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Network structure, equilibrium and dynamics in a monopolistically competitive economy

  • Tamás SebestyénEmail author
  • Dóra Longauer
Article
  • 13 Downloads

Abstract

Although network theory has been busy to emphasize the role of connection structures in shaping aggregate level phenomena of complex systems, there are only few attempts in economic modeling which try to build this dimension into the analysis. Macroeconomic models typically build on complete connectedness among economic actors (frictionless flow of information, perfect information on prices), thus these models typically oversee the possible effects of complex, incomplete network structures among economic agents on emergent macroeconomic phenomena. In this paper we try to fill this gap by incorporating possibly incomplete relationship structures between economic actors in a standard model of monopolistic competition and then analyze the effect of different network structures on the equilibrium and dynamic properties of the model. Analytical and simulation results of the model show that incomplete connectedness give rise to deadweight loss, shrinking output below the level observed in standard models with complete networks. Also, the dynamics of link formation has an effect on the steady state of the economy as well as on its response to shocks.

Keywords

Monopolistic competition Network structure Incomplete information Preferential attachment 

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Notes

Acknowledgements

The research presented in this paper was supported by the ÚNKP-17-4-III New National Excellence Program of the Hungarian Ministry of Human Capacities.

References

  1. 1.
    Acemoglu, D., Carvalho, V.M., Ozdaglar, A., Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80(5), 1977–2016.CrossRefGoogle Scholar
  2. 2.
    Acemoglu, D., Ozdaglar, A., Tahbaz-Salehi, A. (2015). Networks and the macroeconomy: an empirical exploration, Bank of Finland Research Discussion Papers 25.Google Scholar
  3. 3.
    Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1–33.CrossRefGoogle Scholar
  4. 4.
    Allen, F., Babus, A., Carletti, E. (2010). Financial connections and systemic risk, NBER Working Papers 16177.Google Scholar
  5. 5.
    Angeletos, G.M., & Lian, C. (2016). Incomplete information in macroeconomics: Accommodating frictions in coordination. In Taylor, J.B., & Uhlig, H. (Eds.) Handbook of macroeconomics (pp. 1065–1240): North-Holland.Google Scholar
  6. 6.
    Bala, V., & Goyal, S. (2000). A noncooperative model of network formation. Econometrica, 68(5), 1181–1230.CrossRefGoogle Scholar
  7. 7.
    Barabási, A.-L. (2016). Network science. Cambridge: Cambridge University Press.Google Scholar
  8. 8.
    Barabási, A.L. (2002). Linked: How everything is connected to everything else what it means for business, science and everyday life. New York: Perseus Publishing.Google Scholar
  9. 9.
    Barabási, A.L., Albert, R., Jeong, H. (2000). Scale-free characteristics of random networks: the topology of the world wide web. Physica A: Statistical Mechanics and its Applications, 281(1), 69–77.CrossRefGoogle Scholar
  10. 10.
    Barabási, A.L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.CrossRefGoogle Scholar
  11. 11.
    Barkman, S., & Heijdra, B. (Eds.). (2004). The monopolistic competition revolution in retrospect. Cambridge: Cambridge University Press.Google Scholar
  12. 12.
    Barro, R.J. (1976). Rational expectations and the role of monetary policy. Journal of Monetary Economics, 2(1), 1–32.CrossRefGoogle Scholar
  13. 13.
    Bougheas, S., & Kirman, A. (2014). Complex financial networks and systemic risk: a review, CESifo Working Paper Series 4756.Google Scholar
  14. 14.
    Caccioli, F., Barucca, P., Kobayashi, T.J. (2018). Network models of financial systemic risk. Journal of Computational Social Science, 1(1), 81–114.CrossRefGoogle Scholar
  15. 15.
    Csermely, P. (2005). A rejtett hálózatok ereje. Budapest: Vince Kiadó.Google Scholar
  16. 16.
    De Grauwe, P. (2010). Top-down versus bottom-Up macroeconomics. CESifo Economic Studies, 56(4), 465–497.CrossRefGoogle Scholar
  17. 17.
    Dixit, A.K., & Stiglitz, J.E. (1977). Monopolistic competition and optimum product diversity. The American Economic Review, 67(3), 297–308.Google Scholar
