Schooling and entrepreneurship: Evidence from a regression discontinuity design
Bibliographic record
Abstract
Does an additional year of formal education affect the decision to become an entrepreneur? Using human capital theory as a conceptual lens, we explore three channels through which it might: productivity, certification, and health impacts. To test the mechanisms and uncover whether there are causal relationships between these variables, we exploit two exogenous changes to British compulsory schooling laws that generated sharp across-cohort differences in years of education. Using a fuzzy regression discontinuity design, we estimate that the reforms significantly reduced self-employment. We go on to explore which channels best explain this finding, and discuss implications for scholars and policymakers. • A Regression Discontinuity Design is used to obtain causal estimates of the relationship between high school education and adult entrepreneurship. • The study exploits as a natural experiment legal reforms to the national minimum school leaving age in Britain. • Estimates indicate a negative relationship between an additional year of schooling and engagement in self-employment. • The findings cast doubt on the notion that the additional schooling affected self-employment through productivity or credentialing channels. • Policy implications might be most applicable to developing countries where governments are exploring whether to raise school leaving ages.
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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 | 0.038 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".