Which firms export? An empirical analysis for the manufacturing sector in the MENA region
Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyse the export behaviour of manufacturing firms located in the Middle East and North Africa (MENA) region using data from the World Bank's Enterprise Surveys Database. Design/methodology/approach – This paper examines the factors influencing the export behaviour of manufacturing firms located in the MENA region through a probit model for export decision and through a fractional logit model for export intensity. Findings – The empirical results show significant positive effects of private foreign ownership, information and communication technology, and firm size on the probability of exporting and on export intensity of MENA manufacturing firms. Government ownership tends to exert negative effects on firms’ propensity to export. The results underscore enhancing effects of national economic development levels on firms’ export performance. Also, they indicate that firms’ propensity to export decreases with larger domestic market size. The empirical analysis reveals considerable heterogeneity in the implications of firm characteristics for firms’ export behaviour through firm size categories and across MENA countries. Originality/value – This paper contributes to the literature by conducting overall and comparative cross-country empirical analyses of the factors influencing the export behaviour of manufacturing firms located in the MENA region. It also explores the specificities of small and large firms’ responses to the factors influencing firms’ export behaviour. The results have implications for policies intended to enhance industrial growth and international competitiveness of the manufacturing sector 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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".