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Record W2949163000 · doi:10.1257/rct.3147-1.0

Welfare effects of a non-contributory old age pension: experimental evidence for Ekiti State in Nigeria

2019· preprint· en· W2949163000 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAEA Randomized Controlled Trials · 2019
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsCarleton University
Fundersnot available
KeywordsPensionWelfareDemographic economicsState (computer science)Welfare stateBusinessEconomicsPolitical scienceComputer scienceFinanceLawMarket economyAlgorithm

Abstract

fetched live from OpenAlex

Many countries in the developing world have implemented non-contributory old-age pensions.Evidence of the impact of such policies on the elderly in Sub-Saharan Africa is scarce, however.In this paper, we provide the first evidence from a randomized evaluation of an unconditional, non-contributory pension scheme targeted at the elderly in Ekiti State, Nigeria.Our findings show that treated beneficiaries self-reported better quality of life, more stable mental health, and better general health.We also provide evidence of spillover effects on labor outcomes and on household expenditure patterns as well as support for demandside interventions aimed at improving the welfare of elderly poor citizens and other household members.

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.

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)0.025
metaresearch head score (Gemma)0.049
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0200.006
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.032
GPT teacher head0.315
Teacher spread0.283 · 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