A Look Back at Merton Miller's “Financial Markets and Economic Growth”
Bibliographic record
Abstract
The author begins by agreeing with Miller's characterization of the fragility of U.S. banks and of the shortcomings of the Asian model of bank finance‐driven growth. The article also expresses “emphatic agreement” with Miller's arguments that the protection of banks through deposit insurance, regulatory forbearance, and other forms of “bailout” have created costly moral‐hazard problems that encourage excessive risk‐taking. And the author endorses, at least in principle, Miller's main argument that the development of capital markets that do not require the direct involvement of banks should make economies if not less prone to financial crises, then at least more resilient in recovering from them. But having acknowledged the limitations of bank‐centered systems and the value of developing non‐bank alternatives for savers and corporate borrowers, the author goes on to point to the surprising durability of some banking systems outside the U.S.—notably Canada's, which has not experienced major problems since the 1830s. And even more important, the author views banks and capital markets not as “substitutes” for one another, but as mutually dependent “complements” whose interdependencies and interactions must be recognized by market participants and regulators alike.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| 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".