Bibliographic record
Abstract
Abstract Cameroon has experienced substantive economic growth in the past two decades between 2001 and 2019, but this growth has not been inclusive enough to provide jobs to hundreds of thousands of new Cameroonian job-seekers entering the market every year. This chapter provides a comprehensive assessment of the Cameroon’s labour market, with a focus on the dynamics and patterns of key labour market indicators and factors affecting the performance in the labour market. Our analysis uses both macro- and micro-economic approaches and combines country-level times-series data from the World Bank World Development Indicators (WDI) and household survey data (ECAM) produced by the Cameroon’s National Institute of Statistics. In terms of findings, first we observe an overall limited increase of the capacity of the Cameroonian economy to absorb its labour force, with a fluctuating pattern over time. Second, we show that the positive dynamics observed in labour force participation, employment and unemployment actually hide the precarity of jobs and poor working conditions prevailing in the country. Third, our results reveal that significant gaps persist across different groups of the population in terms of labour market outcomes, and women, youth, low-educated people, and population of northern regions of the country are the most affected groups by the scarcity of formal and decent jobs. Finally, our analysis also corroborates previous findings of the literature showing that gender, education, area of residence, and economic sector of employment are key drivers of the labour market outcomes in Cameroon. From a policy perspective, we argue that to mitigate the growing demographic pressure in Cameroon’s labour market and convert it into a demographic dividend, there is a need to accelerate the structural transformation of the Cameroonian economy, invest more in education and human capital, and therefore set up conditions for extensive job creation in more formal and more productive sectors. Additionally, targeted economic policies aiming to formalize the labour market, expand social protection programs, especially for women and youth, would help improving employment and earning prospects for the growing and young Cameroonian population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".