Measuring Infrastructure in BEA's National Economic Accounts
Bibliographic record
Abstract
Infrastructure provides critical support for economic activity, and assessing its role requires reliable measures. This paper provides an overview of U.S. infrastructure data in the National Economic Accounts. After developing definitions of basic, social, and digital infrastructure, we assess trends in each of these categories and their components. Results are mixed depending on the category. Investment in some important types of basic infrastructure has barely or not kept up with depreciation and population growth in recent decades, while some other categories look better. We also show that the average age of most types of infrastructure in the U.S. has been rising, and the remaining service life has been falling. This paper also presents new prototype estimates of state-level investment in highways, highlighting the wide variation across states. In addition, we present new prototype data on maintenance expenditures for highways. In terms of future research, we believe that deprecation rates warrant additional attention given that current estimates are based on 40-year old research and are well below those used in Canada and other countries. We also believe that additional creative work on price indexes for infrastructure would be valuable. Finally, all of the data in this paper will be downloadable on the BEA website, and we hope that the analysis in this paper and the availability of data will spur additional research. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.039 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".