Why aren't all Truck Drivers Owner‐Operators? Asset Ownership and the Employment Relation in Interstate for‐hire Trucking
Bibliographic record
Abstract
Transaction‐cost and agency theorists have frequently cited trucks as prototypical user‐owned assets, and have consequently predicted a predominance of self‐employed drivers who contract with motor carriers. In fact, owner‐operators accounted for less than one‐third of US trucking activity conducted by large interstate trucking firms in 1991, a proportion that has changed little since deregulation. Given the predictions of organizational economists, why is self‐employment in the interstate trucking industry not the dominant organization form? We propose that transaction costs and agency costs are indeed important in the trucking industry. In the absence of externalities across hauls, contracting between carriers and owner‐operators is preferred for traditional agency reasons. However, when the outcome of one haul imposes externalities on other hauls or on the carrier's reputation, an owner‐operator will not internalize all costs associated with poor outcomes. Given problems of noncontractibility of maintenance effort, carrier ownership of the vehicle is the preferred organizational form in such a case. We also propose that vehicle idiosyncrasy can create thin market conditions that encourage carrier ownership of vehicles. A study of the organization and operations of 354 trucking firms for 1991 provides evidence consistent with these predictions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".