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

  • Approaching the measurement of the critical mass of science,technology and innovation: how far off is Mexico?Gabriela Dutrénit* and Martín Puchet**1AbstractNewly industrialized countries behave well in the indicators related to their domesticscience, technology and innovation (STI) capabilities (e.g. Korea and Singapore), whichsuggest that in some way they have achieved critical masses of STI capabilities. This mayhave allowed them to spawn endogenous processes that contribute to a developmentprocess. Such processes are also clear in developed economies, where a quite balancedstructure of STI populations is observed. In contrast, other emerging economies areachieving remarkable success in some variables, like Brazil or India, but they still observeimbalances between the STI populations, they have probably not achieved yet criticalmasses in STI. The aim of this paper is twofold, first to discuss the concept of a criticalmass 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 andimplementation of STI policy has contributed to the development of critical masses of STIleading to the consolidation of a NSI. The empirical analysis focuses on the evolution ofthe main inputs and outputs of the NSI of some developed countries as a point of referenceof what having critical masses may mean, and of some newly industrializing countries andothers that are observing a remarkable performance, with others like Mexico, India andRussia that are moving, but still at a low pace. Introduction2A number of newly industrialized countries have achieved remarkable success in terms ofeconomic 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 toinitiate a successful development trajectory, with different degrees of advance.There is a growing consensus about the centrality of scientific and technological advancesin driving economic progress, and that increasing national investments in innovation areessential to ensure the countries’ economic growth (Schumpeter, 1942; Solow, 1956;Abramovitz, 1956 and 1986). However, no agreement has been reached concerning theprocesses linking innovation and growth, even less so when development is introduced intothe 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 AutonómaMetropolitana-Xochimilco, Mexico, **Professor, Faculty of Economics,Universidad Nacional Autónoma de México, We would like to thank Carlos Ramos and Rodrigo Magaldi for their research assistance.
  • 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 affectseconomic 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 structuralchange-led economic development has to be placed in the context of the construction ofNSI, as agents, functions and structures are important for the dynamics of change.Newly industrialized countries behave well in the indicators related to their domesticscience, technology and innovation (STI) capabilities (e.g. Korea and Singapore), whichsuggest that in some way they have achieved critical masses of STI capabilities, includingboth science and technology (ST) and Innovation (Innov). This may have allowed them tospawn endogenous processes that contribute to a development process. Such processes arealso clear in developed economies, where a quite balanced structure of STI populations isobserved. In contrast, other emerging economies are achieving remarkable success in somevariables, like Brazil or India, but they still observe imbalances between the STIpopulations.In many cases, the government ignited this process with a right design of STI policies andthe assignment of resources in order to generate the accurate incentives. Hence, the STIpolicy, and also industrial policy, is called to play a key role in this process by fosteringchanges in the agents’ behaviours to increase demand and supply of knowledge (and abalance between both), stimulating the emergence of strategic sectors and new areas ofcompetitiveness, and promoting cooperation and balance between regions within thecountry. Along this line, the coevolution of STI arenas emerges as a relevant process forbuilding up such critical masses in order to accelerate a trajectory of innovation andstructural change-led economic development. The promotion of such coevolutionaryprocesses 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-sustainingendogenous processes may be generated.3 However, a critical mass is a dynamicdimension, 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 builtand how they dynamically evolve, what is their relationship with co evolutionary processes !
