A Border Strategy Analysis of Ad Source and Message Tone in Senatorial Campaigns
Bibliographic record
Abstract
Political advertising is controversial, as there is widespread concern about money from political action committees (PACs and super PACs) distorting the democratic process. Studying advertising effectiveness is, however, a challenging topic for several reasons, including the endogenous nature of fundraising and ad spending rates. However, the extensive use of targeting based on designated marketing areas (DMAs) creates a setting in which neighboring counties with comparable demographics receive different levels of advertising exposure. In this paper, we leverage these advertising discontinuities along DMA borders to study the relative effectiveness of political advertising on vote shares and turnout rates in 2010 and 2012 senatorial elections. We find that negative advertising sponsored by PACs is significantly less effective than that sponsored by candidates in affecting two-party vote shares and voter turnout. A 1% increase in negative advertising by the candidate produces a significant 0.015% lift in the candidate’s unconditional vote shares. By contrast, negative advertising from PACs is ineffective in increasing its supported candidate’s unconditional vote share. Further analysis reveals that the competitiveness of races moderates the effectiveness of political advertising, providing implications for those managing candidates’ campaigns, PACs, and super PACs. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1079 .
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".