A forecasting assessment of the affordability of a balanced diet for residents of Russian regions: an agent-based approach

Автор: Mashkova Aleksandra L., Dukhi Natisha, Nevolin Ivan V., Savina Olga A.

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Рубрика: Regional economy

Статья в выпуске: 6 т.14, 2021 года.

Бесплатный доступ

Amid the coronavirus pandemic in Russia, the financial situation of households is deteriorating, as well as their ability to provide themselves with a full-fledged and balanced diet with an optimal content of nutrients, vitamins and minerals. The aim of the work is to conduct a forecasting assessment of the affordability of a balanced diet for households in the context of various scenarios of the economic and epidemiological situation. The applied research methodology involves creating a comprehensive agent-based model reflecting the course of demographic and economic processes that determine food production, people’s income and health. When assessing the affordability of a balanced diet, we consider the number and income of households and the share of income allocated for the purchase of foodstuffs. The information content of the model is based on data arrays available at the websites of the Federal State Statistics Service of Russia, ministries and departments; we also use the data of a product prices monitoring conducted specifically for this purpose. We develop balanced diets that ensure the intake of 75 and 90% of key vitamins and minerals with the necessary caloric content. The products included in the diets are optimized by price; on the basis of the results of the prices monitoring, we estimate the cost of the diets and their affordability for the population in each region of the Russian Federation, according to the data as of 2020. The affordability of diets in different regions varies greatly: from 35% in the Chechen Republic to 95% in Moscow, with an average value of 83%. Scenario-based modeling of the affordability of high-quality food for Russian population is carried out for the period through to 2025. Under the optimistic scenario, there is a decrease in the affordability of diets to 81%; under the conservative and pessimistic scenarios, we observe a steady decline in the affordability of vitamin diets to 76 and 72%, respectively. The results we have obtained indicate serious risk of deterioration of the quality of nutrition of Russian residents; it is associated with falling incomes and rising food prices. Besides, within the framework of scenario-based calculations, we have determined the amount of subsidies to be provided to low-income population groups so that they could afford a balanced diet.


Standard of living, food affordability, balanced diet, agent-based modeling, computational experiment, prices monitoring

Короткий адрес: https://sciup.org/147236293

IDR: 147236293   |   DOI: 10.15838/esc.2021.6.78.6

Список литературы A forecasting assessment of the affordability of a balanced diet for residents of Russian regions: an agent-based approach

