Research

We find that people who experience social marginalization are more likely to share COVID-19 news indiscriminately, that is, sharing news that is factually untrue and true, as well as news that seems surprising and unsurprising. This effect, driven by their general motivation to seek meaning, holds when people self-identify as being socially marginalized (i.e., experiencing frequent feelings of discrimination) and when they are situationally induced to feel marginalized. We demonstrate that an intervention to help people obtain a temporary sense of meaning by having high (vs. low) power can reduce indiscriminate news sharing. For socially marginalized individuals, sharing news on social media appears to reflect a need to make sense of their world and comprehend it.

Emergency Departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8-12-hour shifts, providing partial flexibility in adjusting staffing levels. In this work, we propose a stochastic queueing network model of patient flow in the ED and study an associated fluid control problem to guide the reassignment decision for two types of nursing staff. We propose a heuristic solution approach and investigate its performance both analytically and using simulation. Analytical results and simulation experiments suggest a significant reduction of waiting times in parameter regimes relevant to the ED setting. We further implement the staffing approach at a large ED. This pilot study highlights several challenges of implementing operational interventions in the ED, including the difficulty of establishing a clean statistical environment in such setting. Despite these challenges, we find that guiding reassignment decisions using our approach is associated with significant improvements to patient flow including a reduction in average total ED length-of-stay (LOS) of 1.7 hours.

The COVID-19 pandemic has caused great disruption to the service sector, and it has, in turn, adapted by implementing measures that reduce physical contact among employees and users; examples include home-office work and the setting of occupancy restrictions at indoor locations. The design of services in the context of a pandemic requires balancing between two objectives: (i) special measures must be implemented to maintain physical separation among people to reduce the risk of infection, and (ii) these sanitary measures also reduce process capacity, thereby increasing the waiting times of users. We study this problem in the context of election processes, in which balancing waiting time with public safety is of first order relevance to ensuring voter turnout, using as a real-world application the Chilean 2020 national referendum. Analyzing this problem requires a multidisciplinary approach that consists of integrating randomized experiments to measure how voters weigh infection risk relative to waiting time and stochastic modeling/discrete event simulation to prescribe recommendations for the service design—specifically setting capacity limits to trade off between overcrowding and process efficiency. Overall, our results shows that infection risk is an important factor affecting voter turnout during a pandemic and that capacity limits can be a useful design tool to balance these risks with other service quality measures. Some of these findings were considered in the guidelines that Servel provided to manage capacity and voter arrival patterns at voting centers.

This research sheds light on consumer motivations for participating in the sharing economy and examines downstream consequences of the uncovered motivations. We use text-mining techniques to extract Airbnb hosts’ motivations from their responses to the question “why did you start hosting.” We find that hosts are driven not only by the monetary motivation “to earn cash” but also by intrinsic motivations such as “to share beauty” and “to meet people.” Using extensive transaction-level data, we find that hosts with intrinsic motivations post more property photos and write longer property descriptions, demonstrating greater engagement with the platform. Consequently, these hosts receive higher guest satisfaction ratings. Compared to hosts who want to earn cash, hosts motivated to meet people are more likely to keep hosting and to stay active on the platform, and hosts motivated to share beauty charge higher prices. As a result, these intrinsically motivated hosts have a higher customer lifetime value compared to those with a monetary motivation. We employ a multimethod approach including text mining, Bayesian latent attrition models, and lab experiments to derive these insights. Our research provides an easy-to-implement approach to uncovering consumer motivations in practice and highlights the consequential role of these motivations for firms.

This research investigates the joint effect of calorie posting and social context on consumers’ food choices and embarrassment. We hypothesize and demonstrate that posting calorie information on a menu becomes more effective in reducing the total calorie of meal orders when the food is ordered in public (vs. in private).

