Are Trade Policies Performance Enhancing? The Special Case of the GCC Countries
Bibliographic record
Abstract
Purpose – The purpose of this study is to investigate the extent to which trade policies affect the performance of firms in the six countries of the Gulf Cooperation Council (GCC): Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Bahrain, and Oman. The finance literature, theoretically and empirically, postulates that the efficient management of trade policies (i.e., trade debtors, inventory, and trade credit policies) relates positively to corporate performance. This study, however, conjectures that in a different and distinct business setting, corporate performance might not be related to how a firm manages its trade policies, or at least not significantly.Design/Methodology/approach – The study sample is comprised of all nonfinancial firms in the Gulf Cooperation Council (GCC) countries. The study uses several ratios in the literature to proxy for trade policies and corporate performance. It also employs related and alternative econometric models in order to corroborate the results.Findings – Contrary to a documented worldwide evidence, the results of the study reveal that short-term trade policies – specifically, trade debtors, inventory, and trade credit – have an insignificant or at most a trifling effect on corporate performance, for a set of nonfinancial listed firms in all six GCC countries.The results are robust to the econometric model, the profitability measure, and the country. This is probably because most GCC corporate managers have no concern about being financially constrained, which leads to less attention to aligning the optimal levels of working capital with the daily financing needs. Most firms in Gulf Cooperation Council (GCC) countries receive generous subsidies from their governments as part of lavish payments, in order to carry out development projects and other services for governmental agencies. The results of this study should be of great importance to GCC corporate managers and investors.Originality/Value – The study contributes to the literature by demonstrating that different cultural managerial practices might skew the already-established association among the financial variables and, hence, bring up new evidence. Also, the study implicitly suggests that further research in this area could reveal additional anomalous findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".