Revised and extended national wealth series: Australia, Canada, France, Germany, Italy, Japan, the UK and the USA
Bibliographic record
Abstract
This paper presents updated series of national wealth and of capital-labor shares of na- tional income for the eight countries covered by Piketty and Zucman (2014a): Australia, Canada, France, Germany, Italy, Japan, the UK and the USA. It discusses the adap- tation of the series from the SNA93 to the SNA2008, the inclusion of natural capital (i.e. forestry land, mineral and energy resources) within the concept of national wealth and the division of national housing across households and other sectors. I find that adopting the SNA2008 has no relevant consequences for aggregate macro wealth or for the net-of-depreciation capital share. However, gross-of-depreciation capital shares are higher, likely due to the inclusion of R&D as investment in the new system of accounts. Overall, new series reveal that average private wealth to national income ratios have been steadily increasing in recent years with capital-labor shares remaining relatively constant at their 2010 values.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".