Human Capital and Production

For the past several decades, macroeconomists and financial analysts have sought to build usable models that describe the many complex relationships between the inputs and outputs of production. In the 1940s, two brilliant economists, Paul Douglas and Richard Cobb, developed what has become known as the Cobb-Douglas Production Function. In its simplest form this model can be described by the formula; Q = F (C, L, T, R) where Q, the quantity of output from the system, is a function (F) of the principal input components, Capital (C), Labor (L), Technology (T), and Raw Materials (R).

This function is generally used to describe the trade-offs of the various inputs that can be made while still maintaining the same level of output Q. For those familiar with this approach, these equal output quantities are referred to as Isoquants.

Dr. Jac Fitz-enz, founder of the Saratoga Institute, would point out that of the components described, Labor, the human component is the only one with the inherent ability to generate value. All the other components have, at best, inert potential, and it is the people who leverage that potential. Labor is also the component with the most variability and least predictability, and it is enormously complex to evaluate. As a result, much of what has happened over the past several decades is a drive towards substitution of other factors for Labor, or Human Capital. Much of what is described as the increase in productivity of the U.S. labor force is in fact the result of substitution of other factors for labor. Dramatic improvements in Information Technology (part of the T in the Cobb-Douglas model) have fueled the information revolution over the past 40 years. IT investment has increased at a rate of 7.4 percent per year since 1960, yet the overall output of the economy as measured by Gross National Product (GNP) has increased by only 3 percent per year. The ratio of IT investment to labor cost has grown from 0.4 percent in 1980 to 5.3 percent today. By substituting IT for labor or ordinary capital, organizations seek to capitalize on the vastly superior price and performance improvements in IT relative to other inputs.

Meanwhile, focus on the human component has been limited in terms of building a solid economic and financial theory around the valuation of human capital. When there are other parts of the model that are easier to deal with and offer the potential of huge returns, why deal with the least tangible component—human capital?

As in all forms of incremental improvement with a drive towards optimization, focus will shift and already is doing so. The elasticity of substitution for human capital is, as one would expect, approaching unity and when it drops below that, when we can not substitute for human capital with a less expensive input and maintain the same output, more and more emphasis will be placed on valuing human capital.

Modeling employees as assets is a fairly recent phenomenon, heralded initially by emergence of the term Human Capital. Theodore Schultz, winner of the Nobel Prize in Economics in 1979, originally coined the term Human Capital and defined it in the following manner:

Consider all human abilities to be either innate or acquired. Every person is born with a particular set of genes, which determines his innate ability. Attributes of acquired population quality, which are valuable and can be augmented by appropriate investment, will be treated as human capital. (Schultz 1981, 21)

A critical concept here is that of “augmentation by appropriate investment.” In general, when we are analyzing and attempting to measure Human Capital, it is in the context of making those augmenting investments. In knowing the absolute value of a pool of human capital (that is, your employee population is not of much interest in and of itself), what we are trying to develop is an ability to measure the change in that asset pool as we make various different augmenting investments. Dr. Schultz was ahead of his time back in the 1960s and 1970s, and much of his thinking is still beyond current practice.