Approaching the Measurement of the Critical Mass of Science, Technology and Innovation How Far Off is Mexico

Approaching the measurement of the critical mass of science, technology and innovation: how far off is Mexico? Gabriela Dutrénit* and Martín Puchet**1 Abstract Newly industrialized countries behave well in the indicators related to their domestic science, technology and innovation (STI) capabilities (e.g. Korea and Singapore), which suggest that in some way they have achieved critical masses of STI capabilities. This may have allowed them to spawn endogenous processes that contribute to a development process. Such processes are also clear in developed economies, where a quite balanced structure of STI populations is observed. In contrast, other emerging economies are achieving remarkable success in some variables, like Brazil or India, but they still observe imbalances between the STI populations, they have probably not achieved yet critical masses in STI. The aim of this paper is twofold, first to discuss the concept of a critical mass in the context of STI and how to measure it, including some indicators, and second, how far off is Mexico in reaching critical masses in STI, and whether the design and implementation of STI policy has contributed to the development of critical masses of STI leading to the consolidation of a NSI. The empirical analysis focuses on the evolution of the main inputs and outputs of the NSI of some developed countries as a point of reference of what having critical masses may mean, and of some newly industrializing countries and others that are observing a remarkable performance, with others like Mexico, India and Russia that are moving, but still at a low pace. � Introduction2

A number of newly industrialized countries have achieved remarkable success in terms of economic and social development. In fact, they are moving towards the developed world. In contrast, most of the countries from the South are still looking for their own way to initiate a successful development trajectory, with different degrees of advance. There is a growing consensus about the centrality of scientific and technological advances in driving economic progress, and that increasing national investments in innovation are essential to ensure the countries’ economic growth (Schumpeter, 1942; Solow, 1956; Abramovitz, 1956 and 1986). However, no agreement has been reached concerning the processes linking innovation and growth, even less so when development is introduced into the analysis. Today it is also quite clear that the structure of linkages at local, regional,

��*Professor, postgraduate Program in Economics and Management of Innovation; Universidad Autonóma Metropolitana-Xochimilco, Mexico, [email protected]. **Professor, Faculty of Economics, Universidad Nacional Autónoma de México, [email protected]. ��We would like to thank Carlos Ramos and Rodrigo Magaldi for their research assistance.

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national and international levels, and the construction of a national system of innovation (NSI) contribute to that success (Freeman, 1987; Lundvall, 1992; Nelson, 1993; Edquist, 1997; Kim, 1997; Niosi, 2000; Cimoli, 2000; Cassiolato, Lastres and Maciel, 2003). From a structuralist and systems-evolutionary perspective (Schumpeter, 1934, 1939; Kuznets, 1971, 1973; and more recently Saviotti and Pyka, 2004) innovation affects economic growth and development if it triggers structural change (World Bank, 2008; Haussman and Klinger, 2007). This could be seen as the emergence of new sectors, markets, clusters, large multinational companies, and other forms of multi-agent structures (e.g networks, regional or sectoral innovation systems). An innovation and structural change-led economic development has to be placed in the context of the construction of NSI, as agents, functions and structures are important for the dynamics of change. Newly industrialized countries behave well in the indicators related to their domestic science, technology and innovation (STI) capabilities (e.g. Korea and Singapore), which suggest that in some way they have achieved critical masses of STI capabilities, including both science and technology (ST) and Innovation (Innov). This may have allowed them to spawn endogenous processes that contribute to a development process. Such processes are also clear in developed economies, where a quite balanced structure of STI populations is observed. In contrast, other emerging economies are achieving remarkable success in some variables, like Brazil or India, but they still observe imbalances between the STI populations. In many cases, the government ignited this process with a right design of STI policies and the assignment of resources in order to generate the accurate incentives. Hence, the STI policy, and also industrial policy, is called to play a key role in this process by fostering changes in the agents’ behaviours to increase demand and supply of knowledge (and a balance between both), stimulating the emergence of strategic sectors and new areas of competitiveness, and promoting cooperation and balance between regions within the country. Along this line, the coevolution of STI arenas emerges as a relevant process for building up such critical masses in order to accelerate a trajectory of innovation and structural change-led economic development. The promotion of such coevolutionary processes requires a systemic/evolutionary approach to STI policy (Nelson, 1994; Murray, 2002; Breznitz, 2007; Sotarauta and Srinivas, 2006; Smits, Kuhlmann and Teubal, 2010; Dutrénit, Puchet and Teubal, 2011). Once critical masses are reached, self-sustaining endogenous processes may be generated.3 However, a critical mass is a dynamic dimension, which evolves over time, thus it can be thought of as a moving target (Somasundaram, 2004). We do not know enough about what these critical masses of STI are, how they may be built and how they dynamically evolve, what is their relationship with co evolutionary processes ��

