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Forecasting labor needs for digitalization : A bi-partite graph machine learning approach.
2023
Details
Title
Forecasting labor needs for digitalization : A bi-partite graph machine learning approach.
Author
Item Type
Journal article
Description
1 volume.
Summary
We use a unique database of digital, and cybersecurity hires from Swiss organizations and develop a method based on a temporal bi-partite network, which combines local and global indices through a Support Vector Machine. We predict the appearance and disappearance of job openings from one to six months horizons. We show that global indices yield the highest predictive power, although the local network does contribute to long-term forecasts. At the one-month horizon, the “area under the curve” and the “average precision” are 0.984 and 0.905, respectively. At the six-month horizon, they reach 0.864 and 0.543, respectively. Our study highlights the link between the skilled workforce and the digital revolution and the policy implications regarding intellectual property and technology forecasting.
Source of Description
Crossref
Series
World Patent Information ; 73, June, 2023.
In
World Patent Information
Linked Resources
Published
Oxford [England] : Elsevier Ltd., 2023
Language
English
Copyright Information
https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108
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