Modelling Poverty in Sub-Saharan Africa and Policy Implications for Poverty Reduction: Evidence from Ghana
Bibliographic record
Abstract
ABSTRACT This study examines the causes of poverty in sub-Saharan Africa by reviewing the relevant literature on poverty and using the Ghanaian economy to shed light on the poverty situation. Using data from the 1998–99 Ghana living standards survey and probit and OLS models, we find that higher levels of education unambiguously reduce the incidence and depth of poverty. Household characteristics, type of economic activity, physical capital ownership, and financial capital accessibility are all important determinants of poverty. These results are true not only for our national model but also for the disaggregated model for wage employment and self-employment. RÉSUMÉ Cette étude examine les causes de la pauvreté dans la région subsaharienne de l'Afrique en révisant la littérature sur la pauvreté et en utilisant l'économie du Ghana comme exemple pour jeter de la lumière sur la situation de la pauvreté. En utilisant des données de recensement sur les niveaux de vie au Ghana pour la période 1998–1999 et avec l'aide des modèles d'estimation de probits et des moindres carrés, nous constatons que l'incidence et la profondeur de la pauvrete sont clairement réduites par des niveaux d'éducation plus élevés. Les caractéristiques des ménages, le type d'activité économique, la propriété et l'accés aux capitaux financiers sont tous des variables explicatives de la pauvreté. Ces résultats sont vrais non seulement pour notre modèle au niveau national mais également pour les modelès désagrégés de l'emploi salarié et non salarié
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".