What determines the performance of SMEs? Evidence from poultry farming in Ghana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate factors influencing the performance of poultry farmers and examine the potential disparities in performance among gender, formalization and association membership and the source of such disparities if they are established. Design/methodology/approach This study focussed on the poultry farmers located in the Bono Region of Ghana. Data was gathered on a total number of 155 poultry farmers located in the study area for two rounds. This study augmented the traditional C-D function and estimate the determinants of performance using panel estimation technique. The Binder-Oaxaca was used to investigate disparities in performance. Findings The empirical results established a significantly positive relation between association membership, size, as well as formalization of farms and performance. However, there existed a negative relation between the level of education of managers and performance. Also, the discrimination analysis revealed the existence of discrimination stemming from association membership and formalization. Research limitations/implications Although the data gathered was adequate for the purpose of this study, further studies on poultry production in Ghana/Africa can broaden the scope to other constructs which are not captured in this study. Originality/value This study contributes to the growing literature that delves into the poultry industry of the Ghanaian economy. Conducting a further discrimination analysis aside the determining factors make the study unique.
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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 | 0.001 |
| Open science | 0.001 | 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 it