Economics of the U.S. highway infrastructure : why are prices rising and how to measure them?
Bibliographic record
Abstract
The rising price to build transport infrastructure poses a challenge to policymakers who are grappling with increasingly limited available resources. The United States now gets less per dollar of infrastructure spending than it used to. Although transport infrastructure is a key input into long-term growth of the broader economy, the knowledge around the infrastructure prices and their potential drivers is limited. Therefore, this thesis aims to fill this gap by documenting and analyzing highway infrastructure spending between 2005 and 2017. More specifically, this thesis aims to improve current measures of productivity and price growth, and identify explanations for the increasing price of highway infrastructure. To improve the productivity measures in the sector, quality-adjusted producer price index of highway infrastructure is generated. The indicator of quality is the deterioration rate of a roadway. The developed deterioration model captures the effect of pavement performance improvements (i.e., quality) across time. By using an iterative-reweighted least squares approach, the model is applied to publicly available roughness data of asphalt concrete pavements collected as part of the FHWA’s long-term pavement performance program. Through analysis of highway projects across the contiguous U.S., the proposed quality-adjusted index reveals that annual productivity growth in the sector was underestimated by 2.0 percent. An econometric analysis of the highway sector aims to evaluate for decision-makers possible explanations for the price growth. The developed model identifies that highway prices are largely influenced by changes in labor, material input prices, and demand for more expensive roads. In addition, the results strongly suggest the presence of the Baumol effect, a phenomenon in which labor compensation growth outpaces productivity growth leading to an increase in prices. The primary contribution of this research is two-fold. First, the findings around price-drivers of the highway construction provide policymakers with new information that may help them evaluate opportunities to mitigate price growth. Second, the proposed methodology to improve productivity and price growth measures motivate economists and statistical agencies to reconsider their current approaches. The new insights around productivity growth in the sector also motivate further investigation of the topic by the research community.
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 | 0.001 | 0.005 |
| 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 | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".