history-friendly models of industrial evolution

History-Friendly Models of Industrial Evolution

Luigi Orsenigo

University of BresciaKITeS – CESPRI, Università Bocconi

L. Orsenigo, Pecs. July 2010

The Principles (from S. Winter)1. Realism! It may not be a necessity for good theory, but it is often a virtue at least at the prevailing margin. There

is no need to take off one head and put on another one when you step reading the business page and start doing economics

2. Dynamics first!To impose on dynamic theory the burden of supporting a pre-existing static equilibrium analysis, is

essentially to put on blinders, making it inevitable that obviously significant issues will be overlooked

3. No free calculation! It is an abiding scandal that the self-proclaimed science of scarcity routinely treats all forms of

deliberation and information processing as free. This scandal reaches Monica-gate proportions in rational expectations and other sophisticated equilibrium concepts that implicitly endow each actor with the ability solve every actor’s problem many times over.

4. Firms are profit seeking! It is a true fact of nature that firms are typically profit seeking, but it is not a true fact of nature that

they are typically profit maximizing. Profit maximization is a theorist’s crutch and ought to be abandoned when it is too stark to capture the reality of profit seeking or too cumbersome to permit analysis of any but the most extremely stylized models

L. Orsenigo, Roma, 24 Aprile 2009

.5. Innovation is always an option!One thing a profit-seeking firm can do rather than optimize over a given set of

possibilities is to think of some new possibilities. Hence, every analysis of such optimizing behavior deserves an asterisk leading to a footnote that says: unless, of course, there is a better idea.

6. Firms are historical entities! They typically display pronounced inertial or quasi-genetic traits (e.g. scale/ routines)

that are clearly persistent enough to shape their actions over interesting prediction periods. They ought to be represented that way in theory, positioned in model history the way real firms are positioned in real history.

7. Firms are repositories of productive knowledge! In most contemporary societies they are in fact the key repositories of technological

and organizational knowledge and among the key agents of historical change. The storage and advance of knowledge, the maintenance and improvement of organizational capabilities, are complementary roles.


8. Progress is co-evolutionary! Technological and organizational innovation is generated by a variety of firm-

level search processes. But firms do not search independently, they look to rivals, suppliers and customers for ideas, technologies and practices. And these firm and industry processes go forward in the context of a variety of public and private institutions and programs, which in turn are shaped by the firms. I could tell you that itís really simpler than that, but That Would be Wrong.

9. Anything can happen for a while! As Schumpeter said, only when things have had time to hammer logic into

men is it safe to assume that some level of rationality will characterize economic outcomes. Market discipline and economic natural selection constrain outcomes over time, but in the short run anything can happen.

10. Feedback, not foresight, drives economic action.


The Evolutionary ApproachAnalysis of changing systems

Change is partly exogenous, but partly endogenousChange is partly stochastic and partly deterministic

Agents are different, do not understand perfectly the world and cannot look too far ahead

Selection LearningInstitutions Methodological commitments: start from stylized factsempirically-based assumptionsappreciative theorizingmodels


Evolutionary Models of Industrial Change

Build a formal argument to reproduce and “explain” specific stylized facts

The argument is derived from appreciative theorizing Dynamic stochastic systems: when analytic treatment is

impossible, simulate the model Derive simplified, compact versions of the model and

solve it analytically


Simulation

Heuristic technique, widely used in other sciences Inductive approach Theory-driven and disciplined Problems of validation: robustness, sensitivity

analysis, ability to reproduce facts, calibration


A. Evolutionary models à la Nelson-Winter 1982

• Micro learning processes; • selection with heterogeneous population of firms;

destrategising conjectures; • processes of experimentation and imperfect trial and error

(Nelson and Winter, 1982; Silverberg, Dosi and Orsenigo, 1989, Dosi, Kaniovski and Winter, 1999;

• Recognition of some stylized facts and development of an evolutionary model able to reproduce those phenomena (i.e. the relationship between innovation and concentration)

• Very abstract models: Focus on some generic basic properties of industrial structure and dynamics


B. Industry life cycle models

• Focus on the relationship between product and process innovation, entry and firm growth, exit, industrial concentration

