Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-28T05:31:25.806Z Has data issue: false hasContentIssue false
  • English
  • Français

EVOLUTIONARY COMPETITION AND PROFIT TAXES: MARKET STABILITY VERSUS TAX BURDEN

Published online by Cambridge University Press:  08 November 2016

Noemi Schmitt  and
Frank Westerhoff
Noemi Schmitt*
Affiliation:
University of Bamberg
Frank Westerhoff
Affiliation:
University of Bamberg
*
Address correspondence to: Noemi Schmitt, Department of Economics, University of Bamberg, Feldkirchenstrasse 21, 96045 Bamberg, Germany; e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

The seminal cobweb model by Brock and Hommes reveals that fixed-point dynamics may turn into increasingly complex dynamics, as firms switch more quickly between competing expectation rules. While policy makers may be able to manage such rational routes to randomness by imposing a proportional profit tax, the stability-ensuring tax rate may cause a very high tax burden for firms. Using a mix of analytical and numerical tools, we show that a rather small profit-dependent lump-sum tax may even be sufficient to take away the competitive edge of cheap destabilizing expectation rules, thereby contributing to market stability.

Keywords

Cobweb ModelsIntensity of ChoiceProfit TaxesTax Burden
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Issue 8 , December 2018 , pp. 2007 - 2031
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This paper was presented at the 21st International Conference on Computing in Economics and Finance, June 20–22, 2015, Taipei, Taiwan, at the Workshop on Complexity Economics and Macroeconomic Dynamics, May 12 and 13, 2016, Hamburg, Germany, and at the GeComplexity Conference, May 26 and 27, 2016, Heraklion, Greece. We thank the participants for their encouraging and stimulating comments. The paper also benefitted from valuable feedback from two referees and an associate editor.

References

REFERENCES

Anufriev, Mishael, Assenza, Tiziana, Hommes, Cars, and Massaro, Domenico (2013) Interest rate rules and macroeconomic stability under heterogeneous expectations. Macroeconomic Dynamics 17, 1574–1604.Google Scholar
Barnett, William, Serletis, Apostolos, and Serletis, Demitre (2015) Nonlinear and complex dynamics in economics. Macroeconomic Dynamics 19, 1749–1779.Google Scholar
Brock, William and Hommes, Cars (1997) A rational route to randomness. Econometrica 65, 1059–1095.Google Scholar
Brock, William and Hommes, Cars (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics Control 22, 1235–1274.Google Scholar
Chiarella, Carl, Dieci, Roberto, and He, Xue-Zhong (2009) Heterogeneity, market mechanisms, and asset price dynamics. In Hens, Thorsten and Schenk-Hoppé, Klaus Reiner (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 277–344. Amsterdam: North-Holland.Google Scholar
Dräger, Lena (2016) Recursive inattentiveness with heterogeneous expectations. Macroeconomic Dynamics 20, 1073–1100.Google Scholar
Droste, Edward, Hommes, Cars, and Tuinstra, Jan (2002) Endogenous fluctuations under evolutionary pressure in Cournot competition. Games and Economic Behavior 40, 232–269.Google Scholar
Gandolfo, Giancarlo (2009) Economic Dynamics. Heidelberg, Germany: Springer.Google Scholar
Goeree, Jacob and Hommes, Cars (2000) Heterogeneous beliefs and the non-linear cobweb model. Journal of Economic Dynamics and Control 24, 761–798.Google Scholar
Hommes, Cars (2006) Heterogeneous agent models in economics and finance. In Tesfatsion, Leigh and Judd, Kenneth (eds.), Handbook of Computational Economics, Agent-Based Computational Economics, vol. 2, pp. 1109–1186. Amsterdam: North-Holland.Google Scholar
Hommes, Cars (2013) Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems. Cambridge, UK: Cambridge University Press.Google Scholar
Hommes, Cars and Lux, Thomas (2013) Individual expectations and aggregate behavior in learning-to-forecast experiments. Macroeconomic Dynamics 17, 373–401.Google Scholar
Hommes, Cars and Wagener, Florian (2009) Complex evolutionary systems in behavioral finance. In Hens, Thorsten and Schenk-Hoppé, Klaus Reiner (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 217–276. Amsterdam: North-Holland.Google Scholar
Hommes, Cars and Zeppini, Paolo (2014) Innovate or imitate? Behavioral technological change. Journal of Economic Dynamics and Control 48, 308–324.Google Scholar
Kirman, Alan (2016) Ants and nonoptimal self-organization: Lessons for macroeconomics. Macro-economic Dynamics 20, 601–621.Google Scholar
Lasselle, Laurence, Svizzero, Serge, and Tisdell, Clem (2005) Stability and cycles in a cobweb model with heterogeneous expectations. Macroeconomic Dynamics 9, 630–650.Google Scholar
Manski, Charles and McFadden, Daniel (1981) Structural Analysis of Discrete Data with Econometric Applications. Cambridge, MA: MIT Press.Google Scholar
Medio, Alfredo and Lines, Marji (2001) Nonlinear Dynamics: A Primer. Cambridge, UK: Cambridge University Press.Google Scholar
Schmitt, Noemi and Westerhoff, Frank (2015) Managing rational routes to randomness. Journal of Economic Behavior and Organization 116, 157–173.Google Scholar
Tuinstra, Jan, Wegener, Michael, and Westerhoff, Frank (2014) Positive welfare effects of trade barriers in a dynamic partial equilibrium model. Journal of Economic Dynamics and Control 48, 246–264.Google Scholar
Westerhoff, Frank and Franke, Reiner (2015) Agent-based models for economic policy design: Two illustrative examples. In Chen, Shu-Heng, Kaboudan, Mak, and Du, Ye-Rong (eds.), OUP Handbook on Computational Economics and Finance, Oxford, UK: Oxford University Press.Google Scholar