Does function follow organizational form? Evidence from the lending practices of large and small banks

Abstract

Theories based on incomplete contracting suggest that small organizations have a comparative advantage in activities that make extensive use of “soft” information. We provide evidence consistent with small banks being better able to collect and act on soft information than large banks. In particular, large banks are less willing to lend to informationally “difficult” credits, such as firms with no financial records. Moreover, after controlling for the endogeneity of bank-firm matching, we find that large banks lend at a greater distance, interact more impersonally with their borrowers, have shorter and less exclusive relationships, and do not alleviate credit constraints as effectively.

Introduction

One of the most enduring questions in economics was posed by Coase (1937): What determines the boundaries of the firm? The question is perhaps most often framed in terms of vertical integration—i.e., when can it make sense for upstream and downstream activities to be combined under the roof of a single firm? But one can also ask about the circumstances under which horizontal integration creates value. A good present-day illustration of this version of the question comes from the commercial banking industry, where ongoing consolidation raises the issue of whether the resulting large banks will behave differently than the small banks that they are displacing.

A partial answer to Coase's question comes from the work on transaction-cost economics of Williamson, 1975, Williamson, 1985 and Klein et al. (1978). These authors focus on the hold-up problems that can accompany market transactions, and argue that such problems can be mitigated by having the firm, rather than the market, mediate trade. While this approach is helpful in identifying the advantages of integration (i.e., a reduction in market hold-up problems), it is less clear on the disadvantages. As such, it is somewhat of a one-sided theory—unless one invokes factors outside the model, like unspecified “costs of bureaucracy,” it has the awkward implication that efficiency would be best served by placing all of the economy's assets inside a single firm.

The disadvantages of integration emerge much more clearly in the framework of Grossman and Hart (1986), Hart and Moore (1990), and Hart (1995), henceforth GHM. At its most general level, the central insight of the GHM paradigm is that, in a world of incomplete contracts, agents’ ex ante incentives are shaped by the extent to which they have control or authority over physical assets. Thus, for example, if firm A acquires firm B, the manager who was previously CEO of firm B might reduce his level of ex ante effort now that he is subordinate to the CEO of firm A and no longer has full control rights over B's assets; herein lies the potential cost of integration.

The GHM paradigm has strongly influenced subsequent work on the theory of the firm. But it has proved challenging to construct sharp empirical tests of the theory. As discussed in Whinston (2001), this is in part due to the fact that the predictions can be sensitive to specific assumptions, such as the nature of the non-contractible investments that need to be made ex ante. Another difficulty is that GHM focus on ownership of physical assets as the exclusive source of power and incentives in the firm, thereby abstracting from other considerations that might be present in a richer, more empirically realistic model. These considerations include differentially informed agents as in Aghion and Tirole (1997), incentive structures as in Holmstrom and Milgrom (1994) and Holmstrom (1999), or access to critical resources as in Rajan and Zingales, 1998, Rajan and Zingales, 2001.

One strategy for dealing with these problems is to not take the original GHM models too literally as a basis for empirical testing, and to work instead with “second-generation” models that build on the basic GHM insights but that are more tailored to delivering clear-cut comparative static predictions, either for a specific type of investment or in a particular institutional setting. This strategy is followed by Baker and Hubbard (2003), whose work centers on the trucking industry and the question of whether drivers should own the trucks they operate, as well as by Simester and Wernerfelt (2003), who look at the ownership of tools in the carpentry industry.

In this paper, we take a broadly similar approach. Unlike the above-mentioned authors, however, our focus is not on how differences in technology influence the ownership of assets, but rather on how the nature of an organization affects both the way it does business and the kinds of activities that it can efficiently undertake. Specifically, we attempt to understand whether small organizations are better at carrying out certain tasks than large organizations. In this regard, our work is closer to Mullainathan and Scharfstein (2001), who document how producers of a particular chemical that are integrated with the downstream users of the chemical have investment behavior that differs from that of stand-alone producers.

Our starting point is the model in Stein (2002). This model adopts the basic GHM insight that the allocation of control affects incentives, but it does so in a setting that is more specific, and thus yields sharper empirical predictions. The predictions have to do with the differing incentives that are created in large and small firms for the production and use of various kinds of information. The model implies that small firms are at a comparative advantage in evaluating investment projects when the information about these projects is naturally “soft” and cannot be credibly communicated from one agent in the firm to another. In contrast, large firms do relatively well when information about investment projects can be easily “hardened” and passed along within the hierarchy.

This model applies naturally to the banking industry, where information is critical to the activity of lending. The model suggests that large banks will tend to shy away from small-business lending, because this is an activity that relies especially heavily on the production of soft information, something that is not their strong suit. For example, consider a loan officer trying to decide whether or not to extend credit to a small start-up company that does not have audited accounting statements. The best the loan officer may be able to do is to spend time with the company president in an effort to determine whether she is honest, prudent, and hardworking—i.e., the classic candidate for a “character loan.” However, given that this information is soft and cannot be verifiably documented in a report that the loan officer can pass on to his superiors, the model predicts (as is explained in more detail below) that his incentives to produce high-quality information are weak when he works inside a large bank.

