Based on mobile phone records for 71 million users and location tracking information for one million users over almost three years, this study examines the labor market impacts of the COVID-19 pandemic in China's Guangdong province, whose GDP is larger than that of all but the top 12 countries in the world. Using a standard difference-in-differences framework, our analysis shows dramatic and protracted effects of the pandemic on the labor market: it increased unemployment by 72% and unemployment benefits claims by 57% even after the full reopening in 2020 relative to their levels in the same period in 2019. The impact was also highly heterogeneous, with women, workers older than 40, and migrants being more affected. Cities that rely more on export or that have a higher share of the hospitality industry in GDP but a lower share of the finance and healthcare industries experienced a more pronounced increase in unemployment. The lingering impact likely reflects the global transmission of the pandemic's effects through the supply chain and trade channels.

In the large literature on the spatial-level correlates of COVID-19, the association between quality of hospital care and outcomes has received little attention to date. To examine whether county-level mortality is correlated with measures of hospital performance, we assess daily cumulative deaths and pre-crisis measures of hospital quality, accounting for state fixed-effects and potential confounders. As a measure of quality, we use the pre-pandemic adjusted five-year penalty rates for excess 30-day readmissions following pneumonia admissions for the hospitals accessible to county residents based on ambulance travel patterns. Our adjustment corrects for socio-economic status and down-weighs observations based on small samples. We find that a one-standard-deviation increase in the quality of local hospitals is associated with a 2% lower death rate (relative to the mean of 20 deaths per 10,000 people) one and a half years after the first recorded death.

This paper investigates the effect of COVID-19 on both housing prices and housing price gradients in China using transaction level data from 60 Chinese cities. After using a difference-in-differences (DID) specification to disentangle the confounding effects of China's annual Spring Festival, we find that housing prices decreased by two percent immediately after the COVID-19 outbreak but gradually recovered by September 2020. Moreover, our findings suggest that COVID-19 flattens the horizontal housing price gradient, reduces the price premium for living in tall buildings, and changes the vertical gradient within residential buildings. This is likely explained by the changing household preferences towards low-density areas associated with lower infection risk.

We document large-scale urban flight in the United States during the COVID-19 pandemic. Regions that saw migrant influx experienced greater subsequent new COVID-19 cases, linking urban flight (as a disease vector) and coronavirus spread in destination areas. Urban residents fled to socially connected areas, consistent with the theory that individuals sheltered with friends and family, or in second homes. Populations that fled were disproportionately younger, whiter, and wealthier. The association between migration and subsequent new cases persists when instrumenting for migration with social networks.

We empirically investigate the impact of COVID-19 shutdowns on domestic violence using incident-level data on both domestic-related calls for service and crime reports of domestic violence assaults from the 18 major US police departments for which both types of records are available. Although we confirm prior reports of an increase in domestic calls for service at the start of the pandemic, we find that the increase preceded mandatory shutdowns, and there was an incremental decline following the government imposition of restrictions. We also find no evidence that domestic violence crimes increased. Rather, police reports of domestic violence assaults declined significantly during the initial shutdown period. There was no significant change in intimate partner homicides during shutdown months and victimization survey reports of intimate partner violence were lower. Our results fail to support claims that shutdowns increased domestic violence and suggest caution before drawing inference or basing policy solely on data from calls to police.

The evidence on the demographics of COVID-19 fatalities points to an overrepresentation of minorities and an underrepresentation of women. We investigate the joint impact of race and gender using individual-level georeferenced death data collected by the Cook County Medical Examiner, which we structure as a cell-level panel at a race, block group, week, and year level. Through an event study approach, we establish that Black individuals are affected more harshly than White and that the effect is driven by Black women. The Black female bias is associated with occupational segregation in the health care and transportation sectors and by commuting on public transport.

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.

Using traffic data from Taiwan for 2020, we quantify how the COVID-19 outbreak affected demand for public and private transportation. Despite there being no governmental restrictions, substantial shifts in travel modes were observed. During the peak of the pandemic in Taiwan within the study period (mid-March 2020), railway ridership declined by 40% to 60%, while highway traffic volume by 20%. Furthermore, railway ridership was well below pre-pandemic levels, though there were no locally transmitted cases in the eight-month period from mid-April to December. These changes in traffic patterns had implications for spatial patterns of economic activity: retail sales and nighttime luminosity data show that during the pandemic, economic activity shifted away from areas in the vicinity of major railway stations.