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of STI populations, and what the role of STI policies is in this process. This paper is inserted into this line of discussion and is a first approach to discuss the concept and measurement of critical masses of STI in emerging economies. The aim of this paper is twofold, first to discuss the concept of a critical mass in the context of STI and how to measure it, including some indicators, and second, how far off is Mexico in reaching critical masses in STI, and whether the design and implementation of STI policy has contributed to the development of critical masses of STI leading to the consolidation of a NSI. The empirical analysis focuses on the evolution of the main inputs and outputs of the NSI of some developed countries as a point of reference of what having critical masses may mean (Canada, Italy, Australia and Spain), and of some newly industrializing countries and others that are observing a remarkable performance (South Korea and Brazil), with others like Mexico, India and Russia that are moving, but still at a low pace. After this introduction, section 2 reviews literature related to critical masses and coevolutionary processes in STI; section 3 approaches the measurement of critical masses in STI, including which countries it makes sense to compare and which indicators may be used, this section discusses where Mexico stands; section 4 discusses the current STI policies in Mexico in the light of the idea of building critical masses of STI; and finally section 5 concludes. � Critical masses and coevolutionary processes Based on coevolutionary approaches to STI and ideas coming from development economics (Gersenkron, 1962; Rosenstein Rodan, 1943; Myrdal, 1957), Dutrénit, Puchet and Teubal (2011) argue that in order to place innovation as a powerful process for the production of structural change, the NSI has to reach a threshold of STI capabilities before emergent behaviour appears to generate a structural change-led development. In other words, critical masses seem to be needed in order to generate self-sustaining endogenous processes.

�� The concept of critical mass and threshold The concept of critical mass has been introduced in different disciplines. It is usually used to determine when a certain level of accumulation of a capability or stock makes it possible to shoot a result that characterizes the process under study, and is maintained from there at a high rate of growth. For instance, in nuclear physic: “A critical mass is the smallest amount of fissile material needed for a sustained nuclear chain reaction. The critical mass of a fissionable material depends upon its nuclear properties (e.g. the nuclear fission cross-section), its density, its shape, its enrichment, its purity, its temperature and its surroundings. When a nuclear chain reaction in a mass of fissile material is self-sustaining, the mass is said to be in a critical state in which there is no increase or decrease in power, temperature or neutron population.” (http://en.wikipedia.org/wiki/Critical_mass)

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The concept has been largely used in relation to collective actions, related to the analysis of the e.market, the emergence of open source communities and other online communities, or in innovation diffusion (Granovetter, 1978; Oliver, Marwell and Teixeira, 1985; Mahler and Rogers, 1999; Somasundaram, 2004; Booij and Helms, 2010). Booij and Helms (2010) relate the concept of critical mass to “… the idea of a point at which a community can suddenly gain a large amount of new members in a short period”. They assert that critical mass is a change in the state of this population, which happens during its growth stage, marking the point at which the community becomes self-sustaining. In growth theory, Azariadis and Drazen (1990) introduced the concept and linked it with another two used in development economics: the Poverty traps and the threshold effects. The motivation and context in which these authors introduced the concepts are associated with the fact that theories of economic growth required more robust models from the empirical point of view. This occurs when the models are insufficient to explain very different growth rates and, in turn, presents the co - existence of economies whose paths diverge in a systematic way, along with others that generate a take off getting them closer to those that grow faster. In this context, Azariadis and Drazen (1990) argue that: “Earlier investigators focused on the ‘preconditions’ that an economy must satisfy to move from low to sustained high growth” (p. 503). They explicitly refer to Preobrazhensky (1926), Rosenstein–Rodan (1943) and Nelson (1956), and also to Rostow (1960). Coming from an evolutionary economics perspective, Dutrénit, Puchet and Teubal (2011) also draw on these authors to build bridges between the analysis of coevolutionary processes of STI and economic development. Azariadis and Drazen (1990) considered that to capture these divergent processes is sufficient to introduce an additional feature in the neoclassical model. This feature is the “…technological externalities with a “threshold” property that permits returns to scale to rise very rapidly whenever economic state variables, …[and they add], take on values in a relatively narrow “critical mass” range.” (p. 504) The threshold effects are “…radical differences in dynamic behaviour arising from local variations in social returns to scale” (p. 508) In particular, for the extended neoclassical model with human capital, they concluded: “There are two ways in which human capital accumulation can result in multiple balanced growth paths and thus explain development takeoffs. Reaching a given level of knowledge either makes it easier to acquire further knowledge (…) or induces a sharp increase in production possibilities (…). Both of these possibilities mean that threshold externalities are due to the attainment of a critical mass in human capital.” (p. 513) Even though this literature introduces the discussion on critical masses and threshold effects generating self-sustaining processes, knowledge about the economic mechanisms that explain the jump from one stage to a higher one is still limited.

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�� The concept of critical masses in the arena of STI capabilities The literature asserts that coevolution is a process that links the evolution of different arenas of a system, where changes of one induce changes on the other –e.g. they are causally linked. There is a growing literature that applies coevolutionary concepts to the study of socio-economic systems, although challenging issues for transferring evolutionary concepts and insights from the biological to the social arenas have been recognized (Norgaard 1984 and 1994; Levinthal and Myatt 1994; March 1994; Nelson 1995; McKelvey, Baun and Donald 1999; Lewin and Volberda 1999; van den Bergh and Gowdy 2003).

