Constrained Consumer Lending: Methods Using the Survey of Consumer Finances by Giovanni Ferri ð, and Peter Simon ¶ *,§ ð
University of Bari (Italy);
¶
Analysis Group/Economics
October 23, 2000 Abstract The existence of credit constraints— and the conditions under which they occur— have important implications for various economic theories and methods of policy implementation. In this paper, we focus on implications for questions of how intermediaries make lending decisions to consumers. Specifically, we examine credit constraints and their determinants within individual years using several waves of the Survey of Consumer Finances. We make two main contributions: i) we introduce two new behaviorally-based measures of credit constraint; ii) we apply lessons learned from the parallel literature on borrowing by firms by using measures of balance-sheet condition and borrower-lender relationships to improve standard models of credit constraints and lending. Our findings on the determinants of credit constraints for consumers are generally consistent across the various proposed measures of the constraints. Keywords: liquidity constraints, consumer credit, credit cards. JEL classification: G21
*
We would like to thank Ben Bernanke, Tullio Jappelli, Marco Pagano, Mark Watson, Robert Shimer, Dean Croushore, Seth Carpenter, Helen Levy, Loic Sadoulet and members of the Princeton Macroeconomics Seminar Group for comments. All errors are our own. §
Contacts: Giovanni Ferri – Via Francesco Berni, 5 – 00185 Rome. Tel. & fax +39-06-7096121 – email:
[email protected] Peter Simon – Economist – Analysis Group/Economics – 601 South Figueroa, Suite 1300 – Tel. 213/8964540 – fax 213/623-4112 – Los Angeles, CA 90017 – email:
[email protected]
1 Introduction Are consumers able to borrow freely in credit markets, or do they face quantitatively significant constraints on their access to credit? If borrowing constraints are important, what determines who is constrained and who gets credit? These questions are relevant to a wide variety of issues in economics, including the short-run dynamics of aggregate consumption and saving, the effects of government debt policy on the economy, and the sources of the equity premium, among many others. In this study we use data from three waves of the Survey of Consumer Finances (SCF) to address some of these issues (most previous studies have considered only one wave, usually the 1983 sample). The SCF is a particularly valuable data source for studying borrowing constraints: it contains relatively complete data on consumer assets, liabilities, and income; it also includes some interesting qualitative questions about consumer attitudes and experiences in the credit market. As Jappelli (1990) was the first to emphasize, the answers of SCF respondents to survey questions about credit-market experience provide some indicators of both desired borrowing and borrowing constraints that are reasonably direct, at least relative to earlier studies that inferred the existence of borrowing constraints indirectly from consumption and borrowing behavior. This paper augments the usual measure of whether a household is constrained— the household's report to the survey-taker that it had been turned down or received less credit than applied for— with a more behaviorally-based measure. Specifically, we define a household to be credit constrained if conventional bank lenders offer less than the household wishes to borrow, and propose that a household that carries month-to-month balances on its credit cards must not have full access to conventional intermediated loans, else the household would not be relying on such an expensive form of credit. We find that reliance on credit cards as a marginal source of credit appears to be a useful indicator of whether households face binding borrowing constraints in the market for intermediated loans. Second, we show that standard models of the probability that a household is turned down for credit can be improved by applying some lessons learned in the parallel literature on firm borrowing. In particular, measures of balance-sheet condition— such as liquidity and leverage— and of relationships with lenders, which have proved significant in various studies of firm borrowing, are shown to matter for households as well. The rest of the paper is organized as follows. Section 2 discusses the motivation for our work and the background on the topic. Section 3 presents a model of the consumer loan market as well as our use of it to motivate a discrete choice model for predicting the incidence of borrowing constraints. Section 4 describes the Survey of Consumer Finances, and how we constructed the measure of permanent income used in estimation. Section 5 presents and discusses the empirical results from the discrete choice models of the probability of constraint using data from 1983, 1989, and 1992 within each year. Section 6 concludes.
2 Motivation and Background Why should we be interested in borrowing constraints for households? Researchers from many diverse fields have interests in credit constraints. First, the extent to which financial markets ration credit has important implications for theoretical models of consumer behavior employing a notion of permanent income. Such constraints 2
necessitate modification of the pure permanent income theory. Further, given research into the importance of uncertainty for consumption behavior, the behavior of borrowing constraints over a short-run horizon (at business cycle frequency, for example) may have important implications for consumption patterns at those frequencies. Second, policy makers need to understand why and under what conditions credit is constrained in order to recommend more effective policies (e.g. housing or education loan or loan guarantee programs). Third, understanding how credit constraints may be imposed on people of different races or genders is important for regulators and legal authorities. Fourth, understanding the conditions in which borrowing constraints occur can tell us something about lender behavior, which in turn can help shed light on how the monetary transmission mechanism operates. Theories of the transmission mechanism of monetary policy, in various ways, rely heavily on lending decisions by banks to explain how open market operations by the central bank translate into changes in the real economy. Standard textbook IS-LM models rely upon changes in reserves affecting bank decisions. Theories of a credit channel for monetary policy rely upon banks altering the size or numbers of loans they make in reaction to action by the monetary authority. Also, changes in bank capital in response to monetary policy changes could affect the lending decisions by banks. We start by examining the basic question of who receives credit in an economy (using data from the U.S. economy); that is, who is credit constrained? Answering this question is not a simple task, and we first relate what others have done to answer it. In an important paper, Jappelli (1990) was the first to use a direct, survey-based indicator of borrowing constraints in an empirical analysis of households. Specifically, using the 1983 SCF, Japelli identified as constrained the union of (1) those respondents who reported that they had been turned down for credit, or received less credit than applied for, and (2) those who said that they did not apply for credit because they felt that they would be turned down (“discouraged borrowers”). The households identified as constrained by this method constituted 19.0 per cent of Jappelli's sample. Using a simple model of borrowing constraints, Jappelli presented logit estimates relating the probability of a household's being constrained to its income, wealth (plus squared and interaction terms), debt, homeownership, demographic variables, and regional dummies. He found that coefficients on income, wealth, and age were significant and had the expected signs. Several other papers have used the 1983 SCF data to estimate qualitative response models of borrowing constraints, often on the way to addressing other issues. Cox and Jappelli (1990) found that borrowing-constrained households are more likely than others to receive private transfers (e.g., gifts from parents), although the quantity of transfers is insufficient to compensate fully for the borrowing constraint. In a subsequent article, Cox and Japelli (1993) estimated that, in the absence of borrowing constraints, liabilities of constrained households would increase significantly; they also found the difference between desired and actual debt to be strongly related to age, with younger households experiencing the tightest constraints. Duca and Rosenthal (1993) reached similar results, finding that about 30% of households whose head is less than 35 years old face binding borrowing constraints. Calem and Mester (1995) estimated a model similar to Japelli's using a later wave (in this case, 1989) of SCF data. Calem and Mester's principal objective was to evaluate empirically some assumptions underlying Ausubel's (1991) theory of the “stickiness” of credit-card interest rates. They found that, all else equal, consumers who are more willing 3
to search for the best financial products tend to borrow less on their credit cards, which they interpret as being consistent with Ausubel's view that stickiness in credit card rates is related to search and switching costs among consumers. As ancillary evidence, Calem and Mester estimate a probit regression of the Jappelli type in which credit card debt is included as an explanatory variable for the probability that a household faces a borrowing constraint. Interestingly, they find that income and wealth are not important determinants of borrowing constraints when credit card debt is included. We will comment further on this result later; for the moment, though, we note that some of the basic results obtained by Jappelli and others using the 1983 SCF data have not been confirmed by other studies using later waves. In an attempt to remedy this omission, we use data from the 1983, 1989, and 1992 waves of the SCF to estimate models of borrowing constraint determination, in the spirit of Jappelli's, Cox and Jappelli's, and Duca and Rosenthal's original analyses.1 However, as already discussed, we also attempt to improve this basic specification in two broad ways: First, following recent literature on the financing of corporations, we introduce several additional explanatory variables to capture aspects of the household's balancesheet structure and its relationship with intermediaries. Second, we propose some alternative proxies for binding borrowing constraints, based on whether the household uses credit cards as a source of credit— as opposed to a transactions medium.
