Long-Run Construction Cost Trends: Baumol’s Cost Disease and a Disaggregate Look at Building Material Price Dynamics
Bibliographic record
Abstract
Decision-support tools for infrastructure planning assume that the real cost of construction (i.e., the cost of construction when adjusting for inflation) will remain constant over the life cycle of a facility. This paper is the first of its kind to evaluate the validity of this assumption by assessing the long-run (i.e., multidecade) nature of construction costs. This study begins by testing for the possibility that Baumol’s cost disease, a phenomenon found in some industries in which labor compensation growth outpaces productivity gains, thus giving way to real cost growth, afflicts the construction sector. To do so, a series of regression models are developed using historical macroeconomic data from the US Bureau of Economic Analysis on construction costs, compensation, productivity, and the price of intermediate and capital goods. Because construction cost growth is also closely tied to price changes for inputs, this research extracts long-run real price trends of important intermediate goods used in construction through time-series methods applied to publicly available data from the US Bureau of Labor Statistics and the US Geological Survey. The results of this study provide strong empirical evidence that Baumol’s cost disease is present within the construction sector, whereas the real price of most construction commodities has not exhibited a negative nor positive secular trend over the last century. These two findings suggest that, contrary to the conventional assumption found in current analytical frameworks, the real cost of construction will rise in the long run. This study’s contribution should motivate decision-makers to re-examine their existing decision-support tools, because the value of policies that reduce project completion times and increase the service life of facilities is potentially much higher than currently anticipated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".