Republican Support and Economic Hardship: Arteaga & Barone (2026)

What this is. A distilled skeleton of Arteaga and Barone (2026). Read the original paper to replicate or extend; this page records headline results with source locators, the estimating equations, and the data used, extracted by an LLM and not human-verified.

TL;DR

Arteaga and Barone use unsealed Purdue Pharma litigation records to show that the pharmaceutical company targeted OxyContin marketing at communities with high cancer mortality in 1996, creating quasi-exogenous geographic variation in opioid exposure. This exposure caused persistent increases in drug-induced mortality, disability, and food-stamp receipt. By 2022, a one standard deviation higher 1996 cancer mortality rate raised the Republican two-party House vote share by 4.5 percentage points. The political shift was not anti-incumbent, was not confined to any demographic group, and was concentrated in communities where economic hardship was greatest. Conservative media covered the epidemic more extensively and framed it around crime and economic distress, themes aligned with the Republican Party’s messaging to white working-class America.

Core results

#	Result	Locator	Magnitude as reported
R1	Drug-induced mortality: 1 SD higher 1996 cancer mortality rate causes persistent rise in drug deaths	Fig. III, p. 523	46% above pre-epidemic average by 2017
R2	Prescription opioid deaths: 1 SD increase causes 61% rise by 2012	p. 523, Fig. III	61% higher than pre-epidemic average at 2012 peak
R3	SSDI applications: 1 SD increase causes 12% rise by 2012	Fig. IV, p. 524	+12% SSDI applications by 2012
R4	SSI applications: 1 SD increase causes 7.6% rise by 2012	Fig. IV, p. 524	+7.6% SSI applications by 2012
R5	SNAP receipt: 1 SD increase predicts 8% more recipients by 2022	Fig. IV, p. 524	+8% SNAP recipients (0.14 SD) by 2022
R6	Republican House vote share: 1 SD increase raises GOP share 4.5 pp in 2022	Fig. V, p. 526	+4.5 pp GOP two-party vote share in 2022 midterms
R7	Republican presidential vote share: 1 SD increase raises GOP share 4.6 pp	Fig. VII, p. 530	+4.6 pp GOP presidential vote share
R8	Republican gubernatorial vote share: 1 SD increase raises GOP share 4.3 pp after six elections	Fig. VIII, p. 532	+4.3 pp GOP gubernatorial vote share
R9	Policy preferences: exposure predicts support for law enforcement and opposition to marijuana legalization	Table V, p. 538	+0.0393*** (more police), +0.125*** (safety around police), -0.0179** (marijuana legalization)
R10	Media: exposure predicts higher Fox News viewership	Table V col. 4, p. 538	+0.0475*** Fox News viewership share (CCES 2020)

Overall (paper’s conclusion). The opioid epidemic causally reshaped the political landscape of affected communities, generating sustained gains for Republicans in House, presidential, and gubernatorial elections from 2006 onward. These gains were mediated by economic hardship (higher disability and SNAP enrollment) and amplified by conservative media coverage that framed the epidemic around crime and economic distress, resonating with the Republican Party’s messaging on working-class economic decline.

Theory / model

The paper has no formal structural model. It tests a set of empirical hypotheses derived from the political economy of economic distress and partisan realignment.

The core identification assumption is that in the absence of OxyContin marketing, commuting zones with higher 1996 cancer mortality would have followed the same trends in health, economic, and political outcomes as zones with lower cancer mortality (parallel trends). The validity of this assumption is supported by pre-trend tests, out-of-sample placebo exercises, and controls for alternative economic and political shocks (NAFTA exposure, China import shock, recessions, robot adoption, Fox News rollout, Southern partisan realignment).

The paper rules out three alternative explanations:

1. Anti-incumbent voting: the GOP vote share increase does not depend on the party of the incumbent at the time of each election (Online Appendix Figure A9, p. 533).
2. Voter turnout: no meaningful changes in turnout rates are estimated across the distribution of opioid exposure (Online Appendix Figure A6, p. 527).
3. Direct mortality effect: a counterfactual calculation shows that missing votes attributable to opioid-related deaths shift the observed vote share by at most 0.22 pp relative to 2020 (p. 527), far smaller than the 4.5 pp House effect.

The mechanisms the paper tests are:

• Economic hardship (SNAP, disability) predicts subsequent Republican gains with a lag: state-level SNAP effects in 2006 are the strongest predictor of GOP vote share shifts in 2022 (Figure IX, p. 536).
• Conservative media engaged more extensively with the epidemic and framed it around crime and economic hardship, themes favoring the Republican Party’s platform (Online Appendix Figure A10, Table V, pp. 538-541).
• Residents preferred Republican law enforcement policy solutions (more police, opposition to harm-reduction policies) over Democratic harm-reduction approaches (Table V, p. 538).

