Johannes Stroebel
David S. Loeb Professor of Finance
Director, Climate Finance Initiative
NBER Research Associate
CEPR Research Affiliate
CESifo Research Fellow
New York University
Leonard N. Stern School of Business
44 West 4th Street, KCM 9-98
New York, NY 10012
Research
Working Papers
The Economics of Biodiversity Loss
View Abstract
ECB 2024 Sintra Forum Presentation
We explore the economic effects of biodiversity loss by developing an ecologically-founded model that captures how different species interact to deliver the ecosystem services that complement other factors of economic production. Aggregate ecosystem services are produced by combining several non-substitutable ecosystem functions such as pollination and water filtration, which are each provided by many substitutable species playing similar roles. As a result, economic output is an increasing but highly concave function of species richness. The marginal economic value of a species depends on three factors: (i) the number of similar species within its ecosystem function, (ii) the marginal importance of the affected function for overall ecosystem productivity, and (iii) the extent to which ecosystem services constrain economic output in each country. Using our framework, we derive expressions for the fragility of ecosystem service provision and its evolution over time, which depends, among other things, on the distribution of biodiversity losses across ecosystem functions. We discuss how these fragility measures can help policymakers assess the risks induced by biodiversity loss and prioritize conservation efforts. We also embed our model of ecosystem service production in a standard economic model to study optimal land use when land use raises output at the cost of reducing biodiversity. We find that even in settings where species loss does not reduce output substantially today, it lowers growth opportunities and reduces resilience to future species loss, especially when past species loss has been asymmetric across functions. Consistent with these predictions of our model, we show empirically that news about biodiversity loss increases spreads on credit default swaps (CDS) more for countries with more depleted ecosystems.
Biodiversity Risk
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We explore the effects of physical and regulatory risks related to biodiversity loss on asset values. We first develop a news-based measure of aggregate biodiversity risk and analyze how it varies over time. We also construct and publicly release several firm- and industry-level measures of exposure to biodiversity risk, based on textual analyses of firms' 10-K statements; the holdings of biodiversity-related funds; firms' responses to a questionnaire fielded by CDP; and a large survey of finance professionals, regulators, and academics. Exposures to biodiversity risk vary substantially across industries in a way that is economically sensible and distinct from exposures to climate risk. We find evidence that biodiversity risks already affect equity prices: returns of portfolios that are sorted on our measures of biodiversity risk exposure covary positively with innovations in aggregate biodiversity risk. However, our survey indicates that market participants do not perceive the current pricing of biodiversity risks in equity markets to be adequate.
Carbon VIX: Carbon Price Uncertainty and Decarbonization Investments
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We study the effects of carbon price uncertainty on firms' decisions to decarbonize their operations. We first use information on the pricing of options for emissions allowances in the European Emissions Trading System to create the Carbon VIX, a market-based high-frequency measure of carbon price uncertainty. Carbon price uncertainty is high, varies substantially over time, and experiences persistent shocks around major climate policy events. To explore the effects of this uncertainty on expected aggregate decarbonization investments, we analyze its effect on the stock returns of firms that help other businesses decarbonize. To identify these ``carbon solution providers,'' we extract common types of decarbonization investments from a large survey of firms, and assess which firms offer the associated goods and services. We find that stock returns of carbon solution providers vary positively with carbon prices, but negatively with carbon price uncertainty. This effect is quantitatively large: a 10 percentage point increase in uncertainty has the same negative impact on expected decarbonization activities as a 12 Euro decline in the carbon price. These findings support the prediction from real options theory that firms may delay investments in decarbonization when faced with uncertainty about the future costs of emissions.
The Social Integration of International Migrants: Evidence from the Networks of Syrians in Germany
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We use de-identified friendship data from Facebook to study the social integration of Syrian migrants in Germany. Our analysis establishes five key findings: (1) Places differ substantially in their propensities to socially integrate migrants. This regional variation in integration outcomes largely reflects causal place-based effects. (2) Spatial variation in migrants' social integration can be decomposed into the rate at which Germans befriend their neighbors in general and the particular rate at which they befriend migrants versus other Germans. We follow the friending behavior of Germans that move across locations to show that both forces are more affected by local institutions and policies than by persistent individual characteristics or preferences of local natives. (3) Integration courses causally affect place-specific equilibrium integration levels by increasing the rate at which Germans befriend Syrian migrants. (4) Social integration helps migrants obtain help from natives across a range of settings such as finding jobs and housing. (5) Natives quasi-randomly exposed to a migrant in high school are more likely to befriend other migrants later in life.
