Estimating the Effect of the Internet on International Trade in Services
Bibliographic record
Abstract
This paper assesses the relationship between the Internet and international trade in services. While there are similarities and discriminating differences between trade in services and goods, it is widely believed that the recent rapid internet penetration has benefitted trade in services more than trade in goods. The study carries out an empirical assessment of the contribution of the internet to services export and import for a total of 63 developed and developing countries over the period of 2000-2014. As most explanatory variables are likely to be jointly endogenous with services export and import, we run GMM regressions developed for dynamic panel data. Our results are, in general, consistent with the previous findings that growth in internet users and GDP as well as measures of trade openness all has positive impact on services export and import. For instance, a 1% increase in internet users in the partner countries leads to 0.27% and 0.08% increase in services export and import, respectively, in the combined group of reporting countries. The impact of internet on services export appear larger for developed countries, 0.52%, and insignificant for developing countries. The estimated coefficients of population appear significant while carry unexpected signs. Finally, the real effective exchange rate is significant for the services import only.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".