Mobile and internet usage, institutions and the trade balance: Evidence from African countries
Bibliographic record
Abstract
Abstract This study examines the influences of institutions, the Internet and mobile usage on the trade balance of African countries between 2003 and 2017. Our empirical results have been estimated with a panel‐corrected standard error method (PSCE) and they have been confirmed by several alternative techniques. First, the increase of internet usage and mobile usage has a significant negative effect on total and inter‐continental trade balances while these factors improve the intra‐African trade balances. Second, better institutions appear to have a negative impact on the total‐, inter‐, and intra‐African trade balances – in other words, better institutions appear to stimulate imports rather than exports. This observation explains the decreasing trends in the current account balances of African countries. Third, the combined effect of the three factors (institutions, internet, and mobile use together) has a significant positive impact on all trade balances: total‐, inter‐, and intra‐continental. Our study shows that an improvement in institutional quality acts as a mitigating factor for any negative impact internet\mobile development might cause on the trade balances of African countries. Further, our analysis examines the influence of institutions, internet usage, and mobile usage on the two parts of the trade: exports and imports. We observe that internet and mobile can influence negatively and differently impact the two wings of the balance trade. However, all improvements in institutions and their associations with internet usage and mobile usage have a significant positive impact on the trade balance especially on exporting activities of African countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".