IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p754-d1021614.html
   My bibliography  Save this article

The Indonesian Digital Workforce Gaps in 2021–2025

Author

Listed:
  • Gati Gayatri

    (Research Center for Society and Culture, The Research and Innovation Agency of the Republic of Indonesia, Jalan Jenderal Gatot Subroto 10, Jakarta 12710, Indonesia)

  • I Gede Nyoman Mindra Jaya

    (Department of Statistics, The Faculty of Math and Science, The University of Padjadjaran, Bandung 45363, Indonesia)

  • Vience Mutiara Rumata

    (Research Center for Society and Culture, The Research and Innovation Agency of the Republic of Indonesia, Jalan Jenderal Gatot Subroto 10, Jakarta 12710, Indonesia
    Faculty of Communication Science, The University of Esa Unggul, Jalan Arjuna Utara, Jakarta 11510, Indonesia)

Abstract

The development and advancement of information and communication technologies have led to major changes in industry and the labor system in Indonesia. In the context of the digital economy, Indonesia needs to immediately improve digital labor policies based on research results. However, studies on Indonesian digital workforces mostly come from global nonacademic publications, which acknowledge the limitation of the workforces. This study addresses the gaps between the supply and demand of digital workforces in 2021–2025 by conducting a Bayesian analysis on the data from the 2018 Indonesian Statistics Bureau and the 2020 ILO ICT job demand forecast. According to the findings, the supply of digital workforces will outnumber the demand, which is expected to be 600,000 workers per year. This surplus number poses a new challenge for the government if the available workforce lacks the competencies needed in the industry. According to the study, IT system programmer/developer/administrator/system analyst and IT web designer/developer will still be popular job roles during this time. It is suggested that improving these digital skills in the current and future workforces should be a top priority for the government.

Suggested Citation

  • Gati Gayatri & I Gede Nyoman Mindra Jaya & Vience Mutiara Rumata, 2022. "The Indonesian Digital Workforce Gaps in 2021–2025," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:754-:d:1021614
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/754/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/1/754/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    2. Cunningham,Wendy & Moroz,Harry Edmund & Muller,Noel & Solatorio,Aivin Vicquierra, 2022. "The Demand for Digital and Complementary Skills in Southeast Asia," Policy Research Working Paper Series 10070, The World Bank.
    3. Suchandra Paul, 2018. "An Analysis of the Skill Shortage Problems in Indian IT Companies," Social Sciences, MDPI, vol. 7(9), pages 1-21, September.
    4. David Morris & Enrico Vanino & Carlo Corradini, 2020. "Effect of regional skill gaps and skill shortages on firm productivity," Environment and Planning A, , vol. 52(5), pages 933-952, August.
    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. Wen-Ze Wu & Chong Liu & Wanli Xie & Mark Goh & Tao Zhang, 2023. "Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China," Energy & Environment, , vol. 34(5), pages 1639-1656, August.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Sandra M. Leitner, 2022. "A skill‐specific dynamic labour supply and labour demand framework: A scenario analysis for the Western Balkan countries to 2030," LABOUR, CEIS, vol. 36(4), pages 471-504, December.
    4. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    5. Ewa Rollnik-Sadowska & Marta Jarocka & Edyta Dabrowska, 2020. "Diversity of Regional Labour Markets in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 33-51.
    6. Muhammad Shoaib & Asaad Y. Shamseldin & Sher Khan & Mudasser Muneer Khan & Zahid Mahmood Khan & Tahir Sultan & Bruce W. Melville, 2018. "A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 83-103, January.
    7. Yusuf Priyo Anggodo & Abba Suganda Girsang, 2024. "A Novel Modified Binning and Logistics Regression to Handle Shifting in Credit Scoring," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2371-2403, June.
    8. Hloušková, Z. & Ženíšková, P. & Prášilová, M., 2018. "Comparison of Agricultural Costs Prediction Approaches," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(1).
    9. Yu, Bolin & Fang, Debin & Pan, Yuling & Jia, Yunxia, 2023. "Countries’ green total-factor productivity towards a low-carbon world: The role of energy trilemma," Energy, Elsevier, vol. 278(PB).
    10. Dittmer, Celina & Krümpel, Johannes & Lemmer, Andreas, 2021. "Power demand forecasting for demand-driven energy production with biogas plants," Renewable Energy, Elsevier, vol. 163(C), pages 1871-1877.
    11. repec:jss:jstsof:27:i03 is not listed on IDEAS
    12. Tomasz Śmiałkowski & Andrzej Czyżewski, 2022. "Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters," Energies, MDPI, vol. 15(24), pages 1-23, December.
    13. Jiří Šindelář, 2019. "Sales forecasting in financial distribution: a comparison of quantitative forecasting methods," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(3), pages 69-80, December.
    14. Wu, Wen-Ze & Zeng, Liang & Liu, Chong & Xie, Wanli & Goh, Mark, 2022. "A time power-based grey model with conformable fractional derivative and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    15. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    16. Anna Manowska & Anna Bluszcz, 2022. "Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network," Energies, MDPI, vol. 15(13), pages 1-23, July.
    17. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
    18. Zhenshan Yang, 2023. "Human capital space: a spatial perspective of the dynamics of people and economic relationships," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    19. Nils Droste & Claudia Becker & Irene Ring & Rui Santos, 2018. "Decentralization Effects in Ecological Fiscal Transfers: A Bayesian Structural Time Series Analysis for Portugal," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(4), pages 1027-1051, December.
    20. Carmona-Benítez, Rafael Bernardo & Nieto, María Rosa, 2020. "SARIMA damp trend grey forecasting model for airline industry," Journal of Air Transport Management, Elsevier, vol. 82(C).
    21. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.

    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:jsusta:v:15:y:2022:i:1:p:754-:d:1021614. 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.