Manufactured exports, disaggregated imports and economic growth: the case of Kuwait
Bibliographic record
Abstract
Abstract This study investigates whether manufactured exports contribute to economic growth and whether imports can augment the role of exports in fostering export diversification. In the case of the latter, the study also examines which categories of imports are most likely to facilitate economic growth in the long run. In particular, the study focuses on the case of Kuwait over the period 1970–2019 and utilizes a Cobb–Douglas production function augmented with manufactured exports and primary and manufactured imports. The long-run relationships between the model variables are explored using two cointegration tests, namely the Johansen test and the dynamic ordinary least squares. The short-run causality is investigated utilizing the multivariate Granger approach in a vector autoregressive model, the parameters of which are assessed for stability using the CUSUM of squares test and recursive residuals plots. To examine the causal relationships in the long run, the Toda and Yamamoto test is applied. The cointegration tests show that the variables are cointegrated, while the Granger causality test shows that manufactured exports and disaggregated imports, together with the inputs of production, cause economic growth in the short run, which, in turn, leads to import growth. In the long run, the expansion of both primary and manufactured imports drives export diversification, whereas manufactured exports do not contribute to economic growth. These findings are very important for Kuwait’s policymakers to consider in their plans to implement Kuwait Vision 2035 as overseas demand for oil wanes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".