Public responses to COVID-19 case disclosure and their spatial implications.

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.

INTRODUCTION

During the COVID‐19 pandemic, a handful of countries and economies mainly in Asia, including South Korea, Hong Kong, and Singapore, chose to disclose detailed location information of the residence and visiting places of individuals who tested positive, along with mass testing and contact tracing. While this strategy is expected to be useful to promote voluntary precautionary behavior and combat the virus's spread, such behavior can also lead to significant economic impacts in some local areas. If the public is informed that there is a potentially higher health risk in a certain neighborhood, this risk should be reflected in the individual choice to visit and/or engage in retail or leisure activities in this neighborhood.

The purpose of this paper is to provide empirical evidence on how the public changed their mobility and retail spending patterns as precautionary responses to the disclosed location of COVID‐19 cases in Seoul, South Korea. Our main research question is on the extent to which disclosed COVID‐19 cases in a given neighborhood led to the reduction in nonresident inflows to and retail spending in that neighborhood. Hence, we focus on neighborhood‐level spatial implications of COVID‐19 case disclosure. To look into underlying mechanisms, we investigate whether spatial attributes such as population/employment density, land‐use diversity, and the level of retail agglomeration play a role in the neighborhood's resilience to demand shock. We also examine whether increasing COVID‐19 risks in geographically or functionally adjacent neighborhoods increase or decrease mobility and retail spending in a subject neighborhood.

To do so, we utilize big data and a carefully designed empirical framework. Two big data sources we use include cell phone‐based mobility and credit card spending between February 10 and May 31 in both 2019 and 2020. They are provided by companies that have a large market share in their respective sectors in South Korea. More importantly, both data are available at a relatively smaller geographic level, called “dong” (hereafter, neighborhood), and on a daily frequency. Therefore, our data provide a fairly complete and accurate picture of neighborhood‐level demand changes at high frequency, allowing us to conduct comprehensive empirical analyses. We use the first‐difference type of empirical models by calculating the year‐over‐year percent change for two outcome variables: nonresident inflow to and retail spending in a given neighborhood. As COVID‐19 has significantly affected these outcomes in a short period, it is important to track down how they have changed during COVID‐19 compared with the prepandemic period.

We find that the disclosure of COVID‐19 cases led to localized demand shock as both nonresident inflow and retail spending decreased significantly in neighborhoods with more cases. Estimation results report that one additional COVID‐19 case during the last 14 days decreased nonresident inflow and retail spending by 0.40 and 0.65 percentage points, respectively. Our results are consistent with Janssen and Shapiro (2020), who find a significant reduction in the probability of individuals to visit the subregion with more cases disclosed 1 day before in Singapore. They find that an additional resident and visitor case decreases the individual's probability of visiting the subregion by 0.081 and 0.017 percentage points, respectively. While results from two studies are not directly comparable due to different empirical approaches and local contexts, we believe that the magnitude of our results is larger because we use a lot smaller geographic areas and the cumulative number of cases.

Our results on retail spending reduction as responses to disclosed COVID‐19 case are consistent with Chetty et al. (2020), who report a significant reduction in credit and debit card spending in the United States. Their result suggests that the average daily spending in food services in March and April 2020 decreased by 25–50 percentage points relative to January 2020 spending while the reduction in total retail spending is less fluctuating. This coincides with our result that the greatest reduction effects are found in retail credit card spending. However, the magnitude of the reduction in retail spending shown in Chetty et al. (2020) is not comparable to ours because our focus is not the nationally aggregated reduction in credit card spending after the COVID‐19 pandemic but the neighborhood‐level reduction responding to an additional case in the neighborhood.

In terms of spatial heterogeneity in the demand shock that we consider as underlying mechanisms of the impact of COVID‐19 case disclosure, our analysis focuses on neighborhood population density, land use, and the level of retail agglomeration. Resident population density and employment density are found to play different roles. On the one hand, mobility and retail performances of neighborhoods with lower resident population density were more affected by COVID‐19 incidences compared with those with higher population density. On the other hand, neighborhoods with different levels of employment density show much less heterogeneous results. In addition, the higher levels of land‐use diversity and retail agglomeration reduced localized economic resilience to the COVID‐19 case shock potentially due to public concerns about the virus's spread. Finally, we test whether there is a spatial spillover or substitution effect of disclosed COVID‐19 case information and find that the spillover effects dominate: an increase in COVID‐19 risks in both geographically and functionally adjacent neighborhoods reduced nonresident inflow and retail spending in the subject neighborhood.

The remainder of the paper is organized as follows. Section 2 presents scholarly contributions of our paper to the relevant literature as well as the institutional backgrounds of the location disclosure of COVID‐19 cases in Seoul, South Korea. Section 3 describes the data and summary statistics while Section 4 presents empirical strategies and identification. Section 5 discusses results from baseline regressions and spatial analyses and Section 6 concludes this study.

BACKGROUNDS

Scholarly background

One strand of the research relevant to our paper is the literature on economic outcomes of the pandemic. Many scholars have focused on how COVID‐19 affected household consumption (e.g., Baker et al., 2020; Carvalho et al., 2020; H. Chen et al., 2021; Chetty et al., 2020; Coibion et al., 2020; Surico et al., 2020), labor market outcomes (e.g., Béland et al., 2020; Bernstein et al., 2020; Chetty et al., 2020; Gupta et al., 2020; Kahn et al., 2020; Marcén & Morales, 2021; Rojas et al., 2020), and health (e.g., Fetzer et al., 2021; Hamermesh, 2020; Kosfeld et al., 2021; Lu et al., 2020). Others studied public behavioral responses to different social‐distancing policies (Abouk & Heydari, 2021; Allcott et al., 2020; Brough et al., 2021; Courtemanche et al., 2020; Dave et al., 2020; Farboodi et al., 2020; Nguyen et al., 2020; Siedner et al., 2020). These studies use cell phone mobility data in the United States and their main goal is to evaluate policy effectiveness on the reduction in virus spread. These studies offer useful insights on a change in general travel behaviors during the pandemic. In particular, the finding of Dave et al. (2020) on the increasing net stay‐at‐home behavior caused by Black Lives Matter protests could be understood as similar precautionary responses to what we find with the increasing number of disclosed COVID‐19 cases in Seoul. However, none of these studies provides spatial implications. Also, as governments in the United States have not released the detailed location information of COVID‐19 cases, findings tend to remain at the macrolevel. For example, Andersen (2020) and Painter and Qiu (2020) consider the variation in the extent of social‐distancing behaviors at the county and state levels, respectively. Our research adds insights on localized responses to the disclosed COVID‐19 case information and associated behavioral and economic outcomes in different neighborhoods within a city.

