We show that social network exposure to COVID-19 cases shapes individuals' beliefs and behaviors concerning the coronavirus. We use de-identified data from Facebook to document that individuals with friends in areas with worse COVID-19 outbreaks reduce their mobility more than otherwise similar individuals with friends in less affected areas. The effects are quantitatively large and long-lasting: a one standard deviation increase in friend-exposure to COVID-19 cases in March 2020 results in a 1.2 percentage point increase in the probability of staying home on a given day through at least the end of May 2020. As the pandemic progresses—and the characteristics of individuals with the highest friend-exposure vary—changes in friend-exposure continue to drive changes in social distancing behavior, ruling out many unobserved effects as drivers of our results. We also show that individuals with higher friend-exposure to COVID-19 are more likely to publicly post in support of social distancing measures and less likely to be members of groups advocating to "reopen" the economy. These findings suggest that friends can influence individuals' beliefs about the risks of the disease and thereby induce them to engage in mitigating public health behavior.
Coauthors: Georgij Alekseev, Safaa Amer, Manasa Gopal, Theresa Kuchler, JW Schneider, and Nils Wernerfelt
We analyze a large-scale survey of owners, managers, and employees of small businesses in the United States to understand the effects of the early stages of the COVID-19 pandemic on those businesses. The survey was fielded in late April 2020 among Facebook business page administrators, frequent sellers on Facebook's e-commerce platform Marketplace, and the general Facebook user population. We observe more than 66,000 responses covering most sectors of the economy, including many businesses that had stopped operating due to the pandemic. The survey asks 136 questions covering topics such as changes in business operations and employment, changes in financing patterns, and the interaction of household and business responsibilities. We characterize the adjustments implemented to survive the pandemic and explore the key challenges to continue operating or to re-open. We show how these patterns differ across industry, firm size, owner gender, and other firm characteristics.
We survey 861 finance academics, professionals, and public sector regulators and policy economists about climate finance topics. They identify regulatory risk as the top climate risk to businesses and investors over the next five years, but they view physical risks as the top risk over the next 30 years. By an overwhelming margin, respondents believe that asset prices underestimate climate risks rather than overestimate them. We also tabulate opinions about the correlation between growth and climate change; social discount rates appropriate for projects that mitigate the effects of climate change; most influential forces for reducing climate risks; and, most important research topics.
We show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties but find no evidence that these investments earn a differential return. Firms in regions with stronger social ties to locations with many institutional investors have higher valuations and higher liquidity. These effects are largest for small firms with little analyst coverage, suggesting that the investors’ behavior is explained by their increased awareness of firms in socially proximate locations. Our results highlight that the social structure of regions affects firms’ access to capital and contributes to geographic differences in economic outcomes.
We use de-identified data from Facebook to study the nature of peer effects in the market for cell phones. To identify peer effects, we exploit variation in friends’ new phone acquisitions resulting from random phone losses. A new phone purchase by a friend has a large and persistent effect on an individual’s own demand for phones of the same brand. While peer effects increase the overall demand for phones, a friend’s purchase of a particular phone brand can reduce an individual’s own demand for phones from competing brands, in particular if they are running on a different operating system.
We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
We review the literature studying interactions between climate change and financial markets. We first discuss various approaches to incorporating climate risk in macro-finance models. We then review the empirical literature that explores the pricing of climate risks across a large number of asset classes including real estate, equities, and fixed income securities. In this context, we also discuss how investors can use these assets to construct portfolios that hedge against climate risk. We conclude by proposing several promising directions for future research in climate finance.
We review an empirical literature that studies the role of social interactions in driving economic and financial decision making. We first summarize recent work that documents an important role of social interactions in explaining household decisions in housing and mortgage markets. This evidence shows, for example, that there are large peer effects in mortgage refinancing decisions and that individuals' beliefs about the attractiveness of housing market investments are affected by the recent house price experiences of their friends. We also summarize the evidence that social interactions affect the stock market investments of both retail and professional investors as well as household financial decisions such as retirement savings, borrowing, and default. Along the way, we describe a number of easily accessible recent data sets for the study of social interactions in finance, including the "Social Connectedness Index," which measures the frequency of Facebook friendship links across geographic regions. We conclude by outlining several promising directions for further research at the intersection of household finance "social finance."
