Measurement of Real Income in the System of National Accounts: An Application to North American Economies
Bibliographic record
Abstract
This paper makes use of both output and income statistics derived from the System of National Accounts to examine performance in the three North American countries. In doing so, the paper follows recommendations contained in the System of National Accounts 1993 (SNA 1993) for calculating aggregate real income statistics such as gross national income (GNI) and gross national disposable income (GNDI) rather than aggregate real gross domestic product (GDP), in order to demonstrate the utility of alternate measures for analyzing aggregate economic performance and the standard of living. To move from estimates of GDP to estimates of GNI and GNDI, adjustments are made for changes in relative prices, referred to as a "trading gain" (the combined effect of changes to the terms of trade and changes in the ratio of traded goods prices to non-traded goods prices), and for current account entries other than the trade balance. The paper compares real output and income measures for Mexico, the United States, and Canada. Differences between the GDP and GNDI estimates illustrate the extent to which non-production factors, such as relative price changes, can influence the economic performance of a nation, either as compared to that of other nations or in terms of a nation's ability to purchase the goods and services its citizens consume. They also illustrate the benefit of using more than one measure when comparing economic performance across countries.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".