Argente et al. (2020) and Janssen and Shapiro (2020) are among few examples that analyze mobility outcomes of disclosed location information of COVID‐19 cases in the contexts of Seoul and Singapore, respectively.1 However, their outcomes of interest differ from ours. Argente et al. (2020) are interested in the virus's spread and model it to be related with commuting flows. They use an SIR meta‐population model to compare the reduction in COVID‐19 infections and deaths in the context of the information disclosure policy with counterfactual outcomes of a policy without such disclosure and a lockdown policy. Utilizing device‐level cell phone data in Singapore, Janssen and Shapiro (2020) analyze how disclosed local cases in the subregion of the individual's residence leads to a change in individual travel behavior. They also look into the extent to which COVID‐19 cases in a given subregion influence the inflow of individuals. As this is partially similar to our first research question associated with nonresident inflows, we aim to test the external validity of results and compare findings. Unlike Janssen and Shapiro (2020), however, our study goes beyond the travel‐related precautionary behavior because our main focus is on localized economic shock observed with both nonresidential inflows and retail spending. Moreover, our spatial unit of analysis is a lot more refined at 424 neighborhoods compared with 25 districts in Argente et al. (2020) and 55 subregions in Janssen and Shapiro (2020).

Next, our research intends to provide a better understanding of the literature on the role of spatial attributes to pandemic outcomes. While it is known that it is more challenging to control epidemics in higher density areas (Wamsler et al., 2013; Wheaton & Kinsella Thompson, 2020), there is little consistent evidence on how areas with different levels of density have survived and recovered during and after the pandemic. Stevens et al. (2010) and Malalgoda et al. (2013) report that higher density is associated with both higher risks and slower recovery from natural disasters. In contrast, Capello et al. (2015) suggest the lower loss of GDP growth in regions with cities with a larger population during the economic crisis in Europe, potentially due to agglomeration benefits. Although the COVID‐19 pandemic is an inherently different shock from natural disasters and economic recession due to no physical destruction and concerns about the virus's spread, these studies offer some motivation. Land‐use diversity is another spatial attribute that could be associated with pandemic outcomes. Researchers tend to find negative effects of nonresidential land‐use components on COVID‐19 incidences. Wheaton and Kinsella Thompson (2020) report a positive impact of commercial–industrial land‐use share on cases in Massachusetts. Y. Chen et al. (2020) similarly show the correlation between green space density and cases in New York City.

Finally, our study is relevant to the literature on the role of spatial patterns in retail resilience. One popular strategy used to enhance this resilience is retail agglomeration.2 Nonetheless, there is little empirical evidence on whether retail agglomeration did enhance retail resilience during and after natural or economic shocks. Meltzer et al. (2020) report that retail businesses serving localized consumer bases and smaller and standalone establishments have been affected most by Hurricane Sandy in New York. Wrigley and Dolega (2011) suggest a higher resilience of retail centers with diversity and corporate‐food‐store entry during a global economic crisis in towns in the United Kingdom. Both studies focus on store‐level analysis rather than spatial implications. Again at the store level, and in the context of the COVID‐19 pandemic, Tucker and Yu (2020) find no demand substitution between restaurant types while reporting that restaurant chains suffer a smaller decrease in visits. This study adds to existing evidence by observing how neighborhoods with a different level of retail agglomeration survive from localized demand shocks during COVID‐19.

Institutional background

South Korea is one of few countries that have never imposed a compulsory lockdown, although it went through quite early outbreaks of COVID‐19 starting from January 20, 2020. Also, most corporations in South Korea did not make working from home mandatory. This means that people in South Korea carried out their daily commuting at a similar level as the pre‐COVID period and chose other activities simply based on their voluntary precaution. This is an important precursor of our analysis because individual demand to visit and shop in local neighborhoods is not limited by any other measures than disclosed location information of COVID‐19 cases. The South Korean government did impose different levels of social‐distancing measures since March as a policy response to increasing or decreasing numbers of COVID‐19 cases. For example, for 1 month from March 22, the operation of religious places, entertainment businesses such as clubs, and some indoor sports facilities was restricted fully or partially. This falls in our study period of February 10–May 31, 2020. However, the level of social distancing has been kept consistent for the entire city of Seoul, which is our study area. As there is no spatial variation in this level, COVID‐19 case effects would not be confounded with the impacts of social distancing. And any temporal change in the social‐distancing level should be captured by date fixed effects.

To promote voluntary precautionary behaviors, government bodies in South Korea, including district offices, the Seoul Metropolitan Government, and Korea Disease Control and Prevention Agency, have actively provided location details about each COVID‐19 case after epidemiologic investigation. For example, district offices in Seoul send text messages for every positive COVID‐19 case to all cell phone users located within the same district as the case's residence.3 , 4 The text is also sent to request the public to report and get a test if they visited the same place the confirmed COVID‐19 case visited in a given district or sometimes in Seoul. This is to identify close contacts of a case and essentially disclose the locations with higher risks. Besides these involuntary text messages, South Korean government bodies widely disseminate the information on the details of the case's residence and visiting places on their websites. Individuals could also use the mobile phone application to follow COVID‐19 case information in any districts of their interest.

DATA AND DESCRIPTIVE STATISTICS

This study uses data from several sources. The first is based on the publicly released information on COVID‐19 cases for the period of February–May 2020. As mentioned in Section 2, this information is from various online platforms. As it is provided in the pure text format, we manually coded it for 862 cases reported in Seoul. Our final data are arranged to generate explanatory variables for our analyses at the neighborhood “dong” level,5 the smallest administrative area in Seoul: the number of residents confirmed as having tested positive for COVID‐19 in each neighborhood within the last 14 days, the number of visits by the confirmed patients to the neighborhood within the last 14 days, and the sum of these two numbers.6 If the patient visits the places in the same neighborhood of his or her residence, we do not add this to the number of visitors in this neighborhood to prevent duplicate counts.7

Second, we use two proprietary data to analyze outcomes of the disclosed location information of COVID‐19 cases. The cell phone‐based daily mobility data were provided by SK Telecom.8 SK Telecom is the largest mobile phone company in South Korea, accounting for a market share of about 42%.9 The data provide daily bilateral flows of people between their residence and destination neighborhood, where they stayed more than 30 min (to exclude movements through car and/or public transportation). The data cover the time period of February 10–May 31 in both 2019 and 2020,10 which enables us to observe differences in mobility in a neighborhood in a given day, adjusting for the day of week, between 2019 and 2020. The main variable we generate for the analysis is the daily year‐over‐year percent change in nonresident inflows reflecting the baseline inflow in each neighborhood.

