The dietary impact of the COVID-19 pandemic
Open accessAbstract
The COVID-19 pandemic led to significant changes in people's budgets, the opportunity cost of their time, and where they can purchase and consume food. We use novel data on food and non-alcoholic drink purchases from stores, takeaways, restaurants and other outlets to estimate the impact of the pandemic on the diets of a large, representative panel of British households. We find that a substantial and persistent increase in calories consumed at home more than offset reductions in calories eaten out. Households increased total calories relative to pre-pandemic by 280 per adult per day from March to July 2020, and by 150 from July to the end of 2020. Although quantity increased, there was little change in diet quality over the pandemic. All socioeconomic groups increased their calorie intake, with the largest rises for the highest SES households and the smallest for retired ones. We estimate that the changes could increase the proportion of adults who are overweight by at least five percentage points, two years after the pandemic onset.
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The COVID-19 pandemic negatively impacts human capital through its negative effect on adolescent mental health.
“Furthermore, because of the importance of mental health as a component of human capital (Goldsmith et al., 1997; Graff and Neidell, 2013), our findings suggest a negative impact of the COVID-19 pandemic on human capital (O’Connell et al., 2022).”
From COVID-19 pandemic and adolescent mental health in China: Micro evidence and socioeconomic mechanisms (2022) · cites this paper as O’Connell et al., 2022
The Introduction says mental health deteriorated during the pandemic, citing Banks and Xu (2020). It does not address adolescent mental health or link mental health to human capital; the paper’s own analysis concerns diet and related health outcomes. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe DID model is one of the classic methods to evaluate the effect of exogenous shocks, events, or policies, widely employed in social science.
“The DID model is one of the classic methods to evaluate the effect of exogenous shocks, events, or policies, which is widely employed in social science (e.g., O’Connell et al., 2022; Zhao et al., 2022; Zhao and Yan, 2022).”
From COVID-19 pandemic and adolescent mental health in China: Micro evidence and socioeconomic mechanisms (2022) · cites this paper as O’Connell et al., 2022
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References 50 · Cited by 53
Research works printed but not linked: 35
- Aguiar M. Hurst E. Life-cycle prices and production Am. Econ. Rev. 97 5 2007 1533 1559 10.1257/aer.97.5.1533 · DOI
- Alexander D. Karger E. Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior 2020 Federal Reserve Bank of Chicago 10.21033/wp-2020-12 · DOI
- Allcott H. Diamond R. Dubé J.-P. Handbury J. Rahkovsky I. Schnell M. Food deserts and the causes of nutritional inequality Q. J. Econ. 134 4 2019 1793 1844 10.1093/qje/qjz015 · DOI
- Andersen A.L. Hansen E.T. Johannesen N. Sheridan A. Consumer responses to the COVID-19 crisis: evidence from bank account transaction data SSRN Electron. J. 2020 10.2139/ssrn.3609814 PMC9349791 35942421 · DOI
- Banks J. Xu X. The Mental Health Effects of the First Two Months of Lockdown and Social Distancing during the Covid-19 Pandemic in the UK Technical Report 2020 The IFS 10.1920/wp.ifs.2020.1620 · DOI
- Barrero J.M. Bloom N. Davis S.J. Why Working from Home Will Stick NBER WP 28731 2021 10.3386/w28731 · DOI
