Sheepskin effects of investment in schooling: Do they signal family background? ; case of Pakistan
Bibliographic record
Abstract
Considerable recent research both for the developing as well as the developed countries has provided evidence for the existence of the sheepskin effect to the economic returns in schooling investment. However, there has not been much empirical work investigating the mechanism that may lie behind the observed sheepskin effects. The few notable exceptions that have started addressing this important yet neglected question of interpreting what do sheepskin effects signal include Flores-Lagunes and Light (2007) for the U. S., Riddell (2008) for Canada and Shabbir and Ashraf (2011) as well as Shabbir (2011) in the case of Pakistan. The present study was undertaken to examine the robustness of sheepskin effects in the face of measured family background in the case of Pakistan. The unique feature of this study is that it utilizes the only nationally representative data set available which allows for a test of sheepskin effects; in fact, Shabbir (1991) was the first of its kind study which used this data set to test (and establish) the existence of sheepskin effects in the case of Pakistan. The present study is an attempt to build on that research finding in order to explore the question of what do sheepskin effects signal? In particular, do they signal measured family background?The important empirical finding of this study is that there is strong evidence of significant sheepskin or diploma effects for all four important certification levels i.e. Matric, Intermediate, Bachelor of Arts (B.A.) and Master of Arts (M.A). Further, and more importantly in terms of the research question posed by this paper, these diploma effects are robust when measured family background effects are controlled for using such measures as father's education, father's income and mother's income. Thus the observed sheepskin effects may be signaling other aspects of ability or other relevant influences besides measured family background (including latent or unmeasured family influences) which keeps the all-important question of the mechanism underlying these observed sheepskin effects open and in need of future research.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".