Evolving Nature of Financial Intermediation and Economic Growth: Insights from a Bayesian Vector-Autoregression Analysis
Bibliographic record
Abstract
We compute the growth of financial intermediary (FI) assets as an indicator of liquidity provided by intermediaries to the economy, as is done in the existing literature, and analyze its impact on the economy. We find that shocks to aggregate FI assets have a significant impact on U.S. real GDP and other macroeconomic indicators. Furthermore, shocks to assets of individual FIs also impact economic growth. However, our sub-sample analysis reveals notable shifts in the nature of financial intermediation: i) an increasing importance of market-based intermediaries, such as securities brokers and dealers, while the relationship between banks and the overall economy has diminished; ii) mutual funds demonstrate a greater impact compared to pension funds, underscoring their relative significance in driving economic outcomes in recent years; iii) insurance companies and shadow banks exhibit consistent significance across the sub-sample periods. These results suggest adopting a holistic approach to policymaking that considers various FIs, enhancing regulation and oversight of systemically important non-bank financial institutions, and monitoring of large insurers to mitigate the risk of financial instability.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".