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 checked against the PDF.

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. 522; text p. 523	46% above pre-epidemic average by 2017
R2	Prescription opioid deaths: 1 SD increase causes 61% rise by 2012	Online Appendix Fig. A3; text p. 523	61% higher than pre-epidemic average at 2012 peak
R3	SSDI applications: 1 SD increase causes 12% rise by 2012	Fig. IV, p. 525; text p. 524	+12% SSDI applications by 2012
R4	SSI applications: 1 SD increase causes 7.6% rise by 2012	Fig. IV, p. 525; text p. 524	+7.6% SSI applications by 2012
R5	SNAP receipt: 1 SD increase predicts 8% more recipients by 2020	Fig. IV, p. 525; text p. 524	+8% SNAP recipients (0.14 SD) by 2020
R6	Republican House vote share: 1 SD increase raises GOP share 4.5 pp in 2022	Fig. V, p. 527; text 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 by 2020	Fig. VII, p. 531; text p. 530	+4.6 pp GOP presidential vote share by 2020
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)
R11	Exposure-proxy validation: baseline cancer mortality predicts early opioid-market exposure conditional on MS Contin use	Table I, cols. (2)-(4), p. 512	MS Contin 1994 coefficient 0.717*** (0.134); cancer mortality coefficient 2.065* (1.161) conditional on MS Contin; population-weighted MS Contin/OxyContin correlation 0.70
R12	Prescription opioid supply: cancer-market exposure predicts greater opioid dispensing	Fig. II, Panel B, p. 514; text p. 523	By 2012, +0.97 opioid doses per capita, 65% above baseline mean, per 1 SD higher 1996 cancer mortality
R13	Descriptive mortality split: drug-induced deaths diverge between high- and low-cancer CZs	text p. 523	Drug-induced mortality in high-cancer CZs was 55% higher by 2020
R14	Mortality identification check: no differential pre-trend before OxyContin	Fig. III caption, p. 522	Joint test of pre-1996 coefficients: p = 0.3462
R15	Disability application effects are concentrated among people under age 55	Fig. IV, Panel C, p. 525; text p. 524	The under-55 group shows the increase; the 55+ group does not show a comparable pattern
R16	House-vote identification check: no differential pre-trend before OxyContin	Fig. V caption, p. 527	Joint test of pre-1996 coefficients: p = 0.7510
R17	Turnout does not account for the Republican vote-share shift	Online Appendix Fig. A6, cited text p. 527	No meaningful change in voter turnout over time or across exposure levels
R18	Direct deaths cannot mechanically explain the House vote-share estimate	text p. 527	Missing votes attributable to opioid deaths change the observed 2020 Republican share by at most 0.22 percentage points under the counterfactual
R19	House-vote effect is similar across demographic groups	Table IV, p. 528	Cancer 1996 coefficients range from 0.0477*** to 0.0721*** (SE 0.0131-0.0165) across race, age, gender, and education groups
R20	Exposure raises the probability of a Republican House seat win on a sustained basis from 2012	Fig. VI, Panel A, p. 529; text p. 528	The probability of a Republican win begins a sustained increase from 2012
R21	House vote-share gains are largest first in CZs with low initial Republican support	Fig. VI, Panel B, p. 529; text p. 529	Vote share rises first in the lowest 1996 Republican-support tercile; the middle tercile begins rising from 2010
R22	Elected House members become more conservative, mainly through composition changes	Online Appendix Fig. A7, cited text p. 529	Roll-call ideology shifts in a conservative direction, especially in initially Democratic-leaning districts; no rise in election of ideological extremes
R23	Campaign giving shifts toward Republicans through fewer Democratic donations	Online Appendix Fig. A8, cited text p. 530	In 2016, the Republican-minus-Democratic donations-per-capita gap rises by 0.44 SD per 1 SD higher cancer mortality; no effect on Republican donations
R24	House vote-share effects are not explained by incumbent party or reelection changes	Online Appendix Fig. A9, cited text p. 533	Republican vote-share changes are not statistically different by incumbent party; incumbent reelection ability shows no change for most of the period and a slight increase after 2016
R25	State-level economic hardship effects predict later Republican gains	Fig. IX, p. 536	Correlation of state-level SNAP effects in 2006 with House vote-share effects rises from 0.198 in 2008 to 0.492 in 2022 (intermediate values: 0.309, 0.392, 0.394, 0.450, 0.397, 0.433)
R26	Republican-leaning local newspapers frame opioids more around hardship, illegal activity, and treatment	Online Appendix Table A4, reported text p. 540	Keyword frequencies are respectively 23%, 19%, and 22% higher than in Democratic-leaning newspapers
R27	Fox News gives opioids more coverage and more crime-centered framing	text pp. 540-541	Coverage is 1.5 times CNN’s and 1.7 times MSNBC’s; crime, trafficking, and cartel stories are covered at double the frequency
R28	Pre-OxyContin 1976 cancer mortality does not predict later health, economic, or political outcomes	Fig. X, p. 542	No relationship for drug-induced mortality, SNAP, House vote share, unemployment, or sector employment before 1996
R29	Placebo mortality measures do not predict the main outcomes	Fig. XI, p. 543	Hypertension and pneumonia mortality among adults over 65 show no relationship with drug mortality, SNAP receipt, or Republican House vote share
R30	The opioid exposure measure does not predict unemployment	text p. 524 n. 24	No significant unemployment response
R31	Main estimates remain stable across expanded controls and treatment definitions	Figs. XII-XIII, pp. 544, 547; text pp. 545-549	Estimates remain similar with added health, economic, and political controls, continuous-exposure heterogeneity checks, a top-quartile indicator, and economic-shock controls