A stream of literature focuses on coevolution in the arenas of science, technology and innovation. Nelson (1994) discusses coevolution between technology, industry and institutions; Murray (2002) focuses on industries and national institutions; Murmann (2003) on industries and academic disciplines; Metcalfe, James and Mina (2005) analyse coevolution between clinical knowledge and technological capabilities; and Nyggard (2008) between technology, markets and institutions, amongst others. Some authors introduce the role of policies in coevolutionary analysis. In this line, Breznitz (2007) analyses the coevolution between technology, policy, industry and state; Fagerberg, Mowery and Verspagen (2008) approach the case of the Norwegian National Innovation System and introduce the innovation policy; 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 coevolution of technologies, firm and industry structure, and economic institutions than as an arena that may coevolve with the others. Following Dutrénit, Puchet and Teubal (2011), we define Science/Technology (ST) and Innovation (Innov) as two activities that transform capabilities into outputs, thus the populations can be defined in terms of either one or both variables.4 The population of capabilities for ST is formed by researchers/teachers working in the research and higher education system, and for Innov by engineers and technicians, including doctors in science and engineering, involved in innovation activities. Outputs of these populations include human resources (graduates and postgraduates), specific knowledge and new capabilities for ST, and new products and processes and patents for Innov. These populations evolve following the three causal processes (variation, selection and retention) as analysed by Campbell (1969), and coevolve as they build bidirectional causal mechanisms that may link their evolutionary trajectories (e.g. cooperation), as discussed by Murmann (2002). Institutions govern the behaviour of the coevolving populations. Based on the literature reviewed in Section 2.1 and the focus of this paper on the STI arenas, critical masses can be defined as the level of capabilities at which the system is able to generate endogenous processes and thus became self-sustaining. Critical masses of ST and Innov correspond to a level of capabilities that allow virtuous coevolutionary processes

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of these populations. Even though coevolutionary processes may appear below critical mass levels, only after that point such processes became self-sustaining. Azariadis and Drazen (1990) incorporate human capital into their neoclassical model in two ways: an easier form to acquire knowledge, or a sharp increase in production possibilities. For both routes technological externalities are generated, and they induce development when they reach a critical mass of accumulated human capital. In the same direction, Dutrénit, Puchet and Teubal (2011), following an evolutionary approach, analyze the interaction between people that can create the easiest way to acquire knowledge, as researchers in the arena of ST, and one that has capabilities to dramatically increase production capacity, as engineers and technicians in the arena of Innov. They argue that when there is co-evolution between the two populations it is possible to generate positive externalities between these populations and build a critical mass of them in order to switch from one to another stage of development. These are the factors that make it possible to move from one stage of development characterized by a NSI that observes only pre-conditions, leading to a trap of low growth, to other steps that may emerge when the NSIs have created the conditions for sustained growth. In the absence of these externalities and critical masses, which depend on elements of organizational, institutional and public policy, the NSI tends to remain at the level of pre-conditions for development. The take-off requires a virtuous combination of qualitative measures with adequate investment levels in size and composition. In these NSI, which only observe pre-conditions, the STI capabilities are characterised by: a narrow ST base/infrastructure, weak institutions and distorted structure of incentives, a profile of innovation activities based on the use of existing knowledge, and little variation (a pre-condition for mutually beneficial selection and reproduction). Under these conditions, innovation policy has not been sufficiently effective to serve as a base for STI evolution and coevolutionary processes in these arenas. Hence, we can assume that the initial conditions are below the critical mass required to trigger endogenous virtuous processes; the system can coevolve but it will only be capable of reaching a low level equilibrium, which will probably be a trap.5 This approach to critical masses of ST and Innov and to coevolutionary processes between these populations seeks to contribute to the understanding of how innovation can be placed as a powerful process in order to produce structural change and to avoid the traps of low growth.

�� How to measure critical masses? An empirical approach

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The measurement of critical masses has two types of problems that are difficult to resolve operatively. The first is common to the measurement of capabilities, particularly those with intangible elements such as the human capital, intellectual capital or capital. The second refers to the case when a critical mass level is added, and such level is critical in the context of the functioning of the system. Given a set of options for adaptation to the environment, if a level of efficient operation is reached, it means that there are capabilities. To measure the size of the capabilities, for example, researchers and teachers, who in turn train new researchers and professionals, and engineers and technicians who develop innovation, measurement are needed both on the level they reach and on the capability composition they have. It is clear that a certain population size is required in order to have variation, enabling selection and allowing retention of a level that allows individuals to trigger growth. At the same time not all compositions of individuals in organizations (by specialty, skills, etc.) ensures that the process is generated. In this regard, generally, levels of capability are measured quantitatively by the amount of members of a given population. The profiles of these members are largely intangible, such as their specialties, which go beyond the certification of knowledge, or their skills, which are formed through experience and are located in the organizations where they work. These profiles are highly difficult to define and measure. In these cases it is not enough to simply list categories of expertise or skills that make up the population, then add their elements. It is about knowing how each category works properly in relation to its environment. However, to qualify as a critical mass, these capabilities should be designed into the system, and their relevance will be to generate a discontinuous change in any output of the system. Therefore, the measurement of critical masses has to put the capabilities in a chain of input-output of the system studied, and must conceive the dynamic behaviour of this link. One way to identify the presence of a critical mass in STI may be to consider an output –e.g. the proportion of high-tech exports, and correlate with an input –e.g. the expenditures on R&D. When the output is stable or growing (but not decreasing on average), then you may think that it has acquired the critical mass associated with the necessary capabilities to enable the magnitude of output is sustained over time. You can think of a timeline in the evolution of a critical mass if the process is seen higher after periods of stagnation or decline. This illustrates that there may be emergence, growth and strengthening of capabilities but we can also observe a decline in them, which would lead to a loss of critical mass. At the same time, as there may be critical masses who are associated with different processes, eg ST and Innov activities, there may be mismatches between the capabilities of each other to cause cross-effects. Failure to achieve critical masses of science will probably not reach stabilization of the innovation processes, although the level of input will stand at the threshold supposedly appropriate. Therefore, accurate measurement of critical masses seems to require a number of indicators that include both inputs and outputs of the populations concerned. Thus, system

