Determinants of Profit Variability among Micro and Small Enterprises (MSEs) in Zambia
Bibliographic record
Abstract
Micro and Small Enterprises (MSEs) in developing economies like Zambia are major contributors of livelihood, job creation, poverty reduction, production and distribution of goods and services, and foreign exchange earnings. All these benefits could be realized if firms are profitable. This paper tried to envisage sources of variations in profitability among micro and small enterprises. By conducting an empirical study using 187 micro and small sized firms selected from four sectors: Trading, Services, Manufacturing, and Agriculture, the paper analyzed the sources of variations in firm profit across time. The study was made with selected firm-level characteristics like sales, cost, market coverage and perception about the level of competition. The analysis is done by using both descriptive statistics and an Ordered Probit Regression Model. Although measuring profit directly is difficult, alternative variables like changes in sales, revenue, cost, competition and market coverage are used. The estimation result revealed that, among firm effects, variations in sales and market coverage over time are the significant variables that explain variations in firm’s profitability.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".