Then, we measure consumption behaviors in neighborhoods within Seoul by using the credit card transaction data from Hyundai Card, which has about 16% of the market share in South Korea.11 Like the cell phone data, credit card data cover the time period of February–May of 2019 and 2020. In addition to the total sales amount, daily sales are reported by industry, which allows us to identify consumption across major consumption categories. Here, we focus on consumption in the retail sector, which includes “retail trade,” “tourism, entertainment, and recreation,” and “food services.” The main variables we generate for the analysis are the daily year‐over‐year percent change in total credit card spending and the change in retail spending in each neighborhood.

While analyzing the impact of the public disclosure of COVID‐19 cases on mobility and consumption in Seoul can provide valuable insights, the averaged estimates may disguise spatial heterogeneity across areas with different characteristics within the city. To explore this as underlying mechanisms for our analysis, we sort all neighborhoods in Seoul into quintiles by the rank of population density, employment density, Theil entropy score of land‐use diversity, and retail agglomeration. The population and employment density data were obtained from Seoul Open Data Plaza, which is a platform where the Seoul Metropolitan Government provides its public data. The Theil entropy score was computed by aggregating total floor area by building use type (e.g., residential, commercial, industrial, and office) at the neighborhood level, based on the building‐level geographic information system (GIS) data constructed and provided by the Ministry of Land, Infrastructure and Transport of South Korea.12 Lastly, for retail agglomeration, we use neighborhood's share of credit card spending in the retail sector in Seoul based on Hyundai Card transaction data in 2015–2019 (prepandemic period).

Table 1 presents descriptive statistics of main variables. On average, there are more visitor cases than resident cases. The average number of cases in the previous 14 days is 0.533 per neighborhood. One may be surprised at how these few cases could have generated a change in public behavior in a large city like Seoul. We note that public fear was very strong, especially at the beginning of the pandemic. In fact, because the number of cases was not very high compared with other cities, the public may have been more sensitive to the location information of one additional case. This is exactly why the government used the location disclosure of cases rather than the lockdown policy as the main measure to combat the spread of COVID‐19. There is evidence that public responses driven by this measure were significant enough to maintain the number of cases low in Seoul (Argente et al., 2020). In addition, a standard deviation is quite large for COVID‐19 cases (Table 1), and this may have led to a significant variation in outcomes between neighborhoods. On the other hand, on average, there is a larger reduction in mobility than spending. However, a standard deviation is much larger for spending, especially spending in the retail sector, indicating that while most neighborhoods experience a relatively similar reduction in foot traffic, retail sectors in certain neighborhoods are more vulnerable than others.

Table 1

Descriptive statistics for regression variables

	Obs.	Mean	Median	S.D.
Cases in 14 days
Total cases	46,592	0.533	0.000	1.578
Resident cases	46,592	0.226	0.000	0.674
Visitor cases	46,592	0.307	0.000	1.269
Outcome variables
Nonresident inflows	46,592	49,512	43,918	27,579
Total spending (in million won)	44,584	1041	396	3865
Retail spending (in million won)	44,584	522	217	1696
% Change in 2019–2020
Nonresident inflows	46,592	−10.601	−9.834	12.916
Total spending	44,584	−7.962	−10.888	29.372
Retail spending	44,584	−7.548	−12.002	40.055

Note: The unit of analysis is neighborhood‐day, and the observations with the percent change in 2019–2020 greater than 95% or less than −95% were winsorized.

As shown in Figure 1, the public in Seoul has actively responded to the case information disclosure by reducing their daily mobility and credit card spending.13 The average year‐over‐year mobility declined up to 26% from February to May 2020 (Panel A, Figure 1). Especially when the disclosed number of cases reached its peak in early March, we see a significant drop in mobility followed by a slow recovery over time.14 The average year‐over‐year credit card spending also declined up to 62% from February to May 2020 (Panel A, Figure 1) although this shows more fluctuation than the daily mobility trend. This is comparable to An et al. (2021) reporting that the average year‐over‐year reduction in US ZIP code‐level credit card spending in April 2020 is 25% and the 75th percentile is 38%.

Figure 1

Disclosed COVID‐19 cases and changes in mobility and consumption in Seoul. (Panel A) Daily disclosed COVID‐19 cases along with year‐over‐year percent changes in nonresident inflow (left) and credit card spending (right). (Panel B) Spatial heterogeneity in year‐over‐year percent changes in nonresident inflow (left) and credit card spending (right). (Panel C) Spatial distribution of COVID‐19 cases through May 31, 2020: Resident (left) and visitor cases (right). Note: Spatial heterogeneity shown at the neighborhood level. A neighborhood, called “dong,” is the smallest administrative area, and there are 424 neighborhoods in Seoul [Color figure can be viewed at wileyonlinelibrary.com]

When we focus on the local level, however, the degrees of reduction in daily mobility and credit card spending varies significantly (Panel B, Figure 1). While some areas, including the three major central business districts (CBDs) in Seoul and transportation hub areas with airport, bus terminal, and train station, experienced more than a 20% reduction in nonresident inflow and retail spending compared with 2019, others saw a lot less reduction or even a slight increase in local economic activities. While this spatial heterogeneity would be somewhat related to the distribution of disclosed cases, no direct association is observed (Panel C, Figure 1). This case distribution in Seoul appears to be less heterogeneous compared with the distribution of economic outcomes.