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performance will be a reference to the threshold required for the capability to qualify as a critical mass. In the absence of both paths of inputs and outputs of STI, which qualify in one economy to identify a critical mass, it is required to act in comparison. For instance, you need to take the critical mass in another economy as reference. This adds an additional difficulty due to the fact that this translation is always mediated by the structural characteristics of economies that are taken as benchmark. Therefore, the proposed measurement of this work involves the structural characteristics of economies; it is understood that a critical mass of economies cannot be mechanically transferred on to others. � Towards measuring critical masses in STI This section is a first approach to the idea of measuring critical masses in STI. Even though the meaning of having critical masses in STI may be ambiguous, the STI capabilities of those countries that are having a remarkable performance may be close to what we can term critical masses in STI. Hence, this section compares the main inputs and outputs of the NSI of some developed countries as a point of reference of what having critical masses may mean (Italy, Canada, Australia, Spain) and a newly successful industrializing country (South Korea). For comparison with Mexico, the analysis includes emerging countries that are part of the BRIC (Brazil, Russia Federation and India).

�� Which countries may be compared: looking at the structural characteristics

Countries differ in their structural characteristics, like economic structure, market size, structure of their export (commodities versus manufactured products, technological contents, etc.), average age of the population, level of education, etc. Some of these characteristics are associated with the country size and endowments while others relate to the level of development, hence differences emerge if they pertain to the developed, emerging or newly developing world. These characteristics may condition their STI capabilities and consequently their STI policies. If we are looking to understand what the critical masses in STI are, it makes sense to make comparisons between countries taking structural characteristics into account. In this line, the countries that were selected to be compared with Mexico include newly industrializing countries that are observing a remarkable performance (South Korea), BRIC countries (Brazil, India, Russia Federation) that have similarities with Mexico in terms of their size, background conditions by the 1960s-1970s or the development model they followed, and developed economies of medium size (Australia, Canada, Italia, Spain). The analysis of structural characteristics of these countries is based on their evolution through 3 periods, which are relevant in terms of Mexico and other emerging and

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developing countries: 1990 (previous to the Washington Consensus), 2000 (post Washington Consensus) and 2008 (current period). For this analysis indicators that reflect percentages and amount of different economic and social aspects of the countries were included. The evaluation of three years illustrates the evolution of the profile. Three set of indicators were included:

� Relative size of the economies (GDP PPP) � Basis for developing the capabilities of the systems: health, education and income

through the Human Development Index (HDI), diffusion of information technologies (Internet users), percentage of age in tertiary education, Studying Population, eg in Higher Education Institutions (HEI Coverage)

� Achievement of economies: GDP per capita, inequality (GINI), export capacity of high technology goods (% High Tech exports)

All the structural graphics are related to the country that has a higher value in the period under review (1990, 2000 and 2008), which mostly refer to 2008:

� HDI: Australia � Internet users and Coverage of HEI and % High Tech exports: Korea � GDP per capita: Canada � GDP: India � Gini: Brazil in 1990

Figure 1 illustrates the structural characteristics of the countries. The structural profiles of the set of developed economies included in the analysis and of a successful new industrializing country like Korea show some similarities:

� The economies are smaller than the other set of countries � They have better life conditions, as revealed by the high HDI, % of Internet users

and Coverage of HEI. � These economies observe a better performance, as they have a very high GDP per

capita, lower inequity (low GINI), and Korea and Italy have the highest coefficient of High Tech exports in their total exports.

In contrast, the profile of the emerging economies has the following features:

� Their economies have a bigger size � The life conditions are deficient as revealed by a relatively low HDI, and low % of

Internet users and Coverage of HEI. � Their performance remains poor, as revealed by a much lower GDP per capita, high

inequality (high Gini) and the low share of high technology exports in the composition of their exports, except for the case of Mexico.

In the case of Mexico, the % of high tech exports in total exports is relatively high, although significantly lower than in Korea. These exports are the result of a particular form of operation of the MNCs, which has neither generated the expected knowledge spillovers nor improvement in living conditions. (Dutrénit and Vera-Cruz, 2007; Carrillo and Hualde, 1997)

Figure 1. Structural profile of the countries

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�� Key indicators for critical masses in STI From the perspective of the NSI, an analysis of critical masses in ST and Innov may include indicators of inputs and outputs of this system. The two populations that were defined in section 2.2 were ST and Innov, thus indicators of inputs and outputs for these populations may be included. According to the focus of this paper on measuring critical masses of ST and Innov to explain the generation of self-sustaining processes, both indicators of composition of the expenditure and results and indicators of the amount of these dimensions are relevant. In this line, the effort in R&D as a percentage of the GDP is important but also the absolute amount of expenditure in R&D. As in the case of the structural characteristics, the analysis of the ST and Innov characteristics of these countries is based on their evolution through three periods: 1990, 2000 and 2008. Six indicators for STI, including indicators for percentages and amount of ST and Innov, were selected to illustrate the evolution of the profiles over the period: � Gross Domestic Expenditure on R&D as percentage of the GDP (GERD/GDP) � Business Expenditure on R&D as percentage of the GERD (BERD %) � Business Expenditure on R&D in millions dollars (BERD $) � Scientific articles per million population (Scientific articles) � Researchers per thousand employed (Researchers) � Triadic patents granted (Patents)

All the STI graphics are related to the country that has a higher value in the period under review (1990, 2000 and 2008), Australia in articles and Korea in all the other variables. Figure 2 illustrates the STI profile of the countries.