We show that housing markets provide information about the appropriate discount rates for valuing investments in climate change abatement. We document that real estate is exposed to both consumption and climate risk and that its term structure of discount rates is downward-sloping, reaching 2.6% for payoffs beyond 100 years. We use a tractable asset pricing model that incorporates features of climate change to show that the term structure of discount rates for climate-hedging investments is thus upward-sloping but bounded above by the risk-free rate. At horizons where risk-free rates are unavailable, the estimated housing discount rates provide an upper bound.
We administer a newly-designed survey to a large panel of wealthy retail investors. The survey elicits beliefs that are important for macroeconomics and finance, and matches respondents with administrative data on their portfolio composition, their log-in behavior, and their trading activity. We establish five facts in this data: (1) Beliefs are reflected in portfolio allocations. The sensitivity of portfolios to beliefs is small on average, but varies significantly with investor wealth, attention, trading frequency, and confidence. (2) Belief changes do not predict when investors trade, but conditional on trading, they affect both the direction and the magnitude of trades. (3) Beliefs are mostly characterized by large and persistent individual heterogeneity; demographic characteristics explain only a small part of why some individuals are optimistic and some are pessimistic. (4) Expected cash flow growth and expected returns are positively related, both within and across investors. (5) Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors. These five facts provide useful guidance for the design of macro-finance models.
We use de-identified data from Facebook to construct a new and publicly available measure of the pairwise social connectedness between 170 countries and 332 European regions. We find that two countries trade more when they are more socially connected, especially for goods where information frictions may be large. The social connections that predict trade in specific products are those between the regions where the product is produced in the exporting country and the regions where it is used in the importing country. Once we control for social connectedness, the estimated effects of geographic distance and country borders on trade decline substantially.
We analyze how investor expectations about economic growth and stock returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic, as well as during the subsequent partial stock market recovery. We surveyed retail investors who are clients of Vanguard at three points in time: (i) on February 11-12, around the all-time stock market high, (ii) on March 11-12, after the stock market had collapsed by over 20%, and (iii) on April 16-17, after the market had rallied 25% from its lowest point. Following the crash, the average investor turned more pessimistic about the short-run performance of both the stock market and the real economy. Investors also perceived higher probabilities of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investor expectations about long-run (10-year) economic and stock market outcomes remained largely unchanged, and, if anything, improved. Disagreement among investors about economic and stock market outcomes also increased substantially following the stock market crash, with the disagreement persisting through the partial market recovery. Those respondents who were the most optimistic in February saw the largest decline in expectations, and sold the most equity. Those respondents who were the most pessimistic in February largely left their portfolios unchanged during and after the crash.
We use aggregated data from Facebook to study the structure of social networks across European regions. Social connectedness declines strongly in geographic distance and at country borders. Historical borders and unions — such as the Austro-Hungarian Empire, Czechoslovakia, and East/West Germany — shape present-day social connectedness over and above today’s political boundaries and other controls. All else equal, social connectedness is stronger between regions with residents of similar ages and education levels, as well as between regions that share a language and religion. In contrast, regionpairs with dissimilar incomes tend to be more connected, likely due to increased migration from poorer to richer regions.
We use anonymized and aggregated data from Facebook to explore the spatial structure of social networks in the New York metro area. We find that a substantial share of urban residents' connections are to individuals who are located nearby. We also highlight the importance of transportation infrastructure in shaping urban social networks by showing that social connectedness declines faster in travel time and travel cost than it does in geographic distance. We find that areas that are more socially connected with each other have stronger commuting flows, even after controlling for geographic distance and ease of travel. We also document significant heterogeneity in the geographic breadth of social networks across New York zip codes, and show that this heterogeneity correlates with access to public transit. Zip codes with geographically broader social networks also have higher incomes, higher education levels, and more high-quality entrepreneurial activity. We also explore the social connections between New York zip codes and foreign countries, and highlight how these are related to past migration movements.
