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Global Poverty and the New Bottom Billion: What if Three‐quarters of the World's Poor Live in Middle‐income Countries?

2010· article· en· W1947226924 on OpenAlexaboutno aff
Andy Sumner

Bibliographic record

VenueIDS Working Papers · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersWorld Bank Group
KeywordsPovertyWarrantDevelopment economicsMiddle incomeQuarter (Canadian coin)Extreme povertyDeveloping countryPoverty thresholdEconomicsGeographyEconomic growthDemographic economics

Abstract

fetched live from OpenAlex

Summary This paper argues that the global poverty problem has changed because most of the world's poor no longer live in low income countries (LICs). Previously, poverty was viewed as an LIC issue predominantly; nowadays such simplistic assumptions/classifications are misleading because some large countries that graduated into the MIC category still have large numbers of poor people. In 1990, we estimate 93 per cent of the world's poor lived in LICs; contrastingly in 2007–8 three quarters of the world's poor approximately 1.3bn lived in middle‐income countries (MICs) and about a quarter of the world's poor, approximately 370mn people live in the remaining 39 low‐income countries – largely in sub‐Saharan Africa. This startling change over two decades implies a new ‘bottom billion’ who do not live in fragile and conflict‐affected states, but in stable, middle‐income countries. Such global patterns are evident across monetary, nutritional and multi‐dimensional poverty measures. This paper argues the general pattern is robust enough to warrant further investigation and discussion.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.025

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 communication0.0030.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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 designNot applicable
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

Citations178
Published2010
Admission routes1
Has abstractyes

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