Skip to main content

An agent-based model for Secular Stagnation in the USA: theory and empirical evidence


This paper deals with the Secular Stagnation in productivity growth that marks the US economy since the end of the Golden Age. I contribute to the understanding of this phenomenon proposing a theoretical and empirical analysis. On the theoretical side, I develop an agent-based, stock-flow consistent model to investigate the deep relationship between functional income distribution and productivity through the channel of innovation. Findings suggest that the continuous shift of income from wages to profits may have resulted in a smaller incentive to invest on R&D, with the corresponding decline in productivity growth that characterizes US Secular Stagnation. In this respect, a social compromise between workers and capitalists in terms of higher wages helps foster innovation and economic growth, but it does not necessarily entail significant improvements in terms of income concentration, because of higher unemployment rates. Additionally, I question the neoclassical belief on the negative interest-elasticity of investments, since decreases in the rate of interest are not associated with increases in capital accumulation, but they decrease income inequality through higher employment rates. On the empirical side, this paper presents a panel cointegration analysis on US manufacturing industries for the period 1958–2011. If, on the one hand, the linkage between R&D and wages is corroborated, on the other hand, I find the lack of any long-run relationship between innovative search and the rate of interest, which does not necessarily conflict with theoretical predictions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Source: see Supplementary Material

Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Data availability

The .R codes and data that support the findings of this study are available from the corresponding author upon request.


  1. A major attempt to provide the topic with a formal treatment is Eggertsson et al. (2019), whose aim consists of contextualizing Summers (2014a, b) in the New-Keynesian framework. However, the overall framework suffers from important limitations: see Pyka and Fagiolo (2005), Fagiolo and Roventini (2016), and Di Bucchianico (2020).

  2. Among the reasons to adopt this framework, I devise macro-to-micro and micro-to-macro channels. On the one hand, crucial macroeconomic phenomena, e.g., the social conflict between workers and capitalists or the final demand from households influence entrepreneurial decisions about innovative search, employment, and competitiveness. On the other hand, the market structure, its evolution, and firms size distribution steer the aggregate dynamics of innovation and productivity which are central to my definition of Secular Stagnation.

  3. For a thorough historical analysis on why we should consider productivity aspects of Secular Stagnation, see Borsato (2021).

  4. See online Supplementary Material for definitions and details.

  5. I recognise that the Golden Age in US was a particular period marked by the necessity of winning the Cold War against the Soviet Union. It would explain why, for instance, space expenditure growth rates surged toward extraordinary values until the end of the Sixties and then fell sharply after the first moon landing. Further evidence and comments in the online Supplementary Material.

  6. Therefore, aggregate labour demand is never above aggregate labour supply, but equal at most. Though this assumption could be thought of as reasonable, recent evidence suggests a shortage of workers in some sub-sectors of the US economy.

  7. Even if production adapts to demand, firms maintain excess capacity, and this excess does not reflect a wrongful process of expectations formation, but rather the rational decision of the firm to be able to accommodate fluctuations in demand (Ciccone 1986). In what follows, lower case letters denote microeconomic variables and parameters while capital letters refer to aggregate variables. Subscripts j and i set the focus on the single firm and the single individual, respectively.

  8. The introduction of the cost component responds to two further reasons. First, it counterbalances the intrinsic instability that an accelerator-like schedule would implicate. Second, there is a broad literature emphasizing that periods of economic stagnation in early-stage capitalism were the result of investments led by profit-seeking behaviour and not (yet) by final demand (Kaldor 1957, 1961). Dutt (2006), Hicks (1963), Marx (1976) and Hein (2012) provide further details.

  9. For simplicity, I posit that firms do not go bankrupt. Moreover, any time they have sufficient means not to incur further debt, i.e., \(\mathrm{d}{ld}_{j,t}<0\), they use those resources to repay (part of) past debt.

  10. In which \({\sigma }_{i,t}^{mh} = \frac{{m}_{i,t}^{h}}{{M}_{h,t}}\), being \({m}_{i,t}^{h}\) and \({M}_{h,t}\) the individual and the aggregate wealth, respectively.

  11. A well-established evolutionary tradition models firms' innovative activity as a two-step stochastic process (Dosi and Nelson 2010; Nelson and Winter 1982). Although I imagine innovation as it took place with the hiring of researchers, I depart from that tradition for two reasons: firstly, I want to keep the model as simple as possible; secondly, I want to respect some empirical regularity in the innovation process in terms of creation and diffusion of novelties (Tarde 2013).

