Bibliographic record
Abstract
The issue of female entrepreneurship has become a globally important topic in recent years, especially for countries in the Middle East and North Africa (MENA) region. Yet, in the case of Iran the topic remains under-researched despite the significance of female entrepreneurship as means of addressing the disproportionately high unemployment within the educated female work force. This article presents the findings from a survey that uses the World Bank Enterprise Survey questionnaire to document the characteristics of a sample of enterprises in Iran. The results suggest that entrepreneurship rate among Iranian women falls within the regional variation and remains low relative to other regions of world outside Asia. Low female entrepreneurship is mostly distinct among small and medium enterprises (SMEs). On the positive side, however, women entrepreneurs in Iran (similar to the rest of MENA) tend to be better represented in larger firms. The research highlights some of the notable characteristics of female entrepreneurship, indicating a high presence in the service sector, especially gender-segregated activities, as well as in some new and growing industries such as electronics and information technology. Our data shows that female-owned enterprises in Iran tend to face particular challenges in accessing some infrastructure services, particularly telecoms and the Internet. Yet, there were fewer complaints among female entrepreneurs regarding other aspects of business, such as obtaining permits and paying taxes, in comparison to the rest of the MENA region. Many female entrepreneurs indicated that international economic sanctions were a major obstacle for their business, predominantly because female-owned firms are new and tend to depend more on technology and foreign trade. Generally, a large part of gender differences in terms of enterprise ownership could be explained by firm size and industrial characteristics of female-owned firms, though one needs also to recognize challenges women face with regard to attitudes toward gender roles and stereotypes.
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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 | 0.001 | 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 | 0.000 |
| 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".