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Record W3119034326 · doi:10.5430/bmr.v9n4p20

Poverty Response to the Household Type of Elderly and Old-Age Pension

2020· article· en· W3119034326 on OpenAlexvenueno aff
Andrews Doeh Agblobi, Anthony Kofi Osei-Fosu, Hadrat Yusif

Bibliographic record

VenueBusiness and Management Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryPensionPovertyReceiptDemographic economicsLogistic regressionEconomicsPoverty levelSocioeconomicsEconomic growthStatisticsFinanceMathematics

Abstract

fetched live from OpenAlex

This paper investigates the effect of household type and old-age pension on poverty in Ghana. Primary data was collected from households with the elderly across six Districts in the country. A binary logistic regression estimation was used for the analyses. The result shows that whereas there was 14.4 times probability of being poor by living in an elderly only household, there is a 2.2 times probability of being poor in a household of the elderly and working-age person. The findings also show that the probability of a pension recipient being poor was 0.143 times less likely as compared to those that were not. Thus, there was a significant negative relationship between receipt of old-age pension and poverty. The study recommends that public policy on old age poverty alleviation must include pension provisions while those that use basic salary should consider using gross salary for pension calculation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.106
GPT teacher head0.361
Teacher spread0.255 · 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.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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