Information and Communication Workforce Forecasting: Evidence from England

Year 2023, Issue: 85, 117 - 126, 30.12.2023

Fethi Aslan

https://doi.org/10.26650/jspc.2023.85.1312322

Abstract

The workforce plays a crucial role in the development of organizations and countries. Therefore, closely monitoring the status of the existing workforce and issues related to individuals entering the workforce is essential. Information and communication technologies (ICT) have resulted in significant consequences in the shift of production and service industries to different areas. This situation implies that advancements in the field of ICT necessitate the development of appropriate skills. Therefore, assessing the current workforce situation and determining future workforce trends are necessary in order to develop the skills required in the ICT field. To achieve this, the article analyzes data from the ICT labor market in England between 1996-2022 and proposes a model to predict the state of the ICT workforce for the upcoming five-year period. As a result, the study predicts the workforce numbers in the ICT field until 2027 and provides a forecast regarding the expected future. According to the findings, this study projects that the workforce in the IT sector will increase during each three-month period until 2027. The increase is expected to occur at a rate of 9.5% during the period of 2023-2027. This result is highly important as it provides a basis of a scenario analysis for different stakeholders on how to plan regarding job loss risks, wages, and education-related matters.

Keywords

Information and Communication, Workforce, England, Time Series, Forecasting

References

• Alisjahbana, A. S., Setiawan, M., Effendi, N., Santoso, T., & Hadibrata, B. (2020). The adoption of digital technology and labor demand in the Indonesian banking sector. International Journal ofSocial Economics, 47(9), 2020, 1109-1122. doi:10.1108/ijse-05-2019-0292. google scholar
• Alyahya, M., & Hadwan, M. (2022). Applying ARIMA Model to Predict Future Jobs in the Saudi Labor Market. International Research Journal of Innovations in Engineering and Technology, 6(4), 1-8. https://doi.org/10.47001/IRJIET/2022.604001. google scholar
• Bajracharya, D. 2010. Econometric Modeling vs Artificial Neural Networks-A Sales Forecasting Comparison. (Master’s thesis). University of Boras. Retrieved from http://bada.hb.se/bitstream/2320/7986/1/2010MI17.pdf. google scholar
• Bakule, M., Czesana, V., & Havlickova, V. (2016). Developing skills foresights, scenarios and forecasts: guide to anticipating and matching skills and jobs: Volume 2. google scholar
• Bockerman, P., Laaksonen, S., & Vainiomaki, J. (2016). Arejobs more polarized in ICT firms? IZADp No. 9851. Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor. google scholar
• Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. McKinsey Global Institute, 1, 3-84. google scholar
• Dachs, B. (2018). The impact of new technologies on the labour market and the social economy. European Parliamentary Research Service. Brussels. doi: 10.2861/68448. google scholar
• Economic Commission for Latin America and the Caribbean (ECLAC) (2021). Digital technologies for a new future (LC/TS.2021/43), Santiago. google scholar
• Eurostat (2023, 9 May). ICT specialists in employment. Retrieved from https://ec.europa.eu/eurostat/statistics explained/index.php?title=ICT_specialists_in_ google scholar
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological forecasting and social change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019. google scholar
• Fukao, K., Miyagawa, T., Kil Pyo, H., Rhee, K., Takizawa, M. (2020), The impact of infor-mation and communications technology investment on employment in Japan and Korea. In B.M. Fraumeni (Ed.), Measuring Economic Growth and Productivity (pp.283-297). Foundations, KLEMS Production Models, and Extensions, Elsevier Inc., London. https://doi.org/10.1016/B978-0-12-817596-5.00013-5. google scholar
• Gates, G. (2023, 9 May). Developing a Global ICT Workforce Fit for the Future. https://e.huawei.com/pl/publications/global/ict_insights/ict32-talent-cosystem/talent- google scholar