3 The Model The last decades have yielded an explosion in research into the effects of imperfect information in various markets. Two prominent works on the subject relevant to lending markets are those by Stiglitz and Weiss (1981) and Jaffee and Russell (1976). Stiglitz and Weiss develop a model in which rationing arises in equilibrium because of adverse selection problems. In their model, a lender rations some borrowers who are otherwise observationally equivalent to those who obtain credit. Jaffee and Russell (1976) propose a model in which credit rationing results in a lender offering amounts smaller than the loan demand at that interest rate. In a framework of profit maximizing banks and utility maximizing borrowers, the authors develop models of supply and demand and point out that the putative equilibrium where supply meets demand will be dominated, for lower risk borrowers, by an equilibrium in which the loan interest rate and loan principal are lower. Since this “new” equilibrium involves lending less than the quantity demanded at that interest rate, some borrowers will be rationed. Their model, realistically, has a lender setting the interest rate and loan maximum. Below, as in the Jaffee-Russell model, we model a bank which offers a maximum loan amount and an interest rate. This and demand determine outcomes, some of which are constrained. The model explains the behavior in the conventional bank consumer lending market. It uses three endogenous variables: one variable for credit demand, Di; another 1
We have also experimented with the 1986 wave; unfortunately, this survey is missing a number of key variables, notably the response to the question of whether the household had been turned down for credit and also the bank relationship variables introduced below. On the contrary, we haven’t yet cast our analysis to SCF waves after the 1992 one, although it is our intention to do it.
4
variable for credit supply, Si ; and a third endogenous variable for the terms on the credit extended, which we can think of as being proxied for by the interest rate, Ri. Di is the level of non-mortgage liabilities an individual wishes to hold, and has the following structure: Di = x1i α1 + x2i α2 − Ri δ + uDi,
(1)
where x1i is a vector of variables known only to the individual (such as her expectations regarding future income), x2i is a vector of observables known to both the individual and the financial intermediary (current income, for example), and Ri is the lending interest rate. We assume that this desired level of borrowing is non-negative. Bank lenders are assumed to select an interest rate and loan amount simultaneously. They select a maximum amount they are willing to lend to individual i, conditional on the observable characteristics of the individual. This amount is given by Si where Si = x2i β1 + x3i β2 + uSi,
(2)
where x2i is as before, and x3i is a vector of variables that the individual doesn't take into account explicitly when making the decision of how much to borrow (past delinquency, for example). We also assume that Si is non-negative. Independently, intermediaries determine a lending rate for individual i, conditional on the individual's characteristics: Ri = x2i γ 1 + x3i γ 2 + uRi,
(3)
where x1i, x2i, and x3i are exogenous. We impose no distributional assumptions on the errors, nor assumptions about the correlation among the residuals. The amount actually observed in the market is assumed given by: Qi = min{Di,Si}.
(4)
For our purposes, we are interested in those who are constrained, namely those individuals in situations with Di > Si.
(5)
For these individuals, the probability of being constrained will be: P(Constrained) = P(Di > Si) = P(x1iα1 + x2iα2 − (x2iγ + (uDi − uRiδ) > x2iβ1 + x3iβ2 + uSi) 1 +x3iγ 2)δ = P(ε > − [x1ia + x2i b + x3ic])
(6)
where
5
ε = (uDi − uRiδ− uSi) a = α1 b = (α2 − γ − β1) 1δ c = (− γ − β2). 2δ Upon estimation, the coefficient on x1i in (6) is predicted, unambiguously, to have the same sign as that of α1. This is the coefficient in the demand equation on the variables that matter only to the borrower. Second, the coefficient on x2 has an ambiguous sign interpretation. Third, since δis predicted to be positive, and we would expect the signs on γ 2 and β2 to be opposite one another (a factor that raises the amount lent to an individual, all else equal, should reduce the interest rate offered to that same individual), the sign on x3i should be ambiguous. Now, the reason we see some of these ambiguities is due to the way we have defined constraint. For example, suppose an individual is constrained, Di > Si. If this individual were to experience an increase in current income, all else equal, she would be viewed more favorably by the lender (Si rises, Ri falls), and would be interested in receiving more credit— assuming for the moment, that the direct effect of income on Di is positive. We can't resolve whether this rise in income is enough to change the inequality Di > Si. For many of the coefficients in the model above, we are unable to identify their signs. However, previous authors estimating similar relationships (for example, Jappelli (1990)) are able to make statements about coefficients' signs. Further, for some of the variables we will use in estimation later, we can reasonably resolve these ambiguities. For example, suppose that while an increase in net wealth both reduces the quantity demanded and increases the quantity offered— thus reducing the probability of being constrained— , it may reduce the interest rate offered, increasing the quantity demanded— and thus, potentially, increasing the probability of being constrained. However, we would argue— either on intuitive grounds or posing that the interest rate effect is weak— that we would expect this coefficient to be positive. In the sections that follow, we make statements about coefficients having signs as we expected, or having the “right” signs. This merely means that the signs accord with intuition, and should not be interepreted as saying that we have identified structural parameters— except, of course, for the attitude variables. Fortunately, the main contributions of this paper don't rely on identifying the structural parameters. Rather, our contributions follow from the definitions of our dependent variables, and having a dataset that describes consumers well. Exactly which variables we use is described in the following sub-sections.
6
4 The Empirical analysis 4.1 Dependent Variables Our objective is to explain the incidence of borrowing constraints among households, and so we need one or more indicators of when households have been turned down for credit. As described above, Jappelli (1990) and a number of subsequent papers classify households as credit constrained when they report either being turned down for credit, or receiving less credit than asked for, during the past five years (TURNDOWN = 1); or if they did not apply for credit on the assumption that they would be turned down (“discouraged borrowers”). Calem and Mester (1995) classify as credit-constrained only potential borrowers reporting being turned down. There are pros and cons in following Japelli in including the so-called “discouraged borrowers” among the credit-constrained. On one hand, one would want to treat as constrained households that genuinely desired to borrow but rationally anticipated being turned down. On the other hand, “discouraged borrowers” may be incorrect in their presumption that they would have been denied credit, or may report that they would have applied for credit if available even though given the actual opportunity and with careful consideration perhaps they would have not. In the following presentation, we weigh in with Jappelli and count as credit-constrained those households reporting that they had been turned down for credit (TURNDOWN = 1) or were discouraged from borrowing (DISCOURAGED = 1). We refer to this variable in the following as TURNDOWN ∪ DISCOURAGED.2 We calculated estimates using both dependent variables, and found that our results didn't change substantially when we use the denial measure excluding the discouraged. Although the survey measure of credit constraints is quite interesting and useful, one does not feel completely comfortable accepting a verbal response to a survey as evidence of a binding credit constraint. For example, respondents may be reluctant to report being turned down for credit to interviewers, leading to a downward bias in the measure. Alternatively, there may be an upward bias if households sometimes request loan amounts which are clearly inconsistent with their intertemporal budget constraints, then report facing a borrowing constraint when these loans are denied. Thus it would be desirable to complement the survey response measure of borrowing constraints with an alternative, perhaps more behaviorally-based indicator. Before presenting our new measures, we must define what we mean by “credit constrained.” We assume a household is constrained if it seeks to intertemporally substitute consumption by obtaining credit in the conventional, bank-intermediated consumer loan market and is denied the desired credit there. The alternative indicators of borrowing constraints that we propose in this paper are based on the observation that, because of the extremely high interest rates charged on credit card balances, households that use credit cards as a source of credit (as opposed to a convenient transactions medium) must have been denied credit by the “normal” sources
2
All of these variables, as well as those mentioned in the following sub-sections are defined in the Data Appendix.
