The Scientific World Journal

Volume 2014 (2014), Article ID 908629, 8 pages

http://dx.doi.org/10.1155/2014/908629

Research Article

Dynamic Properties of the Solow Model with Bounded Technological Progress and Time-to-Build Technology

Luca Guerrini¹ and Mauro Sodini²

¹Department of Management, Polytechnic University of Marche, 60121 Ancona, Italy

²Department of Economics and Management, University of Pisa, 56124 Pisa, Italy

Received 30 August 2013; Accepted 12 February 2014; Published 19 March 2014

Academic Editors: F. Borondo and A. Favini

Copyright © 2014 Luca Guerrini and Mauro Sodini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We introduce a time-to-build technology in a Solow model with bounded technological progress. Our analysis shows that the system may be asymptotically stable, or it can produce stability switches and Hopf bifurcations when time delay varies. The direction and the stability criteria of the bifurcating periodic solutions are obtained by the normal form theory and the center manifold theorem. Numerical simulations confirms the theoretical results.

1. Introduction

In one of the most influential papers on economic growth, Jones [1] argued that R&D-based models of growth à la Romer [2] are characterized by the counterfactual property that an increase of the level of resources invested on R&D sector implies an increase in the growth rate of economy. However, from empirical studies it appears clear that this result is inconsistent with time series evidence (see [3]) that suggests decreasing returns in the production of new technology, probably due to negative externalities (see [4]) or difficulties in creating new knowledge. Thus, in order to have a more realistic description of technological progress, Jones proposes an equation of accumulation with decreasing returns. The main finding of this approach is that the evolution of long-run economic growth is not endogenous but depends on classical factors usually taken as exogenous such as the rate of labor force. Given these styled facts, we introduce an exogenous growth model à la Solow [5] (thus, we do not model the allocative problem between research and manufacturing) in which technological progress is bounded from above and the rate of growth of the economy (i.e., capital accumulation) on the balanced growth path is equal to the rate of population growth. On the other hand, we introduce a time-to-build technology in which we assume the existence of a production lag that corresponds to the time for new capital to be produced and installed (see [6, 7]). The main objectives of the present paper are (i) to improve the understanding of the dynamic interaction between technological findings and the economic system; (ii) to characterize the possibility of self-sustained oscillations (business cycles phenomena) which are not possible in formulations without time delays (see [8, 9]).

From a mathematical point of view, it is worth mentioning that the characteristic equation associated to the dynamical system involves delay-dependent coefficients. Then, the corresponding dynamics are dramatically different with respect to models with delay independent parameters (e.g., [6, 10–15]) and stability switches as well as Hopf bifurcations may arise when time delay varies. In order to perform such analysis, we will use the procedure described in Beretta and Kuang [16] based on the existence of real zeros of particular functions .

The paper is organized as follows. In Section 2, the model is described. In Section 3, by choosing the delay parameter as a bifurcation parameter and using the procedure introduced by Beretta and Kuang [16], we determine the condition for Hopf bifurcation occurrence for the model. In Section 4, an analysis on Hopf bifurcation including the direction and stability of bifurcation periodic solutions is made. In order to support the theoretical results, simulations are presented in Section 5. Some conclusions are drawn in Section 6.

2. The Mathematical Model

As explained in the introduction we are concerned with the modelling of à Solow model with bounded technological progress, where the Cobb-Douglas technology displays a delay of periods before capital can be used for production. Specifically, technological progress evolves according to the -shaped power law logistic technology [9]

where is the maximum level of technology and are positive constants. The rate of change of the capital stock at moment is a function of the productive capital stock at ; namely, we have , where denotes physical capital, , is capital's share, is the constant saving rate, and represents labor force. As usual, population size and labor force are assumed to be interchangeable. Setting , the evolution of per capita physical capital over time is given by

Population grows at a constant rate . Hence, normalizing the number of people at time zero to one yields . Setting , so that , and by substituting the above equations, we can rearrange terms to have the model described by the following delay differential system with delay-dependent coefficients

Letting , , , and , the equilibria of system (3) are determined. We derive that there exists a unique nontrivial equilibrium , where

