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Growth theory under heterogeneous heuristic behavior

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Abstract

Over the last decades, conceptual frameworks formulated to address the dynamics of economic growth have hypothesized, discussed and tested a large number of different assumptions concerning the role of capital accumulation, labor productivity, learning-by-doing, formal education, innovation, and diffusion of ideas. Underlying all these theoretical contributions is, on the demand side, a pervasive and apparently unshakable structure of analysis: economic agents invariably set an optimal intertemporal consumption plan which allows then to maximize utility over an infinite horizon. Such behavior, however, suggests a planning ability that agents often lack. In fact, household decisions are frequently designed on the basis of heuristics or rules-of-thumb that, although not optimal, are reachable under the cognitive constraints typically faced by human beings. This paper revisits some of the most prominent models of the mainstream growth theory, taking a specific heuristic to account for consumption-savings decisions. The heuristic, which allows for the consideration of distinct profiles of saving behavior across individual agents, is a static rule, which might suggest a return to a Solow-like growth analysis. Notwithstanding, the adopted rule-of-thumb encloses a series of novel and relevant implications for growth theory. Such implications are duly highlighted and discussed in this study.

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Notes

  1. See Sequeira et al. (2018) for an effort to integrate and explore the role of complexity in standard dynamic growth analysis, namely, in the context of an endogenous growth model. Studies on agent-based evolutionary growth e.g., Dosi et al. (2010, 2013), Guerini et al. (2018), to which we will come back later, also interpret the economy as a complex evolving system.

  2. See Gerhard et al. (2018) for an empirical characterization of the psychological drivers of savings. In particular, these authors identify the following relevant psychological characteristics as determinants of savings behavior: personality traits (agreableness, conscientiousness, extraversion, neuroticism, openess to experience), self-control, optimism, attitude towards savings, and regulatory focus. These features vary across individual agents (for genetic reasons and due to socialization), thus generating distinct savings profiles.

  3. The dynamics of the distribution of the worldviews might be interpreted as an evolutionary process that can be modeled, e.g., under a discrete choice switching mechanism as the one proposed by Brock and Hommes (1997). The growth models to explore in the following sections focus on the impact of heterogeneous savings behavior over growth dynamics, but they overlook the analysis of the dynamics of the distribution of worldviews. Although this is a relevant topic of research, it is left for future work.

  4. See Appendix D for the derivation of this equation.

  5. The role of automation and robotics as an engine of long-term growth has been explored in recent literature. See, e.g., Benzell et al. (2015), Sachs et al. (2015), Prettner (2016), Gasteiger and Prettner (2017), and Gomes (2019).

  6. Note, in this case, that saddle-path stability guarantees convergence to the balanced growth path. This occurs because the representative agent chooses u(t) optimally and, thus, he is capable of triggering an initial jump that places this control variable over the stable trajectory, which is then followed until the steady-state locus is reached.

References

  • Acemoglu D, Cao D (2015) Innovation by Entrants and Incumbents. Journal of Economic Theory 157:255–294

    Google Scholar 

  • Acemoglu D, Akcigit U, Alp H, Bloom N, Kerr W (2018) Innovation, reallocation, and growth. American Economic Review 108:3450–3491

    Google Scholar 

  • Aghion P, Howitt P (1992) A Model of Growth through Creative Destruction. Econometrica 60:323–351

    Google Scholar 

  • Aghion P, Akcigit U, Howitt P (2015) Lessons from Schumpeterian Growth Theory. American Economic Review 105:94–99

    Google Scholar 

  • Aghion P, Roulet A (2014) Growth and the Smart State. Annual Review of Economics 6:913–926

    Google Scholar 

  • Akcigit U (2017) Economic Growth:, the Past, the Present, and the Future. Journal of Political Economy 125:1736–1747

    Google Scholar 

  • Akcigit U, Kerr WR (2018) Growth through Heterogeneous Innovations. Journal of Political Economy 126:1374–1443

    Google Scholar 

  • Akcigit U, Celik MA, Greenwood J (2016) Buy, Keep, or Sell:, Economic Growth and the Market for Ideas. Econometrica 84:943–984

    Google Scholar 

  • Benhabib J, Perla J, Tonetti C (2014) Catch-up and Fall-Back through Innovation and Imitation. Journal of Economic Growth 19:1–35

    Google Scholar 

  • Benzell SG, Kotlikoff LJ, LaGarda G, Sachs JD (2015) Robots Are Us: Some Economics of Human Replacement NBER working paper n o 20941

  • Brock WA, Hommes CH (1997) A Rational Route to Randomness. Econometrica 65:1059–1095

    Google Scholar 

  • Buera FJ, Lucas RE (2018) Idea Flows and Economic Growth. Annual Review of Economics 10:315–345

    Google Scholar 

  • Cadena BC, Keys BJ (2015) Human Capital and the Lifetime Costs of Impatience, American Economic Journal: Economic Policy, vol. 7, pp. 126–153

