The Effect of Gig Workers Across Sector on Asean Economy: A Panel ARDL Approach
Keywords:
ASEAN ARDL Gig Workers SectorAbstract
The gig economy has emerged as a strategic topic of study in the context of rapid digital transformation. The rise of various online work platforms has driven an increase in the number of gig workers across sectors. This study aims to analyze the impact of the number of Gig workers per sector on the national economies of ASEAN countries. The method employed is the Panel Autoregressive Distributed Lag (Panel ARDL) for the period 2017-2024 using quarterly data, focusing on five ASEAN countries, namely Indonesia, Malaysia, Singapore, Thailand, and Vietnam. The data used in this study are secondary data obtained from CEIC and the Online Labour Observatory. The independent variables include the sectors of clerical and data entry, sales and marketing support, as well as software development and technology. Meanwhile, the dependent variable is Real Gross Domestic Product (GDP). This study yields four main findings. First, in the long run, all sectors—clerical data entry, sales and marketing support, as well as software and development technology—exhibit a significant positive effect on GDP. Second, in the short run, the clerical data entry sector has a significant negative impact on GDP in Vietnam. Third, the sales and marketing support sector shows a significant positive effect on GDP in Singapore, whereas it has a significant negative effect in Vietnam in the short run. Fourth, the software and development technology sector demonstrates a significant positive impact on GDP in Malaysia and Singapore.
References
Abraham, K., Haltiwanger, J., Sandusky, K., & Spletzer, J. (2018). Measuring The Gig Economy: Current Knowledge and Open Issues. In American Behavioral Scientist (Vol. 63, Issue 3). https://doi.org/10.1016
Ahmad, N. (2021). Gig Workers: The New Employment Form in the New Economy. Ulum Islamiyyah, 33(S4), 131–145. https://doi.org/10.33102/uij.vol33nos4.419
Ashford, S. J., Caza, B. B., & Reid, E. M. (2018). From surviving to Thriving in The Gig Economy: A research Agenda for Individuals in The New World of Work. Research in Organizational Behavior, 38, 23–41. https://doi.org/10.1016/j.riob.2018.11.001
Banik, N. (2019). Could Online Gig Work Drive Economic Growth? World Economy Brief, 9(17). Basri, R., & Kongcharoen, C. (2021). Emissions: A Linear, Nonlinear, and Panel ARDL Model. International Journal of Economy, Energy and Environment, 6(2), 46.
https://doi.org/10.11648/j.ijeee.20210602.14
Celestin, M., Vanitha, N., & Arts, M. (2021). The Gig Economy and Its Effects on Personal Finance.
5(2).
Cetrulo, A., Cirillo, V., & Guarascio, D. (2018). Weaker Jobs, Weaker Innovation. Exploring The Temporary Employment-Product Innovation Nexus 1 Introduction. February.
Codagnone, C., Abadie, F., & Biagi, F. (2016). The Future of Work in the “Sharing Economy” Market Efficiency and Equitable Opportunities or Unfair Precarisation? https://doi.org/10.2791/431485
De Stefano, V., Durri, I., Stylogiannis, C., & Wouters, M. (2021). Platform Work and The Employment Relationship. In ILO Working Paper (Issue 27).
Fourie, D. (2023). The Individual in The Gig Society: Is The Gig Economy Exploitative of The Informal Economy, or A Means of Empowerment? Acta Academica, 55(2), 154–169. https://doi.org/10.38140/aa.v55i2.7725
Freudenstein, D., & Duane, B. (2020). The Rise of The Gig Economy and Its Impact on The Australian Workforce. In Institute of Actuaries of Australia (Issue December).
Furrer, M., Kässi, O., & Rani, U. (2025). Decoding The Online Labour Index ( 2016 – 2024 ): Methodology and Caveats. Research Gate, 1–10.
Graham, M., Hjorth, I., & Lehdonvirta, V. (2017). Digital labour and Development: Impacts of Global Digital Labour Platforms and The Gig Economy on Worker Livelihoods. Transfer, 23(2), 135–
162. https://doi.org/10.1177/1024258916687250
Gutierrez, L. (2003). On The Power of Panel Cointegration Tests: A Monte Carlo Comparison.
