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Record W3180434418 · doi:10.55365/1923.x2021.19.33

Financial Deepening, Stock Market, Inequality and Poverty: Some African Evidence

2021· article· en· W3180434418 on OpenAlex
Jelson Serafim

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReview of Economics and Finance · 2021
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsDistributed lagEconomicsPovertyStock marketEconomic inequalityInequalityStock (firearms)Causality (physics)MacroeconomicsMonetary economicsEconometricsEconomic growth

Abstract

fetched live from OpenAlex

This paper presents evidence about the relationship between private credit, stock market indicators, income inequality and poverty, using the annual data that ranges from 1992 to 2018 on nine African economies. We applied the estimation method of Autoregressive Distributed Lag (ARDL) to model the long-run effect. In Addition, we used Dumitrescu and Hurlin Panel causality to check the direction of causality. The results of long-run estimates show that the stock market indicators have a significant positive impact on income inequalities, but have a negative and significant impact on poverty. Further, our findings show that private credit adversely reduces income inequalities. Our results also establish significant short-run causalities among stock market indicators, private credit, income inequalities, and poverty.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.630
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.235
Teacher spread0.209 · 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