  • Campbell JY, Mankiw NG (1990) Permanent income, current income, and consumption. Journal of Business & Economic Statistics 8:265–279

    Google Scholar 

  • Cass D (1965) Optimum Growth in an Aggregative Model of Capital Accumulation. Review of Economic Studies 32:233–240

    Google Scholar 

  • Ciarli T, Lorenz A, Valente M, Savona M (2019) Structural Changes and Growth Regimes. Journal of Evolutionary Economics 29:119–176

    Google Scholar 

  • Dawid H, Harting P, Neugart M (2014) Economic Convergence: Policy Implications from a Heterogeneous Agent Model. Journal of Economic Dynamics and Control 44:54–80

    Google Scholar 

  • Dawid H, Harting P, Neugart M (2018) Cohesion Policy and Inequality Dynamics: Insights from a Heterogeneous Agents Macroeconomic Model. Journal of Economic Behavior & Organization 150:220–255

    Google Scholar 

  • Dawid H, Harting P, Hoog S, Neugart M (2019) Macroeconomics with heterogeneous agent models: fostering transparency, reproducibility and replication. Journal of Evolutionary Economics 29:467–538

    Google Scholar 

  • De Grauwe P (2011) Animal Spirits and Monetary Policy. Economic Theory 47:423–457

    Google Scholar 

  • Deaton A (1992) Household Saving in LDCs: Credit Markets, Insurance and Welfare. Scandinavian Journal of Economics 94:253–273

    Google Scholar 

  • Dohmen T., Enke B, Falk A, Huffman D, Sunde U (2016) Patience and the Wealth of Nations Working Paper 2016-012, Human Capital and Economic Opportunity Working Group

  • Dosi G, Fagiolo G, Roventini A (2010) Schumpeter Meeting Keynes: a Policy-friendly Model of Endogenous Growth and Business Cycles. Journal of Economic Dynamics and Control 34:1748–1767

    Google Scholar 

  • Dosi G, Fagiolo G, Napoletano M, Roventini A (2013) Income Distribution, Credit and Fiscal Policies in an Agent-based Keynesian Model. Journal of Economic Dynamics and Control 37:1598–1625

    Google Scholar 

  • Dosi G, Fagiolo G, Napoletano M, Roventini A, Treibich T (2015) Fiscal and Monetary Policies in Complex Evolving Economies. Journal of Economic Dynamics and Control 52:166–189

    Google Scholar 

  • Dosi G, Napoletano M, Roventini A, Stiglitz JE, Treibich T (2017) Rational Heuristics? Expectations and Behaviors in Evolving Economies with Heterogeneous Interacting Agents. LEM papers series 2017/31 Sant’Anna School of advanced Studies. Pisa, Italy

    Google Scholar 

  • Dosi G, Roventini A (2019) More is Different ... Complex! The Case for Agent-based Macroeconomics. Journal of Evolutionary Economics 29:1–37

    Google Scholar 

  • Dosi G, Roventini A, Russo E (2019) Endogenous Growth and Global Divergence in a Multi-country Agent-based Model. Journal of Economic Dynamics and Control 101:101–129

    Google Scholar 

  • Ellison G, Fudenberg D (1993) Rules of Thumb for Social Learning. Journal of Political Economy 101:612–643

    Google Scholar 

  • Gabaix X (2014) A Sparsity-based Model of Bounded Rationality. Quarterly Journal of Economics 129:1661–1710..

    Google Scholar 

  • Gasteiger E, Prettner K (2017) A note on automation, stagnation, and the implications of a robot tax freie universitat Berlin, discussion paper 2017/17

  • Gerhard P, Gladstone JJ, Hoffmann AOI (2018) Psychological Characteristics and Household Savings Behavior:, the Importance of Accounting for Latent Heterogeneity. Journal of Economic Behavior & Organization, 148:66–82

    Google Scholar 

  • Gigerenzer G, Todd PM (1999) Simple Heuristics that Make Us Smart. Oxford University Press, New York

    Google Scholar 

  • Gigerenzer G, Selten R (2002) Bounded Rationality: the Adaptive Toolbox Cambridge, MA: MIT Press

  • Gigerenzer G (2008) Rationality for Mortals: How People Cope with Uncertainty New York: Oxford University Press

  • Gigerenzer G, Brighton H (2009) Homo Heuristicus:, Why Biased Minds Make Better Inferences. Topics in Cognitive Science 1:107–143

    Google Scholar 

  • Gigerenzer G, Gaissmaier W (2011) Heuristic Decision Making. Annual Review of Psychology 62:451–482

    Google Scholar 

  • Gomes O (2019) Growth in the Age of Automation:, Foundations of a Theoretical Framework. Metroeconomica 70:77–97

    Google Scholar 

  • Grossman GM, Helpman E (1991) Innovation and Growth in the Global Economy Cambridge Mass. MIT Press