7
of consumer credit, such as bank or finance company loans.3 Fortunately for our purposes, the SCF asks households to report outstanding balances on credit cards after the last payment on each card was made, thereby allowing us to identify households who carry positive credit-card balances from month to month. Since a rational individual who was unconstrained in the credit market would not choose to borrow at the high rate of interest typically charged on credit cards, in some of our estimations we designate a household as “constrained” if it carries positive credit card balances (CARDBAL > 0). We construct this variable in two ways. One method is to drop from the sample those without credit cards, and define the variable to be equal to one if the household carries a positive balance. The rationale here is that those without credit cards are not in a situation to be constrained in the sense we mean here. We call this measure (CARDBAL > 0 ex. HAVECC=0) below. On the other hand, we argue that those without credit cards are probably severely constrained either because they are operating outside of conventional financial markets, or are very risky borrowers. It is also worth noting that credit card issuers usually do not approve applications for credit cards by households having either very low incomes or poor credit histories. Thus, it is likely that those households not having a credit card have either seen their applications turned down, or refrained from applying in the expectation of denial. For these reasons, we include them as constrained, along with those borrowing on credit cards in an alternative definition. We call this measure (CARDBAL > 0 ∪ HAVECC=0) below. At this point, the reader might point out some objections to these measures. One is that individuals borrow small amounts on credit cards all the time for short-term, unexpected liquidity needs; it might be questionable to assume these individuals are credit constrained. We argue that this kind of borrowing need is randomly distributed throughout the population, so their inclusion only introduces a measurement error in the measure of constraint. While it may increase the variance of the error term in our estimations below, it won't bias our results. A second objection, and the more serious, is that those who we observe borrowing on credit cards, at high rates, are simply paying the higher price for being riskier borrowers. Further, these people are obtaining credit, just not cheaply. We argue that since our interest here is in finding those who are constrained in the conventional bank consumer loan market, these people are constrained by our definition: unable to obtain credit from conventional sources. Means and medians of the alternative proxies for credit constraints are shown in Tables (1) and (2). We note that the criterion of borrowing on credit cards identifies a much higher fraction of the sample as being borrowing-constrained (about 40% to 50%) more than does the TURNDOWN ∪ DISCOURAGED indicator (15-25%). The first of the three indicators shows an increasing incidence of constraints over time (1983, 1989, 1992). The other measures don't show such a strong pattern; however, later in the paper we will show that such an increasing pattern is present once borrower characteristics are controlled for. Within these three years though, a particularly interesting comparison, on which we will focus below, is between 1989 (a year which occurred at the end of a prolonged expansion) and 1992 (a year following a recession): The TURNDOWN ∪ DISCOURAGED indicator shows significant increases in the incidence of borrowing 3
Gross and Souleses (2000) seem to confirm that credit card borrowers are quantity constrained. They find that, controlling for other variables, increases in credit limits on credit cards, in fact, generate an immediate and significant rise in credit card debt. Since credit limits on credit cards are unilaterally upgraded by the bank— often automatically with time (given non-delinquency) and not under the borrower’s request— this evidence suggests quantity constraints prevail for credit card borrowers.
8
constraints between the former year and the latter one, as predicted by theories of timevarying stringency in credit markets; the CARDBAL > 0 indicator shows a slight drop in value between the two years. Of course, neither of these simple observations controls for changes in the creditworthiness of borrowers, which we will attempt to do below. The criterion of holding some positive credit card balances month-to-month (CARDBAL > 0) also shows an increase in borrowing constraints between 1989 and 1992, but the change is much smaller. Table (3) presents correlations between the (TURNDOWN ∪ DISCOURAGED) measure and the various alternative measures of credit constraints. Interestingly, there is little correlation across all the measures, although for all of the measures we use we can reject the hypothesis that the variables are unrelated. Also, the correlations have been increasing over time. However, given that these are binary variables, it may be easier to compare them using cross tabulations of the variables. These are presented in Table (4). The CARDBAL > 0 measures seem only to differ in that they classify many of the unconstrained by the (TURNDOWN ∪ DISCOURAGED) measure as constrained. They don't reclassify many of the constrained. Lastly, Table (5) presents means on some of the explanatory variables— described in the next section— for the constrained by each measure. Notice that the constrained by each measure tend to have lower incomes, less wealth, have been delinquent on payments more often, and are more leveraged than in the full sample.
4.2 Explanatory Variables Key explanatory variables in the Jappelli (1990) specification include income, income squared, wealth, wealth squared, age, and age squared. We include these variables in all of our specifications as well (mnemonics are INCOME, INCOMESQ, WEALTH, WEALTHSQ, AGE, and AGESQ). We also generate a measure of permanent income (described below) and include it in our specifications. Wealth, permanent income, and other dollar-denominated variables in this study are all expressed in 1983 prices. Permanent income should be important for the determination of borrowing constraints for two reasons. First, higher permanent income signals greater lifetime resources with which to repay a loan, increasing the borrower's likelihood of being granted a loan, and hence a lower likelihood of being constrained.4 Second, all else equal, higher permanent income indicates a higher desired level of current consumption— in particular, relative to a given level of income— on the part of a consumer, and hence a greater desired level of borrowing and a greater probability of requesting more resources than a lender is willing to grant. So overall, the effect is ambiguous. The inclusion of wealth and age follows directly from the standard theory, which implies that borrowing constraints are more likely when desired consumption— which is proportional to the sum of human and financial wealth— is high relative to current financial resources. All else equal, an older person— whose ratio of human wealth to total wealth is lower— is less likely to be constrained in credit markets; similarly, a person or household with high financial wealth is unlikely to need to borrow to finance the desired 4
The link between permanent income and desired current consumption will be weakened if the desire to leave bequests rises more than proportionally with permanent income.
9
level of current consumption. For these reasons, we suspect the estimated coefficients on age and wealth in our equation (6) will be negative. We should note that, although we include wealth in the specifications reported here, we do have some concerns about possible measurement error in the wealth variable: First, as Jappelli (1990) noted, the reported wealth variable in the SCF is end-of-period, rather than beginning-of-period as one would like. More seriously, large components of wealth, such as the values of homes, businesses, and pensions— which omit Social Security— are likely to be poorly measured, relative to more liquid assets. Further, because different components of wealth are measured with different amounts of error, the measurement error in total wealth is likely to be correlated with the household's balancesheet composition, which— recent research suggests— should play a role in the determination of borrowing constraints independent of the total level of wealth. An alternative approach, followed by Duca and Rosenthal, is to use instruments for wealth in the estimation. We have not tried that approach thus far, but we have experimented with specifications that omit wealth, effectively allowing the included demographic variables to proxy for wealth effects. As these experiments had little effect on the results it may be that the measurement error problems with wealth are not overly serious. Current income should affect the probability of being borrowing constrained for several reasons. A high level of transitory income raises current financial resources relative to desired consumption, reducing the probability of binding borrowing constraints. Another perspective on the role of current income is provided by the parallel literature on credit constraints faced by firms— see, e.g., the seminal work of Fazzari, Hubbard, and Petersen (1988); Bernanke, Gertler, and Gilchrist (1996) survey this literature. This research has emphasized the importance of firms' current cash flow— analogous to the current income of consumers— in ameliorating borrowing constraints. The difference in the two literatures is that the work on firms has examined the effects of higher current cash flow while controlling for the long-run prospects of the firm, usually proxied— admittedly imperfectly— by Tobin's q. No control for the household's long-run prospects analogous to Tobin's q exists, of course, and thus the measured effects of current income will confound “cash flow” effects and permanent-income effects. To overcome this problem, we use both permanent income and current income in the following estimations. The parallel literature on corporate borrowing has also focused on the liquid assets held by firms. For firms, a reserve of liquid assets should ease borrowing constraints in much the same way that high current cash flow does, and the firm-based literature has generally found a positive relationship between liquid assets and investment or other forms of spending. Analogously, one would expect to see households with a healthy reserve of liquid assets facing fewer borrowing constraints, all else equal. However, there are some factors working in the opposite direction: First, given wealth, households that are less sophisticated financially are more likely to hold liquid assets, such as checking accounts, rather than— for example— stocks or bonds. Less financial sophistication may be correlated with greater difficulties in obtaining credit. Second, because of down-payment constraints and the like, households that are planning to apply for credit may first liquefy their assets, leading to a spurious correlation between liquidity holdings and being turned down for credit. Similarly, households turned down for credit may have to resort to converting illiquid to liquid wealth in order to finance expenditures. To avoid these ambiguities, we include liquidity in the specification as the ratio of liquid assets— e.g., checking accounts— to the sum of liquid assets, stocks, and bonds 10
