Improving the <scp>SNA</scp>: Alternative measures of output, input, income, and productivity
Bibliographic record
Abstract
Abstract The current System of National Accounts (SNA) Gross Domestic Product (GDP) concept does not measure the income generated by the production sector since it includes depreciation and excludes capital gains and losses on assets used in the production sector. The paper suggests an accounting framework that measures the income generated by the production sector of an economy and implements this measure using the Augmented Productivity Database (APDB) developed by Asian Productivity Organization and Keio University for China over the years 1970–2020. Real gross and real net income generated by the Chinese production sector are decomposed into explanatory factors including TFP growth using the framework suggested by Jorgenson and Diewert and Morrison. TFP growth is further decomposed into technical progress and inefficiency components using the nonparametric approach developed by Diewert and Fox. The APDB has estimates for the price and quantity of agricultural, industrial, commercial, and residential land used in China. The paper argues that changes in land use should be treated in the same manner as inventory change and added to the alternative output measures. It turns out that Jorgensonian user costs for land are frequently negative. The problems associated with negative user costs are discussed in the paper.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".