Agent-based Simulation of Entry and Exit of Production Firms in Supplier-Dominated Industries

Subject Areas : Labor and Demographic Economics

Zahra  Asadollahi Sohi ¹ (Department of economics,, Alzahra University,, Tehran,, Iran.)
Hossein  Raghfar ² (Professor of Economics, Department of Economics, Faculty of Social Science and Economics, Alzahra University, Tehran, Iran)

Accepted : 0621-03-23 Published : 2021-12-12

Keywords: Number of Firms, . Agent-Based Modeling. Industry Entry and Exit. Supplier-Dominated Industries. Simulation Experiments, JEL Classification: C63, D22, L14,

Abstract :

This article explores how different factors influence the number and dynamics of firms in supplier-dominated industries. These industries are typically traditional, small, and depend on external suppliers for innovation. The article uses a hybrid simulation of system dynamics and agent-based modeling to capture the realistic assumptions that firms do not have complete market information and make decisions based on simple heuristics and past and current conditions. The article conducts experiments to examine how initial conditions, machine life, economic parameters, producer optimism, production growth rate, and demand elasticity affect the entry and exit patterns and the number of firms in the industry. The article uses variables such as the time to reach the peak number of firms, the number of firms at the peak, and the number of firms at the end of the simulation period to represent the shape of the industry distribution. The experiments show that the initial number of firms, demand elasticity, machine life, and financial resources have the most significant effects on the distribution shape, while other factors such as economic growth also have some nonlinear effects.

References:

-‌ بهشتی، محمدباقر، صنوبر، ناصر و فرزانه کجاباد، حسن (1388). بررسی عوامل موثر بر ورود و خروج خالص بنگاه‌ها در بخش صنعت ایران. پژوهش‌های اقتصادی ایران، 13(38)، 157-179.

-‌ عازم، افسر، گرایی‌نژاد، غلامرضا، دقیقی اصلی، علیرضا و خسروی نژاد، علی اکبر (1400). بررسی عوامل ساختاری و محیطی بر پویایی‌های ورود و خروج بنگاه‌ها به بازار در صنایع ایران. بررسی‌های مسایل اقتصاد ایران، 8(2)، 225-253.

-‌ Akcigit, U., Baslandze, S., & Lotti, F. (2023). Connecting to power: Political connections, innovation, and firm dynamics. Econometrica, 91(2), 529-564.

-‌ Ayres, J., & Raveendranathan, G. (2023). Firm entry and exit during recessions. Review of Economic Dynamics, 47, 47-66. https://doi.org/10.1016/j.red.2021.12.001

- Sönmez, Alper. (2013). Firm Entry, Survival, and Exit. Academic Journal of Interdisciplinary Studie, 2. https://doi.org/10.5901/ajis.2013.v2n9p160.

-‌ Antony, J. (2014). 7 - Fractional Factorial Designs. In J. Antony (Ed.), Design of Experiments for Engineers and Scientists (Second Edition) (pp. 87-112). Elsevier. https:// doi.org/ 10. 1016/B978-0-08-099417-8.00007-9

-‌ Chang, M. H. (2011). Agent-Based modeling and computational experiments in industrial organization: Growing Firms and industries in silico. Eastern Economic Journal, 37, 28-34.

-‌ Corradini, C., & Vanino, E. (2021). Path dependency, regional variety and the dynamics of new firm creation in rooted and pioneering industries. Journal of Economic Geography, 22(3), 631-651. https://doi.org/10.1093/jeg/lbab021

-‌ Delli Gatti, D., Gallegati, M., Giulioni, G., & Palestrini, A. (2003). Financial fragility, patterns of firms’ entry and exit and aggregate dynamics. Journal of Economic Behavior & Organization, 51(1), 79-97. https://doi.org/10.1016/S0167-2681(02)00138-5

-‌ Farmer, J. D., & Axtell, R. L. (2022). Agent-Based modeling in economics and finance: Past, present, and future [working paper]. INET Oxford Working Papers, 10.

-‌ Kimbrough, S., & Murphy, F. (2009). Learning to collude tacitly on production levels by oligopolistic agents. Computational Economics, 33, 47-78. https://doi.org/10.1007/s10614-008-9150-6

-‌ Martin, R., & Sunley, P. (2006). Path dependence and regional economic evolution. Journal of Economic Geography, 6(4), 395-437. http://www.jstor.org/stable/26160962

-‌ Nelson, R., & Winter, S. (1982). An evolutionary theory of economic change. Cambridge, MA: The Belknap Press of Harvard University Press.

-‌ Pavitt, K. (1984). Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy, 13(6), 343-373. https://doi.org/https://doi.org/10.1016/0048-7333(84)90018-0

-‌ Shimogawa, S., Shinno, M., & Saito, H. (2012). Structure of s-shaped growth in innovation diffusion. Physical Review E, 85. https://doi.org/10.1103/PhysRevE.85.056121

-‌ Steinbacher, M., Raddant, M., Karimi, F., Camacho Cuena, E., Alfarano, S., Iori, G., & Lux, T. (2021). Advances in the agent-based modeling of economic and social behavior. SN Business & Economics, 1(7), 99-118. https://doi.org/10.1007/s43546-021-00103-3

-‌ Terano, T. (2008). Beyond the KISS principle for agent-based social simulation. Journal of Socio-Informatics, 1(1), 175-187. https://doi.org/10.14836/jsi.1.1_175

-‌ Tesfatsion, L. (2006). Chapter 16 agent-based computational economics: A constructive approach to economic theory. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of Computational Economics (2, pp. 831-880). Elsevier. https://doi.org/10.1016/S1574-0021(05)02016-2

-‌ Wu, Y., & Zhang, J. (2001). The effects of inflation on the number of firms and firm size. Journal of Money, Credit and Banking, 33(2), 251-271.

-‌ Zhang, J. (2005). Growing silicon valley on a landscape: An agent-based approach to high-tech industrial clusters. In L. Cantner, E. Dinopoulos, & R. F. Lanzillotti (Eds.), Entrepreneurships, the New Economy and Public Policy (pp. 71-90). Springer.