Building bridges between co-evolutionary approaches to science, technology and innovation and...

This article was downloaded by: [189.146.218.83]On: 29 August 2014, At: 15:34Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Innovation and DevelopmentPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/riad20

Building bridges between co-evolutionary approaches to science,technology and innovation anddevelopment economics: aninterpretive modelGabriela Dutrénit a , Martín Puchet Anyul b & Morris Teubal ca Program of Economics and Management of Innovation,Universidad Autónoma Metropolitana , Mexico Cityb Faculty of Economics , Universidad Nacional Autónoma deMéxicoc Department of Economics , Hebrew University , JerusalemPublished online: 04 May 2011.

To cite this article: Gabriela Dutrénit , Martín Puchet Anyul & Morris Teubal (2011) Building bridgesbetween co-evolutionary approaches to science, technology and innovation and developmenteconomics: an interpretive model, Innovation and Development, 1:1, 51-74

To link to this article: http://dx.doi.org/10.1080/2157930X.2010.551061

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Building bridges between co-evolutionary approaches to science,technology and innovation and development economics: an interpretivemodel

Gabriela Dutrenita∗, Martın Puchet Anyulb and Morris Teubalc

aProgram of Economics and Management of Innovation, Universidad Autonoma Metropolitana, MexicoCity; bFaculty of Economics, Universidad Nacional Autonoma de Mexico; cDepartment of Economics,Hebrew University, Jerusalem

Innovation can be a powerful process to produce structural change to avoid the traps of lowgrowth. Innovation requires capabilities in the business sector as well as knowledgeproduction and human resource formation processes, which are functions of other agents ofthe national innovation system. Based on anecdotal evidence on the formation and evolutionof the national innovation systems of Israel and Mexico, and on a stylization of theseprocesses, this paper proposes an interpretive model based on the co-evolution betweenscience, technology and higher education, on the one side, and innovation, on the other.Some bridges that connect co-evolutionary approaches and development economics are builtto highlight the role of this co-evolution as an engine of economies’ development processand their structural changes.

Keywords: co-evolution; innovation; science and technology; development; structural change

Introduction

A number of new industrializing countries have achieved remarkable success in terms of econ-omic and social development. In fact, they are moving in the direction of becoming integratedinto the so-called developed world. In contrast, most of the countries from the South are stilllooking for their own way to initiate an appropriate successful development trajectory. Innovationis increasingly seen as the core of this process, substituting the role assigned to technologicalchange or technical progress.

From a structuralist/evolutionary perspective, innovation affects economic growth if it trig-gers a structural change, which for the purposes of this discussion is identified with newsectors (or widely defined product classes), markets, clusters, large multinational companies,and other forms of multi-agent structures.1 This perspective goes back to Schumpeter (1934,1939) and Kuznets (1971, 1973), and has been propagated by many other more recent authors(see Saviotti and Pyka, 2004, among others). Some analysis of co-evolutionary processes ofvarious kinds has gradually become central in research that follows an evolutionary perspective(see Nelson, 1994, 2007; Saviotti, 1996, 1997; Murmann, 2002, 2003; Breznitz, 2007a, b).

In development economics literature, industrialization is seen as the engine of the develop-ment process. Some approaches analyze the structural change between sectors either as jumps

ISSN 2157-930X print/ISSN 2157-9318 online

# 2011 Taylor & FrancisDOI: 10.1080/2157930X.2010.551061

http://www.informaworld.com

∗Corresponding author. Email: gdutrenit@laneta.apc.org

Innovation and DevelopmentVol. 1, No. 1, April 2011, 51–74

or as discontinuous transitions, assuming imbalances between sectors or regions of the econom-ies, and institutional modifications (see Gersenkron, 1962; Rosenstein Rodan, 1943). At the sametime, some authors describe trajectories characterized by the reinforcement of negative trendsthrough mechanisms of cumulative causation (Myrdal, 1957), or those that converge on a situ-ation of slow growth (Nelson, 1956; Kaldor, 1966) rather than towards a balanced one.

In the light of recent research it is possible to place innovation as a powerful process toproduce structural change to avoid the traps of low growth. Innovation requires capabilities inthe business sector as well as knowledge production and human resources formation processes,which are functions of other agents of the national innovation system (NIS).

Drawing on the co-evolutionary literature and seeking to build bridges with developmenteconomics, this paper proposes an interpretive model on the role of co-evolution of science, tech-nology and higher education with innovation in the development process.2 Based on case studies,at different stages of economic development (Israel and Mexico), an attempt is made to extract anumber of common features about the nature and importance of co-evolutionary and emergentprocesses for growth and development.

Literature review and methodology

This section reviews literature on co-evolutionary approaches to science, technology and inno-vation, and on the role of industrialization in development processes by development economics.It also presents the methodology used in this paper to base an interpretive model on the role of co-evolutionary processes in trajectories that lead to development.

Co-evolutionary approaches in the arenas of science, technology and innovation

There is a growing literature that applies co-evolutionary concepts to the study of socio-economicsystems, although challenging issues for transferring evolutionary concepts and insights from thebiological to the social arenas have been recognized (Norgaard, 1984, 1994; Levinthal and Myatt,1994; March, 1994; Nelson, 1995; McKelvey et al., 1999; Lewin and Volberda, 1999; van denBergh and Gowdy, 2003).

Co-evolution involves three causal processes (variation, selection and retention) followed bythe populations, as analysed by Campbell (1969), and bidirectional causal mechanisms that maylink their evolutionary trajectories (e.g., cooperation), as discussed by Murmann (2002).

Nelson (1994) discusses co-evolution between technology, industry and institutions; Murray(2002) focuses on industries and national institutions; Murmann (2003) on industries and aca-demic disciplines; Metcalfe et al. (2005) analyse co-evolution between clinical knowledge andtechnological capabilities; and Noggard (2008) between technology, market and institutions,amongst others. Some authors introduce the role of policies. In this line, Breznitz (2007b) ana-lyses the co-evolution between technology policy, industry and state; Sotarauta and Srinivas(2006) relate public policy with economic development in technologically innovative regions;and Nelson (2008) highlights policies more as part of the picture related to the co-evolution oftechnologies, firm and industry structure, and economic institutions than as an arena that mayco-evolve with the others.

Most developing countries can have a narrow science, technology and higher education andinnovation infrastructure; limited science and technology human resources; a business sector inwhich short-term profits are biased against innovation; weak institution building; and dramaticsocial needs. Most of the catching up processes in the last decades were driven by an extremelyacute accumulation of innovation capabilities, which were fundamentally driven by learningfrom experience instead of by science or research and development (R&D) activities (Hobday,

52 G. Dutrenit et al.

1995; Kim, 1997). However, it seems that the conditions for catching up and development havechanged. There is little dispute about the argument that both scientific and technological knowledgeand innovation are essential for the development process. But, the Schumpeterian literature onco-evolution still seems to lack a basic analytical framework for incorporating such processesinto a broader evolutionary process, and even less one linking innovation to economic development(see also Fagerberg et al., 1999, Sotarauta and Srinivas, 2006; Fagerberg and Verspagen, 2007).

Emergence also has to be brought to this analysis (Kauffman, 1995; Holland, 1998), as bothco-evolution and emergence can be linked to explain paths of development. However, it is worthnoting that in some cases the system has to reach a combined threshold of diversity, organisationand connectivity before emergent behaviour appears; in other words, critical masses are needed.

This article focuses on co-evolutionary and emergence processes in structural change anddevelopment following an industry lifecycle perspective (Abernathy and Utterback, 1978;Klepper, 1996, among others).

The role of industrialization in development economics

Development economics theory sought to explain underdevelopment (CEPAL, 1998) or slowgrowth (Kaldor, 1966, 1967); the nucleus was located in the theory of industrialization. Themain ideas were the following:

(1) The emergence and spread of an industrial sector, as a link between technical progressand growth, shows that technological change has a role to play in a part of the productionbut not in the economy as a whole.

(2) Industry is the core explanatory of growth; industrial processes and organization areintroduced as its main determinants. Forms of industrial organization are those whichallow economies that create increasing returns to scale. These returns can be eitherone-time or continuing on as they become dynamic and provide feedback from firm tobranch level, and hence to the entire sector.

(3) Industrialization transforms traditional production processes extending the division oflabour and intra-firm cooperation between workers, substitutes labour by machineryand introduces ways to incorporate knowledge. These changes introduce new routines,conventions and rules that increase the opportunities for the use of technology, social effi-ciency, incorporation of effective work units and return on capital through newinvestment.

(4) The diversification of production and the growth of the industrial sector also induceincreases in productivity in the economy, based on backward and forward linkages.These processes are fuelled by the inter-sectoral disequilibria generated by the creationof new industrial branches (Hirschman, 1958).

Hence, the role of technology in development was mediated by the expansion of the industrialsector. The gestation of underdevelopment (or slow growth) is explained by the difficulties andobstacles to trigger virtuous circles to explain the successive stages of industrial development– from light towards heavy industry, from production of consumer goods towards durablegoods and capital goods, from craft goods to those based on manufacturing, machine industriesor automated complexes.

