Jan 6

Prospect theory

Prospect theory belongs to behavioural economics and outstands as an alternative model to expected utility theory, as the neoclassical assumption of the rational agent is put into question. This theory was developed by Nobel laureate Daniel Kahneman and his collaborator Amos Tversky in their “Prospect Theory: An Analysis of Decision under Risk”, 1979. They used results they gathered both from their own empirical observations and from various experiments.

Individuals establish preferences depending on a certain situation and specific circumstances, rather than in absolute terms. This means that depending on their initial situation, agents will act one way or another. One of the outcomes of this reasoning leads to behavioural asymmetries between situations of potential loss or gain. Individuals, for example, are generally more adverse to losses than they are attracted to gains. An endowment effect has also been distinguished, as the compensation required by someone to give away a good, is larger than he would be willing to pay in order to acquire it.

Prospect theory postulates the existence of a value function V (Δw) that depends on the deviation from a reference point (determined in terms of wealth), and a function ∏ (p_i) that turns the weighted probabilities applied in decision-making. In this context A is preferred to B if: ∑∏(p_A) V (Δw_A) > ∑∏(q_B) V (Δw_B)

Function ∏ (p) is defined on the interval [0, 1] transforms the probabilities into decision making weighted probabilities. It is a function characterized by being monotonically increasing that intersects the 45 degrees line near the origin, so that the weights in decision-making are smaller than the probabilities, except for the very low probabilities.

The value function V (Δw), which is shown in the adjacent diagram, is dependent on changes in wealth relative to a reference situation, having a sigmoid-shape: being concave for gains (risk aversion) and convex for losses (risk seeking). It presents a discontinuity at the origin with a steeper slope for losses than for gains.

This theory helps understand why the same individual can be, at different situations, risk-avoiding or risk-seeking. It explains behaviours observed in the economy such as the disposition effector why the same person may buy both a lottery ticket and an insurance policy.

Dec 16

Lope Gallego

Economies of learning

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.

Dec 13

Lope Gallego

Experience curve

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:

	Learning curve	Experience curve
Conception:	microeconomics	macroeconomics
Origins:	Theodore P. Wright, 1936	Bruce D. Henderson, mid-1960s
Variable considered:	average time (labour cost) per unit	direct costs: production, labour, distribution, etc.(includes learning curve effects)
Measures:	labour productivity	total efficiency

Dec 12

Lope Gallego

Learning curve

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:

Y = aX^b

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:

	Learning curve	Experience curve
Conception:	microeconomics	macroeconomics
Origins:	Theodore P. Wright, 1936	Bruce D. Henderson, mid-1960s
Variable considered:	average time (labour cost) per unit	direct costs: production, labour, distribution, etc.(includes learning curve effects)
Measures:	labour productivity	total efficiency

Dec 5

Lope Gallego

Laissez-faire

Laissez-faire, laissez-passer is a French expression that translates as “to let do, let pass”, that is letting things work on their own. In a sense it sums up the economic doctrine of physiocracy, expressing that there is a natural order of things, with its own laws, and it is best to let them run by themselves without laying restrictions.

Leaving the economy to run at its own pace, in a laissez-faire way, where the government’s only concern is to assure sufficient property rights, has been praised by some economic doctrines such as neoclassical economics, monetarism and new classical macroeconomics. Government should only be responsible of providing and ensuring protection for firms and people; the forces of demand and supply should be left to act when economic cycles require it.

Nov 30

Lope Gallego

X-inefficiency

X-inefficiency is known as the result of inputs not producing their maximum output as a consequence of an “X” factor. This translates into both cost minimisation and production maximisation failure and, hence, implies a loss of efficiency. This term was first introduced by Harvey Leibenstein in his “Allocative efficiency vs “X-efficiency”, 1966, since concepts such as organisation inefficiency or motivational inefficiency were not yet available. Therefore X-inefficiency makes reference to all non-allocative inefficiencies.

There are four main identified reasons that try to answer for X-inefficiencies:

Relaxing maximisation behaviour: as the psychological Yerkes-Dodson Law postulates, individuals who are exposed to low pressure will not put much effort in their actions. As pressure builds up, maximising behaviours are reached until a point where too much pressure will again result in inefficiency. Other non-maximising decision making processes include habits, conventions, moral imperatives, etc.
Incomplete contracts: employment contracts define workers economic compensation but other concerns such as the effort, the work load or specific tasks may remain undefined, which creates an incompleteness in contracts.
Inertia: forces that are external to the company may exert pressure to make the firm become more competitive. Only with this external force efforts will increase.
Discretion: it is assumed that workers will not show their “full potential” or maximize their efforts, and at the same time, that employers will not pay the maximum wages they could offer.

However empirical tests and models about X-inefficiency relevance have mixed results so no final transcendence can be assured. However, is interesting to realise the debate it has created, specially regarding incomplete contracts and managerial inefficiency, both also studied in the field of business management.

Nov 29

Lope Gallego

Stigler’s cost analysis theory

As a contribution to industrial organization, George Stigler developed his “own” theory on cost analysis in his article “Production and Distribution in the Short Run”, 1939, which moved slightly away from neoclassical cost analysis.

