Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis

Jing Wang, Jing Wang, Hui Zhang

2022

Abstract

In recent years, the rapid growth of the internet has brought about an era of big data, bringing opportunities, challenges and changes to both higher education and people’s income levels. The labour market and the education market are closely linked and the level of education is crucial to a country’s economic development. This paper uses data from CLDS 2018 and regression analysis method in big data analysis to argue for a relationship between them and to test for endogeneity. The findings show that there is a significant positive correlation between the level of higher education and wage, and this feature will be maintained over time. Therefore, the country and government should focus on how to make higher education more accessible and should make higher levels of higher education accessible to those in the labour market.

Paper Citation

in Harvard Style

Wang J. and Zhang H. (2022). Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis. In Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM, ISBN 978-989-758-593-7, pages 979-985. DOI: 10.5220/0011360600003440

in Bibtex Style

@conference{bdedm22,
author={Jing Wang and Hui Zhang},
title={Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis},
booktitle={Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM,},
year={2022},
pages={979-985},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011360600003440},
isbn={978-989-758-593-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Big Data Economy and Digital Management - Volume 1: BDEDM,
TI - Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis
SN - 978-989-758-593-7
AU - Wang J.
AU - Zhang H.
PY - 2022
SP - 979
EP - 985
DO - 10.5220/0011360600003440