According to Gerschekron (1962), looking at the economy as a whole and not only focusingon the industrial sector, the degree of backwardness of an economy shapes the potential for indus-trialization. In this approach, accumulation and institutional changes are two of the main drivers toovercome backwardness (Gerschekron, 1962) and for the big-push (Rosenstein Rodan, 1943).

Innovation and Development 53

Both components provide a vision of development that assumes structural changes and not onlypermanent structures that may condition growth and development.

The possibility to move from backwardness to progress, or from underdevelopment to devel-opment, are conditioned by the presence or absence of feedback mechanisms that reinforce ten-dencies. If the situation of the economy is one of deteriorated capabilities and the predominance ofinstitutions that inhibit their generation, the presence of feedback processes reinforces suchdeterioration, and the systems are subject to a regressive cumulative causation that traps themin the previous stage (Myrdal, 1957).

The development stages originated in both the industrialization processes and the structuralchanges was theorized like phases of ‘traditional society’, ‘previous conditions to take-off’,‘take-off’, ‘progress to maturity and consumption society’ (Rostow, 1960), as semi-industrializedand industrialized economies (Kaldor, 1966, 1967), or as primitive, intermediate and moderndevelopment styles (Pinto, 1976, 1978). The transit from one stage to another was seen as aresult of a combination of structural and institutional factors, according to different authors.

Gercheskron’s theory of late industrialization, based on increasing returns to scale to generategrowth (Young, 1928), explains the transit from backward economies toward a developed stage.The fact that the former co-exists with developed economies allows it to introduce technicalchanges at lower costs and in less time. Hence, this late industrialization is founded on the appro-priation of techniques generated in developed economies. The analytical line is the following: thechanges in the returns to scale caused by imported capital goods are the generator of new indus-trial processes with higher productivity and, simultaneously, these processes produce commod-ities with lower costs than the previous ones, and they demand a work force with more education.

Unlike the recent focus on innovation, this literature centred on technical change and mostlyon embodied technology. The processes of industrialization would be examples of co-evolutionbetween technical progress and effective demand, even though they were not analyzed as co-evol-utionary processes. In addition, this literature lacked a systemic view of the processes; nonethelessthe Sabato triangle (Sabato and Botana, 1968) was probably a first approach to a systemic reason-ing within these theories.

The relationships between economies of scale and industrialization along the developmentprocesses were amply treated in development economics. Most of the contributions are syn-thesized in a formal manner by Ros (2000), but the specific connections between the causes ofthe changes of the economies of scale and the innovation and education processes are scarcelyreferred to.

There have been different efforts to build bridges between evolutionary economics anddevelopment economics. In the long run analysis of the development processes from an economicevolutionary perspective, Freeman and Perez (1988) and Perez (1985) made an important contri-bution. A more specific connection between technical change and structural modifications can befound in Katz (2000). Based on a catalytic approach to technology policy, Teubal (1997)analyses processes, agents and policy instruments to foster development. More recently Cimoliand Porcile (2009) make connections between the dynamics of learning and capability accumulationand both the low-growth traps and the building of virtuous growth cycles. The importance of theindustrial sector for development was again brought to the debate by Szirmai (2009). This articledraws on this literature but suggests a different eye to understand the core of development today.

Methodological approach/research design

This article proposes an interpretive model of the role of the co-evolution of science, technologyand higher education with innovation in the development process. The ideas underlying thismodel are based on stylised facts emerging from the cases of Israel and Mexico.

The methodology followed a set of steps:

(1) A description of the evolution of the NIS of each country is carried out based on publicinformation, evaluation by international organisations and studies made by differentresearchers, some of which were made by the authors of this paper.

(2) The underlying perspective of the descriptive analysis include the following dimensions:size of populations, structures and HLO; organisations and behaviour; institutions,markets and governance; and policies.

(3) Phases of evolution and trajectories of key variables are identified. Following the litera-ture, an effort was made to identify the variation, selection and retention processes as wellas the bidirectional links between the populations under study.

(4) The phases of each country were identified, based on the trajectory and levels of the keyvariables.

(5) A stylisation process is followed (Kaldor, 1961; Gerschenkron, 1962), which is rooted inidentifying common features of the dimensions of both NIS. The stylised facts aregrouped into three stages, which are named according to the main co-evolutionaryprocess that dominated it.

(6) An interpretive model is proposed which includes an environment, a core and endogen-ous and exogenous components.

(7) This model is formally represented by two diagrams: (i) interaction amongst the key vari-ables; and (ii) a type of phase diagram.

In this article, we define science, technology and innovation (STE) and innovation (Innov) astwo activities that transform capabilities into outputs, thus the populations can be defined in termsof either one or both variables.3 The population of capabilities for STE is integrated by research-ers/teachers and for Innov by engineers and technicians, including doctors in science and engin-eering, involved in innovation activities. Outputs of these populations include human resources(graduates and postgraduates), specific knowledge and new capabilities for STE, and new pro-ducts and processes and patents for Innov.

Institutions govern the behaviour of the co-evolving populations through a set of norms thatthey have internalized over time, and this is also influenced by restrictions associated with thesenorms. It is worthwhile differentiating between those norms that shape informal institutionsassociated with routines, habits, codes and agents’ modes of behaviour, and the formal institutionsthat emanate from constitutions, laws or regulations and set up the rules of the game. Both normsand rules of the game condition the variation, selection and retention processes, and the bidirec-tional mechanisms.

This approach takes into account the initial conditions based on the endowment ofcapabilities (whether or not they constitute a critical mass to generate dynamics) and theinstitutional framework. But to transit from a situation without co-evolutive processestowards another where those processes are endogenously generated may require an insti-tutional change. In particular, this calls for the emergence and consolidation of thoseinstitutions and policies that favour variation, selection and retention processes and bidirec-tional mechanisms. Exogenous factors also may be needed to trigger new co-evolutiveprocesses.

Learning from the cases of Mexico and Israel

This section identifies different phases of the evolution of the NIS of Israel and Mexico, focusingon their STE and Innov populations.

The case of Israel

Israel is an example of a dynamic process that has successfully led to the emergence of a venturecapital industry and market (VC) with an associated entrepreneurial high-tech cluster. Thisprocess started in 1969 when the agency in charge of supporting business innovation wascreated under the Office of the Chief Scientist (OCS). Once initial conditions were established,three phases were identified covering the 1969–2000 period.4

Phase 0. Initial conditions (before 1969)

The establishment of Israel’s STE infrastructure began long before the state achieved indepen-dence in 1948. With the expansion of agricultural and urban settlement, scientific teaching andresearch organisations were also being established. Highly accomplished, active scientists whohad obtained their knowledge and training abroad frequently staffed these organisations. Theyincluded the Hebrew University, the Technion (Israel Institute of Technology), the WeizmannInstitute of Science and the Volcani Centre for Agricultural Research.

The first attempts to foster applied or directed scientific research involved the creation of pub-licly owned and managed, specialized applied research institutes. In 1959 the National Councilfor Research and Development was established and entrusted with the formulation of a nationalpolicy for directed/applied research and the coordination of activities of government ministriesresponsible for R&D. Since agricultural research was already flourishing, the emphasis wasgiven to public institutes for industrial research.

Military expenditure played a key role in the evolution of R&D during this period.Through the 1950s, RAFAEL (Armaments Development Authority) was the first, and formany years, almost the only public research institute in Israel to conduct high-tech industrialR&D. Its R&D capabilities were diffused to other defence and civilian companies and organ-izations (e.g., Israel Aircraft Industry in 1962; Elbit, a pioneering private company which laterdeveloped the country’s first minicomputer; and the Technion’s newly created Electronics Lab,two of whose members were instrumental in founding Elscint in 1969, the most prominentcivilian high-tech firm of the 1970s). Even though by the late 1960s a significant STE infra-structure, with some public training and technology transfer centres (PT3C), had been estab-lished, R&D in the business sector was practically non-existent, and the Innov populationwas reduced.

Phase 1. Background conditions (1969–1984)

A new institutional setting for innovation policy was established based on the creation of the OCSin 1969. From the outset, its policy was to enhance socio-economic welfare by inducing an inno-vation-based economic growth process through the diffusion of R&D to the business sector (OCS,2008). Three new universities and a new set of government-owned, applied public research insti-tutes and some PT3C were established. These and the existing STE organisations led to anincreasingly large pool of qualified scientists and engineers. In addition, innovation policy wasinitiated with a grant to firms’ R&D programs and a bi-national industrial R&D program(BIRD), which promoted collaborative commercial innovation between Israeli and US firms.5

Financial incentives were also extended to multinational corporations, a fact that contributed toa strong and relatively early multinational R&D presence. Finally, huge investments weremade in defence R&D (software, communication and instrumentation). The outcome was theestablishment and growth of R&D performing firms, particularly in the communications/elec-tronic areas, which has had significant direct and indirect effect on the growth of the Innov

population and the evolution of the NIS. During this period, the first attempts of searching forinnovation finance mechanisms were undertaken.

