IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0308287.html
   My bibliography  Save this article

The impact of internet usage preferences on labor income: Evidence from China

Author

Listed:
  • Kefeng Yuan
  • Xiaoxia Zhang

Abstract

Background: The widespread application and iterative updating of computers and Internet communication technologies have not only increased productivity and enhanced intra- and inter-enterprise collaboration, but have also led to significant changes in the labor market and residents’ labor income. In the digital era, accepting digital technology and possessing a certain degree of digital literacy have become the necessary abilities for people to survive and develop. However, the differences in digital literacy caused by individual differences will inevitably bring about a series of chain reactions. Therefore, it is necessary to study the subtle impact of Internet usage preference on residents’ labor income in the context of digital transformation to promote digital equity. Objective: This study aims to empirically analyze micro-level survey data to reveal the impact of individual differences in internet usage preferences on their labor income. The findings provide theoretical references for government policy formulation and individual development. Methods: A function model was established to analyze the impact of individual internet usage preferences on labor income. Relevant data from the authoritative Chinese General Social Survey (CGSS2017) were selected, and empirical analyses for significance, heterogeneity, and robustness were conducted using the ZINB and CMP models in the Stata statistical software. Conclusion: (1) Higher Internet Usage Frequency (IUF) increases the likelihood of higher income. (2) Engaging in Online Social Networking (OSN) helps in accumulating social capital, leading to higher labor income. Meanwhile, participating in Online Entertainment (OE) relieves work and life stresses, thereby increasing labor income. Proficiency in Accessing Online Information (AOI) is associated with higher labor income, while frequent involvement in Online Business (OB) is correlated with higher personal income. Additionally, the Marginal utility of these internet usage preferences indicate that OB > AOI > OSN > OE. (3) Individual variations in physical, psychological, and social characteristics significantly influence the labor income effects of internet usage preferences. (4) There are substantial differences in the labor income effects of internet usage preferences between urban and rural areas and across different regions. (5) Education attainment has a positive mediating effect on the labour income effect of individual Internet use preferences, and enhancing residents’ digital literacy has a positive effect on increasing their labour income and alleviating inequality in digital gains. (6) The popularity of Internet technology is the background that triggers an individual’s Internet use, and the acceptance of a particular Internet technology is catalyzed by an individual’s perception of the value and difficulty of mastering that technology; an individual’s biased learning or proficiency in a particular Internet technology in order to gain higher competitiveness and value in the labour market is an important internal driving force.

