Financial Structure and Macroeconomic Volatility: A Panel Data Analysis
Bibliographic record
Abstract
In 2015, the European Commission (EC) launched its action plan for the creation of a European Capital Markets Union. The EC aims to return the European economy to sustainable growth and to enhance its shock-absorbing capacity by reducing the reliance on bank finance and stimulating financial deepening and cross-border integration of Europe’s capital markets. Financial diversification and integrated European capital markets are expected to improve risk sharing among households, supporting economic stability. However, the economic literature reveals a lack of theoretical and empirical consensus on the superiority of either a bank-based or a market-based financial system in promoting growth or reducing macroeconomic volatility. This article is the first to include bond markets in its financial structure indicators, besides stock markets and bank lending. Using panel data on 55 countries between 1975 and 2014 and three different measures of financial structure, we investigate the effect of the structure of the financial system on the volatility of output and investment growth as well as their cyclical components. We do not find evidence that market-based financial structures dampen volatility of output or overall investment. Increase of the stock market size relative to that of the banking sector has a significant positive effect on the business cycle volatility of investments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".