The case for welfare state universalism, or the lasting relevance of the paradox of redistribution
Bibliographic record
Abstract
In 1998, Walter Korpi and Joakim Palme proposed a political and institutional explanation to account for the greater redistributive success of welfare states that relied more on universal than on targeted programmes. Effective redistribution, they argued, resulted less from a Robin Hood logic – taking from the rich to give to the poor – than from a broad and egalitarian provision of services and transfers. Hence, the paradox: a country obtained more redistribution when it took from all to give to all than when it sought to take from the rich to help the poor. Recent studies, however, failed to confirm the existence of this paradox. This article suggests that the original argument was theoretically sound but inadequately operationalized. Korpi and Palme measured universalism indirectly, not by the design or character of social programmes, but rather by their outcomes, namely, by their income effects. These outcomes, however, are influenced by exogenous factors. We use two new Organisation for Economic Co-operation and Development (OECD) indicators to capture universalism directly, through the institutional design of social programmes: (1) the percentage of social benefits that are means or income tested and (2) the proportion of private spending in total social expenditures. These two indicators are combined into a universalism index and tested with a time-series cross-sectional design for 20 OECD countries between 2000 and 2011. This approach, we argue, better captures institutional design, in a way that is consistent with Korpi and Palme’s original argument, and it suggests that there is still a paradox of redistribution in the 21st-century welfare state.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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".