History-Friendly Models of Industrial Evolution

1. History-Friendly Models of Industrial Evolution Luigi Orsenigo University of Brescia KITeS – CESPRI, Università Bocconi L. Orsenigo, Pecs. July 2010

2. 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

3. .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. L. Orsenigo, Roma, 24 Aprile 2009

4. 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. L. Orsenigo, Roma, 24 Aprile 2009

5. The Evolutionary Approach Analysis of changing systems Change is partly exogenous, but partly endogenous Change is partly stochastic and partly deterministic Agents are different, do not understand perfectly the world and cannot look too far ahead Selection Learning Institutions Methodological commitments: start from stylized facts empirically-based assumptions appreciative theorizing models L. Orsenigo, Pecs. July 2010

6. 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 L. Orsenigo, Roma, 24 Aprile 2009

7. 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 L. Orsenigo, Pecs. July 2010

8. 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 L. Orsenigo, Pecs. July 2010

9. 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 ofcompetition and innovation, as from the several models by Klepper and associates L. Orsenigo, Pecs. July 2010

10. 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 L. Orsenigo, Pecs. July 2010

11. History Friendly Models CLOSER 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) L. Orsenigo, Pecs. July 2010

12. 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 L. Orsenigo, Pecs. July 2010

13. 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 L. Orsenigo, Pecs. July 2010

14. 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 L. Orsenigo, Pecs. July 2010

15. 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) L. Orsenigo, Pecs. July 2010

16. 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) L. Orsenigo, Pecs. July 2010

17. 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) L. Orsenigo, Pecs. July 2010

18. 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. L. Orsenigo, Pecs. July 2010

19. 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? L. Orsenigo, Pecs. July 2010

20. 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. L. Orsenigo, Pecs. July 2010

21. 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 market L. Orsenigo, Pecs. July 2010

22. Computers in the space of characteristics L. Orsenigo, Pecs. July 2010

23. 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. L. Orsenigo, Pecs. July 2010

24. 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. L. Orsenigo, Pecs. July 2010

25. Innovation In 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. L. Orsenigo, Pecs. July 2010

26. 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: L. Orsenigo, Pecs. July 2010

27. The dynamics of concentration L. Orsenigo, Pecs. July 2010

28. Counterfactuals L. Orsenigo, Pecs. July 2010

29. Counterfactuals 2 L. Orsenigo, Pecs. July 2010

30. Policy experiments L. Orsenigo, Pecs. July 2010

31. Theoretical experiments: failed adoption L. Orsenigo, Pecs. July 2010

32. Experimental Users L. Orsenigo, Pecs. July 2010

33. Pharmaceuticals • Innovation as a quasi random process Innovation and imitation • Market fragmentation • Low concentration, despite high R&D and marketing L. Orsenigo, Pecs. July 2010

34. Pharmaceuticals L. Orsenigo, Pecs. July 2010

35. Random search, patent • Development • Product launch and marketing • Imitation L. Orsenigo, Pecs. July 2010

36. 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

37. 1.b TA markets SubMKT2 SubMKT1 SubMKT3 SubMKT4 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). GMOP, Bocconi, Sept 09 37

38. 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 GMOP, Bocconi, Sept 09

39. 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 GMOP, Bocconi, Sept 09

40. 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: Firm 3 1 2 n=3 40 NB = Imitative firm doesn’t draw and doesn’t pay the cost of draw GMOP, Bocconi, Sept 09

41. 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. GMOP, Bocconi, Sept 09

42. Imitation • Firms may also imitate already discovered molecules when patent has expired • Search and development if a drug is less esxpensive for imitators GMOP, Bocconi, Sept 09

43. Development • 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. 43 GMOP, Bocconi, Sept 09

44. 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”). GMOP, Bocconi, Sept 09

45. 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. 45 GMOP, Bocconi, Sept 09

46. Pricing LEGEND: S = share of patients in a submarket (sub) and total share in a TC (TC) caughtby a product n.sub= number of submarkets reached STC=total share of firm in the TC i=product GMOP, Bocconi, Sept 09 46

47. Demand LEGEND: PQ= product’s quality P= product’s price A= expenditures in advertising Usub=total utility in a submarket • Product’s market share is then: LEGEND: S = share of patients in a submarket (sub) and total share in a TC (TC) caughtby a product n.sub= number of submarkets reached That is to say: totalNumberOf Patents/Patients Reached firms’ products compete within each submarket. Each product has its own UTILITY: U=f(quality, price, advertising): GMOP, Bocconi, Sept 09 47

48. Entry • 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. 48 GMOP, Bocconi, Sept 09

49. 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. 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 Firm’s exit rules 1) 2) If number of draw n is 0 more than ψ times GMOP, Bocconi, Sept 09 49

50. 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 GMOP, Bocconi, Sept 09