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Coping with seasonality in a quarterly CGE model: COVID‐19 and U.S. agriculture

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  • Peter B. Dixon
  • Maureen T. Rimmer

Abstract

Most dynamic CGE models work with periods of 1 year. This limits their applicability for analysing the effects of shocks that operate over a short period or with different intensities through a year. It is relatively easy to convert an annual CGE model to shorter periodicity, for example a quarter, if we ignore seasonal differences in the pattern of economic activity, but this is not acceptable for agriculture. This paper introduces seasonal factors to the agricultural specification in a detailed quarterly CGE model of the United States. The model is then applied to analyse the effects of the COVID pandemic on U.S. farm industries. Taking account of the general features of the pandemic such as the reduction in household spending, we find that these effects are mild relative to the effects on most other industries. However, agriculture is subject to potential supply‐chain disruptions. We apply our quarterly model to analyse two such possibilities: loss of labour at harvest time in Fruit & nut farms, and temporary closure of meat‐processing plants. We find that these disruptions are unlikely to cause noticeable reductions in the supply of food products to U.S. households.

Suggested Citation

  • Peter B. Dixon & Maureen T. Rimmer, 2021. "Coping with seasonality in a quarterly CGE model: COVID‐19 and U.S. agriculture," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(4), pages 802-821, October.
    • Handle: RePEc:bla:ajarec:v:65:y:2021:i:4:p:802-821
    DOI: 10.1111/1467-8489.12442
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    References listed on IDEAS

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    1. Dixon, Peter B. & Koopman, Robert B. & Rimmer, Maureen T., 2013. "The MONASH Style of Computable General Equilibrium Modeling: A Framework for Practical Policy Analysis," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 23-103, Elsevier.
    2. Dixon Peter B & Lee Bumsoo & Muehlenbeck Todd & Rimmer Maureen T. & Rose Adam & Verikios George, 2010. "Effects on the U.S. of an H1N1 Epidemic: Analysis with a Quarterly CGE Model," Journal of Homeland Security and Emergency Management, De Gruyter, vol. 7(1), pages 1-19, December.
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    Cited by:

    1. Arndt Feuerbacher & Jonas Luckmann, 2023. "Labour‐saving technologies in smallholder agriculture: An economy‐wide model with field operations," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(1), pages 56-82, January.
    2. Deborah C. Peterson & Lin Crase, 2021. "Disaster, disruption, recovery and resilience: lessons from and for agricultural and resource‐based industries," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(4), pages 767-775, October.
    3. Barun Deb Pal & Sanjib Pohit & Meenakshi Rajeev, 2024. "Unpacking India’s fiscal responses to COVID-19: a computable general equilibrium modelling analysis," Indian Economic Review, Springer, vol. 59(1), pages 201-231, June.

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    More about this item

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • I19 - Health, Education, and Welfare - - Health - - - Other

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