Bank Expansion after the Riegle-Neal Act: The Role of Diversification of Geographic Risk
Bibliographic record
Abstract
The Riegle Neal Act of 1994 established the conditions for the removal of restrictions on interstate banking and branching in US. One of the primary motivations for its enactment was to permit banks to diversify geographic risk. The purpose of this paper is to study the actual role of diversification of geographic risk as a motive for bank expansion during the period 19942006. We propose and estimate an equilibrium model of the US banking industry where a bank’s decision of where to operate branches is modeled as a portfolio choice between risky assets. A key building block in our empirical approach is a measure of geographic risk that is based on the estimation of the variances and covariances of deposits-per-branch between all the US counties. We find that the impact of Riegle Neal on the possibilities of geographic risk diversification varies substantially across states. The effect is negligible for large and economically diverse states, but important for small and homogeneous states. Banks have also responded differently to the new possibilities for diversification. While banks’ portfolio efficiency positions have improved in general, the process has been slow, especially for smaller banks. Diseconomies of scale, economies of density, and merging costs, have played important roles in this slow process of adjustment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".