Technological unemployment and income inequality: a stock-flow consistent agent-based approach

Abstract

The paper presents a stock-flow consistent agent-based model with effective demand, endogenous credit creation, and labor-saving technological progress. The aim is to study the joint dynamics of both personal and functional distribution of income as a result of technological unemployment, together with the effect on household debt. Numerical simulations show the potentially destabilizing effect of technological unemployment and reveal that an increase in the profit share of income amplifies the negative effect of income inequality on the business cycle and growth. The sensitivity analysis provides indications on the effectiveness of possible mixes of fiscal and redistributive policies, but also demonstrates that the effectiveness of policy measures is strongly dependent on behavioral and institutional factors.

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Notes

  1. 1.

    Besides allowing for formal Minskyan analyses of debt accumulation and financial fragility (Dos Santos 2005), SFC models have recently been used to study the macroeconomic effects of shareholder value orientation and ‘financialisation’ (Van Treeck 2009), as well as that of household debt accumulation (Kim and Isaac 2010). For an exhaustive survey, see Caverzasi and Godin (2015).

  2. 2.

    See also Riccetti et al. (2013a, b, 2016).

  3. 3.

    We will focus our entire analysis in the region where the economy is not capital-constrained and output is below potential, so that \(\bar {Q}=\gamma K\) and \(Q<\bar {Q}\). Model parameters have been calibrated in the simulations such as to avoid the problem that the capital constraint becomes binding after the burnout phase.

  4. 4.

    See Rowthorn (1982), Dutt (1984) and Taylor (1985) for early models of growth and distribution in this tradition.

  5. 5.

    Since the focus of the paper is systemic financial fragility as the result of households’ leverage, we implicitly assume that the firm sector is fully rationed on the credit market. This assumption does not imply a loss of generality, as a perfectly elastic supply of credit rules out any possible crowding out effect on the credit market.

  6. 6.

    The consumption rule applies to all workers, employed and unempoyed. Therefore it is theoretically possible that an unemployed worker consumes more than an employed one, with a consequent increase in leverage for the former. This simplifying assumption allows for aggregation and closure of the model through the multiplier.

  7. 7.

    The priority given to debt repayment follows from the assumption that money deposits do not hold any interest. Setterfield and Kim (2016) and Lusardi et al. (2011) provide a theoretical explanation for this assumption based on a “pecking order”-type behavior of households.

  8. 8.

    The numerical simulations confirm that, in each period, the accounting consistency is mantained.

  9. 9.

    Due to Eq. 4, the actual inflation is always roughly equal to πe as confirmed by simulations.

  10. 10.

    Given that the main focus of this paper is household leverage in the presence of technological progress, we abstract from unnecessary complications, such as the inclusion of a government budget constraint or the study of the sustainability of public debt. In the simulations, we do not observe in any scenario an explosive dynamic of debt. When its absolute size grows, the system collapses before public debt goes out of control. The introduction of a range in public debt would negatively affect aggregate demand and lead to an earlier collapse, without changing the underlying conclusions and the basic intuitions.

  11. 11.

    Systemic financial fragility can be further analyzed by extending the model to include microfounded firms and banks. However, the introduction of the negative term ϕDt− 1 in Eq. 11 partially addresses this shortcoming. The accumulation of debt progressively reduces consumption and aggregate demand, replicating in substance the effect of a debt default.

  12. 12.

    Equation 31 simply represents the accounting closure of the demand-driven model in order to determine the aggregate level of output as in standard textbook Keynesian models.

  13. 13.

    The generation of macroeconomic patterns more aligned with the empirical evidence would require a specific analysis of real data and the use of more sophisticated calibration techniques which, given the length and the scope of the present paper, we prefer to postpone to future developments of this project.

  14. 14.

    Other agent-based models (for example Caiani et al. 2016; Dosi et al. 2015) use a higher value of 5%. This parameter is not central in our model and its effect is mostly to increase the frequency of fluctuations.

  15. 15.

    This outcome is due to the high heterogeneity of wages and their dynamics. The unemployment benefit is the major factor in reducing the overall income inequality.

  16. 16.

    Also Ciarli et al. (2012) refer to Verdoorn’s law, but only as a positive correlation between output and technical change that they find ex-post in their simulation data, while the determinant of the latter is on the supply side as in the cited Keynes-Schumpeter models and in other Schumpeterian growth models such as Silverberg and Lehnert (1993).

  17. 17.

    For simplicity, we abstract from the cost of innovation because the firm sector is modeled as a single aggregate and the R&D costs for one firm would be the revenue for another in a stock-flow consistent perspective. A more refined modeling of the innovation financing process would imply the modeling of an additional capital or innovation producing sector as in the cited agent-based models or in aggregate models such as Caiani et al. (2014) for example. Since the paper focuses on the effects of technological progress on household income distribution this aspect is left to possible future developments of this research.

