This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle.

The health impact of the COVID-19 pandemic across OECD (Organisation for Economic Co-operation and Development) and European regions has been strikingly uneven. In 2020, excess mortality rates in the hardest-hit regions were, on average, 17 percentage points higher than those in the least affected regions of the same country. This paper shows that low health system capacity, followed by population density, air pollution, the share of elderly people, and low institutional quality were associated with higher excess mortality during the first year of the pandemic. Finally, reduced home-to-work mobility, following governments' COVID-19 responses, was associated with lower excess mortality 2 months after implementation of the measures.

This paper examines the impact of Covid-19 lockdowns on exports by Chinese cities. We use city-level export data at a monthly frequency from January 2018 through April 2020. Differences-in-differences estimates suggest cities in lockdown experienced a 34 percentage points reduction in the year-on-year growth rate of exports. The lockdown impacted the intensive and extensive margin, with higher exit and lower new entry into foreign markets. The drop in exports was smaller in (i) coastal cities; (ii) cities with better-developed ICT infrastructure; and (iii) cities with a larger share of potential teleworkers. Time-sensitive and differentiated goods experienced a more pronounced decline in export growth. Global supply chain characteristics matter, with more upstream products and industries that had accumulated larger inventories experiencing a smaller decline in export growth. Also, products that relied more on imported (domestic) intermediates experienced a sharper (flatter) slowdown in export growth. The rapid recovery in cities' exports after lockdowns were lifted suggests the policy was cost-effective in terms of its effects on trade.

We investigate whether pandemic-induced contagion disamenities and income effects arising due to COVID-related unemployment adversely affected real estate prices of one- or two-family owner-occupied properties across New York City (NYC). First, ordinary least squares hedonic results indicate that greater COVID case numbers are concentrated in neighborhoods with lower-valued properties. Second, we use a repeat-sales approach for the period 2003-2020, and we find that both the possibility of contagion and pandemic-induced income effects adversely impacted home sale prices. Estimates suggest sale prices fell by roughly $60,000 or around 8% in response to both of the following: 1000 additional infections per 100,000 residents and a 10-percentage point increase in unemployment in a given Modified Zip Code Tabulation Area (MODZCTA). These price effects were more pronounced during the second wave of infections. On the basis of cumulative MODZCTA infection rates through 2020, the estimated COVID-19 price discount ranged from approximately 1% to 50% in the most affected neighborhoods, and averaged 14%. The effect intensified in the more affluent, but less densely populated NYC neighborhoods, while the effect was more pronounced in the most densely populated neighborhoods with more rental properties and greater population shares of foreign-born residents. This disparity implies the pandemic may have been correlated with a wider gap in housing wealth in NYC between homeowners in lower-priced and higher-priced neighborhoods.

We study how the public changes their mobility and retail spending patterns as precautionary responses to the disclosed location of COVID-19 cases. To look into the underlying mechanisms, we investigate how such change varies spatially and whether there is any spatial spillover or substitution. We use the daily data of cell phone-based mobility and credit card transactions between February 10 and May 31 in both 2019 and 2020 in Seoul, South Korea, and employ the empirical approach analyzing the year-over-year percent change for the mobility and consumption outcomes. Results report that one additional COVID-19 case within the last 14 days decreased nonresident inflow and retail spending by 0.40 and 0.65 percentage points, respectively. Then, we also find evidence of spatial heterogeneity: the mobility and retail performances of neighborhoods with higher residential population density were more resilient to COVID-19 case information while neighborhoods with higher levels of land-use diversity and retail agglomeration experienced a greater localized demand shock. This heterogeneity is not negligible. For example, one additional COVID-19 case in neighborhoods in the bottom 20% for population density led to a decline of 1.2 percentage points in retail spending, while other neighborhoods experienced a less negative impact. Finally, we find a significant spatial spillover effect of disclosed COVID-19 information instead of spatial substitution. One additional COVID-19 case in geographically adjacent areas within the last 14 days reduced nonresident inflow and retail spending in the subject neighborhood by 0.06 and 0.09 percentage points, respectively.