  • Redchikova N., Semenova A. Economic access to food in the Russian Federation. Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika=Tomsk State University Journal of Economics, 2015, no. 4(32), pp. 71–87 (in Russian).
  • Vodyasov P.V., Minenko A.V. Estimation of economic factors of demand in the food market. Ekonomika: vchera, segodnya, zavtra=Economics: Yesterday, Today and Tomorrow, 2018, vol. 8, no. 2А, no. 26–32 (in Russian).
  • Yashkova N.V. Methodology for assessing the physical availability of food. Fundamental’nye issledovaniya=Fundamental Research, 2020, no. 8, pp. 92–96. Available at: http://fundamental-research.ru/ru/article/view?id=42833 (accessed: July 7, 2021; in Russian).
  • Borodin K.G. Economic access to food: Factors and methods of assessment. Ekonomicheskii zhurnal VShE=HSE Economic Journal, 2018, vol. 22, no. 4, pp. 563–582 (in Russian).
  • Akerlof G.A. Behavioral macroeconomics and macroeconomic behavior. American Economic Review, 2002, vol. 92, pp. 411–433. DOI: 10.1257/00028280260136192
  • Fagiolo G., Roventini A. Macroeconomic policy in DSGE and agent-based models redux: New developments and challenges ahead. Journal of Artificial Societies and Social Simulation, 2017, vol. 20(1). Available at: http://jasss.soc.surrey.ac.uk/20/1/1.html. DOI: 10.18564/jasss.3280
  • Tesfatsion L., Judd K. (Eds.) Handbook of Computational Economics, vol. 2: Agent-Based Computational Economics. Amsterdam, North Holland, 2006. 904 p.
  • Haber G. Monetary and fiscal policies analysis with an agent-based macroeconomic model. Journal of Economics and Statistics, 2008, vol. 228, pp. 276–295. DOI: 10.1515/jbnst-2008-2-308
  • Bassi F., Lang D. Investment hysteresis and potential output: A post-Keynesian–Kaleckian agent-based approach. Economic Modelling, 2016, vol. 52, pp. 35–49. DOI: 10.1016/j.econmod.2015.06.022
  • Napoletano M., Dosi G., Fagiolo G., Roventini A. Wage formation, investment behavior and growth regimes: An agent-based analysis. Revue de l’OFCE, 2012, vol. 124, pp. 235–261 DOI: 10.3917/reof.124.0235.
  • Branch W.A., Evans G.W. Monetary policy and heterogeneous expectations. Economic Theory, 2011, vol. 47, pp. 365–393, DOI: 10.1007/s00199-010-0539-9
  • Delli Gatti D., Desiderio S. Monetary policy experiments in an agent-based model with financial frictions. Journal of Economic Interaction and Coordination, 2015, vol. 10(2), pp. 265–286, DOI: 10.1007/s11403-014-0123-7
  • Alexandre M., Lima G.T. Combining monetary policy and prudential regulation: An agent-based modeling approach. J Econ Interact Coord, 2020, vol. 15, pp. 385–411, DOI: 10.1007/s11403-017-0209-0
  • Popoyan L., Napoletano M. Roventini A. Taming macroeconomic instability: Monetary and macro prudential policy interactions in an agent-based model. Journal of Economic Behavior & Organization, 2017, vol. 134 (February), pp.117–140. DOI: 10.1016/j.jebo.2016.12.017
  • Blanchard O., Galí J. Labor markets and monetary policy: A new Keynesian model with unemployment. American Economic Journal: Macroeconomics, 2010, vol. 2, pp.1–30. DOI: 10.2139/ssrn.920959
  • Dawid H., Gemkow S., Harting P., Neugart M. Labor market integration policies and the convergence of regions: The role of skills and technology diffusion. Journal of Evolutionary Economics, 2012, vol. 22, pp. 543–562. DOI: 10.1007/s00191-011-0245-1
  • Riccetti L., Russo A., Gallegati M. Unemployment benefits and financial leverage in an agent based macroeconomic model. Economics: The Open-Access, Open-Assessment E-Journal, 2013b, vol. 7, no. 2013–42. DOI: 10.5018/economics-ejournal.ja.2013-42
  • Anufriev M., Assenza T., Hommes C., Massaro D. Interest rate rules and macroeconomic stability under heterogeneous expectations. Macroeconomic Dynamics, 2013, vol. 17(08), pp.1574–1604. DOI: 10.2139/ssrn.1400748
  • Battiston S., Delli Gatti D., Gallegati M., Greenwald B., Stiglitz J. Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk. Journal of Economic Dynamics & Control, 2012, vol. 36(8), pp.1121–1141. DOI: 10.1016/j.jedc.2012.04.001
  • Russo A., Riccetti L., Gallegati M. Increasing inequality, consumer credit and financial fragility in an agent based macroeconomic model. Journal of Evolutionary Economics, 2016, vol. 26, pp.25–47. DOI: 10.2139/ssrn.2356249
  • Raberto M., Teglio A., Cincotti S. Integrating real and financial markets in an agent-based economic model: An application to monetary policy design. Computational Economics, 2008, vol. 32(1), pp. 147–162. DOI: 10.1007/s10614-008-9138-2,
  • Gerst M., Wang P., Roventini A., Fagiolo G., Dosi G., Howarth R., Borsuk M. Agent-based modeling of climate policy: An introduction to the engage multi-level model framework. Environmental Modelling & Software, 2013, vol. 44, pp. 62–75. DOI: 10.1016/j.envsoft.2012.09.002
  • Ge J., Polhill J., Macdiarmid J., Fitton N., Smith P., Clark H., Dawson T., Aphale M. Food and nutrition security under global trade: A relation-driven agent-based global trade model. Royal Society Open Science, 2021, vol. 8, no. 201587. DOI: https://doi.org/10.1098/rsos.201587
  • Dobbie S., Schreckenberg K., Dyke J., Schaafsma M., Balbi S. Agent-based modelling to assess community food security and sustainable livelihoods. Journal of Artificial Societies and Social Simulation, 2018 vol. 21(1), no. 9. DOI: 10.18564/jasss.3639
  • Buurma J., Hennen W., Verwaart T. How social unrest started innovations in a food supply chain. Journal of Artificial Societies and Social Simulation, 2017, vol. 20(1), no. 8. DOI: 10.18564/jasss.3350
  • McPhee-Knowles S. Growing food safety from the bottom up: An agent-based model of food safety inspections. Journal of Artificial Societies and Social Simulation, 2015, vol. 18(2), no. 9. DOI: 10.18564/jasss.2717
  • Mashkova A.L., Novikova E.V., Savina O.A., Mamatov A.V., Mashkov E.A. Simulating budget system in the agent model of the Russian Federation spatial development. In: Chugunov A., Khodachek I., Misnikov Y., Trutnev D. (Eds.). Electronic Governance and Open Society: Challenges in Eurasia. EGOSE 2019. 2020, Communications in Computer and Information Science. Vol. 1135. Pp.17–31. DOI: 10.1007/978-3-030-39296-3_2.
  • Mashkova A.L., Novikova E.V., Savina O.A., Mashkov E.A. Generating synthetic population for the agent-based model of the Russian Federation spatial development. In: Ahrweiler P., Neumann M. (Eds.). Advances in Social Simulation. ESSA 2019. Springer Proceedings in Complexity. Springer, Cham. 2021. Pp. 183–187. DOI: 10.1007/978-3-030-61503-1_17
  • Goncharuk I.V. Review of research on the impact of the COVID-19 pandemic on the development of global and Russian e-commerce. Tamozhennaya politika Rossii na Dal’nem Vostoke=Customs Policy of Russia in the Far East, 2021, no. 1(94), pp. 66–82. DOI: 10.24866/1815-0683/2021-1/66-82 (in Russian).
  • Alfonso V., Boar C., Frost J., Gambacorta L., Liu J. E-commerce in the pandemic and beyond. BIS Bulletin, 2021, vol. 36, pp. 1–7. Available at: https://www.bis.org/publ/bisbull36.htm
Статья научная