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Figure 2. STI profiles of the countries Profile 1.

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Profile 2.

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Three profiles of STI emerge from the graphics: 1. Balanced profile of the highest values: There is a balance between all indicators of

ST and Innov (Korea and Italy); there are differences in the magnitudes of the indicators between the two countries but Korea has higher values in all variables.

2. High values profile biased towards ST: High values in all variables, a high GERD but minor BERD and patents, in other words, there is relatively less effort and performance of the business sector, and greater importance of indicators of effort and results of ST (Australia, Canada, Italy and Spain). It is worth mentioning that the first two countries with this profile have a very small population relatively to the very large land.

3. Low values profile biased towards Innov: Low values in all variables except the BERD%, the business sector contribution to the GERD (Brazil, India, Mexico and Russia). The four countries that observe this profile have both a very large population and land. Hence, even though they have high potential, they observe the worst profile. India, Brazil and Mexico are quite similar, but the Mexican GERD is lower. Russia observes some differences, with a much higher level of researchers, and a decrease in the level of the researchers and GERD from 1990 on.

In general, Figure 2 reveals: � A high value of the BERD%, in other words, the business sector makes a high

contribution to the total GERD. � Korea has the highest values; it is overcome by Australia in the case of scientific

papers. It has an outstanding performance in terms of the private and public expenditure in R&D and in patents granted.

� All the countries, except Russia, observe an increase in the value of the indicators between 1990 and 2008.

� The BERD% was already high in 1990; hence it observes moderate increases. � In all developed countries, including Korea, there is a balance between the levels of

the ST and Innov indicators, and in terms of inputs and outputs. This balance is not observed in the case of the emerging economies.

Figure 3 shows the current situation, data from 2008. Korea has the highest values for all the indicators, except for scientific papers per million populations, where it is overcome by Australia. This illustrates that Korea ha based its development more on Innov than on science, and has a relative weaknesses in ST. This trajectory is similar to that followed by Japan. (Kim, 1997)

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Figure 3. STI profile of the countries, 2008

Concerning Mexico, Figure 2 reveals the low and stagnated GERD, which has not grown from the 1980s. The BERD$ is very low, but as the public expenditure in R&D is also low, this contributes to explain the high BERD%. The analysis of the STI profiles suggests that: � More developed countries, like Italy, Canada, Australia and even Spain, or successful

new industrializing countries like Korea have more balanced trajectories of ST and Innov capabilities, as observed in Profiles 1 and 2.

o The trajectory of both populations is coherent; both populations have grown over time.

o Inputs and outputs have grown, in some cases more success of ST while in others of Innov.

o If these countries have reached critical masses, or are closed to them, this suggests that in order to reach critical masses it is required to invest in both populations.

o This balance also suggests that the evolutionary trajectories of these capabilities are linked.

� The evolution of the trajectories of ST and Innov in emerging economies like those of Profile 3 is not balanced; they observe a bias towards one of the populations.

o The populations follow different trajectories; the population of Innov, particularly related to effort, has grown the most.

o The outputs are not related to the growth of the inputs o The imbalance suggests that the evolutionary trajectories of these

capabilities are not linked, thus the existence of coevolutionary processes with an endogenous self–sustaining tendency, at least a strong one is not clear.

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The comparison between the structural features and NSI profiles of the studied countries offers lessons. The conditions relating to human development, particularly in health which is reflected in life expectancy at birth and education that are expressed in literacy and education, with tertiary education coverage and internet access are a substantial part of the basis for the performance of countries and are pre - conditions for the evolution of the NSI. Therefore, when comparing the profiles of the NSI obtained by measuring inputs and outputs relating to both ST and to Innov, clear differences emerge between the developed and newly industrialized countries regarding emerging countries. Furthermore, in the cases of Brazil and Mexico it is important to note that these conditions, engendered in the country structure, are distorted by the high degree of inequality that they record. In these countries, moving towards a sustainable co-evolution of ST and Innov not only requires the levels of coverage and access to better living conditions, but also what must be considered as building more equal societies. Confronting Russia and India with Brazil and Mexico, shows that although all living conditions are deficient, and even more disharmonious in the Russian case, the first two countries have the largest economies and lower levels of inequality, although India has high levels of poverty. It is also important to note that the proportion of high tech exports is not among the conditions that necessarily equate NSI with adequate critical masses. The cases of Korea and Italy stand out as those that have achieved a NSI profile clearly associated with this structural characteristic. Nonetheless, Canada, Australia and Spain show a much smaller presence of such exports but they also have a NSI profile with levels of their capabilities that are approximate to the critical mass. In Australia and Spain critical masses are clearly observed which are not yet harmonious in relation to Innov. The critical masses required to reach a NSI that presents a sustained co-evolution are conditioned by certain structural characteristics. However, the relationship between these conditions and the attainment of the masses are mediated by the different ways in which these conditions are articulated with size, high tech exports, per capita product and income distribution in the economies. These different ways are relevant when institutional and policy designs, which promote the achievement of levels and compositions of the corresponding capacities that will build critical masses, are being thought of. It is required then to highlight that critical masses for the attainment of mature NSI do exist, those where a sustained co-evolution reigns, but the initial structural characteristics must be considered as conditions that are configured in different ways. The balances between types of markets and the orientation of production towards the domestic or foreign markets, the degrees of inequality in the income distribution regarding access to health coverage, education and information and communication technologies, and the relative sizes of the economies must be considered in order to formulate long term policies that aim towards the emergence, structure and consolidation of critical masses of the NSI capabilities.