  • Grossman GM, Helpman E (2015) Globalization and Growth. American Economic Review 105:100–104

    Google Scholar 

  • Grossman GM, Helpman E (2018) Growth, trade, and inequality. Econometrica 86:37–83

    Google Scholar 

  • Guerini M, Napoletano M, Roventini A (2018) No Man is an Island:, the Impact of Heterogeneity and Local Interactions on Macroeconomic Dynamics. Economic Modelling 68:82–95

    Google Scholar 

  • Haldane AG, Turrell AE (2019) Drawing on Different Disciplines:, Macroeconomic Agent-based Models. Journal of Evolutionary Economics 29:39–66

    Google Scholar 

  • Jaimovich N, Rebelo S (2017) Nonlinear Effects of Taxation on Growth. Journal of Political Economy 125:265–291

    Google Scholar 

  • Jones CI (1995) R&D-Based Models of Economic Growth. Journal of Political Economy 103:759–784

    Google Scholar 

  • Keynes JM (1936) The General Theory of Employment, Interest, and Money 2018 edition Cham, Switzerland: Palgrave Macmillan

  • Kim YJ, Song J (2014) Romer Meets Heterogeneous Workers in an Endogenous Growth Model. Hitotsubashi Journal of Economics 55:121–146

    Google Scholar 

  • Koopmans TC (1965) On the Concept of Optimal Economic Growth in The Econometric Approach to Development Planning Amsterdam: North Holland

  • Krusell P, Smith AA (1996) Rules of thumb in macroeconomic equilibrium. a quantitative analysis. Journal of Economic Dynamics and Control 20:527–558

    Google Scholar 

  • Lamperti F, Dosi G, Napoletano M, Roventini A, Sapio A (2018) Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model. Ecological Economics 150:315–339

    Google Scholar 

  • Lettau M, Uhlig H (1999) Rules of Thumb versus Dynamic Programming. American Economic Review 89:148–174

    Google Scholar 

  • Lucas RE (1988) On the Mechanics of Economic Development. Journal of Monetary Economics 22:3–42

    Google Scholar 

  • Lucas RE (2009) Ideas and Growth. Economica 76:1–19

    Google Scholar 

  • Lucas RE (2015) Human Capital and Growth. American Economic Review 105:85–88

    Google Scholar 

  • Lucas RE, Moll B (2014) Knowledge Growth and the Allocation of Time. Journal of Political Economy 122:1–51

    Google Scholar 

  • Martin A, Ventura J (2012) Economic Growth with Bubbles. American Economic Review 102:3033–3058

    Google Scholar 

  • Phelps ES (1966) Golden Rules of Economic Growth New York: Norton

  • Prettner K (2016) The Implications of Automation for Economic Growth and the Labor Share of Income Vienna University of Technology working papers in Economic Theory and Policy, n o 04/2016

  • Rebelo S (1991) Long-Run Policy Analysis and Long-Run Growth. Journal of Political Economy 99:500–521

    Google Scholar 

  • Romer PM (1987) Growth Based on Increasing Returns Due to Specialization. American Economic Review 77:56–62

    Google Scholar 

  • Romer PM (1990) Endogenous Technological Change. Journal of Political Economy 98:S71–S102

    Google Scholar 

  • Sachs JD, Benzell S, LaGarda G (2015) Robots: Curse or Blessing? A Basic Framework NBER working pape, n o21091

  • Sequeira TN, Gil PM, Afonso O (2018) Endogenous Growth and Entropy. Journal of Economic Behavior and Organization 154:100–120

    Google Scholar 

  • Simon HA (1955) A Behavioral Model of Rational Choice. Quarterly Journal of Economics 69:99–118

    Google Scholar 

  • Solow RM (1956) A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics 70:65–94

    Google Scholar 

  • Stokey NL (2015) Catching Up and Falling Behind. Journal of Economic Growth 20:1–36

    Google Scholar 

  • Swan TW (1956) Economic Growth and Capital Accumulation. Economic Record 32:334–361

    Google Scholar 

  • Tversky A, Kahneman D (1974) Judgement Under Uncertainty:, Heuristics and Biases. Science 185:1124–1131

    Google Scholar 

  • Uzawa H (1965) Optimum Technical Change in an Aggregative Model of Economic Growth. International Economic Review 6:18–31

    Google Scholar 

  • Weber CE (2000) Rule-of-thumb Consumption, Intertemporal Substitution, and Risk Aversion. Journal of Business & Economic Statistics 18:497–502

    Google Scholar 

  • Weber CE (2002) Intertemporal Non-separability and Rule of Thumb Consumption. J Monet Econ 49:293–308

    Google Scholar 

  • Winter JK, Schlafmann K, Rodepeter R (2012) Rules of Thumb in Life-cycle Saving Decisions. Economic Journal 122:479–501

    Google Scholar 

  • Zeira J, Zoabi H (2015) Economic Growth and Sector Dynamics. European Economic Review 79:1–15

    Google Scholar 

Download references

Acknowledgments

Financial support from CEFAGE research center, under FCT (Portuguese Foundation for Science and Technology) strategic program UID/ECO/04007/2019, and from MacroViews project (Lisbon Polytechnic Institute), is gratefully acknowledged. I thank participants in the CEFAGE seminar series, in which a first version of the paper was presented. I also thank the insightful comments of two anonymous referees, which led to a substantial revision of the paper, and the thorough English editing that the journal’s editorial team has provided. The usual disclaimer applies.

