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

Abstract

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.

Breakpoint Analysis for the COVID-19 Pandemic and Its Effect on the Stock Markets

“…In this research, world stock market indexes are used to evaluate the evolution and performance of the economies of different countries during the periods both before and after the declaration of the global health crisis produced by the spread of COVID-19 [ 33 , 36 ], which can be considered as a shock event. As an example of structural breakdown associated with stock market indexes, Figure 1 shows the evolution of the S&P500 index, in which a structural breakdown in its price can be observed during the second week of March 2020.…”

“…By using the data collected for the stock indexes defined in Table 1 , and based on the models supported by the background [ 30 , 31 , 32 , 33 , 36 ], we state a functional relation defined as where is the effect of the structural breakdown in country i ; is the value of X 1 related to the health security of country i measured by the global health security (GHS) index for 2019 [ 37 ]; is the value of X 2; and is the value of X 3 , both of them associated with the average risk value and its standard deviation for country i , respectively; and two control variables is the value of X 4 , an indicator of whether or not country i belongs to the OECD group; as well as is the value of X 5 , which is linked to the gross domestic product (GDP) of country i [ 35 ]. Note that the response variable Y is measured as a percentage of the variation in the index between the average of the last three months (Diff3M), or six months (Diff6M), before the structural change and the value of the index as an average after two month of this change—which represents a loss of wealth of the countries present in this study; whereas represents a benchmarking of the health security of 195 countries [ 37 ].…”

Land-Use Impact on Stand Structure and Fruit Yield of Tamarindus indica L. in the Drylands of Southeastern Ethiopia

“…The relationship between fruit yield and individual tree attributes was determined through multinomial classification model approach. The models were fitted to the responses to predict the probabilities of the different possible outcomes of fruit yield categories [ 28 ]. The analysis used total fruit yield categories as a dependent variable, whereas combinations of dendrometric characteristics were used as explanatory variables.…”

Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries

“…Based on [40][41][42][43][44][45], we formulated a statistical model to describe the relationship between the response variable and covariates mentioned above, stated as:…”