  18. 18.
    Elliott, M., Golub, B., Jackson, M.O. (2014). Financial networks and contagion. American Economic Review, 104(10), 3115–3153.CrossRefGoogle Scholar
  19. 19.
    Erdős, P., & Rényi, A. (1959). On random graphs I. Publicationes Mathematicae, 6, 290–297.Google Scholar
  20. 20.
    Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. Hungarian Academy of Sciences Institute of Mathematics, 5, 17–61.Google Scholar
  21. 21.
    Farmer, J.D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460, 685–686.CrossRefGoogle Scholar
  22. 22.
    Farmer, J.D. (2013). Economics needs to treat the economy as a complex system. CRISIS publications working paper.Google Scholar
  23. 23.
    Galí, J. (2008). Monetary policy, inflation, and the business cycle. An introduction to the new Keynesian framework. Princeton: Princeton University Press.Google Scholar
  24. 24.
    Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.CrossRefGoogle Scholar
  25. 25.
    Granovetter, M. (1983). The strength of weak ties: a network theory revisited. Sociological Theory, 1, 201–233.CrossRefGoogle Scholar
  26. 26.
    Gueriniac, M., Napoletano, M., Roventinica, A. (2018). No man is an island: the impact of heterogeneity and local interactions on macroeconomic dynamics. Economic Modelling, 68, 82–95.CrossRefGoogle Scholar
  27. 27.
    Hau, O., Mellár, T., Sebestyén, T. (2013). Láthatóvá tehető-e a láthatatlan kéz. Hungarian Economic Review (Közgazdasági Szemle), 60(9), 992–1024.Google Scholar
  28. 28.
    Jackson, M.O., & Wolinsky, A. (1996). A strategic model of social and economic networks. Journal of Economic Theory, 71(1), 44–74.CrossRefGoogle Scholar
  29. 29.
    Kónya, I. (2015). Fejezetek a haladó makroökonómiából. Az RBC-DGSE modellcsalád és a munkapiac makroökonómiája. University of Pécs Faculty of Business and Economics, Pécs.Google Scholar
  30. 30.
    L’Huillier, J.P. (2012). Consumers’ imperfect information and price rigidities. EIEF Working Papers Series 1209.Google Scholar
  31. 31.
    Lucas, R.E. Jr. (1972). Expectations and the neutrality of money. Journal of Economic Theory, 4(2), 103–124.CrossRefGoogle Scholar
  32. 32.
    Lucas, R.E. Jr. (1973). Some international evidence on output-inflation trade-offs. American Economic Review, 63(3), 326–334.Google Scholar
  33. 33.
    Lucas, R.E. Jr. (1975). An equilibrium model of the business cycle. Journal of Political Economy, 83(6), 1113–1144.CrossRefGoogle Scholar
  34. 34.
    Mankiw, N.G., & Reis, R. (2002). Sticky information versus sticky prices: a proposal to replace the new Keynesian Phillips curve. Quarterly Journal of Economics, 117(4), 1295–1328.CrossRefGoogle Scholar
  35. 35.
    Mankiw, N.G., & Reis, R. (2010). Imperfect information and aggregate supply. In Friedman, B.M., & Woodford, M. (Eds.) Handbook of monetary economics (pp. 183–229): North Holland.Google Scholar
  36. 36.
    Stigler, G. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225.CrossRefGoogle Scholar
  37. 37.
    Tesfatsion, L. (2006). Agent-based computational economics: a constructive approach to economic theory. In Tesfatsion, L, & Judd, K.L. (Eds.) Handbook of computational economics (pp. 831–880): North-Holland.Google Scholar
  38. 38.
    Townsend, R. (1983). Forecasting the forecasts of others. Journal of Political Economy, 91(4), 546–588.CrossRefGoogle Scholar
  39. 39.
    Wims, G., Martens, D., De Backer, M. (2011). Network models of financial contagion: a definition and literature review. Working papers of the faculty of economics and business administration, Ghent University Faculty of Economics and Business Administration.Google Scholar
  40. 40.
    Woodford, M. (2002). Imperfect common knowledge and the effects of monetary policy. In Aghion, P., Frydman, R., Stiglitz, J., Woodford, M. (Eds.) Knowledge, information, and expectations in modern macroeconomics: In honor of Edmund S. Phelps (pp. 25–58). Princeton: Princeton University Press.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economics and Econometrics and MTA-PTE Innovation and Economic Growth Research GroupUniversity of Pécs Faculty of Business and EconomicsPécsHungary
  2. 2.PhD Program in Regional Policy and EconomicsUniversity of Pécs Faculty of Business and EconomicsPécsHungary

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