• Basic model of industry life cycle derived from the evidence of the auto industry (Klepper, 1996)

• Different industry life cycles and divergence from the standard model due to factors such as the characteristics of demand,

technological discontinuities, the type of competition and innovation, as from the several models by Klepper and associates


VARIETY IN THE EVOLUTION OF INDUSTRIES

• From the empirical cases and the historical analyses of semiconductors, computers, pharmaceuticals, aircraft, chemicals it is evident that:

• the evolution of industries presents a wide variety of patterns• a richer set of factors and variables than those examined by evolutionary

and industry life cycle models can be identified: various types of capabilities, innovative users, vertical and horizontal boundaries of firms, actors such as universities or government, specific institutions, and so on.

• In sum, except for some versions of the standard industry life cycle model, there are no models which focus on the evolution of industries and on the factors that have been identified and examined by historical analyses and case studies


History Friendly ModelsCLOSER RELATIONSHIP WITH HISTORICAL AND EMPIRICAL ANALYSIS INDUSTRY-SPECIFICITIES PUT MORE RESTRICTIONS ON MODELS DERIVE TIME-PATHS, NOT “SIMPLY” LIMIT PROPERTIES FORMALIZE AN APPRECIATIVE ARGUMENT (Sources of industrial

leadership)


• HFMs play a bridging role between general and abstract theories and detailed case studies

• To the theorists, HFMs suggest that abstract and general modeling should take into account some degree of realism and contain empirical foundations in their models

• To the historian/empirical scholars, HFMs suggest some degree of formal discipline and modeling of the empirical analyses and historical works, so that rigorous and consistent explanations of industry evolution could be developed


Empirical validation• It is not the purpose of history-friendly modeling to produce simulations

that closely match the quantitative values observed in the histories under investigation.

• The goal is to match overall patterns in qualitative features, in particular the trend behaviour of the key descriptors of industry structure and performance of a sector

• In a sense, HFMs represent also an abstraction from the specific motivating historical episode

• The goal is to feature some particular causal mechanisms that have been proposed by the appreciative theories for the empirical phenomena under examination

• So, HFMs do not attempt detailed quantitative matching to historical data, nor detailed calibration of the parameters


Empirical validation (ctd)• There is some common sense guidance and some basic learning from the

case studies in the choice of the plausible orders of magnitude of the parameters

• Moreover some of the dimensions known as relevant are not easily measurable, for example some rules and behaviors

• Some value choices for parameters involve implicit unit choices for variables, which means that the quantitative variables are at the end somewhat arbitrary. However the relations among parameters have to be made with a view to consistency

• So the methodology is different from the one by Werker and Brenner (2004) in which models are constructed using detailed empirical data on assumptions and on implications


• The computer industry (1950-1985) (Malerba, Nelson, Orsenigo and Winter, 1999)

• The pharmaceutical industry (from the early period to molecular biology)(Malerba and Orsenigo, 2002)

• The synthetic dye industry (late XIX-early XX century) (Brenner and Murmann, 2003)

• The DRAM industry (early 1970s- late 1980s) (Kim and Lee, 2003)

• The recent evolution of the semiconductor industry (1985-2010)(Yoon and Malerba, 2009)

• The coevolution of the semiconductor and computer industries (1950s-1985). (Malerba,Nelson,Orsenigo and Winter,2008)


• Technological bifurcation between US and Britain in the XIX century (Fontana,Guerzoni and Nuvolari,2008)

• The dynamics of environmental technologies (Oltra and Saint Jean, 2003)

• The dynamics of Korean and Taiwanese national innovation systems and their international specialization (Yoon, 2009)


FROM CASES TO MORE GENERAL ISSUES

• Demand and industry evolution(Malerba,Nelson,Orsenigo and Winter, JEE 2007)

• Public policy, innovation and industry evolution (Malerba, Nelson, Orsenigo and Winter, IJIO 2001 and JEBO,2008)

• Entry and the dynamics of concentration (Garavaglia, Malerba and Orsenigo, 2006)

• IPR (Garavaglia, Malerba, Orsenigo and Pezzoni (2010)


The Evolution of the Computer Industry

Four eras:

early experimentation and mainframes (transistors)introduction of integrated circuits and subsequent development of minicomputers.personal computer, made possible by the invention of the microprocessor. networked PCs and the Internet.