(LIQSHARE). The prediction is that households for which this ratio is high, because of lower financial sophistication, are more likely to encounter problems in the credit market. Some other implications from the literature on firm borrowing are more straightforward to adapt to the case of households. First, research on firms has emphasized the importance of credit-worthiness— i.e., as reflected in balance sheet composition and available collateral— in determining access to credit. In our specifications we include several variables indicative of the credit-worthiness of individual households: These include leverage (the ratio of debt to wealth,5 LEVERAGE); leverage squared (LEVERAGESQ); a dummy for whether the household owns its own home, and thus has potential access to home equity as collateral (HOMEOWNER); and a dummy for whether the household has been delinquent on previous borrowings (DELINQUENT). Second, the literature on firms has emphasized that borrowing is easier when the quality of information on the prospective borrower is better. A related point is that borrowing constraints should be less likely to bind when the borrower has maintained a long-term and exclusive relationship with a lender— which facilitates efficient information transmission. For example, according to Boot (2000), facilitating a Paretoimproving exchange of information between the borrower and the bank, relationship banking reduces asymmetries in information between the two parties and expands credit availability for borrowers. Petersen and Rajan (1994) show that the availability of credit is greater for small firms participating in a long-term relationship with a bank, and that the average cost of borrowing increases for firms doing business with a larger number of banks; Cole (1998) finds that a lender is less likely to grant credit to a firm if the firm deals with other financial counterparts. Angelini, Di Salvo, and Ferri (1998) show that, all else equal, the reported intensity of credit rationing increases with the number of lending banks. Nakamura (1993) has argued for economies of scope in exclusive banking relationships— i.e., if a bank manages a small firm's checking account as well as being its source of loans, problems of asymmetric information are reduced. As measures of the quality of information about each household, we include several variables— which are responses to questions appearing on credit card application forms— in our specifications: the number of years the respondent has been employed at her current job (YRSEMPLOYED), the number of years she has lived at the current address (YRSCURRADDR), and a dummy variable for home-owning households that have been at the current address for less than two years (NEWARRIVAL)— the first two of which should have negative coefficients in the supply equation, and the last should have a positive coefficient. We also include two variables proxying for the quality of households' customer relationships with banks: first, a variable that equals the number of financial institutions with which the household has accounts or loans, or regularly does financial business (NUMBANKS); and, second, a dummy that equals one if the household has its checking account and credit card account at the same bank (SAMEBANK). Households who have exclusive banking relationships, or deal with a smaller number of banks, should face less serious informational problems when applying for credit. We also include a number of demographic controls in our specification, comprising the respondent's gender (MALE = 1 if male), race (WHITE = 1 if Caucasian), whether the respondent is married (MARRIED = 1), the respondent's years of education 5
Jappelli (1990) includes debt in his specification.
11
(EDUCATION), and the number of people in the household (HHSIZE). The demographic variables should pick up various unobserved characteristics of the households in our sample, and also allow for the possibility of discrimination in credit markets against particular demographic groups. We are mute on predictions for these variables. Finally, we include “attitude” variables in some of the specifications as demand shifters. All of these are dummy variables for the respective trait. Some of these can be classified as individual expectations: if the head of household expects to earn adequate retirement income, EXPRETINC; if the household head expects to earn pension benefits from the current job or past jobs, EXPPENSION; if the household head expects an inheritance, EXPINHERIT; and, if the household head or spouse expect to stop working before age 65, EXPSTOPWRK, EXPSTOPWRKS. These variables reveal information about how the household views the certainty of its future income. An increase in the certainty of future income, to the extent that it isn't known to the bank, will raise the desired level of current borrowing (Di) relative to the level the bank is willing to lend (Si). So, the first three of these variables should enter with positive signs; the last two are harder to sign, but we suspect they will enter with negative signs. Others of the “attitude” variables could be classified as financial market attitudes: if the household head is risk averse when making investments, RISKAVERSE; if the household head doesn't save, or only does so for necessities, SAVEREASON; if the household head has a short planning horizon for saving, SHORTHORIZON; if the household head and spouse report usually spending as much or more than their income, SAVEHABIT; if the household head reports shopping around for the best rate when borrowing and saving, SHOPRATE. We don't have strong priors on how these variables should relate to the probability of being constrained. The last set reflects attitudes toward borrowing: whether the household head considers it is not all right to borrow on the installment plan (NOINSTPLAN), to borrow for vacation (NOBORRVAC), to borrow for jewelry (NOBORJEWEL), to borrow for the purchase of a car (NOBORRCAR), to borrow to cover living expenses when income is cut (NOBORLIVEX), and to borrow to finance educational expenses (NOBOREDUC). We expect these to enter with negative signs. Sample means and medians for all the variables described above for each of the three years that we use are presented in Tables (1) and (2).6 All of the dollar denominated values are in thousands of constant dollars. On average, the households in our sample are an older— mean age of the respondent is in the late forties— and more affluent group— mean wealth levels, which include housing and pension wealth, are fairly high. The mean education level is around 13 years. The mean household has been at its current address for more than 10 years— the median is 7-10 years— , and the mean job tenure is 7-8 years— 3-4 years at the median. About two-thirds of the respondents are married, and a slightly higher percentage are homeowners. Average leverage ratios are around .3, reflecting primarily mortgage borrowing, with the median leverage a good bit lower, in the range of .10-.15. Non-mortgage liabilities are not particularly high relative to wealth, between $3000 and $5000 in 1983 dollars at the mean, only about one-fifth of that at the median. The typical household deals primarily with one bank, but does not have its credit card account at the same bank as its checking account.
6
See the Data section for a description of the dataset.
12
5 Predicting the Incidence of Borrowing Constraints: Empirical Results In this section we discuss the basic estimation results linking the probability of being credit-constrained to its determinants. In what follows, we assume that the error terms are normally distributed, which allows us to estimate coefficients by probit MLE methods. Standard errors were calculated using robust methods. The next section takes up the issue of whether and/or how the severity of credit constraints may change over time. Table (6) reports the results for separate probit models estimated for the 1983, 1989, and 1992 waves, where— as in previous work— credit-constrained households are identified by their answer to the question about whether they had been turned down for credit (TURNDOWN ∪ DISCOURAGED). For each year, since the estimated coefficients are not interpretable as the marginal effects of the explanatory variable on the probability of being turned down for credit, we report the partial derivative— with significance level— of the expected value of the dependent variable with respect to each independent variable. The partial derivative, or marginal effect, equals the coefficient of the independent variable times the value of the normal density predicted by the equation at the sample means. Significance levels for the partial derivatives are also reported; these were determined by likelihood ratio tests. For 1989 and 1992, we report two specifications in each year, one is the same as used in 1983, the other includes attitudinal variables. Levels of significance of the tests for zero coefficients are denoted, here and in all the results which follow, as *** for the 1% level or lower, ** for levels between 1%and 5%, and * for levels between 5% and 10%. The results are generally quite consistent across the three samples, and a substantial portion of the coefficients are statistically significant. Notably, 1) Higher current income reduces the probability of being turned down; 2) Households that have lower leverage, are homeowners— except in the 1992 sample estimation using CCBAL > 0 ex. HAVECC=0— and have not been delinquent on previous debts are less likely to be turned down; 3) Households with a low ratio of liquid assets to liquid assets plus stocks and bonds are less likely to be turned down for credit— financial sophistication implies fewer problems in credit markets; 4) Households that deal with several banks face a higher probability of being turned down for credit, and households with checking accounts and credit card accounts at the same bank face a lower probability of being tuned down; 5) Being older decreases the probability that an individual is turned down; caucasians face lower probabilities of being turned down; 6) Permanent income enters with a negative sign in the TURNDOWN ∪ DISCOURAGED probit, demonstrating the “repayment potential” given by a high permanent income.
