PRICE NEGOTIATION IN DIFFERENTIATED PRODUCTS MARKETS: THE CASE OF INSURED MORTGAGES IN CANADA.
Bibliographic record
Abstract
ABSTRACT. This paper measures market power in a decentralized market where contracts are determined through a search and negotiation process. The mortgage industry has many institutional features which suggest it should be competitive: homogeneous contracts, negotiable rates, and, for a given consumer, common lending costs across lenders. As a result, even with a small number of competing lenders, informed borrowers can gather multiple quotes. However, there is important heterogeneity in the ability of consumers to understand the subtleties of financial contracts, in their ability or willingness to negotiate and search for multiple quotes, and also in their degree of loyalty to their main financial institutions. We propose and estimate a model to disentangle the different channels through which market power can arise for a given transaction in this environment. There are two main sources of market power. The first is search frictions. We find that over the five year period of the contract the average search cost corresponds to an upfront sunk cost of between $1,047 and $1,590. The second main source of market power is switching costs. We estimate that consumers are willing to pay between $759 and $1,617 upfront to avoid having to switch banks.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".