Discontinuities concerning both components technology (transistors, integrated circuits, and microprocessors) and the opening of new markets (minicomputers, PCs).

One firm - IBM - emerges as a leader in the first era and keeps its leadership also in the successive ones, surviving every potential "competence-destroying" technological discontinuity.

In each era, however, new firms have been the vehicles through which new technologies opened up new market segments.

The old established leaders have been able to adopt the new technologies and - not always and often facing some difficulties - to enter in the new market segments, where they gained significant market shares but did not acquired the dominant position they previously had.


Questions

What determines the emergence of a dominant leader in the mainframe segment?

What are the conditions that explain the persistence of one firm's leadership in mainframe computer, despite a series of big technological "shocks"?

What allowed IBM to enter profitably into new markets (PCs) but not to achieve dominance?


The era of transistors, entry and the mainframe industry

At the beginning, the only available technology for computer designs is transistors.

N firms engage in efforts to design a computer, using funds provided by "venture capitalists" to finance their R&D expenditures.

Some firms succeed in achieving a computer that meets a positive demand and begin to sell. This way they first break into the mainframe market. Some other firms exhaust their capital endowment and fail.

Firms with positive sales uses their profits to pay back their initial debt, to invest in R&D and in marketing.

With R&D activity firms acquire technological competencies and become able to design better computers. Different firms gain different market shares, according to their profits and their decision rules concerning pricing, R&D and advertising expenditure.

Over time firms come closer to the technological frontier defined by transistor technology, and technical advance becomes slower.


The introduction of microprocessors

After a period t', microprocessors become exogenously available. This shifts the technological frontier, so that it is possible to achieve better computer designs.

A new group of firms tries to design new computers exploiting the new technology, in the same way it happened for transistors.

Some of these firms fail. Some enter the mainframe market and compete with the incumbents.

Some others open up the PC market. Incumbents may choose to adopt the new

technology to achieve more powerful mainframe computers.

Diversification in the PC marketL. Orsenigo, Pecs. July 2010

Computers in the space of characteristics



Customers and Markets

• Computers are offered to two quite separate groups of potential customers:.

• "large firms", greatly values performance and wants to buy mainframes.

• "individuals", or "small users", has less need for high performance but values cheapness. It provides a potential market for personal computers.

• Each of the two user groups requires a minimum level" of performance and cheapness before they are enticed to buy any computer at all. Then, the value that customers place on a computer design is an increasing function of its performance and its cheapness.

Demand• The probability, Pi, that a particular submarket will buy a computer

i is:

• c0 is specified so that the sum of the probabilities adds to one. • Mi denotes the "value" of computer i. • "mi" is the market share of the firm who produces computer i• the market share variable can be interpreted either in terms of a

"bandwagon" effect, or a (probabilistical) lock-in of consumers who previously had bought products of a particular brand.

• The constant parameter d1 assures that even computers that have just broken into the market, and have no previous sales, can attract some sales.

• "A" is the advertising expenditure of a firm. • The constant parameter d2 performs here a similar role to d1 for

firms who have just broken into the market and have not yet invested in advertising.

• If consumers in a particular submarket decide to buy computer i, then M is the number of machines they buy.


InnovationIn every period the "merit " of the computer each firm is

able to achieve along its technological trajectory --performance and cheapness— improves according to:

R, is the firm's R&D expenditure, where i=1 is performance and i=2 is cheapness.

T represents the number of periods that a firm has been working with a particular technology.

Li-Xi, measures the distance of the achieved design from the technological frontier. The closer one gets to the frontier, the more technological progress slows down, for every given level of R&D expenditure. There is also a random element to what firm achieves, given by e.