EMPIRICAL STRATEGY AND IDENTIFICATION

Although the COVID‐19 outbreak is a seemingly exogenous shock, we should still be concerned about the presence of other concurrent shocks to neighborhoods and households within Seoul. To overcome such an identification challenge, most existing research on the outcomes of the COVID‐19 pandemic uses either the difference‐in‐difference approach (e.g., Chetty et al., 2020) or the estimation of year‐over‐year (or month‐over‐month/day‐over‐day) changes in outcomes with some fixed effects (e.g., Janssen & Shapiro, 2020). For our analysis, we choose the latter approach to estimate the causal effects because we assume that case location disclosure arrives as exogenous shocks after we control for date‐ and neighborhood‐specific trends of mobility and retail spending. Specifically, we employ the first‐difference type of empirical models by calculating the year‐over‐year difference for our outcome variables. To identify the public behavioral responses to the case disclosure, we consider the following specification15: where y represents an outcome variable of interest: (1) the year‐over‐year percent change in nonresident flows and (2) the year‐over‐year percent change in (retail) credit card spending in neighborhood i on day t. COVID19 denotes the number of disclosed COVID‐19 cases for neighborhood i on day t, and s refers to three types of local cases: cases who reside in neighborhood i (resident), cases who visited neighborhood i (visitor), and the sum of both numbers (total). Given that 14 days is known as the longest likely incubation period for COVID‐19 and thus has been recommended as a quarantine period, we are interested in the effects of the number of publicly disclosed COVID‐19 cases within the last 14 days, LocalCases . Individual neighborhood fixed effects σ absorb time‐invariant area‐specific factors, and date fixed effects τ capture time‐varying shocks that are common to all neighborhoods each day.

To examine more precisely the high frequency changes in an outcome variable before and after 2 weeks of COVID‐19 case disclosure, we also run a dynamic model which is an event‐study type of regressions with temporal trends. We use 14‐day leads and lags of the number of neighborhood‐level COVID‐19 cases because its effects on nonresident inflow and retail spending change dynamically over time and would be rather transient, especially with the survival period of the virus and government disinfection measures.16 where we specify the number of disclosed local COVID‐19 cases for neighborhood i from 14 days ago to 14 days later (i.e., −14, −13, …, +13, +14 days) from day t. In addition to these continuous variables, we also consider the dummy variables for COVID‐19 cases (i.e., whether there was any disclosed case) from 14 days ago to 14 days later.

To test spatial heterogeneity in public responses to COVID‐19 case disclosure, we add the interaction terms between local cases and neighborhood characteristics as follows: where is a set of neighborhood status dummies for spatial heterogeneity: top 20%, middle 60% (reference group), and bottom 20%. As mentioned above, we test whether the effects differ across neighborhoods with varying population density, employment density, land‐use diversity, and retail agglomeration. While the estimated coefficients for the neighborhood status dummies will be absorbed in neighborhood fixed‐effects, those for the interactions can be interpreted as the difference in the effects of public disclosure of COVID‐19 cases between the neighborhood groups.

Finally, to test the potential spatial spillover or substitution from adjacent neighborhoods,17 we add the number of cases tested positive in these neighborhoods controlling for the number of local cases as follows: where NeighborCases denotes (1) the number of case announcements for geographically or (2) functionally adjacent neighborhoods of i (neighbors), and s refers three different types of neighboring cases within the last 14 days as before. The geographically adjacent neighborhoods were identified as the neighborhoods that share a boundary and/or share a node using GIS techniques. The functionally adjacent neighborhoods are those within the same quintile of retail sales, which are not necessarily geographically adjacent.

RESULTS

Baseline regression results

Table 2 summarizes the regression results from the baseline models with public responses to disclosed COVID‐19 cases with different outcomes: nonresident inflow (Panel A), total credit card spending (Panel B), and card spending in the retail sector, which is comprised of the “retail trade,” “tourism, entertainment, and recreation,” and “food services” industries (Panel C). We use four different combinations of the number of cases in a given neighborhood within the last 14 days: total cases (Model 1), resident cases (Model 2), visitor cases (Model 3), and resident and visitor cases separately (Model 4).

Table 2

Baseline effects of disclosed COVID‐19 cases on nonresident inflow and credit card spending

	Model 1	Model 2	Model 3	Model 4
Panel A: Dependent variable: year‐over‐year percent change in nonresident inflow
Total cases in 14 days	−0.396***
	(0.040)
Residents in 14 days		−0.451***		−0.276***
		(0.073)		(0.075)
Visitors in 14 days			−0.469***	−0.438***
			(0.054)	(0.055)
Neighborhood FEs	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes
Number of Obs.	46,592	46,592	46,592	46,592
Panel B: Dependent variable: year‐over‐year percent change in credit card spending
Total cases in 14 days	−0.466***
	(0.076)
Residents in 14 days		−0.490*		−0.276
		(0.196)		(0.200)
Visitors in 14 days			−0.561***	−0.531***
			(0.089)	(0.090)
Neighborhood FEs	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes
Number of Obs.	44,584	44,584	44,584	44,584
Panel C: Dependent variable: year‐over‐year percent change in retail credit card spending
Total cases in 14 days	−0.652***
	(0.094)
Residents in 14 days		−0.586*		−0.271
		(0.291)		(0.298)
Visitors in 14 days			−0.812***	−0.782***
			(0.103)	(0.105)
Neighborhood FEs	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes
Number of Obs.	44,584	44,584	44,584	44,584

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector, including retail trade, tourism, entertainment, recreation, and food services.

Abbreviation: FEs, fixed effects.

In all specifications, the number of disclosed COVID‐19 cases reduced both mobility and consumer spending. For example, one additional COVID‐19 case in a neighborhood over the last 14 days decreased nonresident inflow to the neighborhood by 0.40 percentage points compared with the same day the previous year (Panel A). Because we control for neighborhood‐ and date‐fixed effects, this reduction is in addition to the neighborhood‐specific general decline in foot traffic in 2020 compared with 2019 and the citywide reduction on the date. The result is consistent with Janssen and Shapiro (2020), who find a significant reduction in the probability of individuals to visit the subregion with more cases disclosed 1 day before. While the magnitudes of effects of disclosed information are not directly comparable because two studies use different empirical methods, it is interesting to find that resident cases have much stronger effects than visitor cases in their study compared with ours.18

Panel B indicates that the number of total COVID‐19 cases in a neighborhood over the last 14 days decreased credit card spending in the neighborhood by 0.47 percentage points (p < 0.001). When we compare results using a one standard deviation, the degree of reduction caused by the disclosed number of COVID‐19 cases is larger for mobility than consumer spending.19 While the effects of publicly disclosed information are similar between resident (0.49 percentage points with p value of 0.013, Model 2) and visitor cases (0.56 percentage points with p < 0.001, Model 3) when they are considered separately, the visitor case effects are greater when both are included in Model 4. A potential explanation is related to information search and acquisition. Public media tends to focus on the places visited by many confirmed COVID‐19 cases, while text messages that include the case's residence information are sent to fewer local residents. Assuming that both involve lower search costs, therefore, most people would have acquired more information on visitor cases for destinations outside their own districts. This may also explain the different results between our study and Janssen and Shapiro (2020) mentioned above because information search costs are similar for resident and visitor cases in Singapore.

