Revisiting the relationship between ability and sheepskin effects of schooling on individual earnings: The case of Pakistan
Bibliographic record
Abstract
Significant amount of recent research continues to produce evidence in support of the presence of sheepskin effects in returns to schooling both for developed and developing countries. However, researchers have not made many attempts to identify or empirically test the possible mechanisms that may generate such effects. A few noteworthy exceptions are Flores-Lagunes and Light (2010) for the U. S., Riddle (2008) for Canada and Shabbir & Ashraf (2011) and Shabbir (2013) for Pakistan. Shabbir and Ashraf (2011) summarily reports that the sheepskin effects for rural Pakistan persist in the face of controls for measures of innate and cognitive ability. The present paper revisits this issue and adds value by presenting and discussing all of the relevant empirical estimates in full detail. Further, the present analysis fully updates the review of the literature as well as the various aspects of the pertinent debate surrounding the nature of the sheepskin effects. This study reconfirms that significant sheepskin effects exist for rural Pakistan for diplomas obtained by completing primary, high school and perhaps also FA and BA levels. Further, according to the detailed empirical regression results presented and discussed in this paper, the sheepskin effects prove to be robust both to an inclusion of measure of innate ability (Raven Progressive Matrices) and „cognitive‟ ability (specially administrated tests of literacy and numeracy). This implies that sheepskin effects „signal‟ individual characteristics unrelated to these measures of ability. The findings have significant policy implications about the nature of the private vs. social returns to schooling
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".