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. The evidence links these gains to economic hardship (higher disability and SNAP enrollment), and the authors argue that conservative media coverage reinforced the shift by framing the epidemic around crime and economic distress, themes aligned with Republican 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 $c$. The regression uses the rate itself; the paper reports effects for a one-standard-deviation increase (0.58 rate units in the House-vote analysis). 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 main panel contains 621 CZs with more than 20,000 residents, and runs from 1982 to 2022 subject to outcome-specific data availability; the ARCOS opioid-supply sample is 1997-2020.

The exposure measure is 1996 commuting-zone 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} \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. The specification expresses outcomes and covariates in changes, equivalent to a level specification with CZ fixed effects, and includes year fixed effects. It uses 621 CZs over the relevant outcome years and the time-varying controls in equation (1). The paper does not state a separate standard-error clustering rule for the state-level correlation exercise.

Empirical specifications

Exposure proxy validation (Table I, p. 512). State-level regressions use 49 states and Medicaid State Drug Utilization Data (SDUD). The specifications regress 1994 MS Contin rates on 1996 cancer mortality, then 1996-1998 OxyContin rates on 1994 MS Contin rates, cancer mortality, or both:

$\text{MSContin}_{s,1994} = a + b\,\text{CancerMR}_{s,1996} + e_s$

$\text{OxyContin}_{s,1996-1998} = a + b_1\,\text{MSContin}_{s,1994} + b_2\,\text{CancerMR}_{s,1996} + e_s$

Table I reports the four columns and parenthetical standard errors; the table does not state fixed effects or a clustering level. In column (4), the MS Contin coefficient is 0.717 (0.134) and the cancer-mortality coefficient is 2.065 (1.161). The population-weighted correlation between 1994 MS Contin and 1996-1998 OxyContin rates is 0.70.

Baseline determinants (Table II, p. 517). The cross-sectional regressions relate the three dependent variables (prescription opioid doses, cancer mortality, and Republican House vote) to demographic, health, and economic characteristics listed in Table II. The sample is the CZ-level cross section described in Section IV; standard errors are heteroskedasticity-robust, with no fixed effects. The table reports coefficients and dependent-variable means but no observation-count row.

$y_c = a + X_c'\beta + e_c$

Opioid-supply exposure check (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).

CCES House vote choice (Table IV, p. 528). For individual respondents in CZs, states, and election years, the repeated cross-section is:

$y_{ict} = \beta\,\text{CancerMR}_{c,1996} + X_{it}'\alpha + \gamma_{st} + \varepsilon_{ict}$

The CCES sample covers 2006-2020; controls are respondent sex, race, and education, regressions use survey weights, state-year fixed effects, and standard errors clustered by CZ. Table IV reports 267,926 observations in the full sample and 105,467-162,459 across its demographic splits.

Policy preferences and media choice (Table V, p. 538). Columns (1), (2), and (4) use individual-level CCES 2020 data with state fixed effects; column (3) uses CZs in states with marijuana ballot initiatives from 2012-2023 and state-year fixed effects:

$y_{ic} = \alpha_1 + \beta\,\text{CancerMR}_{c,1996} + X_i'\alpha + \gamma_s + \varepsilon_{ic}$

$y_{ct} = \alpha_1 + \beta\,\text{CancerMR}_{c,1996} + X_{ct}'\alpha + \gamma_{st} + \varepsilon_{ct}$

The reported samples are 60,054, 59,989, 198, and 25,417 observations by columns (1)-(4); standard errors are clustered by CZ. The table reports individual-level controls in columns (1), (2), and (4), and state-year effects for marijuana ballot results in column (3).

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 (opioid-supply outcome); 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 exposure measure	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 the exposure proxy (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 differences in conservative and liberal media coverage of a crisis and the political context around that 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 (gpt-6-luna), 2026-10-04; checked against the source PDF by paper-verifier (gpt-6-luna), 2026-10-04. Not reproduced.