13
The only financial variable that does not enter the TURNDOWN ∪ DISCOURAGED equations as expected is real wealth; we find that, controlling of course for many other factors, there is a slight positive relationship between wealth and being turned down for credit. Quantitatively, however, this effect is very small, and it may reflect the measurement problems discussed above. We recall that a weak relationship between wealth and borrowing constraints was also found by Calem and Mester (1995). Many of the attitude variables don't enter significantly or with expected signs. Results for our alternative measures of credit constraints, that the household is borrowing on credit cards— sample excluding those without credit cards— and that the household is borrowing on credit cards or has no credit card, are given in Tables (7) and (8). Estimation is by the same methods— probit, with robust standard errors— as in Table (6). Again the results are largely in line with expectations, reinforcing our confidence in the results obtained using the TURNDOWN ∪ DISCOURAGED variable. Results using the two new measures of credit constraints are very similar. Interestingly, for these measures of borrowing constraints, wealth enters with the expected— negative— sign, and its partial derivative is significant at the .01 level in two of the three years. Other variables strongly influencing the probability of borrowing on credit cards— and with the expected signs— are leverage, previous delinquency in repayments, age, race, and education. In all, it appears that using credit-card borrowing to indicate that the household faces borrowing constraints in “standard” consumer loan markets yields quite reasonable results. An important and interesting question is which of the three measures of constraints examined so far is closer to being the “right” one, since— although they have similar relationships to the explanatory variables— the proportion of the population facing constraints is much higher by the credit-cardborrowing measures than by the survey response measure. On this point, we don't take a stand. To summarize, the positive contributions of this section are three: first, we have extended the analysis of Jappelli (1990) and others using the TURNDOWN ∪ DISCOURAGED variable to two later waves of data. We have confirmed that the relationship of this variable to its determinants is as predicted by the theory, with tightly estimated coefficients that appear reasonably stable over time.7 Thus, the TURNDOWN ∪ DISCOURAGED variable does appear to be a reasonable indicator of borrowing constraints. Second, we have proposed two alternative indicators of borrowing constraints, based on whether households are using their credit cards as a source of medium— or long— term credit, rather than as only a convenience for transactions. This indicator seems to perform as well as the Jappelli measure, though it has a strongly different implication for the share of the population that faces credit constraints: In particular, about 40% to 50% of households borrow on credit cards, as opposed to 15-25% who report being turned down for credit or were discouraged from borrowing. The final contribution of this section is to extend the list of variables useful in explaining the incidence of credit constraints to include various measures of creditworthiness and the severity of asymmetric information. As in the parallel literature The exception to this statement is that we do not find a strong relationship between TURNDOWN ∪ DISCOURAGED and a household's real wealth. 7
14
on firms, these two types of variables do seem to play an important role in determining credit constraints.
6 Conclusion This paper makes two main contributions. First, we have found that borrowing constraints may be usefully proxied by household borrowing on credit cards. Our rationale for identifying as credit constrained a household borrowing on credit cards stems from the consideration that it must not have full access to conventional intermediated loans, else the household would not be relying on such an expensive form of credit. Second, we have developed robust specifications explaining the determinants of consumer borrowing constraints, which include an expanded set of explanatory variables based on the parallel literature for firms’borrowing, such as measures of balance-sheet condition and of relationships with lenders. The determinants of consumer borrowing constraints are generally consistent whether one refers to the usual measure of a household being constrained— the household's report to the survey-taker that it had been turned down or received less credit than applied for— or one casts the analysis to our more behaviorally-based proxy. This provides important confirmation to the extant literature.
15
References Angelini, Paolo, Roberto Di Salvo, and Giovanni Ferri. “Availability and cost of credit for small businesses: customer relationships and credit cooperatives”, Journal of Banking and Finance, 1998. Ausubel, Lawrence. “The Failure of Competition in the Credit Card Market”, American Economic Review, vol. 81, no. 1, 1991, pp. 50-81. Bernanke, Ben, Mark Gertler, and Simon Gilchrist. “The Financial Accelerator and the Flight to Quality”, Review of Economics and Statistics, vol. 78, no. 1, February 1996, pp. 1-15. Bernanke, Ben, and Mark Gertler. “Inside the Black Box: The Credit Channel of Monetary Policy Transmission”, Journal of Economic Perspectives, vol. 9, no. 4, Fall 1995, pp. 27-48. Boot, A.W.A.. “Relationship banking: what do we know?”, Journal of Financial Intermediation 9, 2000, No. 1. Calem, Paul, and Loretta Mester. “Consumer Behavior and the Stickiness of Credit Card Interest Rates”, American Economic Review, vol. 85, no. 5, December 1995, pp. 132736. Carroll, Christopher. “Does Future Income Affect Current Consumption?”, Quarterly Journal of Economics, vol. 109, no. 1, February 1994, pp. 111-147. Cole, R.A.. “The importance of relationships to the availability of credit”, Journal of Banking and Finance 22, 1998, 959-77. Cox, D., and Tullio Jappelli. “Credit Rationing and Private Transfers: Evidence from Survey Data”, Review of Economics and Statistics, vol. 72, no. 3, August 1990, pp. 44554. Cox, D., and Tullio Jappelli. “The Effect of Borrowing Constraints on Consumer Liabilities”, Journal of Money, Credit, and Banking, vol. 25, no. 2, May 1993, pp. 197213. Duca, John, and Stuart Rosenthal. “Borrowing Constraints, Household Debt, and Racial Discrimination in Loan Markets”, Journal of Financial Intermediation, vol. 3, no. 1, October 1993, pp. 77-103. Fazzari, Steven, R. Glenn Hubbard, and Bruce Petersen. “Financing Constraints and Corporate Investment”, Brookings Papers on Economic Activity, 1988:1, pp. 141-95. Gertler, Mark, and Simon Gilchrist. “Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms”, Quarterly Journal of Economics, vol. 109, no. 2, May 1994, pp. 309-40. Gross, David B., and Nicholas S, Souleles. “Consumer Response to Changes in Credit Supply: Evidence from Credit Card Data”, University of Pennsylvania, Wharton Financial Institutions Center, w.p. 00-04-B, 2000. 16
Jaffee, Dwight M, and Thomas Russell. “Imperfect Information, Uncertainty, and Credit Rationing” Quarterly Journal of Economics, vol. 90, no. 4, November 1976, pp. 651-666. Jappelli, Tullio. “Who is Credit-Constrained in the U.S. Economy?”, Quarterly Journal of Economics, vol. 105, no. 1, February 1990, pp. 219-34. Nakamura, Leonard. “Commercial Bank Information: Implications for the Structure of Banking”, in M. Klausner and L.J. White, eds., Structural Change in Banking. Homewood, IL, 1993. Petersen, Mitchell, and Raghuram Rajan. “The Benefit of Firm-Creditor Relationships: Evidence from Small Business Data”, Journal of Finance, vol. 49, no. 1, March 1994, pp. 3-37. Stiglitz, Joseph E, and Andrew Weiss. “Credit Rationing in Markets with Imperfect Information”, American Economic Review, vol. 71, no. 3, pp. 393-410.