We explore firms' ability to collect, verify, and use information as a mechanism that facilitates the role of companies as powerful corporate citizens who can mitigate societal problems. Specifically, we study whether US firms' business networks with China and Italy are information networks and whether firms use relevant information to mitigate the negative impacts of COVID-19. We first show descriptive evidence that firms with these networks have relevant information about COVID-19. We then document higher stay-at-home ratios in areas with more information-exposure companies before local city governments impose measures, suggesting that firms act on relevant information to safeguard employee health. We find the effects are more pronounced when industries are teleworkable, when regulators have information constraints, and when the information is verifiable or trustworthy. Finally, we show a lower growth in COVID-19 in areas with higher stay-at-home ratios, suggesting a positive impact of firms’ corporate citizenship

Are female founding CEOs penalized when raising funds for their ventures based on industry served? Across an observational study conducted on ventures seeking funding (N = 392) and an experimental study conducted on investors allocating venture funding (N = 130), we find evidence for a “lack of fit” effect: Female-led ventures catering to male-dominated industries receive significantly less funding at significantly lower valuations than female-led ventures catering to female-dominated industries. In contrast, male-led ventures attain similar funding and valuation outcomes regardless of the gender dominance of the industries to which they cater. We confirm that this is because investors perceive lower degrees of fit between founding CEO and venture for female-led ventures catering to male- as opposed to female-dominated industries (with no perceived fit differences for male-led ventures across industries). Degree of investor sophistication emerges as a potential attenuating factor, appearing to help reduce gender bias from perceived lack of fit.

We develop a dynamic model of board decision-making akin to dynamic voting models in the political economy literature. We show a board could retain a policy all directors agree is worse than an available alternative. Thus, directors may retain a CEO they agree is bad—deadlocked boards lead to entrenched CEOs. We explore how to compose boards and appoint directors to mitigate deadlock. We find board diversity and long director tenure can exacerbate deadlock. We rationalize why CEOs and incumbent directors have power to appoint new directors: to avoid deadlock. Our model speaks to short-termism, staggered boards, and proxy access.

This paper investigates whether political connections affect individuals' propensity to engage in white-collar crime. We identify connections by campaign donations or direct friendships and use the 2007 French Presidential election as a marker of change in the value of political connections to the winning candidate. We compare the behavior of Directors of publicly listed companies who were connected to the future President to the behavior of other non-connected Directors, before and after the election. Consistent with the belief that connections to a powerful politician can protect someone from prosecution or punishment, we uncover indirect evidence that connected Directors are more likely to engage in suspicious insider trading after the election: Purchases by connected Directors trigger larger abnormal returns, connected Directors are less likely to comply with trading disclosure requirements in a timely fashion, and connected Directors trade closer in time to their firms' announcements of results.

The recent pandemic has caused many businesses to alter their offerings, at times providing inferior value to their customers or incurring higher costs. Many classes moved online, leading to a lower-value offering without significant cost reductions, and many firms adopted costly hygiene measures, such as stringent cleaning or reducing capacity to maintain social distancing. This article explores consumers’ fairness perceptions regarding pricing decisions made in response to unique scenarios caused by the pandemic. We present three key findings: (i) maintaining prices following a product downgrade is viewed as less fair than maintaining prices following an equivalent decrease in costs; (ii) price decreases following a product downgrade are viewed as more fair when positioned as passing on a cost saving rather than making up for decreased value; and (iii) price increases due to hygiene measures are perceived as more fair when they result from direct (compared with indirect) cost changes.

Social media users face a tension between presenting themselves in an idealized or authentic way. Here, we explore how prioritizing one over the other impacts users’ well-being. We estimate the degree of self-idealized vs. authentic self-expression as the proximity between a user’s self-reported personality and the automated personality judgements made on the basis Facebook Likes and status updates. Analyzing data of 10,560 Facebook users, we find that individuals who are more authentic in their self-expression also report greater Life Satisfaction. This effect appears consistent across different personality profiles, countering the proposition that individuals with socially desirable personalities benefit from authentic self-expression more than others. We extend this finding in a pre-registered, longitudinal experiment, demonstrating the causal relationship between authentic posting and positive affect and mood on a within-person level. Our findings suggest that the extent to which social media use is related to well-being depends on how individuals use it.

