We study the extent to which individuals' consumption decisions are influenced by firms' stances on controversial social issues and the implied incentives for firms to take such stances. We use transactions from a major payment card company to predict cardholders’ likely social alignment with firm stances and to quantify effects on consumption. The social stances taken by firms increase revenue on average, with significant heterogeneity across consumers and firm stances. Consumers most aligned with a firm's social stance increase their consumption at the firm by 19 percent in the month following widely known social stance events, and consumers most opposed to the firm's stance decrease their consumption by 11 percent. These diverging consumption responses attenuate over time but persist even a year later. Firms tend to take stances that align with their consumers' and employees' social preferences and that correlate with the firm's ownership structure. Together our results show that consumers meaningfully respond to their social alignment with firms, and that this consumer response can incentivize profit-maximizing firms to engage with social issues.

What role do journalists play in determining the political slant of the news they produce? We develop a model where journalists and newspaper outlets contract over both slant and wages. The model implies a set of conditions under which we can consistently estimate the role of journalist preferences in driving the observed variation in slant across outlets by leveraging journalist transitions between outlets. To measure slant, we train a transformer-based, machine learning model using articles tweeted by politicians and apply it to a full-text database of 20+ million newspaper articles published in the US between 2013 and 2018. Applying our model-informed estimators to the data, our estimates (a) reject the hypothesis that journalists have zero ideological preferences over the content they produce and (b) imply that 16 percent of observed variation in slant across outlets can be explained by journalist preferences.

We study the impact of the Community Reinvestment Act (CRA) on access to consumer credit since 1999 using an individual-level panel and three distinct identification strategies: a regression discontinuity design centered on a CRA-eligibility cutoff; a comparison of neighboring census blocks; and an event study of changes in eligibility. All three rule out a significant effect of the CRA on consumer borrowing. We show this is in part explained by a shift in mortgages from non-banks, which are free from CRA obligations, to banks in need of CRA-eligible mortgages. Our findings underscore the pitfalls of a circumscribed regulatory regime.

We study differences in economic outcomes by perceived skin tone among African Americans using full-count U.S. decennial census data from the late-19th and early-20th centuries. Comparing children coded as “Black” or “Mulatto” by census enumerators and linking these children across population censuses, we first document large gaps in educational attainment and income among African Americans with darker and lighter perceived skin tones. To disentangle the drivers of these gaps, we identify all 36,329 families in which enumerators assigned same-gender siblings different Black/Mulatto classifications. Relative to sisters coded as Mulatto, sisters coded as Black had lower educational attainment, were less likely to marry, and had lower-earning, less-educated husbands. These patterns are consistent with more severe contemporaneous discrimination against African American women with darker perceived skin tones. In contrast, we find similar educational attainment, marital outcomes, and incomes among differently-classified brothers. Men perceived as African Americans of any skin tone faced similar contemporaneous discrimination, consistent with the “one-drop” racial classification rule that grouped together individuals with any known Black ancestry. Lower incomes for African American men perceived as having darker skin tone in the general population were driven by differences in opportunities and resources that varied across families, likely reflecting the impacts of historical or family-level discrimination.

We provide new evidence on the drivers of the early US COVID-19 pandemic and develop a methodology that future researchers can use to similarly analyze the outbreaks of new diseases. We combine an epidemiological model of disease transmission with quasi-random variation arising from the timing of stay-at-home-orders to estimate the causal roles of policy interventions and voluntary social distancing. We then relate the residual variation in disease transmission rates to observable features of cities. We estimate significant impacts of policy and social distancing responses, but we show that the magnitude of policy effects was modest, and most social distancing was driven by voluntary responses. Moreover, we show that neither policy nor rates of voluntary social distancing explained a meaningful share of geographic variation. The most important predictors of which cities were hardest hit by the pandemic were exogenous characteristics such as population and density.

We document trends in affective polarization during the coronavirus pandemic. In our main measure, affective polarization is relatively flat between July 2019 and February 2020, then falls significantly around the onset of the pandemic. Three of five other data sources display a similar downward trend, with two of five data sources showing no significant change. A survey experiment shows that priming respondents to think about the coronavirus pandemic significantly reduces affective polarization.

We study partisan differences in Americans' response to the COVID-19 pandemic. Political leaders and media outlets on the right and left have sent divergent messages about the severity of the crisis, which could impact the extent to which Republicans and Democrats engage in social distancing and other efforts to reduce disease transmission. We develop a simple model of a pandemic response with heterogeneous agents that clarifies the causes and consequences of heterogeneous responses. We use location data from a large sample of smartphones to show that areas with more Republicans engaged in less social distancing, controlling for other factors including public policies, population density, and local COVID cases and deaths. We then present new survey evidence of significant gaps at the individual level between Republicans and Democrats in self-reported social distancing, beliefs about personal COVID risk, and beliefs about the future severity of the pandemic.