Credit Constraints and the Racial Gap in Post-Secondary Education in South Africa
Bibliographic record
Abstract
This paper analyzes the impact of high school household income and scholastic ability on post-secondary enrollment in South Africa. Using longitudinal data from the Cape Area Panel Study (CAPS), we analyze the large racial gaps in the proportion of high school graduates who enroll in university and other forms of post-secondary education. Our results indicate that family background and high school achievement (measured by a literacy and numeracy exam and performance on the grade 12 matriculation exam) are strong predictors of post-secondary enrollment and statistically account for all of the black-white difference in enrollment. Controlling for parental education and baseline scholastic ability reduces the estimated impact of household income on university enrollment, though there continues to be an effect at the top of the income distribution. We also find evidence of credit constraints on non-university forms of post-secondary enrollment. Counterfactual estimates indicate that if all South Africans had the incomes of the richest whites, African university enrollment would increase by 65%, even without changing parental education or high school academic achievement. The racial gap in university enrollment would narrow only slightly, however as our results suggest that this gap in postsecondary enrollment results mainly from the large racial gap in high school academic achievement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".