Recent work has explored the dynamics of secrecy and its outcomes, but has yet to examine the motivations people have for engaging in secrecy and how such motivations shape the experience of secrecy and its implications. We present a motivational model of secrecy, and test this model in diverse contexts: (a) politics (secret votes in the 2016 United States election), (b) common secrets people keep, and (c) romantic relationships (secrets from partners) across a large sample of participants (total N = 1,839). We explored the motivations people have for keeping a secret, and the psychological implications of having a secret for one’s self and relationships. We found that mind wandering to secrets (but not concealing secrets) was associated with feelings of inauthenticity and regret. Moreover, it was secrecy motivated by concern for one’s reputation rather than one’s relationships that predicted these harms of secrecy.

In light of the widely discussed political divide post the 2016 election, we investigate in this paper whether this divide extends to the preferences of individuals for commercial brands, media sources and nonprofit organizations and how it evolved post the election. Using publicly available social media data of over 150 million Twitter users’ brand followerships we establish that commercial brands and organizations are affiliated with the consumers political ideology. We create a mosaic of brand preferences that are associated with either sides of the political spectrum, which we term preference partisanship, and explore the extent to which the political divide manifests itself also in the daily lives of individuals. Moreover, we identify an increasing polarization in preference partisanship since Donald Trump became President of the United States. Consistent with compensatory consumption theory, we find the increase in polarization post-election is stronger for liberals relative to conservatives. From a brand perspective, we show that brands can affect their degree of the political polarization by taking a political stand. Finally, after coloring brands as conservative or liberal we investigate the systematic differences and commonalities between them. We provide a publicly available API that allows access to our data and results.

We estimate the effects of hospital mergers, using detailed data containing medical supply transactions (representing 23 percent of operating costs) from a sample of US hospitals 2009-2015. Pre-merger price variation across hospitals (Gini coefficient 7 percent) suggests significant opportunities for cost decreases. However, we observe limited evidence of actual savings. On average, targets realize 1.9 percent savings; acquirers realize no significant savings. Examining treatment effect heterogeneity to shed light on theories of “buyer power,” we find that savings, when they occur, tend to be local, and potential benefits of savings may be offset by managerial costs of merging.

People often judge how much “the rich” deserve to be rich by taking into consideration how they had made their wealth. How do people make such judgments about the origins of others' wealth? In nine studies (N = 1707) and two supplemental analyses (N = 197), we examine whether the attributions people make about wealth are influenced by the way wealthy people spend their money. We find that people are more likely to attribute economic success to internal factors (such as hard work and competence) when “the rich” spend their money charitably versus when they spend it in a more luxurious manner. Moreover, we find that the tendency to attribute wealth to internal factors is due to judgments about wealthy individuals' character, and that the influence of spending on trait attribution is substantially larger for merit-related traits (e.g., persistence or industriousness) than other positive traits that are unrelated to merit (e.g., elegance or youthfulness). Finally, we find that how “the rich” spend their fortunes influences beliefs about how much they deserve to be rich. The more wealthy people give their money to charity, the more people believe that they deserve to have it in the first place.

Expanding unemployment insurance (UI) not only reduces the burden for the unemployed but also the moral cost of layoffs to firms and their managers. Using staggered expansions of UI across US states, we show that expanding UI leads to larger layoffs in firms experiencing negative economic shocks. The effects are stronger in weakly governed and financially unconstrained firms, where managers have greater discretion to avoid moral cost. This study presents moral cost as a novel microeconomic channel through which UI affects layoff decisions, which can compromise its effectiveness as a social insurance program and an automatic stabilizer.

I examine how financial markets interact with news about the COVID-19 pandemic. A twelve topic model optimizes the trade-off between number of topics and topic coherence. Using this model, I show that before mid-March 2020 markets react more to the same quantum of news when volatility is higher – a phenomenon I call hypersensitivity. Formal tests identify a structural break in mid-March, post which markets are no longer hypersensitive. In the hypersensitive stage, markets are overly volatile and overreact to news. Despite hypersensitivity, lagged prices better forecast future COVID-19 case counts than do lagged news.