Funding

Funding: This study was funded by CEFAGE research center, under FCT (Portuguese Foundation for Science and Technology) strategic program UID/ECO/04007/2019, and by MacroViews project (Lisbon Polytechnic Institute)

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Appendices

Appendix A: Proof of proposition 1

For x ≥ 0, the process of capital accumulation may initiate under one of two dynamic rules, depending on whether \(x<\ln \left [ 1+\frac {K(0)}{Y(0)} \right ] \) or \(x\geq \ln \left [ 1+\frac {K(0)}{Y(0)}\right ] \). In the first of these two cases, the agent adopts the middle capital accumulation rule in heuristic (3); otherwise, the first rule, such that capital accumulation grows at the constant negative rate 1 + δ, is followed. Observe, as well, that, in both cases, households will not invest and the level of capital will progressively fall to zero. Given the properties of the neoclassical production function, the value of the ratio \(\frac {K(t)}{ Y(t)}\) will also decline over time and, as a result, someone starting at the second capital accumulation dynamic rule will necessarily pass onto the first one at a given point in time (a point that is as much further away in time as the smaller is the value of x). Therefore, in the long-term, as long as x ≥ 0, capital depletion will invariably occur at rate 1 + δ, which obviously implies zero steady-state levels of income and consumption, given the production technology and the consumption heuristic.

For x < 0, we are faced with a Solow-like capital accumulation constraint, with the constant savings rate given by \((1-\zeta )\left (1-e^{x}\right ) \). In this case, it is trivial to determine a unique zero-growth steady-state point by solving \(\overset {\text {{\Large .}}}{K}(t)=0\). The solution is

$$ K^{\ast }:\frac{Y^{\ast }}{K^{\ast }}=\frac{\delta }{(1-\zeta )\left( 1-e^{x}\right) } $$
(62)

The steady-state output-capital ratio, \(\frac {Y^{\ast }}{K^{\ast }}\), is, under the neoclassical specification, a continuous, twice-differentiable, decreasing function, which diverges to infinity for K = 0 and converges to zero whenever \(K^{\ast }\rightarrow \infty \). Therefore, it crosses the constant value in the right hand side of (62) once and only once. If K is unique, positive and constant, then Y and C are also unique positive and constant values, given the production function and the consumption rule.

Appendix : B: Proof of proposition 2

Start by observing that income in a given sentiment class is presentable as in equation (5). The main point to highlight concerning this equation is that all agents, regardless of their sentiment, will receive the same wage, w(t), and the same return rate from capital, r(t), because they are identical to one another except, eventually, for the value of x. This argument is precisely what makes the analysis under heterogeneous sentiments relevant: everyone will always get an income regardless of the trajectory followed by the capital variable, as long as there is at least one ant among grasshoppers. Agents who suffer a complete depletion of their capital stock will continue to be able to consume, because they continue to receive a wage for the participation in the productive activity.

If x ≥ 0, then \(K(t,x)\rightarrow 0\), and the income of the agents in the specific sentiment class converges to the steady-state outcome

$$ Y^{\ast }(x)=w^{\ast }L(x) $$
(63)

The steady-state wage level is

$$ w^{\ast }=F_{L}\left( \int\limits_{-\infty }^{0}K^{\ast }(x)dx,L\right) $$
(64)

In the Cobb-Douglas specification,

$$ w^{\ast }=(1-\alpha )A\left( \frac{\int\limits_{-\infty }^{0}K^{\ast }(x)dx}{ L}\right)^{\alpha } $$
(65)

Under the heuristic, the consumption equality that subsists in the steady-state will be, as long as x ≥ 0, C(x) = K(x) + Y(x). Because K(x) = 0, the long-term level of consumption of any class of grasshoppers in this economy is identical to the respective income, as is expressed in (63). Grasshoppers will take advantage, in the long-run, of the presence of ants in the economy; they can work for them (ants will be those who hold all capital in the steady-state) and continue to consume their wage income level at each period.

