Technical Efficiency of Manufacturing Firms in Cameroon: Sources and Determinants
Bibliographic record
Abstract
The primary objective of this study is to analyze the determinants of efficiency in manufacturing firms in Cameroon. The study used a stochastic frontier model employing RPED data of 319 firms from different manufacturing industries. The data are micro-level which is the most adequate type of data used in the estimation of these models. The model used is that outlined by Battese and Coelli (1995) which determines the causes of inefficiency in the manufacturing sector in Cameroon. The estimates of the stochastic production frontier with inefficiency effects model indicates that firms in Cameroon exhibit various degrees of technical inefficiency for the sample of firms considered. The results show that firm size plays an important role in explaining technical efficiency in the sub-sector of food processing. However, large firms reduce technical inefficiency levels of firms in all the sub sectors. Another important variable which has an effect in determining technical efficiency level is the foreign ownership variable. It is significant in food processing, wood processing, textile and garments as well as in the overall sample. Hence, it increases technical efficiency in all the sub-sectors. Finally, since an increase in age of firms leads to a reduction in efficiency levels in manufacturing firms, policies should be adopted to replace the existing capital in the large firms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".