Suggested Citation

  • Kefeng Yuan & Xiaoxia Zhang, 2024. "The impact of internet usage preferences on labor income: Evidence from China," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-25, August.
    • Handle: RePEc:plo:pone00:0308287
    DOI: 10.1371/journal.pone.0308287
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308287
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0308287&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0308287?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lex Borghans & Bas ter Weel, 2008. "Understanding the Technology of Computer Technology Diffusion: Explaining Computer Adoption Patterns and Implications for the Wage Structure," Journal of Income Distribution, Ad libros publications inc., vol. 17(3-4), pages 37-70, September.
    2. Eric TC Lai & Ruby Yu & Jean Woo, 2020. "The Associations of Income, Education and Income Inequality and Subjective Well-Being among Elderly in Hong Kong—A Multilevel Analysis," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    3. Entorf, Horst & Kramarz, Francis, 1997. "Does unmeasured ability explain the higher wages of new technology workers?," European Economic Review, Elsevier, vol. 41(8), pages 1489-1509, August.
    4. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    5. Bauer, Johannes M., 2018. "The Internet and income inequality: Socio-economic challenges in a hyperconnected society," Telecommunications Policy, Elsevier, vol. 42(4), pages 333-343.
    6. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
    7. DeCanio, Stephen J., 2016. "Robots and humans – complements or substitutes?," Journal of Macroeconomics, Elsevier, vol. 49(C), pages 280-291.
    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. Berg, Andrew & Buffie, Edward F. & Zanna, Luis-Felipe, 2018. "Should we fear the robot revolution? (The correct answer is yes)," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 117-148.
    2. Jae Song & David J Price & Fatih Guvenen & Nicholas Bloom & Till von Wachter, 2019. "Firming Up Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(1), pages 1-50.
    3. Keller, Elisa, 2019. "Labor supply and gender differences in occupational choice," European Economic Review, Elsevier, vol. 115(C), pages 221-241.
    4. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    5. Hamid Boustanifar & Everett Grant & Ariell Reshef, 2018. "Wages and Human Capital in Finance: International Evidence, 1970–2011 [Financial reform: what shakes it? What shapes it?]," Review of Finance, European Finance Association, vol. 22(2), pages 699-745.
    6. Tschopp, Jeanne, 2015. "The Wage Response to Shocks: The Role of Inter-Occupational Labour Adjustment," Labour Economics, Elsevier, vol. 37(C), pages 28-37.
    7. Thanos Fragkandreas, 2022. "Three Decades of Research on Innovation and Inequality: Causal Scenarios, Explanatory Factors, and Suggestions," Working Papers 60, Birkbeck Centre for Innovation Management Research, revised Feb 2022.
    8. Stephan Brunow & Stefanie Lösch & Ostap Okhrin, 2022. "Labor market tightness and individual wage growth: evidence from Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-21, December.
    9. Rebecca Riley & Simon Kirby, 2006. "The Returns to General versus Job-Specific Skills: the Role of Information and Communication Technology," National Institute of Economic and Social Research (NIESR) Discussion Papers 274, National Institute of Economic and Social Research.
    10. Theodore Koutmeridis, 2013. "The Market for "Rough Diamonds": Information, Finance and Wage Inequality," CDMA Working Paper Series 201307, Centre for Dynamic Macroeconomic Analysis, revised 14 Oct 2013.
    11. Silvia Vannutelli & Sergio Scicchitano & Marco Biagetti, 2022. "Routine-biased technological change and wage inequality: do workers’ perceptions matter?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 409-450, September.
    12. Lex Borghans & Bas ter Weel, 2008. "Understanding the Technology of Computer Technology Diffusion: Explaining Computer Adoption Patterns and Implications for the Wage Structure," Journal of Income Distribution, Ad libros publications inc., vol. 17(3-4), pages 37-70, September.
    13. Caselli, Mauro & Fracasso, Andrea & Scicchitano, Sergio & Traverso, Silvio & Tundis, Enrico, 2025. "What workers and robots do: An activity-based analysis of the impact of robotization on changes in local employment," Research Policy, Elsevier, vol. 54(1).
    14. Jaewon Jung, 2024. "The Labor Market and Growth Implications of Skill Distribution: A Dynamic General Equilibrium Model with Skill Heterogeneity," Mathematics, MDPI, vol. 12(21), pages 1-22, October.
    15. Borghans, Lex & Weel, Bas ter, 2001. "What happens when agent T gets a computer?," Research Memorandum 017, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    16. Boneva, Teodora & Golin, Marta & Rauh, Christopher, 2022. "Can perceived returns explain enrollment gaps in postgraduate education?," Labour Economics, Elsevier, vol. 77(C).
    17. Michelle Rendall & Andrew Rendall, 2013. "Math Matters: Student Ability, College Majors, and Wage Inequality," 2013 Meeting Papers 1196, Society for Economic Dynamics.
    18. Nicholas Bloom & Scott Ohlmacher & Cristina Tello-Trillo & Melanie Wallskog, 2021. "Pay, Productivity and Management," Working Papers 21-31, Center for Economic Studies, U.S. Census Bureau.
    19. Emilio Colombo & Alberto Marcato, 2021. "Skill Demand and Labour Market Concentration: Theory and Evidence from Italian Vacancies," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis2104, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    20. Thomsen, Stephan L, 2018. "Die Rolle der Computerisierung und Digitalisierung für Beschäftigung und Einkommen," Hannover Economic Papers (HEP) dp-645, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

    More about this item

    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:plo:pone00:0308287. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.