  12. In which \({\sigma }_{j,t}^{ld}=\frac{l{d}_{j,t}}{{L}_{d,t}}\); \({L}_{d,t}\) is the aggregate amount of loans.

  13. The presence of a passive bank is a limitation but not a major problem. Albeit this assumption does not allow to study the relationship households’ and corporates’ debt has with growth, this is not the object of the present paper, and an active banking system would only add further complexity. Additionally, opting for a passive banking sector is not uncommon, even when the interactions between stocks and flows require the crucial presence of a banking system: recent examples from the literature are Brochier and Macedo e Silva (2019), and Missaglia and Botta (2020).

  14. \({Y}_{t}\) is aggregate production.

  15. As clarified by Sawyer and Passarella (2021), regressive expectations provide a more accurate approximation of how economic agents make their decision in the real worlds. Moreover, “Unlike adaptive expectations, regressive expectations are not systematically wrong” (ibid., p. 393). Yet, that of expectations formation is a hot debate in the literature: see Assenza et al. (2014), Coibon and Gorodnichenko (2015), Coibon et al. (2018), Sorić et al. (2020), and Hommes (2021).

  16. Simulations show the long and gradual convergence of capacity utilization toward an average 70 percent. Details available upon request.

  17. The cyclicality of R&D fuels an interesting debate in the literature and the empirical evidence is mixed: details in Aghion et al. (2010, 2012), Chiao (2001), and Rafferty and Funk (2004).

  18. As above, the random pattern of interaction in the market for “capital” goods leads every firm to produce and sell an amount of goods to the other firms equal to the average. This simplification does not contrast with a log-Gaussian distribution, for firms differ in how they perform on the “consumption” good market in terms of sales and employment. Therefore, this market acts as an important source of heterogeneity.

  19. Representativeness concerns to evidence of lumpiness only and does not extend to other features.

  20. We should not forget the Structuralist perspective, which aims at studying the long-run outcome of short-term interactions between growth and distribution (Barbosa-Filho and Taylor 2006; Rezai 2012; Di Matteo and Sordi 2015; Taylor 2021). Within the evolutionary tradition, a variety of research joins the analysis of innovation creation to the surge of growth cycles (Silverberg and Verspagen 1995; Perez 2003; Fatás-Villafranca et al. 2012; Dosi et al. 2015; Spinola 2021).

  21. A lower amount of Monte Carlo runs with respect to the baseline case does not alter the general properties of the model. See Supplementary Material for details.

  22. The fact that in the model there is not such a clear and well-established influence of the interest rate on investments could be also the consequence of a banking system providing all the demanded credit at a given interest rate. Different results might arise with some sort of endogenous movement of the interest rate on loans as outcome of monetary contractions and credit rationing. In this case, an interest rate hike could constrain investment choices and decrease the investment rate. I will focus on it in future research.

  23. The configuration of parameters in the baseline scenarios with its specific wage rate shows that the decreasing and convergent pattern followed by the wage share does not reduce productivity growth. The reason lies in the increased normal profit rate which keeps feeding entrepreneurial willingness to undertake innovative search. Moreover, the increase in the investment expenditure does not lead automatically to a positive spiral in the need of workers. The skewness in firms’ size distribution shows indeed that, even though they attract a higher level of demand, large firms benefit from productivity gains because they are involved in a more intensive innovative search. Smaller firms, in contrast, will experience a decrease in the target capital stock, because of a lower market share: in this case, investment would decline and the need of workers as well.

  24. The presence of Secular Stagnation in productivity, R&D, and wage share is visible at the industrial level too. Details in the online Supplementary Material.

  25. Interestingly, Fleissig and Strauss (1997) applied the LLC test on real wage panel data finding that real wage innovations for the G7 countries, except for the US, are temporary with half-lives generally less than three years.

  26. Two main messages from Table 9: firstly, since the importance given throughout the analysis to the social conflict and to the distribution of the social product between workers and entrepreneurs, these aspects can be embodied by the influence of unit labour costs on innovative search, the former being a proxy for the wage share at microeconomic level; secondly, this robustness exercise helps understand as the relationship between R&D and wages would not qualitatively change if we switched from labour to unit labour costs.

