The Impact of Linguistic Distance from English on Economic Growth: A Cross-Country Analysis
Bibliographic record
Abstract
Increasing levels of globalization have contributed to English becoming a preferred language of international communication, education and trade. With countries rapidly embedding English into a large variety of curricula, the demand for English as a medium of instruction is ever growing. Using information from the US Foreign Service Institute's Professional Working Proficiency values, we identify seven distinct linguistic distance from English (LDE) categories, and examine the effects of linguistic distance on income and economic growth across 97 countries over the 1980-2018 period. In addition to the direct effects of linguistic distance on incomes and growth, we further explore its impact through the channels of education and trade. Our results demonstrate that linguistic distance from English affects income levels and growth non-linearly across countries- increased distance from English does not necessarily translate into declining levels of income or growth rates. Conversely, we find that, via the trade channel, more distant languages tend to experience positive rates of growth. Controlling for regional effects or the existence of multiple languages of instruction do not alter our findings. Sub-dividing our dataset by national income levels, we show that the largest negative effects on growth are among Upper-Middle-Income countries.
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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) | 0.001 | 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".