• Goldin, C. and Katz, L.F. (1995). The decline of non-competing groups: Changes in the premium to education, 1890 to 1940. Tech. Rep., NBER Working Paper No. 5202, National Bureau of Economic Research. google scholar
• Herman, E. (2019). The Influence of ICT Sector on the Romanian Labour Market in the European Context. In Proceedings of the 13th International Conference Interdisciplinarity in Engineering (INTER-ENG 2019), Targu Mures, Romania, pp. 344—351. google scholar
• Hüsnüoğlu, N., & Oda, V. (2022). Applying Artificial Neural Networks and Arima Models to Analyze the Impact of ICT on the Economic Growth in Turkey. Journal of the Knowledge Economy, 1-18. https://doi.org/10.1007/s13132-022-01031-9. google scholar
• Köppelova, J. and Jindrova, A. (2019). Application of Exponential Smoothing Models and Arima Models in Time Series Analysis from Telco Area. Agris on-line Papers in Economics and Informatics, 11(3), 73-84. https://doi.10.7160/aol.2019.110307. google scholar
• Minitab (2023, May). Interpret the key results for ARIMA. Retrieved from https://support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/ time- series/how-to/arima/interpret-the-results/key-results/. google scholar
• Nau, R (2014)., Lecture notes on forecasting, Fuqua School of Business Duke University. Lecturere Note. Retrieved from http://people.duke.edu/ rnau/forecasting.html. google scholar
• Navarro-Espigares, J. L., Martfa-Segura, J. A., & Hernandez-Torres, E. (2012). The role of the service sector in regional economic resilience. The Service Industries Journal, 32(4), 571-590. http://dx.doi.org/10.1080/02642069.2011.596535. google scholar
• Ning, Y., Kazemi, H., & Tahmasebi, P. (2022). A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet. Computers & Geosciences, 164, 105126. google scholar
• Nomisweb (2023, 9 May). Workforce jobs by industry (SIC 2007). Retrieved from https://www.nomisweb.co.uk/query/construct/submit.asp?menuopt =201&subcomp= google scholar
• Organisation for Economic Co-operation and Development (OECD) (2019). OECD Employment Outlook 2019: TheFuture of Work, OECD Publishing. https://doi.org/10.1787/9ee00155-en. google scholar
• Organisation for Economic Co-operation and Development (OECD) (2016). Automation and Independent Work in a Digital Economy. Policy Brief on The Future of Work. OECD Publishing, Paris google scholar
• Pantea, S., Sabadash, A., & Biagi, F. (2017). Are ICT displacing workers in the short run? Evidence from seven European countries. Information Economics and Policy, 39, 36-44. http://dx.doi.org/10.1016/j.infoecopol.2017.03.002. google scholar
• Peinecke, D., Forland, C., Wheeler, S., Roubinchtein, A., & Nimmo, B. 2017 Employment Projections Technical Report. google scholar
• Queensland Government (2020). Strategic health workforce planning framework toolkit. google scholar
• Quinn J. (2023, 9 May). Forecasting made easy with SPSS Statistics. Retrieved from https://www.sv-europe.com/wp-content/uploads/Forecasting-with-SPSS-Statistics-Made-Easy-v1.1-Read-Only.pdf. google scholar
• Radda, L., O, Rydhem A.,(2022). Closing the skills gap-A study of the ICT-sector and the higher education system in Mauritius. (Bachelor’s thesis). University of Gothenburg. google scholar
• U.S Bureau of Labor Statistics (2023, 9 May). Retrieved from https://www.bls.gov/opub/mlr/2022/article/growth-trends-for-selected-occupations-considered-at-risk-from-automation.html. google scholar
• Vicente, M. R., Lopez-Menendez, A. J., & Perez, R. (2015). Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing? Technological Forecasting and Social Change, 92, 132-139. http://dx.doi.org/10.1016/j.techfore.2014.12.005. google scholar
• Voumik, L. C., Hossain, M. I., Dewan, M. F., Rahman, M., & Rahman, M. (2020). Forecasting Employment Rate in Service Sectors in Bangladesh: An Application of Autoregressive Integrated Moving Average Model. IRE Journals, 3(11), 125-131. google scholar
• Zatonatska, T., Klapkiv, Y., Dluhopolskyi, O., & Fedirko, O. (2022). Forecasting of the Employment Rate in the EU ICT Field. Comparative Economic Research. Central and Eastern Europe, 25(3), 7-25. https://doi.org/10.18778/1508-2008.25.19. google scholar