How Much Does Work Matter for Inequality? Time, Money and Inequality in International Perspective
Bibliographic record
Abstract
How much of the difference between countries in inequality of the distribution of income can be explained by work i.e. by differing probabilities of any employment? Across OECD countries there are large differences in the average level and distribution of working hours. These differences arise from differing common entitlements to leisure (e.g. paid Public Holidays), plus differences in working hours per employee and in the percentage of the working age population who have some paid employment. The participation level is particularly important for inequality differences and there is persuasive evidence that country attitudes to paid employment, particularly for women, differ significantly. This paper uses Luxembourg Income Study data on Canada and the USA, UK, Germany, France and Sweden to simulate the income distributions that other countries would have if they had the Canadian pattern of workforce participation. Because employment rates in the US are quite similar to those in Canada, inequality in the USA would change only fractionally. In every other case, poverty and inequality would fall, indicating that measured differences in the inequality of money income between the North America and Europe understate the extent of cross country differences in well being. Put simply, in North America the relatively poor have to work harder, and still end up poorer, than in Europe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".