The existence of borrowing constraints in the market for consumer loans has important implications at both the micro and macro levels. At the micro level, credit constraints can affect both the intra- and intertemporal allocations of resources and have important consequences for the effects of various policy measures. At the macro level, liquidity constraints, as borrowing restrictions are often characterized, have been invoked to explain the observed correlation between expected consumption and income growth, and the rejection of the permanent income hypothesis. Moreover, the possibility that individual agents have limited means of smoothing consumption over time has been for a long time considered as a justification for a Keynesian consumption function (see for instance Flemming, 1973). But despite the importance of the topic, and the substantial amount of theoretical and empirical research that has been devoted to it, there is still no conclusive evidence on the significance of credit rationing in consumer loan markets.
A potential explanation for this lack of consensus is the fact that most empirical work on the subject has utilized only consumption data, and not data on loans. The majority of this work has been framed in terms of a test of the life cycle – permanent income hypothesis, focusing on the excess sensitivity of consumption to expected labor income (see, for example, Hall and Mishkin (1982), Hansen and Singleton (1982, 1983), Altonji and Siow (1987), Zeldes (1989), Runkle (1991)). The problem with this approach is that the interpretation of the results critically depends on explicit or implicit assumptions about the utility function. In particular, the inference of the existence of credit constraints often rests on the assumption of separability between consumption and leisure, which has been empirically rejected (Browning and Meghir (1991)).
More recently, another set of papers has tried to exploit the idea that in the presence of (at least partly) collateralizable loans (this is the case with the financing of durables), liquidity constraints introduce distortions in the intratemporal allocation of resources between durables and non-durables (Brugiavini and Weber (1992), Chah et al. (1995), Alessie et al (1997)). But this idea was again implemented using only data on aggregate or household consumption.
Departing from this tradition, Jappelli (1990) relied on survey questions to identify individuals who have been denied credit, or feel that they would have been denied, had they applied for it. While this approach is direct, and circumvents the interpretation difficulties associated with the previous ones, there is some concern as to the accuracy of the responses to the questions. Given that liquidity constraints are primarily restrictions placed on borrowing, it is rather surprising that none of the above papers have utilized data on borrowing behavior to test for credit rationing. To take a credit in case of money lack is the way out but Speedy Payday Loans via speedy-payday-loans.com suggests loans at more favourable terms.
This paper attempts to fill in this gap by proposing and implementing a novel approach for testing for borrowing constraints that exploits micro data on car loans. Our basic idea is that borrowing restrictions have specific implications for certain features of the demand for loans, and in particular for its interest rate and maturity elasticities. By testing these implications, one can shed some light on the empirical significance of credit restrictions. The strength of this approach is that it does not rely on functional form assumptions concerning the utility function. It is particularly promising if information on loan contracts is combined with data on socioeconomic characteristics to identify households that are likely to face liquidity constraints.
Our focus on the demand for loans forces us to be specific about what we mean by borrowing constraints. Our starting point is Jaffee and Stiglitz’s (1990) definition of credit rationing as a situation in which there exists an excess demand for loans at the current interest rates of primary lenders. A strict interpretation of the above definition identifies liquidity constrained consumers as individuals who face an absolute limit in the amount they can borrow against their future income. A weaker interpretation extends the definition to consumers for whom interest rates are not independent of their net asset positions (Pissarides (1978)); of course, the former interpretation can be thought of as a special case of the latter one, if the borrowing rate goes to infinity at the borrowing limit.
Whatever interpretation one adopts, the implication for the optimization problem facing the consumer is the same; credit constraints introduce kinks and convexities in the intertemporal budget set. Liquidity constrained individuals are the ones who are either at a kink, or in the steeper portion of the budget set. This leads to the following testable implication which will be discussed in the theoretical section below. The demand for loans of unconstrained individuals, consuming at the flatter portion of the budget set, should be a function of the price of the loan (the primary interest rate), but independent of the loan maturity; liquidity constrained consumers, on the other hand, should respond less to changes in the primary interest rates, and more to changes in the borrowing limit. In consumer loan markets, changes in the borrowing limit are primarily achieved through changes in loan maturities; a longer maturity decreases the size of the monthly payment, allowing the consumer to assume a larger amount of debt.3 4 Hence, one can test for the presence of credit rationing, by estimating the elasticities of loan demand with respect to interest rate and maturity, and testing the null hypothesis that the maturity elasticity is equal to zero.
