A Study on the Layoff Problem After COVID-19 Based on Decision Tree Ensemble Methods

Abstract

The COVID-19 pandemic, a cataclysmic event in modern history, has upended the global economy, precipitating extensive layoffs across diverse sectors. This study delves into the determinants of these workforce reductions through the lens of decision tree ensemble methods. By scrutinizing a comprehensive dataset, we harnessed statistical and machine learning algorithms to assess the significance of key variablesincluding industry type, geographic location, company development stage, and capital acquisitionon the scale of layoffs. This analysis further entailed predicting the total number of employees laid off and examining their distribution. The use of decision tree ensemble methods, such as random forests and gradient boosting, provided robust insights into the complex interplay of factors influencing layoff decisions. We discovered that industry type and company development stage were particularly critical in predicting layoff patterns, while geographic location and capital acquisition also played notable roles. This research offers a data-driven perspective on the layoff phenomena, shedding light on the multifaceted influences at play and offering a foundation for further inquiry and strategic policy development in response to economic downturns. By understanding these dynamics, policymakers and business leaders can better navigate future economic crises, potentially mitigating the adverse impacts on the workforce and promoting more resilient economic structures.