Bibliographic record
Abstract
Although Kenya was once viewed as being among the African countries with the most favorable growth prospects, the last two decades witnessed significant declines in many measures of economic performance and social standards. As a result, Kenya’s share of world trade is now less than one-half its average level in the early-1980s. How can Kenya halt, and then reverse, these negative trends? While the answer to this question has multiple dimensions, trade policy certainly can play an important positive role. This report examines reasons why Kenya and other African trade did not provide the “engine of growth” that it did elsewhere. During the last quarter century many Sub-Saharan African countries non-oil exports either declined in absolute terms, or expanded at a slower pace than world trade. Evidence suggests African countries, including Kenya, experienced serious supply constraints that limited their ability to capitalize on opportunities of international production sharing in foreign markets. Inappropriate governance policies, and a general unfavorable commercial environment, were largely responsible for Africa’s supply problems. In addition, a decomposition of recent trade changes into supply and demand factors shows that, with two important exceptions (cut flowers and apparel exports) Kenya experienced a general erosion of its US and EU import market shares. These findings support the conclusion of the recent Africa Competitiveness Report that concludes Kenya is at a competitive disadvantage vis-a-vis more than one half the other African countries surveyed. Finally, the authors suggest that the diversifications of Kenya’s exports away from traditional products must have a very high priority in practice. For the developments in regional markets, it would be more advantageous for Kenya to pursue the trade liberalization on an MFN basis, rather than through the exchange of regional preferences. Kenya also need to set itself up to attract private investment, and that means a clean regulatory environment, a judicial system that works, proper police enforcement and corporate law, capacity building, and development and maintenance of infrastructure necessary to support manufacturing activity. The Africa Region Working Paper Series expedites dissemination of applied research and policy studies with potential for improving economic performance and social conditions in Sub-Saharan Africa. The series publishes papers at preliminary stages to stimulate timely discussions within the Region and among client countries, donors, and the policy research community. The editorial board for the series consists of representatives from professional families appointed by the Region’s Sector Directors. For additional information, please contact Momar Gueye, (82220), Email: mgueye@worldbank.org or visit the Web Site: http://www.worldbank.org/afr/wps/index.htm. The findings, interpretations, and conclusions in this paper are those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries that they represent and should not be attributed to them. Authors’Affiliation and Sponsorship Francis Ng, Economist, Trade Team of Development Research Group, The World Bank fng@worldbank.org Alexander Yeats Consultant, Trade Team and Africa Region, The World Bank ayeats@msn.com
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".