Profits, prices, R&D

• Profits: t = M*p – M*k,

• Price: pt= k * (1+t)

• Mark-up: t = 0.9*t-1 + 0.1*(mi/( - mi ),

• Where is demand elasticity• R&D expenditures: Rt, = * t (1- )

• Advertising:


The dynamics of concentration


Counterfactuals


Counterfactuals 2


Policy experiments


Theoretical experiments: failed adoption


Herfindhal Index for old market

0

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1 9 17 25 33 41 49 57 65 73 81 89 97 105

113

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145

bw 4 ds 1

bw 1 ds 3

First generation firms

0

0,5

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1,5

2

2,5

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1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

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ber

of f

irms

bw 4 ds 1

bw 1 ds 3

Adoptions

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1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

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of f

irms

bw 4 ds 1

bw 1 ds 3

Second generation firms

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1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

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of f

irms

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bw 1 ds 3

Experimental Users


He rfindha l Inde x for o ld m a rke t

0

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e x per 0 .1

ex per 0 .2

ex per 0 .3

First ge ne ra tion firm s

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1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 1 0 1 1 1 1 1 2 1 1 3 1 1 4 1

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mb

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of

firm

s

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ex per 0 .2

ex per 0 .3

Adoptions

0

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1,2

1,4

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1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

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mb

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of

firm

s

e x per 0 .1

ex per 0 .2

ex per 0 .3

S e cond ge ne ra tion firm s

0

1

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3

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9

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1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

nu

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of

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ex per 0 .2

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Le a de rship of first ge ne ra tion in O ld m a rke t

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Le a de rship of se cond ge ne ra tion in O ld m a rke t

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ex per 0 .2

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Pharmaceuticals

• Innovation as a quasi random process

Innovation and imitation

• Market fragmentation

• Low concentration, despite high R&D and marketing


Pharmaceuticals


• Random search, patent• Development• Product launch and marketing• Imitation


An (evolutionary) model of the pharmaceutical industry

We use a history-friendly model of the evolution of the bio-pharmaceutical industry

Might be used to explore the logic and the effects of alternative policies

The technological and market environment in which pharmaceutical firms are active is composed by several therapeutic areas or fields (TA).

Each therapeutic areas (TA) has a different economic size (nr of patients)

Within each TA there are a certain number of molecules M, which firms aim to discover and which are at the base of pharmaceutical products that later on are introduced in the market.

Each molecule M has a certain quality Q. Q is expressed in terms of “height” of a certain molecule and it is set randomly. In most of the cases, it is equal to zero; in few cases, it has a positive value drawn from a normal distribution.

GMOP, Bocconi, Sept 09

1.b TA markets

• In each TA (each characterized by a fixed number of patients, i.e. the value of this market) firms may sell products.

• The patients in each TA are grouped in a fixed number of submarkets), where products can be sold if they reach an exogenous minimum level of quality (this means that low-quality products catch few patients, even if they are the only available drug).

SubMKT1

37

SubMKT2 SubMKT3 SubMKT4


The firms

Firms are born with a given budget

Firms are characterized by three activities -search, development and marketing- but with different propensities in these activities.

In each period, firms can be innovators or imitators


The “landscape”

Figure 1: Therapeutic areas and molecules

Firms do not know the “height” Q of a molecule: they only know whether Q is greater than zero or not: a lottery model

Firms engage in a search process in a specific therapeutic area and may (or may not) “discover” a molecule. Discovery means that the firm knows whether that search has found a potentially interesting molecule (Q > 0). If Q > 0, the firm patents the molecule and start a research process. If successful, they invest in marketing and sell it

• • •

• •• • •

• • •

TA 1 TA 2 TA 3


Search

• Firms randomly screen the molecules, spending a given amount of money (a fixed share of their initial budget is used for the search activity,

• The firm draws from the environment n molecules and adds them to the array of (potential) projects.

• n is given by:

cost draw

searchBudget n

n=3

Firm

12

3

NB = Imitative firm doesn’t draw and doesn’t pay the cost of draw 40GMOP, Bocconi, Sept 09

Patents

A patent has a specific duration and width (extension). Once patent duration expires, the molecule becomes free for all the firms. A patent gives the firm also the right to extend the protection on the molecules situated in the “neighbourhood” of the molecule that has been patented. Competing firms are blocked in the developments of potential molecules near the patented one.


Imitation

• Firms may also imitate already discovered molecules when patent has expired

• Search and development if a drug is less esxpensive for imitators


Development

43

Choice of TA (and Molecule) in each period:

• Firms own a portfolio of potential projects.• In each period, according to budget availability, firms start “some” new parallel projects.

