Do Exports of Services Lengthen the Duration of Export of Goods? Evidence from Kenya
Bibliographic record
Abstract
This study investigates the effect of service exports disaggregated into nine categories (transport, travel, construction, government, ICT, personal, cultural and recreational, financial, insurance, and other business services) on the survival of goods exports in Kenya. Estimates from the discrete-time probit model with random effects on bilateral trade data covering 1996 to 2019 show that export of services positively and significantly impact the duration of goods exports. A disaggregation of the goods into intermediate, consumption, capital, differentiated and homogenous products shows that transport and ICT service exports increase the duration of export of goods. Therefore, the simultaneous advancement of goods and service exports would foster the export survival of goods. In particular, the results indicate that export survival of value chain commodities would be enhanced through enhancing modes of transportation alongside communication and connectivity infrastructure. Our results also have implications for the African Continental Free Trade Area (AfCFTA) which seeks to synchronously negotiate and promote trade in goods and trade in services within Africa.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".