Globalization, Inequality and the Rich Countries of the G-20: Evidence from the Luxembourg Income Study
Bibliographic record
Abstract
The purpose of this study is to summarize and comment upon what we know about the determinants of both the level and trend in economic inequality over the past two decades, and to relate these findings to the progress of globalization in these nations. While the fruits of economic progress in rich nations have not been equally spread, we argue that most citizens in rich Organization for Economic Cooperation and Development (OECD) nations have benefited from the trend toward global economic progress. We begin with a summary of the differences in overall economic inequality within the G-20 nations based on LIS (Luxembourg Income Study) data and recent work by others. Here we find that social policies, wage distributions, time worked, social and labor market institutions and demographic differences all have some influence on why there are large differences in inequality among rich nations at any point in time. In contrast, trade policy has not been shown to have any major impact on economic inequality. Next, we turn to trends in inequality. We find modest and sometimes dissimilar changes in the distribution of income have taken place within most advanced nations, with most finding a higher level of inequality in the mid-to-late 1990s than in the 1980s. Inequality, however, has not risen markedly in some nations (e.g., Denmark, Germany, France, and Canada) over this period, while its rise has slowed in several other nations during the late 1990s. The explanations for rising inequality in rich countries are many, and no one single set of explanations is ultimately convincing. In particular, there is no evidence that we know of that trade and globalization is bad for rich countries. This suggests that rising economic inequality is not inevitable, or that it necessarily hurts low skill-low income families. Rather it suggests that globalization does not force any single outcome on any country. Domestic policies and institutions still have large effects on the level and trend of inequality within rich and middle-income nations, even in a globalizing world economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.234 | 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".