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Record W4281860617 · doi:10.3390/jrfm15060243

Wealth Inequality in South Africa—The Role of Government Policy

2022· article· en· W4281860617 on OpenAlexvenueno aff
Marlin Jason Fortuin, Gerhard Philip Maree Grebe, Patricia Lindelwa Makoni

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsRedistribution (election)EconomicsInequalityNational wealthRedistribution of income and wealthPopulationEconomic inequalityLabour economicsGovernment (linguistics)Private capitalCapital (architecture)Development economicsForeign direct investmentEconomic growthMacroeconomicsFinancePolitical scienceGeography

Abstract

fetched live from OpenAlex

In South Africa, high levels of wealth inequality have persisted since 1994, to the extent that 1% of the population owns 50% of the wealth. This study examines how macroeconomic policies influenced wealth inequality in South Africa over the period 2010 to 2019 using a behavioural life-cycle model. Despite a decrease in wealth inequality over this period, the extent of this decrease is almost negligible. Results show government’s current policy model to redirect wealth from a very small tax base that is under increasing financial strain is unable to meet wealth redistributive targets. The South African government should change the wealth redistribution policy from redistribution through predominantly lump sums to creating an environment in which private enterprises are able to absorb the labour capital that South Africa possesses. An open labour market would support private and foreign direct investment into the economy, thereby strengthening economic growth and upliftment through increased income and the consequent ability to accumulate wealth.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.021
GPT teacher head0.348
Teacher spread0.327 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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