Phase 2. Pre-emergence (1985–1992)

This phase involved a number of macroeconomic and liberalization policies, such as the success-ful price stabilization program of 1985 and the liberalization of capital, foreign trade andexchange markets. This phase coincided with global changes, including enhanced capital move-ments and opportunities for foreign high-tech start-up companies, and liberalization of communi-cations markets in the US, the UK and Japan, and the internationalization of US investment banks.There was also a sharp restructuring of the military industry including the very significant cancel-ling of the Lavi fighter plane project. This contraction caused an increase in the variety and stockof engineers and technicians in civilian industry and in the flows of engineers and scientists. It alsogenerated variation in the business sector; a pool of technological entrepreneurs benefited fromthe selection process coming from the OCS’s Grants to R&D program strengthening the Innovpopulation. A strong learning and experimentation process with respect to entrepreneurshipand VC also characterized this phase. It led to identification of the limited partnership form ofVC organization, which was subsequently selected by policy-makers and embodied in thedesign of the Yozma Program in 1993.6

The outcome was an expansion of informal VC activity, an increased rate of start-ups for-mation leading to a critical mass of these firms (300 in 1992), the appearance of the first VCfunds (starting with Athena in 1985), and the creation of companies like Lannet and M-Systems (IVC, 2008; OECD, 2004). Moreover, individuals (foreign and returning Israelis) andsome organizations, such as Advent Private Equity, came to Israel to search for new investmentopportunities in high-tech. Underpinning the above was an additional OCS priority: promotingentrepreneurship and start-ups, and the establishment of a domestic VC industry to supportthem. New government programs were implemented: the Inbal Program (1991), which targetedVC. In addition, changes in governance at universities strengthened the STE system.

Phase 3. Emergence (1993–2000)

During the early 1990s Israel benefited from a wave of hundreds of thousands of immigrants fromthe former Soviet Union. Many of them were highly skilled engineers, technicians, and medicaldoctors, among others, who made a singular contribution to high-tech industry, business inno-vation and Academia. This strengthened the human resources side of STE and Innov populations.This phase was triggered by the implementation of the successful Yozma Program, a policyresponse to both the weakened impact of the regular grants to industrial R&D program and thenew opportunities for start-ups opened up by the expansion and globalization of NASDAQduring the 1980s. It targeted a domestic VC market and indirectly, an entrepreneurial high-techcluster, which can be seen as an HLO. It triggered a cumulative process with positive feedbackbased on the entry of new funds and VC organizations, strong bidirectional interactionsbetween VC and start-ups, cluster effects in the sense of enhanced scope for the local productionof non-tradable intermediate goods and services, and enhanced activity in Israel of large multina-tionals and foreign investment banks. As a result, the number of start-ups increased from 300 toapproximately 3000, and VCs and private equity funds from three to more than 100.

Intense interdependence and bidirectional interactions between STE, Innov, innovation policyand innovation finance fuelled these phases.7 Two types of links were particularly important in thecase of Israel: (a) between Innov and innovation policy, which started in Phase 1; and (b) betweenInnov and innovation finance, which started in the early 1980s when the expansion of actual and

desired R&D to be performed in the business sector by far exceeded the increasingly expanded,albeit still limited budgets. These processes were important for the eventual emergence of the VCindustry and the entrepreneurial high-tech cluster, an HLO. The new-targeted policy (the YozmaVC-directed program) triggered and sustained such an emergence process (Avnimelech andTeubal, 2008a; Avnimelech et al., 2010). While previous work strongly suggests that the emer-gence of VC was a central vector of cluster emergence, the relevant evolutionary processesstarted before, with the creation of the OCS in charge of direct support of R&D in firms.Towards the end of the 1990s, VC had become the main source of finance of R&D in the businesssector, having substituted the government’s previous dominant role (IVC, 2008).

During Phases 1–3, the STE infrastructure continuously adapted itself or expanded to allowfor the expansion of innovative activity in firms. Following Allen (2004), we observe that theagents (mostly of the entrepreneurial high-tech cluster), linkages structures and products and ser-vices evolved qualitatively during Phases 1 and 2 (1969–1992). Quantitative changes were rel-evant mostly in Phase 3. The post-2000 period was one of increasing difficulties in the STEsystem, particularly due to decreases in government support, which suggest that critical massesof STE became an issue. The evolution of STE was slower than Innov, generating an imbalancebetween the two.

The Israeli case is an example of successful transition from background conditions and pre-emergence phases to the phase of emergence of a VC industry and an entrepreneurial high-techcluster in the 1990s. This led to strong increases in Innov, in its share in gross expenditure in R&D(GERD), as well as in the GERD/gross domestic product ratio (over 4.5 per cent towards 2007).The success was supported by a strong STE infrastructure and sharp increases in the output andstock of engineers. This transition however would not have been possible without an increasinglystrong innovative segment of the business sector. Innovation policy was an important underpin-ning of Innov.

The case of Mexico

The case of Mexico shows how policy may shift a situation characterized by extremely limitedinteractions between STE and Innov to trigger a process of building up the population andtheir bidirectional links. Once initial conditions were established, two phases were identified cov-ering the 1970–2000s period.8

Phase 0. Initial conditions (before 1970)

The building up of the STE infrastructure started in 1910 with the re-foundation of the NationalAutonomous University of Mexico. There were two waves of infrastructure building: 1935–1945and 1970–1982, where almost all the national institutes, public research centres and universitieswere created. Both waves were related to an economic policy focused on the intervention of thestate in the economy and the promotion of industrial development. It is worth mentioning the cre-ation of the National Polytechnic Institute (1935–1938), the Mexican Academy of Sciences(1958), the Centre for Research and Advanced Studies (1961) and the public institutes for scien-tific research and technological development connected to the main public firms, such as theMexican Oil Institute (1965) and the Electric Research Institute (1975).

During this period, STE strategies were modelled following different approaches and practicalactions adopted by the most prestigious public research centres and higher education institutions.At the same time, a number of government promotion agencies were created, which had someinfluence on technological activities. Initially, a linear model was at the base of the fostering ofSTE and Innov, based on a top-down approach and a supply-driven strategy. R&D activities

by the business sector were carried out more as a playful activity than as a means for increasingcompetitiveness. By the late 1960s an STE infrastructure was in place but R&D activities in thebusiness sector were extremely limited.

Phase 1. Background conditions (1970–end 1990s)

This phase started with the creation of the National Council for Science and Technology(CONACyT) in 1970, which became the locus to foster the building of domestic capabilitiesin STE and Innov. This allowed the introduction of explicit STE policies. From then, many initiat-ives to foster national development of STE capabilities and outputs were designed andimplemented, strengthening the supply-driven strategy. To this end, several policies, programs,and mechanisms were designed and implemented to create and strengthen a set of public aca-demic organisations that were predominantly involved in scientific knowledge production andhuman resource formation in science and technology.

During this period new public research centres were created. They were mostly oriented tobasic and applied science. In contrast, it was rare to find a centre playing the role of a PT3C.On the STE side, a special effort was made to generate a pool of qualified scientists and engineersthrough the creation of a large scholarship program for postgraduate studies in 1971, and by theimplementation of the National System of Researchers Program.9 STE policies moved to ademand-pull approach, although still in the context of a rather linear model of innovation. Onthe Innov side, the first programs to promote private sector R&D were implemented. Theseincluded the fund for R&D and technological modernization, the program to support technicalmodernization, the fund for strengthening scientific and technological capacities, and the incuba-tor program for technology-based enterprises. In addition, during the 1990s the Mexican govern-ment enacted regulatory changes intended to strengthen innovation and technology transfer.

Along this period, many large domestic firms became R&D performers, establishing R&Dlabs, having success in a set of sectors, such as steel, glass, beer amongst others. Some ofthem became transnational corporations, with facilities in several countries, mostly LatinAmerican.

However, this trajectory was affected by the 1982 crisis and the economic reforms of 1983.Macroeconomic instability characterised this period with volatility of interest rate and exchangerate, and high level of inflation (Moreno and Ros, 2010). This affected the investment strategies offirms and introduced a systemic uncertainty to the business climate. Subsequently a new approachto development was adopted, where market forces appeared as the only effective way to regulatethe economy and provide direction for policy-making decisions. This was strengthened by thesignature of the NAFTA agreement, which continued to open up the economy to internationalcompetition. All this reduced to an extreme the industrial policies that may have complementedthe STE policies. Firms had to adapt to this completely different context, and there was an involu-tion of the Innov due to these radical changes in the rules of the game.

Phase 2. Pre-emergence (2000 onwards)

This phase built on the existent STE infrastructure and the pool of qualified scientists and engin-eers. The approval of the Science and Technology Law in 1999 represented a break in the evol-ution of STE policies and created the bases for novel forms of governance (Puchet Anyul, 2008).The new Science and Technology Law of 2002, the new Organic Law of CONACyT, and theimplementation of the Special Program for Science and Technology 2001–2006 (PECYT) con-tributed to changing the previous policy focus on STE to a broader view comprising STE andInnov. In fact, it represents the first formal attempt to design innovation policies based on the

double objective of increasing both the share of GERD/gross domestic product and the private-sector contribution to R&D activities. While this program was elaborated with an interactive STEand Innov perspective, resource allocation evidence reveals the persistence of a linear, science-push approach both by policy-makers and the scientific and technological community (FCCT,2006; OCDE, 2009a).