  18. 18.

    Such predator-prey dynamics between demand and distribution are studied, for instance, in Skott (1989) and Barbosa-Filho and Taylor (2006).

  19. 19.

    An alternative solution to introduce a more progressive tax system would involve, for example, different rates for brackets of income earners. However, this option would make it impossible to calculate the multiplier and have a neat and simple closure of the model as the one presented in Section 2.5.

  20. 20.

    The toolbox is available at the address http://www.sumowiki.intec.ugent.be/Main_Page.

  21. 21.

    The choice of the sub-period depends on the amplitude and frequency of the fluctuations for each value of the parameter and has the aim to isolate the effect of the parameter’s changes.

References

  1. Atkinson AB, Piketty T, Saez E (2011) Top incomes in the long run of history. J Econ Lit 49(1):3–71. https://doi.org/10.1257/jel.49.1.3. http://www.aeaweb.org/articles?id=10.1257/jel.49.1.3

    Google Scholar 

  2. Barba A, Pivetti M (2009) Rising household debt: its causes and macroeconomic implications—a long-period analysis. Camb J Econ 33(1):113–137

    Google Scholar 

  3. Barbosa-Filho N, Taylor L (2006) Distributive and demand cycles in the us-economy: a structuralist goodwin model. Metroeconomica 57(3):389–411

    Google Scholar 

  4. Caiani A, Godin A, Lucarelli S (2014) Innovation and finance: a stock flow consistent analysis of great surges of development. J Evol Econ 24(2):421–448. https://doi.org/10.1007/s00191-014-0346-8

    Article  Google Scholar 

  5. Caiani A, Godin A, Caverzasi E, Gallegati M, Kinsella S, Stiglitz JE (2016) Agent based-stock flow consistent macroeconomics: towards a benchmark model. J Econ Dyn Control 69:375–408. https://doi.org/10.1016/j.jedc.2016.06.001. http://www.sciencedirect.com/science/article/pii/S0165188915301020

    Google Scholar 

  6. Carvalho L, Di Guilmi C (2014) Income inequality and macroeconomic instability: a stock-flow consistent approach with heterogeneous agents. CAMA Working Papers 2014-60 Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

  7. Carvalho L, Rezai A (2016) Personal income inequality and aggregate demand. Camb J Econ 40(2):491. https://doi.org/10.1093/cje/beu085

    Article  Google Scholar 

  8. Caverzasi E, Godin A (2015) Post-keynesian stock-flow-consistent modelling: a survey. Camb J Econ 39(1):157–187

    Google Scholar 

  9. Chiarella C, Di Guilmi C (2011) The financial instability hypothesis: a stochastic microfoundation framework. J Econ Dyn Control 35(8):1151–1171

    Google Scholar 

  10. Ciarli T, Lorentz A, Savona M, Valente M (2012) The role of technology, organisation, and demand in growth and income distribution. LEM Papers Series 2012/06, Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Pisa, Italy. https://ideas.repec.org/p/ssa/lemwps/2012-06.html

  11. Clementi F, Di Matteo T, Gallegati M (2006) The power-law tail exponent of income distributions. Physica A: Statistical and Theoretical Physics (370)

    Google Scholar 

  12. Cynamon B, Fazzari S (2013) Inequality and household finance during the consumer age. Working Paper 752, Levy Economics Institute

  13. Dafermos Y, Papatheodorou C (2015) Linking functional with personal income distribution: a stock-flow consistent approach. Int Rev Appl Econ 29(6):787–815

    Google Scholar 

  14. Dawid H, Harting P, Neugart M (2014) Economic convergence: policy implications from a heterogeneous agent model. J Econ Dyn Control 44:54–80. https://doi.org/10.1016/j.jedc.2014.04.004. http://www.sciencedirect.com/science/article/pii/S0165188914000852

    Google Scholar 

  15. Di Guilmi C, Carvalho L (2017) The dynamics of leverage in a demand-driven model with heterogeneous firms. J Econ Behav Organs 140:70–90. https://doi.org/10.1016/j.jebo.2017.04.016

    Article  Google Scholar 

  16. Dos Santos CH (2005) A stock-flow consistent general framework for formal minskyan analyses of closed economies. Journal of Post Keynesian Economics 27(4):712–735

    Google Scholar 

  17. Dosi G, Fagiolo G, Roventini A (2008) The microfoundations of business cycles: an evolutionary, multi-agent model. J Evol Econ 18(3):413–432

    Google Scholar 

  18. Dosi G, Fagiolo G, Roventini A (2010) Schumpeter meeting keynes: a policy-friendly model of endogenous growth and business cycles. J Econ Dyn Control 34(9):1748–1767