With respect to x < 0, the capital accumulation dynamic equation of households in some sentiment class x is

$$ \overset{\text{{\Large .}}}{K}(t,x)=(1-\zeta )\left( 1-e^{x}\right) \left[ w(t)L(x)+r(t)K(t,x)\right] -\delta K(t,x) $$
(66)

The steady-state value of the capital stock is the value of K(x) such that

$$ \frac{w^{\ast }L(x)+r^{\ast }K^{\ast }(x)}{K^{\ast }(x)}=\frac{\delta }{ (1-\zeta )\left( 1-e^{x}\right) } $$
(67)

with w defined in (64) or (65), and

$$ r^{\ast }=F_{K}\left( \int\limits_{-\infty }^{0}K^{\ast }(x)dx,L\right) =\alpha A\left( \frac{L}{\int\limits_{-\infty }^{0}K^{\ast }(x)dx}\right)^{1-\alpha } $$
(68)

To determine K(x) explicitly, it would be necessary to solve a system of dimension equal to the number of sentiment classes that possibly exists for the ant type of households. Nevertheless, the simple observation of expression (67) indicates that the right-hand side of the equation is a constant value, while the left-hand side is, as is for (62), a continuous, twice-differentiable, decreasing function that falls from infinity to zero as we make K(x) to vary from zero to infinity. Hence, the two sides of (67) cross once and only once at some positive K(x) value, which signifies that we guarantee the existence of a unique steady-state amount of K(x), and, consequently, also unique steady-state levels of income and consumption.

Appendix C: Proof of Proposition 3

The examination of differential equation (27) directly suggests the result in the proposition: the two first rules in the capital accumulation equation yield negative growth and a convergence to a zero capital long-term state. Note that unlike what one has observed in the neoclassical growth model, now the threshold between the two first dynamic rules in the heuristic is a constant value, meaning that there is no transition between rules as the stock of capital falls. This, however does not change the fact that, in both cases, the absence of savings and investment throws the economy to an extinction state. For x < 0, there is a constant growth rate \(\gamma =\frac {\overset {\text {{\Large .}}}{K}(t,x)}{ K(t,x)}\) that is perpetuated over time. For this growth rate to be positive, the sentiment level must be not only negative but sufficiently strong, in absolute value, in order to guarantee γ > 0; this condition is equivalent to the one in the proposition setting an upper bound on the value of x.

Appendix D: Derivation of the differential equation representing the motion of the human capital share in the Uzawa-Lucas model (x < 0

Let the current-value Hamiltonian function for the intertemporal Uzawa-Lucas growth problem be written as follows, with p(t) and q(t) the co-state variables associated, respectively, to physical capital and human capital,

$$ \begin{array}{@{}rcl@{}} \mathbf{H}\left[ K(t),H(t),u(t),p(t),q(t)\right] &=&p(t)\left\{ F\left[ K(t),u(t)H(t)\right] -C(t)-\delta K(t)\right\} \\ &&+q(t)\left( G\left\{ \left[ 1-u(t)\right] H(t)\right\} -\delta H(t)\right) \end{array} $$
(69)

First-order optimality conditions are as follows:

$$ \frac{\partial \mathbf{H}(t)}{\partial u(t)}=0\Rightarrow \frac{F_{u}}{-G_{u} }=\frac{q(t)}{p(t)} $$
(70)
$$ \overset{\text{{\Large .}}}{p}(t)=\rho p(t)-\frac{\partial \mathbf{H}(t)}{ \partial K(t)}\Rightarrow \overset{\text{{\Large .}}}{p}(t)=\left( \rho +\delta -F_{K}\right) p(t) $$
(71)
$$ \overset{\text{{\Large .}}}{q}(t)=-\frac{\partial \mathbf{H}(t)}{\partial H(t)}\Rightarrow \overset{\text{{\Large .}}}{q}(t)=\left( \rho +\delta -G_{H}\right) q(t)-F_{H}p(t) $$
(72)

and the transversality conditions come

$$ \underset{t\rightarrow \infty }{\lim }K(t)e^{-\rho t}p(t)=\underset{ t\rightarrow \infty }{\lim }H(t)e^{-\rho t}q(t)=0 $$
(73)

where ρ > 0 is the intertemporal discount rate. Defining \(Q(t)\equiv \frac {p(t)}{q(t)}\), equations (71) and (72) can be condensed in a unique equation for the dynamics of ratio Q(t),

$$ \frac{\overset{\text{{\Large .}}}{Q}(t)}{Q(t)}=G_{H}-F_{K}+F_{H}Q(t) $$
(74)

Equation (74) might be further simplified by resorting to (70), i.e.,

$$ \frac{\overset{\text{{\Large .}}}{Q}(t)}{Q(t)}=G_{H}-F_{K}-F_{H}\frac{G_{u}}{ F_{u}} $$
(75)

Further insights require specifying functional forms for the production functions. Consider a Cobb-Douglas production function for the goods sector and a linear production function for the human capital sector. The second is already displayed in (34). The first one is

$$ F\left[ K(t),u(t)H(t)\right] =AK(t)^{\alpha }\left[ u(t)H(t)\right]^{1-\alpha }\text{, \ }A>0\text{, }\alpha \in (0,1) $$
(76)