  • of STI populations, and what the role of STI policies is in this process. This paper isinserted into this line of discussion and is a first approach to discuss the concept andmeasurement of critical masses of STI in emerging economies. The aim of this paper istwofold, first to discuss the concept of a critical mass in the context of STI and how tomeasure it, including some indicators, and second, how far off is Mexico in reachingcritical masses in STI, and whether the design and implementation of STI policy hascontributed to the development of critical masses of STI leading to the consolidation of aNSI. The empirical analysis focuses on the evolution of the main inputs and outputs of theNSI of some developed countries as a point of reference of what having critical masses maymean (Canada, Italy, Australia and Spain), and of some newly industrializing countries andothers that are observing a remarkable performance (South Korea and Brazil), with otherslike Mexico, India and Russia that are moving, but still at a low pace.After this introduction, section 2 reviews literature related to critical masses andcoevolutionary processes in STI; section 3 approaches the measurement of critical massesin STI, including which countries it makes sense to compare and which indicators may beused, this section discusses where Mexico stands; section 4 discusses the current STIpolicies in Mexico in the light of the idea of building critical masses of STI; and finallysection 5 concludes. Critical masses and coevolutionary processesBased on coevolutionary approaches to STI and ideas coming from developmenteconomics (Gersenkron, 1962; Rosenstein Rodan, 1943; Myrdal, 1957), Dutrénit, Puchetand Teubal (2011) argue that in order to place innovation as a powerful process for theproduction of structural change, the NSI has to reach a threshold of STI capabilities beforeemergent behaviour appears to generate a structural change-led development. In otherwords, critical masses seem to be needed in order to generate self-sustaining endogenousprocesses. The concept of critical mass and thresholdThe concept of critical mass has been introduced in different disciplines. It is usually usedto determine when a certain level of accumulation of a capability or stock makes it possibleto shoot a result that characterizes the process under study, and is maintained from there ata high rate of growth.For instance, in nuclear physic: “A critical mass is the smallest amount of fissile materialneeded for a sustained nuclear chain reaction. The critical mass of a fissionable materialdepends upon its nuclear properties (e.g. the nuclear fission cross-section), its density, itsshape, its enrichment, its purity, its temperature and its surroundings. When a nuclear chainreaction in a mass of fissile material is self-sustaining, the mass is said to be in a criticalstate in which there is no increase or decrease in power, temperature or neutronpopulation.” (
  • "The concept has been largely used in relation to collective actions, related to the analysis ofthe, the emergence of open source communities and other online communities, orin innovation diffusion (Granovetter, 1978; Oliver, Marwell and Teixeira, 1985; Mahlerand 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 cansuddenly gain a large amount of new members in a short period”. They assert that criticalmass 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 withanother two used in development economics: the Poverty traps and the threshold effects.The motivation and context in which these authors introduced the concepts are associatedwith the fact that theories of economic growth required more robust models from theempirical point of view. This occurs when the models are insufficient to explain verydifferent growth rates and, in turn, presents the co - existence of economies whose pathsdiverge in a systematic way, along with others that generate a take off getting them closerto those that grow faster.In this context, Azariadis and Drazen (1990) argue that: “Earlier investigators focused onthe ‘preconditions’ that an economy must satisfy to move from low to sustained highgrowth” (p. 503). They explicitly refer to Preobrazhensky (1926), Rosenstein–Rodan(1943) and Nelson (1956), and also to Rostow (1960). Coming from an evolutionaryeconomics perspective, Dutrénit, Puchet and Teubal (2011) also draw on these authors tobuild bridges between the analysis of coevolutionary processes of STI and economicdevelopment.Azariadis and Drazen (1990) considered that to capture these divergent processes issufficient to introduce an additional feature in the neoclassical model. This feature is the“…technological externalities with a “threshold” property that permits returns to scale torise very rapidly whenever economic state variables, …[and they add], take on values in arelatively narrow “critical mass” range.” (p. 504)The threshold effects are “…radical differences in dynamic behaviour arising from localvariations in social returns to scale” (p. 508) In particular, for the extended neoclassicalmodel with human capital, they concluded: “There are two ways in which human capitalaccumulation can result in multiple balanced growth paths and thus explain developmenttakeoffs. Reaching a given level of knowledge either makes it easier to acquire furtherknowledge (…) or induces a sharp increase in production possibilities (…). Both of thesepossibilities mean that threshold externalities are due to the attainment of a critical mass inhuman capital.” (p. 513)Even though this literature introduces the discussion on critical masses and thresholdeffects generating self-sustaining processes, knowledge about the economic mechanismsthat explain the jump from one stage to a higher one is still limited.
