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Record W2607521404

Poverty and institutional regimes: A generalised budget approach in 11 countries

2009· preprint· en· W2607521404 on OpenAlexaboutno aff
J.C. Vrooman

Bibliographic record

VenueEconstor (Econstor) · 2009
Typepreprint
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyProsperityEconomicsDevelopment economicsPoliticsDemocracyEmpirical researchDemographic economicsPolitical scienceEconomic growthStatistics
DOInot available

Abstract

fetched live from OpenAlex

The standard poverty lines applied in empirical research tend to be problematic in terms of validity, reliability, ease of application or socio-political credibility. This paper introduces an international version of an alternative method, which originally has been developed for the Netherlands. The approach starts from a detailed expert reference budget for a single person, which is subsequently generalised to other household types and over time. The empirical analyses try to assess whether Esping-Andersen's famous distinction between social-democratic, liberal and corporatist institutional regimes is related to actual differences in the 'production of poverty' in 11 countries, as measured by the generalised budget approach. Bivariate results from the Luxembourg Income Study indicate that liberal regimes (Australia, Canada, UK, USA) attain a substantially higher degree of poverty than representatives of the corporatist type (Belgium, Germany, France). Poverty in the latter group exceeds the level reached by exponents of the social-democratic regime (Denmark, Norway, Sweden) and the hybrid Dutch system. Multi-level analysis, however, shows that much of these differences have to be attributed to the characteristics of individuals, and to the divergent level of prosperity of these countries. The 'pure' effects of the regime type on poverty all run in the direction that was expected on theoretical grounds, but are rather modest. Only the difference between the high poverty rates in the liberal group and the lower incidences in other countries turned out to be statistically significant.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.281
Teacher spread0.253 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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