The results in Panel C of Table 2 present that, not surprisingly, the reduction is even greater for credit card spending in the retail sector, which includes industries that may be more vulnerable to COVID‐19 shocks (e.g., restaurants, retail shops, and tourism). For example, while an additional COVID‐19 case leads to the decline in overall credit card spending by 0.47 percentage points (p < 0.001), the decline is concentrated in the retail sector where spending declined by 0.65 percentage points (p < 0.001) per each COVID‐19 case. This indicates that as a response to disclosed information of COVID‐19 cases, people would reduce their mobility or avoid destinations with higher COVID‐19 risks and pay even more attention to these risks when they want to dine out, go shopping, or watch a movie. When we repeat the regression analysis separately for “retail trade,” “tourism, entertainment, and recreation,” and “food services,” the greatest effects are found in food services.20 Given the findings, we will focus on the credit card spending in the retail sector for further analyses.

Next, considering a potential concern on the restrictive time horizon of the previous 14 days for counting COVID‐19 cases in each neighborhood, we use an event‐study type of specification with 14‐day leads and lags. As shown in Table 3, the public responses to disclosed case information are quite immediate. With almost no pretrend before the date of disclosed information on the number of COVID‐19 cases in a given neighborhood, we find a significant reduction in nonresident inflow to the neighborhood starting from 1 day after information disclosure. While the magnitude of the reduction becomes slightly smaller 5 days after information disclosure, it lasts for about 2 weeks. Results are consistent when we use the dummy variable for COVID‐19 incidences in a given neighborhood instead of the number of cases (Panel A of Table 3 and Appendix A). Similarly, public responses with their retail spending reduction to disclosed location information of the number of COVID‐19 cases start 1 day after information disclosure (Panel B of Table 3 and Appendix A). Although temporal patterns of this reduction are less linear compared with those for the reduction in nonresident inflow, these results demonstrate the robustness of our main results to the dynamic modeling approach.

Table 3

Event‐study results for effects of disclosed COVID‐19 cases on nonresident inflow and retail spending

	Panel A: Nonresident inflow			Panel B: Retail spending
	Total	Residents	Visitors	Total	Residents	Visitors
Day −14	−0.311*	−0.407+	−0.314*	0.020	−0.369	0.183
Day −13	−0.126	−0.290	−0.132	−0.850*	−0.771	−1.056*
Day −12	−0.170	−0.410+	−0.095	−0.078	−0.476	0.181
Day −11	−0.099	−0.053	−0.185	0.378	1.079	−0.168
Day −10	−0.213	−0.288	−0.227	−0.089	−0.241	0.021
Day −9	−0.111	−0.428+	−0.020	0.274	0.033	0.446
Day −8	0.033	−0.164	0.078	−0.075	0.267	−0.376
Day −7	−0.009	−0.019	−0.027	−0.689+	−1.099	−0.480
Day −6	0.003	−0.008	−0.027	0.248	0.040	0.168
Day −5	−0.135	−0.359	−0.080	−0.555	−1.153	−0.377
Day −4	−0.077	−0.254	−0.057	0.178	−0.690	0.311
Day −3	−0.054	−0.069	−0.085	−0.080	−1.242	0.334
Day −2	−0.272*	−0.177	−0.389*	−0.720*	−0.699	−0.939*
Day −1	−0.246	−0.491*	−0.184	0.015	0.030	−0.133
Day 0	−0.205	−0.250	−0.324	−0.741+	−1.139	−0.746
Day +1	−0.535**	−0.827***	−0.529*	−1.504***	−1.589+	−1.678***
Day +2	−0.385*	−0.776**	−0.352+	0.499	−0.083	0.638
Day +3	−0.430**	−0.474+	−0.534*	−1.374***	−0.606	−1.971***
Day +4	−0.605***	−0.301	−0.846***	−0.974**	−1.425*	−1.030*
Day +5	−0.486**	−0.432	−0.573*	−0.980*	−1.865*	−0.722+
Day +6	−0.386+	−0.259	−0.513	−0.012	0.445	−0.412
Day +7	−0.404+	−0.261	−0.584+	−0.586	−0.267	−0.843
Day +8	−0.346+	−0.674*	−0.295	−1.340*	−1.948*	−1.234*
Day +9	−0.318*	−0.593+	−0.348+	−0.392	−0.475	−0.579
Day +10	−0.218	−0.547*	−0.174	0.002	0.207	−0.092
Day +11	−0.516***	−0.467	−0.657**	−0.277	0.472	−0.609
Day +12	−0.451**	−0.538+	−0.516**	−0.515	−0.281	−0.580
Day +13	−0.315	−0.505+	−0.274	−0.826	−0.248	−1.225**
Day +14	−0.303	0.105	−0.537+	−1.231*	−0.861	−1.473**
Neighborhood FEs	Yes	Yes	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes	Yes	Yes
Number of Obs.	46,592	46,592	46,592	44,584	44,584	44,584

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector including retail trade, tourism, entertainment, recreation, and food services.

Abbreviation: FEs, fixed effects.

Finally, we perform some robustness tests. Considering that the full sample analysis does not distinguish demand from commuters and noncommuters, we also try to stratify the sample by weekdays and weekends. As expected, the reductions in both mobility and retail spending are more significant for the subsample of weekends across most specifications, meaning that public responses were more active when they make discretionary choices (Appendix B).21 We also run placebo regressions using the false numbers of COVID‐19 cases that are randomly assigned to each neighborhood on a given date. As shown in Appendix C, results reveal no significant effect of COVID‐19 cases on nonresident inflow and retail credit card spending.

