--- title: "Republican Support and Economic Hardship: Arteaga & Barone (2026)" description: >- Distilled: Using quasi-exogenous variation in Purdue Pharma's OxyContin cancer-market targeting, this paper establishes a causal link between opioid epidemic exposure and a 4.5 percentage-point increase in Republican House vote share by 2022, operating through economic hardship and conservative media framing. QJE 2026, paywalled. Ten core results with source locators, datasets used, the empirical specification with equations, and identification strategy. sidebar: label: Arteaga-Barone 2026 order: 1 tags: [paper-summary, political-economy, health-economics, opioids, elections, partisan-realignment, natural-experiment, panel-regression, event-study, peer-reviewed, unreplicated, data:arcos, data:nvss, data:ssa-disability, data:snap, data:us-elections, data:cces] paper: authors: Carolina Arteaga, Victoria Barone authorList: - { family: Arteaga, given: Carolina, affiliation: "University of Toronto and National Bureau of Economic Research" } - { family: Barone, given: Victoria, affiliation: University of Notre Dame } year: 2026 venue: "The Quarterly Journal of Economics 141(1), 2026, 499-558" venueShort: QJE 2026 doi: 10.1093/qje/qjaf051 jel: codes: [D72, I12, H55] assignedBy: claude-sonnet-4-6 date: 2026-06-28 topics: ["Opioid Use Disorder Treatment", "HIV, Drug Use, Sexual Risk", "Gun Ownership and Violence Research"] dataAccess: licensed-commercial outcome: - Republican two-party vote share in House, presidential, and gubernatorial elections - drug-induced mortality rate - SSDI and SSI application and receipt rates - SNAP receipt rate - policy preferences for law enforcement and drug legalization - Fox News viewership outcomeClass: [electoral-outcomes, social-welfare, labor-careers-health] license: >- Copyright The Author(s) 2025. Published by Oxford University Press on behalf of President and Fellows of Harvard College. All rights reserved; commercial re-use requires RightsLink permission (reprints@oup.com). licenseShort: paywalled access: paywalled machineAccess: "blocked-paywall (OUP site, 2026-06-28)" redistribution: extract-only resultsCount: 10 citedByCount: 1 methods: role: applies-method family: reduced-form-causal buildsFrom: [difference-in-differences, event-study, panel-regression] identification: natural-experiment contributionType: [new-fact, new-data] mechanisms: [economic-hardship-partisan-sorting, media-framing] introducesData: true scope: region: US period: 1982-01..2022-12 frequency: annual dataType: [administrative, survey, text] granularity: [aggregate] n: "621 commuting zones (CZs), 1982-2022; 438 CZs for SSDI/SSI data" findings: - { ref: R1, outcome: drug-induced mortality rate, metric: coefficient, value: "46% above pre-epidemic average by 2017 (per 1 SD higher 1996 cancer mortality)", direction: positive } - { ref: R2, outcome: prescription opioid death rate, metric: coefficient, value: "61% increase relative to pre-epidemic average by 2012 (per 1 SD)", direction: positive } - { ref: R3, outcome: SSDI application rate, metric: coefficient, value: "12% rise in SSDI applications by 2012 (per 1 SD)", direction: positive } - { ref: R4, outcome: SSI application rate, metric: coefficient, value: "7.6% rise in SSI applications by 2012 (per 1 SD)", direction: positive } - { ref: R5, outcome: SNAP receipt rate, metric: coefficient, value: "8% increase in SNAP recipients by 2022, equivalent to 0.14 SD (per 1 SD)", direction: positive } - { ref: R6, outcome: "Republican two-party House vote share", metric: pp-effect, value: "4.5 pp increase by 2022 midterms (per 1 SD)", direction: positive } - { ref: R7, outcome: "Republican two-party presidential vote share", metric: pp-effect, value: "4.6 pp increase (per 1 SD)", direction: positive } - { ref: R8, outcome: "Republican two-party gubernatorial vote share", metric: pp-effect, value: "4.3 pp increase after six elections post-1996 (per 1 SD)", direction: positive } - { ref: R9, outcome: support for law enforcement and drug policy, metric: coefficient, value: "0.0393*** (police officers), 0.125*** (safety around police), -0.0179** (marijuana legalization)", direction: mixed } - { ref: R10, outcome: Fox News viewership, metric: coefficient, value: "0.0475*** increase in Fox News viewership share (CCES 2020)", direction: positive } resultType: new-finding relatesTo: - { cite: "Autor, Dorn, Hanson, and Majlesi (2020)", doi: "10.1257/aer.20170011", relation: extends, note: "extends the trade-shock-to-political-polarization design to a public-health crisis instrument" } - { cite: "Alpert, Evans, Lieber, and Powell (2022)", doi: "10.1093/qje/qjab043", relation: builds-on, note: "uses their triplicate-state identification of opioid supply shocks as a benchmark; extends to welfare and political outcomes" } - { cite: "Voigtländer and Voth (2012)", doi: "10.1093/qje/qjs019", relation: extends, note: "extends the epidemics-and-right-wing-politics framework from historical Europe to the contemporary US" } - { cite: "Evans, Lieber, and Power (2019)", doi: "10.1162/rest_a_00755", relation: builds-on, note: "uses their OxyContin reformulation evidence to document the prescription-to-illicit-opioid transition" } - { cite: "Goodwin et al. (2018)", doi: '10.1001/jamanetworkopen.2018.0450', relation: extends, note: "extends their observational association between chronic opioid use and GOP voting to a causal quasi-experimental design" } openQuestions: - "Why conservative media engaged far more extensively with the opioid epidemic than liberal-leaning outlets, and what consequences this asymmetry has for public perception and political behavior (p. 550)." - "How different types of health shocks, from chronic conditions to infectious disease outbreaks, affect community economic conditions and translate into long-term political shifts (p. 550)." replicationCode: { url: "https://doi.org/10.7910/DVN/R5PKQL", status: available } extraction: - { by: "paper-distiller (claude-sonnet-4-6)", date: 2026-06-28, role: extracted, note: "Read full PDF (60 pages, pp. 499-558); all locators confirmed against PDF tables and figures; not human-verified; not reproduced." } - { by: "paper-verifier (claude-sonnet-4-6)", date: 2026-06-28, role: verified, note: "Locators and reported magnitudes re-checked against source PDF; four fixes applied: Table I column reference corrected from (1)&(3) to (2)&(3); Table IV coefficient range corrected from 0.0588-0.0722 to 0.0477-0.0721; Alpert et al. fourth author corrected from Pacula to Powell; Autor et al. author list corrected to include Dorn." } licenceVerification: - { source: "Crossref works/10.1093/qje/qjaf051", checked: 2026-06-28, by: "paper-distiller (claude-sonnet-4-6)", found: "license URL https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (content-version vor, delay-in-days 0, start 2025-11-04); OUP CHORUS standard publication model, not Creative Commons; paywalled" } --- **What this is.** A distilled skeleton of Arteaga and Barone (2026). Read the [original paper](https://doi.org/10.1093/qje/qjaf051) 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.