Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2019

Abstract

The labor market refers to the market between job seekers and employers. As much of job seeking and talent hiring activities are now performed online, a large amount of job posting and application data have been collected and can be re-purposed for labor market analysis. In the labor market, both supply and demand are the key factors in determining an appropriate salary for both job applicants and employers in the market. However, it is challenging to discover the supply and demand for any labor market. In this paper, we propose a novel framework to built a labor market model using a large amount of job post and applicant data. For each labor market, the supply and demand of the labor market are constructed by using offer salaries of job posts and the response of applicants. The equilibrium salary and the equilibrium job quantity are calculated by considering the supply and demand. This labor market modeling framework is then applied to a large job repository dataset containing job post and applicant data of Singapore, a developed economy in Southeast Asia. Several issues are discussed thoroughly in the paper including developing and evaluate salary prediction models to predict missing offer salaries and estimate reserved salaries. Moreover, we propose a way to empirically evaluate of equilibrium salary of the proposed model. The constructed labor market models are then used to explain the job seeker and employer specific challenges in various market segments. We also report gender and age biases that exist in labor markets. Finally, we present a wage dashboard system that yields interesting salary insights using the model.

Keywords

Equilibrium salary, Labor market, Salary prediction, Supply demand, Wage dashboard

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

2019 IEEE International Conference on Data Science and Advanced Analytics 6th DSAA: Washington, October 5-8: Proceedings

First Page

511

Last Page

520

ISBN

9781728144931

Identifier

10.1109/DSAA.2019.00066

Publisher

IEEE

City or Country

Pistacaway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/DSAA.2019.00066

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