www.elsevier.com/locate/jfi Estimating switching costs: the case of banking
Bibliographic record
Abstract
We present an empirical model of firm behavior in the presence of switching costs. Customers’ transition probabilities, embedded in firms ’ value maximization, are used in a multiperiod model to derive estimable equations of a first-order condition, market share (demand), and supply equations. The novelty of the model is in its ability to extract information on both the magnitude and significance of switching costs, as well as on customers ’ transition probabilities, from conventionally available highly aggregated data which do not contain customer-specific information. As a matter of illustration, the model is applied to a panel data of banks, to assess the switching costs in the market for bank loans. The point estimate of the average switching cost is 4.1%, about one-third of the market average interest rate on loans. More than a quarter of the customer’s added value is attributed to the lock-in phenomenon generated by these switching costs. About a third of the average bank’s market share is due to its established bank–borrower relationship.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".