Multinomial Logistic Regression to Estimate and Predict the Perceptions of Individuals and Companies in the Face of the COVID-19 Pandemic in the Ñuble Region, Chile

• Main Authors: Benito Umaña-Hermosilla, Hanns de la Fuente-Mella, Claudio Elórtegui-Gómez, Marisela Fonseca-Fuentes
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Sustainability
• Subjects: COVID-19 pandemic; local community; perception analysis; econometric modeling; data science
• Online Access: https://www.mdpi.com/2071-1050/12/22/9553

The Coronavirus Disease 2019 (COVID-19) pandemic is transforming the world we live in, revealing our health, economic, and social weaknesses. In the local economy, the loss of job opportunities, the uncertainty about the future of small and medium-sized companies and the difficulties of families to face the effects of this crisis, invite us to investigate the perception of the local community. Based on a questionnaire applied to 313 citizens and 51 companies, this study explored the perception of these actors on the effects of the pandemic at the local level and determined the main factors that influenced their assessment using a multinomial logistic regression model. The results indicated a systematic concern for issues of employment, job security, and household debt. The variables of age and sex were significant when analyzing the vulnerability of certain groups, especially women and the elderly, to face the effects of the crisis and their role as citizens. At the business level, the focus was on economic policies that support its operational continuity and management capacity to face a changing scenario.