Regulatory competition and cross‐fertilization in bank performance in the US banking markets
Bibliographic record
Abstract
Abstract This paper examines empirically cross‐fertilization in the productivity growth of banks between a state and its neighbouring and non‐neighbouring states (i) before (i.e. 1971–1977) the interstate multibank holding company (IMBHC) deregulations and (ii) during (i.e. 1982–1995) the IMBHC deregulations, which, through cross‐border bank M&As mainly among neighbouring states, could inject new blood, awaken the market for corporate control and enhance cross‐fertilization in bank performance among neighbouring states. Further, the 1978–1981 period offers a natural experiment to examine Baumol's Contestable Markets Hypothesis (CMH). The legislature of Maine made the first IMBHC deregulatory move in 1978. There was no reciprocity until New York and Alaska made their moves in 1982. Under CMH, Maine's move should inject a competitive spirit and alter bank performance for better across all—neighbouring or non‐neighbouring – banking markets during this period. Theoretically, Kane's regulatory equilibrium framework provides guidance to address these matters and Tiebout's people vote with their feet framework extends and supplements this guidance. Empirically, FDIC's annual banking data, aggregated at the state level, constitute the main input in computing the productivity growth indices for each of the 48 contiguous sample states between 1971 and 1995. Estimations of a novel spatially driven fixed effects model that uses these indices produce empirical results. The empirical model exploits the proximity of one sample state to its neighbouring states while also embracing a set of randomly chosen non‐neighbouring states as a control sample. Results show that cross‐fertilization in bank performance, observed among neighbouring states before the introduction of the IMBHC deregulations during 1971–1977, gets stronger in response to the dynamically evolving IMBHC deregulations during 1982–1995 and that improvements in banks' productivity growth during 1978–1981 support Baumol's CMH. Overall, our results demonstrate the importance and influence of cross‐fertilization, as a matter of proximity of subjects, on banks' performance and suggest promise for future research that embraces the spatial dimension of banking markets and data.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".