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�� How far off is Mexico? Tables 1 and 2 list some of the indicators used to analyse critical masses of ST and Innov in 2008, respectively, only for Mexico, Korea, Brazil and India. Referring to the critical mass in ST, table 1 shows a set of indicators that give an account of different aspects of the ST capabilities of the selected countries. Concerning the inputs:

� The indicator of the national effort to foster basic science reveals that Mexico is making a lower effort than Korea, but its efforts are higher than those made by Brazil. It is worth mentioning that even though the effort deployed by Brazil as a percentage of the GDP is lower than Mexico’s, the Brazilian economy is fifty percent larger the Mexican one, hence in terms of the amount of resources devoted to funding science, Brazil is devoting the same amount of resources.

� The indicator relating to the academic researchers in the NSI shows that Korea has an outstanding behaviour in terms of the percentage of researchers in relation to the total employment (10.02 per 1000 total employment); however, the number of academic researchers (53,274) relates to its small population comparing to the other countries. In contrast, the percentage of researchers in relation to the total employment in Brazil is much lower (1.48 per 1000 total employment), however, it observes a much larger number of academic researchers (158,314). The combination between the percentage and the amount suggest that Brazil has strength in ST capabilities. Unfortunately, the Mexican capabilities measured by both indicators of human resources are quite low.

Table 1. Critical mass in ST (2008)

Inputs Outputs

Country

Population,

millions

(2010)

Basic Research

Expenditure as % of

GDP

Researchers

(per 1000 employed)

Researchers in

universities and

research centres

PhD awarded

(per 100.000

population)

Scientific Articles

(per million

population)

World Share of Scientific Publicatio

ns (%)

Mexico 112,3 0.09* 0.9* 20,891*

3.2* 73.3 0.8

Korea 49.0 0.50 9.5* 53,274*

19.8 762.2 3.3

Brazil 190,7 0.06 2.2* 158,314 5.2* 141.4 2.7

India 1,210,2 NA 0.4** NA NA 35.5 3.7

Note: *The data is for 2007, ** The data is for 2004, NA: not available. Source: Own elaboration with information from OECD (2010c, d), CONACyT (2009), UNESCO (2010) Concerning the outputs:

� Even though Korea overcomes the other countries, in terms of articles per population, its contribution to the world scientific publications is similar to that of Brazil and India, again as a result of the amount of researchers of each country. Mexico is below the levels of the other countries, with exception of the articles per population in India.

� There are no significant differences between Mexico, Brazil and India in terms of the PhD awarded, while Korea has much higher levels.

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Summing up, in terms of the inputs and outputs for science, Korea performs much better than the other selected countries, having outstanding levels in most of the science capabilities related variables. Mexico’s indicators for science suggest that this country is below the critical mass in science, and that the number of researchers working in universities and research centres does not correspond to the expenditure in basic research. Referring to the critical mass in Innov, table 2 shows a set of indicators that give an account of different aspects of the Innov capabilities of the selected countries. Concerning the inputs:

� Data related to the national public and private financial effort devoted to R&D (GERD/GDP) reveals that the effort made by Mexico is far below the other countries.

� The amount in million dollars devoted to Innov is also important in order to reach scales; the Mexican GDP is larger than the Korean one but the expenditure in R&D is eight times lower. Brazil doubles the expenditure made by Mexico, having a national effort almost three times larger. A significant portion of this expenditure is generated by the business sector in Korea, while in Mexico and Brazil the public sector is still the main funder of R&D expenditure.

� Mexico is behaving better in terms of the proportion of researchers working in the business sector, approaching the levels of Korea. Brazil is still far below in this indicator.

Table 2. Critical mass in Innov (2008)

Inputs Outputs

Country

GDP world position (millions

dollars, IMF)

GERD (million dollars)

GERD/GDP (%)

BERD/GERD (%)

Researchers

in the Business Sector

(%)

Innovative manufact

uring firms in

total firms (%)

Triadic patents granted

(per million

habitants) Mexico (2007)

14 (1,004,042) 5,856 0.38 44,6 52.7 29.8 0.14

Korea 15 (986,256) 44,026 3.37 73.7 65.6 42.0 43.9 Brazil 8 (2,023,528) 11,269 1.13 47.5 19.8 33.3 0.34 India 11 (1,430,020) 29,021 0.88 29.6* 31.0 NA 0.14

Notes: * The data is for 2007. Source: OECD (2010a, b, c), Bogliacino, et al. (2009), MEST (2011), MCT (2011) and MOST (2011). Concerning the outputs:

� Korea reports the highest percentage of innovative firms. No significant differences can be observed between the other countries. Even though this data comes from the national innovation surveys, which are perception surveys, it provides an idea whether the firms are introducing innovations or not.

