The impact of corruption on the export intensity of SMEs in Tunisia: moderating effects of political instability and regulatory obstacles
Bibliographic record
Abstract
Purpose This paper aims to investigate the moderating effect of political instability and regulatory obstacles on the relationship between corruption and export intensity in the context of Tunisian small- and medium-sized enterprises (SMEs). Design/methodology/approach This study uses data from the World Bank Enterprise Survey (WBES). The sample consists of 537 Tunisian SMEs. The partial least squares method was used to analyse the data. Findings The direct effect of corruption on export intensity was found to be non-significant. It was significantly negative when corruption was combined with regulatory obstacles, whereas it was positive when corruption coexisted with political instability. Additional analyses revealed that results were sensitive to firm size (small versus medium) and sector of activity (service versus manufacturing). Research limitations/implications This paper has some limitations related to the use of secondary data. Enhanced variable measurements and more detailed data collection are recommended for future studies. Practical implications This paper is useful to researchers and policymakers who are interested in understanding the effects of a poor institutional environment on SME exports in developing countries. Originality/value This paper considers the impact of corruption on the export intensity of SMEs in the presence of political instability and regulatory obstacles in Tunisia. To the best of the authors’ knowledge, the joint effect of these institutional variables on the exports of firms has not been examined in previous research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".