How do consumers respond to COVID-19? Application of Bayesian approach on credit card transaction data

Determining how consumers respond to unexpected outbreaks has been one of the core research areas in risk analysis. Using the case of the COVID-19 pandemic, this study estimates consumption behavior and pays significant attention to understanding the information updating process of consumers regarding the spread of the pandemic. We propose four different models of information updating: the naïve expectation, adaptive expectation, perfect and non-perfect Bayesian models. Using the real-time credit card transactions in Taiwan, we find that consumers respond to the spread of COVID-19 confirmed cases in the way predicted by the perfect Bayesian model. Moreover, we find that COVID-19 increases consumers’ expenditure on clothing and transportation in offline markets. With respect to food consumption, we find a decrease in offline and an increased expenditure in online markets. Our findings are robust to different measurements of COVID-19 spread.