Evaluating Railroad Duopoly Behavior: A Market Level Analysis
Bibliographic record
Abstract
Abstract Railroads remain a critical transportation mode for the movement of U.S. agricultural freight. Within much of the northwest and mid-central U.S., rail is often the only viable mode to transport bulky agricultural commodities, including wheat. With the potential for exploitation of market power by railroads over such movements, regulations exist that are designed to mitigate the effects of monopoly railroad situations. But what of duopoly railroad markets? Economic theory offers reliable predictions of firm behavior when there are either many firms serving a market, or conversely when there is just a single firm serving the market. But behavioral predictions are not as straightforward when evaluating oligopolistic market structures. Relevant to this research, it is not clear a priori what kind of firm and market behavior might emerge under a duopoly. Clarifying what happens in such cases ultimately becomes an empirical issue. In this paper, we investigate a significant U.S. wheat transportation market currently served by a Class 1 railroad duopoly, but railroad behavior in this market may also be moderated by intermodal competition from water barge. While our findings about railroad behavior over time indicate a tendency towards Cournot duopoly behavior, latent variable analysis offers a more granular understanding of how both intra- and inter-modal competition affect the chosen transportation market. With only a very limited number of Class 1 railroads left serving the entire country, the future of U.S. freight transportation by rail will be comprised of numerous important products and regions served by only one or two railroads. While some of the methods we use in this analysis are novel to the industrial economics literature, we believe this effort will help better inform future regulatory policy design for rail, further strengthening market vigilance for freight shippers who are destined to transport goods in increasingly concentrated railroad markets.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".