Spatial wage inequality in North America and Western Europe: changes between and within local labour markets 1975-2019
Why this work is in the frame
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Bibliographic record
Abstract
The rise of economic inequalities in advanced economies has been often linked with the growth of spatial inequalities within countries, yet there is limited comparative research that studies the relationship between national and subnational economic inequality. This paper presents the first systematic attempt to create internationally comparable evidence showing how different countries perform in terms of geographic wage inequalities. We create cross-country comparable measures of spatial wage disparities between and within similarly-defined local labour market areas (LLMAs) for Canada, France, (West) Germany, the UK and the US since the 1970s, and assess their contribution to national inequality. By the end of the 2010s, spatial inequalities in LLMA mean wages are similar in Canada, France, Germany and the UK; the US exhibits the highest degree of spatial inequality. Over the study period, spatial inequalities have nearly doubled in all countries, except for France where spatial inequalities have fallen back to 1970s levels. Due to a concomitant increase in within-place inequality, the contribution of places in explaining national wage inequality has remained fairly constant over the 40-year study period, except in the UK where we document a significant increase. Whilst common global social, economic and technological shocks are important drivers of spatial inequality, this variation in levels and trends of spatial inequality opens the way to comparative research exploring the role of national institutions in mediating how global shocks translate into economic disparities between places.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it