Economic and political inequality and the quality of public goods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it