Do Partisans Make Riskier Financial Decisions When their Party is in Power?

(with David Rothschild and Shawndra Hill)

Link to Paper

This article studies the effect of an electoral victory or loss on partisans’ economic behaviors. Using a novel dataset of searches for cars, houses, and stock among over 50,000 searchers with known partisanship and demographics, as well as New York car registration data, we investigate the consequences of the 2016 election on partisan purchasing behavior. We find that Democrats were significantly less likely to search for cars and houses after Trump’s victory, and they registered fewer new cars than did Republicans. These findings were not explained by broader changes in search patterns, nor were they the result of differential partisan concerns about their personal losses from the Republican administration’s economic or healthcare policies. These findings show that partisans are likely to engage in partisan motivated reasoning about the economy following an electoral loss, with significant behavioral consequences.

This article studies the role of partisanship in American's willingness to follow government recommendations. I combine survey and behavioral data to examine partisans' vaccination rates during the Bush and Obama administrations. I find that presidential co-partisans are more likely to believe that vaccines are safe and more likely to vaccinate themselves and their children than presidential out-partisans. Depending on the vaccine, presidential co-partisans are 4-10 percentage points more likely to vaccinate than presidential out-partisans. This effect is not the result of differences in partisan media coverage of vaccine safety, but rather in differing levels of trust in government. This finding sheds light on the far-reaching role of partisanship in Americans' interactions with the federal government.

Does Partisanship Affect Compliance with Government Recommendations?

Link to Paper

President Trump Stress Disorder:
Partisanship, Ethnicity, and
Expressive Reporting of Mental
Distress after the 2016 Election

(With David Rothschild, Shawndra Hill, and Elad Yom-Tov)

Link to Paper

Using Natural Language Processing to Detect Partisan Polarization in Text

Link to slides

In the aftermath of the 2016 election, many Democrats reported significant increases in stress, depression and anxiety. Were these increases real, or the product of expressive reporting? Using a unique data set of searches by over 1 million Bing users before and after the election, we examine the changes in mental health related searches among Democrats and Republicans. We then compare these changes to shifts in searches among Spanish-speaking Latinos in the US. We find that while Democrats may report greater increases in post-election mental distress, their mental health search behavior did not change after the election. On the other hand, Spanish-speaking Latinos had clear, significant, and sustained increases in searches for ’depression’, ’anxiety’, ’therapy’, and anti-depressant medications. This suggests that for many Democrats, expressing mental distress after the election was a form of partisan cheerleading.

 

In this paper, I propose a supervised model of targeted sentiment analysis which relies on Natural Language Processing to identify a text’s sentiment towards specific political figures or parties. My model assigns “relevance” to words within a sentence based on the structure of the sentence. I  train a model using a human-coded sample of 500 sentences to classify words as “relevant” or “irrelevant” to the subject based on their dependency relationships. Words that the model recognizes as relevant to the political figure or party are then analyzed to produce a sentiment score for that entity. I find that for the task of assigning targeted sentiment to political figures and parties, this model significantly outperforms both a simple dictionary-based sentiment model and a proximity-based sentiment model. Finally, I demonstrate an application of this model by examining over 5000 Congressional candidate websites from 2002 to 2016. Using this model, I show that candidates’ expressed negativity towards the opposing party (not just the opposing candidate) has risen dramatically since 2002.