Health Policy Opinion

Public Opinion and the State Politics of the Affordable Care Act

(with Ryan T. Moore)

Since passage of the Patient Protection and Affordable Care Act, several attempts have been made to block its provisions in the states. Among these, ballot propositions challenging the individual mandate have occurred in five states, with four more scheduled for November 2012. We first provide state-level estimates of public opinion on the ACA since the beginning of 2010, and we show that, consistent with models of partisan resonance, polarization of public opinion is greatest near elections that politicize health care. We then use synthetic control methods to estimate the causal effects of the high-profile public campaigns surrounding these proposition elections, finding these effects to be conditioned by the broader political context of the campaign. In Ohio, where the campaign took place without simultaneous major candidate elections, we find effects on opinion of about seven percentage points. These effects are fairly short-lived, persisting a few months.

Economic Voting and State Context

Red State/Blue State Divisions in the 2012 Presidential Election

(with Avi Feller and Andrew Gelman)

Published February 2013 in The Forum

The so-called “red/blue paradox” is that rich individuals are more likely to vote Republican but rich states are more likely to support the Democrats. Previous research argued that this seeming paradox could be explained by comparing rich and poor voters within each state – the difference in the Republican vote share between rich and poor voters was much larger in low-income, conservative, middle-American states like Mississippi than in high-income, liberal, coastal states like Connecticut. We use exit poll and other survey data to assess whether this was still the case for the 2012 Presidential election. Based on this preliminary analysis, we find that, while the red/blue paradox is still strong, the explanation offered by Gelman et al. no longer appears to hold. We explore several empirical patterns from this election and suggest possible avenues for resolving the questions posed by the new data.

Blog Post: In 2008, Rich States Vote Democratic, Poor States Vote Republican — Again

Rich State, Poor State, Red State, Blue State: Why Americans Vote the Way They Do

(with Andrew Gelman, David Park, Joseph Bafumi, and Jeronimo Cortina)

Published September 2008 by Princeton University Press

On the night of the 2000 presidential election, Americans sat riveted in front of their televisions as polling results divided the nation’s map into red and blue states. Since then the color divide has become a symbol of a culture war that thrives on stereotypes–pickup-driving red-state Republicans and elitist, latte-sipping blue-state Democrats. Red State, Blue State, Rich State, Poor State debunks these and other political myths.

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Rich state, poor state, red state, blue state: What’s the matter with Connecticut?

(with Andrew Gelman, Joseph Bafumi, and David Park)

Published in the Quarterly Journal of Political Science, November 2007

Slide Presentation

Abstract: For decades, the Democrats have been viewed as the party of the poor, with the Republicans representing the rich. Recent presidential elections, however, have shown a reverse pattern, with Democrats performing well in the richer blue states in the northeast and coasts, and Republicans dominating in the red states in the middle of the country and the south. Through multilevel modeling of individuallevel survey data and county- and state-level demographic and electoral data, we reconcile these patterns.

Furthermore, we find that income matters more in red America than in blue America. In poor states, rich people are much more likely than poor people to vote for the Republican presidential candidate, but in rich states (such as Connecticut), income has a very low correlation with vote preference.

Key methods used in this research are: (1) plots of repeated cross-sectional analyses, (2) varying-intercept, varying-slope multilevel models, and (3) a graph that simultaneously shows within-group and between-group patterns in a multilevel model. These statistical tools help us understand patterns of variation within and between states in a way that would not be possible from classical regressions or by looking at tables of coefficient estimates.


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