• Projects are selected according to the value of their TA: i.e. firms will select more likely a project whose TA is high valued (congestion effects).

• BUT: if the patent of the Molecule of the project is close to expiration, then firms are less attracted by this project and will not chose it very likely.


Development

• All projects have the same number of steps (i.e. Same cost of development)

• Firms, developing a project (i) of an innovative or imitative product (or

both), pay a fixed number of “steps” each period (In this way the periods needed to develop a product are fixed).

• A firm starts a new project if it thinks it has, in advance, enough money to finish it.

• Hence, it is possible to develop multiple projects at the same time (innovative and imitative too) (i.e. in this model we have firms developing “parallel projects”).


Commercialization

• If the development phase is successul, the firm has a product

• But its quality must be higher that a minimum level (FDA approval): otherwise,the drug cannot be launched

• Once the product is developed and approved, the firm commercializes it spending money for advertising; this amount has been saved up during the development period.

The higher the value of a TA, the higher the amount spent in marketing for the product in that TA.

45GMOP, Bocconi, Sept 09

Pricing

)1( ii mupkP

)()1(1,

1,1,

tTC

tTCtii S

Setamupetamup

LEGEND:

S = share of patients in a submarket (sub) and total share

in a TC (TC) caught by a product

n.sub= number of submarkets reached

STC=total share of firm in the TC

i=product


Demand

• firms’ products compete within each submarket.

• Each product has its own UTILITY: U=f(quality, price, advertising): LEGEND:

PQ= product’s quality

P= product’s price

A= expenditures in advertising

Usub=total utility in a submarket

LEGEND:

S = share of patients in a submarket (sub) and total share

in a TC (TC) caught by a product

n.sub= number of submarkets reached

• Product’s market share is then:

That is to say: totalNumberOf Patents/Patients Reached


Entry

48

• All firms start their research activity at time = 0.

• When a firm successfully develops its first product, then it enters the market

• All firms start as innovators.

•After the first patent expired, then firms behave as innovators or imitators accordingly to their own firm-specific propensity.


Exit

Products with a market share lower than 5% exit the market.

i.e. Firms that own more than one product, then, might stop producing some of them without exiting the market.

If Firms have no more products and are not researching anymore, obviously exit the market.

49

Firm’s exit rules

2) If number of draw n is 0 more than ψ times

LEGEND:

Ef = fixed level to exit

F = number of firms at the beginning of the simulation

r = weight factor

Stot=total share of the firm in the market

1)


Results• The model does a good job in generating qualitively a series of stylised

facts:• Low overall concentration• Concentration is higher in individual TAs, but it declines over time• Patterns of competition within individual markets• Dynamics of drug prices• As time goes by, an increasing number of TAs is explored• Skewed distributions of firms’ size, products quality, firms’ innovative

performance• Firms’ growth is basically consistent with empirical evidence (deviations

form Gibrat’s Law)• Relationships between profits and innovation


Counterfactuals

• Costs and economies of scale• Market size and demand growth• Market fragmentation• Innovative opportunities• Patent protection


CHANGING VERTICAL SCOPE OF FIRMS IN

OF THE COMPUTER AND SEMICONDUCTOR INDUSTRIES


Understanding the determinants of specialization and vertical integration in related industries

in uncertain and dynamic environments,

characterized by technological discontinuities.

Major factors: capabilities, technical change and market size

Co-evolutionary processes


A capability-based, dynamic theory of vertical integration and A capability-based, dynamic theory of vertical integration and specializationspecialization

Competence accumulation in specific technological and market domains

Competence destroying technological change

Coordination and integration capabilities

Capabilities take time to develop

Decisions to specialize and vertically integrate are not symmetrical

The distribution of capabilities among all industry participants are relevant

Market selection amplifies the effects of capabilities on the vertical scope of firms

The identity of firms affects the development of capabilities


SPECIALIZATION AND INTEGRATION DECISIONS

VERTICAL INTEGRATION decision is led by:

- the relative size of the computer firm compared to the largest SC component producer (capabilities, R&D, innovation)

- the age of the SC component technology

SPECIALIZATION decision is led by:

Comparison between the quality of SC components produced in-house and the quality of SC components available on the market


Market for components► Specifically, a specialized computer producer will “sign” a contract with a

component producer selected by using a probability function that considers the technical quality of the components: the higher the quality of the component , the higher the probability of signing a contract with a computer producer.