  27. Table 11 does not distinguish between investments in tangible and intangible capital. I could have alternatively amended the table by creating a new variable for physical investments, say IK, and a new one for R&D investments, say IRD. However, this specification is not very necessary. I assume that firms fund their net investments with their accumulated entrepreneurial profits and out of new loans. There is not such a distinction between sources based on the kind of net investments they are going to fund.


  • Aghion P, Angeletos G-M, Banerjee A, Manova K (2010) Volatility and Growth: Credit Constraints and the Composition of Investment. J Monet Econ 57(3):246–265

    Article  Google Scholar 

  • Aghion P, Askenazy P, Berman N, Cette G, Eymard L (2012) Credit Constraints and the Cyclicality of R&D Investment: Evidence from France. J Eur Econ Assoc 10(5):1001–1024

    Article  Google Scholar 

  • Allen RC (2011) Global economic history: a very short introduction. Oxford University Press, Oxford

  • Allen RC (2009) The British industrial revolution in global perspective. Cambridge University Press, Cambridge

  • Assenza T, Bao T, Hommes C, Massaro D (2014) ‘Experiments on Expectations in Macroeconomics and Finance’. In experiments in macroeconomics. Emerald Group Publishing Limited, Bingley

  • Autor D, Dorn D, Katz LF, Patterson C, Van Reenen J (2020) The Fall of the Labor Share and the Rise of Superstar Firms. Q J Econ 135.2:645–709

    Article  Google Scholar 

  • Baltagi BH, Baltagi BH (2008) Econometric Analysis of Panel Data. Springer, New York

  • Barbosa-Filho NH, Taylor L (2006) Distributive and Demand Cycles in the US Economy—a Structuralist Goodwin Model. Metroeconomica 57(3):389–411

    Article  Google Scholar 

  • Bartelsman EJ, Doms M (2000) Understanding Productivity: Lessons from Longitudinal Microdata. J Econ Lit 38(3):569–594

    Article  Google Scholar 

  • Bartelsman EJ, Gray W (1996) The NBER Manufacturing Productivity Database (National Bureau of Economic Research)

  • Borsato, A (2021) Does the Secular Stagnation Hypothesis Match with Data? Evidence from USA (Bureau d’Economie Théorique et Appliquée, UDS, Strasbourg)

  • Botta A, Caverzasi E, Russo A, Gallegati M, Stiglitz JE (2021) Inequality and Finance in a Rent Economy. J Econ Behav Organ 183:998–1029

    Article  Google Scholar 

  • Bottazzi G, Secchi A (2003) Common Properties and Sectoral Specificities in the Dynamics of US Manufacturing Companies. Rev Ind Organ 23(3–4):217–232

    Article  Google Scholar 

  • Bottazzi G, Secchi A (2006) Explaining the Distribution of Firm Growth Rates. Rand J Econ 37(2):235–256

    Article  Google Scholar 

  • Botte F (2019) Endogenous Business Cycles and Harrodian Instability in an Agent-Based Model. J Post Keynesian Econ 42(2):232–254

    Article  Google Scholar 

  • Brochier L, Macedo e Silva AC (2019) A Supermultiplier Stock-Flow Consistent Model: The “Return” of the Paradoxes of Thrift and Costs in the Long Run? Camb J Econ 43(2):413–42

    Article  Google Scholar 

  • Caballero RJ (1999) Aggregate Investment. Handb Macroecon 1:813–862

    Article  Google Scholar 

  • Caballero RJ, Hammour ML (1991) The Cleansing Effect of Recessions (National Bureau of Economic Research)

  • Caiani A, Godin A, Caverzasi E, Gallegati M, Kinsella S, Stiglitz JE (2016a) Agent Based-Stock Flow Consistent Macroeconomics: Towards a Benchmark Model. J Econ Dyn Control 69:375–408

    Article  Google Scholar 

  • Caiani A, Russo A, Gallegati M (2019) Does Inequality Hamper Innovation and Growth? An AB-SFC Analysis. J Evol Econ 29(1):177–228

    Article  Google Scholar 

  • Caiani A, Russo A, Palestrini A, Gallegati M (2016b) ‘Economics with Heterogeneous Interacting Agents’. New Economic Windows, Springer Series, Berlin/Heidelberg

  • Cardaci A, Saraceno F (2019) Between Scylla and Charybdis: Income Distribution, Consumer Credit, and Business Cycles. Econ Inq 57(2):953–971

