The Impact of Globalisation on Rising Earnings Inequality: Empirical Evidence from OECD Countries
Bibliographic record
Abstract
This paper examines the distributive consequences of economic globalisation in 23 OECD countries over the past quarter century, taking into account the multi-faceted dimensions of globalisation and controlling for influence from other concurrent trends, in particular technological progress and changes in labour market institutions. The paper also identifies the relevant pathways between globalisation and earning inequality among the whole working-age population by accounting for both rising wage dispersion among workers and widening earnings gaps between employed and non-employed. The results show that financial deepening (through increased foreign investment) is a key driver behind the upward trend of income inequality; it transmitted inequality through both raising wage dispersion and reducing employment rates. Trade integration in general exerted little distributional effect. Technical advancement also contributed to widening the earnings distribution among the whole working-age population, while the growth in the supply of skilled workers provided a sizable counterweight to increasing inequality. Moreover, the declining strength of labour market institutions tended to be distributional neutral overall, as the (increasing) employment effect
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".