Inequality and Taxation: Evidence from the Americas on how Inequality may Influence Tax Institutions
Bibliographic record
Abstract
Tax scholars generally focus on how taxation influences inequality. In this article, we examine how inequality may influence the design and implementation of tax systems. We focus on the societies of the Americas over the 19th and 20th centuries to see how and why institutions of taxation differ across and within countries, and how they evolve over time. We examine North America and Latin America for two major reasons. First, despite the region having the most extreme inequality in the world, the tax structures of Latin America are generally recognized as among the most regressive in the world, even by developing country standards. Second, the colonization and development of the Americas constitute a natural experiment of sorts that students of economic and social development can exploit. The different circumstances meant that largely exogenous differences existed across these societies, not only in national heritage, but also in the extent of inequality. Several salient patterns emerge. The United States and Canada (like Britain, France, Germany and even Spain) were much more inclined to tax wealth and income during their early stages of growth, and into the 20th century, than developing countries are today. Although the United States and Canadian federal governments were similar to those of their counterparts in Latin America in relying primarily on the taxation of foreign trade (overwhelmingly tariffs) and excise taxes, the greater success or inclination of state (provincial) and local governments in North America to tax wealth (primarily in the form of property or estate taxes) and income (primarily in the form of business taxes), as well as the much larger relative sizes of these sub-national governments in North America, accounted for a radical divergence in the overall structure of taxation. Tapping these progressive sources of government revenue, state and local governments in the United States and Canada, even before independence, began directing substantial resources toward public schools, improvements in infrastructure involving transportation and health, and other social programs. In contrast, the societies of Latin America, which had come to be characterized soon after initial settlement by rather extreme inequality in wealth, human capital, and political influence, tended to adopt tax structures that were significantly less progressive in incidence and manifested greater reluctance or inability to impose local taxes to fund local public investments and services. These patterns persisted well into the 20th century, indeed up to the present day.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".