Using province-level establishments and employment data from the Korean Employment Insurance Database, this paper investigates how the regional spread of COVID-19 affects local businesses and unemployment by establishment size and industry. We find that the number of small establishments declines substantially after the COVID-19 pandemic through a decrease in new establishment creation and a surge in establishment closures. By contrast, large establishments are not affected significantly. Examining the numbers of unemployment benefits (UB) applicants, an indicator of unemployment, we find that the higher the rate of COVID-19 confirmed cases in a province, the higher the number of UB applicants, regardless of their previous workplace size. Our analysis of employment insurance subscribers further confirms that the regional spread of COVID-19 leads to a significant reduction in employment and job mobility in small establishments. Regarding industry heterogeneity in the COVID-19 effects, we find that local COVID-19 outbreaks affect local industries more through the reduction in establishment creation and new employment than through an increase in establishment closures. Industries that require face-to-face operations, such as lodging & restaurant, experience a substantial adverse impact in the early phase, and the impact also tends to last longer as COVID-19 situations prolong.

We use novel and comprehensive monthly data on electronic payments, by municipality and sector, together with cash withdrawals, to study the impact of Covid-19 in Portugal. Our difference-in-differences event study identifies a causal decrease of 17 and 40 percentage points on the year-on-year growth rate of overall purchases in March and April 2020. We document a stronger impact of the crisis in more central and more urban municipalities, due to a combination of the sectorial of the local economy and the sharper confinement in these locations. We discuss the importance of tourism for the results.

In this paper, we study the short-run evolution of political trust during the recent covid-19 pandemic using survey data for a sample of young individuals living in Germany, France, Italy, and Spain. In particular, we analyze whether pre-pandemic perceptions and experiences of citizens about various dimensions of local governments and institutional quality had any mediating effect on the evolution of political trust after the outbreak of the covid-19 pandemic. The results show a relative increase in political trust of about 9% in regions with high institutional quality (75th percentile) compared with regions with low institutional quality (25th percentile) over the period 2019-2020. This divergence can be associated with either a better performance of policymakers in high-quality institutions regions, or to more positive attitudes toward politicians by citizens that, before the pandemic, believed to live in regions with efficient institutions. Overall results are not affected by the inclusion of regional fixed effects or by possible differential evolution of political trust according to a large set of observable regional characteristics.

Since the pioneering study by Banfield, the North-South gap in Italian social capital has been considered by international scholars as an example of how cultural diversity within a country can generate different developmental outcomes. Most studies, however, suffer from limited external validity and measurement error. This paper exploits a new and representative online lab-experiment to assess social-capital patterns in Italy. Unlike previous experiments, we do not inform participants about the geographic origins of their counterparts. This feature allows us to assess the North-South gap in universal, as opposed to parochial, behavior. Results suggest that Southerners and Northerners do not systematically differ in generalized prosocial preferences. Only trustworthiness is higher among. Northerners, while they are statistically similar to Southerners in many other economic preferences such as cooperation, trust, expected trustworthiness, altruism, and risk tolerance. We also show that the gap in trustworthiness stems from the lower reciprocity of Southerners in response to large transfers, and it is characterized by the intergenerational transmission of norms. Possible policy implications are discussed.

Using daily US county-level data on consumption, employment, mobility, and the coronavirus disease 2019 (COVID-19) cases, this paper investigates the welfare costs of COVID-19. The investigation is achieved by using implications of a model, where there is a trade-off between consumption and COVID-19 cases that are both determined by the optimal mobility decision of individuals. The empirical results show evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December 2020 for the average county. There is also evidence for heterogeneous welfare costs across US counties and days, where certain counties have experienced welfare reductions up to on average across days and up to in late March 2020 that are further connected to the socioeconomic characteristics of the US counties.

During the coronavirus disease 2019 (COVID-19) pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant activity data, we find that the implementation of stay-at-home orders initially had a limited impact, but that activity rose quickly after states' reopenings. The results suggest that consumers inferred from reopening that it was safer to eat out. The rational, but mistaken inference that occurs in our model may explain why a sharp rise of COVID-19 cases followed reopening in some states.

This paper examines the uneven geography of COVID-19-related excess mortality during the first wave of the pandemic in Europe, before assessing the factors behind the geographical differences in impact. The analysis of 206 regions across 23 European countries reveals a distinct COVID-19 geography. Excess deaths were concentrated in a limited number of regions-expected deaths exceeded 20% in just 16 regions-with more than 40% of the regions considered experiencing no excess mortality during the first 6 months of 2020. Highly connected regions, in colder and dryer climates, with high air pollution levels, and relatively poorly endowed health systems witnessed the highest incidence of excess mortality. Institutional factors also played an important role. The first wave hit regions with a combination of weak and declining formal institutional quality and fragile informal institutions hardest. Low and declining national government effectiveness, together with a limited capacity to reach out across societal divides, and a frequent tendency to meet with friends and family were powerful drivers of regional excess mortality.

The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.

