Assessing a feasible degree of product market integration: a pilot analysis
Bibliographic record
Abstract
Purpose This paper aims to make a preliminary estimate of the degree of integration in the US product market (widely acknowledged to be the most integrated among geographically large economies) as an upper bound of spatial integration that is practically achievable in markets covering fairly large territories. Design/methodology/approach The approach takes the form of an econometric model derived from the fact that local price of a tradable good should not be dependent on local demand under the law of “one price is a tool to measure market integration”. It is applied to data on the cost of a grocery basket and prices for three individual goods in 2000 across 29 US cities. Findings The regression results suggest that the US market is not perfectly integrated. Thus, the estimated degree of its integration can be deemed, indeed, as a feasible maximum. Applying this benchmark to the European part of Russia in 2000, its degree of market integration turns out to be comparable – by the order of magnitude – with the feasible one. The roles of a few factors that could potentially cause segmentation of the US market are estimated. Research limitations/implications The estimated degree of US market integration is crude because of the relatively small spatial sample. Further research has to substantially widen the spatial sample and estimate integration of the US market across a number of points in time. Originality/value The paper suggests a realistic benchmark standard for judging the extent of market integration in various (geographically large) economies.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".