Data-Driven Labor Market Analysis, Modeling and Prediction
PI: Dr. Jia Pan, HKU
Co-I: Dr. Wenfeng Wang, City University of Hong Kong
In the innovation-driven economy, the labor market must respond efficiently and effectively to support our economic life. Every day, new jobs appear, and new skills are added to the scope of existing job profiles. Some skills that were once assumed to be “must-haves” are no longer requested, and some jobs are becoming obsolete. What new skills are required for the labor force? Besides the new skill requirements, spatial changes in the economy also need a geographically redistributed labor force to match. Does a specific economic area, such as Hong Kong, have a suitable labor force composition? In this research proposal, we aim to use large quantities of firms’ online job posting information to generate real-time knowledge of the labor demand of the economy. Such information could have many useful applications. For example, the overall temporal evolution of the labor demand can be used to understand industrial upgrading. An individual firm’s labor demand information can be used to evaluate its investment activities in human capital and predict its future performance.
Specifically, we propose to first homogenize the definition of jobs and firms by applying NLP word embedding learning and graph neural network on the big data of job posts. Second, no existing explicit model describes the spatial and temporal evolution of the job market. We will use data-driven dynamics identification tools such as dynamic mode decomposition to extract the model from data for further prediction and decision making.