Bibliographic record
Abstract
Economic and social shifts have led to rising income inequality in the world's affluent countries. This is worrisome for reasons of fairness and because inequality has adverse effects on other socioeconomic goods. Redistribution can help, but government revenues are threatened by globalization and population aging. A way out of this impasse is for countries to increase their employment rate. Increasing employment enlarges the tax base, allowing tax revenues to rise without an increase in tax rates; it also reduces welfare state costs by decreasing the amount of government benefits going to individuals and households. The question is: Can egalitarian institutions and policies be coupled with employment growth? For two decades conventional wisdom has held that the answer is no. In Jobs with Equality, Lane Kenworthy provides a comprehensive and systematic assessment of the experiences of rich nations since the late 1970s. This book examines the impact on employment of six key policies and institutions: wage levels at the low end of the labor market, employment protection regulations, government benefit generosity, taxes, skills, and women-friendly policies. The analysis includes twenty countries, with a focus on Australia, Canada, Denmark, Finland, France, Germany, Italy, the Netherlands, Norway, Sweden, the United Kingdom, and the United States. Kenworthy concludes that there is some indication of tradeoffs, but that they tend to be small in magnitude. There is no parsimonious set of policies and institutions that have been the key to good or bad employment performance. Instead, there are multiple paths to employment success. The comparative experience suggests reason for optimism about possibilities for a high-employment, high-equality society.
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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 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.013 |
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".