    Article  Google Scholar 

  • Carnevali E, Godin A, Lucarelli S, Veronese Passarella M (2020) Productivity Growth, Smith Effects and Ricardo Effects in Euro Area’s Manufacturing Industries. Metroeconomica 711:129–55

    Article  Google Scholar 

  • Chiao C (2001) The Relationship between R&D and Physical Investment of Firms in Science-Based Industries. Appl Econ 33(1):23–35

    Article  Google Scholar 

  • Ciarli T, Lorentz A, Savona M, Valente M (2010) The Effect of Consumption and Production Structure on Growth and Distribution. A Micro to Macro Model. Metroeconomica 61(1):180–218

    Article  Google Scholar 

  • Ciarli T, Lorentz A, Valente M, Savona M (2019) Structural Changes and Growth Regimes. J Evol Econ 29(1):119–176

    Article  Google Scholar 

  • Ciccone R (1986) Accumulation and Capacity Utilization: Some Critical Considerations on Joan Robinson’s Theory of Distribution. Polit Econ 2(1):17–36

    Google Scholar 

  • Coibion O, Gorodnichenko Y (2015) Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts. Am Econ Rev 105(8):2644–2678

    Article  Google Scholar 

  • Coibion O, Gorodnichenko Y, Kamdar R (2018) The Formation of Expectations, Inflation, and the Phillips Curve. J Econ Lit 56(4):1447–1491

    Article  Google Scholar 

  • Jong De, Pieter J (2007) The Relationship between Capital Investment and R&D Spending: A Panel Cointegration Analysis. Appl Financ Econ 17(11):871–880

    Article  Google Scholar 

  • Deissenberg C, Van Der Hoog S, Dawid H (2008) EURACE: A Massively Parallel Agent-Based Model of the European Economy. Appl Math Comput 204(2):541–552

    Google Scholar 

  • Bucchianico Di (2020) Stefano, ‘Discussing Secular Stagnation: A Case for Freeing Good Ideas from Theoretical Constraints?’ Struct Chang Econ Dyn 55:288–297

    Article  Google Scholar 

  • Di Matteo M, Sordi S (2015) Goodwin in Siena: Economist, Social Philosopher and Artist. Camb J Econ 39(6):1507–1527

    Article  Google Scholar 

  • Doms M, Dunne T (1998) Capital Adjustment Patterns in Manufacturing Plants. Rev Econ Dyn 1(2):409–429

    Article  Google Scholar 

  • Dopfer K, Foster J, Potts J (2004) Micro-Meso-Macro. J Evol Econ 14(3):263–279

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dosi G, Fagiolo G, Roventini A (2006) An Evolutionary Model of Endogenous Business Cycles. Comput Econ 27(1):3–34

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dosi G, Napoletano M, Roventini A, Treibich T (2016) ‘The Short-and Long-Run Damages of Fiscal Austerity: Keynes beyond Schumpeter’, in Contemporary Issues in Macroeconomics (Springer), pp. 79–100

  • Dosi G, Nelson RR (2010) Technical Change and Industrial Dynamics as Evolutionary Processes. Handbook of the Economics of Innovation 1:51–127

    Article  Google Scholar 

  • Dosi G, Pereira MC, Roventini A, EnricaVirgillito M (2018) Causes and Consequences of Hysteresis: Aggregate Demand, Productivity, and Employment. Ind Corp Chang 27.6:1015–44

    Article  Google Scholar 

  • Dosi G, Sodini M, Virgillito ME (2015) ‘Profit-Driven and Demand-Driven Investment Growth and Fluctuations in Different Accumulation Regimes.’ J Evol Econ 25(4):707–728

    Article  Google Scholar 

  • Dutt AK (2006) Aggregate Demand, Aggregate Supply and Economic Growth. Int Rev Appl Econ 20(3):319–336

    Article  Google Scholar 

  • Eggertsson GB, Mehrotra NR, Robbins JA (2019) A Model of Secular Stagnation: Theory and Quantitative Evaluation. Am Econ J Macroecon 11(1):1–48

    Article  Google Scholar 

  • Eichengreen B (2015) Secular Stagnation: The Long View. Am Econ Rev 105(5):66–70

    Article  Google Scholar 

  • Fagiolo G, Napoletano M, Roventini A (2008) Are Output Growth-Rate Distributions Fat-Tailed? Some Evidence from OECD Countries. J Appl Economet 23(5):639–669