We study housing markets with multiple segments searched by heterogeneous clienteles. In the San Francisco Bay Area, search activity and inventory covary negatively across cities, but positively across market segments within cities. A quantitative search model shows how the endogenous flow of broad searchers to high-inventory segments within their search ranges induces a positive relationship between inventory and search activity across segments with a large common clientele. The prevalence of broad searchers shapes the response of housing markets to localized supply and demand shocks. Broad searchers help spread shocks across many segments and reduce their effect on local market activity.
We propose and implement a procedure to dynamically hedge climate change risk. To create our hedge target, we extract innovations in climate news series that we construct through textual analysis of high-dimensional data on newspaper coverage of climate change. We then use a mimicking-portfolio approach to build climate change hedge portfolios using a large panel of equity returns. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. The resulting hedge portfolios outperform alternative hedging strategies based primarily on industry tilts. We discuss multiple directions for future research on financial approaches to managing climate risk.
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 detailed micro data to document a causal response of local retail price to changes in house prices, with elasticities of 15%-20% across housing booms and busts. Notably, these price responses are largest in zip codes with many homeowners, and non-existent in zip codes with mostly renters. We provide evidence that these retail price responses are driven by changes in markups rather than by changes in local costs. We then argue that markups rise with house prices, particularly in high homeownership locations, because greater housing wealth reduces homeowners' demand elasticity, and firms raise markups in response. Consistent with this explanation, shopping data confirms that house price changes have opposite effects on the price sensitivity of homeowners and renters. Our evidence has implications for monetary, labor and urban economics, and suggests a new source of markup variation in business cycle models.
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.
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.
We propose a new approach to studying the pass-through of credit expansion policies that focuses on frictions, such as asymmetric information, that arise in the interaction between banks and borrowers. We decompose the effect of changes in banks' shadow cost of funds on aggregate borrowing into the product of banks' marginal propensity to lend (MPL) to borrowers and those borrowers' marginal propensity to borrow (MPB), aggregated over all borrowers in the economy. We apply our framework by estimating heterogeneous MPBs and MPLs in the U.S. credit card market. Using panel data on 8.5 million credit cards and 743 credit limit regression discontinuities, we find that the MPB is declining in credit score, falling from 59% for consumers with FICO scores below 660 to essentially zero for consumers with FICO scores above 740. We use a simple model of optimal credit limits to show that a bank's MPL depends on a small number of "sufficient statistics" that capture forces such as asymmetric information, and that can be estimated using our credit limit discontinuities. For the lowest FICO score consumers, higher credit limits sharply reduce profits from lending, limiting banks' optimal MPL to these consumers. The negative correlation between MPB and MPL reduces the impact of changes in banks' cost of funds on aggregate household borrowing, and highlights the importance of frictions in bank-borrower interactions for understanding the pass-through of credit expansions.
I empirically analyze credit market outcomes when competing lenders are differentially informed about the expected return from making a loan. I study the residential mortgage market where property developers often cooperate with vertically integrated mortgage lenders to offer financing to buyers of new homes. I show that these integrated lenders have superior information about the construction quality of individual homes and exploit this information to lend against higher-quality collateral, decreasing foreclosures by up to 40%. To compensate for this adverse selection on collateral values, non-integrated lenders charge higher interest rates when competing against a better-informed integrated lender.
Many U.S. government policies aim to encourage homeownership. We use a general equilibrium model with heterogeneous agents to consider the effects of temporary homebuyer tax credits and the asymmetric tax treatment of owner-occupied and rental housing on prices, quantities, allocations, and welfare. The model suggests that homebuyer tax credits temporarily raise house prices and transaction volumes, but have negative effects on welfare. Removing the asymmetric tax treatment of owner-occupied and rental housing can generate welfare gains for a majority of agents across steady states, but welfare impacts are substantially more varied along the transitions between steady states.
We test for the existence of housing bubbles associated with a failure of the transversality condition that requires the present value of payments occurring infinitely far in the future to be zero. The most prominent such bubble is the classic rational bubble. We study housing markets in the U.K. and Singapore, where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are finite-maturity, pre-paid, and tradable ownership contracts with maturities often exceeding 700 years. Freeholds are infinite-maturity ownership contracts. The price difference between leaseholds with extremely-long maturities and freeholds reflects the present value of a claim to the freehold after leasehold expiry, and is thus a direct empirical measure of the transversality condition. We estimate this price difference, and find no evidence for failures of the transversality condition in housing markets in the U.K. and Singapore, even during periods when a sizeable bubble was regularly thought to be present.
