
Analysis and Forecast of China's Unemployment Rate
- 1 University of Mississauga
* Author to whom correspondence should be addressed.
Abstract
The unemployment rate is an important economic indicator that measures the proportion of the unemployed labor force. The natural unemployment rate is the normal unemployment rate based on economic fluctuations. Analyze the unemployment rate to determine its root causes. By forecasting the unemployment rate, people can obtain an estimate of the future conditions of the labor market. Not only can the government make policy adjustments based on this, people can use this prediction to make wise career choices and planning, or to learn the necessary skills. The risk of unemployment is closely related to everyone, especially now during the economic downturn due to the impact of global catastrophic events such as COVID-19. Understanding the unemployment rate is an important part of analyzing the characteristics of the current labor market. If we can make more connections about the labor market and even make rough predictions about future trends, college students will be able to make future career choices that are more suitable for them. This study predicts the unemployment rate in the next nine years based on China's unemployment rate from 2012 to 2022, which will fluctuate within a fixed range. The government can increase spending (such as on education) and use monetary and finance polices to ensure that unemployment does not exceed forecasts.
Keywords
unemployment, unemployment rate prediction, unemployment rate analysis
[1]. World Bank Data China Unemployment, total (% of total labor force) (modeled ILO estimate).
[2]. Zhang, Yi. An Empirical Test of the Effectiveness of Main Variables of Economic Policy in Controlling China's Unemployment Rate. China Information News. 2020 Oct 21. 003.
[3]. Han, Xin. The employment situation gradually recovers and is generally stable. People's Daily. 2023 April 25. 007.
[4]. Jia, LIjun. And Wang, Rong. An empirical test of the effectiveness of the main variables of economic policy in controlling China's unemployment rate. Economic Research Guide. 2014. 222.
[5]. Ye, Ting. Research on fiscal policy promoting the reduction of unemployment rate in the declining economic cycle. Master's thesis of Kunming University of Science and Technology. 2018 Nov.
[6]. Ji Bicong,Zhang Pinyi. Financial time series forecasting based on ARIMA-LSTM model[J]. Statistics and Decision Making,2022,38(11):145-149.
[7]. Jia Qianying. Empirical analysis of domestic carbon financial transaction risk based on financial time series analysis[D]. Shandong University,2022.
[8]. Liu H,Sun Z,Liu X. Research on Financial Market Price Direction Based on ARIMA Model[J]. Academic Journal of Business & Management,2022,4.0(5.0).
[9]. Shiwei S. Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting[J]. Journal of Mathematics,2021,2021.
[10]. Sun Yi,Zhou Longlong. Teaching financial time series ARIMA modeling based on Python[J]. Modern Information Technology,2021,5(10):192-195.
Cite this article
Li,M. (2024). Analysis and Forecast of China's Unemployment Rate. Advances in Economics, Management and Political Sciences,60,9-15.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).