Ability and Preferences as Key Determinants of the Socio-Economic Gradient in Dropping out of High School
Bibliographic record
Abstract
What factors influence a youth’s decision to drop out of high-school? What is the relative importance of circumstances beyond the control of youths? Is the final decision to drop out the result of permanent family preferences and incomplete information about returns? Or rather the rational reflection of lower expected returns associated to lower ability? This paper uses Canadian micro-level data from the Youth in Transition Survey to examine the channels through which family socio-economic status and unobservable characteristics affect children’s decisions to drop out of high school. First, we document the strength of observable socio-economic factors: our data suggest that teenage boys with two parents who are themselves high school drop outs have a 14 % chance of dropping out, compared to a drop-out rate of approximately 1 % for boys whose parents both have a university degree. We provide reduced form evidence that parental hopes and expectations for their children’s education substantially reduce the family socio-economic gradient in the drop-out decision. For young males, simply controlling for parental hopes, and reading skills halves the gap in drop-out probability between a child with two drop-out parents and a child whose parents both have a BA. For young females, the inclusion of these variables similarly reduces the socio-economic
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".