    Article  Google Scholar 

  • Fagiolo G, Roventini A (2016) Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead. Available at SSRN 2763735

  • Falk M (2006) What Drives Business Research and Development (R&D) Intensity across Organisation for Economic Co-Operation and Development (OECD) Countries? Appl Econ 38(5):533–547

    Article  Google Scholar 

  • Farmer JD, Foley D (2009) The Economy Needs Agent-Based Modelling. Nature 460.7256:685–86

    Article  Google Scholar 

  • Fatás-Villafranca F, Jarne G, Sánchez-Chóliz J (2012) Innovation, Cycles and Growth. J Evol Econ 22(2):207–233

    Article  Google Scholar 

  • Fleissig AR, Strauss J (1997) Unit Root Tests on Real Wage Panel Data for the G7. Econ Lett 56(2):149–155

    Article  Google Scholar 

  • Franke R (2019) Heterogeneity in the Harrodian Sentiment Dynamics, Entailing Also Some Scope for Stability. J Evol Econ 30(2):347–374

  • Godley W, Lavoie M (2006) Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth (Springer)

  • Gordon RJ (2015) Secular Stagnation: A Supply-Side View. Am Econ Rev 105(5):54–59

    Article  Google Scholar 

  • Hansen AH (1939) Economic Progress and Declining Population Growth. Am Econ Rev 29(1):1–15

    Google Scholar 

  • Hanusch H, Pyka A (2007) Principles of Neo-Schumpeterian Economics. Camb J Econ 31(2):275–289

    Article  Google Scholar 

  • Harhoff D (2000) ‘Are There Financing Constraints for R&D and Investment in German Manufacturing Firms?’. In the economics and econometrics of innovation. Springer, Berlin/Heidelberg, pp 399–434

  • Hein E (2012) “ Financialization”, Distribution, Capital Accumulation, and Productivity Growth in a Post-Kaleckian Model. J Post Keynesian Econ 34(3):475–496

    Article  Google Scholar 

  • Hein E (2016) Secular Stagnation or Stagnation Policy? A Post-Steindlian View. Eur J Econ and Eco Policies Interv 13(2):160–171

    Google Scholar 

  • Hicks J (1963) The theory of wages. Springer, New York

  • Hommes C (2021) Behavioral and Experimental Macroeconomics and Policy Analysis: A Complex Systems Approach. J Econ Lit 59(1):149–219

    Article  Google Scholar 

  • Kaldor N (1957) A Model of Economic Growth. Econ J 67(268):591–624

    Article  Google Scholar 

  • Kaldor N (1955) Alternative Theories of Distribution. Rev Econ Stud 23(2):83–100

    Article  Google Scholar 

  • Kaldor N (1961) ‘Capital accumulation and economic growth’. In the theory of capital. Springer, New York, pp 177–222

  • Kao C, Chiang M-H (2001) ‘On the Estimation and Inference of a Cointegrated Regression in Panel Data’. In nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, Bingley

  • Kao C, Chiang M-H, Chen B (1999) International R&D Spillovers: An Application of Estimation and Inference in Panel Cointegration. Oxford Bull Econ Stat 61(S1):691–709

    Article  Google Scholar 

  • Lavoie M (2022) Post-keynesian economics: new foundations. Edward Elgar Publishing, Cheltenham

  • Le Bas C, Scellato G (2014) Firm innovation persistence: a fresh look at the frameworks of analysis. Economics of Innovation and New Technology, Taylor & Francis, Oxford

  • Lorentz A, Ciarli T, Savona M, Valente M (2016) The Effect of Demand-Driven Structural Transformations on Growth and Technological Change. J Evol Econ 26(1):219–246

    Article  Google Scholar 

  • Mairesse J, Hall BH, Mulkay B (2001) ‘Firm-Level Investment in France and the United States: An Exploration of What We Have Learned in Twenty Years’ (National bureau of economic research Cambridge, Mass., USA)

  • Marx K (1976) Capital: a critique of political economy

  • Mazzucato M (2011) The Entrepreneurial State. Soundings 49(49):131–142

    Article  Google Scholar 

  • Missaglia M, Botta A (2020) The role of liquidity preference in a framework of endogenous money

