IDEAS home Printed from https://ideas.repec.org/a/ura/ecregj/v1y2016i3p951-965.html
   My bibliography  Save this article

Simulation of the Role of Government in Spatial Agent-Based Model

Author

Listed:
  • Victor Suslov

    (Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences)

  • Tatyana Novikova

    (National Research University Novosibirsk State University , Institute of Economics and Industrial Engineering of the Siberian Branch of the Russian Academy of Sciences)

  • Alexander Tsyplakov

    (Institute of Economics and Industrial Engineering of the Siberian Branch of the Russian Academy of Sciences and National Research University Novosibirsk State University)

Abstract

The paper describes the further development of an agent-based multiregional input-output model of the Russian economy. We consider the idea of incorporating the government into the model and analyze the results of experimental calculations for the conditional example of spatial economy. New agents are included into the model such as the federal and regional governments, pension fund and also the state enterprises producing public goods at the federal and regional levels. The government sets four types of taxes (personal and business income taxes, VAT and payroll taxes), ensures the provision of public goods and provides social, investment and interbudgetary transfers to households, firms and budgets. Social transfers consist of social assistance and unemployment benefits. The utility functions of households are expanded by the terms associated with national and regional public goods. The budget policy is designed in accordance with the maximization of isoelastic function of social welfare that formalizes the choice between the different concepts of social justice. The Gini index is used for the monitoring the inequality of income distribution. The results of experimental calculations present the convergence of the new version of the model to the state of quasi-equilibrium. The special attention is paid an optimal level of the taxation maximizing the social welfare function. Four variants of the optimal tax rates are defined: for three major taxes at a fixed proportion of rates and for each of the tax separately at zero rates of two other taxes. The further directions of modelling are identified, they allow to investigate the spatial development of the Russian economy taking into account the decision-making by private agents in responding to government policies.

Suggested Citation

  • Victor Suslov & Tatyana Novikova & Alexander Tsyplakov, 2016. "Simulation of the Role of Government in Spatial Agent-Based Model," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 951-965.
  • Handle: RePEc:ura:ecregj:v:1:y:2016:i:3:p:951-965
    as

    Download full text from publisher

    File URL: http://economyofregion.ru/Data/Issues/ER2016/September_2016/ERSeptember2016_951_965.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sarah Wolf & Steffen Fürst & Antoine Mandel & Wiebke Lass & Daniel Lincke & Federico Pablo-Marti & Carlo Jaeger, 2013. "A multi-agent model of several economic regions," PSE - Labex "OSE-Ouvrir la Science Economique" halshs-00825217, HAL.
    2. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    3. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Nigar Hashimzade & Gareth Myles & Frank Page & Matthew Rablen, 2015. "The use of agent-based modelling to investigate tax compliance," Economics of Governance, Springer, vol. 16(2), pages 143-164, May.
    6. Theodore Tsekeris & Klimis Vogiatzoglou & Stelios Bekiros, 2011. "Multi-Regional Agent-Based Modeling of Household and Firm Location Choices with Endogenous Transport Costs," ERSA conference papers ersa10p479, European Regional Science Association.
    7. Theodore Tsekeris & Klimis Vogiatzoglou, 2011. "Spatial agent-based modeling of household and firm location with endogenous transport costs," Netnomics, Springer, vol. 12(2), pages 77-98, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Domozhirov D. A. & Ibragimov N. M. & Melnikova L. V. & Tsyplakov A. A., 2017. "Integration of input–output approach into agent-based modeling. Part 1. Methodological principles," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 17(1), pages 86-99.
    2. Доможиров Д. А. & Ибрагимов Н. М. & Мельникова Л. В. & Цыплаков А. А., 2017. "Интеграция подхода «затраты – выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы. Integration of input–output approach into agent-based modeling. Part 1. Methodological pr," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(1), pages 86-99.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ciarli, Tommaso & Savona, Maria, 2019. "Modelling the Evolution of Economic Structure and Climate Change: A Review," Ecological Economics, Elsevier, vol. 158(C), pages 51-64.
    2. Tommaso Ciarli & Karolina Safarzynska, 2020. "Sustainability and Industrial Challenge: The Hindering Role of Complexity," SPRU Working Paper Series 2020-18, SPRU - Science Policy Research Unit, University of Sussex Business School.
    3. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    4. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    5. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    7. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    8. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    9. Richard Holt & J. Barkley Rosser & David Colander, 2011. "The Complexity Era in Economics," Review of Political Economy, Taylor & Francis Journals, vol. 23(3), pages 357-369.
    10. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(1), pages 19-37, April.
    11. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    12. Доможиров Д. А. & Ибрагимов Н. М. & Мельникова Л. В. & Цыплаков А. А., 2017. "Интеграция подхода «затраты – выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы. Integration of input–output approach into agent-based modeling. Part 1. Methodological pr," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(1), pages 86-99.
    13. repec:zbw:iamodp:109915 is not listed on IDEAS
    14. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    16. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    17. Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021. "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers 450, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    18. Oeffner, Marc, 2008. "Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation," MPRA Paper 18199, University Library of Munich, Germany, revised Oct 2009.
    19. Paul De Grauwe, 2012. "Booms and busts: New Keynesian and behavioural explanations," Chapters, in: Robert M. Solow & Jean-Philippe Touffut (ed.), What’s Right with Macroeconomics?, chapter 6, pages 149-180, Edward Elgar Publishing.
    20. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    21. Jonathan F. Cogliano & Roberto Veneziani & Naoki Yoshihara, 2022. "Computational methods and classical‐Marxian economics," Journal of Economic Surveys, Wiley Blackwell, vol. 36(2), pages 310-349, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ura:ecregj:v:1:y:2016:i:3:p:951-965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alexey Naydenov (email available below). General contact details of provider: http://www.economyofregion.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.