PECYT triggered a pre-emergence process (which is now followed by the new program2007–2012), based on a horizontal/neutral program (generating variation), oriented to stimulatethe emergence of new sectors of production. Other more consistent instruments to promote privateR&D were introduced as well, such as the fiscal benefit for R&D,10 the sectoral fund for inno-vation and the program for the creation of new businesses from scientific and technological devel-opments, aimed towards the promotion of high-tech start-ups. Although CONACyT has beenprimarily responsible for formulation, implementation, and coordination of the differentscience, technology and innovation policies, changes in the laws contributed to the appearanceof new actors and multi-actor structures, such as the Advisory Forum for Science and Technologyand the National Network of State Councils and Institutions for Science and Technology.11 Thesecontributed to the emergence processes but made governance of the system a complex process.

Interactions between STE and Innov started during this phase with changes in the institutionalset-up, the introduction of the PECYT, and the consequent growth of innovation in firms. A basicconstraint to such interactions was the relatively small size and still weak growth of these popu-lations, since it meant a narrow process of variation in each one, and a low potential for selectionand retention processes, and for bi-directional links. Scholarships were used to increase the supplyof STE postgraduates. From 1974 this program sponsored 136,000 scholarships for postgraduatestudies in Mexico and abroad. The total researcher population has grown from 40,000 in 2000 to83,000 in 2005; however, its size is still relatively small by international standards and relative tothe population and its needs (Dutrenit et al., 2008, 2010; OCDE, 2009a).

For the Innov population, fiscal benefits for R&D were the most successful new instrument interms of resources committed and firms that benefited. The number of benefited firms increasedfrom 679 in 2001 to 1616 in 2006. This instrument contributed significantly to the increase ofbusiness sector R&D as a percentage of the GERD from 14.1 per cent to 41.5 per centbetween 1995 and 2005. There was also an important increase in the number of trained engineersand technicians employed in the productive sector. The employment figures reveal that thenumber of engineers and technicians in industry grew faster than the number of academicresearchers (FCCT, 2006). However, they are still very low in comparison to internationaltrends and reveal the possible existence of significant shortages in this resource. In contrast,private sources of finance for R&D continue to be limited. The VC market remains limitedboth in terms of the number of funding institutions and the volume of resources to fund inno-vation. The lack of a VC market is one of the weaknesses of the Mexican NIS.

Clusters have emerged in several sectors (electronic, leather and footwear, software, automo-tive) and regions (e.g., Jalisco, Nuevo Leon, Baja California); they are still in a building upprocess (OCDE, 2009b; Hualde, 2010). Some Mexican industrial groups became multinationalcorporations, like Cemex (cement), Carso Group (diversified group in communications, automo-tive, construction, etc) and Gruma (corn flour and tortilla production). However, their operationslack domestic R&D labs; many of these activities are developed abroad. These new successfulmultinationals based in Mexico differ from the transnational corporations of the previous phase.

Meanwhile, public resource disbursements were neither sufficient nor consistent with science,technology and innovation policy design and objectives. Thus, the share of GERD/gross dom-estic product was maintained at its historical level of 0.4 per cent. One of the main factors to influ-ence this process has been the limited political and social priority that government and societyhave traditionally assigned to this policy.

Overall, Mexico is still below the critical masses required for a dynamic NIS. Following Allen(2004), it can be argued that also in this case the organisations, linkages structures and productsevolved qualitatively during Phases 1 and 2. However, limited quantitative changes are stillobserved. Institutions and policies are aligned towards the required changes but the still fragileinstitutional edifice, the evolving multi-actor related governance profile, and the weaknesses ofscience, technology and innovation policy implementation due to resource constraints suggestthat this country has a way to go.

Features and stylized trajectories towards economic development

This section extracts some common features of the NIS of Israel and Mexico that help to depict abroad conceptual framework on innovation and structural change-based economic development.It takes the interaction and interdependence of STE and Innov as the central axis of the analysis topropose stylised trajectories towards development.

The description of the two cases suggests the existence of common features concerning thefour analytical dimensions (size of populations, structures and HLOs; organisations and behav-iour; institutions, markets and governance; and policies). Based on the cases, and on the phasesthat each country experienced, Table 1 describes a successful trajectory from the pre-conditions(Stage I) to the continuing STE–Innov interactions and widespread emergence (Stage III).

Each stage is characterised by qualitative and quantitative levels and the changes each popu-lations observed and the main co-evolutionary process that dominated it. It is worth noting that thepre-conditions are associated with the creation of critical masses to generate endogenousdynamics. The three stages are:

. Stage I. Preconditions: Achieving critical masses.

. Stage II. Strengthening of bidirectional interactions between STE and Innov and the cre-ation of ‘critical’ financial and technical infrastructures.

. Stage III. Continuing STE–Innov interactions and widespread emergence.

Along the rows of the table we can see the evolution of the common features grouped bydimensions, the core of the stages and the phases used to analyze each individual country.

The evidence reveals that Mexico is situated in Stage I, with a relatively high level of STErelative to Innov but low quantitative levels of both variables. From 2000 onwards, there hasbeen a stronger pace of both STE and Innov evolution, quicker in the case of the Innov. Aspecial effort was made to promote R&D in the business sector, increase the pool of researchersand engineers, promote linkage between the agents and establish conditions for better govern-ance, but these conditions were still insufficient to generate an endogenous dynamic. In addition,the financial system has neglected to introduce new schemes (like VC) to fund Innov. However, itmust be noted that the time available for introducing changes was insufficient for improved gov-ernance to evolve, one that spurred radical changes in agents’ behaviour. Moreover, the financialeffort by the government was far below minimum magnitudes and standard international levels(and shares of overall effort) to spark or trigger a self-reinforcing co-evolutionary process ofSTE–Innov, which would involve the economy and society as a whole.

Israel has accomplished Stage II, and it is ready to start Stage III. Strong support of Innov afterthe establishment of the OCS in 1969 along with a strong and growing STE infrastructure led to acritical mass at the end of that country’s Phase 2. In addition, the search and adoption of new non-OCS mechanisms of financing R&D in firms assured the timely expansion of Innov and contrib-uted to the creation of a critical mass of start-up firms by 1992. The early activities of OCS,together with the strong STE infrastructure, spurred a strong industrial R&D response that in

Table 1. Phases of the countries and stages of the trajectories of STE and innovation

Stage I Stage II Stage III

Size ofpopulations,structures andHLOs

Limited size of STE and Ipopulations, limited STEinfrastructure andinexistence of PT3C

Critical masses of STE and Ipopulations, infrastructureof STE and a set of well-established PT3C

Quantitative growth in bothpopulations

Emergence of importantinnovative SME/SUsegment in the BusinessSector and associatessupport structures (VCs andPT3C)

Higher level of criticalmasses of STE and Ipopulations, broadlydeveloped STEinfrastructure andefficient PT3C network

Quantitative changes inboth populations,particularly new criticalmasses and enhancedcapabilities in emergingareas

Widespread emergence ofnew HLO (e.g., clusters)

Organisationsand behaviour

Qualitative changes in bothpopulations

Creation of some PT3C toabsorb technologies,provide technical servicesand employ/trainimmigrant & returningengineers/scientists andstudents

Creation of innovativefirms, or development ofinnovative activities inexistent firms

New qualitative changes andcollective learning in newHLO

Emergence of new forms ofVC

Consolidation of a network ofPT3C

Qualitative changes inboth populations

Widespread mission-oriented R&D involvingorganisations in the STEand I arenas

Widespread emergence ofnew forms of VC

Institutions,markets andgovernance

Institutional set up andbuilding up of effectivesystem governance

Emergence of new markets fortraining, R&D services andother technical services,especially for the growingsegment of innovative firms

Emergence of new financialsegments and markets tofinance innovation in SMEand innovation moregenerally speaking

Improving governance,particularly at STEinstitutions

Widespread systemicintermediation offinancial and technicalsupport systems

Improving governance toinclude new actors

Policies Horizontal policies to fostervariation in STE and Ipopulations,experimentation of newprograms, and learning(including collectivelearning) aboutinnovation throughdesign andimplementation

Design and implementation ofnew programs, morevertical/targeted policies,greater policy mix

Implementing a strategicapproach, set strategicpriorities in a supra-ministerial setting, anddevelop a nationalvision concerning STEand I (and related issuesand areas)

Design andimplementation of newprograms to foster newsectors (targeting)

(Continued)

turn led to expansion of OCS budgets and to some new programs. This is evidence of a strong co-evolution between Innov and innovation policy during the 1980s. We should also mentionchanges in STE governance and the establishment of technology transfer offices at the HebrewUniversity and Weizmann Institute during that decade.