SARS-CoV-2 has had a greater burden, as measured by rate of infection, in poorer communities within cities. For example, 55% of Mumbai slums residents had antibodies to COVID-19, 3.2 times the seroprevalence in non-slum areas of the city according to a sero-survey done in July 2020. One explanation is that government suppression was less severe in poorer communities, either because the poor were more likely to be exempt or unable to comply. Another explanation is that effective suppression itself accelerated the epidemic in poor neighborhoods because households are more crowded and residents share toilet and water facilities. We show there is little evidence for the first hypothesis in the context of Mumbai. Using location data from smart phones, we find that slum residents had nominally but not significantly (economically or statistically) higher mobility than non-slums prior to the sero-survey. We also find little evidence that mobility in non-slums was lower than in slums during lockdown, a subset of the period before the survey.

How does interconnectedness affect the course of a pandemic? What are the optimal containment policies in an economy with connected regions? We embed a spatial SIR model into a multi-sector quantitative trade model. We calibrate it to US states and the COVID-19 pandemic and find that interconnectedness increases the death toll by 146,200 lives. State-level policies that reduce within-state economic activity mitigate welfare losses by more than a uniform national policy or a policy that only reduces mobility between states. The optimal policy in mitigating welfare losses generated by the pandemic combines local within- and between-state restrictions and saves 289,300 lives, despite significantly exacerbating economic losses and imposing mobility restrictions across states. Different timing of policies across states is key to minimize welfare losses. States like South Carolina might have imposed internal lockdowns too early but travel restrictions too late.

We argue that occupations are a key explanatory variable for understanding the early transmission of COVID-19 in New York City, finding that they play a larger role than other key demographics such as race or income. Moreover, we find no evidence that commuting patterns are significant after controlling for occupations. On the other hand, racial disparities still persist for Blacks and Hispanics compared with Whites, although the disparities' magnitudes are economically small. We perform our analysis over a range of several weeks to evaluate how different channels interact with the progression of the pandemic and the stay-at-home order. While the coefficient magnitudes of many occupations and demographics decrease, we find evidence consistent with higher intra-household contagion over time. Finally, our results also suggest that crowded spaces play a more important role than population density in the spread of COVID-19.

How effective are restrictions on mobility in limiting COVID-19 spread? Using zip code data across five U.S. cities, we estimate that total cases per capita decrease by 19% for every ten percentage point fall in mobility. Addressing endogeneity concerns, we instrument for travel by residential teleworkable and essential shares and find a 25% decline in cases per capita. Using panel data for NYC with week and zip code fixed effects, we estimate a decline of 30%. We find substantial spatial and temporal heterogeneity; east coast cities have stronger effects, with the largest for NYC in the pandemic's early stages.

Tracking human activity in real time and at fine spatial scale is particularly valuable during episodes such as the COVID-19 pandemic. In this paper, we discuss the suitability of smartphone data for quantifying movement and social contact. These data cover broad sections of the US population and exhibit pre-pandemic patterns similar to conventional survey data. We develop and make publicly available a location exposure index that summarizes county-to-county movements and a device exposure index that quantifies social contact within venues. We also investigate the reliability of smartphone movement data during the pandemic.

What factors explain spatial variation in the severity of COVID-19 across the United States? To answer this question, we analyze the correlates of COVID-19 cases and deaths across US counties. We document four sets of facts. First, effective density is an important and persistent determinant of COVID-19 severity. Second, counties with more nursing home residents, lower income, higher poverty rates, and a greater presence of African Americans and Hispanics are disproportionately impacted, and these effects show no sign of disappearing over time. Third, the effect of certain characteristics, such as the distance to major international airports and the share of elderly individuals, dies out over time. Fourth, Trump-leaning counties are less severely affected early on, but later suffer from a large severity penalty.

