Obstacles and Performance of Agribusiness Enterprises: Evidence from South-Kivu Eastern Democratic Republic of Congo
Bibliographic record
Abstract
The objective of this study is to analyze differences between business obstacles and performance by their location using 92 food and agribusiness firms operating in rural and urban areas. Descriptive statistics, chi-square and analysis of variance are used to evaluate data collected through household surveys. Chi-square test are used to identify relationships between location and enterprises characteristics and business obstacles. Results reveal a less participation of female in ownership and management. Access to finance still the main obstacle faced by all firms. Scarcity of electricity, transportation cost and lack of equipment are perceived as severe and moderate obstacles. The analysis of variance show that urban firms are able generate higher profit margin than rural firms. These results are helpful for policymakers to promote food and agribusiness sector in order to reduce poverty and enable SME growth in Eastern of the Democratic Republic of Congo (South Kivu region).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".