INSTITUTIONS, INEQUALITY, AND LONG-TERM DEVELOPMENT: A PERSPECTIVE FROM BRAZILIAN REGIONS
Bibliographic record
Abstract
In this paper, we present evidence on the relationship between inequality and long-term development using data on different Brazilian regions. A new framework of analysis is provided in the sense that our empirical approach is developed within a constant de jure institutional environment - Brazil - accounting for possible differences in the de facto institutional environments (Brazilian regions) rooted in distinct colonial experiences within the same national territory. New inequality indicators are constructed from scratch for Brazilian municipalities in 1920 (using the Census of 1920, which, surprisingly, had thus far been ignored for such purposes). We find no significant relationship between economic (land) inequality (proxied by the Land Gini) and political concentration (proxied by the percentage of eligible voters) for Brazilian municipalities in the early twentieth century. And although our econometric analysis indicates a positive robust relationship between economic inequality and long-term development indicators for Southeastern states (São Paulo, the center of coffee production in the nineteenth and twentieth centuries and a state with a large influx of European immigrants, which became the most dynamic Brazilian region; and Minas Gerais, the gold cycle region, shaped also by cattle-farming and coffee production), we find no relationship for Pernambuco, a state in the Northeast region representative of the old agrarian structure of colonial sugar plantations; and a positive and robust relationship for Rio Grande do Sul, a Southern state with a colonial experience more similar to that of the United States and Canada. We found no evidence of a robust relationship between the percentage of eligible voters and long-term development, a surprising result in light of the results provided in development literature, but likely consistent with a politically captured system with very low levels of enfranchisement. These results are shown to hold even when controlling for proxies for structural changes that happened in this time span, namely: urbanization, industrialization, and immigration. Moreover, land inequality in 1920 is at most weakly related to contemporaneous income inequality for Minas Gerais and São Paulo, but significant for Pernambuco and Rio Grande do Sul. In other words, evidence suggests that the positive effects of inequality are associated to a particular structural organization at a specific time, in contrast to a more structural inequality, which, as exemplified by the cases of Pernambuco and Rio Grande do Sul, would have negative or no significant effects on long-term development. Finally, we find no robust relationship between the overall land Gini and long-term economic development. These results highlight the importance of the study of historical and social elements in their respective context, as the results are consistent with the picture of a rural Brazil dominated by agrarian elites within a complex institutional environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".