TY  - BOOK
N2  - 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.
DO  - 10.1016/j.wpi.2023.102193
DO  - doi
AB  - 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.
T1  - Forecasting labor needs for digitalization :A bi-partite graph machine learning approach.
AU  - David, Dimitri Percia,
AU  - Moreno, Santiago Anton,
AU  - Maréchal, Loïc,
AU  - Maillart, Thomas,
AU  - Mermoud, Alain,
JF  - World Patent Information
VL  - 73, June, 2023
LA  - eng
ID  - 48570
KW  - Intellectual property.
KW  - Skilled labor.
KW  - Patents.
KW  - Copyright.
TI  - Forecasting labor needs for digitalization :A bi-partite graph machine learning approach.
LK  - https://doi.org/10.1016/j.wpi.2023.102193
UR  - https://doi.org/10.1016/j.wpi.2023.102193
ER  -