Examining the Impact of ICT Development Level in Addis Ababa, Ethiopia, on China- Ethiopia Bilateral Trade
Bibliographic record
Abstract
Purpose – This study examines the impact of Information and Communication Technology (ICT) development in Addis Ababa, Ethiopia, on China-Ethiopia bilateral trade, a crucial aspect of their growing economic partnership. Rapid advancements in ICT are reshaping global trade dynamics, particularly impacting developing nations like Ethiopia. The research integrates key ICT indicators, including mobile and broadband penetration, internet usage, and e-government initiatives, with trade metrics such as export volumes, trade growth rates, and bilateral trade indices.Design/methodology/approach – Employing a mixed-methods approach, this study combines quantitative analyses using international (ITU, World Bank) and national (Ethiopian reports) databases with qualitative insights from primary surveys and stakeholder interviews across trade and ICT sectors. Analytical methods incorporate descriptive statistics to evaluate trends, correlation analysis to assess the relationships between ICT indicators and trade metrics, and regression models to establish causality. A fixed-effects model accounted for unobserved heterogeneity, ensuring the findings' robustness.Findings – Results indicate that ICT development —especially broadband penetration and internet usage—significantly boosts trade volumes, with broadband access showing the highest impact. The exponential growth in Ethiopia’s ICT sector over the past decade correlates strongly with rising trade levels—confirming ICT’s critical role in enhancing connectivity, reducing transaction costs, and facilitating market access.Originality/value – This research contributes to the theoretical and practical understanding of ICT’s role in facilitating trade, offering specific policy recommendations to leverage technology in strengthening Ethiopia’s trade with China. The findings underscore ICT’s transformative potential in bridging economic disparities and fostering sustainable trade relations in the digital era.
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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".