OPPORTUNITY IN MOTION: HOW EDUCATIONAL MOBILITY SHAPES DEVELOPMENT WORLDWIDE
Bibliographic record
Abstract
This study investigates the mechanism of how per capita GDP impacts intergenerational mobility in education. We propose an analytic framework in which per capita GDP affects educational mobility through government spending on education and other channels. Following this framework, this study conducts five-round estimations to examine the connections among per capita GDP, educational mobility, and government expenditure on education, using multiple data sources. The estimations demonstrate the following findings: (1) there is a positive non-linear relationship between per capita GDP and educational mobility, with higher disparities in less developed countries. This suggests that other factors, such as social arrangements, mediate the relationship. (2) Government expenditure on education is positively associated with intergenerational mobility in education. However, the effectiveness of government expenditure on education varies, particularly in developing countries. (3) The Granger causality test indicates a relationship between per capita GDP and Government expenditure on education for a short term (2-7 years), although a bidirectional relationship emerges between these variables in the longer term of 8-12 years. Government expenditure on education is more responsive to per capita GDP in developed countries than in less developed countries. (4) Through 2SLS estimations, two paths from per capita GDP to educational mobility are identified: One through increased average schooling and another through direct policy interventions. These paths highlight the importance of both economic development and targeted educational policies in enhancing educational mobility. In addition, the study suggests that higher educational mobility can lead to economic growth, though identifying the precise causal mechanisms remains challenging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".