Analysing the Impact of Natural Disasters, Pandemics and Other Crisis on Employment and Workforce Dynamics
Abdul-waliyyu Bello
*
Austin Peay State University, Clarksville, USA.
Tochukwu Njoku
Austin Peay State University, Clarksville, USA.
Idris Wonuola
Austin Peay State University, Clarksville, USA.
Callistus Obunadike
Austin Peay State University, Clarksville, USA.
Olumuyiwa Idowu
Austin Peay State University, Clarksville, USA.
Esther Ajadi
Austin Peay State University, Clarksville, USA.
Aderonke Adebisi
Austin Peay State University, Clarksville, USA.
Ebiere Aroks
University of New Haven, Connecticut, USA.
Adesola Adeyeri
Queen Margaret University Edinburgh, Scotland, UK.
*Author to whom correspondence should be addressed.
Abstract
This study investigates the impact of recent crises such as natural disasters and pandemics on employment and workforce dynamics using a Random Forest machine learning model. The data was collected through a detailed questionnaire analyzing various factors, including demographic information, employment status, the impact of crises, job security, coping mechanisms, and future outlook. The findings reveal that most respondents are young adults, with a higher percentage engaged in full-time or self-employment roles. Crises, notably COVID-19, led to significant financial impacts such as reduced salaries and layoffs, with many experiencing jobs in security and moderate to high stress levels. A substantial portion of the workforce transitioned to remote work, though many reported inadequate employer support. The analysis of the Random Forest model indicates a moderate performance in predicting workforce dynamics, with accuracy, precision, recall, and F1 scores highlighting the complexity of this issue. The study provides practical recommendations such as enhancing remote work infrastructure, improving financial and mental health support, and promoting workforce adaptability to mitigate the adverse effects of future crises and ensure a resilient workforce.
Keywords: Workforce dynamics, crisis impact, remote work, random forest model, job security, mental health support, machine learning analysis