITC rebates, we classified applicants into 12 groups depending on their monthly household income and their number of dependents. For each group, we calculated the earned income tax credit for the average household in the group, and also the percent increase in applications, close rate, and sales in February relative to the other months. Figure 2B plots the relationship between the calculated ElTC rebate and the seasonal spike in demand for each group. There is a sharp correlation. For households with monthly incomes below $1, 500 and at least two dependents, for whom the EITC rebate could be around $4, 000, the number of applications doubles in February and the number of purchases more than triples. In contrast, for households with monthly incomes above $3, 500 and no dependents, for whom the EITC rebate is likely zero, the number of applications and purchases exhibits virtually no increase in tax rebate season Because minimum down payment requirements are raised during tax season, it is interesting to isolate the seasonal effect in demand, holding all else constant. Our demand estimates in the next section. which control for the relevant offer terms as well as individual characteristics such as credit score and household income, indicate that the demand of applicants who arrive on the 13The details of the EITC schedule did not change much over our observation period (2001-2005). The particular numbers we report are based on the 2003 schedule
VOL. 99 NO. 1 Adams Et al.: Subprime Lending 57 over the rest of the year. These seasonal patterns cannot be attributed to sales or other changes in the firm’s offers. In fact, required down payments are almost $150 higher in February, averaging across applicants in our data, than in the other months of the year. Indeed we initially thought these patterns indicated a data problem, until the company pointed out that prospective buyers receive their tax rebates at precisely this time of year. But can tax rebates be large enough to explain such a dramatic spike in demand? All loan applicants must hold a job to be eligible for a loan, and most are relatively low earners, making them eligible for the earned income tax credit (EITC). The associated rebate, which varies with income and the number of dependents, can be as high as $4,500. To assess whether purchasing patterns might reflect EITC rebates, we classified applicants into 12 groups depending on their monthly household income and their number of dependents. For each group, we calculated the earned income tax credit for the average household in the group,13 and also the percent increase in applications, close rate, and sales in February relative to the other months. Figure 2B plots the relationship between the calculated EITC rebate and the seasonal spike in demand for each group. There is a sharp correlation. For households with monthly incomes below $1,500 and at least two dependents, for whom the EITC rebate could be around $4,000, the number of applications doubles in February and the number of purchases more than triples. In contrast, for households with monthly incomes above $3,500 and no dependents, for whom the EITC rebate is likely zero, the number of applications and purchases exhibits virtually no increase in tax rebate season. Because minimum down payment requirements are raised during tax season, it is interesting to isolate the seasonal effect in demand, holding all else constant. Our demand estimates in the next section, which control for the relevant offer terms as well as individual characteristics such as credit score and household income, indicate that the demand of applicants who arrive on the 13 The details of the EITC schedule did not change much over our observation period (2001–2005). The particular numbers we report are based on the 2003 schedule. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Sales Applications Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Calendar week Normalized count per week Figure 2A. Seasonality in Applications and Sales Notes: Figure based on data from all applications. Both number of applications and sales are normalized by the average number of applications per week
THEAMERICAN ECONOMIC REVIEW MARCH 2009 Applications s中 Earned Income Tax Credit(EITc) Sales 1L s1000 s1,000 s2000 Close rate FIGURE 2B. TAX CREDIT EFFECTS ON APPLICATIONS AND SALES Notes: Figures based com all a ions. Each point represents a group of applicants with a nber of dependents are: 0= no dependents, 1 =1 dependent, and 2 or more dependen for incom (in dollars per month) are: VL less than 1,500, L 1, 500-2.000 M=2.000-3.000. and H more than 3.000
58 THE AMERICAN ECONOMIC REVIEW March 2009 0 20 40 60 80 100 120 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 50 100 150 200 250 300 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 20 40 60 80 2VL 2M 1L 2L 1VL 2H 1M 0H 1H 0VL 0M 0L Applications Percent increase in applications in February Earned Income Tax Credit (EITC) EITC Percent increase in sales in February Percent increase in close rate in February 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Sales Close Rate EITC Figure 2B. Tax Credit Effects on Applications and Sales Notes: Figures based on data from all applications. Each point represents a group of applicants with a given income level and number of dependents. Labels for number of dependents are: 0 5 no dependents, 1 5 1 dependent, and 2 5 2 or more dependents. Labels for income level (in dollars per month) are: VL 5 less than 1,500, L 5 1,500–2,000, M 5 2,000–3,000, and H 5 more than 3,000. 0 20 40 60 80 100 120 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 50 100 150 200 250 300 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 20 40 60 80 2VL 2M 1L 2L 1VL 2H 1M 0H 1H 0VL 0M 0L Applications Percent increase in applications in February Earned Income Tax Credit (EITC) EITC Percent increase in sales in February Percent increase in close rate in February 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Sales Close Rate EITC 0 20 40 60 80 100 120 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 50 100 150 200 250 300 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 20 40 60 80 2VL 2M 1L 2L 1VL 2H 1M 0H 1H 0VL 0M 0L Applications Percent increase in applications in February Earned Income Tax Credit (EITC) EITC Percent increase in sales in February Percent increase in close rate in February 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Sales Close Rate EITC