17
Table 1: Sample Means and Medians
Financial TURNDOWN ∪ DISC. CARDBAL > 0 ∪ HAVECC=0 CARDBAL > 0 ex. HAVECC=0 TURNDOWN HAVECC CARDBAL > 0 INCOME PERMINCOME WEALTH LEVERAGE LIQSHARE DELINQUENT NUMBANKS SAMEBANK HOMEOWNER NONMLIAB PREAPPROVED NEWAPPROVED NEWAPPFLOW TOTALSTOCK PERLOANFIN PERLOANNONFIN CARLOANFIN CARLOANNONFIN Demographic AGE MALE WHITE MARRIED EDUCATION HHSIZE YRSEMPLOYED YRSCURRADDR NEWARRIVAL Observations
1983 Mean
1989 Mean
Median
0.19 0.69 0.57 0.14 0.72 0.41 27.56 21.42 104.33 0.28 0.84 0.12 0.75 0.16 0.68 3.55
46.68 0.77 0.88 0.65 12.56 2.40 7.35 14.48 0.04 3285
Median
1992 Mean
Median
0 1 1 0 1 0 22 21.82 42.14 0.12 1 0 1 0 1 0.64
0.21 0.66 0.58 0.17 0.80 0.46 30.72 24.15 122.3 0.31 0.86 0.15 0.72 0.24 0.71 4.24 1.55 3.28 2.55 4.83 0.57 0.36 2.09 0.21
0 1 1 0 1 1 24.19 25.24 50.8 0.14 1 0 1 0 1 1.21 0 0.12 0 1.61 0 0 0 0
0.25 0.65 0.56 0.22 0.80 0.44 31.03 24.55 109.58 0.34 0.86 0.11 0.77 0.21 0.68 3.39 1.48 2.41 1.74 3.89 0.50 0.29 1.52 0.08
0 1 1 0 1 0 23.17 25.77 42.72 0.15 1 0 1 0 1 0.78 0 0 0 0.78 0 0 0 0
44 1 1 1 12 2 3 10 0
49.23 0.75 0.82 0.62 12.92 2.57 7.01 11.94 0.11 2273
46 1 1 1 12 2 3 9 0
49.34 0.74 0.81 0.60 13.34 2.42 6.49 11.62 0.10 2722
47 1 1 1 13 2 3 7 0
Note: statistics have been calculated using analytical weights; the number of observations for CARDBAL > 0 ex. HAVECC=0 is smaller in each year than for the other variables (1983 - 2395 obs.; 1989 - 1866 obs.; 1992 - 2265 obs.)
18
Table 2: Sample Means and Medians (Cont.)
Attitudes EXPRETINC EXPSTOPWRK EXPSTOPWRKS EXPPENSION EXPINHERIT RISKAVERSE SAVEREASON SHORTHORIZ SAVEHABIT SHOPRATE NOINSTPLAN NOBORRVAC NOBORRJEWEL NOBORRCAR NOBORRLIVEX NOBORREDUC Observations
1983 Mean NA
Median NA
1989 Mean
2996
0.6668 0.695 0.7532 0.0819 0.1922 0.466 0.1393 0.5584 0.2661 0.3818 0.3387 0.8786 0.9389 0.1843 0.5932 0.1861 2273
Median 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0
1992 Mean
Median
0.4924 0.7347 0.7561 0.1003 0.1635 0.4685 0.1504 0.5597 0.2071 0.5806 0.3791 0.874 0.9515 0.1955 0.5985 0.1669 2722
0 1 1 0 0 0 0 1 0 1 0 1 1 0 1 0
Note: statistics have been calculated using analytical weights
19
Table 3: Correlations Among Indicators of Credit Constraints Dependent Variable
1983 ρ
P-value
1989 ρ
P-value
1992 ρ
P-value
(CARDBAL > 0 ex. HAVECC = 0), (TURNDOWN ∪ DISCOURAGED)
0.143
0
0.223
0
0.252
0
0.16
0
0.223
0
0.256
0
(CARDBAL > 0 ∪ HAVECC=0), (TURNDOWN ∪ DISCOURAGED)
Note: P-value is from the Pearson Chi-squared statistic.
20
Table 4: Cross Tabulations Among Measures of Credit Constraint 1983 CARDBAL > 0 U HAVECC=0
CARDBAL > 0 ex. HAVECC=0
TURNDOWN ∪ DISCOURAGED 0
0 996
1 1719
0 996
1 1059
1 Total
96 1092
474 2193
96 1092
244 1303
1989 CARDBAL > 0 U HAVECC=0 TURNDOWN ∪ DISCOURAGED 0 1 Total
0 867 74 941
CARDBAL > 0 ex. HAVECC=0 1 995 337 1332
0 867 74 941
1 701 224 925
1992 CARDBAL > 0 U HAVECC=0 TURNDOWN ∪ DISCOURAGED 0 1 Total
0 1019 128 1147
CARDBAL > 0 ex. HAVECC=0 1 1051 524 1575
0 1019 128 1147
1 762 356 1118
21
Table 5: Means and Medians by Measure of Credit Constraint 1983 Mean
Median
1992 Mean
Median
Observations with TURNDOWN ∪ DISCOURAGED 1 1 1 1
1
1
1 1 1 1 1 22.58 30.74 16.55 0.3912 0 411
0.8414 0.7746 0.8522 0.7039 0.5452 27.43 30.84 63.36 0.6215 0.2458 652
1 1 1 1 1 19.94 29.23 12.68 0.3533 0 652
Observations with CARDBAL > 0 ∪ HAVECC=0 0.2402 0 0.2817 0
0.3423
0
1 1 0 1 1 19.38 24.67 22.76 0.2353 0 1584
1 1 0.2742 0.5814 0.5814 24.54 23.59 56.18 0.4355 0.1487 1842
1 1 0 1 1 17.8 24.19 20.57 0.2761 0 1842
Observations with CARDBAL > 0 ex. HAVECC=0 0.1934 0 0.27 0
0.3103
0
1 1 0.2679 1 1 31.99 29.39 76.55 0.489 0.1438 1118
1 1 0 1 1 26.34 29.64 36.17 0.3238 0 1118
Variable Name: TURNDOWN ∪ DISC.
CARDBAL > 0 ∪ HAVECC=0 CARDBAL > 0 ex. HAVECC=0 TURNDOWN HAVECC CARDBAL > 0 INCOME PERMINCOME WEALTH LEVERAGE DELINQUENT Nobs. TURNDOWN ∪ DISC.
CARDBAL > 0 ∪ HAVECC=0 CARDBAL > 0 ex. HAVECC=0 TURNDOWN HAVECC CARDBAL > 0 INCOME PERMINCOME WEALTH LEVERAGE DELINQUENT Nobs. TURNDOWN ∪ DISC.
CARDBAL > 0 ∪ HAVECC=0 CARDBAL > 0 ex. HAVECC=0 TURNDOWN HAVECC CARDBAL > 0 INCOME PERMINCOME WEALTH LEVERAGE DELINQUENT Nobs.
