Pinheiro, Flávio Luís PortasDemetriades, Manuel2022-07-292022-07-292022-05-09http://hdl.handle.net/10362/142636Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis paper aims to investigate and understand the drivers of unemployment rates in OECD Nations during the COVID-19 Outbreak and the associated measures and events. Previous research on unemployment models was consulted in order to consolidate a diverse list of possible explanatory variables for modelling unemployment rates. We further assess whether technology or healthcare availability impact said figures, particularly with lockdown measures imposed and a large amount of operations having shifted online. The model identified to best describe unemployment rates was the auto-regressive 2-factor model whilst a multiple linear regression was used to attain more interpretable insights. The proportion of a population in self employment as well as the availability of hospital beds appear to be the most influential factors as opposed to other more conventional factors during this pandemic environment.engUnemploymentEmploymentCOVID-19Technologymaster thesis203045262