An empirical analysis of vote buying among the poor
Bibliographic record
Abstract
Recent literature suggests that the poor are more likely to be targeted for vote buying and to sell their votes. However, there is limited empirical analysis on the patterns of vote buying among low-income voters. This paper attempts to fill this gap using a survey conducted in Metro Manila, Philippines after the 2016 elections. Data analysis shows that vote buying among the poor is indeed very common, but the incidence varies depending on the vote buying type. The most prevalent form uses more benign goods such as food and clothing, but offers of money is still reported by more than a quarter of respondents. Different vote-buying types also have different correlates, including some socio-economic factors, suggesting that it is a finely targeted activity. In addition, money vote buying is predominant in tight elections, but buying votes using non-monetary offers is more common when there is a clear winner even before the election. Most of those who were offered accepted the goods or money, but only about two-thirds voted for the candidate. In addition, evidence suggests that the good or money is not the deciding factor in voting for the candidate. This supports the premise that vote buying is just part of a bigger effort by politicians to build clientelism and patronage among his/her constituencies. Dependency and loyalty is merely punctuated by election-related transfers, rather than an effort to completely change votes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".