Bibliographic record
Abstract
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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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.064 | 0.037 |
| Insufficient payload (model declined to judge) | 0.018 | 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".