نابرابری‌های روستایی-شهری در تقاضای غذای حیوانی و زیان‌های رفاهی در ایران طی کووید-۱۹: رویکرد QUAIDS

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

• علیرضا کرباسی
• سعید جلالیان

گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

https://doi.org/10.22067/jead.2025.88095.1267

چکیده

پاندمی کووید-۱۹، چالش‌های جهانی عمده‌ای ایجاد کرد، از جمله نرخ رشد منفی درآمد سرانه در تمامی گروه‌های درآمدی کشورها در سال ۲۰۲۰. زنجیره تأمین پروتئین، به‌ویژه با منشأ حیوانی (ASF)، با فشارهای فزاینده‌ای از هر دو سوی عرضه و تقاضا مواجه شد که منجر به نوسانات قیمتی گردید. این مطالعه، تأثیر شوک‌ درآمدی بر الگوهای مخارج غذایی و رفتار مصرفی را با تمرکز بر این نوع غذاها بررسی می‌کند. داده‌های بودجه خانوارهای ایرانی برای سال‌های ۲۰۱۹ (پیش از پاندمی) و ۲۰۲۰ (طی پاندمی) با بکارگیری مدل سیستم تقاضای تقریباً ایده‌آل درجه دوم (QUAIDS) تحلیل شد. یافته‌ها سه بینش کلیدی ارائه دادند: 1) سهم متوسط مخارج غذایی از 37% به 42% افزایش یافته است، ضمن رشد شدیدتر در مناطق روستایی؛ 2) کشش‌های مخارج برای هر شش گروه ASF شامل گوشت دام، آبزیان، طیور، محصولات لبنی، تخم‌مرغ و چربی‌ها، مثبت مشاهده شد در حالی‌که کشش‌های خودقیمتی به‌طور نسبی کوچکتر بودند؛ و 3) زیان‌های رفاهی در این شش گروه‌، از 2% تا 24% متغیر بود، که ناشی از عدم تعادل سیاستی و اختلالات زنجیره تأمین بود. خانوارهای روستایی به جز در گروه چربی‌ها، زیان‌های رفاهی بیشتری متحمل شدند. این مطالعه مداخلات هدفمند به شکل سیاست‌های حمایتی قیمتی برای مناطق شهری و حمایت اجتماعی برای مناطق روستایی، پیشنهاد می‌کند. برای تقویت واکنش‌های سیاستی و بهبود امنیت غذایی بلندمدت، تحقیقات آتی می‌تواند پتانسیل جایگزینی پروتئین‌های گیاهی را به‌عنوان گزینه‌های پایدار و مقرون‌به‌صرفه ارزیابی کند. این یافته‌ها راهنمایی ارزشمندی برای سیاست‌گذاران در راستای بهبود تاب‌آوری و ثبات اقتصادی در دوران پساپاندمی ارائه می‌دهند.

کلیدواژه‌ها

• زیان رفاهی
• کووید-19
• غذاهای با منبع حیوانی (ASF)
• مدل QUAIDS
• ایران

موضوعات

• اقتصاد کشاورزی

عنوان مقاله [English]

Rural-Urban Disparities in Animal-Source Food Demand and Welfare Losses during COVID-19 in Iran: A QUAIDS Approach

نویسندگان [English]

• A. Karbasi
• S. Jalalian

Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

چکیده [English]

Abstract
The COVID-19 pandemic presented major global challenges, including a decline in per capita income growth across all income groups in 2020. The protein sector, particularly Animal-Source Foods (ASF) faced increased pressure on both supply and demand, resulting in price volatility. This study examines how income shocks affected food expenditure patterns and consumption behavior, with a focus on protein-rich ASF. Utilizing the QUAIDS model, budget data from Iranian households in rural and urban areas were analyzed for 2019 (pre-pandemic) and 2020 (during pandemic). The findings yield three key insights: (1) The average food expenditure share rose from 37% to 42%, with a sharper increase in rural areas; (2) Positive expenditure elasticities were observed across the six ASF groups including livestock meat, poultry, aquatic animal products, dairy, eggs, and fats, while own-price elasticities were relatively smaller; and (3) Welfare losses across ASF groups ranged from 2% to 24.2%, driven by policy imbalances, supply chain disruptions, and unequal utility distribution. Rural households experienced greater welfare losses in all ASF categories except fats. The study recommends targeted interventions: price-based support for urban areas and expanded social services for rural regions. To strengthen policy responses and enhance long-term food security, future research should assess the potential for substituting plant-based proteins as sustainable and cost-effective alternatives. These findings offer valuable guidance for policymakers aiming to improve nutritional resilience and economic stability in the post-pandemic era.