There are also differences between the countries in terms of the time to accomplish each stage.After reaching the initial conditions, Mexico took 29 years and is still on the way to accomplish-ing Stage I, while Israel was able to accomplish this stage in 15 years. Phases 2 and 3 were muchquicker in Israel: Phase 2 was accomplished in only seven years and Phase 3 in another sevenyears, moving along Stage II. Macroeconomic facts (changes of key prices, adjustment policies,etc), international changes (e.g., war, commercial agreements, emerging of new technologies) andother factors (e.g., changes in drug trafficking and its impact on the institutional set up) that intro-duce uncertainty have certainly affected the analytical dimensions and then the timing of thesecountries (Arza, 2005; Katz and Bernat, 2010; BBVA, 2010)

An interpretive model

This section describes an interpretive model based on the stylized facts and designed under a co-evolutionary approach. It includes mechanisms of variation, selection and retention as well asbidirectional links, which are behind the relationships between the STE and Innov populations.The model illustrates the trajectories of the populations along stages, reflecting their qualitativeand quantitative changes.12

The model relates the key variables that originate in the process of stylization of the facts, par-ticularly the supply and demand of STE and Innov. Table 2 contains a definition of the variables.

Table 1. Continued.

Stage I Stage II Stage III

Core of the stage Pre-conditions: Achievingcritical masses

Strengthening of bidirectionalinteractions between STEand Innov and creation of‘critical’ financial &technical infrastructures

Continuing STE-Innovinteractions andwidespread emergence

Cases Israel:Phase 0: Strong STE and

Innov system and somepublic technologicalcentres for training andtechnology transfer:before 1969.

Phase 1 Backgroundconditions: 1969–1984(15 years)

Mexico:Phase 0: Limited STE and

Innov system: before1970

Phase 1 Backgroundconditions: 1970–1999(29 years)

Phase 2 Pre-emergence:2000 and beyond(unaccomplished)

Israel:Phase 2 Pre-Emergence:

1985–1992 (7 years)Phase 3 VC and

entrepreneurial high techcluster Emergence: 1993–2000 (7 years)

Table 3 contains the main relationships between the variables.The supply of graduates in science and technology in t period (STEt) responds to the following

demands:

(i) The short-term demand generated by the Innov supplied at t through the functionSTEd

t (Innovt; aSTE, SSTE(t), GVSTE); this demand is conditioned by independent variables:aSTE, SSTE(t) and GVSTE. This demand operates as a pull process. This relationship is shownin Table 3 (row 1, column 1), and on the top right of the endogenous component in Figure 1.

(ii) The long-term demand generated by strategic decisions of capacity building of STE at Tthrough the function STEd

T (R, K, PT 3C; SSTE(T )); these capacities are integrated by K, Rand PT3C, which for its creation largely depend on SSTE(T ). This relationship is shown inTable 3 (row 1, column 2) and on the top left of the exogenous component in Figure 1.

The supply of Innov in t period (Innovt), which is integrated by a different type of products,responds to the following demands:

(i) The short-term demand of graduates generated by the STE supplied at t through the func-tion Innovd

t (STEt;aInnov, SInnov(t), GVInnov). These graduates are incorporated in themarket of innovative firms. This demand is also determined by independent variables:aInnov, SInnov(t) and GVInnov. This supply operates as a push process. This relationship isshown in Table 3 (row 2, column 1) and on the bottom right of the endogenous componentin Figure 1.

(ii) The long-term demand generated by strategic decisions of capacity building of Innov at Tthrough the function Innovd

T (N , VC; SInnov(T )); these capacities are integrated by VC andN, which depend on SInnov(T). This relationship is shown in Table 3 (row 2, column 2) andon the bottom left of the exogenous component in Figure 1.

Table 2 Definition of variables

Variable DefinitionType ofvariable

STE Graduates and postgraduates FlowInnov Amount of sales associated with new products Flowaj ¼ 1 j¼STE, Innov Capability level Initial

capabilitySi

i¼STE, Innov

Short-term operational subsidies of (SSTE(t), SI(t))Long-term subsidies (SSTE(T), SInnov(T)):SSTE(T) includes subsidies to teaching, learning and research in

existing facilities; to support of students abroad and of efforts tolocate potential highly skilled immigrants; and to absorb returningstudents, graduates and immigrants in PT3C prior to theiremployment in the business sector.

SInnov(T) includes subsidies to firms undertaking Innov.

Expenditure/flow

i¼STE, Innov

Changes in Governance at STE and Innov organizations

K Physical facilities comprising the STE infrastructure (buildings,teaching equipment, research facilities, etc)

Capacity/stock

R STE researchers/teachers Capacity/stockPT3C Public technology transfer and training centres (technical

infrastructure), located in: public research centres, highereducation institutions or in private organizations

Capacity/stock

VC Venture capital (financial infrastructure) Capacity/stockN Innovative firms Capacity/stock

Table 3. Main relationships and variables

Dependent variables

Components

Capacity constraintsEndogenous Exogenous

STE Pull:STEd

t (Innovt; aSTE, SSTE(t), GVSTE)STEd

t : short-term demandInnovt: supplied at t

STEdT (R, K, PT3C; SSTE(T ))

STEdT : long-term demand

STE∗(R, K, PT3C) , STEdT

Innov Push:Innovd

t (STEt; aInnov, SInnov(t), GVInnov)Innovd

t : short-term demandSTEt: supplied at t

InnovdT (N , VC; SInnov(T))

InnovdT : long-term demand

I∗(N , VC) , InnovdT

Independent variables aSTE, SSTE(t), GVSTE

aInnov, SInnov(t), GVInnov

R, K, PT3C; SSTE(T )N , VC; SInnov(T )

Innovationand

Developm

The relationships described above are not conventional supply and demand equations at leastin two respects: (i) their slopes are positive in some sections and other negative according as theyare the respective returns to scale of a dependent variable on the other, of STE on Innov or viceversa; and (ii) they determine quantities of both flows rather than prices, and adjustments are madeonly by movements of both quantities. The processes of pull and push may observe decreasingreturns or increasing returns to scale. In the first case of Innovt on STEt and in the second ofSTEt on Innovt.

These relationships involve exchanges that take place in the markets but also in other circuitsthat interact with HLO, higher education institutions, public research centres, PT3C, N and otherorganizations in order to transfer signals of supply and demand. These spaces can be configuredby intermediary institutions and can be defined either by explicit contracts or agreements or byinformal arrangements.

Figure 1 depicts the model. The diagram includes the endogenous and exogenous com-ponents, a core and an environment.

The endogenous component, presented in the right part of Figure 1, describes the relationshipsbetween the STE and Innov populations. It shows pull or push processes with inputs entering ascapacity or other independent variables that become outputs of supply or demand. The directionsof change between the dependent variables are shown in this diagram.

The changes in populations and processes are supported by organizations of various kinds (HLO,higher education institutions, public research centres, PT3C, N). Populations, processes and organ-izations are generators and carriers of knowledge that circulate through the model components.

The interactions represented in the core account for the behaviour of populations, the linkingprocesses and the organizations that support them. The environment is integrated by formal andinformal institutions expressed by rules, norms, routines, codes of conduct and policies. Thisaffects all participants, and conditions their interactions.

Figure 1. Diagram of the model

It is difficult to clarify to what extent the emergence of organizations or the appearance of newcomponents is caused by the activities of STE or Innov, or the other way round. Therefore, neitherthe influence and determination of organizations on the model’s components nor the conse-quences are defined (e.g., if Innov affects the emergence of HLO).

In the model the way to achieve critical masses, which contributes heavily to both STE andInnov flows, goes through the ability to accumulate graduates in the PT3C; this helps feed the criti-cal masses that trigger the co-evolution.

It is possible to visualise different trajectories of the STE and Innov populations coming fromthe observed processes that were abstracted in the model. These trajectories depend on thechanges in the relationships between STE and Innov that are generated when the system movesfrom one stage to the other. Using a diagram of phase, Figure 2 shows a successful trajectoryof the endogenous component with a high degree of formalization.

This figure shows two kinds of behaviour. The first is shaped by the interaction of the evol-utionary processes of variation, retention and selection observed by both populations. This inter-action generates forms of growth and the emergence of linkages between them. These types ofbehaviour are registered in each of the vertical subsets of the figure. If the linkages betweenthe populations were to remain without substantial modifications, then the NIS would betrapped either in Stage I or in a more successful Stage III. The second behaviour encouragesthe transit between stages. The first type of dynamic is internal to each stage. The second requirespassing from one stage to another, and in this sense, is external to the stages.

The relationships between the two dependent variables show that there are different kinds ofreturns to scale between them. They characterize the stages of evolution, described earlier. Hencethe STE and Innov schedules show diminishing returns of a variable on the other and create a situ-ation where a balance appears with the characteristics of a low-level equilibrium trap (LLET).Then the schedules STE′ and Innov′ shift to being characterized by increasing returns to pushand pull. This makes possible a new equilibrium involving dynamically efficient critical mass(DECM). This point is unstable, because some paths converge while others diverge. Weassume that after this point and once we approach capacity constraints, the nature of the

Figure 2. Stages of evolution and scale returns

returns to push and pull again changes to decreasing returns, as indicated by the schedules STE”and Innov”. This leads to the emergence of a new high-level equilibrium (HLE), which is stableand attracts all the paths.

