Global Value Chains, Trade and Structural Transformation in Developing Countries
Bibliographic record
Abstract
The study explores the role of global value chains (GVCs) – related trade and conventional trade in the structural transformation of resource-rich and non-resource-rich developing countries. Using data from various sources the study adopts panel data fixed effects estimation to achieve its objectives. The results show that the share of domestic value added in gross GVC-related exports and conventional trade have the tendency to aggravate employment and value addition respectively in the agricultural sector of Non-Resource-Rich Countries (NRRCs). In Resource Rich Countries (RRCs), the findings show that conventional trade has negative and significant impact on value-added in manufacturing while the share of foreign value added in gross GVC-related trade reports positive and significant impact on share of labour employment in services but not on the value added in the sub-sector. Thus, developing countries must be willing to pay the huge price for correcting their structural imbalances by increasing investments in the development of domestic capabilities and infrastructure which are important ingredients for the development of GVCs and trade. Hence, the study contributes to the literature on the effect GVCs could have on structural transformation particularly in developing countries in terms of the performance of the economic sub-sectors and their employment shares. The sub-sample approach gives unique evidence on the subject as it relates to the natural resource endowments in developing countries.
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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 | 0.000 | 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".