Commentary on Life-cycle dynamics in industrial sectors: the role of banking market structure
Bibliographic record
Abstract
JULY/AUGUST 2003 149 D oes credit market competition aid or hinder the formation of firm-creditor relationships? Economists have offered contradictory answers. In an insightful analysis, Mayer (1988) argued that in a world where contracts are incomplete, limited competition in the credit markets might allow creditors to take the long-term view. Intuitively, a certain degree of monopoly power can create the kind of rents ex post that allow the monopolist creditor to invest in nurturing young firms. Put another way, if the borrower cannot commit to stay with the lender via long-term contracts, but it is optimal for him to commit, he may be better off when the creditor is a monopolist because commitment is achieved de facto. A number of assumptions are necessary for this result. First, for the relationship to start up, the lender has to make a fixed investment up front, regardless of whether he is a monopolist. Second, once the relationship starts up, there is little ongoing relationship-specific investment by either party during the course of the relationship. These two assumptions ensure the lender will have more of an incentive to make the required investment when he is faced with little competition ex post and that competition ex ante does not spur more investment. Petersen and Rajan (1995) formalize this intuition in a model and take it to the data. One measure of a creditor’s up-front investment in a relationship is his willingness to offer lower-than-market rates to start-up firms. They find that loan rates are indeed lower for young firms and higher for older firms in concentrated banking markets than for comparable firms in competitive banking markets. They also find a greater availability of credit for firms in concentrated markets. Of course, if the upfront investment is discretionary, the potential monopolistic lender will have less of an incentive to make that investment if he knows that the borrower will be captive anyway. (This assumes, of course, that the lender cannot appropriate all the surplus the borrower generates.) Similarly, if the relationship demands ongoing investment by the borrower, he may have less of an incentive to commit to that investment if he knows the lender will enjoy a monopoly regardless. A marriage where there is no possibility of divorce is one where neither party has the incentive to work very hard at keeping the marriage exciting. Thus the traditional effect of monopolies, that they distort the incentive to invest, can imply that firm-creditor relationships can be shallow and unsatisfying. (See, for example, Dinc, 2000, for a nice development of this point.) This means that one cannot make a blanket assertion about whether credit market competition is good or bad for firm-creditor relationships—it depends, at the very least, on the nature of the investments that are required by either party. To test the theory, we have to go deeper into the data and look at the details of the theory—for example, the intertemporal loan rate smoothing observed by Petersen and Rajan (1995). However, work has moved beyond testing the detailed implications of the theory to testing whether some of its predictions hold up. In particular, if firmcreditor relationships are stronger in more concentrated areas and if they especially benefit small and young firms who would otherwise have limited access to credit, we should see more entry by industrial firms in areas where there is more credit market concentration. (See Cetorelli’s paper, as well as Black and Strahan, 2002.) While exploring a link between credit market competition and entry is interesting, I am not sure we can attribute any finding solely to stronger (or weaker) firm-creditor relationships. There are at least two other explanations that have to be ruled out. The first is a selection bias. For example, it could be that areas where there is little entry into banking (so that the banking sector is concentrated) are also areas where entry into industry is difficult. A correlation would then be seen between limited industrial entry and banking sector concentration; the cause would not be weak relationships, however, but a Raghuram G. Rajan is the Joseph L. Gidwitz Professor of Finance at the Graduate School of Business, University of Chicago .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.054 | 0.040 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".