Segmentation of consumer markets in the US: What do intercity price differences tell us?
Bibliographic record
Abstract
Abstract We quantify the magnitude of market segmentation in US consumer market and explore the underlying factors behind this segmentation, using a quarterly panel of retail prices for 45 products in 48 US cities from 1985 to 2009. The extent of market segmentation is estimated using city‐pair price differences within the framework of both linear autoregressive (AR) and nonlinear threshold autoregressive (TAR) models. We find that the magnitude of market segmentation varies from one product to another, but even more across city pairs in each product. Contrary to a widespread perception, market segmentation within the US is not necessarily larger for non‐tradable services compared to tradable goods. We identify potential drivers of market segmentation by relating the cross‐city and cross‐product variations of market segmentation to location‐specific and product‐specific characteristics—distance, relative city sizes, differences in wage and rent, type of product and proximity to marketplace. Distance, which captures more than transport costs, turns out to be the most salient factor even after controlling for a range of other potential factors. The effect of distance, however, varies substantially across products, with perishable products and locally produced products showing larger distance effect on market segmentation. We find that the magnitude of market segmentation has been somewhat stable during the sample period, but intercity price differences have become more sensitive to distance over time in many products under study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".