Monetary and Multidimensional Poverty in Cameroon: Measurements, Determinants, and Policy Implications
Bibliographic record
Abstract
Abstract Cameroon has witnessed substantial economic growth in the new millennium, while poverty reduction has been limited and inequality has worsened. In this context, this chapter investigates the different facets of poverty in Cameroon, factors affecting them and policy options to tackle poverty and achieve inclusive and sustainable development. We apply two prominent poverty measurement methods (Alkire-Foster and Foster-Greer-Thorbecke) to a series of household consumption and living standards (ECAM) surveys and Demographic and Health Surveys (DHS) collected between 2001 and 2018, and perform various empirical analyses to elucidate poverty dynamics and features. Our results indicate that both monetary and multidimensional poverty have decreased in Cameroon between 2001 and 2018, albeit slowly and to varying degrees across the different demographic, socioeconomic and spatial groups of the population. We find that the proportion of multidimensional poor people is always higher than the proportion of the monetary poor. At the same time, multidimensional poverty has reduced much faster than monetary poverty at the national level. Lastly, we find that higher levels of poverty in Cameroon are strongly associated with rural livelihoods, large family size, less education, employment in agriculture and the Northern regions of the country. Our micro-economic analysis is complemented with a review of structural factors affecting poverty at the macro level. We point out the need to accelerate the structural transformation of the Cameroonian economy, to reduce inequalities across the different regions and sub-groups of the population and expand economic opportunities for the youth to achieve the demographic dividend.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".