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Record W2245784613

Welfare states, real income and poverty

2004· preprint· en· W2245784613 on OpenAlexaboutno aff
Lane Kenworthy

Bibliographic record

VenueEconstor (Econstor) · 2004
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGenerosityPovertyWelfare stateWelfareEconomicsLabour economicsRedistribution (election)Demographic economicsDevelopment economicsPolitical scienceEconomic growthMarket economyLaw
DOInot available

Abstract

fetched live from OpenAlex

Welfare state supporters typically contend that social-welfare programs boost the incomes of low-earning households. Critics argue that, over time, such programs reduce the growth of economic output and/or employment. As a result, redistribution may produce stagnant or even declining real incomes for those at the bottom. A number of recent cross-country empirical studies have found that welfare state generosity is strongly associated with low relative poverty, but there has been virtually no cross-national analysis of welfare state effects on absolute poverty, which is at the heart of the critics' argument. I use Luxembourg Income Study (LIS) data to examine the relationship between welfare state generosity and absolute poverty for working-age households in Sweden, Germany, the United Kingdom, Canada, and the United States from the mid-1970s to 2000. Consistent with the critics' charge, the countries with the most generous welfare states experienced rising pretax-pretransfer absolute poverty. Yet the actual causal significance of welfare state generosity in this development is questionable. On the whole, the comparative evidence seems more consistent with the view of welfare state supporters. Germany, with its relatively generous social-welfare programs, had the lowest levels of both pretax-pretransfer and posttax-posttransfer absolute poverty throughout the period. And the sharpest decline in posttax-posttransfer absolute poverty, as well as the second lowest level as of 2000, were found in Sweden, the country with by far the most generous welfare state.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.294
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2004
Admission routes1
Has abstractyes

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