  • Napoletano M, Dosi G, Fagiolo G, Roventini A (2012) ‘Wage Formation, Investment Behavior and Growth Regimes: An Agent-Based Analysis’, Revue de l’OFCE, 5 235–61

  • Napoletano M, Roventini A, Sapio S (2006) Are Business Cycles All Alike? A Bandpass Filter Analysis of the Italian and US Cycles. Rivista Italiana Degli Economisti 11(1):87–118

    Google Scholar 

  • Nelson CR, Plosser CR (1982) Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications. J Monet Econ 10(2):139–162

    Article  Google Scholar 

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

  • Onaran Ö, Galanis G (2012) ‘Is Aggregate Demand Wage-Led or Profit-Led’, National and Global Effects. ILO Conditions of Work and Employment Series, 31.3 1–51

  • Palagi E, Napoletano M, Roventini A, Gaffard J-L (2021) ‘An Agent-Based Model of Trickle-up Growth and Income Inequality’. Available at SSRN 3873764

  • Palley TI (2019) The Fallacy of the Natural Rate of Interest and Zero Lower Bound Economics: Why Negative Interest Rates May Not Remedy Keynesian Unemployment. Rev Keynes Econ 7(2):151–170

    Article  Google Scholar 

  • Pedroni P (2004) ‘Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the ppp hypothesis’. Econometric Theory 20(3):597–625

  • Pedroni P (2001) Purchasing Power Parity Tests in Cointegrated Panels. Rev Econ Stat 83(4):727–731

    Article  Google Scholar 

  • Perez C (2003) Technological Revolutions and Financial Capital. Edward Elgar Publishing, Cheltenham

  • Pesaran MH, Shin Y, Smith RP (1999) Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. J Am Stat Assoc 94.446:621–34

    Article  Google Scholar 

  • Phillips PCB, Moon HR (2000) Nonstationary Panel Data Analysis: An Overview of Some Recent Developments. Econ Rev 193:263–86

    Article  Google Scholar 

  • Piketty T (2018) Capital in the Twenty-First Century. Harvard University Press

  • Pyka A, Fagiolo G (2005) The Elgar Companion to Neo-Schumpeterian Economics, Chapter Agent-Based Modelling: A Methodology for Neo-Schumpeterian Economics (Edward Elgar, Cheltenham)

  • Rafferty M, Funk M* (2004) ‘Demand Shocks and Firm-Financed R&D Expenditures’. Appl Econ 36.14: 1529–36

  • Rezai A (2012) Goodwin Cycles, Distributional Conflict and Productivity Growth. Metroeconomica 63(1):29–39

    Article  Google Scholar 

  • Riccetti L, Russo A, Gallegati M (2015) An Agent Based Decentralized Matching Macroeconomic Model. J Econ Interac Coord 10(2):305–332

    Article  Google Scholar 

  • Rosenberg N (1982) Inside the black box: technology and economics. Cambridge University Press, Cambridge

  • Russo E (2021) ‘Harrodian instability in decentralized economies: an agent-based approach’. Economia Politica 38(2): 539–567

  • Sawyer M, Passarella MV (2021) A Comprehensive Comparison of Fiscal and Monetary Policies: A Comparative Dynamics Approach. Struct Chang Econ Dyn 59:384–404

    Article  Google Scholar 

  • Schumpeter JA (1934) ‘The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle (1912/1934)’, Transaction Publishers.–1982.–January, 1: 244

  • Silverberg G, Verspagen B (1995) An Evolutionary Model of Long Term Cyclical Variations of Catching up and Falling Behind. J Evol Econ 5(3):209–227

    Article  Google Scholar 

  • Sorić P, Lolić I, Matošec M (2020) Some Properties of Inflation Expectations in the Euro Area. Metroeconomica 71(1):176–203

    Article  Google Scholar 

  • Spinola D (2021) The La Marca Model Revisited: Structuralist Goodwin Cycles with Evolutionary Supply Side and Balance of Payments Constraints. Metroeconomica 72(1):189–212

    Article  Google Scholar 

  • Stock JH, Watson MW (1999) Business Cycle Fluctuations in US Macroeconomic Time Series. Handb Macroecon 1:3–64

    Article  Google Scholar 

  • Stockhammer E (2017) Wage-Led versus Profit-Led Demand: What Have We Learned? A Kaleckian-Minskyan View. Rev Keynes Econ 5(1):25–42

