Measuring Micro and Small Enterprises (MSEs) Market Performance in Zambia
Bibliographic record
Abstract
Given the role of Micro and Small Enterprises (MSEs) in developing economies in terms of job creation, poverty reduction, production and distribution of goods and services, and foreign exchange earning, it is important to understand the determinants of firm performance.Business firm performance is usually measured by revenue, profitability, employment, stock price, production efficiency.This paper considers profitability as a major indicator of firm market performance.By conducting an empirical study using 187 micro and small sized firms from Lusaka and Central provinces of Zambia, the paper analyzed the determinants of firm performance by considering profitability as a proxy variable.This study seeks to look at the role of firm-specific factors in profitability of MSEs by employing a quantitative method from qualitative responses collected on the performance of enterprises.The analysis is done by using both Descriptive statistics and a Ordered Probit Regression Model.Explanatory variables, to explain changes in profit across time by a business firm, included are sales/revenue, cost, market coverage, competition, training, and owning more than one business.The Ordered Probit Regression result showed that increase in sales and expansion in 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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".