  • # The concept of critical masses in the arena of STI capabilitiesThe literature asserts that coevolution is a process that links the evolution of differentarenas of a system, where changes of one induce changes on the other –e.g. they arecausally linked. There is a growing literature that applies coevolutionary concepts to thestudy of socio-economic systems, although challenging issues for transferring evolutionaryconcepts 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 Gowdy2003).A stream of literature focuses on coevolution in the arenas of science, technology andinnovation. Nelson (1994) discusses coevolution between technology, industry andinstitutions; Murray (2002) focuses on industries and national institutions; Murmann (2003)on industries and academic disciplines; Metcalfe, James and Mina (2005) analysecoevolution between clinical knowledge and technological capabilities; and Nyggard(2008) between technology, markets and institutions, amongst others. Some authorsintroduce 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 InnovationSystem and introduce the innovation policy; Sotarauta and Srinivas (2006) relate publicpolicy with economic development in technologically innovative regions; and Nelson(2008) highlights policies more as part of the picture related to the coevolution oftechnologies, firm and industry structure, and economic institutions than as an arena thatmay coevolve with the others.Following Dutrénit, Puchet and Teubal (2011), we define Science/Technology (ST) andInnovation (Innov) as two activities that transform capabilities into outputs, thus thepopulations can be defined in terms of either one or both variables.4 The population ofcapabilities for ST is formed by researchers/teachers working in the research and highereducation system, and for Innov by engineers and technicians, including doctors in scienceand engineering, involved in innovation activities. Outputs of these populations includehuman resources (graduates and postgraduates), specific knowledge and new capabilitiesfor ST, and new products and processes and patents for Innov. These populations evolvefollowing the three causal processes (variation, selection and retention) as analysed byCampbell (1969), and coevolve as they build bidirectional causal mechanisms that may linktheir 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 STIarenas, critical masses can be defined as the level of capabilities at which the system is ableto generate endogenous processes and thus became self-sustaining. Critical masses of STand Innov correspond to a level of capabilities that allow virtuous coevolutionary processes" $ % % &% ( ( ) %
  • *of these populations. Even though coevolutionary processes may appear below criticalmass levels, only after that point such processes became self-sustaining.Azariadis and Drazen (1990) incorporate human capital into their neoclassical model in twoways: an easier form to acquire knowledge, or a sharp increase in production possibilities.For both routes technological externalities are generated, and they induce developmentwhen they reach a critical mass of accumulated human capital.In the same direction, Dutrénit, Puchet and Teubal (2011), following an evolutionaryapproach, analyze the interaction between people that can create the easiest way to acquireknowledge, as researchers in the arena of ST, and one that has capabilities to dramaticallyincrease production capacity, as engineers and technicians in the arena of Innov. Theyargue that when there is co-evolution between the two populations it is possible to generatepositive externalities between these populations and build a critical mass of them in orderto switch from one to another stage of development. These are the factors that make itpossible to move from one stage of development characterized by a NSI that observes onlypre-conditions, leading to a trap of low growth, to other steps that may emerge when theNSIs have created the conditions for sustained growth.In the absence of these externalities and critical masses, which depend on elements oforganizational, 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 qualitativemeasures 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, aprofile of innovation activities based on the use of existing knowledge, and little variation(a pre-condition for mutually beneficial selection and reproduction). Under theseconditions, innovation policy has not been sufficiently effective to serve as a base for STIevolution and coevolutionary processes in these arenas. Hence, we can assume that theinitial conditions are below the critical mass required to trigger endogenous virtuousprocesses; the system can coevolve but it will only be capable of reaching a low levelequilibrium, which will probably be a trap.5This approach to critical masses of ST and Innov and to coevolutionary processes betweenthese populations seeks to contribute to the understanding of how innovation can be placedas a powerful process in order to produce structural change and to avoid the traps of lowgrowth. How to measure critical masses? An empirical approach# % + , - . ) / 0 %$ 0 !
