George Joseph Stigler, 1911-1991, was an American economist and Professor at Brown University, Columbia and University of Chicago.  Furthermore, and regarding the University of Chicago, he stands out as one of the most prominent members of the School of Chicago. Stigler was awarded the Nobel Prize in Economic Sciences in 1982 for his researches regarding industrial structures, functioning of markets and causes and effects of public regulations.

From Stigler’s contribution to the microeconomic theory and industrial organization we can highlight his work of enhancement and adaptation of microeconomics in such a way, that it allows its use and analysis in a great range of real phenomena. In this field we find Stigler’s cost analysis theory, which changed the way we understand economies of scale. Amongst his top works we can find “The Economics of Information”, 1961 and “The Theory of Economic Regulation”, 1971.

Stigler also produced a great amount of work as a historian of the economic thought. His merit does not related to the amount he covered, but the depth and originality of his analysis, and how clarifying they were. We can highlight his first economic history book “Production and Distribution Theories” 1941, that studies the formative period of neoclassical economics.

Industrial Organization is the economic field that studies the strategic behaviour of firms, and their interaction to determine the structure of markets. Knowing their evolution helps to understand them. The traditional neoclassical theory made the relation between “industrial” and “manufacturing”, making the manufacturing market the main target of studies focusing in the technological result. It left aside aspects, such as organization, management and property. This perspective was criticized for using unrealistic assumptions and because it failed to explain a series of phenomena such as imperfect competition and asymmetric information amongst others.

During the 1930s, a new perspective started to take shape, especially from ideas coming from the Harvard school, and theory began to focus in the behaviour of agents in the industry and in the structure of both firms and industry. In his book “The Theory of Monopolistic Competition”, 1933, Edward H. Chamberlin wrote about imperfect competition and found that many markets were differentiated, thus turning markets into unique. There were many works and papers produced on how markets were arranged in terms of the concentration of suppliers, describing how some markets were dominated by a relatively small number of firms.

In his book “Industrial Organization”, 1959, Joe S. Bain Jr. provided the Structure, Conduct and Performance paradigm (SCP), which is used as an analytical framework, to make relations amongst market structure, market conduct and market performance. The market structure will determine its conduct and thereby its performance.

As implications of all this, regarding economic policies, Harvard School recognizes market power as being dangerous, and establishes a relation between, the concentration ratio, and the harmful effects on social welfare. The dynamic behaviour of buyers and sellers have an effect on the markets, making it harder to predict and establish fixed market structures. Difficulties arise when trying to explain the paradigm and this is due to data shortage, and the multiple definitions and extension of markets.

The Chicago school criticized the “empiricism without theory” of the SCP paradigm, and so returned to the Neoclassical pricing theory, having a more benign view of the market’s results, and having a theoretical (rather than empirical) approach. They start from the framework of perfect competition, and consider interventionism as being the source of monopolies. From Chicago’s point of view, the market’s efficiency guarantees the achievement of social welfare, and therefore firms are encouraged to seek efficiency in their production and distribution. As to the implications regarding economic policies, government intervention is considered undesirable, as the market’s forces should be left to act, and the cost of the intervention is believed to be higher than the social benefit, producing a deadweight loss.

The Austrian school also contributed to the development of the Industrial Organization theory, breaking with the Neoclassical approach. From the same school we can distinguish two main views: von Mises and his disciples’ view and Schumpeter‘s.

Mises describes the structure of the market as a dynamic process of exploration of new methods and opportunities to improve the use of resources, and thus competition is beneficial, as it encourages efficiency and innovations. Therefore, markets have to be free, and it dismays government intervention, although some regulation is praised when innovation is promoted.

From Schumpeter’s point of view, technical progress comes from a process of creative destruction, in which innovations make prior technology out-dated and originate from the innovative entrepreneur. Monopolies have a positive consideration, as they carry out the function of promoting innovation and investments, and government intervention should guarantee both of these.

Contestable markets came up as an alternative approach to the problems concerning market power. These ideas were first introduced by William J. Baumol in his “Contestable Markets: An Uprising in the Theory of Industry Structure”, 1982. In contestable markets there are no entry or exit barriers, no sunk costs and firms have access to the same level of technology. All this makes the number of firms irrelevant, as many will access it and then leave, following different strategies. This view has important limitations: for instance, its starting assumptions are quite restrictive; the definition of cost is not fully suitable; and that, in the real world, a market with such characteristics, is unconceivable.

