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Online labour market analytics for the green economy: The case of electric vehicles

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  • Papoutsoglou, Maria
  • Rigas, Emmanouil S.
  • Kapitsaki, Georgia M.
  • Angelis, Lefteris
  • Wachs, Johannes

Abstract

Since job characteristics in areas related to the green economy and Industry 4.0 are changing rapidly, combined methodologies to measure the labour demand and supply are needed. One substantial aspect of this emerging sector is the shift of the automotive industry towards the production of electric vehicles (EVs). The automotive sector is a major employer in Europe, directly employing over 2.8 million people. However, little is known about the effects this structural transformation of the automotive industry will have on labor markets, in particular in the area of information and communications technology (ICT). This prevents effective planning by educational institutions, who seek to prepare their students for future labor markets, and industry stakeholders aiming to assemble effective teams. In this paper, we develop a framework to analyze labor market trends using digital trace data, and apply it to the case of the EV industry. We track demand-side trends in the labor market using job advertisements from LinkedIn and supply-side trends using data from StackExchange and GitHub. Using natural language processing methods, we categorize the skills sought by EV industry employers on the demand side and topics of interest to individuals on the supply side. We also highlight those programming languages and frameworks most salient in the EV industry.

Suggested Citation

  • Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:tefoso:v:177:y:2022:i:c:s004016252200049x
    DOI: 10.1016/j.techfore.2022.121517
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    Cited by:

    1. Emmanouil S. Rigas & Tatiana Pourliaka & Maria Papoutsoglou & Hariklia Proios, 2023. "Towards a topic modeling approach to semi-automatically detect self-reported stroke symptoms (FAST symptoms) and their correlation with aphasia types," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1321-1336, April.
    2. Yu, Quanqing & Nie, Yuwei & Peng, Simin & Miao, Yifan & Zhai, Chengzhi & Zhang, Runfeng & Han, Jinsong & Zhao, Shuo & Pecht, Michael, 2023. "Evaluation of the safety standards system of power batteries for electric vehicles in China," Applied Energy, Elsevier, vol. 349(C).
    3. S. Di Luozzo & A. Fronzetti Colladon & M. M. Schiraldi, 2024. "Decoding excellence: Mapping the demand for psychological traits of operations and supply chain professionals through text mining," Papers 2403.17546, arXiv.org.
    4. Seyed Mohammad Ali Jafari & Ehsan Chitsaz, 2024. "Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market," Papers 2409.00658, arXiv.org.

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