Title:
USING CATEGORICAL DATA ANALYSIS TO EVALUATE THE INFLUX OF JOB SEEKERS IN EU15/UK DURING THE CORONAVIRUS PANDEMIC

Authors:
Dr Joel C. Nwaubani, Dr Ogechi Roseline Obiozo, Rao M. Kashif Khan, Vincentia Boham and Muhammad Wajid

Abstract:
Numerous articles have been written about the migration of job seekers, and the majority of them have focused on the main causes of high job demands or job loss, which include intense competition, modern skill-biased, technological advancements, and excessive pressure from the economic recession brought on by the coronavirus pandemic. Economists, statisticians, the media, and policymakers undoubtedly continue to place a great deal of importance on evaluating the influx of job seekers; however, a transition from economic theories to economic analysis is necessary to comprehend how the economy functions. The purpose of this study is to draw attention to statistical problems with the influx of job seekers during the coronavirus pandemic in 15 EU nations, including the UK. As a substitute for form methods, the association model will be taken into consideration. To determine the proportion of the data that each model covers, the analysis of association (ANOAS) table is provided. The Column Effects Association Model (C) has the best fit of all the models because it covers 88% of the total data, according to an estimation made to determine which model was acceptable and best fit

Keywords:
Association model, Log-linear and non-linear models, influx of jobseekers, COVID19 and EU15/UK

DOI:
https://doi.org/10.37500/IJESSR.2024.7605

Date of Publication: 16-11-2024