Spatial heterogeneity

We perform additional analysis to understand spatial heterogeneity in the effects of disclosed COVID‐19 cases on nonresident inflow and retail credit card spending. We select top and bottom 20% neighborhoods in Seoul, by their population density, employment density, land‐use mix, and retail agglomeration, and analyze heterogeneous results by neighborhood status in terms of these four categories (Tables 4 and 5).

Table 4

Spatially heterogeneous effects of disclosed COVID‐19 cases on nonresident inflow

Panel A
Spatial attribute	Population density			Employment density
Case type	Total	Resident	Visitor	Total	Resident	Visitor
___cases in 14 days	−0.422***	−0.612***	−0.462***	−0.361***	−0.347**	−0.428***
	(0.057)	(0.089)	(0.066)	(0.059)	(0.116)	(0.066)
(ref: Middle 60%)
Top 20%	0.478**	0.985***	0.360*	−0.098	−0.228	−0.152
	(0.155)	(0.255)	(0.173)	(0.139)	(0.280)	(0.163)
Bottom 20%	−0.258+	−0.111	−0.413*	−0.185+	−0.290+	−0.212
	(0.153)	(0.323)	(0.187)	(0.101)	(0.161)	(0.162)
Neighborhood FEs	Yes	Yes	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes	Yes	Yes
Number of Obs.	49,592	49,592	49,592	49,592	49,592	49,592

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector including retail trade, tourism, entertainment, recreation, and food services.

Note 5: Mixed land‐use quintiles are assigned based on the Theil entropy score of building use (e.g., residential, commercial, industrial, and office) in a given neighborhood. Retail agglomeration quintiles are determined by the neighborhood's share of commercial building areas in Seoul in 2015–2019.

Abbreviation: FEs, fixed effects.

Table 5

Spatially heterogeneous effects of disclosed COVID‐19 cases on retail credit card spending

Panel A
Spatial attributes	Population density			Employment density
Case type	Total	Resident	Visitor	Total	Resident	Visitor
___cases in 14 days	−0.621***	−0.326	−0.800***	−0.603***	−0.716+	−0.685***
	(0.115)	(0.369)	(0.122)	(0.100)	(0.397)	(0.102)
(ref: Middle 60%)
Top 20%	0.190	−0.464	0.443	−0.116	0.640	−0.527*
	(0.259)	(0.628)	(0.271)	(0.211)	(0.565)	(0.258)
Bottom 20%	−0.575*	−1.221*	−0.590	−0.330	−0.329	−0.453
	(0.288)	(0.606)	(0.436)	(0.394)	(0.684)	(0.655)
Neighborhood FEs	Yes	Yes	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes	Yes	Yes
Number of Obs.	44,584	44,584	44,584	44,584	44,584	44,584

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector including retail trade, tourism, entertainment, recreation, and food services.

Note 5: Mixed land‐use quintiles are assigned based on the Theil entropy score of building use (e.g., residential, commercial, industrial, and office) in a given neighborhood. Retail agglomeration quintiles are determined by the neighborhood's share of commercial building areas in Seoul in 2015–2019.

Abbreviation: FEs, fixed effects.

Unlike existing evidence on the role of population density to the virus's spread (e.g., Wheaton & Kinsella Thompson, 2020), our results indicate that public responses to disclosed COVID‐19 cases vary by different types of density. One additional COVID‐19 case in a neighborhood in the bottom 20% for resident population density led to a decline of 0.68 percentage points (−0.680 = −0.422 to 0.258, p < 0.001) in nonresident inflow (Panel A, Table 4) and a decline of 1.20 percentage points (−1.196 = −0.621 to 0.575, p < 0.001) in retail spending (Panel A, Table 5). On the other hand, neighborhoods with higher population density experienced a much less severe decline in their local demand. For example, one additional COVID‐19 case in the top 20% neighborhoods for resident population density increased nonresident inflow merely by 0.06 percentage points with p value of 0.700 (Panel A, Table 4). This is because people would prefer to make shorter trips in their own or nearby neighborhoods or perceive lower COVID‐19 risk in largely residential areas. The heterogeneous results in retail spending by population density are in line with the existing evidence of growing consumption of groceries compared with a large reduction in spending for durable goods (An et al., 2021) as neighborhoods with higher population density are more likely to have grocery stores to serve local residents than large shops or department stores that sell durable goods and target a wider group of nonresident consumers.

Results on heterogeneity by employment density completely differ. Compared with the previous result by population density, neighborhoods across different levels of employment density show much less heterogeneous impacts of disclosed COVID‐19 cases on mobility and consumption (Panel A, Tables 4 and 5). We believe that this is relevant to the institutional context of South Korea, which did not implement a substantial transition into remote working during our study period. Because of the mandatory nature of commuting trips, workers in Seoul would have continued to commute even when they perceive higher risk in neighborhoods concentrated with jobs and this may have led to a negligible difference in mobility and consumption between job centers and others. However, results in Table 5 (Panel A) suggest that retail spending declined by an additional 0.53 percentage point in the most job‐concentrated neighborhoods when one additional visitor case was confirmed in these neighborhoods within the last 14 days. This indirectly suggests that even workers who were not able to avoid commuting may have reduced retail spending in their workplace neighborhoods when these neighborhoods experience a major outbreak with visits of many COVID‐19 cases.22

Next, we test whether the effects of COVID‐19 case disclosure vary by different levels of land‐use diversity. It is measured by a Theil entropy score and has a greater value for the neighborhood with more mixed‐use buildings and a lower value for the neighborhood largely dominated by single‐use buildings. The results show that the disclosed COVID‐19 cases, especially visitor cases, significantly reduced mobility into and retail spending in neighborhoods in the top 20% for land‐use diversity, while the reduction was not statistically significant in those in the bottom 20% (Panel B, Tables 4 and 5). Given that the least diverse neighborhoods are largely dominated by residential properties (more than 70%) while the most diverse neighborhoods are mainly composed of commercial, transportation facilities, and/or mixed‐use buildings, this echoes the above result on heterogeneity by resident population density.