The paper is related to three strands of literature. First, the causal identification design extends Autor, Dorn, Hanson, and Majlesi (2020), who use the China trade shock as an instrument for local economic distress and document downstream political polarization. Second, the finding that acute epidemics can shift politics rightward complements Voigtländer and Voth (2012), who document that the Black Death increased anti-Semitic persecution in medieval Germany. Third, the paper extends Goodwin et al. (2018), who document an observational association between chronic opioid use and presidential voting patterns in US counties, to a causal quasi-experimental framework. Evans, Lieber, and Power (2019) provide the evidence that OxyContin’s 2010 abuse-deterrent reformulation pushed users toward heroin, used here to contextualize the epidemic’s transition from prescription to illicit drugs.

Method

The main estimating equation is an event-study specification on a panel of commuting zones (CZs), interacting a pre-determined exposure proxy with year indicators (p. 515):

y_{ct} = \sum_{\tau=1982}^{2022} \phi_\tau \, \text{CancerMR}_{c,1996} \cdot \mathbf{1}(\text{Year} = \tau) + \alpha X_{ct} + \gamma_c + \gamma_{st} + \upsilon_{ct} \tag{1}

where $c$ indexes the CZ, $s$ the state, and $t$ the year. $\text{CancerMR}_{c,1996}$ is the 1996 cancer mortality rate per 1,000 in CZ $c$, standardized so that a one-unit change equals one standard deviation. The sum runs from 1982 (or the earliest available year for each outcome) to 2022. Coefficients $\phi_{1982}$ to $\phi_{1995}$ are pre-trend estimates that test for differential trends before OxyContin’s 1996 launch; $\phi_{1997}$ to $\phi_{2022}$ trace the cumulative effect of opioid exposure over time. The interaction for 1996 is omitted, so all coefficients are relative to that base year.

$\gamma_c$ are CZ fixed effects capturing time-invariant unobserved heterogeneity. $\gamma_{st}$ are state-year fixed effects controlling for state-level policy changes (prescription drug monitoring programs, pill-mill regulations, naloxone access laws). $X_{ct}$ are time-varying controls at the CZ level: contemporaneous cancer mortality, white and female population shares, and age shares (18-29, 30-49, 50-64, 65+, under 1). Standard errors are clustered at the CZ level.

The instrument for opioid exposure is 1996 county-level cancer mortality, which proxies the cancer pain market that Purdue targeted for OxyContin at launch. The validity rests on Purdue’s documented strategy: internal records state “OxyContin will be marketed at the cancer pain market” (Purdue Pharma 1994, cited p. 509) and cancer-market penetration was used as a springboard into the far larger noncancer pain market. Columns (2) and (3) of Table I (p. 512) confirm that 1994 MS Contin prescription rates (the pre-OxyContin cancer opioid) and 1996 cancer mortality both strongly predict 1996-1998 OxyContin prescription rates.

To identify geographic concentration of political effects and test whether communities with the largest economic effects saw the largest political shifts, the paper also estimates a state-level in-differences model (p. 535):

\Delta y_{ct} = \sum_{s=1}^{50} \sum_{\tau=1982}^{2022} \phi_\tau^s \, \text{CancerMR}_{c,1996} \cdot \mathbf{1}(\text{Year} = \tau \text{ and State} = s) + \alpha \Delta X_{ct} + \gamma_t + \upsilon_{ct} \tag{2}

The state-by-year interactions $\phi_\tau^s$ capture state-specific exposure effects at each point in time, allowing a within-state comparison that is equivalent to the level specification augmented with state-by-year dummies.

Empirical specifications

First stage (opioid supply, Figure II, p. 514). The estimating equation is equation (1) above, with $y_{ct}$ replaced by DEA ARCOS prescription opioid doses per capita (1997-2020). The peak coefficient is in 2012, when a one standard deviation higher cancer mortality corresponded to 0.97 additional opioid doses prescribed per capita (65% above the baseline mean), consistent with the magnitude of Alpert, Evans, Lieber, and Powell (2022).

Mortality (Figure III, p. 522-523). Equation (1) with $y_{ct}$ = drug-induced mortality per 1,000. Drug-induced mortality is the broadest measure, covering poisoning and medical conditions caused by legal or illegal drugs (ICD-9/10 codes linked per CDC 2013 guidance). The coefficient rises steadily from 1996 onward, peaks around 2015-2017, then begins to decline as fentanyl-related deaths shift the geographic distribution. Pre-trend test: $p$-value for joint test of $\phi_{1982}, \ldots, \phi_{1995} = 0$ is 0.3462 (Figure III caption, p. 522). Deaths are concentrated among individuals under 55 (Online Appendix Figure A4).

