Impervious to Elite Influence: Americans' ACA Attitudes, 2009-2020.

The Affordable Care Act (ACA) was a central issue dividing Republicans and Democrats for the decade following its 2010 enactment. Given that, it offers key lessons about policymaking and public opinion during a highly polarized political period. Here, I draw out some of those lessons from my 2023 book Stable Condition: Elites' Limited Influence on Health Care Attitudes, detailing how polarization shaped both the elite- and mass-level politics of the ACA. At the elite level, polarization and nationalization within the federal and state governments laid the groundwork for a highly complex law that was a patchwork of policies experienced very differently by different Americans. At the mass level, polarization and nationalization contributed to a remarkable level of stability in public opinion, so much so that even direct beneficiaries of the law did not typically become markedly more positive toward it. Elite efforts at opinion leadership, through policymaking and messaging, were largely unsuccessful.

Effectiveness of Behaviorally Informed Letters on Health Insurance Marketplace Enrollment: A Randomized Clinical Trial.

Importance: Every year during the open enrollment period, hundreds of thousands of individuals across the Affordable Care Act marketplaces begin the enrollment process but fail to complete it, thereby resulting in coverage gaps or going uninsured. Objective: To investigate if low-cost ($0.55 per person) letters can increase health insurance enrollment. Design Setting and Participants: This intent-to-treat randomized clinical trial was conducted during the final 2 weeks of the 2015 open enrollment period among the 37 states on the HealthCare.gov platform. The trial targeted 744 510 individuals who started the enrollment process but had yet to complete it. Data were analyzed from January through August 2021. Interventions: Study participants were randomized to either a no-letter control group or to 1 of 8 letter variants that drew on evidence from the behavioral sciences about what motivates individuals to take action. Main Outcomes and Measures: The primary outcome was the health insurance enrollment rate at the end of the open enrollment period. Results: Of the 744 510 individuals (mean [SD] age, 41.9 [19.6] years; 53.9% women), 136 122 (18.3%) were in the control group and 608 388 (81.7%) were in the treatment group. Most lived in Medicaid nonexpansion states (72.7%), and a plurality were between 30 and 50 years old (41.0%). For race and ethnicity, 3.0% self-identified as Asian, 14.0% as Black, 5.1% as Hispanic, 39.8% as non-Hispanic White, and 38.2% as other or unknown. By the end of the open enrollment period, 4.0% of the control group enrolled in health insurance coverage. Comparatively, the enrollment rate in the pooled treatment group was 4.3%, which demonstrated an increase of 0.3 percentage points (95% CI, 0.2-0.4 percentage points; P<.001), yielding 1753 marginal enrollments. Letters that used action language caused larger enrollment effects, particularly among Black individuals (increase of 1.6 percentage points; 95% CI, 0.6-2.7 percentage points; P = .003) and Hispanic individuals (increase of 1.5 percentage points; 95% CI, 0.0-3.0 percentage points; P = .046) in Medicaid expansion states. Conclusions and Relevance: This randomized clinical trial shows that letters designed with best practices from the behavioral sciences literature were a low-cost way to increase health insurance enrollment in the Affordable Care Act marketplaces. More research is needed to understand what messages are most effective amid the recently passed American Rescue Plan. Trial Registration: ClinicalTrials.gov Identifier: NCT05010395.

Partisanship, Messaging, and the COVID-19 Vaccine: Evidence From Survey Experiments.

PURPOSE: To investigate partisanship in COVID-19 attitudes, and assess partisan or scientific messaging effects on COVID-19 vaccination intentions. DESIGN: Two-wave survey with two-arm randomized experiment. SETTING: Recruited Pennsylvania residents online. SAMPLE: 2037 (May 2020) and 1577 (October 2020) Pennsylvania residents, aged 18-94 years. INTERVENTION: Respondents saw messaging that presented either President Trump or scientists endorsing the vaccine, then reported their vaccination intentions. MEASURES: Likert scale items measuring COVID-19 attitudes (May), including mask wearing and vaccination intentions (May and October). ANALYSIS: Partisan differences in attitudes were analyzed by chi-square; differences in support for mask wearing and vaccination intentions were also analyzed by Mann-Whitney U. The messaging experiment was analyzed by chi-square, Mann-Whitney U, and survey-weighted multivariate logistic regression. RESULTS: Significant partisan differences were found in all attitudes. The partisan split in support for mask wearing increased from May to October, whereas the split in vaccination intentions decreased. Compared to partisan messaging, scientific messaging increased overall odds of intending to vaccinate by 32% in May (adjusted odds ratio [AOR]=1.32, 95% confidence interval [CI] = 1.06-1.65), and increased odds among Democrats by 142% in October (AOR = 2.42, CI = 1.29-4.55). Scientific messaging had no significant effect on independents or Republicans. CONCLUSION: Partisan COVID-19 attitudes were widespread and persistent. Partisan endorsement of the vaccine positively influenced those with congruent beliefs, while scientific messaging produced consistent effects across political affiliation.

Not by turnout alone: Measuring the sources of electoral change, 2012 to 2016.

Changes in partisan outcomes between consecutive elections must come from changes in the composition of the electorate or changes in the vote choices of consistent voters. How much composition versus conversion drives electoral change has critical implications for the policy mandates of election victories and campaigning and governing strategies. Here, we analyze electoral change between the 2012 and 2016 U.S. presidential elections using administrative data. We merge precinct-level election returns, the smallest geography at which vote counts are available, with individual-level turnout records from 37 million registered voters in six key states. We find that both factors were substantively meaningful drivers of electoral change, but the balance varied by state. We estimate that pro-Republican Party (GOP) conversion among two-election voters was particularly important in states including Ohio, Michigan, and Pennsylvania where the pro-GOP swings were largest. Our results suggest conversion remains a crucial component of electoral change.

Results from a 2020 field experiment encouraging voting by mail.

The ability to cast a mail ballot can safeguard the franchise. However, because there are often additional procedural protections to ensure that a ballot cast in person counts, voting by mail can also jeopardize people's ability to cast a recorded vote. An experiment carried out during the COVID-19 pandemic illustrates both forces. Philadelphia officials randomly sent 46,960 Philadelphia registrants postcards encouraging them to apply to vote by mail in the lead-up to the June 2020 primary election. While the intervention increased the likelihood a registrant cast a mail ballot by 0.4 percentage points (P = 0.017)-or 3%-many of these additional mail ballots counted only because a last-minute policy intervention allowed most mail ballots postmarked by Election Day to count.

Local demographic changes and US presidential voting, 2012 to 2016.

Immigration and demographic change have become highly salient in American politics, partly because of the 2016 campaign of Donald Trump. Previous research indicates that local influxes of immigrants or unfamiliar ethnic groups can generate threatened responses, but has either focused on nonelectoral outcomes or analyzed elections in large geographic units, such as counties. Here, we examine whether demographic changes at low levels of aggregation were associated with vote shifts toward an anti-immigration presidential candidate between 2012 and 2016. To do so, we compile a precinct-level dataset of election results and demographic measures for almost 32,000 precincts in the states of Florida, Georgia, Michigan, Nevada, Ohio, Pennsylvania, and Washington. We employ regression analyses varying model specifications and measures of demographic change. Our estimates uncover little evidence that influxes of Hispanics or noncitizen immigrants benefited Trump relative to past Republicans, instead consistently showing that such changes were associated with shifts to Trump's opponent.