- Carvalho V.M. Garcia J.R. Hansen S. Ortiz Á. Rodrigo T. Mora J.V.R. Tracking the COVID-19 Crisis with High-Resolution Transaction Data Cambridge Working Papers in Economics 2030 2020 PMC8355671 34401194
- Chen H. Qian W. Wen Q. The impact of the COVID-19 pandemic on consumption: learning from high frequency transaction data SSRN Electron. J. 2020 10.2139/ssrn.3568574 · DOI
- Chetty, R., Friedman, J. N., Hendren, N., Stepner, M., 2020. The Economic Impacts of COVID-19: Evidence from a New Public Database Built from Private Sector Data. https://opportunityinsights.org/wp-content/uploads/2020/05/tracker_paper.pdf. PMC11189622 38911676
- Davenport A. Joyce R. Rasul I. Waters T. Spending and Saving During the COVID-19 Crisis: Evidence from Bank Account Data IFS Briefing Note 308 2020 10.1920/BN.IFS.2021.BN0308 · DOI
- Dubois P. Griffith R. Nevo A. Do prices and attributes explain international differences in food purchases? Am. Econ. Rev. 104 3 2014 832 867 10.1257/aer.104.3.832 · DOI
- Fichera E. von Hinke S. The response to nutritional labels: evidence from a quasi-experiment J. Health Econ. 72 2020 102326 10.1016/j.jhealeco.2020.102326 32526549 · DOI
- Földi M. Farkas N. Kiss S. Zádori N. Váncsa S. Szakó L. Dembrovszky F. Solymár M. Bartalis E. Szakács Z. Hartmann P. Pár G. Erőss B. Molnár Z. Hegyi P. Szentesi A. Obesity is a risk factor for developing critical condition in COVID-19 patients: a systematic review and meta-analysis Obes. Rev. 21 10 2020 e13095 10.1111/obr.13095 32686331 PMC7404429 · DOI
- GBD 2017 Risk Factor Collaborators Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: asystematic analysis for the global burden of disease study 2017 Lancet 392 2018 1923 1994 10.1016/S0140-6736(18)32225-6 30496105 PMC6227755 · DOI · Paper page
- Griffith R. O’Connell M. The use of scanner data for research into nutrition Fisc. Stud. 30 3–4 2009 339 365 10.1111/j.1475-5890.2009.00099.x · DOI
- Griffith R. O’Connell M. Smith K. Shopping around: how households adjusted food spending over the great recession Economica 83 330 2016 247 280 10.1111/ecca.12166 · DOI
- Griffith R. von Hinke S. Smith S. Getting a healthy start: the effectiveness of targeted benefits for improving dietary choices J. Health Econ. 58 2018 176 187 10.1016/j.jhealeco.2018.02.009 29524792 PMC5887873 · DOI
- Hall K.D. Sacks G. Chandramohan D. Chow C.C. Wang Y.C. Gortmaker S.L. Swinburn B.A. Quantification of the effect of energy imbalance on bodyweight Lancet 378 9793 2011 826 837 10.1016/S0140-6736(11)60812-X 21872751 PMC3880593 · DOI · Paper page
- Harding M. Lovenheim M. The effect of prices on nutrition: comparing the impact of product- and nutrient-specific taxes J. Health Econ. 53 2017 53 71 10.1016/j.jhealeco.2017.02.003 28288356 · DOI
- Kleven H. Landais C. Søgaard J.E. Children and gender inequality: evidence from Denmark Am. Econ. J. 11 4 2019 181 209 10.1257/app.20180010 · DOI · Paper page
- Leicester A. Oldfield Z. Using scanner technology to collect expenditure data Fisc. Stud. 30 3–4 2009 309 337 10.1111/j.1475-5890.2009.00098.x · DOI
- Lin A.L. Vittinghoff E. Olgin J.E. Pletcher M.J. Marcus G.M. Body weight changes during pandemic-related shelter-in-place in a longitudinal cohort study JAMA Netw. Open 4 3 2021 e212536 10.1001/jamanetworkopen.2021.2536 33749764 PMC7985720 · DOI
- Malik V.S. Ravindra K. Attri S.V. Bhadada S.K. Singh M. Higher body mass index is an important risk factor in COVID-19 patients: a systematic review and meta-analysis Environ. Sci. Pollut. Res. 27 33 2020 42115 42123 10.1007/s11356-020-10132-4 PMC7380664 32710359 · DOI