Finally, we test the role of retail agglomeration to the effects of public disclosure of COVID‐19 cases by selecting top/bottom 20% neighborhoods by their share of retail sales amount in 2015–2019.23 Results suggest that an additional total COVID‐19 case disclosed to the public reduced nonresident inflow to neighborhoods in the top 20% neighborhoods by 0.65 percentage points (p < 0.001) while the same COVID‐19 shock led to a 0.03 percentage point increase (p = 0.884) in mobility into neighborhoods in the bottom 20% neighborhoods (Panel B, Table 4). With respect to retail spending, an increase in COVID‐19 cases, especially visitor cases, leads to a significantly higher reduction in demand in the top 20% neighborhoods for retail agglomeration. These results suggest that neighborhoods that used to attract a lot of shoppers may be perceived as riskier and thus people may avoid visiting and spending money in the retail sector there.

Spatial spillover versus substitution

Localized demand shock may be affected not only by disclosed information of COVID‐19 cases in a given neighborhood but also by cases in adjacent neighborhoods.24 To test this, we add the number of COVID‐19 cases in the geographically or functionally adjacent neighborhoods (i.e., those in the same quintile of retail sales within Seoul) to a given neighborhood.25 The results are summarized in Table 6.

Table 6

Spatial spillover from geographically and functionally adjacent neighborhoods

Case type	Total		Resident		Visitor
	Geographic	Functional	Geographic	Functional	Geographic	Functional
Panel A: Dependent variable: year‐over‐year percent change in nonresident inflow
___cases of own	−0.335***	−0.372***	−0.347***	−0.448***	−0.42***	−0.442***
neighborhoods in 14 days	(0.048)	(0.049)	(0.084)	(0.085)	(0.059)	(0.059)
___cases in geographically adjacent	−0.057***		−0.212***		−0.062***
neighborhoods in 14 days	(0.012)		(0.032)		(0.013)
___cases in functionally adjacent neighborhoods in 14 days		−0.026**		−0.069**		−0.029**
		(0.009)		(0.025)		(0.010)
Neighborhood FEs	Yes	Yes	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes	Yes	Yes
Number of Obs.	49,592	49,592	49,592	49,592	49,592	49,592

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector, including retail trade, tourism, entertainment, recreation, and food services. Geographically adjacent neighborhoods are those that share a physical boundary and/or a node. Functionally adjected neighborhoods are those in the same quintile for retail sales by ranking all neighborhoods in Seoul by retail credit card spending in 2015–2019.

Abbreviation: FEs, fixed effects.

Controlling for the number of cases in a given neighborhood, one additional total case disclosed to the public in geographically adjacent neighborhoods led to a decrease of 0.06 percentage points (p < 0.001) in nonresident inflow to the neighborhood (Panel A) and a decline of 0.09 percentage points (p = 0.048) in retail spending (Panel B). The results also indicate that people were more sensitive to resident cases than visitor cases in geographically adjacent neighborhoods. One additional resident case in geographically adjacent neighborhoods led to a decrease of 0.21 percentage points (p < 0.001) in nonresident inflow (Panel A) and a decline of 0.23 percentage points (p = 0.014) in retail spending (Panel B). On the other hand, one additional visitor case in geographically adjacent neighborhoods led to a decrease of 0.06 (p < 0.001) and 0.13 (p = 0.016) percentage points in nonresident inflow (Panel A) and retail spending (Panel B), respectively. In other words, spatial spillover effects are greater when infected people reside in geographically adjacent neighborhoods rather than shortly visiting them. A big customer pool to a given neighborhood would be from residents in geographically adjacent neighborhoods because of shorter travel distances. As mentioned before, the cost for information acquisition is generally lower for resident cases for local residents because people get involuntary text messages from the district office. Hence, more resident cases in geographically adjacent neighborhoods are likely to reduce the travel demand of their residents to a given neighborhood.

We also examine the potential spillover or substitution effects of publicly disclosed COVID‐19 cases in neighborhoods that are not necessarily geographically adjacent but are in the same quintile of retail sales. Since they are functionally similar, the subject neighborhood could be an alternative location for consumers if the neighborhood has fewer cases of COVID‐19. The regression results in Panel A of Table 6 indicate that, however, there are negative spillover effects on nonresident inflows rather than substitution among neighborhoods that are functionally adjacent. Panel B also shows negative spillover effects on retail sales that are significant for both total and visitor cases. Therefore, whether it is geographic proximity or functional similarity, spatial spillover effects appear to dominate over substitution effects in the context of the COVID‐19 pandemic.

CONCLUSION

A policy to disclose the detailed location information of COVID‐19 cases can be an effective measure to mitigate the virus's spread by promoting precautionary behavioral responses of the public. However, such information disclosure can also lead to substantial demand shocks, especially for retail businesses. If the distribution of COVID‐19 cases has a significant spatial variation within a city, the demand shock would be localized at the neighborhood level. Moreover, if perceived risks of the virus's spread and behavioral responses differ by neighborhood attributes, such as the density of population or employment, land‐use diversity, or retail agglomeration, the negative demand shock would be greater for certain neighborhoods than others even though they are reported to contain a similar number of COVID‐19 cases.

We focus on these spatial implications in our study. Using the big data of daily mobility and credit card spending at the fine geographic level in Seoul, South Korea, we investigate public responses to the disclosed number of COVID‐19 cases. We find that the public actively responded to the information disclosure by reducing their visits to and retail spending in neighborhoods with more cases. Moreover, there is spatial heterogeneity in public responses to the disclosed information of cases. People reduced visits and retail spending disproportionately more if neighborhoods have higher employment density, land‐use diversity, and retail agglomeration. In contrast, neighborhoods with higher population density experienced little reduction in nonresident visits and retail spending. Finally, we demonstrate significant spatial spillover with no evidence on spatial substitution of localized demand toward neighborhoods with lower COVID‐19 risks. The public further reduced their visits and retail spending if a neighborhood is surrounded by areas with a higher number of COVID‐19 cases or if COVID‐19 risks increase in other areas with similar functions within the city.

Our findings suggest important implications for economic resilience of local neighborhoods during the pandemic period. Higher employment density and more mixed‐use buildings in a neighborhood are associated with lower resilience. Retail agglomeration also appears to exacerbate local demand shock controlling for the number of COVID‐19 cases. This is opposite from the evidence that areas with higher retail agglomeration were more resilient to economic crisis. In other words, spatial attributes that used to help the local economy may increase perceived COVID‐19 risks and lead to lower economic resilience. On the other hand, stronger local demand and being located in more homogeneous residential areas help neighborhoods survive in terms of retail demand, potentially due to public preference for shorter trips as well as lower perceived risks of COVID‐19. Also, in the context of COVID‐19, demand substitution hardly occurs. Therefore, neighborhoods that used to be commercially viable would be the ones hit most severely during COVID‐19.