One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through shelter-in-place orders (SIPOs). This study examines the role of localized urban SIPO policy in curbing COVID-19 cases. Specifically, we explore (i) the comparative effectiveness of county-level SIPOs in urbanized as compared to non-urbanized areas, (ii) the mechanisms through which SIPO adoption in urban counties yields COVID-related health benefits, and (iii) whether late adoption of a statewide SIPO yields health benefits beyond those achieved from early adopting counties. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state's population prior to adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with an 8 percent increase in the percent of residents who remain at home full-time and between a 13 to 19 percent decrease in foot-traffic at venues that may contribute to the spread of COVID-19 such as restaurants, bars, hotels, and entertainment venues. These social distancing effects are largest in urbanized and densely populated counties. Then, we find that in early adopting urban counties, COVID-19 case growth fell by 21 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in COVID-19 outbreak timing, coronavirus testing, the age distribution, and political preferences. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.

Due to the suspension of in-person classes in response to the COVID-19 pandemic, students at universities with earlier spring breaks traveled and returned to campus while those with later spring breaks largely did not. We use variation in academic calendars to study how travel affected the evolution of COVID-19 cases and mortality. Estimates imply that counties with more early spring break students had a higher growth rate of cases than counties with fewer early spring break students. The increase in case growth rates peaked two weeks after spring break. Effects are larger for universities with students more likely to travel through airports, to New York City, and to popular Florida destinations. Consistent with secondary spread to more vulnerable populations, we find a delayed increase in mortality growth rates. Lastly, we present evidence that viral infection transmission due to college student travel also occurred prior to the COVID-19 pandemic.

This paper evaluates the impacts of migration flows and transportation infrastructure on the spatial transmission of COVID-19 in China. Prefectures with larger bilateral migration flows and shorter travel distances with Hubei, the epicenter of the outbreak, experienced a wider spread of COVID-19. In addition, richer prefectures with higher incomes were better able to contain the virus at the early stages of community transmission. Using a spatial general equilibrium model, we show that around 28% of the infections outside Hubei province can be explained by the rapid development in transportation infrastructure and the liberalization of migration restrictions in the recent decade.

We use U.S. county-level location data derived from smartphones to examine travel behavior and its relationship with COVID-19 cases in the early stages of the outbreak. People traveled less overall and notably avoided areas with relatively larger outbreaks. A doubling of new cases in a county led to a 3 to 4 percent decrease in trips to and from that county. Without this change in travel activity, exposure to out-of-county virus cases could have been twice as high at the end of April 2020. Limiting travel-induced exposure was important because such exposure generated new cases locally. We find a one percent increase in case exposure from travel led to a 0.21 percent increase in new cases added within a county. This suggests the outbreak would have spread faster and to a greater degree had travel activity not dropped accordingly. Our findings imply that the scale and geographic network of travel activity and the travel response of individuals are important for understanding the spread of COVID-19 and for policies that seek to control it.

We examine the potential for exploiting retailer location choice in targeting health interventions. Using geospatial data, we quantify proximity to vaccines created by a U.S. federal program distributing COVID-19 vaccines to commercial retail pharmacies. We assess the distributional impacts of a proposal to provide vaccines at Dollar General, a low-priced general merchandise retailer. Adding Dollar General to the federal program would substantially decrease the distance to vaccine sites for low-income, rural, and minority U.S. households, groups for which COVID-19 vaccine take-up has been disproportionately slow.

Debate over safe-storage gun regulations has captured public attention in the aftermath of several high-profile shootings committed by minors. To date, the existing literature provides no evidence that these laws are effective at deterring gun crime, a conclusion that has prompted the National Rifle Association to assert that such regulations are "unnecessary" and "ineffective." Using data from the FBI's for the period 1985-2013, we find that child access prevention (CAP) laws are associated with a 17 percent reduction in firearm-related homicides committed by juveniles. The estimated effect is stronger among whites than nonwhites and is driven by states enforcing the strictest safe-storage standard. We find no evidence that CAP laws are associated with firearm-related homicides committed by adults or with non-firearm-related homicides committed by juveniles, suggesting that the observed relationship between CAP laws and juvenile firearm-related homicides is causal.