A Quantity-Based Approach to Constructing Climate Risk Hedge Portfolios
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We propose a new methodology to build hedge portfolios for climate risks. Our quantity-based approach exploits information on mutual funds' trading responses to idiosyncratic changes in fund managers' climate beliefs. We identify these belief shocks based on (i) managers experiencing local extreme heat events that shift climate beliefs and (ii) changes in the way funds’ shareholder disclosures discuss climate risks. We show that a portfolio that is long industries that investors tend to buy after experiencing negative idiosyncratic climate belief shocks, and short industries that investors tend to sell, appreciates in value in periods with negative aggregate climate news shocks.
Published or Forthcoming Papers
Four Facts about ESG Beliefs and Investor Portfolios
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We analyze survey data on ESG beliefs and preferences in a large panel of retail investors linked to administrative data on their investment portfolios. The survey elicits investors' expectations of longterm ESG equity returns and asks about their motivations, if any, to invest in ESG assets. We document four facts. First, investors generally expected ESG investments to underperform the market. Between mid-2021 and late-2022, the average expected 10-year annualized return of ESG investments relative to the overall stock market was −1.4%. Second, there is substantial heterogeneity across investors in their ESG return expectations and their motives for ESG investing: 45% of survey respondents do not see any reason to invest in ESG, 25% are primarily motivated by ethical considerations, 22% are driven by climate hedging motives, and 7% are motivated by return expectations. Third, there is a link between individuals’ reported ESG investment motives and their actual investment behaviors, with the highest ESG portfolio holdings among individuals who report ethics-driven investment motives. Fourth, financial considerations matter independently of other investment motives: we find meaningful ESG holdings only for investors who expect these investments to outperform the market, even among those investors who reported that their most important ESG investment motives were ethical or hedging reasons.
Social Networks Shape Beliefs and Behavior: Evidence from Social Distancing during the Covid-19 Pandemic
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We use de-identified data from Facebook to study how social connections affect beliefs and behaviors in high-stakes settings. During the Covid-19 pandemic, individuals with friends in areas currently experiencing worse disease outbreaks reduced their mobility substantially more than their otherwise similar neighbors with friends in less affected areas. To explore the mechanisms through which social connections shape behaviors, we 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 seeking to "reopen" the economy. These findings suggest that friends influence individuals’ behaviors in part through their beliefs, even in the presence of ubiquitous information from expert sources.
Lender Automation and Racial Disparities in Credit Access
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Process automation reduces racial disparities in credit access through enabling smaller loans, broadening banks’ geographic reach, and removing human biases from decision-making. We document these findings in the context of the Paycheck Protection Program (PPP), a setting where private lenders faced no credit risk but decided which firms to serve. Black-owned firms primarily obtained PPP loans from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black-owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, pre-existing banking relationships, firm performance, or fraud rates.
Climate Stress Testing
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We explore the design of climate stress tests to assess and manage macro-prudential risks from climate change in the financial sector. We review the climate stress scenarios currently employed by regulators, highlighting the need to (i) consider many transition risks as dynamic policy choices; (ii) better understand and incorporate feedback loops between climate change and the economy; and (iii) further explore "compound risk" scenarios in which climate risks co-occur with other risks. We discuss how the process of mapping climate stress scenarios into financial firm outcomes can incorporate existing evidence on the effects of various climate-related risks on credit and market outcomes. We argue that more research is required to (i) identify channels through which plausible scenarios can lead to meaningful short-run impact on credit risks given typical bank loan maturities; (ii) incorporate bank-lending responses to climate risks; (iii) assess the adequacy of climate risk pricing in financial markets; and (iv) better understand and incorporate the process of expectations formation around the realizations of climate risks. Finally, we discuss the relative advantages and disadvantages of using market-based climate stress tests that can be conducted using publicly available data to complement existing stress testing frameworks.
