Production Function as a Growth Model

Any science is tempted by the naive attitude of describing its object of enquiry by means of input-output representations, regardless of state. Typically, microeconomics describes the behavior of firms by means of a production function:

y = f(x) —– (1)

where x ∈ R is a p×1 vector of production factors (the input) and y ∈ R is a q × 1 vector of products (the output).

Both y and x are flows expressed in terms of physical magnitudes per unit time. Thus, they may refer to both goods and services.

Clearly, (1) is independent of state. Economics knows state variables as capital, which may take the form of financial capital (the financial assets owned by a firm), physical capital (the machinery owned by a firm) and human capital (the skills of its employees). These variables should appear as arguments in (1).

This is done in the Georgescu-Roegen production function, which may be expressed as follows:

y= f(k,x) —– (2)

where k ∈ R is a m × 1 vector of capital endowments, measured in physical magnitudes. Without loss of generality, we may assume that the first m_p elements represent physical capital, the subsequent m_h elements represent human capital and the last m_f elements represent financial capital, with m_p + m_h + m_f = m.

Contrary to input and output flows, capital is a stock. Physical capital is measured by physical magnitudes such as the number of machines of a given type. Human capital is generally proxied by educational degrees. Financial capital is measured in monetary terms.

Georgescu-Roegen called the stocks of capital funds, to be contrasted to the flows of products and production factors. Thus, Georgescu-Roegen’s production function is also known as the flows-funds model.

Georgescu-Roegen’s production function is little known and seldom used, but macroeconomics often employs aggregate production functions of the following form:

Y = f(K,L) —– (3)

where Y ∈ R is aggregate income, K ∈ R is aggregate capital and L ∈ R is aggregate labor. Though this connection is never made, (3) is a special case of (2).

The examination of (3) highlighted a fundamental difficulty. In fact, general equilibrium theory requires that the remunerations of production factors are proportional to the corresponding partial derivatives of the production function. In particular, the wage must be proportional to ∂f/∂L and the interest rate must be proportional to ∂f/∂K. These partial derivatives are uniquely determined if df is an exact differential.

If the production function is (1), this translates into requiring that:

∂²f/∂x_i∂x_j = ∂²f/∂x_j∂xi ∀i, j —– (4)

which are surely satisfied because all x_i are flows so they can be easily reverted. If the production function is expressed by (2), but m = 1 the following conditions must be added to (4):

∂²f/∂k∂x_i ∂²f/∂x_i∂k ∀i —– (5)

Conditions 5 are still surely satisfied because there is only one capital good. However, if m > 1 the following conditions must be added to conditions 4:

∂²f/∂k_i∂x_j = ∂²f/∂x_j∂k_i ∀i, j —– (6)

∂²f/∂k_i∂k_j = ∂²f/∂k_j∂k_i ∀i, j —– (7)

Conditions 6 and 7 are not necessarily satisfied because each derivative depends on all stocks of capital k_i. In particular, conditions 6 and 7 do not hold if, after capital k_i has been accumulated in order to use the technique i, capital k_j is accumulated in order to use the technique j but, subsequently, production reverts to technique i. This possibility, known as reswitching of techniques, undermines the validity of general equilibrium theory.

For many years, the reswitching of techniques has been regarded as a theoretical curiosum. However, the recent resurgence of coal as a source of energy may be regarded as instances of reswitching.

Finally, it should be noted that as any input-state-output representation, (2) must be complemented by the dynamics of the state variables:

k ̇ = g ( k , x , y ) —– ( 8 )

which updates the vector k in (2) making it dependent on time. In the case of aggregate production function (3), (8) combines with (3) to constitute a growth model.

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Catastrophe Revisited. Note Quote.

Transversality and structural stability are the topics of Thom’s important transversality and isotopy theorems; the first one says that transversality is a stable property, the second one that transverse crossings are themselves stable. These theorems can be extended to families of functions: If f: Rⁿ x R^r–>R is equivalent to any family f + p: Rⁿ x R^r–>R, where p is a sufficiently small family Rⁿ x R^r–> R, then f is structurally stable. There may be individual functions with degenerate critical points in such a family, but these exceptions from the rule are in a sense “checked” by the other family members. Such families can be obtained e.g. by parametrizing the original function with one or several extra variables. Thom’s classification theorem, comes in at this level.

So, in a given state function, catastrophe theory separates between two kinds of functions: one “Morse” piece, containing the nondegenerate critical points, and one piece, where the (parametrized) family contains at least one degenerate critical point. The second piece has two sets of variables; the state variables (denoted x, y…) responsible for the critical points, and the control variables or parameters (denoted a, b, c…), capable of stabilizing a degenerate critical point or steering away from it to nondegenerate members of the same function family. Each control parameter can control the degenerate point only in one direction; the more degenerate a singular point is (the number of independent directions equal to the corank), the more control parameters will be needed. The number of control parameters needed to stabilize a degenerate point (“the universal unfolding of the singularity” with the same dimension as the number of control parameters) is called the codimension of the system. With these considerations in mind, keeping close to surveyable, four-dimensional spacetime, Thom defined an “elementary catastrophe theory” with seven elementary catastrophes, where the number of state variables is one or two: x, y, and the number of control parameters, equal to the codimension, at most four: a, b, c, d. (With five parameters there will be eleven catastrophes). The tool used here is the above mentioned classification theorem, which lists all possible organizing centers (quadratic, cubic forms etc.) in which there are stable unfoldings (by means of control parameters acting on state variables). 

