We study the nature of peer effects in the market for new cell phones. Our analysis builds on de-identified data from Facebook that combine information on social networks with information on users' cell phone models. To identify peer effects, we use variation in friends' new phone acquisitions resulting from random phone losses and carrier-specific contract terms. A new phone purchase by a friend has a substantial positive and long-term effect on an individual's own demand for phones of the same brand, most of which is concentrated on the particular model purchased by the friend. We provide evidence that social learning contributes substantially to the observed peer effects. While peer effects increase the overall demand for cell phones, a friend's purchase of a new phone of a particular brand can reduce individuals' own demand for phones from competing brands---in particular those running on a different operating system. We discuss the implications of these findings for the nature of firm competition. We also find that stronger peer effects are exerted by more price-sensitive individuals. This positive correlation suggests that the elasticity of aggregate demand is substantially larger than the elasticity of individual demand. Through this channel, peer effects reduce firms' markups and, in many models, contribute to higher consumer surplus and more efficient resource allocation.
We use social network data from Facebook to show that institutional investors invest more in firms that are located in regions to which the investors have stronger social ties. This effect is distinct from the effect of geographic distance, and is particularly strong for investments in small and informationally opaque firms. Building on these results, we show that firms in regions that are more socially proximate to institutional capital have higher valuations and higher liquidity, especially when the firms are small or have low analyst coverage. Consistent with this cross-sectional result, liquidity was lower during Hurricane Sandy for firms that are socially proximate to institutional capital in the areas that were affected by Sandy. We find no evidence that investors realize differential returns from their investments in socially connected areas, suggesting that investors do not obtain an informational advantage through friends as captured by Facebook. Our results suggest that the social structures of a region affect its firms' access to capital and thereby contributes to geographic differences in economic outcomes.
We use anonymized and aggregated data from Facebook to explore the spatial structure of social networks in the New York metro area. We highlight the importance of transportation infrastructure in shaping urban social networks by showing that travel time and travel costs are substantially stronger predictors of social connectedness between zip codes than geographic distance is. We also document significant heterogeneity in the geographic breadth of social networks across New York zip codes, and show that much of this heterogeneity is explained by the ease of access to public transit, even after controlling for socioeconomic characteristics of the zip codes' residents. When we group zip codes with strong social ties into hypothetical communities using an agglomerative clustering algorithm, we find that geographically non-contiguous locations are grouped into socially connected communities, again highlighting that geographic distance is an imperfect proxy for urban social connectedness. We also explore the social connections between New York zip codes and foreign countries, and highlight how these are related to past migration movements.
Published or Forthcoming Papers
Using high-frequency transaction-level income, spending, balances, and credit limits data from an online financial service, we show that many consumers fail to stick to their self-set debt paydown plans and argue that this behavior is best explained by a model of present bias. Theoretically, we show that (i) a present-biased agent's sensitivity of consumption spending to paycheck receipt reflects his or her short-run impatience and that (ii) this sensitivity varies with available resources only for agents who are aware (sophisticated) rather than unaware (naive) of their future impatience. In turn, we classify users in our data accordingly. Consistent with present bias, we find that (i) sophisticated users' average paydown falls with higher measured impatience and that (ii) their planned paydown is more predictive of actual paydown than that of naives. We are the first to provide a theoretically-founded empirical methodology to measure sophistication and naivete from spending and income data and to validate this measure using our information on planned versus actual debt paydown. Moreover, our results highlight the importance of distinguishing between sophisticated and naive present-biased individuals in understanding their financial decision making.
We study the relationship between homebuyers' beliefs about future house price changes and their mortgage leverage choices. Whether more pessimistic homebuyers choose higher or lower leverage depends on their willingness and ability to reduce the size of their housing market investments. When households primarily maximize the levered return of their property investments, more pessimistic homebuyers reduce their leverage to purchase smaller houses. On the other hand, when considerations such as family size pin down the desired property size, pessimistic homebuyers reduce their financial exposure to the housing market by making smaller downpayments to buy similarly-sized homes. To determine which scenario better describes the data, we investigate the cross-sectional relationship between house price beliefs and mortgage leverage choices in the U.S. housing market. We use plausibly exogenous variation in house price beliefs to show that more pessimistic homebuyers make smaller downpayments and choose higher leverage, in particular in states where default costs are relatively low, as well as during periods when house prices are expected to fall on average. Our results highlight the important role of heterogeneous beliefs in explaining households' financial decisions.
We use novel survey data to document that individuals extrapolate from recent personal experiences when forming expectations about aggregate economic outcomes. Recent locally experienced house price movements affect expectations about future US house price changes, and higher experienced house price volatility causes respondents to report a wider distribution over expected US house price movements. Similarly, we exploit within-individual variation in employment status to show that individuals who personally experience unemployment become more pessimistic about future nationwide unemployment. The extent of extrapolation is unrelated to how informative personal experiences are; it is also inconsistent with risk-adjustment, and more pronounced for less sophisticated individuals.
We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We use anonymized data from Facebook, the world's largest online social network, to first explore heterogeneity in the structure of individuals' social networks. We then exploit the rich variation in the data to analyze the effects of social interactions on housing market investments. To do this, we combine the social network information with housing transaction data. Variation in the geographic dispersion of social networks, combined with time-varying regional house price changes, induces heterogeneity in the house price experiences of different individuals' friends. We show that individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Similarly, when homeowners' friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We find that these relationships are driven by the effect of social interactions on individuals' housing market expectations. Survey data show that individuals whose geographically distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends.
Public Health Post
New York Times
We introduce a new measure of social connectedness between U.S. county pairs, as well as between U.S. counties and foreign countries. Our measure, which we call the Social Connectedness Index (SCI), is based on the number of friendship links on Facebook, the world's largest online social network. Within the U.S., social connectedness is strongly decreasing in geographic distance between counties. The population of counties with more geographically-dispersed social networks is richer, more educated, and has higher life expectancy. Region-pairs that are more socially connected have higher trade flows, even after controlling for geographic distance and the similarity of regions along other demographic and socioeconomic measures. Higher social connectedness is also associated with more cross-county migration and patent citations. Social connectedness between U.S. counties and foreign countries is correlated with past migration patterns, with social connectedness decaying in the time since the primary migration wave from that country. Trade with foreign countries is also strongly related to the social connectedness with those countries. These results suggest that the SCI captures an important role of social networks in facilitating economic and social interactions. Our findings highlight the potential for the SCI to mitigate the measurement challenges that pervade empirical literatures that study the role of social interactions across the social sciences.
The internet has dramatically reduced the cost of varying prices, displays and information provided to consumers, facilitating both active and passive experimentation. We document the prevalence of targeted pricing and auction design variation on eBay, and identify hundreds of thousands of experiments conducted by sellers across a wide array of retail products. We use the data to measure the dispersion in auction prices for identical goods sold by the same seller, to estimate nonparametric auction demand curves, to analyze the effect of "buy it now" options and other auction design parameters, and to assess consumer sensitivity to shipping fees. We also investigate the robustness of the results by isolating different types of identifying variation, as well as the heterogeneity of the estimates across item categories. We argue that leveraging the experiments of market participants takes advantage of the scale and heterogeneity of online markets and can be a powerful approach for testing and measurement.