Between the 1970s and 1980s, a series of theoretical models formulated using game theory in a framework of imperfect competition, made obsolete previous models, such as the SCP paradigm. As a result, there is more emphasis in imperfect information, in firms’ behaviour and, also, in mathematical tools used to calculate social or optimal equilibrium and market equilibrium. In general, there is a shift towards analysing the internal structure of firms, and different views of the firm arose.

Ronald H. Coase, described businesses as being a series of contracts amongst its workers, and explains that it is more beneficial to form firms, since there are a number of transaction costs that can be reduced or even eliminated.

When a business grows in size, there will normally be a distinction between managers and owners. Olivier E. Williamson took notice of the internal boundaries that firms have, and distinguished between decision-making, management and business ownership. The agency theory describes and explains the issues that arise between business managers and owners, as their respective objectives differ.

George Stigler’s contributions to industrial organization must also be mentioned, even though they were more focused on the economical aspect of the theory, rather than opening a debate on how firms interact with each other. Stigler stressed the importance of economies of scale, since it would make a big difference when concentration in the market and firms’ size increased. Furthermore, Stigler developed his “own” theory on cost analysis in his article “Production and Distribution in the Short Run”, 1939 (see Stigler’s cost analysis theory), which moved slightly away from neoclassical cost analysis. When considering the size of firms, economies of scope are also a possibility, as Baumol pointed out in his co-authored book “Contestable Markets: An Uprising in the Theory of Industry Structure”, 1982.

To conclude, it is necessary to clarify that, nowadays, the industrial organization theory accepts multiple perspectives. This theory has not only grown within its field, but also in others, such as applied microeconomics and business management, and, as we have seen during the last years, the boundaries of business and markets have become less clear, and many new definitions and shapes are appearing. This explains the flexibility and the relevance of the industrial organization theory.

The experience curve (not to be confused with learning curve) is a graphical representation of the phenomenon explained in the mid-1960s by Bruce D. Henderson, founder of the Boston Consulting Group. It refers to the effect that firms learn from doing, which means that the higher the cumulative volume of production (X), the lower the direct cost per new unit produced (C). Therefore, the experience curve will be convex and have a downward slope, as shown in the adjacent diagram.

There is a simple rationalisation behind all this: there is a reduction in the average cost of production of a particular product, as a consequence of an increase in the firm’s experience.  The time and cost of producing a unit of output will be reduced, as learning economies, economies of scale, economies of scope, etc. appear due to the cumulative output increase and other process related growth. The difference between learning curves and experience curves is that learning curves only consider time of production (only in terms of labour costs), while experience curve is a broader phenomenon related to the total output of any function such as manufacturing, marketing, or distribution. The ex­perience curve is defined by the following function: 

C_n = C₁X^-a

where:

C₁ = direct cost of first unit of production
C_n = direct cost of n^th unit of production
X = cumulative volume of production
a = experience rate (%)

The experience rate corresponds to the elasticity of cost with regard to output. In other words, the rate at which direct costs decrease (in percentage points) when output increases (also in percentage points).

Some important implications arise from this curve. If direct costs decrease as the cumulative output increases, this will mean that firms that have been producing more and for a longer period, will have lower direct costs per unit and thus dominate the market.

We must consider what happens when two firms compete with the same product and the same experience curve (blue curve, EC). In the diagram on the left, we have a starting situation where two firms compete. Firm B has been in that market for a longer time (or has produced more volume during the same period of time), therefore its direct costs are lower than A’s. If price equals the direct costs of A, firm B will have profits while firm A will just survive. It must be noted that B could decrease its price, which will force firm A to lower its price, thus incurring in losses. Firm A will ultimately leave this market.

However, if A and B maintain a price level at which firm A can endure, direct costs for A will decrease at a higher rate than B, because of the steeper slope for lower cumulative volumes of production. Therefore, the longer A stays in the market, the lower the profits for B, as it is drawn in the diagram on the right, where the difference between direct costs (from C’_A to C’_B) is smaller. Game theory and the analysis of oligopolies tell us that, since B is able to anticipate all these scenarios, firm B will try to banish firm A from the market.

