MODELING CATCH-UP WITH HISTORY FRIENDLY MODELS Franco Malerba

MODELING CATCH-UP WITH HISTORY FRIENDLY MODELS Franco Malerba
ICRIOS The Invernizzi Center for Research on Innovation, Organization, Strategy and Entrepreneurship MODELING CATCH-UP WITH HISTORY FRIENDLY MODELS Franco Malerba Globelics Academy, Tampere, May 2017

The characteristics of HFM The 2016 CUP book 2. Examples
1. Why history friendly models (HFM) ? The characteristics of HFM The 2016 CUP book 2. Examples HFM and catch-up HFM and public policies for catch-up 3. The road ahead

WHY HISTORY FRIENDLY MODELS
WHY HISTORY FRIENDLY MODELS? VARIETY IN THE CHARACTERISTICS AND EVOLUTION OF INDUSTRIES From the empirical cases and the historical analyses of semiconductors, computers, pharmaceuticals, aircraft, chemicals, textiles, and so many other industries it is evident that: The characteristics of industries differ The evolution of industries presents a wide variety of patterns A rich set of factors can be identified: various types of capabilities, vertical and horizontal boundaries of firms, actors such as users, universities or government, specific institutions, and so on.

CAN EXISTING MODELS ADDRESS EXAMINE THESE EVOLUTIONS?
NO Evolutionary models à la Nelson-Winter 1982 These models have microeconomic learning processes; selection with heterogeneous population of firms; de-strategising conjectures; processes of experimentation and imperfect trial and error Nelson and Winter, 1982; 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; diffusion curves) Very abstract models. Focus on some generic basic properties of industrial structure and dynamics

B. Industry life cycle models
They mainly 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, pre-entry experience and first mover advantages, as in several models by Klepper and associates

THEN, HOW TO MODEL THE EVOLUTION OF DIFFERENT INDUSTRIES AND THE VARIETY OF FACTORS AFFECTING EVOLUTION ? In sum, except for some versions of the standard industry life cycle model, there are no models which focus on the different characteristics and evolution of industries and on the factors that have been examined by historical analyses and case studies This has been the initial original motivation for the development of history-friendly models (HFMs).

WHAT ARE HFMs ? HFMs are agent based simulation models which aim to capture in stylised form qualitative theories about mechanisms and factors affecting innovation and industry evolution These mechanisms and factors are put forth by case studies, empirical research in innovation, industrial organization, business strategy and organization and by the histories of industries HFMs aim at establishing a link between empirical evidence and formal theory , developing stand alone simulation models HFMs aim to explore whether particular mechanisms and forces built into the model can generate (explain) the patterns examined HFMs are guided by verbal explanations and appreciative theorizing

THE MAIN FEATURES OF HFMs
HFMs are evolutionary models: firms are boundedly rational agents; their behavior is guided by routines; learning is a key process; heterogeneity of agents and capabilities characterizes the industry These models aim to analyze factors affecting the (short-run and long-run) dynamics of technology, innovation, market structure, industry architecture and industrial leadership HFMs are complex dynamic stochastic systems Non linearities are present Bottom up perspective: aggregate properties are emergent out of the repeated interaction among agents

THE STEPS IN HFM Study of the characteristics of the phenomenon under examination (innovation, evolution of the industry...). Identification of the main features to be analyzed and of the appreciative explanatory model, and consequently also of the hypotheses to be examined and tested. Development of the model. Translation of the theoretical structure into the programming language (variables, methods and algorithm) of the model. Repeated process of internal verification of the correctedness of the computer implementation, and balance between accuracy of the model and explanatory power. Runs, calibration, analysis of the results and sensitivity analysis Good surveys are Garavaglia, 2009 and Yoon 2009

THE EMPIRICAL VALIDATION OF HFM (1)
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 behavior of the key descriptors of industry structure and performance of a sector In a sense, HFMs represent 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

THE EMPIRICAL VALIDATION OF HFM (2)
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

RUNS THAT MATCH AND THAT DO NOT MATCH THE QUALITATIVE FEATURES OF THE HISTORICAL PATTERNS
Once the model is developed and the basic runs done, HFMs test the possibility of different outcomes if the parameter values of some key variables are changed. More than developing different histories, this is a way of testing the causation mechanisms which are present in the model For example, if the model proposes and the simulations show that one of the reason for industrial concentration is the presence of a high bandwagon effect at the demand level, would industrial concentration be lower if the bandwagon parameter is significantly smaller? This is particularly useful as a normative tool in order to examine different policies or socio-economic settings

WHICH DIMENSIONS, ACTORS AND PROCESSES EXAMINED IN EMPIRICAL ANALYSES DO THESE MODELS TAKE INTO ACCOUNT ? Opportunity conditions and technological discontinuities Capabilities of various types Learning and search processes Established firms Entrants Vertical and horizontal structure of production and innovation Demand: consumers Demand: industrial users Institutions and other non-firm organizations such as public authorities and universities Cooperation and vertical interactions (such as user-producer) Competition and selection

INNOVATION AND INDUSTRY EVOLUTION: HISTORY-FRIENDLY MODELS Malerba Nelson Orsenigo and Winter CUP 2016 Part 1: C. 1 The subject matter C. 2 Methodology Part 2: Models of industries C.3 Computers C.4 Computers and semiconductors C. 5 Pharmaceuticals Part 3: Conclusions

The computer industry (1950-1985)
Factors affecting the evolution of specific industries from the 2016 Book The computer industry ( ) Cumulativeness and increasing returns along different product trajectories Technological and market discontinuities Bandwagon effects in demand Emergence and consolidation of concentration in some product segments, entry and competition in others, depending on the level of bandwagon effects.