کلیدواژه‌ها [English]

• : Animal-source food (ASF)
• COVID-19
• Iran
• QUAIDS model
• Welfare losses JEL Classifications: D12
• Q11

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

مراجع

1. Aday, S., & Aday, M.S. (2020). Impact of COVID-19 on the food supply chain. Food Quality and Safety, 4(4), 167–180. https://doi.org/10.1093/fqsafe/fyaa024
2. Adekunle, C.P., Akinbode, S.O., Shittu, A.M., & Momoh, S. (2020). Food price changes and farm households’ welfare in Nigeria: direct and indirect approach. Journal of Applied Economics, 23(1), 409–425. https://doi.org/10.1080/15140326.2020.1743103
3. Adelaja, A., George, J., Fox, L., Fuglie, K., & Jayne, T. (2021). Shocks, Resilience and Structural Transformation in Sub-Saharan Africa. Sustainability, 13(24), 13620. https://doi.org/10.3390/SU132413620
4. Ahmed, M.Y., & Sarkodie, S.A. (2021). How COVID‐19 pandemic may hamper sustainable economic development. Journal of Public Affairs, 21(4). https://doi.org/10.1002/pa.2675
5. Akaichi, F., & Revoredo-Giha, C. (2014). The demand for dairy products in Malawi. African Journal of Agricultural and Resource Economics, 9(3), 214–225. https://doi.org/10.22004/AG.ECON.183894
6. Akter, S. (2020). The impact of COVID-19 related ‘stay-at-home’ restrictions on food prices in Europe: findings from a preliminary analysis. Food Security, 12(4), 719–725. https://doi.org/10.1007/S12571-020-01082-3/TABLES/3
7. Alston, J.M., Norton, G.W., & Pardey, P.G. (1995). Science under scarcity : principles and practice for agricultural research evaluation and priority setting. 585.
8. Anderson, J.D., Mitchell, J.L., & Maples, J.G. (2021). Invited Review: Lessons from the COVID-19 pandemic for food supply chains. Applied Animal Science, 37(6), 738–747. https://doi.org/10.15232/aas.2021-02223
9. Angrist, N., Goldberg, P.K., & Jolliffe, D. (2021). Why is growth in developing countries so hard to measure? Journal of Economic Perspectives, 35(3), 215–242. https://doi.org/10.1257/JEP.35.3.215
10. Araar, A., & Verme, P. (2016). Prices and Welfare. World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-7566
11. Badolo, F., & Traoré, F. (2015). Impact of rising world rice prices on poverty and inequality in Burkina Faso. Development Policy Review, 33(2), 221–244. https://doi.org/10.1111/dpr.12099
12. Bai, Y., Costlow, L., Ebel, A., Laves, S., Ueda, Y., Volin, N., Zamek, M., & Masters, W.A. (2022). Retail prices of nutritious food rose more in countries with higher COVID-19 case counts. Nature Food, 3(5), 325–330. https://doi.org/10.1038/s43016-022-00502-1
13. Banks, J., Blundell, R., & Lewbel, A. (1997). Quadratic engel curves and consumer demand on JSTOR. The Review of Economics and Statistics. https://www.jstor.org/stable/2951405
14. Barrett, C.B., Benton, T.G., Cooper, K.A., Fanzo, J., Gandhi, R., Herrero, M., James, S., Kahn, M., Mason-D’Croz, D., Mathys, A., Nelson, R. J., Shen, J., Thornton, P., Bageant, E., Fan, S., Mude, A.G., Sibanda, L.M., & Wood, S. (2020). Bundling innovations to transform agri-food systems. Nature Sustainability, 3(12), 974–976. https://doi.org/10.1038/s41893-020-00661-8
15. Barten, A.P. (1969). Maximum likelihood estimation of a complete system of demand equations. European Economic Review, 1(1), 7–73. https://doi.org/10.1016/0014-2921(69)90017-8
16. Batlle-Bayer, L., Aldaco, R., Bala, A., Puig, R., Laso, J., Margallo, M., Vázquez-Rowe, I., Antó, J.M., & Fullana-i-Palmer, P. (2020). Environmental and nutritional impacts of dietary changes in Spain during the COVID-19 lockdown. Science of the Total Environment, 748, 141410. https://doi.org/10.1016/J.SCITOTENV.2020.141410
