Income Distribution and Public Social Expenditure: Theories, Effects and Evidence
Bibliographic record
Abstract
Bonney, and Kati Foley for their excellent help with manuscript preparation. All errors are our own. “Political irrelevance not withstanding, inequality is exacting a considerable cost on the society and turning attention to it would hardly be ‘frivolous’. ” – Smolensky (2002) What is the relationship between economic inequality and public social expenditure? Why might it matter? Because income distribution and social spending are jointly determined by economic growth and political decisions, these issues have long been analyzed by both economists and political scientists. Over seventy years ago, R. H. Tawney, discussed the growth and significance of public provision for education, health and social services, and noted that “the standard of living of the great mass of the nation depends, not merely on the remuneration which they are paid for their labour, but on the social income which they receive as citizens”—and he saw the expansion of such public spending for “purposes of common advantage ” as the primary route to overcome inequalities of opportunity and circumstance (1964:133, 121). In his now-classic book, Esping-Andersen has more recently (1990) argued that there are significant differences between
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".