Economic analysis through alternative data and big data techniques: what do they tell about Brazil?

Alternative data are now widely used in economic analyses worldwide but still infrequent in studies on the Brazilian economy. This research demonstrates how alternative data extracted from Google Trends and Google Mobility contribute to innovative economic analysis. First, it demonstrates that the search for the future on the internet is correlated (R = 0.62) with the average household income in Brazilian states. The three Brazilian states with the most people looking for the future on the internet have an average household income 1.6 times higher than people from states that do not have this behavior. The search for the future represents 10.9% of the economic development potential of the states, while the proportion of people with university degrees, scientific publications, and researchers represents another 60.4%. The reduction in mobility in retail/recreation locations averaged 34.28% in Brazil, Ecuador, Paraguay, and Uruguay. This group of countries had COVID-19 infection and death rates 1.25 and 1.74 times higher than in countries that reduced their mobility in retail/recreation locations by 45.03%. The impact of reduced mobility in retail/recreation locations on the unemployment rate, gross domestic product degrowth, and inflation in countries such as Brazil was 1.1, 2.2, and 2.6 times lower than in countries that reduced mobility more of people. The research contributions are associated with identifying new indicators extracted from alternative data and their application to carry out innovative economic analyses.

Social vulnerability and COVID-19 in Maringá, Brazil

This research explores the relationship between COVID-19 and social vulnerability on an intra-urban scale. For this, two composite indicators of social vulnerability have been constructed. The composite indicator constructed by the Benefit-of-the-Doubt considers spatial heterogeneity. It weakly captures the conceptually most significant individual indicator of social vulnerability (R=-0.39), as it overestimates the above-average performance sub-indicators. The composite indicator constructed by the Principal Component Analysis considers that the sub-indicators have the same weights in different census tracts, resulting in a highly consistent composite indicator as a multidimensional phenomenon concept (R=-0.93). These findings allow reaching four conclusions. First, the direction and strength of correlations associated with COVID-19 are sensitive to the method employed to construct the composite indicator and not just the geographic scale and space. Second, Medium and High social vulnerability census tracts concentrate 97% of the population but only 93% of COVID-19 cases and deaths. Third, people living in census tracts of None and Low social vulnerability are 3.87 and 2.13 times more likely to be infected or die from COVID-19. Fourth, policies to combat COVID-19 in the study area should prioritize older populations regardless of their social conditions.

Factors that most expose countries to COVID-19: a composite indicators-based approach.

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R > 0.60) with composite indicator scores and moderately concordant ( K > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.