Economics Letters, 80(1), 105–111. https://doi.org/10.1016/S0165-1765(03)00066-1
Hashem Pesaran, M., & Smith, R. (1995). Estimating Long-Run Relationships from Dynamic Heterogeneous Panels. Journal of Econometrics, 68, 79–113. https://doi.org/doi.org/10.1016/0304-4076(94)01644-F
Heeks, R. (2017). Decent Work and the Digital Gig Economy: A Developing Country Perspective on Employment Impacts and Standardsin Online Outsourcing, Crowdwork, etc.
Hidayah, N., Nawawi, A., Ramli, R., Khalid, N., Fatimah, S., Rashid, A., Kampus, M., & Gajah, A. (2025). The Gig Economy Revolution: Evaluating Income Streams and Economic Contributions in Malaysia. Information Management and Business Review, 17(1), 12–21. https://doi.org/10.22610/imbr.v17i1(I).4293
Holly, S., & Raissi, M. (2009). Project funded under the Socio-economic Sciences and Humanities The Macroeconomic Effects of European Financial Development: a heterogenous panel analysis The Macroeconomic E¤ects of European Financial Development: a heterogenous panel analysis.
Idowu, A., & Elbanna, A. (2022). Digital Platforms of Work and The Crafting of Career Path: The Crowdworkers’ Perspective. Information Systems Frontiers, 24(2), 441–457. https://doi.org/10.1007/s10796-020-10036-1
Iortyom, U. I., Sabo, A. A., & Joseph, P. (2024). Welfare Effect of Gig Economy on Urban Households in Nigeria. African Journal of Economics and Sustainable Development, 7(4), 203–218. https://doi.org/10.52589/ajesd-sx6xoomb
Judijanto, L., & Andhiyan Mergining Mei, N. (2025). Impact Analysis of Gig Economy on Traditional Labour Structure. Sinomics Journal, Volume, 4. https://doi.org/10.54443/sj.v4i2.511
Keith Chen, M., Chevalier, J. A., Rossi, P. E., & Oehlsen, E. (2019). The value of flexible work: Evidence from uber drivers. Journal of Political Economy, 127(6), 2735–2794. https://doi.org/10.1086/702171
Kittur A, Jeffrey V, Michael S, Bernstein, Gerber E. M, Zimmerman J, Lease M, H. J. . (2013).
Proceedings of the 2013 conference on Computer supported cooperative work. ACM.
Lehdonvirta, V. (2018). Flexibility in the gig economy: managing time on three online piecework platforms. New Technology, Work and Employment, 33(1), 13–29. https://doi.org/10.1111/ntwe.12102
Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24. https://doi.org/10.1016/S0304-4076(01)00098-7
Lisi, D., & Malo, M. A. (2017). The impact of temporary employment on productivity. Journal for Labour Market Research, 50(1), 91–112. https://doi.org/10.1007/s12651-017-0222-8
Loayza, N. V, & Rancière, R. (2006). Financial Development, Financial Fragility, and Growth. Source: Journal of Money, Credit and Banking, 38(4), 1051–1076. https://doi.org/https://www.jstor.org/stable/3838993
Malik, F. (2025). The Paradox of Gig Economy: Labour Productivity, Unemployment and Economic Growth. In SSRN. https://doi.org/doi.org/10.2139/ssrn.5197862
Mehmet, S., Yusuf Ekrem, A., & Gokcen, O. (2014). Cross Sectional Dependence and Cointegration Analysis Among the Gdp-Foreign Direct Investment and Aggregate Credits: Evidence From Selected Developing Countries. Asian Economic and Financial Review, 4(11), 1485–1501. http://www.aessweb.com/journals/5002
Muhyi, S. N. A., Omar, S. I., & Adnan, S. F. (2023). The Drivers Force The Gig Workers Into Gig Economy: The Case of Malaysia. Journal of Business And Social Development, 11(1), 1–12.
https://doi.org/10.46754/jbsd.2023.03.001
Munir, K., & Iftikhar, M. (2021). Asymmetric Impact of FDI and Exchange Rate on Tourism: Evidence From Panel Linear and Nonlinear ARDL Model. SAGE Open, 11(3). https://doi.org/10.1177/21582440211046589
Munir, K., & Riaz, N. (2019). Energy consumption and environmental quality in South Asia: evidence from panel non-linear ARDL. Environmental Science and Pollution Research, 26(28), 29307–29315. https://doi.org/10.1007/s11356-019-06116-8
Najiha, I., & Herman, S. (2023). Factors Affecting the Gig Economy of Labour Productivity in Ride Hailing Services. 1(1). https://doi.org/https://doi.org/10.58968/der.v1i1.474
Pande, N. (2024). EPH-International Journal of Business Management Science What are The Economic Implications of The Rise of The Gig Economy on Employment Stability and Benefits? International Journal of Business Management Science, 10(1). https://doi.org/10.53555/eijbms.v10i1.174
Parker, J. (2019). Theories of Endogenous Growth. In Economics.
