Notice bibliographique
Résumé
JULY/AUGUST 2003 27 The conventional wisdom concerning the interaction between economic development and financial system structure is that there are three stages (see, e.g., Gurley and Shaw, 1960, Goldsmith, 1969, and Allen and Gale, 2000). In this process of historical development, increasing per capita income and financial depth reinforce each other, and the transaction costs of establishing financial institutions and markets play a key role. In the first stage, where the level of development is low, investment is self-financed. The only financial instrument is money. At moderate levels of development, the second stage, banks and other financial institutions start to play a role. These financial institutions transfer resources from agents with excess funds to agents that need funds to invest and consume. They also provide liquidity insurance and a range of other services. At the third stage, formalized markets develop for agents (including financial institutions) to trade in. These markets improve the efficiency of the allocation from surplus units to deficit units and allow risk sharing. This interesting paper contributes to the literature on financial system structure and growth by showing that it is not just transactions costs that matter for the development of banking systems. Monetary policy is also an important determinant of the extent of intermediation. The paper develops a model based on the interaction of the transactions costs of intermediation and monetary policy. The main result is that some low-income countries that have high inflation and a poorly developed banking system may be able to improve the banking sector by lowering the rate of inflation. They give the examples of Argentina in the 1980s and early 1990s, Brazil in the 1990s, and Bolivia in the 1980s. In all these countries a reduction in inflation was accompanied by a significant growth in the financial sector. The model assumes an overlapping generations framework with two-period-lived individuals. These people are endowed with 1 unit of labor when they are young, which provides their income. They save their labor income for their old age, which is when they consume. The individuals have constant relative risk aversion utility functions with a degree of risk aversion between 0 and 1. An important role is played by liquidity shocks. These are modeled by assuming there are two islands with limited communication between them but perfect communication within each one. After they have made their saving decisions, individuals find out whether they have to relocate to the other island. Initially, the proportion that relocates is known but the identities of who has to relocate are not. Production takes place on each island using capital and labor. The production function is CobbDouglas and displays constant returns to scale. The assets available for saving are physical capital and money. Physical capital cannot be moved between the islands but money can be. If there are no banks, you have to abandon your capital if you are relocated and the capital is lost to you and to society as a whole. In contrast, if there is a bank, a person who is forced to relocate can withdraw money from the bank before moving and take it with her. There is no private or social loss of capital. Banks thus provide liquidity insurance. Money is printed by the government in order to purchase the final good. Government expenditure does not have any direct effect on people’s behavior. In the first case analyzed, there are no banks and people save using direct holdings of physical capital and money. Physical capital has a higher return but cannot be relocated and is wasted if relocation occurs. Currency has the advantage that it can be transported. It has an opportunity cost that depends on the rate of inflation and the marginal product of capital. The optimal portfolio of physical capital and money depends on the trade-off between the opportunity cost of holding currency and the probability of relocation. The main result is that there is a unique steady state for the economy. This is a fairly simple case, so the result is not particularly surprising. In the second case, individuals put their savings Franklin Allen is a professor of finance and economics at The Wharton School, University of Pennsylvania.
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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 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,064 | 0,037 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 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 ».