In housing markets, neighborhood characteristics are a key source of information heterogeneity: sellers are usually better informed about neighborhood values than buyers are, but some sellers and buyers are better informed than their peers are. Consistent with predictions from a new framework for analyzing such markets with heterogeneous assets and differentially informed agents, we find that changes in the composition of sellers toward more informed sellers and sellers with a larger supply elasticity predict subsequent house price declines. This effect is larger for houses with more price exposure to neighborhood characteristics, and smaller for houses bought by buyers that are more informed.
We estimate how households trade off immediate costs and uncertain future benefits that occur in the very long run, 100 or more years away. We exploit a unique feature of housing markets in the U.K. and Singapore, where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are temporary, pre-paid, and tradable ownership contracts with maturities between 99 and 999 years, while freeholds are perpetual ownership contracts. The price difference between leaseholds and freeholds reflects the present value of perpetual rental income starting at leasehold expiry, and is thus informative about very long-run discount rates. We estimate the price discounts for varying leasehold maturities compared to freeholds and extremely long-run leaseholds via hedonic regressions using proprietary datasets of the universe of transactions in each country. Households discount very long-run cash flows at low rates, assigning high present value to cash flows hundreds of years in the future. For example, 100-year leaseholds are valued at more than 10% less than otherwise identical freeholds, implying discount rates below 2.6% for 100-year claims.
We analyze the effectiveness of consumer financial regulation by considering the 2009 Credit Card Accountability Responsibility and Disclosure (CARD) Act. We use a panel data set covering 160 million credit card accounts and a difference-in-differences research design that compares changes in outcomes over time for consumer credit cards, which were subject to the regulations, to changes for small business credit cards, which the law did not cover. We estimate that regulatory limits on credit card fees reduced overall borrowing costs by an annualized 1.6% of average daily balances, with a decline of more than 5.3% for consumers with FICO scores below 660. We find no evidence of an offsetting increase in interest charges or a reduction in the volume of credit. Taken together, we estimate that the CARD Act saved consumers $11.9 billion per year. We also analyze a nudge that disclosed the interest savings from paying off balances in 36 months rather than making minimum payments. We detect a small increase in the share of accounts making the 36-month payment value but no evidence of a change in overall payments.
Policymakers are increasingly turning to regulation to reduce hidden or non-salient fees. Yet the overall consumer benefits from these polices are uncertain because firms may increase other prices to offset lost fee revenue. We show that the extent to which firms offset reduced hidden fee revenue is determined by a simple equation that combines two "sufficient statistics," which can be estimated or calibrated in a wide range of settings: (i) a parameter that captures the degree of market competitiveness and (ii) a parameter that captures the salience of the hidden fee. We provide corroborating evidence for this approach by drawing upon evidence on the effect of fee regulation under the 2009 CARD Act. We also illustrate the applicability of our approach by using the framework to assess a hypothetical regulation of airline baggage fees.
We use fiscal data on 2,468 oil extraction agreements in 38 countries to study tax contracts between resource-rich countries and independent oil companies. We analyze why expropriations occur and what determines the degree of oil price exposure of host countries. With asymmetric information about a country's expropriation cost even optimal contracts feature expropriations. Near-linearity in the oil price of real-world hydrocarbon contracts also helps to explain expropriations. We show theoretically and verify empirically that oil price insurance provided by tax contracts is increasing in a country's cost of expropriation, and decreasing in its production expertise. The timing of actual expropriations is consistent with our model.
The largest credit or liquidity program created by the Federal Reserve during the financial crisis was the mortgage-backed securities (MBS) purchase program. In this paper, we examine the quantitative impact of this program on mortgage interest rate spreads. This is more difficult than frequently perceived because of simultaneous changes in prepayment risk and default risk. Our empirical results attribute a sizeable portion of the decline in mortgage rates to such risks and a relatively small and uncertain portion to the program. For specifications where the existence or announcement of the program appears to have lowered spreads, we find no separate effect of the stock of MBS purchased by the Fed.