Effects of Market Concentration and Competition in the Paving Sector
Bibliographic record
Abstract
Planning agencies are searching for innovative techniques to cost effectively preserve their existing infrastructure systems. In this study, we investigate the effects of two indicators of increased competition in the paving market as a mechanism to reduce roadway construction costs. The highway construction sector makes for a unique case study due to a rich dataset that allows for the simultaneous consideration of two indicators of competitive intensity: number of bidders on a project (an indicator of intra-industry competition – between firms who pave with the same material) and market concentration (an indicator of inter-industry competition – between firms who pave with material substitutes). To evaluate the relationship among these indicators and pricing, we develop panel data regression models using bid data that spans 10 years for 47 states within the United States. The models embed several covariates that account for cross-sectional and time-varying heterogeneity. Results from the analyses indicate both that a) the paving market functions as a private value auction, in which an increase in bidders reduces construction prices and b) states with more uniform market shares among pavement materials pay lower prices for all materials. For a “typical” roadway project, the parameterized model indicates that states that with the lowest quartile of market concentration pay at least 7% less than states with the highest quartile of market concentration. These findings support the notion that policies that reduce material market concentration have the potential to reduce an agency’s costs, allowing it to be more efficient with its limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".