Using Taylor expansion on the right-hand side of (3) around , we get the following linearized system:

where

The corresponding characteristic equation is of the following form:

Equation (7) has always the root . Other possible roots are those solutions of

Equation (8) is a transcendental equation which, in general, has an infinite number of complex roots. As usual, one begins by considering the case with no delay. In this case, it is straightforward to see that the characteristic equation (7) has only and as roots. Then for all of the roots of the polynomial (7) have negative real part. Hence, the equilibrium point is locally asymptotically stable in the absence of the delay. As varies, these roots change. The question is whether the equilibrium can undergo any stability switch as is increased. To identify a stability switch, we seek solutions of the characteristic equation of the form and , .

3. Stability and Existence of Hopf Bifurcation

In this section, we mainly study stability and existence of limit cycle of system (3). We begin by investigating the existence of the critical stability boundary . In this case, implies that is not a characteristic root of (8). Next, we look for a purely imaginary root , , of (8). Without loss of generality, one can assume , with , to be a root of (8), since the roots of (8) appear as complex conjugate pair. Therefore, one has

Using Euler's identity in the above equation, and then separating the polynomial into its real and imaginary parts, we get

Squaring each equation and summing the results yield

A stability switch cannot occur for such that , that is, if , while it may occur as is varied when . We have the following result.

Lemma 1. There exists as a unique positive root of (11), if

Proof. The condition means , from which we have and . Therefore, we find

The conclusion hold according to the fact that the term is less or bigger than one.

We have shown that pairs of eigenvalues cross the imaginary axis as passes through certain critical values. From (10), we can derive that the critical values of and the corresponding purely imaginary eigenvalues , , where

are given implicitly by

It is impossible to solve these equations for explicitly, so we will use the procedure described in Beretta and Kuang [16]. According to this procedure, one defines as solution of (15) with given by (14). This defines in a form suitable for numerical evaluation using standard software. Then is still given implicitly by

From (10), we have

The occurrence of stability switches takes place at the zeros of the functions

We can locate the zero of functions to provide thresholds for stability switches by Maple or other popular mathematical software. With the aid of these plots, a designer can determine by a glance what values of delay must be chosen in order to have a stable system.

Proposition 2. A pair of simple conjugate pure imaginary roots , with , of (8) exists at , if for some , where

The pair of conjugate pure imaginary roots crosses the imaginary axis from left to right if and crosses the imaginary axis from right to left if , where

Proof. From the previous Lemma, we know that the existence of is guaranteed. Also, must be a simple root for (8), otherwise would lead to the contradiction . Next, differentiation of (8) with respect to gives

where . Then (21) yields

Differentiating (17) with respect to , and noting, from (11), that , one has

We remark that

Hence, comparing (23) evaluated at with (22) leads to the conclusion.

We have seen that the couple of simple pure imaginary roots of (8),, solution of (11), occur at the values which are the zeros of the functions in (18). In the light of the previous analysis and recalling that is locally asymptotically stable when , certain conclusions follow.

Theorem 3. Let be defined as in the previous proposition.

(1)The positive equilibrium of (3) is locally asymptotically stable for all if the following condition: , holds and for all otherwise.(2)For all , if , , have positive simple zeros, say , then (3) undergoes a Hopf bifurcation at when , . Moreover, the equilibrium may change its stability finitely many times through stability switches.

4. Stability and Direction of the Hopf Bifurcation

In the previous section, we have obtained conditions which guarantee that system (3) undergoes the Hopf bifurcation at the critical values . In this section, we will study the direction, stability, and the period of the bifurcating periodic solutions, based on the normal form approach and the center manifold theory introduced by Hassard et al. [17]. For notation convenience, let , where . Then is the Hopf bifurcation value for system (3) in terms of the new bifurcation parameter . Set . Rescaling the time by to normalize the delay, system (3) can be written as a functional differential equation in the phase space . Applying Taylor expansion to the right-hand side of system (3) at the equilibrium point and then separating the linear from the nonlinear terms, system (3) becomes

where , , for , and the maps and are defined as follows:

where . Let

Then the nonlinear parts , are given by

where

Here, we use the notation , .