Economic outcomes (Figure IV, p. 524-525). Same specification with outcomes: (i) share of working-age population (18-65) applying for SSDI, (ii) share of population applying for SSI (blindness/disability), and (iii) share of population receiving SNAP benefits. SSDI and SSI data available for 1990-2015 (438 CZs); SNAP data from USDA Food and Nutrition Service, January counts, 1989-2022. Unemployment does not respond significantly (Online Appendix Section C.3, p. 524n).

Political outcomes: House elections (Figure V, Table IV, pp. 526-528). Equation (1) with $y_{ct}$ = two-party Republican vote share in House elections. Outcome is the ratio of Republican votes to total Republican plus Democratic votes. CZs cover 616 House election observations. Pre-trend: $p$-value for joint zero of $\phi_{1982}, \ldots, \phi_{1994}$ is 0.7510 (Figure V caption). Cross-demographic stability: Table IV shows coefficients of 0.0477-0.0721 (standard errors 0.0131-0.0165) across race (white vs. non-white), age (under vs. over 50), gender, and education groups, all statistically indistinguishable. The effect on Republican seat wins appears from 2012 onward (Figure VI, Panel A, p. 529).

Policy preferences (Table V, p. 538). Cross-sectional specification:

$y_{it} = \alpha_i + \beta \, \text{CancerMR}_{c,1996} + \alpha X_i + \gamma_s + \varepsilon_{it}$

where $i$ indexes CCES respondents in 2020 and $\gamma_s$ are state fixed effects. Columns (1)-(2) and (4) use 2020 CCES data; column (3) uses secretary of state vote records for marijuana ballot initiatives in 18 states (2012-2023).

Datasets used

Dataset	Role in paper	Wiki page
DEA ARCOS (Automation of Reports and Consolidated Orders System), 1997-2020	CZ-level opioid prescription doses per capita (first stage); digitized from DEA records	no page yet
CDC/NCHS National Vital Statistics System (NVSS) Detailed Multiple Cause of Death files, 1976-2022; restricted-use county identifiers	Drug-induced, prescription opioid, and all-opioid mortality rates; cancer mortality rate (instrument)	no page yet
SSA SSDI and SSI application and receipt data, 1990-2015 (applications), 1998-2020 (receipts); 438 CZs	SSDI and SSI application and receipt rates (economic hardship measures)	no page yet
USDA Food and Nutrition Service SNAP county-level participation, January 1989-2022	SNAP receipt rate (economic hardship measure)	no page yet
Dave Leip’s Atlas of U.S. Elections + ICPSR U.S. Historical Election Returns, 1976-2022	Two-party Republican vote shares for House, presidential, and gubernatorial elections; voter turnout	no page yet
Cooperative Congressional Election Study (CCES), 2006-2020	Individual-level survey data: vote choice, demographic heterogeneity analysis, policy preferences (police, marijuana), Fox News viewership	no page yet
Purdue Pharma unsealed litigation documents	OxyContin marketing strategy; source of identification rationale (cancer market targeting strategy)	no page yet
State Drug Utilization Data (SDUD), 1994-1998	MS Contin and OxyContin prescription rates at state level for validating instrument (Table I)	no page yet
Newspapers.com local newspaper archive, 1995-2020; TV News Internet Archive (Fox News/CNN/MSNBC), 2009-2020	Media coverage content and framing analysis; newspaper political affiliation from Gentzkow and Shapiro (2011)	no page yet

Sample: 621 CZs (restricted to those with more than 20,000 residents, covering more than 99% of all opioid deaths and 99% of total US population). Panel period 1982-2022 for political and mortality outcomes; 1989-2022 for SNAP; 1990-2015/1998-2020 for disability.

When to read the full paper

Read Arteaga and Barone (2026) if you need:

• A credible causal estimate of the opioid epidemic’s effect on partisan realignment (Table I, Figures V-VIII are the key evidence).
• A detailed treatment of how economic hardship mediates the link from a public health crisis to political preferences (Figure IX, Online Appendix Table A2).
• Evidence on how media framing of a crisis shapes political outcomes, with a useful contrast between conservative and liberal coverage (Online Appendix Figure A10, Table V columns 1-4).
• A worked example of using industry marketing documents (unsealed litigation records) as a source of quasi-exogenous variation (Sections III.A-B, pp. 509-516).

Attribution and rights

Arteaga, Carolina, and Victoria Barone. “Republican Support and Economic Hardship: The Enduring Effects of the Opioid Epidemic.” The Quarterly Journal of Economics 141, no. 1 (2026): 499-558. https://doi.org/10.1093/qje/qjaf051

Copyright The Author(s) 2025. Published by Oxford University Press on behalf of President and Fellows of Harvard College. All rights reserved. This page contains only extracts for academic reference (titles, locators, magnitudes, descriptions). Replication data are available at Harvard Dataverse: https://doi.org/10.7910/DVN/R5PKQL.

LLM-distilled by paper-distiller (claude-sonnet-4-6), 2026-06-28. Not human-verified. Not reproduced.