The covid-19 crisis has led to a sharp deterioration in firm and bank balance sheets. The government has responded with a massive intervention in corporate credit markets. We study equilibrium dynamics of macroeconomic quantities and prices, and how they are affected by government policy. The interventions prevent a much deeper crisis by reducing corporate bankruptcies by about half and short-circuiting the doom loop between corporate and financial sector fragility. The additional fiscal cost is zero since program spending replaces what would otherwise have been spent on intermediary bailouts. The model predicts rising interest rates on government debt. We propose a more effective intervention with lower fiscal cost. Finally, we study longer-run consequences for firm leverage and intermediary health when pandemics become the new normal.

This note studies optimal lockdown policy in a model in which the government can limit a pandemic's impact via a lockdown at the cost of lower economic output. A government would like to commit to limit the extent of future lockdown in order to support more optimistic investor expectations in the present. However, such a commitment is not credible since investment decisions are sunk when the government makes the lockdown decision in the future. The commitment problem is more severe if lockdown is sufficiently effective at limiting disease spread or if the size of the susceptible population is sufficiently large. Credible rules that limit a government's ability to lock down the economy in the future can improve the efficiency of lockdown policy.

The largest economic cost of the COVID-19 pandemic could arise if it changed behavior long after the immediate health crisis is resolved. A common explanation for such a long-lived effect is the scarring of beliefs. We show how to quantify the extent of such belief changes and determine their impact on future economic outcomes. We find that the long-run effect of the COVID crisis depends crucially on whether bankruptcies and changes in habit make existing capital obsolete. A policy that avoided most permanent separation of workers from capital could generate a much larger benefit than originally thought, that could easily be 180% of annual GDP, in present value.

This note studies optimal lockdown policy in a model in which the government can limit a pandemic's impact via a lockdown at the cost of lower economic output. A government would like to commit to limit the extent of future lockdown in order to support more optimistic investor expectations in the present. However, such a commitment is not credible since investment decisions are sunk when the government makes the lockdown decision in the future. The commitment problem is more severe if lockdown is sufficiently effective at limiting disease spread or if the size of the susceptible population is sufficiently large. Credible rules that limit a government's ability to lock down the economy in the future can improve the efficiency of lockdown policy.

A crucial element of navigating group conflict is how group members manage stigma imposed on them by other groups. Across three experiments, we propose that group identification is a cause and consequence of self‐labeling with stigmatizing group labels, a practice known to reduce stigma. Experiment 1 found that group identification increased self‐labeling with a stigmatizing group label. In Experiment 2, individuals who self‐labeled with a stigmatizing group label felt more identified with their group, which reduced the label's perceived negativity; they also persisted longer on an in‐group helping task, an effect that was partially mediated by group identification. In Experiment 3, observers perceived self‐labelers as more identified with their group and as viewing the label less negatively; perceived group identification mediated the relationship. Group identification is a critical component in reappropriating stigmatizing labels and provides insight into how highly identified members can navigate group conflict by negotiating their group's identity.

This article examines the diffusion of protest tactics among social movement organizations (SMOs) through their collaboration in protest groups. Using a longitudinal data set of SMO protest activity between 1960 and 1995, the authors adapt novel methods for dealing with two forms of selection and measurement bias in network analysis: (i) the mechanism that renders some SMOs more likely to select into collaboration and (ii) the notion that diffusion is an artifact of homophily or indirect learning rather than influence. The authors find that collaboration is an important channel of tactical diffusion and that SMOs with broader tactical repertoires adopt more tactics via their collaboration with other SMOs, but only up to a point. Engaging in more collaboration also makes SMOs more active transmitters and adopters of new tactics. Finally, initial overlap in respective tactical repertoires facilitates the diffusion of tactics among collaborating SMOs.

We perform an econometric analysis of the effect of new drug launches on longevity, using data from the IMS Health Drug Launches database and the WHO Mortality Database. Under conservative assumptions, our estimates imply that the average annual increase in life expectancy of the entire population resulting from new drug launches is about one week, and that the incremental cost effectiveness ratio (new drug expenditure per person per year divided by the increase in life-years per person per year attributable to new drug launches) is about $6750 — far lower than most estimates of the value of a statistical life-year.