IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p5978-d815688.html
   My bibliography  Save this article

Impact of Digital Economy on the Provision Efficiency for Public Health Services: Empirical Study of 31 Provinces in China

Author

Listed:
  • Yuwen Lyu

    (School of Economics and Statistics, Guangzhou University, Guangzhou 511400, China)

  • Yuqing Peng

    (School of Journalism and Communication, Guangzhou University, Guangzhou 511400, China
    Institude of Communication Studies, Communication University of China, Beijing 100024, China)

  • Hejian Liu

    (School of Education, Guangzhou University, Guangzhou 511400, China)

  • Ji-Jen Hwang

    (School of Policy and Government, George Mason University, Arlington, VA 20301, USA
    Institute for Global Public Affairs Research, Bethesda, MD 20817, USA)

Abstract

The digital economy is booming in China and has become the world’s largest after the United States’. Since China entered the era of the digital economy, its digital technology has radiated into various fields. This study is to examine the impact of China’s digital economy on the provision efficiency of public health institutions and the mechanism of action between them. Specifically, it measures the development level of China’s digital economy, and the provision efficiency of public health institutions from 2009 to 2018. The research also explores the relationship between China’s digital economy and its provision efficiency, through the Tobit-DEA model. An analysis of the regional heterogeneity indicated that the performance of China’s digital economy in the eastern region has a significant positive effect on improving the efficiency of the public health sector. This further confirms that the digital economy has strengthened China’s ability to deal with public health crises during the COVID-19 pandemic. A further mediation effect analysis showed that China’s digital economy optimizes the efficiency of public health provision by improving governmental performance and regulatory quality. This shows that the development of the digital economy promotes the construction of digital government, and thus improves the quality of governmental supervision and governmental performance, which has a significant positive effect on the efficiency of the supply of public health services. During the COVID-19 pandemic especially, government delivery of public health services was critical in addressing public health crises. Therefore, based on the results of our empirical analysis, this study provides policy suggestions for improving the efficiency of public health service provision in the era of the digital economy.

Suggested Citation

  • Yuwen Lyu & Yuqing Peng & Hejian Liu & Ji-Jen Hwang, 2022. "Impact of Digital Economy on the Provision Efficiency for Public Health Services: Empirical Study of 31 Provinces in China," IJERPH, MDPI, vol. 19(10), pages 1-17, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5978-:d:815688
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/5978/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/10/5978/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Douglass C. North, 1990. "A Transaction Cost Theory of Politics," Journal of Theoretical Politics, , vol. 2(4), pages 355-367, October.
    2. José França & João Figueiredo & Jair Lapa, 2010. "A DEA methodology to evaluate the impact of information asymmetry on the efficiency of not-for-profit organizations with an application to higher education in Brazil," Annals of Operations Research, Springer, vol. 173(1), pages 39-56, January.
    3. Touhami Abdelkhalek & Aziz Ajbilou & Mohamed Benayad & Dorothée Boccanfuso & Luc Savard, 2021. "How Can the Digital Economy Benefit Morocco and All Moroccans?," Working Papers 1503, Economic Research Forum, revised 20 Nov 2021.
    4. Yiru Guo & Yan Hu & Ke Shi & Yuriy Bilan, 2020. "Valuation of Water Resource Green Efficiency Based on SBM–TOBIT Panel Model: Case Study from Henan Province, China," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    5. Carlsson, Bo, 2004. "The Digital Economy: what is new and what is not?," Structural Change and Economic Dynamics, Elsevier, vol. 15(3), pages 245-264, September.
    6. Aarthi Raghavan & Mehmet Akif Demircioglu & Araz Taeihagh, 2021. "Public Health Innovation through Cloud Adoption: A Comparative Analysis of Drivers and Barriers in Japan, South Korea, and Singapore," IJERPH, MDPI, vol. 18(1), pages 1-30, January.
    7. Mann, Stefan & Wüstemann, Henry, 2010. "Public governance of information asymmetries--The gap between reality and economic theory," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 39(2), pages 278-285, April.
    8. Rongen, Gunnar, 1995. "Efficiency in the provision of local public goods in Norway," European Journal of Political Economy, Elsevier, vol. 11(2), pages 253-264, June.
    9. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, October.
    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. Peng, Yue & Wang, Wei & Zhen, Shangsong & Liu, Yunqiang, 2024. "Does digitalization help green consumption? Empirical test based on the perspective of supply and demand of green products," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).

