Corruption and Turnout in Presidential Elections: A Macro‐Level Quantitative Analysis
Bibliographic record
Abstract
This article tests the impact of corruption on electoral turnout for over 200 elections from over 70 presidential systems conducted between 1990 and 2011. Differentiating among three corruption indicators (i.e., the International Country Risk Guide corruption indicator, the Transparency International Corruption Perceptions Index, and the World Bank Control of Corruption measure), I evaluate corruption's precise impact on electoral participation in a pooled time series framework. Controlling for compulsory voting, semi‐presidentialism, regime type, development, political culture, the closeness of the election, and state size, my results are nuanced. I find that corruption more narrowly defined as political corruption stifles turnout, whereas a rather broad definition of corruption, which includes societal and financial corruption, has no impact on macro‐level turnout. Finally, I discover that the interactive impact of corruption on other variables in the turnout function is rather limited. Related Articles Kostadinova , Tatiana 2009 . “.” Politics & Policy 37 (): 691 ‐ 714 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2009.00194.x/abstract Caillier , James . 2010 . “.” Politics & Policy 38 (): 1015 ‐ 1035 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2010.00267.x/abstract Lagunes , Paul F. 2012 . “.” Politics & Policy 40 (): 802 ‐ 826 . http://onlinelibrary.wiley.com/doi/10.1111/j.1747‐1346.2012.00384.x/abstract
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".