Industry Specificities (2)
The pharmaceutical industry (from the early period to molecular biology) Low cumulativeness of technological advance Technological regimes with IPR and imitation Fragmented demand Generation of low level of overall concentration, with higher level of concentration in individual market segments. Coexistence of large innovators and small imitators.

Industry Specificities (3)
The co-evolution of the semiconductor and computer industries (1950s-1985) High opportunity conditions Major technological and market discontinuities Vertically linked industries Specialization and vertical integration as co-determined by the dynamics of capabilities, technology and firm size in the upstream and downstream Industries

So, are history-friendly models suitable only for examining the evolution of specific industries?
History-friendly models may be used also to identify and examine generic mechanisms that drive industry evolution: - firm growth and changing industry structure - innovation and increasing returns - technological regimes and demand regimes …. and to explore more general issues relevant for broader contexts or issues that cut across different idustries: - entry - public policy

History-friendly models and catch-up Two examples

A history-friendly model of successive catch-up cycles Landini, Lee Malerba RP 2017
We have developed a history-friendly simulation model of catch-up, which is general enough to investigate the role of different windows of opportunities and of the speed and type of domestic firms learning We have examined firms in three countries: a leader country and two follower countries. Firms in both countries learn. Learning is cumulative and systemic. Firms may sell in one country only or on both countries Technological windows are examined Windows open at a certain time during the evolution of an industry

History-friendly model of technology-driven catch-up cycles
Text with No Subtitle 1st generation technology 2nd generation technology

Historical Cases (I) Mobile phone industry:
Until the beginning of the 1990s Motorola (US) dominated the analog-based cell phone sector; With the emergence of digital technologies, Nokia (Finland) dethrones Motorola, which tended to stay along with analog technologies; In the smart phone Era, finally, Samsung (Korea) dethroned Nokia; Two shifts in the techno-economic paradigm, causes two waves of successive catch-up. Text with No Subtitle

Historical Cases (II) Camera industry:
Until 1959 German companies (Leica, Contax) dominated the rangefinder camera market The emergence of analog and digital single lens camera led Japanese companies (Nikon, Canon) to take industrial leadership away from Germany In the era of compact system camera, finally, Korean companies (Olympus, Panasonic, Samsung Elec) are the new industry leaders; Once again, two technology shifts led to two leadership change…. Text with No Subtitle

History-friendly Model
Structure of the model: rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B firms of country C Text with No Subtitle (market activities) market of country A market of country B market of country C

Firm-level effects: Firms are born with a technology that allows them to search a restricted potion of the technology space; over time new technologies emerge and firms have the chance to adopt them; Firms have heterogeneous capabilities: Higher capabilities allow firms to look for better techniques and improve market share; Demand is vertically segmented: the higher the firm’s capabilities and technical merit, the higher the product quality, the higher the market share; Firms have a competitive advantage in serving their national market. Text with No Subtitle

Country-level effects: System of innovation: the higher the average value of the technique in a country, the higher the probability that firms of that country find better techniques; Lock-in effect: the probability that a firm of a given country perceives a new technology decreases with the country’s market share using the old technology; Learning: firms improve their capabilities over time; the growth of a firm’s capabilities is faster the higher the average level of capabilities in the firm’s country Text with No Subtitle

(innovation activities)
Simulation Dynamics 1st Gen. technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B firms of country C Text with No Subtitle (market activities) market of country A market of country B market of country C

Simulation Dynamics 2nd gen. technology 1st gen. technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) Window of opp. 1 firms of country A firms of country B firms of country C Text with No Subtitle (market activities) market of country A market of country B market of country C

Simulation Dynamics 2nd gen. technology 1st gen. technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B firms of country C Text with No Subtitle (market activities) market of country A market of country B market of country C

Simulation Dynamics 3rd gen. technology 2nd gen. technology 1st gen. technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) Window of opp. 2 firms of country A firms of country B firms of country C Text with No Subtitle (market activities) market of country A market of country B market of country C

Results ‘History-friendly’ runs:
Symmetric advantage in national markets; markets of country B and C starts relatively small (compared to A) and enlarge over time; Two technology shifts in periods 100 and 200 respectively; country B enter at period 50, country C enter at period 150. Evolution of Herfindal index Evolution of market shares Text with No Subtitle