    Google Scholar 

  19. Dosi G, Fagiolo G, Napoletano M, Roventini A (2012) Income distribution, credit and fiscal policies in an agent-based keynesian model. Documents de Travail de l’OFCE 2012-06, Observatoire Francais des Conjonctures Economiques (OFCE)

  20. Dosi G, Fagiolo G, Napoletano M, Roventini A (2013) Income distribution, credit and fiscal policies in an agent-based Keynesian model. J Econ Dyn Control 37 (8):1598–1625

    Google Scholar 

  21. Dosi G, Fagiolo G, Napoletano M, Roventini A, Treibich T (2015) Fiscal and monetary policies in complex evolving economies. J Econ Dyn Control 52(C):166–189

    Google Scholar 

  22. Dosi G, Napoletano M, Roventini A, Treibich T (2017) Micro and macro policies in the keynes+schumpeter evolutionary models. J Evol Econ 27(1):63–90

    Google Scholar 

  23. Dutt AK (1984) Stagnation, income distribution and monopoly power. Camb J Econ 8(1):25–40

    Google Scholar 

  24. Dutt AK (1990) Growth, distribution and uneven development. Cambridge University Press, Cambridge

    Google Scholar 

  25. Dutt AK (2006) Maturity, stagnation and consumer debt: a steindlian approach. Metroeconomica 57(3):339–364. http://ideas.repec.org/a/bla/metroe/v57y2006i3p339-364.html

    Google Scholar 

  26. Garcia-Peñalosa C, Orgiazzi E (2013) Factor components of inequality: a cross-country study. Rev Income Wealth 59(4):689–727. https://doi.org/10.1111/roiw.12054

    Article  Google Scholar 

  27. Giovannoni O (2010) Functional distribution of income, inequality and the incidence of poverty: stylized facts and the role of macroeconomic policy. UTIP 58

  28. Godin A, Kinsella S (2012) Leverage, liquidity and crisis: a simulation study. ASSRU Discussion Papers 1205, ASSRU - Algorithmic Social Science Research Unit

  29. Godley W, Lavoie M (2007) Monetary economics: an integrated approach to credit, money, income, production and wealth. Palgrave Macmillan, London

    Google Scholar 

  30. Goodwin R (1967) A growth cycle. In: Feinstein C (ed) Socialism, capitalism, and growth. Cambridge University Press

  31. Gorissen D, Couckuyt I, Demeester P, Dhaene T, Crombecq K (2010) A surrogate modeling and adaptive sampling toolbox for computer based design. J Mach Learn Res 11:2051–2055

    Google Scholar 

  32. Hein E, Tarassow A (2010) Distribution, aggregate demand and productivity growth: theory and empirical results for six oecd countries based on a post-kaleckian model. Cambridge Journal of Economics 34(4):727–754. https://doi.org/10.1093/cje/bep066. http://cje.oxfordjournals.org/content/34/4/727.abstract, http://cje.oxfordjournals.org/content/34/4/727.full.pdf+html

    Google Scholar 

  33. Hoover G, Giedeman D, Dibooglu S (2009) Income inequality and the business cycle: a threshold cointegration approach. Econ Syst 33(3):278–292

    Google Scholar 

  34. Kalecki M (1971) Selected essays on the dynamics of the capitalist economy. Cambridge University Press, Cambridge

    Google Scholar 

  35. Kim Y, Isaac A (2010) The macrodyamics of household debt. Working Papers 1010, Trinity College, Department of Economics

  36. Kinsella S, Greiff M, Nell EJ (2011) Income distribution in a stock-flow consistent model with education and technological change. East Econ J 37(1):134–149

    Google Scholar 

  37. Kumhof M, Ranciere R (2010) Inequality leverage and crises. Imf working papers. International Monetary Fund

  38. Lavoie M (2014) Post-Keynesian economics: new foundations. Edward Elgar: Cheltenham

  39. Levy M (2003) Are rich people smarter? J Econ Theory 110(1):42–64

    Google Scholar 

  40. Lusardi A, Schneider D, Tufano P (2011) Financially fragile households: evidence and implications. Brook. Pap. Econ. Act 42(1 (Spring)):83–150

    Google Scholar 

  41. McCombie J, Thirlwall AP (1994) Economic growth and the balance of payments constraint. St Martin’s Press, New York

    Google Scholar 

  42. Nelson RR, Winter SG (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge

    Google Scholar 

  43. Nirei M, Souma W (2007) A two factor model of income distribution dynamics. Rev Income Wealth 53(3):440–459

    Google Scholar 

  44. Palley T (2012) Americas exhausted paradigm: macroeconomic causes of the financial crisis and great recession. In: Cynamon B, Fazzari S, Setterfield M (eds) After the great recession: the struggle for economic recovery and growth. Cambridge University Press, pp 31–60

