Financialization and top incomes in emerging economies: A comparative distributional analysis of the financial wage premium in the BRIC
Bibliographic record
Abstract
Prior studies on emerging economies contend that increasing returns to human capital has contributed to the growth of wage inequality over the last few decades. However, this explanation fails to account for an important dynamic of contemporary wage inequality: the growth of top labor incomes. Research on advanced economies show the emergence of a wage premium in the financial sector increased top labor incomes, but studies have yet to investigate whether a financial wage premium is contributing to the growth of top labor incomes in emerging economies. The present study addresses this theoretical and empirical gap by conceptualizing and measuring the financial wage premium across the distributions of labor income in the most important subset of emerging economies: Brazil, Russia, India, and China (BRIC). Drawing on harmonized labor force data from the Luxembourg Income Study, we utilize unconditional quantile regression modeling and treatment effect estimation to examine the financial wage premium across the distributions of labor income in the BRIC before and after the Great Recession. Consistent with studies on advanced economies, we find a substantial wage premium among top earners in the financial sectors of the BRIC, which has grew in the post-recession period. However, we find significant variation in size and growth of the financial wage premium because of the variegated nature of financialization across the BRIC. We conclude by suggesting that subsequent studies should explore the heterogeneous effects of subordinate and state financialization on wage dynamics in emerging economies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".