Published Papers

The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interpersonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad information is processed more thoroughly than good. The self is more motivated to avoid bad self-definitions than to pursue good ones. Bad impressions and bad stereotypes are quicker to form and more resistant to disconfirmation than good ones. Various explanations such as diagnosticity and salience help explain some findings, but the greater power of bad events is still found when such variables are controlled. Hardly any exceptions (indicating greater power of good) can be found. Taken together, these findings suggest that bad is stronger than good, as a general principle across a broad range of psychological phenomena.

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual. We describe experimental designs that instead randomly assign individuals' peers to encouragements to behaviors that directly affect those individuals. We illustrate this method with a large peer encouragement design on Facebook for estimating the effects of receiving feedback from peers on posts shared by focal individuals. We find evidence for substantial effects of receiving marginal feedback on multiple behaviors, including giving feedback to others and continued posting. These findings provide experimental evidence for the role of behaviors directed at specific individuals in the adoption and continued use of communication technologies. In comparison, observational estimates differ substantially, both underestimating and overestimating effects, suggesting that researchers and policy makers should be cautious in relying on them.

User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the "best-value" hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.

Many businesses today have adopted tweeting as a new form of product marketing. However, whether and how tweeting affects product demand remains inconclusive. The authors explore this question using a randomized field experiment on Sina Weibo, the top tweeting website in China. The authors collaborate with a major global media company and examine how the viewing of its TV shows is affected by (1) the media company's tweets about its shows, and (2) recruited Weibo influentials' retweets of the company tweets. The authors find that both company tweets and influential retweets increase show viewing, but in different ways. Company tweets directly boost viewing, whereas influential retweets increase viewing if the show tweet is informative. Meanwhile, influential retweets are more effective than company tweets in bringing new Weibo followers to the company, which indirectly increases viewing. The authors discuss recommendations on how to manage tweeting as a marketing tool.

This research examines how the positive or negative valence of proprietary information affects both the likelihood that people diffuse this information through their social networks and the likelihood that recipients' access to this information provides them with a source of comparative advantage. Using a unique dataset of over 2 million stock trades and associated profits and losses, and 1 million instant messages exchanged between professional day traders at a U.S. hedge fund, we show that day traders are more likely to talk about their gains than their losses with their close contacts, suggesting that positive information is more likely to be shared among one's close network of strong ties. However, by examining the subsequent behaviors of message recipients, we find that recipients tend to discount the value of positive, gains related information, being both more likely to pass on and profit from negative information related to trading losses, particularly from their strong ties. Our results suggest that although individuals are more likely to share positive information with their contacts, message recipients appear to account for the asymmetry in their subsequent communications and decision-making.

This paper exploits a novel hand-collected data set to provide a comprehensive analysis of the social relationships that underlie illegal insider trading networks. I find that inside information flows through strong social ties based on family, friends, and geographic proximity. On average, inside tips originate from corporate executives and reach buy-side investors after three links in the network. Inside traders earn prodigious returns of 35% over 21 days, with more central traders earning greater returns, as information conveyed through social networks improves price efficiency. More broadly, this paper provides some of the only direct evidence of person-to-person communication among investors.

We find that firms headquartered in U.S. counties with higher levels of social capital incur lower bank loan spreads. This finding is robust to using organ donation as an alternative social capital measure and incremental to the effects of religiosity, corporate social responsibility, and tax avoidance. We identify the causal relation using companies with a social-capital-changing headquarters relocation. We also find that high-social-capital firms face loosened nonprice loan terms, incur lower at-issue bond spreads, and prefer public bonds over bank loans. We conclude that debt holders perceive social capital as providing environmental pressure that constrains opportunistic firm behaviors in debt contracting.

Using large-scale administrative data, I track the employment and entrepreneurship of over forty million Americans and investigate entrepreneurial spillovers across coworkers, based on the idea that individuals who start their own firms learn institutional knowledge and entrepreneurial skills that they may teach others. I find that an individual whose current coworkers have more prior entrepreneurship experience is more likely to become an entrepreneur themself within the next five years, and these spillovers are strongest among workers with similar jobs and demographics. Furthermore, an individual is more likely to become a successful entrepreneur if those coworkers were themselves successful entrepreneurs. To quantify the role of these spillovers, I build a structural model of entrepreneurship and learning and estimate that the aggregate entrepreneurship rate would be 10% lower in the absence of learning.

We study how information sharing within an organization affects individual performance. We look at situations in which the same analyst, while working at the same broker, covers multiple mergers and acquisitions (M&As), in particular the acquirer prior to the M&A and the merged firm thereafter. We find that earnings forecasts for the merged firm are significantly more accurate when the analyst has a colleague (working at the same broker) covering the target prior to the M&A. This holds particularly true if acquirer analysts and target analysts reside in the same locale, if they are part of a smaller team, and if the target analyst is of higher quality. Our findings highlight the importance of information spillovers on individual performance in knowledge-based industries.

This paper uses unique administrative data and a quasi-field experiment of exogenous allocation to apartments in Sweden to estimate medium- and longer-run effects on financial behavior from exposure to financially literate neighbors. It contributes evidence of causal impact of financial literacy and points to a social multiplier of effective programs to enhance it. Exposure promotes saving in private retirement accounts and stockholding, especially when neighbors have economics or business education, but only for educated or male-headed households. Findings point to relevant knowledge transfer through social interactions rather than to labor market or other channels linked to local economic conditions.

The limits of arbitrage explain how a speculative bubble is sustained; they do not explain how likely one is to occur. To do that, you need a theory about the thing that sporadically causes arbitrageur constraints to bind. I propose a first such theory, which is based on social interactions between speculators. The theory says that bubbles should be more likely in assets where increases in past returns make excited-speculators relatively more persuasive to their peers. I empirically verify this ex ante prediction about bubble likelihoods and show that it is robust to some ex post disagreement about bubble definitions.

Using the introduction of Fox News as a natural experiment, we investigate whether partisanship in television news coverage influences fundamental corporate decisions.We find that during the George W. Bush presidency, firms led by Republican-leaning managers headquartered in regions into which Fox was introduced shift upward their total investment expenditures and financial leverage. Our findings imply that in making fundamental corporate decisions, Republican-leaning managers are swayed by the Republican slant of Fox that presents an optimistic macroeconomic outlook. The results highlight the importance of heterogeneity in media slant in understanding the role of the media incorporate decision making.