Economic and political inequality and the quality of public goods
Bibliographic record
Abstract
Purpose To formalize and test the hypotheses that economic and political inequality tend to lower the quality of public education and thereby the overall quality of education in developing countries. Design/methodology/approach The paper uses both international cross‐section data and panel data from almost 100 countries to test these hypothesized effects of the two types of inequality on educational quality. Three different indicators of school quality, all at the primary level, are used. The paper tests the robustness of the findings to different estimation methods, specifications and the use of instruments for a potentially endogenous variable. Findings There is clear empirical support for the hypothesized negative effects of political inequality and ethnic fragmentation on educational quality. The evidence for the hypothesized effect of income inequality, however, is very weak at best. Research limitations/implications The educational quality measures are crude and the analysis is at the country level. Future work can use more direct, achievement‐based measures of quality and data at the district or county levels. Practical implications Redistribution of income and democratization can have beneficial effects on educational quality. Originality/value The paper provides a theoretical model that formalizes the hypothesis that economic and political inequality can lower the quality of public education and thereby the overall quality of education. It empirically tests this model using panel and cross‐sectional data.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".