IDEAS home Printed from https://ideas.repec.org/p/max/cprwps/251.html
   My bibliography  Save this paper

Cities in a Pandemic: Evidence from China

Author

Listed:

Abstract

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 inter-temporal dynamics of the disease, and (iv) the unobserved city- 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.

Suggested Citation

  • Badi H. Baltagi & Ying Deng & Jing Li & Zhenlin Yang, 2022. "Cities in a Pandemic: Evidence from China," Center for Policy Research Working Papers 251, Center for Policy Research, Maxwell School, Syracuse University.
    • Handle: RePEc:max:cprwps:251
    as

    Download full text from publisher

    File URL: https://surface.syr.edu/cpr/462/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Andrés Rodríguez‐Pose & Chiara Burlina, 2021. "Institutions and the uneven geography of the first wave of the COVID‐19 pandemic," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 728-752, September.
    2. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    3. Brzezinski, Adam & Deiana, Guido & Kecht, Valentin & Van Dijcke, David, 2020. "The COVID-19 Pandemic: Government vs. Community Action Across the United States," INET Oxford Working Papers 2020-06, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    4. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    5. Combes, Pierre-Philippe & Gobillon, Laurent, 2015. "The Empirics of Agglomeration Economies," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 247-348, Elsevier.
    6. Jérôme Adda, 2016. "Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 891-941.
    7. Edward L. Glaeser & Caitlin S. Gorback & Stephen J. Redding, 2020. "How Much Does COVID-19 Increase with Mobility? Evidence from New York and Four Other U.S. Cities," Working Papers 2020-22, Princeton University. Economics Department..
    8. Daron Acemoglu & Jacob Moscona & James A. Robinson, 2016. "State Capacity and American Technology: Evidence from the 19th Century," NBER Working Papers 21932, National Bureau of Economic Research, Inc.
    9. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    10. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    11. Dhaval M. Dave & Andrew I. Friedson & Kyutaro Matsuzawa & Joseph J. Sabia & Samuel Safford, 2020. "Were Urban Cowboys Enough to Control COVID-19? Local Shelter-in-Place Orders and Coronavirus Case Growth," NBER Working Papers 27229, National Bureau of Economic Research, Inc.
    12. Troesken, Werner, 2002. "The Limits Of Jim Crow: Race And The Provision Of Water And Sewerage Services In American Cities, 1880–1925," The Journal of Economic History, Cambridge University Press, vol. 62(3), pages 734-772, September.
    13. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).
    14. Yang, Kai & Lee, Lung-fei, 2019. "Identification and estimation of spatial dynamic panel simultaneous equations models," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 32-46.
    15. Daron Acemoglu & Camilo García-Jimeno & James A. Robinson, 2015. "State Capacity and Economic Development: A Network Approach," American Economic Review, American Economic Association, vol. 105(8), pages 2364-2409, August.
    16. Yusuke Narita & Ayumi Sudo, 2021. "Curse of Democracy: Evidence from 2020," Cowles Foundation Discussion Papers 2281, Cowles Foundation for Research in Economics, Yale University.
    17. Robert F. Phillips, 2019. "A Comparison of First-Difference and Forward Orthogonal Deviations GMM," Papers 1907.12880, arXiv.org.
    18. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    19. Gilles Duranton & Diego Puga, 2020. "The Economics of Urban Density," Journal of Economic Perspectives, American Economic Association, vol. 34(3), pages 3-26, Summer.
    20. Klaus Desmet & Romain Wacziarg, 2020. "Understanding Spatial Variation in COVID-19 across the United States," NBER Working Papers 27329, National Bureau of Economic Research, Inc.
    21. Kazuhiko Hayakawa, 2009. "First Difference or Forward Orthogonal Deviation- Which Transformation Should be Used in Dynamic Panel Data Models?: A Simulation Study," Economics Bulletin, AccessEcon, vol. 29(3), pages 2008-2017.
    22. Li, Jing, 2014. "The influence of state policy and proximity to medical services on health outcomes," Journal of Urban Economics, Elsevier, vol. 80(C), pages 97-109.
    23. Bisin, Alberto & Moro, Andrea, 2020. "Learning Epidemiology by Doing: The Empirical Implications of a Spatial SIR Model with Behavioral Responses," MPRA Paper 101059, University Library of Munich, Germany.
    24. Badi H. Baltagi, 2021. "Econometric Analysis of Panel Data," Springer Texts in Business and Economics, Springer, edition 6, number 978-3-030-53953-5, August.
    25. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    26. David Cutler & Grant Miller, 2005. "The role of public health improvements in health advances: The twentieth-century United States," Demography, Springer;Population Association of America (PAA), vol. 42(1), pages 1-22, February.
    27. Michael Greenstone & Vishan Nigam, 2020. "Does Social Distancing Matter?," Working Papers 2020-26, Becker Friedman Institute for Research In Economics.
    28. Cuñat, Alejandro & Zymek, Robert, 2022. "The (structural) gravity of epidemics," European Economic Review, Elsevier, vol. 144(C).
