Political determinants of COVID-19 restrictions and vaccine rollouts: The case of regional elections in Italy and Spain
Bibliographic record
Abstract
The COVID-19 pandemic is one of the most significant public health crises in modern history, with considerable impacts on the policy frameworks of national governments. In response to the pandemic, non-pharmaceutical interventions (NPIs) and mass vaccination campaigns have been employed to protect vulnerable groups. Through the lens of Political Budget Cycle (PBC) theory, this study explores the interplay between incumbent electoral concerns and political dynamics in influencing the implementation of NPIs and vaccination rollout within the administrative regions of Italy and Spain during the period spanning June 2020 to July 2021. The results reveal that incumbents up for the next scheduled election are 5.8 % more likely to increase the stringency of containment measures than those that face a term limit. The findings also demonstrate that the seats of the incumbent and coalition parties in parliament and the number of parties in the coalition have a negative effect on both the efficiency of the vaccination rollout and the stringency of NPIs. Additionally, the competitiveness of the election emerges as an important predictor of the strictness of NPIs. Therefore, our results suggest that incumbents may strategically manipulate COVID-19 policy measures to optimize electoral outcomes. The study underscores the substantive influence of political incentives, competitive electoral environments, and government coalitions on policy formulation during health emergencies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".