  45. Piketty T (2014) Capital in the twenty-first century. Harvard University Press, Cambridge

    Google Scholar 

  46. Piketty T, Saez E (2003) Income inequality in the united states, 1913-1998. Q J Econ 118(1):1–39

    Google Scholar 

  47. Riccetti L, Russo A, Gallegati M (2013a) Leveraged network-based financial accelerator. J Econ Dyn Control 37(8):1626–1640

    Google Scholar 

  48. Riccetti L, Russo A, Gallegati M (2013b) Unemployment benefits and financial factors in an agent-based macroeconomic model. Tech. rep

  49. Riccetti L, Russo A, Gallegati M (2016) Financialisation and crisis in an agent based macroeconomic model. Econ Model 52(PA):162–172. https://doi.org/10.1016/j.econmod.2014.11

    Article  Google Scholar 

  50. Rowthorn R (1982) Demand, real wages and economic growth. Studi Economici 18:3–53

    Google Scholar 

  51. Russo A (2014) A stochastic model of wealth accumulation with class division. Metroeconomica 65(1):1–35

    Google Scholar 

  52. Russo A, Riccetti L, Gallegati M (2016) Increasing inequality, consumer credit and financial fragility in an agent based macroeconomic model. J Evol Econ 26(1):25–47. https://doi.org/10.1007/s00191-015-0410-z

    Article  Google Scholar 

  53. Salle I, Yildizoglu M (2014) Efficient sampling and Meta-Modeling for computational economic models. Comput Econ 44(4):507–536. https://doi.org/10.1007/s10614-013-9406-7

    Article  Google Scholar 

  54. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis: the primer. Wiley, Cornwall

    Google Scholar 

  55. Seppecher P, Salle I (2015) Deleveraging crises and deep recessions: a behavioural approach. Appl Econ 47(34-35):3771–3790

    Google Scholar 

  56. Setterfield M (2012) Wages, demand, and u.s. macroeconomic travails: diagnosis and prognosis. In: Cynamon B, Fazzari S, Setterfield M (eds) After the great recession: the struggle for economic recovery and growth. Cambridge University Press, pp 158–84

  57. Setterfield M, Kim Y (2016) Debt servicing, aggregate consumption, and growth. Struct Chang Econ Dyn 36(C):22–33

    Google Scholar 

  58. Shorrocks AF (1982) Inequality decomposition by factor components. Econometrica 50(1):193–211

    Google Scholar 

  59. Silverberg G, Lehnert D (1993) Long waves and evolutionary chaos in a simple schumpeterian model of embodied technical change. Struct Chang Econ Dyn 4(1):9–37

    Google Scholar 

  60. Skott P (1989) Effective demand, class struggle and cyclical growth. Int Econ Rev 30(1):231–247

    Google Scholar 

  61. Stockhammer E (2013) Why have wage shares fallen? an analysis of the determinants of functional income distribution. Palgrave Macmillan, London, pp 40–70. https://doi.org/10.1057/9781137357939_3

    Google Scholar 

  62. Taylor L (1985) A stagnationist model of economic growth. Camb J Econ 9 (4):383–403

    Google Scholar 

  63. Taylor L (2010) Maynard’s revenge: the collapse of free market macroeconomics. Harvard University Press, Cambridge

    Google Scholar 

  64. Tobin J (1969) A general equilibrium approach to monetary theory. J Money, Credit, Bank 1(1):15–29

    Google Scholar 

  65. Van Treeck T (2009) A synthetic, stock-flow consistent macroeconomic model of ‘financialisation’. Camb J Econ 33:467–493

    Google Scholar 

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Acknowledgements

We thank Evgeniya Goryacheva for the excellent research assistance. We are also thankful to Andre Diniz and Andre Cieplinski who have helped us for the simulations of previous versions of this model. This paper presents an extension to the models built in Carvalho and Di Guilmi (2014) and Di Guilmi and Carvalho (2017), which themselves have largely benefited from comments from Daniele Tavani and Peter Skott, as well participants at the Eastern Economic Association, World Keynes Conference, FMM Conference, Crisis2016 and numerous seminar/workshop presentations. We also thank two anonymous reviewers whose comments have led to this improved version of the paper. Financial support by the Institute for New Economic Thinking is gratefully acknowledged.

Funding

This study was funded by the Institute for New Economic Thinking (INET Grant INO13-00007) and by the Business School of the University of Technology Sydney (grant 2210094).

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Carvalho, L., Di Guilmi, C. Technological unemployment and income inequality: a stock-flow consistent agent-based approach. J Evol Econ 30, 39–73 (2020). https://doi.org/10.1007/s00191-019-00628-9

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Keywords

  • Stock-flow agent-based consistent model
  • Income inequality
  • Functional distribution
  • Technological unemployment
  • Social imitation

JEL Classification

  • C63
  • D31
  • E21
  • E25