The Impact of Business Support Services for Small and Medium Enterprises on Firm Performance in Low- and Middle-Income Countries: A Meta-Analysis
Bibliographic record
Abstract
Interventions designed to support small and medium enterprises are popular among policy makers, given the role small and medium enterprises play in job creation around the world. Business support interventions in low- and middle-income countries are often based on the assumption that market failures and institutional constraints impede the growth of small and medium enterprises. Significant resources from governments and international organizations are directed to small and medium enterprises to maximize their socioeconomic impact. Business-support interventions for small and medium enterprises in low- and middle-income countries most often relate to formalization and business environments, exports, value chains and clusters, training and technical assistance, and access to credit and innovation. Very little is known about the impact of such interventions despite the abundance of resources directed to small and medium enterprise business-support services. This paper systematically reviews and summarizes 40 rigorous evaluations of small and medium enterprise support services in low- and middle-income countries, and presents evidence to help inform policy debates. The study found indicative evidence that overall business-support interventions help improve firm performance and create jobs. However, little is still known about which interventions work best for small and medium enterprises and why. More rigorous impact evaluations are needed to fill the large knowledge gap in the field.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".