Intangible Assets and Their Contribution to Productivity Growth in Ontario∗
Bibliographic record
Abstract
Recent empirical studies confirm that investment in intangible assets is a non-negligible component of business sector output. The contribution of intan-gible capital to total labour productivity growth is comparable to that of tangi-ble capital for a wide range of the countries, including the US, UK, Canada, Ger-many, France etc. Following Corrado et al. (2005) and Baldwin et al. (2012), this paper focuses on an assessment of business sector investment in intangible assets and an analysis of the contribution of intangible capital to labour produc-tivity growth at the provincial level in Canada, namely in Ontario. The findings indicate that the Ontario business investment in intangible assets accounts on average for 10 percent of revised business sector output in the 1998-2008 period. The growth rate of real investment in intangibles exceeds that of investment in tangible assets. Investment in economic competencies is as large as investment in innovative property and computerized information combined. The results of this growth accounting exercise demonstrate that intangible capital contributes significantly to the total labour productivity growth in Ontario. In 1998-2008 intangible capital contributed on average 26.2 percentage points to total labour productivity growth while tangible capital contributed 17.9 percentage points and labour composition contributed 8.7 percentage points. Innovative property contributed the most among all categories of intangible capital, followed by economic competencies and computerized information.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".