Under (34) and (76), we rewrite equation (75) as

$$ \frac{\overset{\text{{\Large .}}}{Q}(t)}{Q(t)}=B-\alpha A\left[ \frac{ u(t)H(t)}{K(t)}\right]^{1-\alpha } $$
(77)

and optimality condition (70) becomes,

$$ Q(t)=(1-\alpha )\frac{A}{B}\left[ \frac{K(t)}{u(t)H(t)}\right]^{\alpha } $$
(78)

From (78) one draws the following relation between growth rates,

$$ \frac{\overset{\text{{\Large .}}}{u}(t)}{u(t)}=\frac{1}{\alpha }\frac{ \overset{\text{{\Large .}}}{Q}(t)}{Q(t)}+\frac{\overset{\text{{\Large .}}}{K} (t)}{K(t)}-\frac{\overset{\text{{\Large .}}}{H}(t)}{H(t)} $$
(79)

Replacing (77), (32), and (33), into (79 ), an equation of motion for the optimal allocation of human capital across sectors is obtained, which is the equation in rule (35) for x < 0.

Appendix E: Proof of Proposition 4

Under systems (38) or (39), it is straightforward to notice that the ratio Ω(t) declines towards zero with the passage of time. This observation implies that variables income and consumption will both converge to a steady-state of complete exhaustion, as mentioned in the proposition.

Only system (40) involves no trivial dynamics. For this system, we can compute steady-state values and analyze transitional dynamics in the vicinity of the steady-state, given a Cobb-Douglas technology. Straightforward algebra allows for calculating the steady-state value of the human capital share allocated to the goods sector. To derive such result, just consider \(\overset {\text {{\Large .}}}{\Omega }(t)=\overset {\text { {\Large .}}}{u}(t)=0\),

$$ u^{\ast }=1-\frac{(1-\zeta )\left( 1-e^{x}\right) }{\alpha } $$
(80)

Given (80), in order to exist an interior solution it is required that the following condition holds: \((1-\zeta )\left (e^{x}-1\right ) <\alpha \). The physical capital – human capital ratio will be, in the long-term equilibrium,

$$ {\Omega}^{\ast }=\left( \frac{1}{\alpha }\frac{B}{A}\right)^{1/(1-\alpha )}u^{\ast } $$
(81)

Replacing the human capital share (80) into the human capital accumulation constraint, (33), one immediately derives the steady-state growth rate of human capital, which is also the balanced growth rate of physical capital, since Ω is constant. Furthermore, given the shape of the production function and the consumption heuristic, the mentioned growth rate is also the steady-state growth rate of these aggregates.

To prove that dynamic system (40) is saddle-path stable, we linearize it in the vicinity of the steady-state, thus computing the following matricial system:

$$ \left[ \begin{array}{c} \overset{\text{{\Large .}}}{u}(t) \\ \overset{\text{{\Large .}}}{\Omega }(t) \end{array} \right] = B\!\left[ \begin{array}{cc} \alpha u^{\ast }-\frac{\left( 1-\alpha \right)^{2}}{\alpha } & \frac{ 1-\alpha }{\alpha }\left[ 1-\alpha \left( 1-u^{\ast }\right) \right] \frac{ u^{\ast }}{{\Omega}^{\ast }} \\ {\Omega}^{\ast }+\left( 1 - \alpha \right) \left( 1 - u^{\ast }\right) \frac{ {\Omega}^{\ast }}{u^{\ast }} & -\left( 1-\alpha \right) \left( 1-u^{\ast }\right) \end{array} \right] \left[ \begin{array}{c} u(t)-u^{\ast } \\ {\Omega} (t)-{\Omega}^{\ast } \end{array} \right] $$
(82)

The determinant and the trace of the Jacobian matrix in system (82) are, respectively,

$$ Det(J)=-\frac{1-\alpha }{\alpha }B^{2}u^{\ast }\text{; }Tr(J)=B\left( u^{\ast }-\frac{1-\alpha }{\alpha }\right) $$
(83)

what implies that the eigenvalues of J are λ1 = Bu and \( \lambda _{2}=-\frac {1-\alpha }{\alpha }B\). With one negative and one positive eigenvalues, one confirms that the system is saddle-path stable.Footnote 6

Appendix F: Proof of proposition 5

For every x ≥ 0, the share of human capital allocated to the production of physical goods is the constant value \(u(t,x)=1-\frac {\delta }{B}\) and the stock of physical capital declines over time to zero. Hence, consumption of grasshoppers will be, in the steady-state,

$$ C^{\ast }(x)=F_{H}^{\ast }u^{\ast }(x)H^{\ast }(x)=(1-\alpha )A\left( \frac{ {\Omega}^{\ast }}{u^{\ast }}\right)^{\alpha }\left( 1-\frac{\delta }{B} \right) H_{0}(x)\text{, \ }\forall x\geq 0 $$
(84)

where

$$ \frac{{\Omega}^{\ast }}{u^{\ast }}=\frac{\int\limits_{-\infty }^{0}K^{\ast }(x)dx}{(1-\vartheta )L\left( 1-\frac{\delta }{B}\right) H_{0}(x)+\int\limits_{-\infty }^{0}u^{\ast }(x)H^{\ast }(x)dx} $$
(85)