The different equilibrium points of Figure 2 conceptualize different situations. The existenceof a LLET is based on the existence of a lower level critical mass of both populations. While a co-evolutionary process will lead to this point, it will stop there, effectively truncating the processuntil and if the conditions change although not all changes in the environment would reignitean adequate feedback between the variables. In this case the system indeed reaches a stable equi-librium with a potential for a ‘low growth trap’.

The situations depicted to the right in Figure 2 correspond to other equilibriums, which implythat the model has reached different configurations depending on the exogenous variables. Toreach this configuration, changes in the returns to scale are necessary. They are motivated bythe organizational changes that make certain levels of STE cause proportional changes in Innovand vice versa. These transformations, which produce a different trajectory of the endogenouscomponent, require modifications to the exogenous component. These include the direction andamount of subsidies, the degree of governance and the creation of several skills associated withhuman resources training, development of innovations and structure of financing. More andwell-targeted resources are required as well as appropriate institutions to move towardsthese higher equilibriums points. These changes should have a size, composition and rhythmsuch that let STE and Innov be interconnected in a virtuous way to reach co-evolutionaryprocesses.

Explanation for the transition from one stage to another is based on the combination of theemergence of organizations, the creation of institutions that determine the performance ofvarious agents, and the formulation of strategic policies. The cases described above show thatthe achievement of this combination is not secured by a list of universal recommendations.Each country has used or wasted opportunities. But in any case, the transition from stagesshows the occurrence of a structural change that is achieved when the system is able to incorpor-ate the dynamics of co-evolution in its operating features. There are gradual transitions; othersmay arise from sudden radical changes in the system environment or in global context.However, involutions may also occur, which means moving from a high level equilibrium toanother equilibrium of a lower level.

Countries gradually create political, educational, scientific and technological institutions, aswell as those related to industrial, innovation and knowledge management policies, amongothers. Initially, these institutions are not specialized but organizationally integrated. Over timethey specialize and science and technology councils, cross-sector innovation agencies and finan-cing devices that reflect the evolutionary processes emerge. Another dimension that plays a role insuch appropriation is the innovation culture, a common vision and a national strategy, which isclear in some countries (e.g., Israel, but also Korea and now China) but this is difficult to introducein the model.

Connections between the interpretive model and development economics

It is possible to establish links between some aspects of development economics and a co-evolutive approach to STE and Innov, particularly with the theorist of the industrializationprocess (as discussed earlier). Differences, similarities and complementarities can be identifiedbetween both approaches.

Concerning differences, development economics conceived industrialization as the nucleus ofthe development process; our model suggests that the co-evolution of STE with Innov become thecore of development today, and this co-evolution is located across different sectors. This

introduces other agents to the scene. The NIS and many of its agents play a key role, and this alsobrings in other organisations, institution and policies.

Moreover, technical progress was an external condition and technical change was present as aresult of industrialization, but they did not always co-evolve with other industrial dimensions(organizations, institutions, policies). In contrast, the co-evolution of STE and Innov, whichembed technical change, is at the core of this model.

The processes of industrialization with limited growth potential originate in negativecumulative causations, like those suggested by Myrdal, or in traps of low growth (povertytraps) as suggested by Nelson (1956). Our model bases the explanation of these stages oflow growth on insufficient evolutive mechanisms, moulded by inadequate productive struc-tures and regulatory frameworks, and on critical masses of capacities/capabilities for STEand Innov.

Concerning similarities, from an analytical point of view, in the theory of industrialization thetransit of the dynamics of decreasing returns towards increasing returns is explained by a struc-tural change. In our model, structural change also plays this role; the dynamic between stages isbased mainly on the changes in linkages exhibited by the dynamic economies of scale of therespective populations.

In our model times and forms of transit between stages depend on the accumulated capabili-ties for generating STE and Innov in the previous stages. As argued by Gerschekron (1962), thedegree of backwardness of an economy moulds the possibilities of industrialization; hence theaccumulated capabilities condition the stages of the economy.

Concerning complementarities, following the ideas of development economics, the proposedmodel takes into account different types of discontinuous changes:

. Structural changes that guarantee in each stage the adequate linkages between dynamiceconomies to scale of STE over Innov and vice versa.

. Institutional changes that create norms and incentives for both populations to behave in theappropriate direction with the required transformations.

In both approaches all these changes require arrangements, policies and programs of differentnature and characteristics. In our model, as in a sequence of development stages, an economy canface conditions under which stages of expansion alternate with others of involution, or at leastwhere sustainable growth alternates with slow growth.

The model proposed in this paper set the bases to overcome the old approaches to phases ofindustrialization and development stages. Today, evidence shows that co-evolution takes place inbroader contexts, is more diverse and has more profound features, such as:

. It is not only industrial but multisectoral.

. It is based on economies of scale that arise in the industry but which also emerge betweenorganizations, branches, districts or local systems that produce a wide range of goods andservices.

. It is conditioned by institutions that regulate various organizational and interactive levels.

Unlike the theory of industrialization, this model highlights the presence of selection mech-anisms in both populations and co-evolution between them, which explain the emergence of thatstage where endogenous impulses are generated toward a sustainable growth. The possibility thatthese impulses overcome a fluctuating behaviour and drive themselves towards a trend of continu-ous growth lies in the fact that the mechanisms of intra-population competence and of interdepen-dence between populations guarantee transit to the emergence stage.

A recent work by Cimoli and Porcile (2009) introduces evolutive arguments on developmenteconomics. They base the microfoundations of development on the dynamics of learning and capa-bility accumulation. Instead of looking for the microfoundations, our model extends this approachto analyse the whole NIS and particularly the co-evolution of two key populations, STE and Innov,and provides a conceptual framework to analyse the stages of the development process.

Final reflections

The two cases analysed in this article strongly suggests that co-evolution and emergence shouldnot be ignored when analyzing innovation and its impact on economic development. Our worksuggests that co-evolutionary processes may play important roles both in generating adequatepre-emergence conditions for desirable structural changes and as part of the emergence processitself.

The interpretive model constructed from the stylization of the two countries attempts to estab-lish causality and feedback, and seeks to formalize how the variables must work to advance throughthe path of development. This model emphasizes the role of critical masses of STE and Innov, PT3Cand VC for building virtuous co-evolutionary processes of STE and Innov, which could make asignificant contribution to innovation-led and structural changed-base economic growth.

This paper claims that the endogenous component of a development process lies in a co-evol-utionary model based on the building up of STE and Innov capabilities within a NIS more than inan industrialization process, as emphasised by development economics. This new endogenouscomponent integrates relationships between key variables (initial conditions of capabilities, sub-sidies, governance, and physical, human resources, technical and financial infrastructures, andinnovative organisations). A type of phase diagram is proposed, which combines both theintra- and inter-stage dynamics.

The exogenous component and the external environment may shift the economy from a lowequilibrium to a higher equilibrium (by shifting curves of the diagrams), or reignite an STE–Innov co-evolutionary process. Concerning the exogenous component, the mass migration ofhighly skilled personnel from the former Soviet Union to Israel helps to increase the level ofthe STE researchers (R). Referring to the external environment, facts like the NAFTA agreementand the economic crisis in Mexico, and the existence of a long-term strategic policy ingredient ofinnovation policy in Israel contributes to explain the rate of changes from one stage to another.

This analysis draws on the stages proposed by development economics theorists, and thetransit from one stage to another is based on discontinued changes, as proposed by them.Undoubtedly, there is an urgency to revisit the old development economics, as many of its con-tributions are important to understand the co-evolutionary trajectories behind the developmentprocess today.

This proposal suggests that policies for development may consider a more comprehensiveconception of science, technology, higher education and innovation policies. However, as inmost models, we introduced the policies largely as part of the environment, but then we wouldbe losing a critical element helping transitions to higher levels of development. If the governmenthas the required (strategic) policy institutions, and this is reflected in knowledge-intensive policyprocesses preceding actual policy-making, then some aspects of government policy affecting theendogenous and exogenous components could be considered as part of the model’s core. Thiswould favourably affect a country’s possibility of shifting to Stage III.

Our model complements the general evolutionary approach to development (Nelson, 2008),giving structure to some aspects of what could become ‘an evolutionary theory of economicdevelopment’. The co-evolutionary processes are different than those in the literature in thesense that these processes are critical for emergence, and this latter is critical for the innovation

and structural change perspective to economic growth. Moreover this paper sets the bases for con-ceptual progress involving the above and including policy. In addition, our analysis further under-pins modern theories of growth based on innovation and structural change (interpreted here asemergence of new HLOs), because they require and are stimulated by new STE outputs.

