Does Education Improve Citizenship? Evidence from the U.S. and the U.K.
Bibliographic record
Abstract
Many economists and educators of diverse political beliefs favor public support for education on the premise that a more educated electorate enhances the quality of democracy. While some earlier studies document an association between schooling and citizenship, little attempt has been made to address the possibility that unobservable characteristics of citizens underlie this relationship. This paper explores the effect of extra schooling induced through compulsory schooling laws on the likelihood of becoming politically involved in the US and the UK. We find that educational attainment is related to several measures of political interest and involvement in both countries. For voter turnout, we find a strong and robust relationship between education and voting for the US, but not for the UK. Using the information on validated voting, we find that misreporting of voter status can not explain our estimates. Our results suggest that the observed drop in voter turnout in the US from 1964 to 2000 would have been 10.4 to 12.3 percentage points greater if high school attainment had stayed at 1964 rates, holding all else constant. However, when we condition on registration, our US results approach the UK findings. This may indicate that registration rules present a barrier to low-educated citizens' participation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".