Bibliographic record
Abstract
Mergers and acquisitions have significantly changed the U.S. banking industry over the past quarter century. This study examines patterns in the 3, 517 mergers consummated among commercial banks and thrift institutions (savings banks, savings and loan associations, and industrial banks) during the ten years from 1994 to 2003. The data used in this study include the vast majority of consolidation activity that took place during the period and are more detailed and comprehensive than any data available for the years preceding 1994.> About $3.1 trillion in assets, $2.1 trillion in deposits, and 47, 300 offices were acquired during the ten-year period. The annual number of mergers was fairly steady between 1994 and 1998 and then declined to a much lower level by 2003. Roughly three-fourths of all deals involved two commercial banking organizations. The remaining mergers involved a thrift institution as the acquirer, the target, or both. The target in the median merger during the period had $102 million in assets, $86 million in deposits, and 3 offices. Mean (average) values are substantially higher because of a relatively small number of extremely large deals: $874 million in assets, $601 million in deposits, and 13 offices.> Whether calculated as a mean or median, roughly 5 percent of the industry's assets, deposits, and offices were acquired in mergers in the typical year in the period. The peak was in 1998. The number of deals completed then (493) was not far larger than the number in earlier years, but the aggregate amounts of assets and deposits purchased in 1998 were roughly twice the second-highest annual levels of the period (recorded in 1996).> Most deals involved the acquisition of a small organization with operations in a fairly limited geographic area. In the aggregate these small mergers tended to account for a relatively small share of the assets, deposits, and offices that were purchased. In contrast, the few acquisitions of very large banks accounted for a large share of the assets, deposits, and offices acquired, and they were responsible for many of the changes to the banking industry caused by consolidation.> Urban markets had disproportionately more mergers than rural markets, and mergers with targets in urban areas accounted for an even larger share of acquired deposits and offices. Urban markets were also more likely than rural markets to be the location of a merger in which the acquirer already had an office in the market.> Acquisitions took place in every state, but the level of activity varied greatly by state. The large majority of mergers involved a target that operated in a single state and an acquirer with at least one office in that state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".