17. Ceylan, R.F., Ozkan, B., & Mulazimogullari, E. (2020). Historical evidence for economic effects of COVID-19. The European Journal of Health Economics, 21(6), 817–823. https://doi.org/10.1007/s10198-020-01206-8
18. Christensen, L.R., Jorgenson, D., & Lau, L.J. (1975). Transcendental logarithmic utility functions. American Economic Review, 65(3), 367–383. https://EconPapers.repec.org/RePEc:aea:aecrev:v:65:y:1975:i:3:p:367-83
19. Christensen, L.R., Jorgenson, D.W., & Lau, L.J. (1973). Transcendental logarithmic production frontiers. The Review of Economics and Statistics, 55(1), 28. https://doi.org/10.2307/1927992
20. Coelho, A.B., Aguiar, D.R.D. de, & Eales, J.S. (2010). Food demand in Brazil: an application of Shonkwiler & Yen Two-Step estimation method. Estudos Econômicos (São Paulo), 40(1), 186–211. https://doi.org/10.1590/S0101-41612010000100007
21. Deaton, A., & Muellbauer, J. (1980). An almost ideal demand system. American Economic Review, 70, 312–376. https://www.scirp.org/reference/referencespapers?referenceid=2020105
22. Engel, E. (1857). Die productions- und consumtionsverhältnisse des Königreichs Sachsen. Zeitschrift Des Statistischen Bureaus Des Königlich Sächsischen Ministerium Des Inneren, 8, 28–29. https://www.statsmodels.org/stable/datasets/generated/engel.html
23. Fan, S., Teng, P., Chew, P., Smith, G., & Copeland, L. (2021). Food system resilience and COVID-19 – Lessons from the Asian experience. Global Food Security, 28, 100501. https://doi.org/10.1016/j.gfs.2021.100501
24. (2020a). FIGURE 53. Average protein supply by region and origin. In FAO Statistical Yearbook 2020 Datasets. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cb1329en-fig53
25. (2020b). Global and regional food availability from 2000 to 2017-an analysis based on supply utilization accounts data. https://doi.org/10.4060/cb1120en
26. Gao, G. (2012). World Food Demand. American Journal of Agricultural Economics, 94(1), 25–51. https://doi.org/10.1093/ajae/aar133
27. Grinberga-Zalite, G., Pilvere, I., Muska, A., & Kruzmetra, Z. (2021). Resilience of Meat Supply Chains during and after COVID-19 Crisis. Emerging Science Journal, 5(1), 57–66. https://doi.org/10.28991/esj-2021-01257
28. Hayakawa, K., & Mukunoki, H. (2021). The impact of COVID-19 on international trade: Evidence from the first shock. Journal of the Japanese and International Economies, 60, 101135. https://doi.org/10.1016/j.jjie.2021.101135
29. Iddir, M., Brito, A., Dingeo, G., Fernandez Del Campo, S.S., Samouda, H., La Frano, M.R., & Bohn, T. (2020). Strengthening the immune system and reducing inflammation and oxidative stress through diet and nutrition: Considerations during the COVID-19 crisis. Nutrients, 12(6), 1562. https://doi.org/10.3390/nu12061562
30. Kaicker, N., Gupta, A., & Gaiha, R. (2022). Covid-19 pandemic and food security in India: Can authorities alleviate the disproportionate burden on the disadvantaged? Journal of Policy Modeling, 44(5), 963–980. https://doi.org/10.1016/j.jpolmod.2022.08.001
31. Khan, A.U., Ema, I.J., Afsana, A. S., Khan, A.U., Zannaty, A., & Faruk, Md.R. (2021). Effects of coronavirus disease (COVID-19) on agricultural sectors in Bangladesh: A Review. Nternational Journal for Asian Contemporary Research, 89–97.
32. Khoiriyah, N., Anindita, R., Hanani, N., & Muhaimin, A.W. (2020). Animal food demand in Indonesia: A quadratic almost ideal demand system approach. Agris On-Line Papers in Economics and Informatics, 12(2), 85–97. https://doi.org/10.7160/aol.2020.120208
