Research

Abstract: Research on policing has focused on documented actions such as stops and arrests—less is known about patrols and presence. We map the neighborhood movement of nearly ten thousand officers across 21 of America’s largest cities using anonymized smartphone data. Police spend 0.36% more time in neighborhoods for each percentage point increase in Black residents. This neighborhood-level disparity persists after controlling for density, socioeconomic, and crime-driven demand for policing, and may be lower in cities with more Black police super- visors (but not officers). Patterns of police presence statistically explain 57% of the higher arrest rate in more Black neighborhoods.

Abstract: This paper examines how investors’ belief disagreement affects the firm’s debt maturity choice. We find that in the presence of belief disagreement, the firm prefers short-term debt to long-term debt. This is because short-term debt is less risky and can attract more optimistic investors, which raises the debt price and reduces the cost of debt financing. Our result is stronger when long-term debt is more risky compared to short-term debt and investors’ belief dispersion is higher.

Abstract: Using smartphone location data and a novel application of publicly available employment data, we map how California communities are connected to nearby prisons through the movement of prison staff, and we measure the role these connections play in spreading infectious diseases. Leveraging an exogenous prisoner transfer-induced COVID-19 outbreak at San Quentin state prison in June 2020 as a quasi-experiment, we examine the unidirectional spread of the disease from the prison to surrounding communities. This outbreak was unique: its origin from outside Northern California was clearly documented and non-staff entry and exit was severely limited during this time. Our identification strategy compares zip codes connected and unconnected to the prison via staff movement. Compared to unconnected zip codes with similar pre-transfer COVID-19 rates and demographic characteristics (race/ethnicity, education, household income, age, and population), zip codes connected to San Quentin had 13% more new COVID-19 cases in July and 30% more in August. Our results suggest that a hypothetical novel infectious disease that emerged in California prisons could lead to almost 15,000 community infections within one month from staff movements alone. These findings identify the degree to which “closed institutions” are—even during lockdowns—epidemiologically porous, highlighting the need for public health interventions to reduce the unintended consequences of such connections on the spread of infectious disease.

Abstract: Extensive research finds that place-based investment reduces crime, leading practi- tioners to propose it as an alternative to police-centered policies. We explore another channel linking local investment to crime—that police patrol is endogenous to the built environment-using smartphone location data. Exploiting quasi-experimental variation in HUD rules designating Qualified Census Tracts (QCTs), we find police increase patrol in QCTs enough to explain all the observed violent crime reduction. Police increase patrol more in neighborhoods with more Black residents and fewer recently-built units. Our findings highlight the importance of understanding police response to local development before framing it as a substitute for policing.

Abstract: Using newly available smartphone mobility data, several recent studies document partisan gaps in compliance with COVID-19 social distancing orders. By relying on cross-county variation in election results and mobility measures, these studies may fail to isolate partisan effects from contemporaneous COVID trends due to spatial confounds: mobility and voting data are aggregated to the county-level, and nine of the ten earliest-hit counties were heavily Democratic. We extend these analyses by merging individual-level geolocation data for ten million smartphones with nationwide precinct-level vote counts, allowing us to estimate partisan differences in compliance with COVID restrictions by comparing Democratic and Republican voters living in close proximity and within the same county. By May 2020—when ninety percent of Americans were under a stay-at-home order—likely Clinton voters reduced travel outside the home by twice as much as their Trump-voting neighbors, and were significantly more likely to remain at home throughout the day. This gap survives aggressive geographic controls and remains one of the strongest predictors of stay-at-home compliance, confirming that strong partisan effects are not the result of spatio-temporal covariates.