Perspectives for export-oriented industrial policy strategies for selected African countries: Case studies Côte d'Ivoire, Ghana and Tunisia
Bibliographic record
Abstract
The European Union (EU) has recently concluded or is currently in the process of negotiating a number of bilateral free trade agreements with both industrialized countries, e.g. Canada and Japan, and developing as well as emerging economies. Negotiations with the latter group include inter alia the Mercosur countries, Vietnam, as well as the African countries of the African, Caribbean and Pacific (ACP) group and Tunisia. Negotiations on the EU-Tunisia Deep and Comprehensive Free Trade Agreement (DCFTA) were launched in 2015 and are still ongoing. Trade agreements between advanced and developing countries like those negotiated by the European Union and the ACP countries as well as with Tunisia, respectively, pose both opportunities and threats to the partner countries involved. While results from model-based economic impact assessments typically indicate that the macroeconomic effects of such agreements tend to be small, the long-term effects depend on the structural change triggered by the agreements. It is a well-known contention in the development economics literature that the marked differences in economic capacities and capabilities between advanced and developing economies pose particular problems that need to be dealt with, if longer-term impediments to late economic development are to be avoided. Thus, in this report, the focus is directed towards discussing the challenges of productive development as well as of export promotion in selected EU partner countries and export sectors in the context of trade liberalisation. To this end, four export sectors in three African countries are analysed, namely (i) the cocoa and mango sectors in Côte d'Ivoire and Ghana, and (ii) the olive oil and the textile and apparel sectors in Tunisia. With the exception of the mango sector, the other sectors - cocoa, olive oil and textile and apparel - are well-established sectors in the respective countries. Attempts to functionally upgrade into processing for the agriculture-based cocoa and olive oil sectors are however recent, while important consolidation processes have been under way in the context of increased global competition and political instability in the context of the Arab Spring in the textile and apparel sector in Tunisia. Mango exports have only recently become a thriving export sector in Côte d'Ivoire and Ghana and have profited from strong demand growth in advanced and increasingly also emerging countries. All sector case studies are located in African countries, which figure among the prioritized partner countries for EU development cooperation. For each scrutinized sector, key policy recommendations for upgrading and diversification into higher value-added products are proposed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".