Is the Construction Industry More Concentrated After the COVID-19 Pandemic? An Empirical Study on the U.S. Revenue Data from 2004 to 2021
Bibliographic record
Abstract
As a most important industry in the U.S., the construction industry faces multiple challenges since the COVID-19 pandemic. Backed by government interventions that have helped streamline construction projects, however, the aftermath of the pandemic is still vague. One of the imperative issues to be examined is the construction market concentration since the pandemic, especially how it is compared to the Great Recession. This study statistically analyzes the revenue gaps among the U.S. construction companies and the changes of their revenue rankings. This study uses the data from Engineering News-Record Top List of Contractors. The results show that unlike the Great Recession, which obviously enlarged the revenue gaps, there is no evidence yet demonstrating that the COVID-19 pandemic caused noteworthy disruptions to the revenue gap among construction companies. The government interventions, such as the Paycheck Protection Program and the Infrastructure Investment and Job Act, are regarded as effective stabilizers, until the cutoff of data collection at the beginning of 2022. The medians of ranking changes across years generally remain stable, including the period of pandemic. The study also indicates the necessity to include more longitudinal data and sectional data to explore long-term impacts and sector-wise conditions.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".