Search Frictions and Efficiency in Decentralized Transport Markets
Bibliographic record
Abstract
Abstract We explore efficiency and optimal policy in decentralized transport markets, such as taxis, trucks, and bulk shipping. We show that in these markets, search frictions distort the transportation network and the dynamic allocation of carriers over space. We derive explicit and intuitive conditions for efficiency and show how they translate into efficient pricing rules, or optimal taxes and subsidies for the planner who cannot set prices directly. The results imply that destination-based pricing is essential to attain efficiency. Then, using data from dry bulk shipping, we demonstrate that search frictions lead to a sizable social loss and substantial misallocation of ships over space. Optimal policy can eliminate about half of the welfare loss. Can a centralizing platform, often arising as a market-based solution to search frictions, do better? Interestingly, the answer is no; although the platform eradicates frictions, it exerts market power, thus eroding the welfare gains. Finally, we use two recent interventions in the industry (China’s Belt and Road Initiative and the environmental initiative IMO 2020) to demonstrate that taking into account the efficiency properties of transport markets is germane to any proposed policy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".