Intangible Capital and Productivity Growth in Canada
Bibliographic record
Abstract
Intangible capital consists of investments that do not take on the solid, physical characteristics of machinery and equipment or buildings. Nevertheless, such investments have some of the properties of other types of investments in that they yield long-lasting benefits as a result of expenditures that are made today. In the National Accounts, these expenditures need to be capitalized rather than expensed as intermediate materials for purposes of estimating gross domestic product (GDP). Recent papers have considered issues surrounding the measurement of intangibles. Baldwin et al. (2005) discussed issues surrounding research and development (R&D). They noted that R&D is only one of the components of innovation expenditures. Baldwin et al. (2009) extended the measurement of intangible investments beyond that of just R&D. At the heart of intangible investments, of course, are software and R&D. However, intangible investments also consist of purchased science services, own-account scientific services, exploration expenses in the resource sector, and advertising expenditures, because these create an intangible asset and yield long-term benefits. This paper extends the authors' previous work in three ways. First, it expands it into several new areas--what are referred to as economic competencies. These involve primarily investments in human capital--via management and training investments as well as management consulting services. This not only provides broader coverage; it also allows cross-country comparisons of Canada to the United States. Second, this paper moves from just measuring investment to also developing capital stock estimates. This requires assumptions about depreciation rates. In both instances, the paper adopts assumptions similar to those used elsewhere in developing estimates for the United States, in order to ensure comparability. Third, the paper incorporates the estimates of intangible capital into the growth-accounting framework so as to understand how it is related to productivity growth. A comparison of Canada and the United States in this regard is also provided.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".