Does Income Inequality Lead to Education Inequality? A Cross Sectional Study of Pakistan
Bibliographic record
Abstract
When firms do not know which labor is capable of efficient work, then paying all employees their average product as wage seems a feasible option. This simplest of ways discourages good workers and makes bad workers costly. Spence proposed to use educational attainment as the indicator of the labor force's capability to solve this problem. Since workers are randomly distributed in terms of their ability, Akerlof would lead us to believe that the level of educational attainment should be proportional to the individual's ability, which is not valid, practically. This study strives to find the determinants of educational inequality, where income inequality of the household is the prime suspect, and other indicators include gender, household size, and age. GMM instrumental variable approach was used to study the effect of income inequality on educational inequality. The results showed that it is income inequality, which restricts people from attaining higher education.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".