  • 1The measurement of critical masses has two types of problems that are difficult to resolveoperatively. The first is common to the measurement of capabilities, particularly those withintangible elements such as the human capital, intellectual capital or capital. The secondrefers to the case when a critical mass level is added, and such level is critical in the contextof the functioning of the system.Given a set of options for adaptation to the environment, if a level of efficient operation isreached, it means that there are capabilities. To measure the size of the capabilities, forexample, researchers and teachers, who in turn train new researchers and professionals, andengineers and technicians who develop innovation, measurement are needed both on thelevel they reach and on the capability composition they have. It is clear that a certainpopulation size is required in order to have variation, enabling selection and allowingretention of a level that allows individuals to trigger growth. At the same time not allcompositions of individuals in organizations (by specialty, skills, etc.) ensures that theprocess is generated.In this regard, generally, levels of capability are measured quantitatively by the amount ofmembers of a given population. The profiles of these members are largely intangible, suchas their specialties, which go beyond the certification of knowledge, or their skills, whichare formed through experience and are located in the organizations where they work. Theseprofiles are highly difficult to define and measure. In these cases it is not enough to simplylist categories of expertise or skills that make up the population, then add their elements. Itis 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 thesystem, and their relevance will be to generate a discontinuous change in any output of thesystem. Therefore, the measurement of critical masses has to put the capabilities in a chainof input-output of the system studied, and must conceive the dynamic behaviour of thislink.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 expenditureson R&D. When the output is stable or growing (but not decreasing on average), then youmay think that it has acquired the critical mass associated with the necessary capabilities toenable 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 higherafter periods of stagnation or decline. This illustrates that there may be emergence, growthand strengthening of capabilities but we can also observe a decline in them, which wouldlead to a loss of critical mass. At the same time, as there may be critical masses who areassociated with different processes, eg ST and Innov activities, there may be mismatchesbetween the capabilities of each other to cause cross-effects. Failure to achieve criticalmasses 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 indicatorsthat include both inputs and outputs of the populations concerned. Thus, system
  • 2performance will be a reference to the threshold required for the capability to qualify as acritical mass.In the absence of both paths of inputs and outputs of STI, which qualify in one economy toidentify a critical mass, it is required to act in comparison. For instance, you need to takethe critical mass in another economy as reference. This adds an additional difficulty due tothe fact that this translation is always mediated by the structural characteristics ofeconomies that are taken as benchmark. Therefore, the proposed measurement of this workinvolves the structural characteristics of economies; it is understood that a critical mass ofeconomies cannot be mechanically transferred on to others. Towards measuring critical masses in STIThis section is a first approach to the idea of measuring critical masses in STI. Even thoughthe meaning of having critical masses in STI may be ambiguous, the STI capabilities ofthose countries that are having a remarkable performance may be close to what we can termcritical masses in STI. Hence, this section compares the main inputs and outputs of the NSIof some developed countries as a point of reference of what having critical masses maymean (Italy, Canada, Australia, Spain) and a newly successful industrializing country(South Korea). For comparison with Mexico, the analysis includes emerging countries thatare part of the BRIC (Brazil, Russia Federation and India). Which countries may be compared: looking at the structural characteristicsCountries differ in their structural characteristics, like economic structure, market size,structure of their export (commodities versus manufactured products, technologicalcontents, etc.), average age of the population, level of education, etc. Some of thesecharacteristics are associated with the country size and endowments while others relate tothe level of development, hence differences emerge if they pertain to the developed,emerging or newly developing world. These characteristics may condition their STIcapabilities and consequently their STI policies.If we are looking to understand what the critical masses in STI are, it makes sense to makecomparisons between countries taking structural characteristics into account. In this line,the countries that were selected to be compared with Mexico include newly industrializingcountries 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, anddeveloped economies of medium size (Australia, Canada, Italia, Spain).The analysis of structural characteristics of these countries is based on their evolutionthrough 3 periods, which are relevant in terms of Mexico and other emerging and
  • 3developing countries: 1990 (previous to the Washington Consensus), 2000 (postWashington Consensus) and 2008 (current period).For this analysis indicators that reflect percentages and amount of different economic andsocial aspects of the countries were included. The evaluation of three years illustrates theevolution 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 periodunder 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 1990Figure 1 illustrates the structural characteristics of the countries. The structural profiles ofthe set of developed economies included in the analysis and of a successful newindustrializing 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 formof operation of the MNCs, which has neither generated the expected knowledge spilloversnor improvement in living conditions. (Dutrénit and Vera-Cruz, 2007; Carrillo and Hualde,1997)
  • Figure 1. Structural profile of the countries
  • Key indicators for critical masses in STIFrom the perspective of the NSI, an analysis of critical masses in ST and Innov mayinclude indicators of inputs and outputs of this system. The two populations that weredefined in section 2.2 were ST and Innov, thus indicators of inputs and outputs for thesepopulations may be included.According to the focus of this paper on measuring critical masses of ST and Innov toexplain the generation of self-sustaining processes, both indicators of composition of theexpenditure and results and indicators of the amount of these dimensions are relevant. Inthis line, the effort in R&D as a percentage of the GDP is important but also the absoluteamount of expenditure in R&D.As in the case of the structural characteristics, the analysis of the ST and Innovcharacteristics 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 ofST 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 underreview (1990, 2000 and 2008), Australia in articles and Korea in all the other variables.Figure 2 illustrates the STI profile of the countries.
