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Record W2408863611 · doi:10.1111/roiw.12318

Estimating the Level and Distribution of Global Wealth, 2000–2014

2017· preprint· en· W2408863611 on OpenAlexaff
James Davies, Rodrigo Lluberas, Anthony Shorrocks

Bibliographic record

VenueReview of Income and Wealth · 2017
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsWestern University
Fundersnot available
KeywordsDistribution (mathematics)Benchmark (surveying)Wealth distributionContext (archaeology)EconometricsDistribution of wealthEstimationEconomicsRegional scienceGeographyInequalityMathematicsCartographyManagement

Abstract

fetched live from OpenAlex

This paper estimates the level and distribution of household wealth globally, as well as for regions and countries, for the period 2000–2014. The data used are mainly from household surveys and national accounts balance sheets, covering about two thirds of the world's population and over 95% of global household wealth. Lists of the most wealthy published in the media are used to adjust the upper tail. Wealth levels and distributions are imputed for countries without data. Estimated global household wealth stood at USD 251 trillion in 2014, having grown from USD 117 trillion in the year 2000. Wealth per adult in 2014 was USD 53,000. The estimated Gini coefficient of global wealth was 92.2% in 2014 and the share of the top 10% was 88.3%. Wealth inequality fell from 2000 to 2007, with the share of the top 10% falling from 89.4% to 86.5%, before rising steadily to 2014. From 2000 to 2008 the share of financial assets in gross wealth, an important driver of wealth inequality, fell from 55.2% to 50.2%, before climbing to 55.0% in 2014. Household debt rose from 13.6% of gross assets in 2000 to 16.0% in 2008, and has since fallen to 13.9%.

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)0.003
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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.312
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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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