►(5)

► where LitCOMP is the propensity of component producer i to be selected and Pri,t is the probability of a supplier to be selected.

► A component firm which signs a contract sells a number of components which is related to the proportion to which components and systems combine in order to build a computer (in the current parametrization, the proportion is one to one). After signing the contract the computer firm is tied to the component supplier for a certain number of periods. When this period expires, a new supplier might be selected, using the same procedure, if the firm still decides to buy component on the open market.

COMPti

COMPtiCOMP

ti

COMPti

COMPti

L

L

L

,

,

,

,,

Pr

mod

L. Orsenigo, ZiF, Sept. 2009

Firms’ behaviour and technical progress

► Firms start with a given (randomly drawn) mod and they start to sell make profits and invest in R&D spending.

► Price is obtained by adding a mark-up, m , to costs which in turn are derived from the merit of design achieved by a computer. The price of components charged by component suppliers is determined symmetrically by adding a fixed mark-up to unit production costs.

► R&D expenditures are calculated as . a constant fraction of profits ► Technical progress: double draw scheme”. In each period firms draw the

value of their Mod from a normal distribution. The number of draws that any one firm can take is set proportional to its R&D spending

► In each period, the values of the Mod obtained through the firms’ draws are compared with the current Mod, and the higher among these values is kept. Thus, more draws increase the likelihood to get a higher Mod for both systems and components.


Public knowledge► The extent to which technical progress is possible for each firm, given their R&D investment

depends in turn on two variables: the level of publicly available knowledge and the value of the Mod achieved by the firm in the previous period

► Public knowledge is specific to each basic component technology and it grows exogenously over time. When a new technology is introduced, its corresponding level of public knowledge is lower than that reached by current technology, but then it grows faster and at a certain time it overtakes the public knowledge of the older technology. The rate of growth of public knowledge starts to slow down as time goes by. An integrated computer firm decides to adopt the new technology when the mean of its own distribution becomes inferior to the level of the public knowledge of the new technology.

► The mean of the normal distribution from which the values of the merit of design (Mod) of system or component are taken, is a linear combination of the Mod at time t-1 of firm i and of the level of publicly available knowledge, PK, at time t:

►

► And

► t>tmcK, lim and nu are parameters and tmck is the date of introduction of the new component technology.

Kttiti PKhModh )1(1,,

K

tgK

Kt

tmctnuePK K

11lim


R&D► Integrated producers enjoy some coordination advantages as

compared to specialized producers As a consequence, the productivity of their R&D efforts on components is enhanced by a spillover effect

► cCOMP*m is the difference between the price of component in the open market and its actual cost for the producer; it represents savings gained by self-production. An integrated computer firm allocate these resources to component R&D.

► Specialized computer producers invest all their R&D on systems and obviously do not enjoy the coordination advantages.

► Component suppliers spend all their R&D on the development of components.

tiCOMPti

COMPti DRspillovercDR ,,, &&


Vertical Integration

Probability of integration:Let:

►

► where: ► AgeOfTechK ( K =TR,IC,MP) = t – (Starting time of Technology K); Qit

is the sales of the computer producer; biggestQt COMP is the sales of the largest component producers and w is a parameter

Then:►

► where B is a parameter. If the probability of integration is bigger than a number drawn from a uniform distribution (0-1), integration occurs.

2,1, )(1;min b

COMPt

tibK

tibiggestQ

Q

w

AgeOfTechZ

ti

ti

ti Z

ZBIntegrateob

,

,

, 1)(Pr


Specialization► The probability of specialization for each firm is:

► where maxModCOMP is the higher component Mod available on the market.

► Then:►

► A is a parameter and if Prob(Specialize) is bigger than a number randomly drawn by a uniform distribution, specialization will occur.

