Emotions and Policy Views

This paper investigates the growing role of emotions in shaping policy views. Analyzing social citizens’ media postings and political party messaging over a large variety of policy issues from 2013 to 2024, we document a sharp rise in negative emotions, particularly anger. Content generating anger drives significantly more engagement. We then conduct two nationwide online experiments in the U.S, exposing participants to video treatments that induce positive or negative emotions to measure their causal effects on policy views. The results show that negative emotions increase support for protectionism, restrictive immigration policies, redistribution, and climate policies but do not reinforce populist attitudes. In contrast, positive emotions have little effect on policy preferences but reduce populist inclinations. Finally, distinguishing between fear and anger, we find that anger exerts a much stronger influence on citizens’ policy views, in line with its growing presence in the political rhetoric.

The Universal Pursuit of Safety and the Demand for (Lethal, Non-Lethal or No) Guns

Personal lethal firearm ownership has for several decades been a hot button political issue in the United States. This article aims to explore the motivations and beliefs underlying sharply different views on the subject through an original large-scale survey of lethal firearm owners (LFAO) and non-owners and experimental information interventions. We start by documenting several facts: First, LFAO and non-owners appear to be driven by a common objective—to be safe. Both groups list protection of family or self as the top rationale for owning or potentially acquiring a lethal firearm (LFA). Second, among non-owners, there are those who are interested in purchasing a lethal firearm (NO-I) and those who are not (NO-UI). NO-I feel the least safe in their daily lives. Third, there are differences in emotional responses to possession of a LFA. LFAO report feeling unsafe and less confident if they did not own the product whereas NO-U report similar feelings if they did own it. Fourth, LFAO are much less concerned about the possibility of personal and social costs associated with lethal firearm possession, a finding heightened across partisan lines. Taken together, these facts motivate three experimental treatments that randomly provide respondents with information on either (1) the personal legal and medical risks of ownership or (2) a non-lethal firearm (NLFA), provided with or without a conservative pundit’s endorsement. The first treatment increases concerns about harms associated with lethal firearm ownership among all respondents, but these results are generally short-lived and do not affect policy views. The second treatment, however, increases respondents’ willingness to pay for a NLFA and their self-reported preference for firearms that incapacitate but do not kill. Moreover, these treatment effects are more persistent than those of the cost treatment, especially when coupled with an endorsement, and affect the support of policies aimed at encouraging NLFA. Importantly, we do not find that exposure to information on NLFA makes current owners want to give up their (lethal) guns. We interpret these findings through an organizing framework in which every household has a demand for safety but differs in how they use firearms or other tools to produce it, due to different perceptions of the safety possibilities frontier (SPF, views about the least harmful ways to achieve protection benefits) or different preferences and incentives influencing the tradeoff over protective benefits vs. harms. Our results suggest that a substantial share of LFAO perceive the SPF differently than non-owners, and that there is a potential demand for less-lethal tools to be and feel safe.

Understanding Economic Behavior Using Open-ended Survey Data

We survey the recent literature in economics measuring what is on top of people’s minds using open-ended questions. We first provide an overview of studies in political economy, macroeconomics, finance, labor economics, and behavioral economics that have employed such measurement. We next describe different ways of measuring the considerations that are on top of people’s minds. We also provide an overview of methods to annotate and analyze such data. Next, we discuss different types of applications, including the measurement of motives, mental models, narratives, attention, information transmission, and recall. Our review highlights the potential of using open-ended questions to gain a deeper understanding of mechanisms underlying observed choices and expectations.

The How and Why of Household Reactions to Income Shocks

This paper studies how and why households adjust their spending, saving, and borrowing in response to transitory income shocks. We leverage new large-scale survey data to first quantitatively assess households’ intertemporal marginal propensities to consume (MPCs) and deleverage (MPDs) (the “how”), and second to dive into the motivations and decision-making processes across households (the “why”). The combination of the quantitative estimation of household response dynamics with a qualitative exploration of the mental models employed during financial decisions provides a more complete view of household behavior. Our findings are as follows. First, we validate the reliability of surveys in predicting actual economic behaviors using a new approach called cross-validation, which compares the responses to hypothetical financial scenarios with observed actions from past studies. Participants’ predicted reactions closely align with real-life behaviors. Second, we show that MPCs are significantly higher immediately following an income shock and diminish over time, with cumulative MPCs over a year showing significant variability. However, MPDs play a critical role in household financial adjustments and display significantly more cross-sectional heterogeneity. Neither is easily explained by socioeconomic or financial characteristics alone, and the explanatory power is improved by adding psychological factors, past experiences, and expectations. Third, using specifically-designed survey questions, we find that there is a broad range of motivations behind households’ financial decisions and identify four household types using machine learning: Strongly Constrained, Precautionary, Quasi-Smoothers, and Spenders. Similar financial actions stem from diverse reasons, challenging the predictability of financial behavior solely based on socioeconomic and financial characteristics. Finally, we use our findings to address some puzzles in household finance.

Zero-Sum Thinking and the Roots of U.S. Political Differences

We investigate the origins and implications of zero-sum thinking — the belief that gains for one individual or group tend to come at the cost of others. Using a new survey of a representative sample of 20,400 US residents, we measure zero-sum thinking, political preferences, policy views, and a rich array of ancestral information spanning four generations.

Perceptions of Racial Gaps, their Causes, and Ways to Reduce Them

We investigate how respondents perceive racial inequities between Black and white Americans, what they believe causes them, and what interventions, if any, they think should be implemented to reduce them.

Understanding of Trade

I study how people understand and reason about trade, and what factors shape their views on trade policy.

Understanding Economics

Using large-scale online surveys and experiments on representative U.S. samples, we study how well people understand, reason, and learn about four economic policies: i) Personal income taxation, ii) Estate taxation, iii) Health insurance, and iv) Trade.