� Regarding triadic patents, Korea exceeds all countries, with an overwhelming 44 per million habitants while other compared countries fail to achieve even 1 patent per million habitants. Only Korea has a high intensity of patenting (OECD, 2009). Therefore, Mexico and the other countries seem to be under the critical mass.

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Summing up, in terms of the inputs and outputs of Innov, Korea performs much better than the other selected countries, having outstanding levels in the GERD as percentage of the GDP, even overcoming the number proposed by the Lisbon strategy. Mexico’s inputs and outputs indicators for Innov suggest that this country is below the critical mass; the distance is particularly significant in the national expenditure on R&D as percentage of the GDP. There is a clear imbalance between this meagre national effort and the relatively high percentage of researchers working in the business sector. Korea stands out in virtually every indicator of the capabilities of ST, both in inputs and in outputs; regarding Innov indicators Korea is also a leader in capabilities building amongst these countries. If we consider that this country has reached or is close to reaching a critical mass in both ST and Innov, the values observed in the case of Mexico suggest that this country is below those capabilities. The limited GERD both in total amount and as percentage of the GDP is one factor that contributes to explain the existing capabilities. � Current STI policies in Mexico: is Mexico looking to reach

critical masses? Since CONACyT started operating in 1970, Mexico has designed a series of national programs dedicated to foster science and technology (and just recently, also innovation). During the last decade, two programs have being designed and implemented: the Special program on Science and Technology (PECYT in Spanish), active from 2002 to 2006 and the Special Program on Science, Technology and Innovation (PECiTI), active from 2007 to 2012. Both programs included core objectives and strategies to foster STI, identify strategic sectors for Mexico to invest in, and establish programs to reach the proposed objectives and allow the country to become a knowledge economy. Both programs recognize strategic areas that are set to become investment priorities in national policy; these include high technology sectors (Pharmaceuticals, IT and computers, electronics, aeronautics, medical and health sectors) and medium high sectors (chemistry and oil related processes and transportations) (Dutrénit et al. 2010: 156). STI policy design in Mexico has evolved by steps; starting from a linear model, policy makers were learning how to introduce a more interactive model. However, implicit priorities in resource allocation (science and human resources), which are rooted in the first years of existence of CONACyT, continue to stamp the policy implementation. In line with the goals of the new STI policy model, CONACyT introduced, reformed or continued implementing several policy instruments and programmes in support of STI activities. The instruments are around 60 funds or programs designed in line with the governing objectives of the PECyT and PECiTI. In this sense, they are oriented to foster basic and problem-oriented research, the regionalisation of the activities, the R&D and innovation activities by the business sector and the formation of human resources. Most of them operate under the scheme of competitive funds. In addition, a program of fiscal incentives for R&D was operating from 2002 to 2008.

� ��

Contrary to the objectives established by PECyT and PCiTI in relation to a growing and continuous public expenditure on STI, expansion in both the Federal Expenditure on STI and CONACyT’s budget was rather slow. This impacted the implementation of the policy mix by limiting resource allocation to some instruments, thereby affecting also their already low interaction. In terms of the implementation of CONACyT’s budget, the persistence of certain inertias, mainly regarding the National Researchers System’s6 payrolls and postgraduate scholarships, has inhibited the achievement of equilibrium in the accomplishment of the core objectives. Figure 4 presents CONACyT’s budget in 2009 allocated to the different programs (728 million dollars); the programs are grouped into the main objective they deal with (human resources, formation, basic science, applied science and innovation). Figure 4. CONACyT policy mix, 2009 (%)

Note: Human resources formation includes, scholarships, NRS, and exchange programs; Basic science includes some sectoral funds; Applied science includes some sectoral funds and all the regional funds; and Innovation includes AVANCE, Program of stimulus for innovation, and some sectoral fund innovation. Source: Own elaboration based on CONACyT information. Budget of 728 million dollars The resource allocation reflects the implicit STI priorities, which differ from what was established first in PECyT and later on in PECiTI: (i) Emphasis on the formation of human resources and the support to basic science, (ii) small amount of resources dedicated to problem oriented research, and, (iii) small amount to foster R&D and innovation of the business sector. The amount dedicated to human resources formation

��*�The NRS is one of the STI instruments with the longest tradition in the country, dating back to 1984; its main goals include the promotion of the formation, development and consolidation of a critical mass of researchers at the highest level, mostly within the public system of higher education and research. The NRS grants both pecuniary (a monthly compensation) and non-pecuniary stimulus (status and recognition) to researchers based on the productivity and quality of their research.�

