Technoscan

How do we observe and measure improvements in individual technological entities?

A convenient approach is to make use of functional performance metrics (FPMs). These express improvements in functional performance. FPMs can be calculated for individual devices, for classes of devices, or for groups of devices working together as systems. FPMs are well known to technology analysts and are studied extensively. They are frequently referred to as technology performance metrics (TPMs). Because they can be quantified, FPMs can be graphed over time. They reflect trends in technological progress.

Improvements in functional performance are captured in terms of three major trends, the so-called triple trends of technological progress:

• Do more with less
• Take less time while doing so
• Use less space in the process

For each technological entity, and for homogenous groups of entities, these trends are reflected in:

• Improvements in the ratio of physical outputs to inputs
• Improvements in the ratio of physical outputs to time required
• Improvements in the ratio of physical outputs to space required

These improvements in physical ratios cause improvemensts in TFP at the macro level, are reflected in postive values of ∆T/T, and help explain overall economic growth.

To enable researchers to focus on technology, they sought clarification of three issues:

• What is technology?
• How is it manifested?
• What is its purpose?

There are many definitions of technology. For the purpose of this analysis we define it as competence, created by people, and expressed in devices, procedures and human skill. The basic unit of analysis is an individual technological entity. It is defined as a unique combination of devices, procedures and skills.

The purpose of technology is the provision of functionality. Functionality is the ability to transform physical reality. Physical reality is composed of three constituents; matter (M), energy (E) and information (I).

To examine the relationship between technological progress and economic growth, we have to probe the link between improvements in technological entities and ∆T/T.

In the next two postings we deal with the following:

• How can we measure progress in individual technological entities?
• How can we visualize progress in the entire technological landscape?

According to one macro-economic model, the growth in TFP can be quantified using the following expression:

• ∆Y/Y = a∆L/L + b∆K/K + ∆T/T

Where:

• Y = GDP
• L = Labor
• K = Capital
• T = Total factor productivity (TFP)
• a and b = parameters that weigh the contribution of L and K

In this model it is possible to find time series data for Y, L and K, and to infer values for a and b. ∆T/T is then calculated as a residual. Typically ∆T/T accounts for between 40% and 60% of ∆Y/Y.  TFP is, therefore, an important element in overall economic growth.

As useful as this insight may be, it is of limited value in assessing the true impact of technological progress. In addition to the impact of technological progress the residual also reflects the impact of other subtle influences, and of errors in calculation. Some observers called it a “measure of our ignorance”.

In general, economists came to the conclusion that “we have reached a dead end”. They had not succeeded in making the link between technological progress and economic growth visible. More work was needed.

As far as we can ascertain there is no generally accepted model to describe the link between technological progress and economic growth. We have not yet succeeded  in making this link visible. This is an inconvenient state of affairs in a technology-dominated world.

In place of an explicit model we proclaim a general  belief in a compelling link  between technological progress and economic growth. In fact we base significant policy initiatives on this belief. However, it would be far more convenient to have an effective model. How far have we come in constructing one?

The best-known model is based on macro-economic theory. It uses an aggregate production function to link economic output on the one hand, to three economic inputs on the other. The three inputs are (1) labor, (2) capital and (3) total factor productivity (TFP).

This approach represents a major step forward. It draws attention to the overriding importance of TFP. This factor contributes about 40%+ to overall economic growth. But unfortunately it does not unravel the pure impact of technological progress. We have to look further.

We describe the macro-economic approach in the next posting.

The purpose of this blog is to contribute to optimizing the functioning of the innovation chain. The innovation chain is the sequence of events that proceeds from scientific discovery, through technological progress, and on to practical application.

To effectively manage the chain we need conceptual models of the landscapes representing the various links: (1) scientific, (2) technological, and (3) fields of application. The last group includes (i) economy, (ii) society, (iii) environment, (iv) health and (v) defense.

The weakest theoretical link in the chain is Item (2). There is no generally accepted model of the technological landscape and of its connections to the various fields of application. Without this link, we cannot optimize the functioning of the chain.

The first contribution to such a model was formulated over fifty years ago. It laid the foundations for countless brilliant studies. However these have not reached the degree of finality required for confident policy and management initiatives. Much needs to be done.

On this blog we wish to explore two themes:

• A new approach to a model for describing the link between technological progress and economic growth
• Procedures to guide technological progress to achieve economic growth

Later we will explore the links to other fields of application.

Revised: September 10, 2009