These findings also call for more attention to localized economic impacts of the public health crisis and preparedness to future shocks. Our findings highlight that public responses to the COVID‐19 pandemic significantly differ from those of natural disasters and economic crisis. Hence, cities' strategies to recover from the pandemic and strengthen the resilience of local communities have to reflect public concerns on social contacts and virus spread. For example, to revitalize commercial centers with agglomerated retail shops and high employment density, stronger safety measures should be considered. With respect to the policy measure of targeted social distancing through public information disclosure of COVID‐19 cases, we not only echo existing evidence on its effectiveness and potentially lower economic cost compared with a lockdown (Argente et al., 2020) but also emphasize paying attention to the localized impact of this measure. Finally, we acknowledge both an advantage and disadvantage of using a sample from one large city. While it is more homogeneous in terms of spatial attributes and policy measures, there is a limitation of generalizability. Even with this limitation, we believe that our study is meaningful because Seoul is one of the most distinguished global cities. Global cities tend to show similar patterns of land uses and hierarchies of commercial spaces. Because Seoul is the rare case that has not gone through any lockdown during the pandemic, our results could provide important implications on urban resilience to other similar global cities.

	Panel A: Nonresident inflow			Panel B: Retail spending
	Total	Residents	Visitors	Total	Residents	Visitors
Day −14	−0.470*	−0.547+	−0.602*	−0.380	−1.454	0.418
Day −13	−0.359+	−0.405	−0.385	−1.565*	−1.377	−1.144
Day −12	−0.433*	−0.542*	−0.391	−1.787**	−0.382	−2.438**
Day −11	−0.025	−0.041	−0.030	−0.277	0.996	−1.028
Day −10	−0.429+	−0.157	−0.752*	−0.173	−0.367	0.001
Day −9	−0.230	−0.512	−0.094	0.364	−0.364	0.577
Day −8	−0.062	−0.137	−0.031	−0.570	−0.352	−0.619
Day −7	0.022	0.044	−0.072	−1.597*	−1.794+	−1.273
Day −6	0.175	−0.027	0.253	0.502	0.472	0.325
Day −5	−0.407+	−0.193	−0.728*	−0.430	−1.167	−0.138
Day −4	−0.265	−0.098	−0.503+	−0.338	−0.798	0.244
Day −3	−0.129	−0.027	−0.257	−0.094	−1.250	0.865
Day −2	−0.097	−0.083	−0.280	−1.542*	−1.567	−1.445
Day −1	−0.462*	−0.578+	−0.460+	−0.006	−0.116	−0.081
Day 0	−0.305	−0.276	−0.451	−1.879**	−1.460	−1.877*
Day +1	−0.885***	−0.981**	−0.753*	−2.373**	−1.972+	−2.842**
Day +2	−0.706**	−0.852*	−0.727*	0.480	−0.704	0.755
Day +3	−0.898***	−0.554	−1.298***	−2.372***	−0.565	−3.808***
Day +4	−0.933***	−0.476	−1.375***	−2.090***	−2.267**	−2.047**
Day +5	−0.728**	−0.604+	−0.925**	−1.801*	−2.200+	−1.477+
Day +6	−0.784***	−0.607+	−1.043***	−0.043	1.134	−1.349+
Day +7	−0.656**	−0.516	−0.959**	−1.467*	−0.900	−1.800*
Day +8	−0.719**	−0.982**	−0.569+	−2.864***	−2.254+	−3.067***
Day +9	−0.618*	−0.956**	−0.569+	−0.236	−0.063	−0.619
Day +10	−0.501*	−0.636+	−0.603+	−0.196	0.786	−1.121
Day +11	−0.889***	−0.734+	−1.146***	−0.947	0.428	−2.088**
Day +12	−0.644**	−0.853**	−0.550+	−1.538*	−1.040	−1.851*
Day +13	−0.616**	−0.786*	−0.497+	−0.309	0.319	−1.391
Day +14	−0.494*	−0.23	−0.730*	−0.907	−0.700	−1.498
Neighborhood FEs	Yes	Yes	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes	Yes	Yes
Number of Obs.	46,592	46,592	46,592	44,584	44,584	44,584

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses; FEs, fixed effects.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector, including retail trade, tourism, entertainment, recreation, and food services.

	Total			Resident			Visitor
	All	Weekdays	Weekends	All	Weekdays	Weekends	All	Weekdays	Weekends
Panel A: Dependent variable: year‐over‐year percent change in nonresident inflow
—cases in 14 days	−0.396***	−0.362***	−0.463***	−0.451***	−0.466***	−0.387*	−0.469***	−0.413***	−0.584***
	(0.040)	(0.040)	(0.082)	(0.073)	(0.073)	(0.153)	(0.054)	(0.053)	(0.114)
Neighborhood FEs	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Number of Obs.	46,592	33,886	12,706	46,592	33,886	12,706	46,592	33,886	12,706

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses; FEs, fixed effects.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector including retail trade, tourism, entertainment, recreation, and food services.

	Model 1	Model 2	Model 3	Model 4
Panel A: Dependent variable: year‐over‐year percent change in nonresident inflow
Total cases in 14 days	−0.044
	(0.054)
Residents in 14 days		−0.081		−0.080
		(0.088)		(0.088)
Visitors in 14 days			−0.011	−0.010
			(0.070)	(0.070)
Neighborhood FEs	Yes	Yes	Yes	Yes
Day FEs	Yes	Yes	Yes	Yes
Number of Obs.	46,592	46,592	46,592	46,592

Note 1: + p < 0.1, *p < 0.05, **p < 0.01, and ***p < 0.001. Robust standard errors in parentheses; FEs, fixed effects.

Note 2: The unit of analysis is neighborhood‐day. The outliers with the percent change greater than 95% or less than −95% are excluded.

Note 3: Resident cases are the number of COVID‐19 cases that reside in a given neighborhood. Visit cases are the number of nonresident COVID‐19 patients that visited the neighborhood.

Note 4: The retail credit card spending refers to credit card spending in the retail sector including retail trade, tourism, entertainment, recreation, and food services.