0.8343 0.7198 0.7761 0.5913 0.4257 21.92 25.39 44.13 0.495 0.2739 570
1 1 0.1764 0.5801 0.5801 22.27 20.67 57.03 0.3369 0.1569 2558
1 1 0.1577 1 1 30.88 26.87 76.39 0.3877 0.1633 1303
Median
1 1 1 1 0 18 25.36 9.66 0.2541 0 570
1 1 0 1 1 17.95 21.07 22.54 0.1537 0 2558
1 1 0 1 1 26.83 26.33 41.53 0.245 0 1303
1989 Mean
0.8576 0.8058 0.824 0.7334 0.5909 27.59 30.9 64.34 0.5511 0.3196 411
1 1 0.2164 0.5735 0.5735 24.91 23.76 69.09 0.4196 0.1979 1584
1 1 0.2247 1 1 33.25 30.45 98.62 0.4401 0.2012 925
1 1 0 1 1 28.22 31.65 43.82 0.3155 0 925
Note: statistics have been calculated using analytical weights
22
Table 6: Determinants of Being Credit Constrained Dependent variable: (TURNDOWN ∪ DISCOURAGED) Full sample
Variable Financial INCOME PERMINCOME WEALTH LEVERAGE LIQSHARE DELINQUENT NUMBANKS SAMEBANK HOMEOWNER Demographic AGE MALE WHITE MARRIED EDUCATION HHSIZE YRSEMPLOYED YRSCURRADDR NEWARRIVAL Attitudes EXPRETINC EXPSTOPWRK EXPSTOPWRKS EXPPENSION EXPINHERIT RISKAVERSE SAVEREASON SHORTHORIZ SAVEHABIT SHOPRATE NOINSTPLAN NOBORRVAC NOBORRJEWEL NOBORRCAR NOBORRLIVEX NOBORREDUC Observations Psuedo R2 Log Likelihood
1983 (a)
MARGINAL EFFECT (SIGNIFICANCE) 1989 1992 (b) (c) (d)
(e)
***-.00113 * -.00071 ** .00010 ** .04564 0.02911 *** .11051 -0.00237 * -.03069 ***-.08960
** -.00075 ***-.00144 ** .00005 *** .11500 0.03132 *** .03397 ** .00827 ***-.05471 ***-.06075
-0.00056 ***-.00132 ** .00005 *** .10258 0.02296 *** .08259 0.01384 ***-.05245 ***-.05553
** -.00078 ** -.00142 *** .00005 *** .14977 0.0315 *** .19120 -0.0072 ** -.04484 ***-.11503
-0.00055 * -.00115 *** .00005 *** .12384 0.03125 *** .16870 0.00732 * -.03581 ***-.11143
***-.00513 -0.0041 ***-.07154 -0.02681 0.00178 0.00679 ***-.00405 0.00012 ** .06493
***-.00400 0.02131 ***-.09334 -0.03269 ** .00827 ** .01480 ** -.00166 -0.00123 0.01269
***-.00420 0.02576 ***-.09725 -0.02853 *** .00871 ** .01221 ** -.00162 -0.00139 0.013
***-.00531 ** .06023 ***-.09387 ** -.07224 0.00521 * .01359 -0.00043 -0.00165 0.02363
***-.00550 ** .06649 ***-.09777 ** -.06522 0.00604 0.01124 0.00002 -0.00178 0.02128
3285
2273
-0.01637 -0.00217 -0.01485 0.00229 0.01367 -0.00078 0.01089 0.01892 *** .05083 -0.00069 0.00941 * -.03603 0.03514 -0.01967 0.00699 0.01203 2273
2722
***-.06522 0.05091 * -.08861 -0.02589 * .03409 0.00958 0.0156 *** .06610 *** .06598 -0.01031 0.00357 0.01789 * -.05704 * -.04005 -0.0054 -0.00399 2722
0.2179 -1185.5
0.1988 -860.8
0.2101 -848.6
0.1956 -1205.5
0.2181 -1171.8
23
Table 7: Determinants of Being Credit Constrained Dependent variable: CARDBAL > 0 Excluded from sample: HAVECC = 0 MARGINAL EFFECT (SIGNIFICANCE) Variable
1983 (a)
1989 (b)
(c)
1992 (d)
***-.00151 *** .00188 ** -.00008 *** .52979 ***-.01406 *** .14409 -0.00383 ** -.07486 0.01639
***-.00270 ** .00132 ** -.00004 *** .57270 0.05997 *** .26240 ** -.08738 0.01557 -0.0037
***-.00249 ** .00178 ** -.00004 *** .50608 *** .05776 *** .21659 ** -.07909 0.01757 *** .21659
***-.00236 ***-.00304 ** -.00327 -0.06576 -0.06794 -0.07467 ***-.11506 ***-.13058 ***-.14946 -0.03354 -0.01612 0.01674 ** -.01434 -0.0161 ***-.01752 *** .05741 *** .05216 *** .03363 -0.00088 -0.0009 0.00165 0.00043 ***-.00121 0.00141 -0.03016 0.01647 0.00516
** -.00369 * -.08239 ***-.16155 0.0455 ***-.01709 *** .03092 0.00168 *** .00052 0.00405
(e)
Financial INCOME PERMINCOME WEALTH LEVERAGE LIQSHARE DELINQUENT NUMBANKS SAMEBANK HOMEOWNER Demographic
***-.00189 *** .00440 ***-.00013 *** .41607 0.03882 *** .27800 0.01583 0.03582 -0.01506
AGE MALE WHITE MARRIED EDUCATION HHSIZE YRSEMPLOYED YRSCURRADDR NEWARRIVAL Attitudes
***-.00010 * -.08912 ***-.18306 * .09546 ***-.01479 0.01399 0.0005 0.00137 0.01298
EXPRETINC EXPSTOPWRK EXPSTOPWRKS EXPPENSION EXPINHERIT RISKAVERSE SAVEREASON SHORTHORIZ SAVEHABIT SHOPRATE NOINSTPLAN NOBORRVAC NOBORRJEWEL NOBORRCAR NOBORRLIVEX NOBORREDUC Observations 2
Psuedo R Log Likelihood
***-.00200 *** .00137 ***-.00009 *** .61135 0.00135 *** .16510 -0.00589 ** -.06319 -0.01287
2395
1866
0.1796 -1354.3
0.2283 -998.1
0.01171 -0.07743 0.02975 -0.06976 0.03002 ** .06335 -0.06887 *** .08423 *** .16104 ***-.08237 ***-.08955 * -.08254 ** -.14085 ***-.13147 -0.02907 0.00447 1866 0.2699 -944.3
2265
-0.01795 -0.03078 0.00064 ** .07437 -0.00158 ** .05911 * .07233 ** .05842 0.04609 ***-.08153 ** -.05255 ***-.13802 -0.03816 ***-.11510 -0.03338 ** -.08866 2265
0.2174 -1228.5
0.2476 -1181.1
24
Table 8: Determinants of Being Credit Constrained Dependent variable: (CARDBAL > 0) U (HAVECC = 0) Full sample
Variable Financial INCOME PERMINCOME WEALTH LEVERAGE LIQSHARE DELINQUENT NUMBANKS SAMEBANK HOMEOWNER Demographic AGE MALE WHITE MARRIED EDUCATION HHSIZE YRSEMPLOYED YRSCURRADDR NEWARRIVAL Attitudes EXPRETINC EXPSTOPWRK EXPSTOPWRKS EXPPENSION EXPINHERIT RISKAVERSE SAVEREASON SHORTHORIZ SAVEHABIT SHOPRATE NOINSTPLAN NOBORRVAC NOBORRJEWEL NOBORRCAR NOBORRLIVEX NOBORREDUC Observations Psuedo R2 Log Likelihood
1983 (a)
MARGINAL EFFECT (SIGNIFICANCE) 1989 1992 (b) (c) (d)
***-.00240 *** .00655 ** -.00009 *** .21198 ** .06104 *** .21369 ***-.06751 ***-.06169 -0.03068
(e)
***-.00243 *** .00321 ***-.00012 *** .36742 0.03795 *** .14504 ***-.06541 ***-.12042 -0.0195
***-.00209 *** .00330 ***-.00010 *** .32588 *** .02635 *** .13291 * -.05557 ***-.13204 -0.00563
***-.00257 *** .00337 ***-.00006 *** .43532 ** .07347 *** .24769 ***-.16452 -0.03415 -0.0469
***-.00242 ***-.00354 ***-.00005 *** .40156 *** .07912 *** .22916 ***-.15883 -0.03518 -0.03136
0.00086 -0.00069 0.0169 -0.01408 ***-.12553 ***-.10142 ** -.06673 ** -.10833 ***-.02592 ***-.02848 *** .03129 *** .05862 -0.00045 -0.00001 0.00088 -0.0012 0.05267 -0.02902
-0.00058 -0.02389 ***-.10150 * -.08467 ***-.03041 *** .05465 -0.00805 ***-.00210 -0.01563
-0.00058 -0.02142 ***-.15170 -0.06384 ***-.03034 *** .04140 0.00036 0.00087 0.01898
** -.00036 -0.02161 ***-.15785 -0.04912 ***-.03191 *** .04060 0.00025 *** .00042 0.01733
3285
2273
0.1679 -1738.1
0.2194 -1203.5
0.01355 -0.04696 0.0639 -0.04604 0.00792 * .04867 -0.03611 *** .08320 *** .12888 ***-.07294 -0.02816 ** -.08077 ** -.12927 -0.04751 ** -.04983 0.015 2273 0.2465 -1161.7
2722
-0.00505 -0.00125 -0.02968 * .06147 -0.01193 ** .06339 ** .08571 ** .04503 0.01077 ** -.06639 -0.01874 ***-.12787 -0.02875 * -.05723 * -.03573 -0.05208 2722
0.2276 -1431.2
0.2467 -1395.8
25
Appendix A:
The Survey and Data Construction
The data used here are from the Survey of Consumer Finances conducted in the years 1983, 1989, and 1992.8 The survey gathers data from between 2500 and 3500 respondents, depending on the year of the survey. The data contain detailed information on wealth, income, and the use of financial markets for those individuals. The wealth data include information about individuals' holdings of both financial and physical assets (specifically: checking, money market, savings account balances; holdings of CDs, stocks, bonds, life insurance, Keogh plans; values of cars, other vehicles, businesses, the principal residence, and other residences). The income data include wages and tips, salary income, rental and asset income, subsidies, and pension income. The survey overcomes some problems faced by all household surveys by using a special sampling technique. Area-probability sampling produces representative distributions for some variables, while other variables (those that tend to be more skewed) aren't captured as well. The distributions of some forms of wealth tend to be particularly skewed, and since one of the main goals of the SCF is to measure wealth holdings, this is a troubling problem. One reason that area-probability sampling doesn't capture skewed variables well is that the response rates of wealthy individuals tend to be lower than for other groups. To overcome this deficiency, the SCF essentially oversamples the wealthy in order to get a representative sample. Specifically, the survey uses a dual frame sample technique, in which an area-probability sample is coupled with a special list sample developed from a sample of tax records. The creators of the survey first put together a list of potential respondents using an area-probability method, then found additional wealthy citizens by examining tax records provided by the IRS. In this way, the SCF creators put together a more representative sample than is employed in other surveys. To construct the data we first eliminated obvious miscodings and large outliers from the raw data. In particular, following Calem and Mester (1995), we eliminated households with total liabilities greater than four times income, with credit card balances exceeding five times the reported credit limit, with major expenditures greater than 30 times income, with liquid assets plus stocks and bonds exceeding $1,000,000, and with income less than zero or greater than $250,000. Finally, we removed observations with exceptionally large values for net wealth, total liabilities, or the ratio of total assets to total liabilities (these latter removals amount to: 17 in 1983, 8 in 1989, and 16 in 1992).