As mentioned before, experience curves is a wider phenomenon than learning curves because they consider not only labour productivity, but also technology, distribution, etc. This difference allows for different experience curves to coexist. Let’s consider two more scenarios, considering open economies.

In the first scenario shown in the adjacent diagram, firm A enters firm B’s market with a different, steeper experience curve. This may happen when firm A is able to learn and get more experience out of the same increase in volume. A few examples include firm A’s workforce being able to learn quicker because it’s highly qualified, or because firm A’s factory allows for quicker modifications. Under these circumstances, even if firm A enters the market with higher direct costs than B (C_A > C_B), it will ultimately catch up with firm B. Actually, after a certain volume, firm A would get more experience and will have lower direct costs than firm B (C’_A < C’_B), even with lower cumulative volume.

In the second scenario, shown in the next diagram, firm C enters firm B’s market with an exclusive technology, which translates into a lower experience curve (let’s assume for the sake of simplicity that this innovative technology cannot be copied or transferred). Then, because of its lower experience curve, firm C will be able to get the same level of direct costs than B with lower cumulative volume. Initially, firm C will have higher direct costs than firm B (C_C > C_B). However, after a while, C will have reached a volume at which it has the same level of direct costs as B. After this point of inflection, firm C will produce each unit with lower direct costs than firm B (C’_C < C’_B).

These two scenarios are usually considered as good examples of the infant industry argument, since trade protectionismin these situations is welcomed. The rationale behind this is quite obvious: if we consider that all these firms (A, B and C) compete in an infant industry, which in these scenarios would be that existing in the home country of B, it is reasonable to limit the extent at which firms such as A and C can act in B’s country. If firm B isn’t kept alone for the time required to gain some experience, national industry in B’s country would not exist, and it would all be controlled by foreign and more powerful firms (A and C). This is usually the case in third world countries, in which infant industries must be protected, in order to help their economic growth.
Even though the term experience curve is usually merged in economics and business management literature with the term learning curve, there are a few differences between the two. These differences are summarized in the following grid:

The learning curve (not to be confused with experience curve) is a graphical representation of the phenomenon explained by Theodore P. Wright in his “Factors Affecting the Cost of Airplanes”, 1936. It refers to the effect that learning had on labour productivity in the aircraft industry, which translates into a relation between the cumulative number of units produced (X) and the average time (or labour cost) per unit (Y), which resulted in a convex downward slope, as seen in the adjacent diagram.

There is a simple rationalisation behind all this: the more units produced by a given worker, the less time this same worker will need to produce the following units, because he will learn how to do it faster and better. Therefore, when a firm has higher cumulative volume of production, its time (or labour cost) per unit will be lower. Wright’s learning curve model is defined by the following function:

where:    Y = average time (or labour cost) per unit

a = time (or labour cost) per unit

X = cumulative volume of production

b = learning rate (%)

Some important implications arise from this curve. If the time (or labour cost) per unit decreases as the cumulative output increases, this will mean that firms that have been producing more and for a longer period, will have lower average time per unit and thus dominate the market.

We must consider what happens when two firms compete with the same product. Since the learning curve considers only time per unit, and we consider that they spend the same amount of time per unit for a given unit when each firm’s cumulative volume of production is at the same level, both firms will have the same learning curve (green curve, LC). In the diagram on the left, we have a starting situation where two firms compete. Firm B has been in that market for a longer time (or has produced more volume during the same period of time), therefore its average time (or labour cost) per unit is lower than A’s. If price equals the average cost of A, firm B will have profits while firm A will just survive. It must be noted that B could decrease its price, which will force firm A to lower its price, thus incurring in losses. Firm A will ultimately leave this market.

However, if A and B maintain a price level at which firm A can endure, time per unit for A will decrease at a higher rate than B, because of the steeper slope for lower cumulative volumes of production. Therefore, the longer A stays in the market, the lower the profits for B, as it is drawn in the diagram on the right, where the difference between average times (from Y’_A to Y’_B) is smaller. Game theory and the analysis of oligopolies tell us that, since B is able to anticipate all these scenarios, firm B will try to banish firm A from the market.

