The determinants of income inequality: a cross- country investigation
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
This thesis investigates recent cross-country patterns of income inequality. A unique dataset is constructed using World Bank and OECD data sources for a sample of 124 countries over the 1980 to 2010 period. Analyses of this dataset reveal three distinct sets of results. First, in terms of the spatial distribution of world income inequality, we find (i) pockets of high levels of inequality in South and Central America along with (ii) clusters of low inequality in Western European countries, particularly in Scandinavian countries. Over the period of study, significant increases in levels of inequality are registered for Russia and North America (especially in Canada and the US). Despite continued high levels of inequality in South America, inequality in the region appears to be receding, if only ever so slightly. Second, regression results support the contention that the Kuznets hypothesis is still relevant today. This finding is also robust to different measures of inequality. Finally, in terms of the determinants of cross-country patterns of inequality, estimates from panel regression models reveal that not one but multiple factors are driving cross-country patterns of income inequality. The level of economic development, age dependency, public sector expenditures and manufacturing activity are all identified as key determinants of international income inequality.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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