The paper studies the containment effects of public health measures to curb the spread of Covid-19 during the first wave of the pandemic in spring 2020 in Germany. To identify the effects of six compound sets of public health measures, we employ a spatial difference-in-differences approach. We find that contact restrictions, mandatory wearing of face masks and closure of schools substantially contributed to flattening the infection curve. The significance of the impact of restaurant closure does not prove to be robust. No incremental effect is evidenced for closure of establishments and the shutdown of nonessential retail stores.

This paper examines whether the intensity of nonpharmaceutical interventions (NPIs) during the coronavirus disease 2019 (COVID-19) pandemic has differentially impacted the public sector labor market outcomes. This extends the analysis of the already documented negative economic consequences of COVID-19 and their dissimilarities with a typical economic crisis. To capture the intensity of the NPIs, we build a novel index (COVINDEX) using daily information on NPIs merged with state-level data on out-of-home mobility (Google data). We show that among individuals living in a typical state, NPI enforcement during COVID-19 reduces the likelihood of being employed (at work) by 5% with respect to the pre-COVID period and the hours worked by 1.3% using data on labor market outcomes from the monthly Current Population Survey and difference-in-difference models. This is a sizable amount representing the sector with the higher job security during the pandemic. Public sector workers in a typical state are 4 percentage points more likely to be at work than salaried workers in the private sector and 7 percentage points more likely to be at work than self-employed workers (the worst so far). Our results are robust to the endogeneity of the NPI measures and present empirical evidence of heterogeneity in response to the NPIs, with those in local employment being the hardest hit.

This paper addresses the important question "Which European areas will be able to better react to the crisis induced by COVID-19 and how regional disparities will look like?" To provide an answer, a "new normality" scenario is built, comprising the structural changes likely to take place in the aftermath of the COVID pandemic. To develop such scenario, two intermediate steps are necessary, in both cases relying on the use of the latest generation of the MAcroeconomic, Sectoral, Social, Territorial (MASST4) model. First, short-run costs of the COVID-induced lockdowns, in terms of missed GDP, are calculated for all European NUTS2 regions, needed because of the lack of short-run statistics about the extent of the regional costs caused by the lockdowns that will only appear in 2 years. Second, a long-run simulation of the economic rebound expected to take place from 2021 through 2030 is presented, assuming, among other trends, that no further national lockdowns will be undertaken in European countries. In the "new normality" scenario, regional disparity trends will decrease as a result of a decisive rebound of those countries mostly hit by the pandemic.

We document the magnitudes of and mechanisms behind socioeconomic differences in travel behavior during the COVID-19 pandemic. We focus on King County, Washington, one of the first places in North America where COVID-19 was detected. We leverage novel and rich administrative and survey data on travel volumes, modes, and preferences for different demographic groups. Large average declines in travel and public transit use due to the pandemic and related policy responses mask substantial heterogeneity across socioeconomic groups. Travel declined considerably less among less-educated and lower-income individuals, even after accounting for mode substitution and variation across neighborhoods in the impacts of public transit service reductions. As policy became less restrictive and travel increased, the size of the socioeconomic gap in travel behavior remained stable, and remote work capabilities became increasingly important in explaining this gap. Our results imply that disparities in travel behavior across socioeconomic groups may become an enduring feature of the urban landscape.

The aim of this article is to analyze the subnational spread of COVID-19 in Italy using an economic geography perspective. The striking spatial unevenness of COVID-19 suggests that the infection has hit economic core locations harder, and this raises questions about whether, and how, the subnational geography of the disease is connected to the economic base of localities. We provide some first evidence consistent with the possibility that the local specialization in geographically concentrated economic activities acts as a vehicle of disease transmission. This could generate a core-periphery pattern in the spatiality of COVID-19, which might follow the lines of the local economic landscape and the tradability of its outputs.

This paper using transaction based data to provide new insights into the link between the geographic proximity of businesses and associated economic activity. It contributes to the literature by developing both two new measures of distance and a set of stylized facts on the distances between observed transactions between vendors and customers for a research intensive sector - universities. We show that spending on research inputs is more likely to be expended at businesses physically closer to universities than those farther away. That relationship is stronger for High Tech and R&D performing businesses than businesses in general, which is consistent with theories emphasizing the role of tacit knowledge. We find that firms behave in a way that is consistent with the notion that propinquity is good for business: if a firm supplies a project at a university in a given year, it is more likely to open an establishment near that university in subsequent years than other firms. We also investigate the link between transactional distance and economic activity and show that when a vendor has been a supplier to a project at least one time, that vendor is subsequently more likely to be a vendor on the same or related project.