Globalization and inequality: insights from municipal level data in Brazil
Bibliographic record
Abstract
Purpose The relationship between globalization – through trade liberalization – and inequality is unclear. The Stolper‐Samuelson theorem, which is a standard result in trade theory, does not offer compelling answers as globalized economies with an abundance of unskilled labour have seen inequality both worsen, as in China and much of Asia, and improve, as in Latin America. Kuznets' classic model also finds scant confirmation in increasingly open economies, with growth associated with declining inequality in poorer Latin America, and with rising inequality in richer OECD countries. The authors aim to suggest that the key to those anomalies lies in the relative weight of industrialization in a country's growth mix. Design/methodology/approach Using census data (for 1991 and 2000) for more than 5,000 municipalities, the authors examine the relationship between income per capita and inequality in Brazil. Findings The authors uncover the existence of an “inverted‐U” relationship in 1991 that flipped into a “straight‐U” relationship in 2000, both of which are statistically significant. They argue that the flip results from the association of economic growth with de‐industrialization that is driven by globalization. Research limitations/implications In terms of future work, there is a need to examine further the role of de‐industrialization, not only in the case of Brazil but also other emerging economies with different patterns of inequality than the ones currently observed in Latin America and Brazil in particular. Practical implications The authors' result reinforces the growing skepticism towards the role of industrialization in economic development, as Brazil sees its most successful period of pro‐poor growth go hand in hand with its de‐industrialization. Social implications The authors' result casts doubts about the role of social policy in the current evolution of inequality and poverty in Brazil. The famous Bolsa Familia program, in particular, may have been exaggerated by both the Brazilian government and social policy specialists, as much of the change could be traced to changes in the structure of the economy itself. Originality/value This paper contributes to the existing literature on globalization and inequality. It uses municipal level data and identifies a “flip” in the Kuznets relationship. This enables us to make sense of growing inequality in poorer but industrializing economies and in rich ones going through processes of de‐industrialization, and also of declining inequality in poorer de‐industrializing countries such as Brazil.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".