Measuring Spatial and Temporal Market Structure in a Transportation Sector: For-hire Grain Trucking on the Alberta-Saskatchewan Border in Canada
Bibliographic record
Abstract
While the trucking industry across North America is now fully de-regulated, truck markets and movements are diverse enough that the level of competition in truck transportation almost certainly varies across space, commodities, and even time. Most studies of market power in transportation do not measure or account for spatial or temporal variation in levels of competition, and in addition, it is not clear to what degree such variation affects shippers. For example, there is anecdotal evidence that trucking of certain commodities in Western Canada is characterized by considerable market power that only manifests at certain times of the year. In this light, we examine both spatial aspects as well as the dynamics of rates in the medium-to-long-haul grain trucking sector in West-Central Alberta and East-Central Saskatchewan on the Canadian prairies. We attempt to characterize market power over both space and time within this regional trucking sector. This is done using a unique and detailed data set of trucking rates charged to shippers (farmers) for grain transportation to a common destination (Lloydminster, AB) from the numerous dispersed farms in the region. To frame the unique spatial aspects of this issue, we begin by using geographic information systems (GIS) software to build freight rate contours for this trucking market through space. A set of suppositions regarding the possible shape of these contours as they relate to transportation market structure is also developed. Subsequently, a subset of the data is used to conduct an econometric estimation of short-run freight rate dynamics. These latter estimates reveal evidence of less than competitive transportation markets through time and space. Ultimately, we find that market power in trucking is not persistent within this market, but we do observe uncompetitive pricing behavior at certain times of the year. Given that trucking is deregulated, the latter finding is somewhat unexpected. We suspect that this set of conditions very likely affects trucking rates across more markets and regions than the one examined here.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".