Financial Development and Income Inequality: Evidence from Latin America, 2001-2021
Bibliographic record
Abstract
Numerous studies delve into the theoretical frameworks on finance and inequality. However, there are too few empirical tests on its theoretical relations due to a lack of data to capture financial development. Additionally, due to the many social and economic dimensions of a large economy such as Brazil or Argentina, it is unrealistic to consider that labor market or political issues are the only culprits of income inequality. More research is needed to understand the dynamics of inequality. In this paper, we evaluate the influence of financial development on income inequality using nineteen countries in Latin America from 2001 to 2021. Two indicators of financial development are employed. First, I use the broader definition of money, M3, as a percentage of GDP to capture the liquid liabilities because M1 or M2 may be a poor proxy in economies with weak financial systems. Secondly, the ratio of credit to private sector to GDP is employed because financial intermediaries with higher volumes of credit are more involved in financial development, such as diversifying risk, saving mobilization, facilitating transactions, allocating funding to economic activities, and monitoring borrowers’ activities. Based on the GMM estimator, our empirical findings support that better-developed financial markets reduce inequality.
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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 | 0.005 |
| Science and technology studies | 0.000 | 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".