Using Logistic Regression and Ensemble Learning for Employment Status Prediction

Guanlin Wan; Guangyu Wu; Huabin Cao

doi:10.54097/w1kmzj48

Authors

Guanlin Wan
Guangyu Wu
Huabin Cao

DOI:

https://doi.org/10.54097/w1kmzj48

Keywords:

Employment Forecasting, Macro Data Fusion, Feature Importance, Logistic Regression, Random Forest

Abstract

As digital technology reshapes the labor market, real-time insights into employment dynamics have become key to the smooth operation of the economy. Based on 5,000 anonymized sampling data in Yichang City, this study constructs a multi-dimensional employment status analysis and prediction framework. The first step is to use data cleaning and visualization to evaluate the influence of age, gender, education and other characteristics to reveal structural characteristics such as high youth unemployment rate and weak female employment stability. In the second step, the chi-square test was used to screen significant variables, and a logistic regression model was constructed to predict the employment status of 20 test samples, with an accuracy of 81.93% and a recall rate of 97.7%. The third step is to introduce macroeconomic indicators such as GDP growth rate, urban registered unemployment rate, and policy support level, and use the random forest model to optimize the prediction after integrating with individual data, with an accuracy of 81.2% and an F1 value of 0.90, confirming the moderating effect of macro factors on employment. This study provides a data-driven decision-making basis for regional employment policy formulation and targeted assistance.

Downloads

Download data is not yet available.

References

[1] Acemoglu, D., & Restrepo, P. (2022). Demographics and automation. Review of Economic Studies, 89(3), 1185-1226.

[2] Bell, B., Bloom, N., & Blundell, J. (2022). The impact of uncertainty on employment dynamics. American Economic Review, 112(5), 1643-1681.

[3] Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2020). Are ideas getting harder to find? American Economic Review, 110(4), 1104-1144.

[4] Autor, D. H., & Dorn, D. (2020). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 110(5), 1553-1597.

[5] Chen, X., & Li, Y. (2021). A hybrid model for employment prediction based on deep learning. IEEE Access, 9, 112345-112356.

[6] Choi, T. M., Wallace, S. W., & Wang, Y. (2022). Big data analytics in operations management. Production and Operations Management, 31(3), 987-1007.

[7] Davenport, T. H., & Ronanki, R. (2020). Artificial intelligence for the real world. Harvard Business Review, 98(1), 108-116.

[8] Goos, M., Manning, A., & Salomons, A. (2021). Job polarization in Europe. American Economic Review, 111(2), 472-512.

[9] Guvenen, F., Kuruscu, B., & Ozkan, S. (2020). Taxation of human capital and wage inequality: A cross-country analysis. Review of Economic Studies, 87(3), 1385-1423.

[10] Heckman, J. J., & Mosso, S. (2020). The economics of human development and social mobility. Annual Review of Economics, 12, 35-68.

[11] Hershbein, B., & Kahn, L. B. (2021). The impact of the Great Recession on the demand for skills. Journal of Labor Economics, 39(S2), S497-S539.

[12] Holzer, H. J. (2022). The role of community colleges in expanding opportunity. The ANNALS of the American Academy of Political and Social Science, 701(1), 110-124.

[13] Jaimovich, N., & Siu, H. E. (2020). The trend is the cycle: Job polarization and jobless recoveries. Review of Economics and Statistics, 102(1), 129-143.

[14] Katz, L. F., & Krueger, A. B. (2021). The rise and nature of alternative work arrangements in the United States, 1995-2015. ILR Review, 72(2), 382-416.20. Muro, M., Maxim, R.,