Pedroni, P. (1999). Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple of Economics and Statistics. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670.
Pesaran, M. H. (2007). A Simple Panel Unit Root Test In The Presence of Cross-Section Dependence
M. Journal of Applied Econometrics, 21(August 2012), 1–21. https://doi.org/10.1002/jae Piketty, T. (2014). Capital in the Twenty-First Century: A multidimensional approach to the history of
capital and social classes. British Journal of Sociology, 65(4), 736–747.
https://doi.org/10.1111/1468-4446.12115
Pilatti, G. R., Pinheiro, F. L., & Montini, A. A. (2024). Systematic Literature Review on Gig Economy: Power Dynamics, Worker Autonomy, and the Role of Social Networks. Administrative Sciences,
14(10). https://doi.org/10.3390/admsci14100267
Porwal, P. D., & Kumar, A. (2023). Exposing The Gig Economy’s Dynamics: Exploring Possibilities, Challenges And Emerging Trends. 2(4), 31–41.
Romer, D. (2016). Advanced Macroeconomics. In Educacao e Sociedade (Vol. 1, Issue 1). http://www.biblioteca.pucminas.br/teses/Educacao_PereiraAS_1.pdf%0Ahttp://www.anpocs.or g.br/portal/publicacoes/rbcs_00_11/rbcs11_01.htm%0Ahttp://repositorio.ipea.gov.br/bitstream/ 11058/7845/1/td_2306.pdf%0Ahttps://direitoufma2010.files.wordpress.com/2010/
S Vallas, J. B. S. (2020). annurev-soc-121919-054857. Annual Reviews, 46, 273–294. https://doi.org/doi.org/10.1146
Sankararaman, Rengaraju. N, Rengarajan. V, Vembu. R, Indhumathi. D. (2024). Gig Economy's Impact on Workforce Dynamics and Economic Resilience. Educational Administration: Theory and Practice. 30(6).
Santoso, A. (2024). Economic Impacts of the Gig Economy. International Journal of Economics and Management Intellectuals [IJEMI], 2(1), 1–6. https://doi.org/doi.org/10.63665/ijemi-y2f1a001
Șaptefrați, A. (2025). Endogenous Economic Growth in the Digital Age: A Neo-Solow-Romer
Analysis of the Impact of Technology on the Transformation of the Labour Market. ACTA Universitatis Danubius, 21(5), 100–110. https://doi.org/danubius.ro/index.php/AUDOE/article/view/3517
Schwellnus C, Geva Assaf, Pak Mathilde, V. R. (2019). Gig economy platforms: Boon or Bane? (OECD Economics Department Working Papers, Vol. 1550). https://doi.org/10.1787/fdb0570b-en
Shyamala K, L Priya, P. D. (2024). Gig Workers Perception on Gig Economy and Abstract : Introduction : Objectives : Scope : Review of Literature : (Vol. 53, Issue 3).
Ungureanu, A. (2019). Industry 4.0 The Role of Gig Economy in The Industrial Revolution of The 21st Century. The USV Annals of Economics and Public Administration, 19(2), 30. http://annals.feaa.usv.ro/index.php/annals/article/viewArticle/1162
Vadavi, S., & Sharmiladevi, J. C. (2024). Analysing the evolution and patterns of the gig economy: a bibliometric examination of growth and trends. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2024.2424480
Wang, T., Beard, R., Hawkins, J., & Chandra, R. (2024). Recursive Deep Learning Framework for Forecasting the Decadal World Economic Outlook. IEEE Access, 12, 152921–152944. https://doi.org/10.1109/ACCESS.2024.3472859
Wood, A. J. (2017). Powerful Times: Flexible Discipline and Schedule Gifts at Work. Work, Employment and Society, 32(6), 1061–1077. https://doi.org/10.1177/0950017017719839
World Bank. (2023). Working Without Borders. www.worldbank.org.
License
Copyright (c) 2025 Indonesia Forum on Labour and Productivity
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.