Labor Market Trends in North America - Has Economic Well-being Improved?
Bibliographic record
Abstract
The last three decades have seen substantial growth in GDP per capita in North America, combined with low unemployment in recent years.However, what does this indicate about trends in economic well-being?GDP per capita includes many items that do not improve individual utility, while employment rates, strictly speaking, concern an input to the process of production, not an enjoyable output.Section 1 of the paper presents some evidence on trends since 1970 in unemployment, employment, hourly wage rates and GDP per capita in the USA and Canada, and discusses their deficiencies as indicators of economic well being.In Section 2, we argue that the economic well-being of a society depends on:(1) effective per capita consumption flows, which includes consumption of marketed goods and services, un-marketed goods and services, and changes in life span and in leisure;(2) net societal accumulation of stocks of productive resources, including tangible capital and housing stocks, human capital and R&D investment, environmental costs, and net change in level of foreign indebtedness; (3) income distribution, (as indicated by the Gini index of inequality, and depth and incidence of poverty); and (4) economic security (from unemployment, ill health, single parent poverty and poverty in old age).The paper then develops an index of economic well-being for Canada and the USA for the period 1970 to 1999 and compares trends in economic well-being to trends in GDP.Since growth in GDP per capita exceeds growth in economic well-being, the paper concludes with a discussion of how the "productivity" of economic growth might be improved.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".