    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. Adwoa Asantewaa & Tooraj Jamasb & Manuel Llorca, 2022. "Electricity Sector Reform Performance in Sub-Saharan Africa: A Parametric Distance Function Approach," Energies, MDPI, vol. 15(6), pages 1-29, March.
    2. Mehdi Toloo & Mona Barat & Atefeh Masoumzadeh, 2015. "Selective measures in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 623-642, March.
    3. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    4. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    5. Lingzhang Kong & Jinye Li, 2022. "Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    6. Oliver Stein & Nathan Sudermann-Merx, 2016. "The Cone Condition and Nonsmoothness in Linear Generalized Nash Games," Journal of Optimization Theory and Applications, Springer, vol. 170(2), pages 687-709, August.
    7. Anastasiou Athanasios & Kalligosfyris Charalampos & Kalamara Eleni, 2022. "Assessing the effectiveness of tax administration in macroeconomic stability: evidence from 26 European Countries," Economic Change and Restructuring, Springer, vol. 55(4), pages 2237-2261, November.
    8. Peter Fernandes Wanke & Rebecca de Mattos, 2014. "Capacity Issues and Efficiency Drivers in Brazilian Bulk Terminals," Brazilian Business Review, Fucape Business School, vol. 11(5), pages 72-98, October.
    9. Yao Zhao & Xuena Kong & Mahmood Ahmad & Zahoor Ahmed, 2023. "Digital Economy, Industrial Structure, and Environmental Quality: Assessing the Roles of Educational Investment, Green Innovation, and Economic Globalization," Sustainability, MDPI, vol. 15(3), pages 1-24, January.
    10. Mohammad Nourani & Qian Long Kweh & Evelyn Shyamala Devadason & V.G.R. Chandran, 2020. "A decomposition analysis of managerial efficiency for the insurance companies: A data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 885-901, September.
    11. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    12. George Halkos & Roman Matousek & Nickolaos Tzeremes, 2016. "Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 47-77, January.
    13. Duk Hee Lee & Il Won Seo & Ho Chull Choe & Hee Dae Kim, 2012. "Collaboration network patterns and research performance: the case of Korean public research institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 925-942, June.
    14. Ran, Qiying & Yang, Xiaodong & Yan, Hongchuan & Xu, Yang & Cao, Jianhong, 2023. "Natural resource consumption and industrial green transformation: Does the digital economy matter?," Resources Policy, Elsevier, vol. 81(C).
    15. Douglass C. North, 2016. "Institutions and Economic Theory," The American Economist, Sage Publications, vol. 61(1), pages 72-76, March.
    16. Pablo T. Spiller, 2003. "The Institutional Foundations of Public Policy: A Transactions Approach with Application to Argentina," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 19(2), pages 281-306, October.
    17. A. M. Aldanondo & V. L. Casasnovas, 2015. "Input aggregation bias in technical efficiency with multiple criteria analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 430-435, April.
    18. Khushalani, Jaya & Ozcan, Yasar A., 2017. "Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 15-23.
    19. Mehdiloozad, Mahmood & Zhu, Joe & Sahoo, Biresh K., 2018. "Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data," European Journal of Operational Research, Elsevier, vol. 265(2), pages 644-654.
    20. Subhash C. Ray & Lei Chen, 2015. "Data Envelopment Analysis for Performance Evaluation: A Child’s Guide," Springer Books, in: Subhash C. Ray & Subal C. Kumbhakar & Pami Dua (ed.), Benchmarking for Performance Evaluation, edition 127, chapter 0, pages 75-116, Springer.

    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:19:y:2022:i:10:p:5978-:d:815688. 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.