� �"�

(National Researchers System and Postgraduate scholarships) represents 62% of the total. If we include basic science the amount increases to 67%. In contrast, the new objectives like applied science (sectoral and regional funds) and innovation only received 8% and 13%, respectively. The effort dedicated to the design of a variety of instruments contrasts with the concentrated allocation of resources to a few. The resources assigned to foster innovation suggest that they are still operating as pilot programs. The fiscal benefits for R&D were an important incentive, but it operated until 2008 because changes in the general taxation system made this specific tax credit not suitable.7 Regarding design, Mexico has followed recommendations by international organisms based on countries with more mature NSI or the experience of successful emerging economies (such as Korea, China, Singapore, or even Brazil). Concerning the implementation, the resource allocation shows a different set of implicit priorities and some rigidity, such as the fact that resources continue to be mostly assigned to programs on basic science and human resources formation, and the new programs designed to foster oriented research and innovation still received limited resources. Referring to the instruments for fostering R&D and innovation activities, most of the instruments focus on the final stages of R&D, which correspond to post R&D activities (last phase of advanced development and development for commercialization), and other innovation activities not R&D based. In contrast, only the fiscal incentives for R&D are directed towards the development of technology. Moreover, the instruments benefit firms who already have some R&D or innovation capabilities; there are no instruments directed to increase the number of firms that deploy such activities. In addition, there are neither instruments to stimulate the demand for innovative products nor to stimulate transfer, assimilation and improvement of existent technologies. (Dutrénit et al, 2010) In other words, the design has focused more on the increase and mould of existent capabilities, without much learning, than on the creation of a critical mass to generate endogenous processes. � Final comments

This paper is a first approach to discuss the concept of critical mass in the STI arenas and to measure them in the context of emerging economies. Particular attention was given to how far off Mexico is in reaching critical masses in STI, and whether the design and implementation of STI policy has contributed to their building. The main results are located on a string of thought that includes: the concept of critical masses in the STI arenas, the problems with its measurement, a proposal on how to measure them, an application of such a proposal taking into consideration the structural differences between the countries that are considered as benchmark, and a consideration of the distance that separates Mexico from the critical masses necessary for a sustained co-evolution within its NSI.

��1�4��56+��

� �#�

In conceptual terms the adopted definition follows others that have been done in Economics and in Social Analysis. Its specificity lies in that it is consistent with the evolutionary analysis applied to the NSI and, in particular, with a co-evolutionary approach of development. The proposed measurement takes into consideration the difficulties of measuring capabilities, of doing so when it regards their evolution and of using references from other systems’ trajectories. Thus, the measurements for nine economies –five of them developed or recently industrialized and four of them emergent- are extremely provisional. This is so because the construction of indicators for the defined concept, and with the noted difficulties for measurement, supposes an analysis of statistical national sources more detailed than the one that has been done from international systemized sources. Nevertheless, the profiles obtained for the NSI and their linkages to the structural characteristics of the countries authorize the relative positioning and the comparisons that have been made. At the same time, these measurements are consistent with the analysis of results of STI policies that aim towards creating critical masses. In this sense the analysis made for Mexico rightfully aims towards alternative formulations of such policies. Since 2000 there has been an effort to redirect the STI building to the construction of the NSI, the institutional and legal changes and STI programs aimed towards this goal. The policy mix has improved from identifying successful programs and introducing new programs to filling the gaps that have emerged. However, policy learning has been slow, as the improvement in the indicators that measure inputs and outputs of the NSI. This poor effort and performance of NSI is seen more clearly by comparing the value of indicators of inputs and outputs with those observed in countries that have similarities in terms of size, the initial conditions that showed up in the 1960s-1970s, or development models that they have followed, like Korea and Brazil. It seems that Mexico has not exceeded the threshold of STI capabilities that can generate an endogenous dynamic allowing the NSI to develop, as perceived in some of these countries. STI policy is called upon to accelerate the building of this critical mass of STI capabilities, but this requires a systemic/evolutionary approach to STI policy, which looks to the system- the NSI, focuses on the generation and absorption of knowledge as nonlinear dynamic models, and on systemic failures, not only on market and government failures. For this approach, learning, accumulated capabilities and time matter, institutions mediate between agents, and there is an increasing concern for the regional level and the governance of the NSI. (Metcalfe, 1995; Teubal, 2002; Woolthuis, Lankhuizen and Gilsing, 2005; Smits, Kuhlmann and Teubal, 2010) Today there is a strong emphasis on innovation, but drawing on this systemic/evolutionary approach, and following Dutrénit, Puchet and Teubal (2011), this paper argues that the focus should be put on building critical masses of STI capabilities, to the extent that a focus on innovation is limited since science capabilities are also still below the critical masses, and these ST capabilities are also needed for knowledge generation, technology transfer and human resources formation. In addition, the existent

� �*�

knowledge base may be enough today, but new knowledge based on higher ST capabilities will be required for the next step on the building of innovation capabilities. One of the principles for building the critical masses of STI capabilities is a more efficient allocation of resources and greater budgets spent on STI activities. This is required to achieve an ample variety of researchers, firms or projects dedicated to STI, which will allow a better selection process, and to generate the conditions for an efficient retention process. Increased budget is required to allocate additional resources to new demands, lacking additional resources it is very difficult to generate a structural change in the economy without the emergence of governance problems. Several questions remain pending to be answered; this paper would like to highlight two key questions. First, the analytical framework used for the STI policy design was conceived on the bases of countries with different initial conditions -the central economies; to what extent is this framework useful to be applied in economies with different initial conditions, like developing countries? Second, if the idea of a threshold is useful and a critical mass has to be achieved, how can the level of a critical mass be identified? These questions require further research.

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