Financialization More Than Globalization!:The Contribution of Global Cities to Inequalities
Bibliographic record
Abstract
Global cities contribute to earnings inequality by concentrating high-paying jobs. But how much and why? In this paper, we quantify the contribution of global cities to earnings inequality and examine whether this contribution is attributable to the financialization of cities or their coordinating role in the global trade of goods and non-financial services. Using administrative linked employer-employee earnings data in nine advanced capitalist democracies and published Internal Revenue Service tables for the United States between 1989 and 2019, we show that global cities account for a substantial portion of national and regional increases in inequality. This contribution to inequality is far greater in financial cities than in the most comparable non-financial cities. In addition, the divergence in pay levels between financial and comparison cities increases with the financialization of respective countries. Our evidence thus shows that the contribution of global cities to inequality is driven more by the concentration of financiers than by other functions of global economic coordination.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".