Identificando fatores associados à ocorrência de demissão em empresas europeias durante a pandemia de COVID-19

Abstract

The aim of this study is to identify the factors associated with firms that have made redundancies during the COVID-19 pandemic in Europe. During this period, the operations of various economic activities had to be reduced or paralyzed, which ended up firms’ turnover and, consequently, layoffs. However, the effect may have been different, depending on the individual characteristics of the firm observed. To two classification models were explored: Logistic Regression and Decision Tree. The data was collected from Enterprise Surveys, a World Bank database with information at the firm level. The model that best fitted the data was Logistic Regression, with a moderate predictive capacity. The results showed that the percentage of products exported, receipt of government aid government aid and the size of the firm in terms of total employees are characteristics associated with a greater chance of dismissal. It was estimated that the chance of dismissal in companies that received government aid was more than twice as high as in companies that did not receive companies that did not receive aid. Although this may be a counter intuitive result, it may be related to the size or other specific characteristics of the firms analyzed. A more detailed analysis is needed in order to understand the relationship between events in a highly complex context.