    29. Daron Acemoglu & Jacob Moscona & James A. Robinson, 2016. "State Capacity and American Technology: Evidence from the Nineteenth Century," American Economic Review, American Economic Association, vol. 106(5), pages 61-67, May.
    30. Combes, Pierre-Philippe & Gobillon, Laurent, 2015. "The Empirics of Agglomeration Economies," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 247-348, Elsevier.
    31. Maloney,William F. & Taskin,Temel, 2020. "Determinants of Social Distancing and Economic Activity during COVID-19 : A Global View," Policy Research Working Paper Series 9242, The World Bank.
    32. Li, Bingjing & Ma, Lin, 2022. "JUE insight: Migration, transportation infrastructure, and the spatial transmission of COVID-19 in China," Journal of Urban Economics, Elsevier, vol. 127(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zbieranek, Piotr, 2022. "Instytucje ramowe. Publiczne instytucje kultury jako katalizator metagovernance w polityce kulturalnej," Studia z Polityki Publicznej / Public Policy Studies, Warsaw School of Economics, vol. 9(4), pages 1-23, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boto-García, David, 2023. "Investigating the two-way relationship between mobility flows and COVID-19 cases," Economic Modelling, Elsevier, vol. 118(C).
    2. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    3. Wan, Liyang & Wan, Qian, 2022. "High-speed railway and the intercity transmission of epidemics: Evidence from COVID-19 in China," Economic Modelling, Elsevier, vol. 114(C).
    4. Borsati, Mattia & Nocera, Silvio & Percoco, Marco, 2022. "Questioning the spatial association between the initial spread of COVID-19 and transit usage in Italy," Research in Transportation Economics, Elsevier, vol. 95(C).
    5. Yu, Jihai & Zhou, Li-An & Zhu, Guozhong, 2016. "Strategic interaction in political competition: Evidence from spatial effects across Chinese cities," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 23-37.
    6. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    7. Nicola Borri & Francesco Drago & Chiara Santantonio & Francesco Sobbrio, 2021. "The “Great Lockdown”: Inactive workers and mortality by Covid‐19," Health Economics, John Wiley & Sons, Ltd., vol. 30(10), pages 2367-2382, September.
    8. Garriga, Carlos & Manuelli, Rody & Sanghi, Siddhartha, 2022. "Optimal management of an epidemic: Lockdown, vaccine and value of life," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    9. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    10. Victor Couture & Jonathan I. Dingel & Allison Green & Jessie Handbury & Kevin R. Williams, 2020. "Measuring Movement and Social Contact with Smartphone Data: A Real-time Application to COVID-19," Cowles Foundation Discussion Papers 2241, Cowles Foundation for Research in Economics, Yale University.
    11. Cristini, Annalisa & Trivin, Pedro, 2022. "Close encounters during a pandemic: Social habits and inter-generational links in the first two waves of COVID-19," Economics & Human Biology, Elsevier, vol. 47(C).
    12. Gonzalo Castex & Evgenia Dechter & Miguel Lorca, 2021. "COVID-19: The impact of social distancing policies, cross-country analysis," Economics of Disasters and Climate Change, Springer, vol. 5(1), pages 135-159, April.
    13. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    14. Alexander Klemm & Stefan Parys, 2012. "Empirical evidence on the effects of tax incentives," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(3), pages 393-423, June.
    15. Nicholas W. Papageorge & Matthew V. Zahn & Michèle Belot & Eline Broek-Altenburg & Syngjoo Choi & Julian C. Jamison & Egon Tripodi, 2021. "Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(2), pages 691-738, April.
    16. Lips, Johannes, 2018. "Debt and the Oil Industry - Analysis on the Firm and Production Level," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181504, Verein für Socialpolitik / German Economic Association.
    17. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    18. Pablo D. Fajgelbaum & Amit Khandelwal & Wookun Kim & Cristiano Mantovani & Edouard Schaal, 2021. "Optimal Lockdown in a Commuting Network," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 503-522, December.
    19. Chen, Simiao & Jin, Zhangfeng & Bloom, David E., 2020. "Act Early to Prevent Infections and Save Lives: Causal Impact of Diagnostic Efficiency on the COVID-19 Pandemic," IZA Discussion Papers 13749, Institute of Labor Economics (IZA).
    20. Emilie Caldeira & Martial Foucault & Gregoire Rota-Graziosi, 2015. "Decentralization in Africa and the nature of local governments’ competition: evidence from Benin," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 22(6), pages 1048-1076, December.

    More about this item

    Keywords

    COVID-19; Urban Density; Government Efficiency Cities;
    All these keywords.

    JEL classification:

    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:max:cprwps:251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Margaret Austin or Zia Jackson or Katrina Fiacchi (email available below). General contact details of provider: https://edirc.repec.org/data/cpsyrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.