33. Mokari-Yamchi, A., Omidvar, N., Tahamipour Zarandi, M., & Eini-Zinab, H. (2022). The effects of food taxes and subsidies on promoting healthier diets in Iranian households. Frontiers in Nutrition, 9. https://doi.org/10.3389/fnut.2022.917932
34. Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery, 78, 185–193. https://doi.org/10.1016/j.ijsu.2020.04.018
35. (2002). Three essays in applied econometrics - Brian P. Poi - Google Books. University of Michigan. https://books.google.fr/books?id=cfodAQAAMAAJ&redir_esc=y
36. Poi, B.P. (2008). Demand-system estimation: Update. The Stata Journal: Promoting Communications on Statistics and Stata, 8(4), 554–556. https://doi.org/10.1177/1536867X0800800407
37. Pollak, R.A., & Wales, T.J. (1978). Estimation of complete demand systems from household budget data: The linear and quadratic expenditure systems. The American Economic Review, 68(3), 348-359. http://www.jstor.org/stable/1805266
38. Poudel, P.B., Poudel, M.R., Gautam, A., Phuyal, S., Tiwari, C.K., Bashyal, N., & Bashyal, S. (2020). COVID-19 and its Global Impact on Food and Agriculture. 生物学与当今世界杂志, 9(5). https://chinese.iomcworld.org/abstract/covid19-and-its-global-impact-on-food-and-agriculture-53429.html
39. Ray, R. (1983). Measuring the costs of children. Journal of Public Economics, 22(1), 89–102. https://doi.org/10.1016/0047-2727(83)90058-0
40. Rodríguez, L., Cervantes, E., & Ortiz, R. (2011). Malnutrition and gastrointestinal and respiratory infections in children: A public health problem. International Journal of Environmental Research and Public Health, 8(4), 1174–1205. https://doi.org/10.3390/ijerph8041174
41. Salehzadeh, H., Soori, M.M., Sadeghi, S., Shahsawari, S., Mohammadi, S., Saifi, M., & Mozaffari, P. (2019). The type and amount of household oil consumption and the influential factors in Sanandaj city, Iran. Journal Advanced Environment Health Research, 7, 1–7. https://doi.org/10.22102/JAEHR.2019.125492.1070
42. Stone, R. (1954). Linear expenditure systems and demand analysis: An application to the pattern of British demand. The Economic Journal, 64(255), 511. https://doi.org/10.2307/2227743
43. Tian, X., Zhou, Y., & Wang, H. (2022). The impact of COVID-19 on food consumption and dietary quality of rural households in China. Foods, 11(4), 510. https://doi.org/10.3390/foods11040510
44. Tonsor, G.T., Lusk, J.L., & Schroeder, T.C. (2023). Market potential of new plant‐based protein alternatives: Insights from four US consumer experiments. Applied Economic Perspectives and Policy, 45(1), 164–181. https://doi.org/10.1002/aepp.13253
45. Vargas-Lopez, A., Cicatiello, C., Principato, L., & Secondi, L. (2022). Consumer expenditure, elasticity and value of food waste: A Quadratic Almost Ideal Demand System for evaluating changes in Mexico during COVID-19. Socio-Economic Planning Sciences, 82, 101065. https://doi.org/10.1016/j.seps.2021.101065
46. (2014). Global Nutrition Targets 2025: Low birth weight policy brief. Geneva. World Health Organization.
47. Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L.J., Fanzo, J., Hawkes, C., Zurayk, R., Rivera, J.A., De Vries, W., Majele Sibanda, L., & Murray, C. J. L. (2019). Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet, 393(10170), 447–492. https://doi.org/10.1016/S0140-6736(18)31788-4
48. World Development Indicators | DataBank. (n.d.). Retrieved May 30, 2025, from https://databank.worldbank.org/source/world-development-indicators