  • "Figure 2. STI profiles of the countriesProfile 1.
  • #Profile 2.
  • *
  • 1Three 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 forall the indicators, except for scientific papers per million populations, where it isovercome by Australia. This illustrates that Korea ha based its development more onInnov than on science, and has a relative weaknesses in ST. This trajectory is similar tothat followed by Japan. (Kim, 1997)
  • 2Figure 3. STI profile of the countries, 2008Concerning Mexico, Figure 2 reveals the low and stagnated GERD, which has notgrown from the 1980s. The BERD$ is very low, but as the public expenditure in R&Dis 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.
  • 3The comparison between the structural features and NSI profiles of the studiedcountries offers lessons.The conditions relating to human development, particularly in health which is reflectedin 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 basisfor 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 andoutputs relating to both ST and to Innov, clear differences emerge between thedeveloped and newly industrialized countries regarding emerging countries.Furthermore, in the cases of Brazil and Mexico it is important to note that theseconditions, engendered in the country structure, are distorted by the high degree ofinequality 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 betterliving conditions, but also what must be considered as building more equal societies.Confronting Russia and India with Brazil and Mexico, shows that although all livingconditions are deficient, and even more disharmonious in the Russian case, the first twocountries have the largest economies and lower levels of inequality, although India hashigh levels of poverty.It is also important to note that the proportion of high tech exports is not among theconditions that necessarily equate NSI with adequate critical masses. The cases ofKorea and Italy stand out as those that have achieved a NSI profile clearly associatedwith this structural characteristic. Nonetheless, Canada, Australia and Spain show amuch smaller presence of such exports but they also have a NSI profile with levels oftheir capabilities that are approximate to the critical mass. In Australia and Spain criticalmasses 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 areconditioned by certain structural characteristics. However, the relationship betweenthese conditions and the attainment of the masses are mediated by the different ways inwhich these conditions are articulated with size, high tech exports, per capita productand income distribution in the economies. These different ways are relevant wheninstitutional and policy designs, which promote the achievement of levels andcompositions of the corresponding capacities that will build critical masses, are beingthought of.It is required then to highlight that critical masses for the attainment of mature NSI doexist, those where a sustained co-evolution reigns, but the initial structuralcharacteristics must be considered as conditions that are configured in different ways.The balances between types of markets and the orientation of production towards thedomestic or foreign markets, the degrees of inequality in the income distributionregarding access to health coverage, education and information and communicationtechnologies, and the relative sizes of the economies must be considered in order toformulate long term policies that aim towards the emergence, structure andconsolidation of critical masses of the NSI capabilities.
  • . 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 Popula Researcher PhD Scientific Basic World tion, Researche s in awarded Articles Research Share of Country million rs universities (per (per Expenditur Scientific s (per 1000 and 100.000 million e as % of Publicatio (2010) employed) research populatio populatio GDP ns (%) centres n) n)Mexico 112,3 0.09* 0.9* 20,891* 3.2* 73.3 0.8Korea 49.0 0.50 9.5* 53,274* 19.8 762.2 3.3Brazil 190,7 0.06 2.2* 158,314 5.2* 141.4 2.7India 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.
  • Summing up, in terms of the inputs and outputs for science, Korea performs muchbetter than the other selected countries, having outstanding levels in most of the sciencecapabilities related variables. Mexico’s indicators for science suggest that this country isbelow the critical mass in science, and that the number of researchers working inuniversities and research centres does not correspond to the expenditure in basicresearch.Referring to the critical mass in Innov, table 2 shows a set of indicators that give anaccount 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 Researc Innovative Triadic GDP world hers manufact patents position GERD GERD/G BERD Country in the uring granted (millions (million DP /GER dollars, IMF) Busines firms in (per dollars) (%) D (%) s Sector total firms million (%) (%) habitants) Mexico 14 (1,004,042) 5,856 0.38 44,6 52.7 29.8 0.14 (2007) 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 0.14Notes: * 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.