In (85), (1 − 𝜗)L represents the number of grasshoppers. Examining result (84), one realizes that C(x) is positive and constant. As in the neoclassical growth model, grasshoppers take advantage of the fact that there are ants who save and apply their capital in production, so that grasshoppers have the possibility of working and receiving a wage, which they integrally apply, at each period, in consumption.

Let us now explore the steady-state result for the ants’ classes. Under x < 0, the relevant dynamic system for agents in class x is, adapting from system (40),

$$ \left\{ \begin{array}{c} \frac{\overset{\text{{\Large .}}}{u}(t,x)}{u(t,x)}=\frac{1-\alpha }{\alpha } B+Bu(t,x) - [e^{x}+\zeta (1 - e^{x})]\left[ w(t)u(t,x)H(t,x)+r(t)K(t,x)\right] \\ \frac{\overset{\text{{\Large .}}}{\Omega }(t,x)}{\Omega (t,x)}=(1-\zeta )\left( 1-e^{x}\right) \left[ w(t)u(t,x)H(t,x)+r(t)K(t,x)\right] -B\left[ 1-u(t,x)\right] \end{array} \right. $$
(86)

Solving \(\overset {\text {{\Large .}}}{\Omega }(t,x)=\overset {\text {{\Large .}} }{u}(t,x)=0\), one gets expressions for the share of human capital u(x) and for the long-term growth rate γ(x) that mimic (80) and (41), respectively. Growth rate γ(x) is the growth rate of both forms of capital and, given the income expression and the consumption function, also the steady-state growth rate of these two variables (income and consumption).

Appendix G: Proof of proposition 6

The observation of differential equations (55) and (56 ) indicates that, under x ≥ 0, there is no capital accumulation in the steady-state. This is true for both types of capital. Therefore, no income and no consumption will subsist in the steady-state in an economy of grasshoppers that give priority to the short-run.

The third rule in the consumption heuristic introduces interesting dynamics into the growth process and allows us to perceive that, in this case, endogenous growth emerges. Defining ratio \({\Phi } (t)\equiv \frac {K(t)}{R(t)}\), the following equation of motion is derived,

$$ \frac{\overset{\text{{\Large .}}}{\Phi }(t)}{\Phi (t)}=(1-\zeta )\left( 1-e^{x}\right) A\left[ \frac{L}{K(t)}+\frac{1}{\Phi (t)}\right]^{1-\alpha } \left[ \sigma -(1-\sigma ){\Phi} (t)\right] $$
(87)

In this case, for x < 0, the capital accumulation constraint is

$$ \frac{\overset{\text{{\Large .}}}{K}(t)}{K(t)}=\sigma (1-\zeta )\left( 1-e^{x}\right) A\left[ \frac{L}{K(t)}+\frac{1}{\Phi (t)}\right]^{1-\alpha }-\delta $$
(88)

From the two above equations, it is possible to derive the steady-state growth of physical capital. Start by noticing that a constant steady-state ratio Φ corresponds, given (87), to:

$$ {\Phi}^{\ast }=\frac{\sigma }{1-\sigma } $$
(89)

From differential equation (88) one may, then, write the steady-state growth rate of physical capital as

$$ \gamma_{K}=\sigma (1-\zeta )\left( 1-e^{x}\right) A\left( \frac{L}{K^{\ast } }+\frac{1-\sigma }{\sigma }\right)^{1-\alpha }-\delta $$
(90)

Because K grows at a constant rate in the steady-state and L remains constant, ratio \(\frac {L}{K^{\ast }}\) falls to zero, and this observation directs us to the explicit growth rate of physical capital, which is (57). This is also the steady-state growth rate of robots, as one understands by noticing that under x < 0,

$$ \frac{\overset{\text{{\Large .}}}{R}(t)}{R(t)}=(1-\sigma )(1-\zeta )\left( 1-e^{x}\right) A{\Phi} (t)\left[ \frac{L}{K(t)}+\frac{1}{\Phi (t)}\right]^{1-\alpha }-\delta $$
(91)

Replacing Φ(t) in (91) by its steady-state value and remarking once again that the labor-capital ratio falls to zero, the steady-state evaluation of the equation of motion for robotic capital allows us to unveil, once again, growth rate (57). Given that the production technology is Cobb-Douglas, then income also grows, at the balanced growth path, at the same rate, which is also the growth rate of consumption, once we take consumption heuristic (54).