By contrast, when dealing with a larger company that has a well-documented track record, the decision to extend credit can be based more heavily on verifiable information, such as the company's income statements, balance sheet, and credit rating. In this case, the model suggests that a large bank will have no problem—indeed, it may do better—at providing incentives for information production.

To test this theory, we make use of a data set on small-business lending that has information not only about the small firms in the sample, but also about their primary bank lenders and the nature of the relationship between the two. The data thus allow us to investigate a number of hypotheses about how the “technology” of lending depends on variables such as bank size. If, as the theory suggests, large banks are at a comparative disadvantage in the production and use of soft information, one would expect this to influence their methods of lending.

We develop six basic pieces of evidence to support this case. First, and most simply, we find that bigger banks are more apt to lend to firms that are larger or that have better accounting records (a good example of hard information). Second, controlling for firm and market characteristics, we find that the physical distance between a firm and the branch office that it deals with increases with the size of the bank. This is consistent with the notion that large banks rely less on the sort of soft information that is typically available through personal contact and observation. Third and relatedly, we find that firms do business with large banks in more impersonal ways—i.e., they meet less often with their banker and instead communicate more by mail or phone.

Of course, a firm chooses the bank from which it borrows. That is, the match between a firm and its bank is to some extent endogenous, and is likely to be related to firm characteristics. Indeed, our first finding—that bigger banks match up with firms with better accounting records—is evidence of just this endogeneity. This suggests that we need to proceed carefully if, as in our second and third findings, we want to use bank size as a right-hand-side variable to explain certain aspects of the lending relationship. For example, perhaps large banks deal with their customers more impersonally not because they are incompetent at personal interaction, but because they tend to match with a type of customer for whom personal interaction is less appropriate.

To deal with this potential endogeneity problem, we try instrumenting for bank size with two variables: (i) the median size of all banks (weighted by number of branches) in the market where the firm is located, and (ii) a regulatory variable that measures how permissive the firm's state has been with respect to branching. Intuitively, if a firm borrows from a large bank because it is located in a market where there are only large banks (say because regulation has not artificially constrained bank size), this does not reflect an endogenous choice on the part of the firm, but rather an exogenous, geographically imposed limitation. We find that when we take this instrumental-variables (IV) approach, the estimated effect of bank size on distance and on the extent of impersonal communication is even larger than when we do not correct for endogeneity.

Our fourth and fifth findings are that bank-firm relationships tend to be stronger—both more long-lived and more exclusive—when the firm in question borrows from a small bank. These findings also emerge both with and without using IV, but again are more pronounced when an IV approach is employed. They are exactly what one would expect based on the theory, given that the soft information produced by small banks is more likely than hard information to be non-transferable. In other words, the theory suggests that small-bank lending should fit more closely with the kind of model in Rajan (1992), where accumulated soft information binds a borrower to its bank over time.

The sixth and final part of our empirical work is to test whether bank size affects the availability of credit to small businesses. If small firms need lenders that are willing to go deeper and acquire soft information, then we would expect those that are forced to go to large banks to be particularly credit constrained. One measure of the degree to which a firm is rationed by financial institutions is the amount of expensive trade credit it relies on (Petersen and Rajan (1994) and Fisman and Love (2003)). We find that all else equal, a firm that borrows from a larger bank is more prone to repay its trade credit late.

Interestingly, this last result holds only when we instrument for bank size. When firms are forced to borrow from large banks because there are no small banks around, they seem to face credit constraints—this is what the IV version of the regression tells us. At the same time, an ordinary-least-squares regression of credit constraints on bank size reveals an offsetting effect due to the endogeneity bias: those firms that are by nature the most difficult credits tend to match with smaller banks, as the theory would suggest.

While our empirical tests are primarily motivated by a model in the control-rights genre, it is important to note that some of the same predictions about the effects of bank size follow from other types of agency models. To take a leading example, Brickley et al. (2003) observe that officers and directors in their sample of small Texas banks own an average of nearly 70% of the stock of these banks. They then go on to propose a theory of small-bank/big-bank differences based on explicit incentive-contracting considerations. In particular, they argue that since managers of small banks have higher-powered ownership incentives, they will devote more effort to soft-information collection, and can be trusted to use this information in a way that is consistent with shareholder objectives. This differs from Stein's (2002) theory, which emphasizes the incentive effects of control rights rather than of direct share ownership.

Our view is that these two types of theories are broadly complementary, and it would be a mistake to try to argue that our basic empirical findings are solely the product of one mechanism or the other. Nevertheless, the two theories have some divergent implications, which in principle allow for a degree of separation.

Section 2 reviews both theories and fleshes out our main hypotheses more fully. Section 3 introduces our data set. Section 4 describes our empirical results. Section 5 discusses how our work fits with the related banking literature, and Section 6 concludes.