Measuring technical efficiency in Zimbabwe's manufacturing sector: a two-stage DEA Tobit approach
Bibliographic record
Abstract
This paper measures and explains efficiency of firms in Zimbabwe's manufacturing sector. The paper uses a panel of 166 firms from 6 subsectors of the manufacturing sector in the financial year 2014. The Data envelopment analysis program is used to measure efficiency and identify its determinants. Exporting, labour quality, size and firm age were found to enhance productivity. Efficiency was found to significantly vary with location with firms in Bulawayo being more efficient. This paper found no evidence of a relationship between foreign ownership and manufacturing productivity in Zimbabwe. By identifying factors that affect efficiency as well as measuring their impact on manufacturing efficiency this paper answers the bigger policy question: how to reindustrialise Zimbabwe following two decades of economic recession. Zimbabwe's manufacturing sector exhibits strong backward and forward linkages with other sectors of the economy. These interlinkages make manufacturing sector productivity growth relevant to the resuscitation and growth of the economy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".