A set of topics remains unclear: (i) the relationships between co-evolution and the growthcycles; and (ii) the connections between the trajectory of the populations and endogenouschanges, triggered by HLO, or exogenous changes, triggered by either targeting innovationpolicies (Avnimelech and Teubal, 2008b; Avnimelech et al., 2010) or industrial policies(Rodrik, 2004). These topics require further research.

AcknowledgementsJorge Katz and two anonymous referees made comments that helped to improve this article significantly.Any remaining errors are the responsibility of the authors.

Notes1. Some of them may be termed higher-level organizations (HLO), in the sense of Potts (2000). In this

view, the impact of innovation will be relatively weak if it does not trigger the emergence of thesehigher levels, multi-agent structures, and will be strong if it does. To exemplify the practical relevanceof this perspective see World Bank (2008) and Haussman and Klinger (2007).

2. A previous paper focusing on co-evolution and emergence will appear as Dutrenit and Teubal (2010) inAntonelli (2010).

3. Moreover, two levels of analysis of populations can be identified: the individual agent level (used inthis paper), and the organizational level. At the organizational level, the STE population (Innov popu-lation) comprises research centers and universities (innovative firms). As systems mature, monitoringat organizational level becomes relatively more important than at the individual level. But, when theinstitutional structures are still immature, as in the case of developing countries, it is more relevant tofocus on individuals.

4. See Pugatch et al. (2010) for a short summary of the pre-1969 STE-related institutional developmentand policy; and Avnimelech and Teubal (2006) for a broader discussion of Phases 1–3. Public infor-mation was also used to base the analysis, including CBS (2008), IAEI (2008), OCS (2008), IVC(2008) and OECD (2004).

5. The grants to industrial R&D program was a horizontal (i.e., open to all firms in the business sector)and rather neutral (i.e., non discriminatory) subsidies-based program. It became the backbone anddominant innovation policy program of Israel (its share of total disbursements by the OCS wasseldom below 80 per cent).

6. Yozma was a VC-directed, targeted program that was implemented during 1993–1996/7 (Avnimelechand Teubal, 2006). This contrasts with the horizontal/neutral grants to industrial R&D program, whichstarted in 1969.

7. Innovation policy includes not only direct support of innovation or R&D in firms (the main focus) butalso other policies, such as VC policies and associated institutional changes; Innovation means notonly commercial innovation in firms but also innovation capabilities and creation/growth of R&D per-forming organizations (such as start-ups); while innovation finance means new private mechanisms offinance of R&D/innovation (e.g., VC).

8. See Dutrenit et al. (2010), FCCT (2006) and OCDE (2009a).9. This is one of the STE instruments with the longest tradition in the country; its main goals include the

promotion of the formation, development and consolidation of a critical mass of researchers at thehighest level, mostly within the public system of higher education and research. The programgrants both pecuniary (a monthly compensation) and non-pecuniary stimulus (status and recognition)to researchers based on the productivity and quality of their research.

10. New instruments, based on subsidies, called stimulus for innovation, replaced this program.11. The Advisory Forum is an independent civil organization for advising the President, the General

Council for Scientific Research and Technological development and CONACyT’s Board of Directors(which comprises directors of the main universities, research centres, industrial association and scien-tific academies). The National Network of State Councils and Institutions for Science and Technology

is a civil association that embodies a permanent forum for discussing and suggesting initiatives aimedat fostering scientific and technological development throughout the different states of the MexicanFederation.

12. For another approach to incorporating qualitative and quantitative dimensions in the analysis of struc-tural changes see Cimoli et al. (2010).

ReferencesAbernathy, J., and Utterback, M. (1978) Patterns of industrial innovation. Technology Review, 80(7), pp. 40–

47.Allen, P. (2004) The complexity of structure, strategy and decision-making, in: J.S. Metcalfe, and J. Foster

(eds) Evolution and Economic Complexity (Cheltenham: Edward Elgar).Antonelli (2010) Elgar Handbook on the System Dynamics of Technological Change (Cheltenham: Edward

Elgar).Arza, V. (2005) Technological performance, economic performance and behaviour: A study of Argentinean

firms during the 1990s. Innovation: management, policy and practice, 7(2/3), pp. 131–151.Avnimelech, G., and Teubal, M. (2006) Creating VC industries which co-evolve with high tech: Insights

from an extended industry life cycle (ILC) perspective to the Israeli Experience. Research Policy, 35,pp. 1477–1498.

Avnimelech, G., and Teubal, M. (2008a) From direct support of business sector R&D/innovation to target-ing venture capital/private equity: A catching-up innovation and technology policy life cycle perspec-tive. Economics of Innovation and New Technology, 17, pp. 153–172.

Avnimelech, G., and Teubal, M. (2008b) Evolutionary targeting. Journal of Evolutionary Economics, 18(2),pp. 151–166.

Avnimelech, G., Rosiello, A., and Teubal, M. (2010) Evolutionary interpretation of venture capital policy inIsrael, Germany, UK and Scotland. Science and Public Policy, 37(2), pp. 101–112.

BBVA (2010) Mexico: Economic Outlook (Mexico: BBVA Research, Fourth Quarter).Breznitz, D. (2007a) Innovation and the State. Political choice and strategies for growth in Israel, Taiwan

and Ireland (New Haven: Yale University Press).Breznitz, D. (2007b) Industrial R&D as a national policy: Horizontal technology policies and industry–state

co-evolution in the growth of the Israeli software industry. Research Policy, 36, pp. 1465–1482.Campbell, D.T. (1969) Variation and selective retention in socio-cultural evolution. General Systems, 14,

pp. 69–85.CBS (2008) Central Bureau of Statistics (Israel), available at ,http://www.cbs.gov.il..CEPAL (1998) El desarrollo economico de la America Latina y algunos de sus principales problemas,

Cincuenta anos de pensamiento en la CEPAL (Mexico: CEPAL/Fondo de Cultura Economica).Cimoli, M., and Porcile, G. (2009) Sources of learning paths and technological capabilities: An introductory

roadmap of development processes. Economics of Innovation and New Technology, 18(7), pp. 675–694.Cimoli, M., Porcile, G., and Rovira, S. (2010) Structural change and the BOP-constraint: Why did Latin

America fail to converge? Cambridge Journal of Economics 34(2), pp. 389–411.Dutrenit, G., Capdevielle, M., Corona Alcantar, J.M., Puchet Anyul, M., Santiago, F., and Vera-Cruz, A.O.

(2010) El sistema nacional de innovacion mexicano: estructuras, polıticas, desempeno y desafıos(Mexico: UAM/Textual S.A).

Dutrenit, G., and Teubal, M. (2010) Co-evolution, emergence and economic development: Some lessonsfrom the Israeli and Mexican experience, in: C. Antonelli (ed.) Handbook on the EconomicComplexity of Technological Change (Cheltenham: Edward Elgar).

Dutrenit, G., Puchet Anyul, M., Sanz-Menendez, L., Teubal, M., and Vera-Cruz, A.O. (2008) Co-evolutionof science and technology and innovation: A three stage model of policies based on the Mexican case, in:S.E. Cozzens, and E. Berger Harari (eds) 2007 Atlanta Conference on Science, Technology, andInnovation Policy (Piscataway, NJ: IEEE).

Fagerberg, J., Guerrieri, P., and Verspagen, B. (1999) The Economic Challenge to Europe: Adapting to inno-vation based economic growth (Cheltenham: Edgar Elgar).

Fagerberg, J., and Verspagen, B. (2007) Innovation, growth and economic development: Have the conditionsfor catch up changed? International Journal of Technological Learning, Innovation and Development, 1,pp. 13–33.

FCCT (2006) Diagnostico de la polıtica cientıfica, tecnologica y de fomento a la innovacion en Mexico(2000–2006) (Mexico: Foro Consultivo Cientıfico y Tecnologico).

Freeman, C., and Perez, C. (1988) Structural crises of adjustment: Business cycles and investment behaviour,in: G. Dosi, C. Freeman, R. Nelson, G. Silverberg, and L. Soete (eds) Technical Change and EconomicTheory (London: Pinter Publishers).

Hirschman, A. (1958) The Strategy of Economic Development (New Haven: Yale University Press).Gerschenkron, A. (1962) Economic Backwardness in Historical Perspective: A book of essays (Cambridge:

Harvard University Press).Hausmann, R., and Klinger, R. (2007) Structural transformation in Chile, Typescript, June.Hobday, M. (1995) Innovation in East Asia: The challenge to Japan (Aldershot: Edward Elgar).Holland, J. (1998) Emergence: From chaos to order MA: Addison Wesley).Hualde, A. (2010) Pymes y sistemas regionales de innovacion: la industria del software en Baja California y

Jalisco (Mexico: UAM/Textual).IAEI (2008) Israel Association of Electronics and Information Industries (Israel), available at: www.iaei.org.il.IVC (2008) Israel Venture Capital Research Center Database (Israel), available at: www.ivc-online.co.il.Kaldor, N. (1961) Capital accumulation and economic growth, in: F.A. Lutz, and D.C. Hague (eds) The

Theory of Capital (New York: St Martin’s Press).Kaldor, N. (1966) Causes of the slow rate of economic growth in the UK, in: J.E. King (ed.) Economic

Growth in Theory and Practice: A Kaldorian perspective (Cambridge: Cambridge University Press),pp. 279–318.