Housing Market Expectations
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We review the recent literature on the determinants and effects of housing market expectations. We begin by providing an overview of existing surveys that elicit housing market expectations, and discuss how those surveys may be expanded in the future. We then document a number of facts about time-series and cross-sectional patterns of housing market expectations in these survey data, before summarizing research that has studied how individuals form these expectations. Housing market expectations are strongly influenced by recently observed house price changes, by personally or locally observed house price changes, by house price changes observed in a person's social network, and by current home ownership status. Similarly, experienced house price volatility affects expectations uncertainty. We also summarize recent work that documents how differences in housing market expectations translate into differences in individuals’ housing market behaviors, including their home purchasing and mortgage financing decisions. Finally, we highlight research on how expectations affect aggregate outcomes in the housing market.
The Effects of Covid-19 on U.S. Small Businesses: Evidence from Owners, Managers, and Employees
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We analyze a large-scale survey of small business owners, managers, and employees in the United States to understand the effects of the Covid-19 pandemic on those businesses. We explore two waves of the survey that were fielded on Facebook in April 2020 and December 2020. We document five facts about the impact of the pandemic on small businesses. (1) Larger firms, older firms, and male-owned firms were more likely to remain open during the early stages of the pandemic, with many of these heterogeneities persisting through the end of 2020. (2) At businesses that remained open, concerns about demand shocks outweighed concerns about supply shocks, though the relative importance of supply shocks grew over time. (3) In response to the pandemic, almost a quarter of the firms reduced their prices, with price reductions concentrated among businesses facing financial constraints and demand shocks; almost no firms raised prices. (4) Only a quarter of small businesses had access to formal sources of financing at the start of the pandemic, and access to formal financing affected how firms responded to the pandemic. (5) Increased household responsibilities affected the ability of managers and employees to focus on their work, while increased business responsibilities impacted their ability to take care of their household members. This effect persisted through December 2020 and was particularly strong for women and parents of school-aged children. We discuss how these facts inform our understanding of the economic effects of the Covid-19 pandemic and how they can help design policy responses to similar shocks.
Social Capital I: Measurement and Associations with Economic Mobility
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Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org.
Social Capital II: Determinants of Economic Connectedness
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Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org.
The effect of climate risks on the interactions between financial markets and energy companies
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In recent years, the investment community has become increasingly aware of the investment risks from both the physical effects of climate change and the regulatory responses to facilitate the transition to a net-zero economy. The potential impact of climate transition risks is especially large for fossil energy companies, given their central role in producing carbon emissions. Here we discuss how concerns about climate risks influence the way investors allocate their capital and exercise their oversight of firms, and how this investor response affects companies in the energy sector. We then explore how different energy firms have responded to climate-related pressures from their investors and other stakeholders. We conclude by highlighting promising areas of research for understanding how climate risks affect the interaction between financial markets and the energy sector.
Peer Effects in Product Adoption
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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.
Social Proximity to Capital: Implications for Investors and Firms
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Social Proximity to Capital Data
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.
The geographic spread of Covid-19 correlates with the structure of social networks as measured by Facebook
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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.
Climate Finance
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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.
Social Finance
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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."
What Do You Think About Climate Finance?
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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.
Climate Change and Long-Run Discount Rates: Evidence from Real Estate
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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.
Five Facts about Beliefs and Portfolios
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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.
International Trade and Social Connectedness
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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.
The Joint Dynamics of Investor Beliefs and Trading During the Covid-19 Crash
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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.
The Determinants of Social Connectedness in Europe
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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.
Social Connectedness in Urban Areas
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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.
Segmented Housing Search
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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.
Hedging Climate Change News
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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.
House Price Beliefs and Mortgage Leverage Choice
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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.
House Prices, Local Demand, and Retail Prices
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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.
The Economic Effects of Social Networks: Evidence from the Housing Market
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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.
Social Connectedness: Measurement, Determinants, and Effects
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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.
Do Banks Pass Through Credit Expansions to Consumers Who Want to Borrow?
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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.
Asymmetric Information about Collateral Values
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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.
Government Intervention in the Housing Market - Who Wins, Who Loses?
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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.
No-Bubble Condition: Model-Free Tests in Housing Markets
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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.
Testing for Information Asymmetries in Real Estate Markets
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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.
Very Long-Run Discount Rates
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Regulating Consumer Financial Products: Evidence from Credit Cards
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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.
A Simple Framework for Estimating Consumer Benefits from Regulating Hidden Fees
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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.
Resource Extraction Contracts Under Threat of Expropriation: Theory and Evidence
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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.
Estimated Impact of the Fed's Mortgage-Backed Securities Purchase Program
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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.