Consumer food stockpiling behavior and willingness to pay for food reserves in COVID-19

Abstract

Consumer behavior changes differently in emergencies. Understanding consumer food stockpiling behavior during COVID-19 pandemic can provide critical information for governments and policymakers to adjust inventory and response strategies. This paper analyzed consumer food stockpiling behavior, including the change of food reserve scale and willingness to pay for fresh food reserves in COVID-19. Our paper shows that the scale of food reserve extends from 3.37 to 7.37 days after the outbreak of COVID-19; if available, consumers on average are willing to pay 18.14 yuan (60.47%) premium for fresh products reserves. The result shows that food stockpiling behavior is fueled by a set of multiple motivations and subjective risk perception. Female, high education level and high-income consumers were more likely to reserve larger scale food reserves, but consumers' willingness to pay for fresh food reserves is determined by income. This study was conducted when new infection cases continued to rise in the world. The story of food stockpiling during the COVID-19 pandemic in China is similar with the rest of world. Consumer stockpiling behavior in China can also be expanded to other countries to predict the change of food demand and understand more about consumer preferences in emergencies.

Do Risk Preferences Change? Evidence from the Great East Japan Earthquake

“…Our findings suggest that men's risk preferences are more likely to change than those of women, and men become less risk averse in response to a negative shock, despite their already low level of risk aversion. While previous studies show that men are less risk averse than women (level), 5 to the best of our knowledge, ours is the first study that documents gender differences in the susceptibility of risk preferences and their direction to a negative shock (changes) in a non-laboratory setting.…”

“…Seismic intensity Table 3 confirms our findings in the figures. Table 3 summarizes the estimates for running specification (5) in which the outcome is the transformed price. Note that a negative coefficient implies a decrease in the risk aversion as the intensity of the Earthquake increases.…”

“…Second, we regress a dummy for attrition (1 if the individual is not observed after the Earthquake and 0 otherwise) on the same set of variables as in equation (5) to check if the attrition is correlated with the Earthquake's intensity. Columns 1-4 in Table 5 show that the attrition is not systematically correlated with the Earthquake's intensity.…”

“…Alternatively, we construct bounds on the estimated treatment effects using a range of assumptions for attriters following an approach based on Horowitz and Manski (2000), Kling and Liebman (2004), and Lee (2009). 32 We also estimate the main specification (5) including the individuals who relocated between 2011 and 2012 in which we assign the intensity measures from the municipalities reported in 2011. Since the migration rate is low, the results remain essentially unchanged (the results are available upon request).…”

“…In online Appendix Section B1, we further control for a dummy for industry, house ownership, as well as assets and property price; however, here as well, the estimates are barely affected. We also directly estimate main specification (5), where outcome is replaced by employment, income, and expected income at the municipality-year level. It is reassuring that none of the estimates are statistically significant at the conventional level.…”