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Food Hardship in the US During the Pandemic: What Can We Learn From Real‐Time Data?

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  • Sara Ayllón
  • Samuel Lado

Abstract

We study the potential effect of the declaration of the state of emergency, the beginning and end of the stay‐at‐home orders, and the one‐off Economic Impact Payments on food hardship in the US during the first wave of the coronavirus pandemic. We use daily data from Google Trends for the search term “foodbank” and document the development of a hunger crisis, as indicated by the number of individuals who need to locate a food pantry through the internet. The demand for charitable food handouts begins to decrease once families start receiving the stimulus payments, but the biggest fall comes when economic activity resumes after the lifting of the lockdown orders. Our estimates indicate that the increased need for emergency help among vulnerable families lasted for at least 10 weeks during the first wave of the pandemic, and we argue that real‐time data can be useful in predicting such urgency.

Suggested Citation

  • Sara Ayllón & Samuel Lado, 2022. "Food Hardship in the US During the Pandemic: What Can We Learn From Real‐Time Data?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(2), pages 518-540, June.
    • Handle: RePEc:bla:revinw:v:68:y:2022:i:2:p:518-540
    DOI: 10.1111/roiw.12564
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    1. Kong, Edward & Prinz, Daniel, 2020. "Disentangling policy effects using proxy data: Which shutdown policies affected unemployment during the COVID-19 pandemic?," Journal of Public Economics, Elsevier, vol. 189(C).
    2. Jérôme Adda, 2016. "Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 891-941.
    3. Ganong, Peter & Noel, Pascal & Vavra, Joseph, 2020. "US unemployment insurance replacement rates during the pandemic," Journal of Public Economics, Elsevier, vol. 191(C).
    4. Scott R. Baker & Andrey Fradkin, 2017. "The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 756-768, December.
    5. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    6. Janet Currie, 2009. "Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 87-122, March.
    7. Cicala, Steve, 2021. "The incidence of extreme economic stress: Evidence from utility disconnections," Journal of Public Economics, Elsevier, vol. 200(C).
    8. Howard, Larry L., 2011. "Does food insecurity at home affect non-cognitive performance at school? A longitudinal analysis of elementary student classroom behavior," Economics of Education Review, Elsevier, vol. 30(1), pages 157-176, February.
    9. Baek, ChaeWon & McCrory, Peter B & Messer, Todd & Mui, Preston, 2020. "Unemployment Effects of Stay-at-Home Orders: Evidence from High Frequency Claims Data," Institute for Research on Labor and Employment, Working Paper Series qt042177j7, Institute of Industrial Relations, UC Berkeley.
    10. Palomino, Juan C. & Rodríguez, Juan G. & Sebastian, Raquel, 2020. "Wage inequality and poverty effects of lockdown and social distancing in Europe," European Economic Review, Elsevier, vol. 129(C).
    11. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    12. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    13. Crossley, Thomas F. & Fisher, Paul & Low, Hamish, 2021. "The heterogeneous and regressive consequences of COVID-19: Evidence from high quality panel data," Journal of Public Economics, Elsevier, vol. 193(C).
    14. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.
    15. Case, Anne & Fertig, Angela & Paxson, Christina, 2005. "The lasting impact of childhood health and circumstance," Journal of Health Economics, Elsevier, vol. 24(2), pages 365-389, March.
    16. Caroline Ratcliffe & Signe-Mary McKernan & Sisi Zhang, 2011. "How Much Does the Supplemental Nutrition Assistance Program Reduce Food Insecurity?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 1082-1098.
    17. Brandon J. Restrepo & Matthew P. Rabbitt & Christian A. Gregory, 2021. "The Effect of Unemployment on Food Spending and Adequacy: Evidence from Coronavirus‐Induced Firm Closures," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(1), pages 185-204, March.
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