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Record W2410327549

Equality and Economic Security Take a Hit: The Index of Economic Well-Being for Selected OECD Countries, 1980-2014

2016· preprint· en· W2410327549 on OpenAlexaboutno aff
Jasmin Thomas, James Uguccioni

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)Per capitaEconomicsEconomic securityConsumption (sociology)EleventhDevelopment economicsGross domestic productEconomic growthPopulationDemography
DOInot available

Abstract

fetched live from OpenAlex

This report presents new estimates of the Index of Economic Well-Being and its four domains (consumption flows, stocks of wealth, economic equality, and economic security) for fourteen OECD countries for the 1980-2014 period. It finds that in 2014 Norway had the highest level of economic well-being and Spain the lowest. Canada ranked eleventh among the fourteen countries. Over the 1980-2014 period, Australia enjoyed the most rapid increase in economic well-being in absolute terms, and Italy the slowest. In all fourteen countries, over the 1980-2014 period, there was growth in the consumption flows index and the stocks of wealth index. Over this same period, the economic security index and the economic equality index were largely stagnant in most countries. Most importantly, in all fourteen countries except France, the IEWB grew slower than GDP per capita, a measure that is often used to provide indications into the state of well-being in a given country. According to our estimates, economic well-being, therefore, has not advanced as rapidly as GDP per capita. Furthermore, since 2008, growth in economic well-being has been slower than growth over the 1980-2008 period for nine of the fourteen countries considered, with two countries showing negative growth (Italy and Spain).

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)0.005
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.298
Teacher spread0.269 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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