Two elementary catastrophes: fold and cusp

1. In the first place the classification theorem points out the simple potential function y = x³ as a candidate for study. It has a degenerate critical point at {0, 0} and is always declining (with minus sign), needing an addition from the outside in order to grow locally. All possible perturbations of this function are essentially of type x³ + x or type x³ – x (more generally x³ + ax); which means that the critical point (y, x = 0) is of codimension one. Fig. below shows the universal unfolding of the organizing centre y = x³, the fold:

This catastrophe, says Thom, can be interpreted as “the start of something” or “the end of something”, in other words as a “limit”, temporal or spatial. In this particular case (and only in this case) the complete graph in internal (x) and external space (y) with the control parameter a running from positive to negative values can be shown in a three-dimensional graph (Fig. below); it is evident why this catastrophe is called “fold”:

One point should be stressed already at this stage, it will be repeated again later on. In “Topological models…”, Thom remarks on the “informational content” of the degenerate critical point: 

This notion of universal unfolding plays a central role in our biological models. To some extent, it replaces the vague and misused term of ‘information’, so frequently found in the writings of geneticists and molecular biologists. The ‘information’ symbolized by the degenerate singularity V(x) is ‘transcribed’, ‘decoded’ or ‘unfolded’ into the morphology appearing in the space of external variables which span the universal unfolding family of the singularity V(x). 

2. Next let us as organizing centre pick the second potential function pointed out by the classification theorem: y = x⁴. It has a unique minimum (0, 0), but it is not generic , since nearby potentials can be of a different qualitative type, e.g. they can have two minima. But the two-parameter function x⁴ + ax² + bx is generic and contains all possible unfoldings of y = x⁴. The graph of this function, with four variables: y, x, a, b, can not be shown, the display must be restricted to three dimensions. The obvious way out is to study the derivative f'(x) = 4x³ + 2ax + b for y = 0 and in the proximity of x = 0. It turns out, that this derivative has the qualities of the fold, shown in the Fig. below; the catastrophes are like Chinese boxes, one contained within the next of the hierarchy. 

Finally we look for the position of the degenerate critical points projected on (a,b)-space, this projection has given the catastrophe its name: the “cusp” (Fig. below). (An arrowhead or a spearhead is a cusp). The edges of the cusp, the bifurcation set, point out the catastrophe zone, above the area between these limits the potential has two Morse minima and one maximum, outside the cusp limits there is one single Morse minimum. With the given configuration (the parameter a perpendicular to the axis of the cusp) a is called the normal factor – since x will increase continuously with a if b < 0, while b is called the splitting factor because the fold surface is split into two sheets if b > 0. If the control axes are instead located on either side of the cusp (A = b + a and B = b – a) A and B are called conflicting factors; A tries to push the result to the upper sheet (attractor), B to the lower sheet of the fold. (Here is an “inlet” for truly external factors; it is well-known how e.g. shadow or excessive light affects the morphogenetic process of plants. 

Thom states: the cusp is a pleat, a fault, its temporal interpretation is “to separate, to unite, to capture, to generate, to change”. Countless attempts to model bimodal distributions are connected with the cusp, it is the most used (and maybe the most misused) of the elementary catastrophes. 

Zeeman has treated stock exchange and currency behaviour from one and the same model, namely what he terms the cusp catastrophe with a slow feedback. Here the rate of change of indexes (or currencies) is considered as dependent variable, while different buying patterns (“fundamental” /N in fig. below and “chartist” /S in fig. below) serve as normal and splitting parameters. Zeeman argues: the response time of X to changes in N and S is much faster than the feedback of X on N and S, so the flow lines will be almost vertical everywhere. If we fix N and S, X will seek a stable equilibrium position, an attractor surface (or: two attractor surfaces, separated by a repellor sheet and “connected” by catastrophes; one sheet is inflation/bull market, one sheet deflation/bear market, one catastrophe collapse of market or currency. Note that the second catastrophe is absent with the given flow direction. This is important, it tells us that the whole pattern can be manipulated, “adapted” by means of feedbacks/flow directions). Close to the attractor surface, N and S become increasingly important; there will be two horizontal components, representing the (slow) feedback effects of N and S on X. The whole sheet (the fold) is given by the equation X³ – (S – S_o)X – N = 0, the edge of the cusp by 3X² + S_o = S, which gives the equation 4(S – S_o)³ = 27 N² for the bifurcation curve. 

Figure: “Cusp with a slow feedback”, according to Zeeman (1977). X, the state variable, measures the rate of change of an index, N = normal parameter, S = splitting parameter, the catastrophic behaviour begins at S_o. On the back part of the upper sheet, N is assumed constant and dS/dt positive, on the fore part dN/dT is assumed to be negative and dS/dt positive; this gives the flow direction of the feedback. On the fore part of the lower sheet both dN/dt and dS/dt are assumed to be negative, on the back part dN/dt is assumed to be positive and dS/dt still negative, this gives the flow direction of feedback on this sheet. The cusp projection on the {N,S}-plane is shaded grey, the visible part of the repellor sheet black. (The reductionist character of these models must always be kept in mind; here two obvious key parameters are considered, while others of a weaker or more ephemeral kind – e.g. interest levels – are ignored.)