AN ANALYSIS OF BANK CONSOLIDATION TRENDS IN RURAL PENNSYLVANIA
Bibliographic record
Abstract
U.S. banking markets have undergone important structural and institutional changes. Overall, the sector has experienced steady consolidation through mergers and acquisitions that have resulted in fewer banks holding a greater value of the total assets. Despite consolidation, new branch offices and the growth of alternative providers has increased the access to banking-type services. This paper documents and describes trends in the banking industry in Pennsylvania, with special emphasis on rural areas. The first section shows that while the number of "bricks and mortar" offices in the state's rural counties has grown, the distribution of the growth has been quite uneven. As a result, access has potentially declined for some of the state's rural residents. In the second section the analysis shows that consolidation is dramatically reducing the number of banks headquartered in Pennsylvania. The analysis shows that, should current trends continue the loss of 1.25 banks per quarter then there will be no banks headquartered in rural Pennsylvania in 2025. Consolidation appears to be having an effect on the competitiveness of rural banking markets. While the analysis suggests that urban county banking markets remain fairly competitive, it also suggests that the state's rural banking markets may have less competition.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".