IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i22p12079-d681357.html
   My bibliography  Save this article

An Investigation of the Initial Recovery Time of Chinese Enterprises Affected by COVID-19 Using an Accelerated Failure Time Model

Author

Listed:
  • Lijiao Yang

    (School of Management, Wuhan University of Technology, Wuhan 430070, China)

  • Yishuang Qi

    (School of Management, Wuhan University of Technology, Wuhan 430070, China)

  • Xinyu Jiang

    (School of Management, Wuhan University of Technology, Wuhan 430070, China)

Abstract

COVID-19 has had a great impact on the economy, society, and people’s lives in China and globally. The production and operations of Chinese enterprises have also faced tremendous challenges. To understand the economic impact of COVID-19 on enterprises and the key affecting factors, this study adds to the literature by investigating the business recovery process of enterprises from the micro perspective. Specific attention is paid to the initial stage of business recovery. A questionnaire survey of 750 enterprises explored the impact during the pandemic period from July to September 2020. An accelerated failure time model in survival analysis was adopted to analyze the data. The results show that the manufacturing industry is mainly faced by affecting factors such as enterprise ownership, employees’ panic and order cancellation on initial enterprise recovery. As for the non-manufacturing industry, more factors, including clients’ distribution, employees’ panic, raw material shortage, cash flow shortage and order cancellation, are found to be significant. Acceleration factors that estimate the effects of those covariates on acceleration/deceleration of the recovery time are presented. For instance, the acceleration factor of employees’ panic is 1.319 for non-manufacturing, which implies that, compared with enterprises where employees are less panicked, enterprises with employees obviously panicked will recover 1.319 times slower at any quantile of probability of recovery time. This study provides a scientific reference for the post-pandemic recovery of enterprises, and can support the formulation of government policies and enterprise decisions.

Suggested Citation

  • Lijiao Yang & Yishuang Qi & Xinyu Jiang, 2021. "An Investigation of the Initial Recovery Time of Chinese Enterprises Affected by COVID-19 Using an Accelerated Failure Time Model," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12079-:d:681357
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/22/12079/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/22/12079/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Meri Davlasheridze & Pinar C. Geylani, 2017. "Small Business vulnerability to floods and the effects of disaster loans," Small Business Economics, Springer, vol. 49(4), pages 865-888, December.
    2. Feroz Ahmed & Monimul Haque, 2011. "Constraints of Manufacture based Small and Medium Enterprise (SME) Development in Bangladesh," Journal of Social and Development Sciences, AMH International, vol. 1(3), pages 91-100.
    3. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    4. Rajshree Agarwal & Michael Gort, 2002. "Firm and Product Life Cycles and Firm Survival," American Economic Review, American Economic Association, vol. 92(2), pages 184-190, May.
    5. Ine Paeleman & Tom Vanacker, 2015. "Less is More, or Not? On the Interplay between Bundles of Slack Resources, Firm Performance and Firm Survival," Journal of Management Studies, Wiley Blackwell, vol. 52(6), pages 819-848, September.
    6. Lijiao Yang & Yoshio Kajitani & Hirokazu Tatano & Xinyu Jiang, 2016. "A methodology for estimating business interruption loss caused by flood disasters: insights from business surveys after Tokai Heavy Rain in Japan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 411-430, November.
    7. Ashraf, Badar Nadeem, 2020. "Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    8. Serguei Kaniovski & Michael Peneder & Egon Smeral, 2008. "Determinants of Firm Survival in the Austrian Accommodation Sector," Tourism Economics, , vol. 14(3), pages 527-543, September.
    9. Barker, Kash & Baroud, Hiba, 2014. "Proportional hazards models of infrastructure system recovery," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 201-206.
    10. Xiaobing Huang, 2019. "Reform of state‐owned enterprises and productivity growth in China," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 33(1), pages 64-77, May.
    11. Yoshio Kajitani & Hirokazu Tatano, 2014. "Estimation Of Production Capacity Loss Rate After The Great East Japan Earthquake And Tsunami In 2011," Economic Systems Research, Taylor & Francis Journals, vol. 26(1), pages 13-38, March.
    12. Ahmed, Walid M.A., 2020. "Stock market reactions to domestic sentiment: Panel CS-ARDL evidence," Research in International Business and Finance, Elsevier, vol. 54(C).
    Full references (including those not matched with items on IDEAS)

