Bibliographic record
Abstract
This article uses data on more than 1,000 political leaders between 1875 and 2004 to investigate whether having a more educated leader affects the rate of economic growth. We use an expanded set of random leadership transitions because of natural death or terminal illness to show, following an earlier paper by Jones and Olken (2005), that leaders matter for growth. We then provide evidence supporting the view that heterogeneity among leaders ’ educational attainment is important with growth being higher by having leaders who are more highly educated. One of the most robust findings in empirical research is the importance of education in explaining economic outcomes. There is overwhelming evidence that education affects earnings – see, for instance, the summary in Card (1997). It has also been shown that education has an impact on charitable giving and other measures of citizenship – see, for example, Dee (2004) and Milligan et al. (2004). This article examines this issue in a new context. It investigates how the educational attainment of a political leader affects economic growth in a country during the leader’s tenure in office. The core dataset for his study is a sample of more than 1000 political leaders who have been in office between 1875 and 2004. We also use educational data for these
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".