A.1 Constructing Permanent Income The idea for constructing permanent income for a cross-section dataset, using the method of Carroll (1994), is to perform two steps. First, estimate current income as a function of age. Second, sum discounted forecasts of income (the forecasts created by 8
While the 1986 data are available, they are from a telephone re-interview and don't contain all of the variables we are interested in. In some of the estimation, we only use the 1989 and 1992 data because: 1) only a few of the attitudinal variables are available in 1983, and 2) classification of 1983, since the data were from the cycle trough— the recession has been dated from July 1981 to November 1982— is not as clear-cut as 1989 or 1992, which comprise a better natural experiment.
26
incrementing age up until retirement). The variable used in estimation in this paper is actually the annuity value of this discounted stream. For the first step, we constructed several explanatory variables. One set of these variables is six occupation dummies, one for each of the following occupations (from the Standard Occupational Classification Manual): 1) managerial and professional specialty occupations; 2) technical, sales, and administrative support occupations; 3) service occupations and unemployed whose last position was in the armed forces; 4) precision production, craft, and repair occupations; 5) operators, fabricators, and laborers; 6) farming, forestry and fishery occupations. Another is years of education. Each of these was interacted with the household head's age and and age squared. We estimated the OLS regression of wage income on age, age squared, education, the occupation dummies, and the interaction terms (age and age squared interacted with education and occupation) and gender. Then, we incremented each household head's age by one, and calculated the predicted value using the estimated coefficients. This value was discounted using a rate of four percent and an assumed growth rate of productivity of one percent. We repeated this prediction and discounting exercise for each household head up to the age of 65. We summed these discounted values, and calculated the annuity value for each household head over the remaining years of life up to age 75, (or one year if the household head was over age 75 at the time of the survey) discounting using four percent and assuming a one percent productivity growth rate. We did this separately for 1989 and 1992.
27
Appendix B: Data Definitions and Descriptions Financial and demographic variables ¯ Mnemonic Description AGE respondent's age at last birthday AGESQ AGE squared CARDBAL reported sum of balances due on all credit cards, after the last payments were made CARDLIMIT total credit limit on bank credit cards CARDLIMITSQ CARDLIMIT squared CARLOANFIN Loans for the purchase of automobiles issued by financial institutions. These include: commercial banks, savings and loans, credit unions, finance companies, insurance companies, etc. CARLOANNONFIN Loans for the purchase of automobiles issued by non-financial organizations. These include: friends, family, store, dealers, etc. DELINQUENT = 1 if respondent reported that he/she was late on or missed any loan payments in the past year; = 0, otherwise, including those having no loans outstanding DISCOURAGED =1 if respondent, any time in the past five years thought of applying for credit, but decided not to because he/she thought he/she would be turned down. D92 = 1 if the sample is 1992; = 0, otherwise EDUCATION highest grade of school or year of college completed by respondent HOMEOWNER = 1 if the respondent is a homeowner; = 0, otherwise HHSIZE number of people in the household INCOME total household income from all sources INCOMESQ INCOME squared LEVERAGE ratio of total liabilities to total assets LEVERAGESQ LEVERAGE squared LIQSHARE ratio of liquid assets (checking accounts, money market accounts, CDs, other savings, mutual funds) to sum of liquid assets, stocks, and bonds MALE = 1 if respondent is male; = 0, if female MARRIED = 1 if the respondent is married; = 0, otherwise NEWARRIVAL = 1 if a homeowner living at current address for less than two years; = 0, otherwise NUMBANKS number of financial institutions with which household has accounts or loans, or regularly does financial business PREAPPROVED Demand determined stock. This consists of the of amounts drawn on home equity lines of credit plus other lines of credit plus credit card 28
SAMEBANK WHITE NEWAPPROVED NEWAPPFLOW
TOTALSTK TURNDOWN
WEALTH WEALTHSQ YRSCURRADDR
YRSEMPLOYED
balances = 1 if credit card and checking account are with the same bank; = 0, otherwise = 1 if respondent is Caucasian; = 0, otherwise Loans for the purchase of automobiles plus personal loans Loans for the purchase of automobiles plus personal loans issued in the last previous two years. PREAPPROVED + NEWAPPROVED = 1 if respondent reports being turned down for credit, or getting less credit than applied for, in last five years; = 0, otherwise, including no answer total assets (real and financial) minus total liabilities WEALTH squared number of years at current address (if not homeowner, years living in current county of residence) number of years the respondent has held his or her current job; = 0, if unemployed
Expectational and attitudinal variables EXPINHERIT = 1 if the respondent (or spouse) expects to receive a substantial inheritance or transfer of assets in the future; = 0, otherwise EXPPENSION = 1 if expects pension benefits from current or past job; = 0, otherwise EXPRETINC = 1 if expected retirement income is ``enough to maintain living standards'' or more; 0, otherwise EXPSTOPWORK = 1 if expect to stop working full-time before age 65; = 0, otherwise EXPSTOPWORKS = 1 if spouse expects to stop working full-time before age 65; = 0, otherwise NOBORRCAR = 1 if the respondent believes it is NOT all right to borrow money to finance the purchase of a car; = 0, otherwise NOBORREDUC = 1 if the respondent believes it is NOT all right to borrow money to finance educational expenses; = 0, otherwise NOBORRJEWEL = 1 if the respondent believes it is NOT all right to borrow money to purchase a fur coat or jewelry; = 0, otherwise NOBORRLIVEXP = 1 if the respondent believes it is NOT all right to borrow money to cover living expenses when income is cut; = 0, otherwise NOBORRVAC = 1 if the respondent believes it is NOT all right to borrow money to cover vacation expenses; = 0, otherwise 29
NOINSTPLAN RISKAVERSE
SAVEHABIT
SAVEREASON
SHOPRATE
SHORTHORIZON
= 1 if the respondent believes it is a bad idea to buy things on an installment plan; = 0, otherwise = 1 if the respondent and spouse are not willing to take risks when making investments; = 0, otherwise = 1 if the respondent and spouse report usually spending as much as or more than their income; = 0, otherwise = 1 if the top two reasons for saving are among buying a car, paying for funeral expenses, paying bills or ordinary living expenses, or if the respondent doesn't/can't save; = 0, otherwise = 1 if the respondent's family shops around for the best rate when borrowing and saving (> 3 on a scale from 1 (``almost no shopping'') to 5 (ä great deal of shopping"); = 0, otherwise = 1 if the ``most important'' planning horizon for savings is the next few months, the next year, or the next few years (OR if the ``least important'' horizon is the next 5-10 years or greater than 10 years); = 0, otherwise.
30