The obvious critique to all this is that, if learning curves were so obvious and could be anticipated as easily, we would live in a world consumed by monopolies. Older firms would keep banishing potential competitors from markets until entry barriers became so great that no other firm would try to enter this market.

Even though the term learning curve is usually merged in economics and business management literature with the term experience curve, there are a few differences between the two. These differences are summarized in the following grid:

Economies of learning derive from the know-howpicked up through experience. The main difference between this and economies of scale or economies of scope is the fact that it is not correlated to production levels in the same way: it does not depend on producing more quantity or a wider portfolio, but from becoming a true specialist in a certain field, by producing a greater cumulative amount of the same product. However, this differs from economies of scale because economies of learning do not stop at a certain level, they keep on improving efficiency.

A classic example of this which is often cited is Japan’s market leadership from the 1980s in technology intensive goods such as TVs and audio equipment. The know how from economies of learning often spreads from a company with employees, leading to clusters of knowledge-rich companies often situated in a relatively small area.

There are many ways in which this learning helps drive down cost: by boosting efficiency and reducing waste in production, by R&D synergies and productivity, by better management which helps coordinate and balance the different functions and by speeding up the productive process. When learning reflects only in labour productivity, it’s analysed with a learning curve. When learning applies to the whole firm, all its processes, it’s analysed with an experience curve.

The concept of economy of learning is actually morphing into that of a ‘learning economy’, one in which efficiency and growth is driven by training and specialisation resulting in profitable, high added value goods and services.

The concept of economy of scope is very similar to that of economies of scale. When we talk about economies of scope, we mean that average costs are reduced by introducing another product into our portfolio that can share some of the infrastructure or know-how, thus reducing overall average cost per product. It is essentially the same concept as economies of scale, but based on producing more than one product instead of producing more of the same one.

This can happen for various reasons. Firstly, because fixed costs do not increase proportionally with production but rather in stages, we can introduce a second production line that benefits from the common infrastructure whilst reducing average cost per unit.

We can also see a reduction in average variable costs. Employees can benefit from shared know how or from a certain skill set that can be transferred to a much wider range of products, raising productivity across the board. In this way, labour need not increase proportionally with production output, as it is the case for economies of scale. What we can also see here are skills being transferred from one production range to another, resulting in synergies that are unique to economies of scope instead of economies of scale.

In the adjacent figure we see the case of two multi-product firms. The fact that the lower production possibility frontier (PPF) is concave rather than a straight line means that firm B will enjoy economies of scope, and be able to produce two goods for a lower average cost than when producing only one. Therefore, economies of scope exist if, for two goods, we have:

C(x₁,x₂) < C(x₁,0) + C(0,x₂)

This economic phenomenon occurs when increasing output is translated into a decline of the firm´s average cost of production. Alfred Marshall was the first economist to distinguish economies of scale depending on their origin:

-Internal economies of scale occur when something inside the firm makes the average cost of production lower:

• Purchasing: buying raw materials in large quantities or big bulks brings better prices and discounts which allow average cost to be reduced.
• Specialisation: covering all areas of management and work can be very difficult, however when operating on a large scale, workers can specialize in the activities in which they are best and more productive.
• Flexibility:  a more efficient production process can be achieved by managers when scale means that different arrangement of the inputs can be made. Better capital can be purchased by firms that have more money, increasing the quality of their products and/or reducing cost of production.

-External economies of scale occur when something outside the business, but inside its industry, makes average cost of production lower. As the industry of the firm grows, more support may come from the suppliers’ side: more variety, more quality, more quantity, etc. Government may give extra aid if it considers that the industry has some special interest.

On the other hand, diseconomies of scale may appear due to the excessive scale of a firm. Diseconomies of scale occur when average cost per unit increases due to excessive size of production.

The main reasons for diseconomies of scale to appear are:

• Demotivation issues: in firms with many workers, standing out becomes harder. Each individual may find himself as meaningless, and therefore cause him to stop working as hard as he would with fewer workers.
• Communication: the communication process of larger firms becomes slower as information has to pass through more and more layers. Information can even become distorted either accidentally or deliberately. The longer it takes managers to make decisions, and these to be known by workers, the less efficient the firm becomes.