Counterfactuals Experiment 1 – Lock-in effects
We reduce the intensity of lock-in effects Evolution of Herfindal index Evolution of market shares Text with No Subtitle

Counterfactuals Experiment 2 – Size of the window:
We reduce the size of the window -> the smaller the window, the lower the probability that a change in leadership occurs Experiment 3 – Shape of technical landscape: Text with No Subtitle We make the linear landscape more linear (i.e. less radical innovations) -> the less radical innovations, the lower the probability that a change in leadership occurs

Conclusion We have developed a history-friendly simulation model of catch-up, which is general enough to investigate the role of windows of opportunities and of the speed and type of domestic firms learning We simulate a technology-led process characterized by three cycles and two times of leadership change. By relying on counterfactuals we test the causal effect of lock-in, size of the window, and shape of technology space. Windows of opportunities do affect catch-up. When they are absent or too small, catch up is very difficult to succeed In the case of a technology-based window, the size of the window matters in a non trivial way Text with No Subtitle

Public policy and catch-up: history-friendly model Landini and Malerba CJE 2017
The questions: Which types of public policies favour catch-up? Is there complementarity among different types of policy? Do policies have the same effect in different technological contexts (incremental vs discontinuous change) ? Policy analysis conducted with a simulation model based on empirical cases of Lee and Malerba (2017). Other simulation models have discussed policies but not w.r.to catch-up - Dosi et al. 2010, 2013; Malerba et al. 2001, 2008; Dawid et al. 2012; Neugart,2008; Happe et al, 2008; Mannaro et al, 2008.

The model used in this paper
Structure of the model (Landini, Lee, Malerba, 2017) : rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

CASE 1: No technological discontinuity
rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

No technological discontinuity
rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

CASE 2: Technological discontinuity
1st Technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

Technological discontinuity
1st Technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

2nd Technology 1st Technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

2nd Technology 1st Technology rJ Technology space r3 r4 … r0 r1 r2 (innovation activities) Lock-in effect firms of country A firms of country B Text with No Subtitle (market activities) market of country A market of country B

Types of public policy Public policy is modelled in terms of different values of country- specific parameters. In particular we consider five policies: a) Capability building: increase in the average level of capabilities newly-born firms are endowed with; b) Support to firms’ learning: strengthening of the link between firms’ learning and country’s average capabilities; c) Market protectionism: higher cost of export (indigenous market advantage, tariff protection and import restrictions); d) Entry support: increase in the probability that new firms enter the industry; e) Technology targeting: increase in the firms’ ability to perceive the emergence of new technologies

Simulations The structure of the simulation is as follows:
First we analyse the case of policy interventions (both in isolation and in pairs) in the latecomer country without discontinuity. Then, same as 2) but with a technological discontinuity. By comparing 1 and 2 we can test how similar policies may produce similar or different effects under different technological conditions. Results show averages over 600 runs

1) Public policy with no technological discontinuity
Result 1: In the absence of technological discontinuity the best performing policies are capability-building and support to learning (literature on development). Protectionism produces a positive effect but smaller than capability building or support for learning. Entry produces a negative effect (technical change is gradual and cumulative)

Result 2: Two positive policy complementarities exist: protectionism and capability building; protectionism and support to learning This complementarity can be related to the capability and the national innovation system literature and to the infant industry argument

Catching up in presence of discontinuities
With a technological discontinuity, an opportunity to catch up emerges, but without policy there is no change in industrial leadership.

2) Public policies with technological discontinuity
Result 3: In presence of a technological discontinuity Capability building and support to learning are still effective policies; Protectionism produces no sizeable effects (contrary to what we have seen before) Entry support and technological targeting become quite effective policy instruments (contrary to what we’ve seen before)

Result 4: In presence of technological discontinuity there exist two policy complementarities that can be quite effective: Entry and Capability Entry and Learning In a sense, these mixes combine exploration and exploitation.

Conclusions Main findings:
Capability building and support for firm learning are always quite important; Protectionism is relevant only when a technological discontinuity is absent; On the contrary, entry support plays a role only in contexts with technological discontinuity; Policy complementarities exist but they also depend on the presence of discontinuities. In general, similar policies can perform differently depending on the technological scenario in which they are implemented.

Which way forward? Here is my set of challenges:
Use HFMs to Investigate the evolution of other industries -Traditional industries -Services ….. Examine the working of sectoral systems This means looking at the co-evolution of - knowledge - actors (users, suppliers, universities.....) - and institutions :

The way forward (2) Examine the performance of alternative firm strategies in the evolution of an industry Look at the co-evolution of two vertically related industries

The way forward (3) Catch up or failure to catch up Use of a HFM to explore more general issues, such as public policy

The way forward (3) Comparisons among different HFMs may identify “in an inductive way” some general properties and some general hypotheses that may apply to a wide range of empirical dynamic phenomena that share some common factors. In a sense, HFMs force to recognize and model what is different and sector specific but also what are the broad similarities and regularities in industry evolution

MODELING CATCH-UP WITH HISTORY FRIENDLY MODELS Franco Malerba

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