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Implications of zero-growth economics analysed with an agent-based model

Dylan C. Terry-Doyle, Adam B. Barrett

Abstract

The ever-approaching limits of the Earth's biosphere and the potentially catastrophic consequences caused by climate change have begun to call into question the endless growth of the economy. There is increasing interest in the prospects of zero economic growth from the degrowth and post-growth literature. In particular, the question arises as to whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable. There have been several answers to this question using macroeconomic models; some find a zero-growth trajectory is stable, while other models show an economic breakdown. However, the capitalist system in a period of growth is not guaranteed to be stable. Hence, a more appropriate methodology is to compare the relative stability between a growth and zero-growth scenario on the same model. Such a question has not yet been answered at any disaggregated level. It's important to investigate the consequences of zero-growth on market share instability and concentration, bankruptcy rates, income distribution, and credit network risk. To answer such questions, we develop a macroeconomic agent-based model incorporating Minskyan financial dynamics. The growth and zero-growth scenarios are accomplished by changing an average productivity growth parameter for the firms in the model. The model results showed that real GDP growth rates were more stable in the zero-growth scenario, there were fewer economic crises, lower unemployment rates, a higher wage share of output for workers, and capital firm and bank market shares were relatively more stable. Some of the consequences of zero-growth were a higher rate of inflation than in the growth scenario, increased market concentration for both firms and banks, and a higher level of financial risk in the credit network.

Implications of zero-growth economics analysed with an agent-based model

Abstract

The ever-approaching limits of the Earth's biosphere and the potentially catastrophic consequences caused by climate change have begun to call into question the endless growth of the economy. There is increasing interest in the prospects of zero economic growth from the degrowth and post-growth literature. In particular, the question arises as to whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable. There have been several answers to this question using macroeconomic models; some find a zero-growth trajectory is stable, while other models show an economic breakdown. However, the capitalist system in a period of growth is not guaranteed to be stable. Hence, a more appropriate methodology is to compare the relative stability between a growth and zero-growth scenario on the same model. Such a question has not yet been answered at any disaggregated level. It's important to investigate the consequences of zero-growth on market share instability and concentration, bankruptcy rates, income distribution, and credit network risk. To answer such questions, we develop a macroeconomic agent-based model incorporating Minskyan financial dynamics. The growth and zero-growth scenarios are accomplished by changing an average productivity growth parameter for the firms in the model. The model results showed that real GDP growth rates were more stable in the zero-growth scenario, there were fewer economic crises, lower unemployment rates, a higher wage share of output for workers, and capital firm and bank market shares were relatively more stable. Some of the consequences of zero-growth were a higher rate of inflation than in the growth scenario, increased market concentration for both firms and banks, and a higher level of financial risk in the credit network.

Paper Structure

This paper contains 41 sections, 90 equations, 13 figures, 7 tables.

Figures (13)

  • Figure 1: Model ontology, the flow diagram of payments between the different agents in the model. Arrows point from paying agents to receiving agents.
  • Figure 2: Stylised facts of time-series for a typical run ($s=5$). The shaded area highlights an economic recession (two or more quarters of negative real GDP growth). Panel (\ref{['subfig:output']}) shows real GDP (solid), consumption (dashed), and investment (dotted). Panel (\ref{['subfig:output_shares']}) shows the debt to GDP ratio (solid), wage share of GDP (dashed), and profit share of GDP (dotted). Panel (\ref{['subfig:rgdp_growth']}) shows the growth rate of real GDP. Panel (\ref{['subfig:inflation']}) shows the CPI inflation rate. Panel (\ref{['subfig:unemployment']}) shows the unemployment rate. Finally, Panel (\ref{['subfig:credit']}) shows the credit rate, defined as the annual percentage change of debt.
  • Figure 3: Stylised facts of the relationship between key economic variable for a typical baseline run ($s=5$). Panel (\ref{['subfig:phillips_curve']}) shows the price-related Phillips curve. Panel (\ref{['subfig:phillips_curve_wage']}) shows the wage-related Phillips curve. Panel (\ref{['subfig:keen_curve']}) shows the credit-related Phillips curve. Finally, panel (\ref{['subfig:okun_curve']}) shows the Okun curve.
  • Figure 4: Autocorrelation of simulated and empirical time-series. The dashed line is the empirical autocorrelation, the solid line is the median autocorrelation across all simulations, and the shaded area is the 95% confidence interval (CI) across all simulations.
  • Figure 5: Cross correlation of simulated and empirical time-series. Where the dashed line is the empirical cross-correlation, the solid line is the median cross-correlation across all simulation, and the shaded area is the 95% confidence interval (CI) across all simulations.
  • ...and 8 more figures