Abstract
We model a macroeconomy with stock-flow consistent national accounts built from the local interactions of heterogenous agents (households, firms, bankers, and a government) through product, labor, and money markets in discrete time. We use this model to show that, without any restrictions on the type of interactions agents can make, and with asymmetric information on the part of firms and households in this economy, power-law dynamics with respect to firm size and firm age, income distribution, skill set choice, returns to innovation, and earnings can emerge from multiplicative processes originating in the labor market.
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Notes
In particular, we use a stock-flow consistent macro framework in the tradition of Taylor [2004] and Godley and Lavoie [2006], the model generates observed empirical regularities such as persistent, endogenously generated dispersions of income in the tradition of Nell [1998] and Angle [2006], and our relates directly to the literature on computational-agent-based macroeconomics, in the vein of Dosi et al. [2006], Gatti et al. [2008], Lorentz and Savona [2008] and Ciarli et al. [2010]. The paper by Ciarli, Lorentz, Savona, and Valente clearly focuses on intra-sectoral dynamics, so it is not very close to our model technically, but has the same spirit. Similarities exist between our paper and Dosi et al. [2006], as well as Lorentz and Savona [2008], with respect to technology. In all papers, innovation and the diffusion of technology shocks are modeled in a similar way. Wright [2005] has a similar model, with no prices and wages drawn from a given distribution, but no banking sector, no government, no education and innovation. Our work is related to agent-based approaches via the assumption of heterogeneous agents, and through the use of simulation to uncover “emergent” phenomena, such as endogenously generated business cycles. Our simple model replicates many of the findings of these agent-based papers, for example, firm size distribution and wealth distributions.
Where a complex system is defined as the simple macroscopic regularity resulting from the interaction of many agents in decentralized exchange [Foley 2003; Angle 2006].
See Singh and Maddala [1976], Ijiri and Simon [1977], Kleiber and Kotz [2003], Cingano and Schivardi [2004], Sinha [2006] and Coad [2008] for summaries of recent work in these diverse fields.
See Dosi et al. [2006], Gatti et al. [2008], Lorentz and Savona [2008] and Ciarli et al. [2010] for survey of such models and related literatures.
There is a risk that new technologies cannot be utilized by the existing workforce, which require retooling, re-skilling, and perhaps new households.
Clearly, when bupper threshold, and blower threshold are set high enough, the government and banks almost never have to borrow from the central bank.
For this set of runs, we set the number of agents as: 500 households, 150 firms, 75 banks. We posit initial amounts of money as: 1,000 for each household and each firm, 10,000 for each bank, 1,000 for the government. The required reserve ratio is rr=0.1, which implies a monetary base of MB=14,01,000.The tax rate is initially set at 10 percent and interest rate for loans is fixed at 1 percent. We parameterize other variables as follows: d=0.1, η=0.7, τ=0.5, γ=1, α1=0.7, α2=0.2, α3=0.1, σH=0.5, σF=0.5, spCollege=100, spB.A.=300, spM.A.=500, spPh.D.=800, θCollege=2, θB.A.=4, θM.A.=6, θPh.D.=8, ponziF=10,000, ponziH=1,000, gthreshold=2,000, bupper threshold=1,000, blower threshold=500. We performed standard robustness checks on our parameters, they did not change the results significantly.
An animation of the money flows between sectors and a dynamic Social Accounting Matrix (http://www.stephenkinsella.net/research) are available from the authors upon request.
See Gonzalez-Estevez et al. [2008] and Hu et al. [2008] for surveys and references.
We thank an anonymous reviewer for the suggestion to discuss the business cycle facet of our model more completely.
Yakovenko [2009] gives the example of a kettle on a gas stove heating slowly, where the kettle has a well defined, but slowly increasing amount.
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Kinsella, S., Greiff, M. & Nell, E. Income Distribution in a Stock-Flow Consistent Model with Education and Technological Change. Eastern Econ J 37, 134–149 (2011). https://doi.org/10.1057/eej.2010.31
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DOI: https://doi.org/10.1057/eej.2010.31