Kaldor, N. (1967) Strategic Factors in Economic Development (New York: Ithaca).Katz, J. (2000) Reformas estructurales, productividad y conducta tecnologica en America Latina (Santiago

de Chile: CEPAL/FCE).Katz, J., and Bernat, G. (2010) Micro to Macro interactions in the context of Argentine manufacturing activi-

ties, Paper presented in the VIII Globelics Conference, Malaysia.Kauffman, S.A. (1995) At Home in the Universe (New York: Oxford University Press).Kim, L. (1997) From Imitation to Innovation: The dynamics of Korea’s technological learning (Boston:

Harvard Business School Press).Klepper, S. (1996) Entry, exit, growth, and innovation over the product life cycle. American Economic

Review, 86(3), pp. 562–583.Klepper, S. (2001) Employee start ups in high-tech industries. Industrial Corporate Change, 10, pp. 639–

674.Kuznets, S. (1971) Economic growth of nations: Total output and production structure (Cambridge, MA:

Harvard University Press).Kuznets, S. (1973) Modern economic growth: Findings and reflections. American Economic Review, 63,

pp. 247–258.Levinthal, D., and Myatt, J. (1994) Co-evolution of capabilities and industry: The evolution of mutual fund

processing. Strategic Management Journal, 15, pp. 11–28.Lewin, A.Y., and Volberda, H.W. (1999) Prolegomena on co-evolution: A framework for research on strat-

egy and new organizational forms. Organization Science, 10, pp. 519–534.March, J.G. (1994) A Primer on Decision Making: How decisions happen (New York: Free Press).Mckelvey, B., Baum, J.A.C., and Donald, T. (1999) Campbell’s evolving influence on organization science,

in: J.A. Baum, and C.B. McKelvey (eds) Variations in Organization Science: In honor of DonaldT. Cambpell (New Delhi: Sage Publications).

Metcalfe, J.S., James, A., and Mina, A. (2005) Emergent innovation systems and the delivery of clinical ser-vices: The case of intra-ocular lenses. Research Policy, 34, pp. 1283–1304.

Moreno-Brid, J.C., and Ros, J. (2009) Development and growth in the Mexican economy: A historical per-spective (Oxford: Oxford University Press).

Murmann, J.P. (2002) The co-evolution of industries and national institutions: Theory and evidence. WorkingPaper, FSIV02.14, Social Science Research Centre, Berlin.

Murmann, J.P. (2003) The co-evolution of industries and academic disciplines. Working Paper WP03-1,Kellogg School of Management, North-Western University.

Murray, F. (2002) Innovation as co-evolution of scientific and technological networks: Exploring tissueengineering. Research Policy, 31, pp. 1389–1403.

Myrdal, G. (1957) Economic Theory and Undeveloped Regions (London: Routledge).Nelson, R. (1956) A theory of the low-level equilibrium trap in underdeveloped economies. American

Economic Review, 46, pp. 894–908.Nelson, R. (1994) The co-evolution of technology, industrial structure and supporting institutions. Industrial

and Corporate Change, 3, pp. 47–63.

Nelson, R. (1995) Recent evolutionary theorizing about economic change. Journal of Economic Literature,33, pp. 48–90.

Nelson, R. (2007) What makes an economy productive and progressive? What are the needed institutions?Staff Papers 13728, University of Minnesota, Department of Applied Economics.

Nelson, R. (2008) Economic development from the perspective of evolutionary economic theory. OxfordDevelopment Studies, 36(1), pp. 9–21.

Norgaard, R. (1984) Co-evolutionary development potential. Land Economics, 60, pp. 160–173.Norgaard, R. (1994) Development Betrayed: The end of progress and a co-evolutionary revisioning of the

future (London: Routledge).OCDE (2009a) OECD Reviews of Innovation Policy: Mexico (Paris: OCDE).OCDE (2009b) Regional Innovation in 15 Mexican States (Paris: OCDE).OECD (2004) Venture capital: Trends and policy recommendations, DSTI/DOC (2004), STI Working

Papers, Paris.OCS (2008) Office of Chief Science (Israel: Ministry of Industry, Trade and Labor), available at: www.moit.

gov.ilPerez, C. (1985) Microelectronics, long waves and world structural change: New perspectives for developing

countries. World Development, 13(3), pp. 411–463.Pinto, A. (1976) Notas sobre los estilos de desarrollo de America Latina. Revista de la CEPAL, first semester,

pp. 97–128.Pinto, A. (1978) Estilos de desarrollo: conceptos, opciones, viabilidad, in: A. Pinto (ed.) America Latina:

una vision estructuralista (Mexico: Facultad de Economıa UNA).Potts, J. (2000) The New Evolutionary Microeconomics: Complexity, competence and adaptive behavior

(Cheltenham: Edgar Elgar).Puchet Anyul, M. (2008) Incentivos, mecanismos e instituciones economicas presupuestas en el ordena-

miento legal mexicano vigente de la ciencia y la tecnologıa, in: G. Valenti (coord.). Ciencia,tecnologıa e innovacion. Hacia una agenda de polıtica publica (Mexico: FLACSO).

Pugatch, M., Teubal, M., and Zlotnick, O. (2010) Israel’s high tech catch up process: The role of IPR andother policies, in: H. Odagiri, A. Goto, A. Sunami, and R. Nelson (eds) IPR, Development and CatchUP (Oxford: Oxford University Press).

Rodrik, D. (2004) Industrial policy for the twenty-first century. Working Paper Series rwp04-047, HarvardUniversity, John F. Kennedy School of Government.

Ros, J. (2000) Development Theory and the Economics Growth (Michigan: The University of MichiganPress).

Rosenstein Rodan, P. (1943) Problems of industrialization of eastern and south-eastern Europe. EconomicJournal, 53, pp. 202–211.

Rostow, W.W. (1960) The Stages of Economic Growth a Non–Communist Manifesto (Cambridge:Cambridge University Press).

Sabato, J.A., and Botana, N. (1968) La ciencia y la tecnologıa en el desarrollo futuro de America Latina,in: J.A. Sabato (ed.) El pensamiento latinoamericano en la problematica ciencia-tecnologıa-desar-rollo-dependencia (Paidos: Buenos Aires).

Saviotti, P. (1996) Technological Evolution, Variety and the Economy (Cheltenham: Edgar Elgar).Saviotti, P. (1997) Innovation systems and evolutionary theories, in: C. Edquist (ed.) Systems of Innovation:

Technologies, institutions and organizations (London: Pinter Publishing).Saviotti, P., and Pyka, A. (2004) Economic development by the creation of new sectors. Journal of

Evolutionary Economics, 14, pp. 1–35.Schumpeter, J. (1934) The Theory of Economic Development (Cambridge, MA: Harvard University Press).Schumpeter, J. (1939) Business Cycles: A theoretical, historical and statistical analysis of the capitalist

process (New York: McGraw Hill).Sotarauta, M., and Srinivas, S. (2006) Co-evolutionary policy processes: Understanding innovative econom-

ies and future resilience. Futures, 38, pp. 312–336.Szirmai, A. (2009) Industrialisation as an engine of growth in developing countries. UNU-MERIT Working

Paper Series 010.Teubal, M. (1997) A catalytic and evolutionary approach to horizontal technological policies. Research

Policy, 25, pp. 1161–1188.van den Bergh, J.C., and Gowdy, J.M. (2003) The micro foundations of macroeconomics: An evolutionary

perspective. Cambridge Journal of Economics, 27, pp. 65–84.World Bank (2008) Commission on Growth and Development (Washington, DC: World Bank).Young, A. (1928) Increasing returns and economic progress. Economic Journal, 38, pp. 117–132.

Building bridges between co-evolutionary approaches to science, technology and innovation and...

Documents

Transcript of Building bridges between co-evolutionary approaches to science, technology and innovation and...

Civil Rules Interpretive Theory - Scholarship Repository

Interpretive Reproduction in Children's Peer Cultures

Interpretive practice and redefining meaning in Harry Potter

Timber Bridges - Institute for Transportation

Inspection of Railway Bridges - E- Learning

standards for old bridges

BRIDGES TO JUSTICE: - SPSSI

UNIT 12 BRIDGES - eGyanKosh

Interpretive Programming at the Caguas Botanical and ...

Bridges to the Future - NSTA

Building Bridges Beyond Boundaries

Bridges - Aware

Exploring the Interpretive Potential of Clovis Waste Flakes

AC BRIDGES - Government Polytechnic Deogarh

Seismic assessment of masonry arch bridges

bridges - Global Government Agreements

1637337273_DECAST Modular Bridges Culverts Brochure ...

Lumber Heritage Region Interpretive Plan - PA Wilds Center

SCIENCE BUILDING / BRIDGES - EuroScience Open Forum

MCMI-IV Annotated Interpretive Sample Report - Pearson ...