Revenue-Based Financing (with Claire Shi and Rowan Clarke). Updated 2023.
[Abstract | Draft]
We use data from a major South African payment processor to study how digital payments mitigate asymmetric information challenges in small business "revenue-based financing" contracts, which tie repayment schedules to future revenue. Eight months post-financing, digital payments through the processor are 15% lower for takers than observably similar non-takers. We show this "gap" can be decomposed into three components: moral hazard from revenue hiding, adverse selection, and the causal effect of financing for takers. Two natural experiments suggest that takers shift more revenue off the platform when competition increases (moral hazard), and that financiers can increase repayment by waiting longer before extending offers (adverse selection). With estimates from both experiments, we bound the gap components, finding substantial adverse selection, but also positive short-run causal effects. Our results suggest digital payment platforms with "sticky" features can alleviate classic risk-sharing frictions by imposing hiding costs and limiting hidden information.
The Social Integration of International Migrants: Evidence from the Networks of Syrians in Germany (with Michael Bailey, Drew Johnston, Martin Koenen, Theresa Kuchler, and Johannes Stroebel). Updated 2023. R&R at Journal of Political Economy
[Abstract | Draft | Summary (English) | Summary (German)]
We use de-identified friendship data from Facebook to study the social integration of Syrian migrants in Germany. We decompose the significant spatial variation in migrants’ integration levels into the rate at which Germans befriend their neighbors in general and the particular rate at which they befriend Syrian 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 persistent individual characteristics or preferences of local natives. We explore the characteristics of places with higher integration levels, and show that integration courses causally affect place-specific equilibrium integration levels by shifting the rates of Germans befriending Syrians.
Social Networks Shape Beliefs and Behavior: Evidence from Social Distancing during the Covid-19 Pandemic (with Michael Bailey, Drew Johnston, Martin Koenen, Theresa Kuchler, and Johannes Stroebel). Updated 2023. Accepted at Journal of Political Economy Microeconomics
[Abstract | Draft | NBER Digest]
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
JUE Insight: The geographic spread of COVID-19 correlates with structure of social networks as measured by Facebook (with Theresa Kuchler and Johannes Stroebel). Journal of Urban Economics, 2022.
[Abstract | Published Version | Code | DSCC-19 Presentation Video | Guardian Coverage | Daily Mail Coverage | FAZ Coverage]
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.
The Determinants of Social Connectedness in Europe (with Michael Bailey, Drew Johnston, Theresa Kuchler, Bogdan State, and Johannes Stroebel). Social Informatics, 2020.
[Abstract | Published Version | Online Appendix | Code | SocInfo 2020 Presentation Video | Slides]
We use de-identified and 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, region-pairs with dissimilar incomes tend to be more connected, likely due to increased migration from poorer to richer regions.