Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023, Beijing, China

Research Article

Predicting Unemployment Rate Using Literacy Rate with Neural Network

Download69 downloads
  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342667,
        author={Xiaoxuan  Peng},
        title={Predicting Unemployment Rate Using Literacy Rate with Neural Network},
        proceedings={Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17--19, 2023, Beijing, China},
        publisher={EAI},
        proceedings_a={ICEMME},
        year={2024},
        month={2},
        keywords={literacy unemployment neural network mathematics},
        doi={10.4108/eai.17-11-2023.2342667}
    }
    
  • Xiaoxuan Peng
    Year: 2024
    Predicting Unemployment Rate Using Literacy Rate with Neural Network
    ICEMME
    EAI
    DOI: 10.4108/eai.17-11-2023.2342667
Xiaoxuan Peng1,*
  • 1: University of California
*Contact email: xiaoxuan.blair.peng@gmail.com

Abstract

The outbreak of the epidemic profoundly affected multiple industries, resulting in a sharp surge in unemployment during its peak. While many sectors have seemingly recovered, unemployment rates in certain regions have not reverted to their pre-epidemic levels. Intriguingly, the literacy rate has remained stable, prompting an examination of its relationship with unemployment. This paper employs a neural network model, incorporating data from 1990 to 2022, that integrates the literacy rate, country code, and year to predict future unemployment trends. However, the model does not accurately predict unemployment based on literacy rates alone, indicating a lack of direct correlation. Consequently, this suggests the necessity of considering a broader range of variables when forecasting future unemployment rates.