Appendix H: Proof of Proposition 7

From differential equations (49) and (50), it is straightforward to observe that K(t,x) and R(t,x) converge to zero for every non-negative x. Thus, the income and consumption levels of grasshoppers will tend to

$$ Y^{\ast }(x)=C^{\ast }(x)=F_{L}^{\ast }L(x)=(1-\alpha )A\left[ \frac{ \int\limits_{-\infty }^{0}K^{\ast }(x)dx}{L+\int\limits_{-\infty }^{0}R^{\ast }(x)dx}\right]^{\alpha }L(x) $$
(92)

Because L is constant and K and R grow at a same constant rate in the steady-state, for every x < 0, grasshoppers consume a positive constant amount in the steady-state (as long as labor is not entirely replaced by robotic capital). Furthermore, notice that \(\frac {\int \limits _{-\infty }^{0}K^{\ast }(x)dx}{L+\int \limits _{-\infty }^{0}R^{\ast }(x)dx}={\Phi } ^{\ast }\) and, thus, expression (92) might in a simpler way be displayed as: \(Y^{\ast }(x)=C^{\ast }(x)=(1-\alpha )A\left (\frac {\sigma }{ 1-\sigma }\right )^{\alpha }L(x)\).

For the ants’ classes, a positive endogenous growth rate is determinable. The growth rate of physical capital held by agents in class x, ∀x < 0, is

$$ \begin{array}{@{}rcl@{}} \frac{\overset{\text{{\Large .}}}{K}(t,x)}{K(t,x)} &=&\sigma (1-\zeta )\left( 1-e^{x}\right) \frac{Y(t,x)}{K(t,x)}-\delta \Leftrightarrow \\ \frac{\overset{\text{{\Large .}}}{K}(t,x)}{K(t,x)} &=&\sigma (1-\zeta )\left( 1-e^{x}\right) \left[ F_{L}\frac{L(x)+R(t,x)}{K(t,x)}+F_{K}\right] -\delta \Leftrightarrow \\ \frac{\overset{\text{{\Large .}}}{K}(t,x)}{K(t,x)} &=&\sigma (1-\zeta )\left( 1-e^{x}\right) \left\{ F_{L}\left[ \frac{L(x)}{K(t,x)}+\frac{1}{\Phi (t,x)}\right] +F_{K}\right\} -\delta \end{array} $$
(93)

In the steady-state, term \(\frac {L(x)}{K(t,x)}\) converges to zero and \({\Phi }^{\ast }(x)=\frac {\sigma }{1-\sigma }\); the wage rate and the rate of return on capital are, respectively, \(w^{\ast }=F_{L}^{\ast }=(1-\alpha )A\left (\frac {\sigma }{1-\sigma }\right )^{\alpha }\) and \(r^{\ast }=F_{K}^{\ast }=\alpha A\left (\frac {1-\sigma }{\sigma }\right )^{1-\alpha }\). Thus, the evaluation of (93) in the steady-state locus conduct directly to growth rate expression (57).

A similar reasoning can be taken for capital variable R(t,x), leading exactly to the same outcome,

$$ \begin{array}{@{}rcl@{}} \frac{\overset{\text{{\Large .}}}{R}(t,x)}{R(t,x)} &=&(1-\sigma )(1-\zeta )\left( 1-e^{x}\right) \frac{Y(t,x)}{R(t,x)}-\delta \Leftrightarrow \\ \frac{\overset{\text{{\Large .}}}{R}(t,x)}{R(t,x)} &=&(1-\sigma )(1-\zeta )\left( 1-e^{x}\right) \left[ F_{L}\frac{L(x)+R(t,x)}{R(t,x)}+F_{K}\frac{ K(t,x)}{R(t,x)}\right] -\delta \Leftrightarrow \\ \frac{\overset{\text{{\Large .}}}{R}(t,x)}{R(t,x)} &=&(1-\sigma )(1-\zeta )\left( 1-e^{x}\right) \left\{ F_{L}\left[ \frac{L(x)}{R(t,x)}+1\right] +F_{K}{\Phi} (t,x)\right\} -\delta \end{array} $$
(94)

To confirm the identical outcome, observe that \(\underset {t\rightarrow \infty }{lim}\left \{ F_{L}\left [ \frac {L(x)}{R(t,x)}+1\right ] +F_{K}\right \} =F_{L}^{\ast }+F_{K}^{\ast }\frac {\sigma }{1-\sigma }=A\left (\frac {\sigma }{ 1-\sigma }\right )^{\alpha }\). Replacing this steady-state value into (94), we get (57). If both forms of capital follow a same balanced growth path, then it is straightforward to infer, as before, that income and consumption share the same long-term growth rate.

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Gomes, O. Growth theory under heterogeneous heuristic behavior. J Evol Econ 31, 533–571 (2021). https://doi.org/10.1007/s00191-020-00674-8

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