► A specialized computer firm may also decide to change its supplier, if a better producer has emerged in the market. The procedure for changing supplier follows the same rule for the specialization process.

0,

mod

modmaxmax

,

,

, COMPti

COMPti

COMPt

ti

ModX

ti

ti

ti X

XASpecializeob

,

,

, 1)(Pr


Exit► Computer firms: the variable► Eit = (1-e)*lshr + e*shareit► is computed, where lshr is the inverse of the number of firms

active in the market at the beginning of the simulation (i.e. the market share that would have been held by n equal firms), shareit is the market share of firm i at time t and 0<e<1 is a parameter. Then, if Eit < E, where E is a constant threshold, the firm exits.

► The rule governing the exit of the semiconductor producers is different and simpler. The probability of exiting of any one firm is an increasing function of the number of consecutive periods in which it doesn’t sell to a computer producer.


Standard simulation►Assumptions:►the size of the external market is relatively small in

the case of transistors and integrated circuits and significantly higher for microprocessors;

►lock-in effects in demand are very important for mainframes and much less so for both PCs and components

►the introduction of microprocessors allows much higher improvements in component designs as compared to the older technology: this technological discontinuity is much sharper than the previous one.


The Standard Simulation: Results► A dominant firm emerges quickly in the mainframe industry and becomes

vertically integrated.► In the semiconductor industry, concentration first rises as demand from

computer producers exert strong selective pressures and firms leave the market. The decrease of the number of mainframe producers gradually softens competition and the Herfindahl index declines in the component market. Concentration begins to grow again as a vertically integrated monopolist comes to dominate the computer market: component suppliers are left with no demand from the mainframe firms and exit continues.

► At the time of the introduction of integrated circuits, new semiconductor companies enter the market and concentration drops sharply.

► The dominant mainframe firm remains vertically integrated, because the external market is not large enough to sustain a significant growth of the new entrants and of the quality of their components. The absence of a demand from the mainframe producer induces a shakeout and concentration gradually increases in the semiconductor market


The age of microprocessors► Microprocessors constitute a major technological advance as compared to

integrated circuit and a large external market supports a significant improvement in the quality of the new components.

► the PC market opens up, generating a substantial new demand and fuelling further advances in the merit of the components.

► The computer leader decides to specialize► Competition in components: large external market► The establishment of a monopoly in the supply of components

contributes however to maintaining competition in the PC market, since all firms get their microprocessors from the same source.

► In the last periods of the simulation, as the microprocessors technology matures, the incentives towards specialization become slightly less compelling and, in some simulations, the mainframe firm and some PC producers decide to vertically integrate


History Friendly Simulation

Figure 1a: Herfindahl index

0

0,2

0,4

0,6

0,8

1

1,2

1 17 33 49 65 81 97 113 129 145 161 177 193 209 225 241

MF

PC

Cmp

Figure 1b: integration ratio

0

0,2

0,4

0,6

0,8

1

1,2

1 19 37 55 73 91 109 127 145 163 181 199 217 235

mf

pc


TESTING THE MODEL:COUNTERFACTUALS

1. Does lack of external markets lead to more vertical integration?

2. Do no demand lock-ins in mainframes lead to more specialization ?

3. Do no demand lock-ins in semiconductors lead to more vertical integration ?

4. Does a minor technological discontinuity in microprocessors lead to more vertical integration?


No external market for SC

Figure 2a: Herfindahl index

0

0,2

0,4

0,6

0,8

1

1,2

1 20 39 58 77 96 115 134 153 172 191 210 229 248

MF

PC

Cmp

Figure 2b: integration ratio

0

0,2

0,4

0,6

0,8

1

1,2

1 19 37 55 73 91 109 127 145 163 181 199 217 235

mf

pc


Policy exercisesAntitrustPublic procurementInvestment in basic research

Unintended consequences- The creation of open standards in computers may lead to the emergence of concentration in components

- Antitrust policy in computers may lead to the emergence of a monopolist in the PC market and the disappearance of a the merchant component industry.-Open standards in systems may lead to the emergence of a merchant component industry


User- Producer relations

• Dynamic matching • Specific and generic bonus• Contract length• Exclusive contracts• Lead users


history-friendly models of industrial evolution

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