Making Policies Matter: Voter Responses to Campaign Promises
Bibliographic record
Abstract
Can campaign promises change voter behavior, even where clientelism and vote buying are pervasive?We elicit multidimensional campaign promises from political candidates in consecutive mayoral elections in the Philippines.Voters who are randomly informed about these promises rationally update their beliefs about candidates, along both policy and valence dimensions.Those who receive information about current promises are more likely to vote for candidates with policy promises closest to their own preferences.Those informed about current and past campaign promises reward incumbents who fulfilled their past promises; they perceive them to be more honest and competent.However, voters with clientelist ties to candidates respond weakly to campaign promises.A structural model allows us to disentangle information effects on beliefs and preferences by comparing actual incumbent vote shares with shares in counterfactual elections: both effects are substantial.Even in a clientelist democracy, counterfactual incumbent vote shares deviate more from actual shares when policy and valence play no role in campaigning than when vote-buying plays no role.Finally, a cost benefit analysis reveals that vote-buying is nevertheless more effective than information campaigns, explaining why candidates do not use them.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".