Research on the Simultaneous Growth of GDP Per Capita and Resident Income in Guangdong Province Based on Linear Regression Analysis
- DOI
- 10.2991/978-94-6463-010-7_73How to use a DOI?
- Keywords
- Guangdong Province; Per Capita Disposable Income; GDP Per Capita; Regression Analysis
- Abstract
In this study, we analyzed the data of urban and rural residents’ income in Guangdong Province from 1978 to 2020 in terms of both absolute and relative differences. The results show that although the urban-rural gap has been expanding, the rate has slowed down. The regression analysis of urban and rural residents’ per capita disposable income and its disparity with per capita GDP was also carried out to establish regression models. The results show that the growth of urban per capita disposable income (UPDI), rural per capita net income (RPFI) and urban-rural income gap are positively correlated with the growth of GDP per capita, and we further analyzed the reasons for the widening of the urban-rural income gap and accordingly put forward relevant suggestions to narrow the urban-rural gap.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Minjing Peng AU - Yao Tong PY - 2022 DA - 2022/12/02 TI - Research on the Simultaneous Growth of GDP Per Capita and Resident Income in Guangdong Province Based on Linear Regression Analysis BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 716 EP - 724 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_73 DO - 10.2991/978-94-6463-010-7_73 ID - Peng2022 ER -