Coping with seasonality in a quarterly CGE model: COVID‐19 and U.S. agriculture
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,...
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| Published in | The Australian journal of agricultural and resource economics Vol. 65; no. 4; pp. 802 - 821 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.10.2021
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1364-985X 1467-8489 |
| DOI | 10.1111/1467-8489.12442 |
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| Summary: | 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. |
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| Bibliography: | being undertaken by the Centre of Policy Studies at Victoria University in Melbourne through DHS’s Center of Excellence for Accelerating Operational Efficiency (CAOE) at Arizona State University. Amendment 01 requires detailed modeling results to be produced for U.S. agriculture. This part of the project is being undertaken in cooperation with DHS’s Center of Excellence for Cross Border Threat Screening and Supply Chain Defense at Texas A&M University. The research for this this paper was supported financially under Amendment 01 to Federal Award no. 17STQAC00001‐03‐00, Subaward no. ASUB00000508 issued by the U.S. Department of Homeland Security (DHS). The award is for a project titled Economic Modeling of the impacts of COVID‐19 Peter B. Dixon (e‐mail: Peter.Dixon@vu.edu.au) and Maureen T. Rimmer are with the Professor at the Centre of Policy Studies, Victoria University, Melbourne, Victoria, Australia. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The research for this this paper was supported financially under Amendment 01 to Federal Award no. 17STQAC00001‐03‐00, Subaward no. ASUB00000508 issued by the U.S. Department of Homeland Security (DHS). The award is for a project titled Economic Modeling of the impacts of COVID‐19 being undertaken by the Centre of Policy Studies at Victoria University in Melbourne through DHS’s Center of Excellence for Accelerating Operational Efficiency (CAOE) at Arizona State University. Amendment 01 requires detailed modeling results to be produced for U.S. agriculture. This part of the project is being undertaken in cooperation with DHS’s Center of Excellence for Cross Border Threat Screening and Supply Chain Defense at Texas A&M University. |
| ISSN: | 1364-985X 1467-8489 |
| DOI: | 10.1111/1467-8489.12442 |