By using the Riesz representation theorem, there exists a matrix whose components are bounded variation function for such that

In fact, we can choose

where denotes the Dirac delta function. For , define

Then system (25) is equivalent to the following system of ordinary differential equations:

where , for . For , define

and a bilinear inner product

where . Then and are adjoint operators. In order to determine the Poincaré normal form of the operator , we need to calculate the eigenvector of corresponding to the eigenvalue and the eigenvector of corresponding to the eigenvalue . Suppose that , with complex, is the eigenvector of corresponding to . Since , then it follows from the definition of , (31), and (32) that we can derive . Similarly, supposing that is the eigenvector of corresponding to , we can get , with the value of chosen to guarantee that .

In the remainder of this section, we will follow the ideas and the same notations as in Hassard et al. [17] and compute the coordinates to describe the center manifold at . Let be the solution of (34) when . Define

On the center manifold, one has

where and are local coordinates for the center manifold in the direction of and . Noticing that is real if is real, we consider only real solution. For the solution , as , from (37) we have

where , with defined as in (27). Denote by . Writing the Taylor expansion, we have

From (37), we get

Substituting into yields

Comparing the coefficients of (42) with those in (40), we find

The term is dependent on (). Hence, in the sequel, we will compute them. From (25) and (37), we have

where

Expanding the above series and comparing the corresponding coefficients, we obtain

By (44), we know that, for ,

Comparing the coefficients with (45),

From (47), (48), and the definition of ,

Recalling that and solving the previous equation for , one has

where is a constant vector. Similarly, again from (47) and (48), we can derive

where . In the following, we will seek for and . From the definition of and (46), we have

From (44) and (45), we have

Substituting (50) and (54) into (52) and noticing that

we obtain

Similarly, from (51) and (53), we can get

These show that and can be determined. Based on the above analysis, each is computed. Therefore, we can calculate the following quantities:

which determine the properties of bifurcating periodic solutions. Specifically, , , and determine the direction, stability, and period of the corresponding Hopf bifurcation, respectively.

Theorem 4. (1) The direction of the Hopf bifurcation of the system (3) at the equilibrium when is subcritical (resp., supercritical) (resp., ); that is, there exists a bifurcating periodic solution for (resp., ) in the sufficiently small -neighbourhood.

(2) The bifurcating periodic solution on the center manifold is unstable (resp., locally asymptotically stable), if (resp., ).

(3) The period of the bifurcating periodic solution decreases (resp., increases), if (resp., ).

5. Numerical Simulations

In this section, we study how the long-run dynamics of the dynamical system (3) change when the time delay parameter varies. The configuration of parameters is the following: , , , , , and .

Figure 1(a) shows the behaviour of and : there exist and such that is locally stable for and while oscillations exist for . Figure 1(b) depicts the maximum and minimum value assumed by with respect to the bifurcation parameter .

(a)

(b)

Figure 1: (a) Graph of stability switch in terms of time delay for the system (3). , ; (b) bifurcation diagram.

The time series of capital stock according to different values of the time delay are shown in Figure 2. In particular, Figure 2(b) describes endogenous oscillations (not driven by stochastic shocks) typical of real world economic variables.

(a)

(b)

(c)

(d)

Figure 2: (c) The time evolution of the function for (a); (b); ; (d) attracting limit cycle in the plane , .

6. Conclusions

We have analysed the dynamical properties of a Solow model with bounded technological progress and time-to-build technology. We have shown that the introduction of these two components drastically changes the results of the classical models with exponential growth of technological progress or without delays. In particular, varying the time delay, the system is able to produce stability switches and Hopf bifurcations. Since problems concerning economic growth and knowledge accumulation are usually studied on BGP (balanced growth path) or when the steady state of the model is stable, we believe that our dynamical analysis may be useful to understand the short run fluctuations of the economic dynamics in a theoretical model. Some possible extensions of the present analysis should also be mentioned. First, a more general structure of time delays may be introduced (different delays for technological and physical productive factors). Second, the allocative process may be endogenized.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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