    Article  Google Scholar 

  • Summers LH (2014a) ‘Reflections on the “New Secular Stagnation Hypothesis”’. Secular Stagnation: Facts, Causes and Cures 1(2014):27–40

  • Summers LH (2014b) US Economic Prospects: Secular Stagnation, Hysteresis, and the Zero Lower Bound. Bus Econ 49(2):65–73

    Article  Google Scholar 

  • Sylos-Labini P (1983) Factors Affecting Changes in Productivity. J Post Keynesian Econ 6(2):161–179

    Article  Google Scholar 

  • Tarde G (2013) The Laws of Imitation. Read Books Ltd, Redditch

  • Taylor L (2021) Reconstructing macroeconomics. In reconstructing macroeconomics. Harvard University Press, Cambridge

  • Tesfatsion L (2006) Agent-Based Computational Economics: A Constructive Approach to Economic Theory. Handbook of Computational Economics 2:831–880

    Article  Google Scholar 

  • Wälde K, Woitek U (2004) R&D Expenditure in G7 Countries and the Implications for Endogenous Fluctuations and Growth. Econ Lett 82(1):91–97

    Article  Google Scholar 

Download references


The author is grateful to Alberto Russo, Alessandro Caiani, André Lorentz, Andrea Roventini, Eugenio Caverzasi, Giacomo Rella, Giovanni Bella, Lorenzo Siddi, Marco Veronese Passarella, Maria Savona, Mauro Caminati, Nicolò Gozzi, Riccardo Pariboni, and Tiziano Razzolini for their suggestions during the development of the work. Moreover, I wish to thank the editor and two anonymous referees for their useful and precise comments. This work was supported by University of Siena, the former institution the author belonged to. All errors remain the author’s.


The author developed the present work when he was a PhD student at the Department of Economics and Statistics at the University of Siena (IT). This work was supported by a three-year PhD scholarship, 2017–2020.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Andrea Borsato.

Ethics declarations

Conflict of interest

The author has no relevant financial or non-financial interest to disclose.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 38 kb)

Supplementary file2 (DOCX 7175 kb)

Appendix - Accounting and balance sheets

Appendix - Accounting and balance sheets

This Appendix provides information on the stock-flow consistent properties of the model. Appendix Table 10 concerns to the balance sheet matrix. Financial variable, such as deposits, M, and loans, L, have specular counterpart, with opposite sign, in the sheet of the bank. By contrast, tangible capital, K, does not match with any other liability, but does with households’ positive net worth, Vh, to balance the model.

The transactions-flow matrix in Appendix Table 11 relates some components of NIPA accounts with changes in financial assets and liabilities. Households apart, both production firms and the bank have current and capital accounts. For what regards to the current account of firms, we have the expenditure side of the aggregate production, Y, composed of aggregate consumption, C, and aggregate investment, I. On the other hand, the national product is divided between the wage bill, WB, the entrepreneurial profits, F, the amortization fund, AF, and the interest payments on previous loans, \({r}_{l}\cdot {L}_{t-1}\). The capital account of production firms shows that aggregate investment expenditure is funded through the amortization fund, part of firms accumulated profits, Fu, and out of new loans, \(\Delta L\). Precisely, AF finances the replacement of worn-out capital while Fu and \(\Delta L\) the net investment.Footnote 27 Households consume and save \(\Delta M\). The sources of their expenditure (and savings) are the wage bill, the bank profits as distributed income, and the interest payments on previously accumulated wealth, \({r}_{h}\cdot {M}_{t-1}\). If the household refers to a capitalist, entrepreneurial profits constitute a further source. Finally, the bank records the interest payments on loans and deposits in the respective current account. On the other hand, its capital account registers additions, or subtractions, to assets and liabilities.

Every SFC model has a redundant equation, a relationship that equals the stock of loans, L, to the stock of wealth, M, and closes the model:


Although the model contains no equilibrium condition which makes M and L equal, they must result identical once the model is solved, in accordance with a Walrasian principle (Godley and Lavoie 2006).

Table 10 Balance sheet matrix
Table 11 Transactions-flow matrix

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Borsato, A. An agent-based model for Secular Stagnation in the USA: theory and empirical evidence. J Evol Econ 32, 1345–1389 (2022).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Secular Stagnation
  • Innovation Dynamics
  • Agent-based SFC Models
  • Panel Cointegration Analysis

JEL Classification

  • C33
  • O30
  • O43
  • O47