Understanding Global Unemployment Patterns: A 1991-2021 Regional Analysis

DOI

10.2991/978-94-6463-538-6_11How to use a DOI?

Keywords

ANOVA; Bivariate Correlations; Hierarchical classification; Unemployment Rate

Abstract

As one of the most important macroeconomic factors, the unemployment rate has played a significant role in individuals’ decision-making and policy-making processes. This paper primarily analyzes unemployment rates and is dedicated to unveiling the connections between regions, years, and unemployment rates. Specifically, I classified all the countries/regions into three clusters according to their unemployment rates spanning from 1991 to 2021. Through Analysis of Variance (ANOVA), I found that cluster 1 represents a relatively low unemployment rate, cluster 2 represents a middle unemployment rate, and cluster 3 represents a relatively high unemployment rate. Moreover, I carried out bivariate correlation tests and identified the general trend of the average unemployment rate of each cluster. Furthermore, I classified all the countries/regions into six continents and performed ANOVA on the unemployment rates of the six continents over the years. This enabled me to discover the differences and similarities between the continents. Additionally, after dividing these 31 years into three time periods, I conducted bivariate correlation tests and determined the general trend of the average unemployment rate of each continent during specific time periods. I also constructed a cross table and discovered the distribution of the three clusters within the six continents. This research serves as guidance for individuals’ decision-making processes and is helpful for people who want to study or work abroad.

Copyright

© 2024 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.

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TY  - CONF
AU  - Tianyu Xia
PY  - 2024
DA  - 2024/10/01
TI  - Understanding Global Unemployment Patterns: A 1991-2021 Regional Analysis
BT  - Proceedings of the 4th International Conference on Economic Development and Business Culture (ICEDBC 2024)
PB  - Atlantis Press
SP  - 76
EP  - 90
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-538-6_11
DO  - 10.2991/978-94-6463-538-6_11
ID  - Xia2024
ER  -