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Geographic restrictions in stimulus spending mitigated COVID-19 transmission in Seoul

Nature Cities (2025)Cite this article

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Abstract

Stimulus payment programs have been instrumental in supporting economic activity during the COVID-19 pandemic, yet their public health implications remain underexplored. This study examines a unique policy in Seoul, South Korea, which restricted stimulus spending to recipients’ residential cities, to assess its potential in mitigating virus transmission. Using credit card transactions, mobility records and COVID-19 case data, we apply a triple difference-in-differences approach to analyze how the policy reshaped spatial consumption patterns. Results indicate that while the stimulus program boosted overall spending, it also significantly redistributed spending geographically. The restriction led to reduced consumption outside Seoul and a greater concentration of local spending, effectively limiting mobility. Spillover analysis shows localized consumption had a lower impact on infection rates than cross-neighborhood consumption. Simulations suggest the geographic restriction reduced COVID-19 cases by 17% versus an unrestricted scenario. These findings suggest geographically targeted stimulus policies can balance economic recovery with public health objectives.

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Fig. 1: Research framework.
Fig. 2: Temporal change in spatial distribution of physical consumption of Seoul residents.
Fig. 3: Counterfactual simulation of cumulative COVID-19 cases in Seoul: scenarios with and without geographic restrictions in stimulus programs.

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Data availability

The mobility dataset is proprietary to SK Telecom, and the dataset is subject to strict privacy regulations. The credit card consumption dataset is proprietary to BC Card and subject to privacy regulations. Both datasets were anonymized and aggregated before being shared with the authors. The data that support the findings of this study are available from the corresponding author upon request. Other datasets, including the official COVID-19 cases, are publicly accessible via Github at https://github.com/jieun0441/SP_Korea. Source data are provided with this paper.

Code availability

Due to the confidentiality of the data used in this study, the code will be shared in compliance with applicable data protection regulations, ensuring privacy. The publicly accessible code can be accessed via Github at https://github.com/jieun0441/SP_Korea. Additional custom code that supports the findings of this study is available from the corresponding author upon request.

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Acknowledgements

We thank SK Telecom and BC Card for providing us access to mobility and credit card transaction data, respectively. This work is supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 Grant (A-0003825-01-00, K.O.L.). The funder had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Urban Systems Institute, University of Hong Kong, Hong Kong, China

    Ji Eun Lee

  2. NUS Business School, National University of Singapore, Singapore, Singapore

    Kwan Ok Lee

  3. Graduate School of Environmental Studies, Seoul National University, Seoul, South Korea

    Hyojung Lee

Authors
  1. Ji Eun Lee
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  2. Kwan Ok Lee
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  3. Hyojung Lee
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Contributions

K.O.L. conceived and designed the study. J.E.L. analyzed the data. All authors wrote the first draft and provided critical revisions. All authors read and approved the submitted paper. All authors accessed and verified the data.

Corresponding author

Correspondence to Kwan Ok Lee.

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The authors declare no competing interests.

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Nature Cities thanks Mozhgan Pourmoradnasseri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Summary of Data and Sources.

This figure shows various data types, sources, spatial- and time- units we employed for this study’s analyses.

Extended Data Fig. 2 Temporal Trend of Card Transactions by Seoul Residents.

The height of bar graph shows trends in total number of card transactions in Seoul, which is sum of the stimulus payments transactions (red) and non-stimulus payments transactions (gray). The dashed line graph shows the percentage of the stimulus payments transactions in each week.

Source data

Extended Data Fig. 3 Trends of Stimulus and Non-Stimulus Transactions.

a, Event study results for all transactions. b, Event study results for non-stimulus transactions. Consistent with Extended Data Fig. 2, our analysis is limited to Seoul residents and focuses solely on physical transactions. We use the aggregate number of transactions rather than individual customer transactions. Data are presented as coefficient values (point estimate) +/- SE. (n = 16,540,750 transaction groups for a; n = 16,479,308 transaction groups for b) Transaction groups refer homogeneous transactions groups having the same O-D and occurred in the same week. Number of transactions in each observation was employed as a weight variable.

Extended Data Fig. 4 Pre- and Post-Stimulus Trends in Public Mobility.

a, Event study results of cross-neighbourhood mobility within Seoul, b, Event study results of cross-city mobility from non-Seoul, c, Weekly new infection rate per 100 K population of Seoul. Data are presented as coefficient values (point estimate) +/- SE. (n = 27,918 homogeneous trip groups with same O-D in each week). Panels a and b present temporal changes in commuting inflows to neighbourhoods in Seoul. In South Korea, the two days in the 18th week were Children’s Day and Mother’s Day, which are peak times for family trips and parent visits. However, the impact of these holidays on public mobility and seasonality is accounted for by the weekly fixed effects.

Extended Data Table 1 Policy Design Comparisons of Stimulus Payment across the Countries
Full size table
Extended Data Table 2 Difference-in-Difference Model Results for Spatial Redistribution with Different Time Windows
Full size table
Extended Data Table 3 Difference-in-Difference Model Results of Post-Stimulus Spatial Redistribution of Physical Consumption
Full size table

Supplementary information

Supplementary Information

Supplementary Discussions 1–4.

Reporting Summary

Supplementary Data 1

Source data for Supplementary Fig. 3.

Supplementary Data 2

Source data for Supplementary Fig. 4.

Supplementary Data 3

Source data for Supplementary Fig. 5.

Supplementary Data 4

Source data for Supplementary Fig. 6.

Supplementary Data 5

Source data for Supplementary Fig. 7.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

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Lee, J.E., Lee, K.O. & Lee, H. Geographic restrictions in stimulus spending mitigated COVID-19 transmission in Seoul. Nat Cities (2025). https://doi.org/10.1038/s44284-025-00318-7

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