Development of MENA SMEs: Constraints and Success Factors
Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) play a critical role in socioeconomic development globally, especially in emerging markets. However, in the MENA region, despite efforts by both the public and private sectors, SMEs face persistent challenges, particularly in countries affected by economic instability and political crises. Policymakers typically rely on governmental data to assess SMEs ecosystems, highlighting the need to identify and understand the key factors influencing the Ease of Doing Business Index in the region. This study aims to identify the key determinants of the Ease of Doing Business Index in three selected countries from the MENA region: Lebanon, Jordan, and Turkey. Using panel data spanning 2004 to 2022, the empirical analysis reveals that factors such as firm investment in R&D, e-government development, the use of bank financing by firms, gender-inclusive policies, commercial and professional infrastructure, and the simplification of business registration procedures, positively influence the Ease of Doing Business Index. Conversely, GDP growth, taxation, and bureaucratic hurdles exert a significant negative impact on the index. Additionally, variables such as startup costs, physical and service infrastructure, and governance show varying degrees of significance across models, highlighting their context-dependent effects. These findings underscore the complex interplay of economic, social, and regulatory factors that shape the business environment in the MENA region.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".