Republican Support and Economic Hardship: The Enduring Effects of the Opioid Epidemic
Bibliographic record
Abstract
Abstract In this article, we establish a causal connection between two of the most salient social developments in the United States over the past decades: the opioid epidemic and the political realignment between the Republican and Democratic parties. Drawing on unsealed records from litigation against Purdue Pharma, we uncover rich geographic variation in the marketing of prescription opioids that serves as a quasi-exogenous source of exposure to the epidemic. We use this variation to document significant increases in drug-related mortality and greater reliance on public transfer programs. This induced economic hardship led to substantial changes in the political landscape of the communities most affected by the opioid epidemic. We estimate that from the mid-2000s to 2022, exposure to the opioid epidemic continuously increased the Republican vote share in House, presidential, and gubernatorial elections. By the 2022 House elections, a one-standard-deviation increase in our measure of exposure led to a 4.5 percentage point increase in the Republican vote share. From 2012 until 2022, this increase in the House vote share translated into Republicans winning additional seats.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".