A Systematic Approach for Developing the Startup Ecosystem in the MENA Region: Empirical Evidence from Lebanon, Jordan, Egypt and Turkey
Bibliographic record
Abstract
Entrepreneurship is a critical driver of economic growth, with entrepreneurial ecosystems increasingly recognized as essential for fostering business creation and development. Despite the growing interest in ecosystems as an approach for understanding the context of entrepreneurship at the macro level, the “startup ecosystems” concept, which plays a crucial role in shaping regional economic landscapes, remains loosely defined and measured, particularly within the MENA region. This study adopts a systematic perspective, employing the PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) framework to evaluate macro-environmental factors influencing the development of the entrepreneurial ecosystem measured through the “Score of Business” in Lebanon, Jordan, Egypt, and Turkey. This study suggests that factors like human capital, access to finance, innovation, access to infrastructure, governance, and technology can be assessed as components of the start-up ecosystem in the context of the selected MENA countries. Using panel data spanning 2004–2020, the analysis incorporates seven datasets, using the World Bank and other international institutions reports, controlled by three main variables: Foreign Direct Investment (FDI), Gross Domestic Product (GDP), and population growth. The data were compiled and analyzed using the Stata 15 software. Findings reveal that access to credit and use of the internet positively influence the “Score of Business” while human capital development and FDI exert a negative impact. Other factors, such as GDP growth, governance, population, and access to electricity in rural areas, demonstrate context-dependent effects. Finally, this study offers insights for policymakers and governments aiming to strengthen regional entrepreneurial ecosystems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".