This theory arises as many economist started to criticize the traditional U-shaped representation of a firm’s average costs. George Stigler used empirical data to question the traditional theory. His cost theory can be divided into the analysis of both the short-term and long-term average costs:

Average Costs in the short-term

According to classical economics, each plant ensured only a specific level of output could be produced in the short-run because of production inflexibility. However, Stigler introduced the notion that firms could adapt to changes in demand and be flexible even in the short-run. His main argument in favour of this idea is that whenever a firms built a plant they would have what is known as a reserve capacity.

The existence of this reserve capacity is justified by economic and technical reasons. From the former we include possible changes in the good’s demand due to cyclical reasons, favourable changes in consumers’ tastes or as a preventive method to avoid a production stop in case there is a breakdown or a repair. The latter can arise as there are some basic installations that are indivisible or as managers and workers are hired in excess to allow future expansions.

-before X₁ : marginal costs < decreasing average costs (increasing returns to scale)

-X₁ to X₂: marginal costs = average costs (constant returns to scale, maximum efficiency)

-after X₂: marginal costs > average costs (both increase, therefore decreasing returns to scale)

It must be noted that this theory allows for maximum efficiency to exist for a while, which completely differs from the neoclassical view, which viewed maximum efficiency as a point of inflection, rather than a straight line.

Average Cost in the long-term

Taking into account production costs and managerial costs, Stigler argued that in the long-run average costs will decrease until a point where they cannot longer do so and from which they will become constant.

Production costs will decrease due to economies of scale until the optimum size is reached and then remain constant. Further average production cost reduction could come along with technological or skills increase. As for managerial cost, he considers, they will start to increase after a certain size is reached. However, managerial diseconomies of scale would be compensated by technical economies of scale, with a zero sum net effect and therefore would remain constant as a result.

Nov 25

Lope Gallego

Structure, conduct, performance paradigm

Structure, Conduct and Performance paradigm (SCP) is used as an analytical framework, to make relations amongst market structure, market conduct and market performance. It was developed in 1959 by Joe S. Bain Jr., who described it in his book “Industrial Organization”. The SCP paradigm is considered a pillar of industrial organization theory, and it has been since its conception a starting point when analysing markets and industries, not only in Economics, but also in the fields of business management and controlling. For instance, the mainline of Michael E. Porter’s works on competition are based on premises derived from this paradigm.

Following its reasoning, an industry performance (which could be considered as the potential benefits to consumers and society as a whole) are determined by the conduct of the firms within the boundaries of this industry, which in turn depend on the structure of the market.

Elements of the Paradigm

– Structure: those set of variables that are relatively stable over time and affect the behaviour of sellers and/or buyers. The way in which markets fail to follow perfect competition conditions, depends basically in the degree of: supply concentration, demand concentration, product differentiation and market entrance barriers. Also, the structure of the market will always be determined by the nature of the product and the technology available.

– Conduct: the way in which buyers and sellers behave, both amongst themselves, and amongst each other. Firms choose their own strategic behaviour, investment in research, in development, advertising levels, collusions, etc.

– Performance: It is measured by comparing the results of firms along the industry in efficiency terms, and different ratios are used to assess different profitability levels. The variables considered at this level are such as pricem quantity, product quality, resource allocation, production efficiency, etc.

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. Actually, the main problem when using this methodology to analyse a market or an industry is the difficulty of defining the limits or boundaries of a given industry.

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.

Nov 18

Lope Gallego

Economies of scope

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)

Nov 14

Lope Gallego

Long run cost analysis

In the long run, no cost is fixed. We can determine our production level and adjust plant sizes, investment in capital and labour accordingly. As we can see in the diagrams below, this gives us unlimited options. Depending on the scale we choose to implement, each level of production will be associated to new, short run cost curves. When we exhaust the infrastructure these provide us, we can upgrade to a new production level and so forth. The actual long run cost curve is made up of all of these individual scenarios, built up year after year.

If we look at average costs, the curve these draw is also the build up of the individual short run curves. These form a U shape, as we can see in the diagram. When average cost is decreasing with each additional investment, we are enjoying economies of scale, but not yet working at maximum efficiency. We reach this maximum efficiency at the minimum point, before the average cost per unit begins growing again.

Marginal costs only really make sense in the long run for each individual level of production. At optimal levels of production for each level, they intersect the long run average cost curve where this meets the short run average cost curve. This is what determines our optimal level of production. In summary, at our optimal level of production:

AC_SR = AC_LR = MC_SR = MC_LR

which is known as Le Châtelier’s principle.

The other important lesson to take away from this is the fact that, when we reach the optimal point where efficiency is at its best, average and marginal costs are the same, as happened in the short run (see detailed graph below these lines). At this point, the slope of the tangent line, that is, the derivative, of the short run total cost (TC_SR) is equal to zero, which is consequent with the fact that we are operating at maximum efficiency. What we can also glean from the graph is the fact that TC_SRand TC_LRare tangent where short run average cost (AC_SR) and AC_LR also meet, which is not necessarily an optimum. In fact, the only time where these junctions do suppose an optimum is in case 2.