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Record W3124932678

Inequality amid income stagnation: Italy over the last quarter of a century

2018· article· en· W3124932678 on OpenAlexaboutno aff
Andrea Brandolini, Romina Gambacorta, Alfonso Rosolia

Bibliographic record

VenueQuestioni di Economia e Finanza (Occasional Papers) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionEconomicsInequalityEconomic inequalityGini coefficientBusiness cyclePovertyCurrencyIncome distributionQuarter (Canadian coin)Demographic economicsPopulationDevelopment economicsKeynesian economicsMonetary economicsGeographyEconomic growthDemography
DOInot available

Abstract

fetched live from OpenAlex

The paper analyses the evolution of inequality in Italy from 1989 to 2014, focusing on three business-cycle phases: the 1992 currency crisis, the moderate growth from 1993 to 2007, and the double-dip recession from 2008 to 2013. Data from the national accounts and the Bank of Italy�s Survey on Household Income and Wealth are used. Results show that income inequality, as measured by the Gini coefficient, rose sharply during the recession of the early 1990s but much less during the recent double-dip recession, though the share of people at risk of poverty rose similarly during the two crises. The stability of (synthetic) distributive inequality measures is explained by the fact that the reduction in income during the double-dip recession hit the whole population. Despite this apparent stability, two changes stand out: the widening gap between the young and the elderly and the fact that the deterioration in living conditions was borne wholly by households whose primary earner was foreign born.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.223
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes1
Has abstractyes

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