    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. David Nortes Martínez & Frédéric Grelot & Pauline Bremond & Stefano Farolfi & Juliette Rouchier, 2021. "Are interactions important in estimating flood damage to economic entities? The case of wine-making in France," Post-Print hal-03609616, HAL.
    2. Weijiang Li & Jiahong Wen & Bo Xu & Xiande Li & Shiqiang Du, 2018. "Integrated Assessment of Economic Losses in Manufacturing Industry in Shanghai Metropolitan Area Under an Extreme Storm Flood Scenario," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    3. Lijiao Yang & Yoshio Kajitani & Hirokazu Tatano & Xinyu Jiang, 2016. "A methodology for estimating business interruption loss caused by flood disasters: insights from business surveys after Tokai Heavy Rain in Japan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 411-430, November.
    4. Xinyu Jiang & Nobuhito Mori & Hirokazu Tatano & Lijiao Yang, 2019. "Simulation-Based Exceedance Probability Curves to Assess the Economic Impact of Storm Surge Inundations due to Climate Change: A Case Study in Ise Bay, Japan," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    5. Zhang, Wei, 2015. "R&D investment and distress risk," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 94-114.
    6. Alex Coad, 2018. "Firm age: a survey," Journal of Evolutionary Economics, Springer, vol. 28(1), pages 13-43, January.
    7. Paul H. Jensen & Elizabeth Webster & Hielke Buddelmeyer, 2008. "Innovation, Technological Conditions and New Firm Survival," The Economic Record, The Economic Society of Australia, vol. 84(267), pages 434-448, December.
    8. Baumöhl, Eduard & Iwasaki, Ichiro & Kočenda, Evžen, 2019. "Institutions and determinants of firm survival in European emerging markets," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 431-453.
    9. Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    10. Mohammad Mojtahedi & Sidney Newton & Jason Meding, 2017. "Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1119-1133, January.
    11. Saara Tamminen, 2017. "Regional effects or none? Firms' profitability during the Great Recession in Finland," Papers in Regional Science, Wiley Blackwell, vol. 96(1), pages 33-59, March.
    12. Sandra M. Leitner & Robert Stehrer, 2016. "R&D and Non-R&D Innovators During the Global Financial Crisis: The Role of Binding Credit Constraints," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 53(1), pages 1-38, December.
    13. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. Md. Mahmudul Alam & Haitian Wei & Abu N. M. Wahid, 2021. "COVID‐19 outbreak and sectoral performance of the Australian stock market: An event study analysis," Australian Economic Papers, Wiley Blackwell, vol. 60(3), pages 482-495, September.
    15. Ponzoa, José M. & Gómez, Andrés & Mas, José M., 2023. "EU27 and USA institutions in the digital ecosystem: Proposal for a digital presence measurement index," Journal of Business Research, Elsevier, vol. 154(C).
    16. Serhan Cevik & Fedor Miryugin, 2022. "Death and taxes: Does taxation matter for firm survival?," Economics and Politics, Wiley Blackwell, vol. 34(1), pages 92-112, March.
    17. Al-Maadid, Alanoud & Alhazbi, Saleh & Al-Thelaya, Khaled, 2022. "Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries," Research in International Business and Finance, Elsevier, vol. 61(C).
    18. Tunyi, Abongeh A. & Ntim, Collins G. & Danbolt, Jo, 2019. "Decoupling management inefficiency: Myopia, hyperopia and takeover likelihood," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 1-20.
    19. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    20. Rui Baptista & Murat Karaöz & João Correia Leitão, 2020. "Diversification by young, small firms: the role of pre-entry resources and entry mistakes," Small Business Economics, Springer, vol. 55(1), pages 103-122, June.

    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:gam:jijerp:v:18:y:2021:i:22:p:12079-:d:681357. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.