Juster and Shay (1964) were the first to stress the implications of borrowing restrictions for the interest rate and maturity elasticities of the demand for loans. It is therefore worth describing the main features of their methodology and results in some detail, and explaining in which major ways our approach differs from theirs. Juster and Shay used experimental data to assess the responsiveness of loan demand to interest rate and maturity. The data were based on a questionnaire that was sent to ca. 16,000 households in 1960, asking them to indicate their preferences among a set of hypothetical financing arrangements. All respondents faced the same problem, namely financing the purchase of a $1,500 automobile. The arrangements, however, differed with respect to finance rates and maturities. Juster and Shay found that, contrary to the widely held view that consumer borrowing did not depend on finance rates, a significant fraction of the households surveyed seemed to respond to interest rates. The response was, however, more pronounced among consumers who, on the basis of various criteria such as age, income, asset holdings, and attitude towards credit, were likely to be unconstrained. Consumers who were likely to be constrained on the basis of the same criteria, were instead shown to be more responsive to changes in the size of monthly payments. The great advantage of the experimental data was that they offered observations on the (hypothetical) loan terms facing individuals who chose not to finance. On the negative side, the results are subject to the usual criticism of survey responses, that the way people talk may not reflect the way they act. Furthermore, the ingenious randomization used by Juster and Shay in the packages offered to different consumers, which allows them to identify interest rate and maturity elasticities of loan demand, yields fairly imprecise estimates given the sample size.
In contrast to Juster and Shay we do not have experimental data, but micro data on auto loan contracts from the Consumer Expenditure Survey (1984-1995). Such contracts are an important, and fast growing component of consumer installment credit – Sullivan (1987), for example, reports that 39% of consumer credit is auto credit. We see the main strengths of our data set as being threefold: First, there is substantial time variation in interest rates and maturities that we exploit to identify the parameters of the loan demand equation; Sullivan (1987) and recent bank sources document that the average maturity on a loan contract for a new car has increased from 40 months in 1977, to 51 months at the end of 1985, 60 months by the end of 1990, and 72 months in recent years, while interest rate ceilings have been removed. To the extent that this variation is exogenous – and we argue that it is – it offers an ideal experiment for the purpose of identifying credit constraints. Second, our information refers to actual household behavior rather than responses to hypothetical questions. Third, the information on demographics allows us to split the sample into various subgroups, some of which are more likely to be credit rationed than others (for example young households), and test for the presence of credit rationing separately in each of them. We are particularly interested in comparing the relative sizes of interest rate and maturity elasticities across groups.
With all its advantages, however, our data also poses several challenges: First, there is potential selection bias -observations on financing are available only for consumers who purchased a car and decided to finance such a purchase. Second, our data is censored: financing is bounded between 0 and the value of the car. If you want to purchase a new car take Speedy Payday Loans and enjoy a purchase immediately. Third, simultaneity issues are likely to be important – both the observed interest rate and maturity of a realized loan are likely to be correlated with unobserved consumer heterogeneity. Finally, normality assumptions often used in the estimation of selectivity models seem particularly inappropriate in our framework. If one considers the loan terms facing an individual consumer to be the results of a search process (this would, for example, be the case if the consumer chooses the lowest interest rate and the maximum maturity among various offered alternatives), then the corresponding loan variables observed in our data would not be distributed normally, even if the original distribution of interest rates and maturities were.
We develop an estimation approach that deals with each of these issues. We first specify an empirical model which – while not directly derived from a full structural model – is informed by a three period model developed in the next section. We next estimate this model by both maximum likelihood (for comparison purposes), and a semiparametric approach that relaxes the joint normality assumption, requiring only that the error terms are independent of the conditioning variables, and that the sampling across households is i.i.d.. We find the employment of the semiparametric method to be a very rewarding exercise indeed, as the results obtained by that method are both significantly different from the ones obtained by maximum likelihood, and consistent with the predictions of the theoretical model. In addition, we believe that the estimation approach we propose represents a methodological contribution. In particular, our method of dealing with endogenous variables in a semi-parametric sample selection model is novel. To the best of our knowledge, this problem has not been considered before in the literature, with the exception of a recent paper by Blundell and Powell (2000), who have proposed an alternative estimator for a binomial model.
In terms of empirical results, we find that while the demand for loans is sensitive to the interest rate, the interest rate sensitivity is largest for older rather than younger consumers, and for consumers with relatively large current income. Moreover, we find strong maturity effects, indicating the presence of binding borrowing restrictions. The maturity effects, once again, are more relevant for the groups that one would expect to be liquidity constrained. Interestingly, the only consumer groups for which we do not find significant maturity effects (but do find the strongest interest rate effects) are the middle age group, and the consumer group with the largest current income.
The remainder of the paper is organized as follows: In the next section, we formalize the above discussion by developing an intertemporal utility maximization model that incorporates two sources of budget set kinks: First, there is an upper bound on the amount that can be borrowed in a single period; second, individuals face different interest rates depending on their net asset positions. We use this model to derive the implications of credit rationing for the interest rate and maturity elasticities of loan demand. Section 3 discusses our empirical model and estimation approach; section 4 describes our data and offers some preliminary descriptive results, and section 5 discusses the results from the estimation of the model. Section 6 concludes.