Financial intermediation cost, rents, and productivity: An international comparison
Bibliographic record
Abstract
Calculation of the unit cost of financial intermediation for 20 countries from 1970 to 2015 has produced the following results. (i) Most countries' unit costs decline and converge in the long run. (ii) Unit costs were much higher in the 1970s and 1980s, coinciding with high nominal rates, as confirmed by panel cointegration tests. (iii) Countries' unit cost aggregation suggests a slight decrease in international unit cost whatever the set of hypotheses used in the calculation. (iv) The break down of unit costs into labor costs, capital costs, and profits shows that most of the decrease stems from reduced input costs. Gross operating surplus and total compensation per output tend to decline while distributed profit per output rises, suggesting increasing intermediation rents per output. (v) The productivity of labor in finance compared to other sectors tends to increase in most countries. (vi) The evidence suggests that most productivity gains have been captured by the financial sector in Canada, the UK, and the US. Elsewhere, productivity gains have benefited the nonfinancial sector through unit cost reduction. (vii) Deregulation is either negatively or not correlated with unit cost. In other words, deregulation is not related to unit cost increases. Finally, the paper discusses the consequences of those results for current debates about finance relative wage changes and inequalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".