Price negotiation in differentiated product markets: An analysis of the market for insured mortgages in Canada
Bibliographic record
Abstract
In many differentiated product markets prices are determined through a negotiation process between buyers and sellers. Sellers post a price, but consumers may be able to negotiate a discount. The extent to which sellers negotiate may depend on consumer characteristics; ignoring the actual pricing mechanism can lead to an incomplete and biased analysis. Moreover, discounting is an important form of price discrimination, and therefore an interesting phenomenon worth measuring and understanding. Despite its prevalence, this form of pricing has been largely ignored by researchers studying market power in differentiated products markets. We analyze detailed transaction-level data on a large set of approved mortgages in Canada between 1992 and 2004 and administered by either the Canadian Mortgage and Housing Corporation or Genworth Financial. These data provide information on features of the mortgage, household characteristics (including place of residence), and market-level characteristics. The richness of these consumer data in combination with lender-level location data (MicroMedia ProQuest) allow us to empirically examine the functioning of this important market. We propose a model of bank choice and price negotiation that incorporates three key features of the market: differentiated services, heterogeneous bank valuations of consumers, and search costs. More generally, our goal is to build and estimate a tractable empirical model to analyze differentiated markets in which prices are negotiated rather than posted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".