Commentary on Life-cycle dynamics in industrial sectors: the role of banking market structure
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,009 |
| Communication savante | 0,006 | 0,012 |
| Science ouverte | 0,008 | 0,003 |
| Intégrité de la recherche | 0,054 | 0,040 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».