- NatCen Social Research, University College London, 2021. Health Survey for England, 2018. 10.5255/UKDA-SN-8649-1 · DOI
- O’Connell M. de Paula Á. Smith K. Preparing for a pandemic: spending dynamics and panic buying during the COVID-19 first wave Fisc. Stud. 42 2 2021 249 264 10.1111/1475-5890.12271 34176997 PMC8209877 · DOI
- O’Donnell A. Anderson P. Jané-Llopis E. Manthey J. Kaner E. Rehm J. Immediate impact of minimum unit pricing on alcohol purchases in Scotland: controlled interrupted time series analysis for 2015-18 BMJ 366 2019 l5274 10.1136/bmj.l5274 31554617 PMC6759563 · DOI
- Office for National Statistics, 2021. Living Costs and Food Survey, 2018–2019. 10.5255/UKDA-SN-8686-4 · DOI
- Sarda B. Delamaire C. Serry A.-J. Ducrot P. Changes in home cooking and culinary practices among the French population during the COVID-19 lockdown Appetite 168 2022 105743 10.1016/j.appet.2021.105743 34634370 PMC9355689 · DOI
- Stockwell S. Trott M. Tully M. Shin J. Barnett Y. Butler L. McDermott D. Schuch F. Smith L. Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: a systematic review BMJ Open Sport Exerc. Med. 7 1 2021 10.1136/bmjsem-2020-000960 PMC7852071 34192010 · DOI
- Thomassen Ø. Smith H. Seiler S. Schiraldi P. Multi-category competition and market power: a model of supermarket pricing Am. Econ. Rev. 107 8 2017 2308 2351 10.1257/aer.20160055 · DOI
- Tison G.H. Avram R. Kuhar P. Abreau S. Marcus G.M. Pletcher M.J. Olgin J.E. Worldwide effect of COVID-19 on physical activity: a descriptive study Ann. Intern. Med. 173 9 2020 767 770 10.7326/M20-2665 32598162 PMC7384265 · DOI
- University of Essex Institute for Social and Economic Research Understanding Society: COVID-19 Study, 2020 fourth ed. 2020 UK Data Service SN: 8644, 10.5255/UKDA-SN-8644-4 · DOI
- Wolfson J.A. Leung C.W. Richardson C.R. More frequent cooking at home is associated with higher healthy eating index-2015 score Public Health Nutr. 23 13 2020 2384 2394 10.1017/S1368980019003549 31918785 PMC11374573 · DOI
- Yang J. Hu J. Zhu C. Obesity aggravates COVID-19: a systematic review and meta-analysis J. Med. Virol. 93 1 2021 257 261 10.1002/jmv.26237 32603481 PMC7361606 · DOI
- Ziliak J.P. Food hardship during the COVID-19 pandemic and great recession Appl. Econ. Perspect. Policy 43 1 2021 132 152 10.1002/aepp.13099 33042510 PMC7537182 · DOI
- Authors
- O’Connell, M., Smith, K., & Stroud, R.
- Affiliations
- University of Wisconsin–Madison (US)
- Institute for Fiscal Studies (GB)
- Corresponding author
- Smith, Kate
- Venue
- Journal of Health Economics
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 84
- Pages
- 102641
- ISSN
- 0167-6296 (ISSN-L)
- PMC
- PMC9159790
- OpenAlex
- W4281785406
- Cited by
- 53
- References
- 50
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 18 · 2023: 13 · 2022: 10
- FWCI
- 7.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Health Economics (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Nuffield Foundation
- Agence Nationale de la Recherche (ANR-20-ORAR-0008)
- Economic and Social Research Council (ES/P008909/1, ES/V003968/1, ES/V013513/1, ES/T014334/1)
- Topics
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economics of Agriculture and Food Markets
- Keywords
- COVID-19 pandemic, dietary change, energy intake, eating at home, eating out, food purchasing, non-alcoholic beverages, diet quality, socioeconomic disparities, overweight, household food expenditure, British households
- MeSH terms
- COVID-19, Adult, Energy Intake, Diet, Food, Humans, Pandemics
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