How Would One Extra Year of High School Affect Wages? Evidence from a Unique Policy Change
Bibliographic record
Abstract
This paper uses a unique policy change in Canada’s most populous province, Ontario, to provide direct evidence on the effect of reducing the length of high school on labour market outcomes for high school graduates. In 1999, the Ontario government eliminated the fifth year of education from its high schools, and mandated a new four-year program. This policy change created two cohorts of students who graduated from high school together with different amounts of education, thus making it possible to identify the effect of one extra year of high school education on earnings. Using restricted survey data, the results demonstrate that students who receive one less year of high school education receive wages that are approximately ten percent lower than their counterparts one year after graduation, and these effects persist two years after graduation. Using birth year to instrument for educational attainment produces estimates that are even higher than the cross-sectional findings, but quite consistent with the existing literature on the return to education. These results are a significant contribution to the literature on the return to education because unlike prior changes to the educational system, this change in schooling laws results in two cohorts entering the labour market simultaneously. As such, business cycle effects do not confound the results.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".