  • Summing up, in terms of the inputs and outputs of Innov, Korea performs much betterthan the other selected countries, having outstanding levels in the GERD as percentageof the GDP, even overcoming the number proposed by the Lisbon strategy. Mexico’sinputs and outputs indicators for Innov suggest that this country is below the criticalmass; the distance is particularly significant in the national expenditure on R&D aspercentage of the GDP. There is a clear imbalance between this meagre national effortand 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 andin outputs; regarding Innov indicators Korea is also a leader in capabilities buildingamongst these countries. If we consider that this country has reached or is close toreaching a critical mass in both ST and Innov, the values observed in the case of Mexicosuggest that this country is below those capabilities. The limited GERD both in totalamount and as percentage of the GDP is one factor that contributes to explain theexisting 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 nationalprograms dedicated to foster science and technology (and just recently, also innovation).During the last decade, two programs have being designed and implemented: theSpecial program on Science and Technology (PECYT in Spanish), active from 2002 to2006 and the Special Program on Science, Technology and Innovation (PECiTI), activefrom 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 theproposed objectives and allow the country to become a knowledge economy.Both programs recognize strategic areas that are set to become investment priorities innational policy; these include high technology sectors (Pharmaceuticals, IT andcomputers, electronics, aeronautics, medical and health sectors) and medium highsectors (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, policymakers were learning how to introduce a more interactive model. However, implicitpriorities in resource allocation (science and human resources), which are rooted in thefirst 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 orcontinued implementing several policy instruments and programmes in support of STIactivities. The instruments are around 60 funds or programs designed in line with thegoverning objectives of the PECyT and PECiTI. In this sense, they are oriented to fosterbasic and problem-oriented research, the regionalisation of the activities, the R&D andinnovation activities by the business sector and the formation of human resources. Mostof them operate under the scheme of competitive funds. In addition, a program of fiscalincentives for R&D was operating from 2002 to 2008.
  • Contrary to the objectives established by PECyT and PCiTI in relation to a growing andcontinuous public expenditure on STI, expansion in both the Federal Expenditure onSTI and CONACyT’s budget was rather slow. This impacted the implementation of thepolicy mix by limiting resource allocation to some instruments, thereby affecting alsotheir already low interaction.In terms of the implementation of CONACyT’s budget, the persistence of certaininertias, mainly regarding the National Researchers System’s6 payrolls and postgraduatescholarships, has inhibited the achievement of equilibrium in the accomplishment of thecore objectives. Figure 4 presents CONACyT’s budget in 2009 allocated to the differentprograms (728 million dollars); the programs are grouped into the main objective theydeal 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 scienceincludes 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 fundinnovation.Source: Own elaboration based on CONACyT information. Budget of 728 million dollarsThe resource allocation reflects the implicit STI priorities, which differ from what wasestablished first in PECyT and later on in PECiTI: (i) Emphasis on the formation ofhuman resources and the support to basic science, (ii) small amount of resourcesdedicated to problem oriented research, and, (iii) small amount to foster R&D andinnovation 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 to1984; its main goals include the promotion of the formation, development and consolidation ofa critical mass of researchers at the highest level, mostly within the public system of highereducation and research. The NRS grants both pecuniary (a monthly compensation) and non-pecuniary stimulus (status and recognition) to researchers based on the productivity and qualityof their research.
  • "(National Researchers System and Postgraduate scholarships) represents 62% of thetotal. If we include basic science the amount increases to 67%. In contrast, the newobjectives like applied science (sectoral and regional funds) and innovation onlyreceived 8% and 13%, respectively. The effort dedicated to the design of a variety ofinstruments contrasts with the concentrated allocation of resources to a few.The resources assigned to foster innovation suggest that they are still operating as pilotprograms. The fiscal benefits for R&D were an important incentive, but it operated until2008 because changes in the general taxation system made this specific tax credit notsuitable.7Regarding design, Mexico has followed recommendations by international organismsbased on countries with more mature NSI or the experience of successful emergingeconomies (such as Korea, China, Singapore, or even Brazil). Concerning theimplementation, the resource allocation shows a different set of implicit priorities andsome rigidity, such as the fact that resources continue to be mostly assigned to programson basic science and human resources formation, and the new programs designed tofoster oriented research and innovation still received limited resources.Referring to the instruments for fostering R&D and innovation activities, most of theinstruments focus on the final stages of R&D, which correspond to post R&D activities(last phase of advanced development and development for commercialization), andother innovation activities not R&D based. In contrast, only the fiscal incentives forR&D are directed towards the development of technology. Moreover, the instrumentsbenefit firms who already have some R&D or innovation capabilities; there are noinstruments directed to increase the number of firms that deploy such activities. Inaddition, there are neither instruments to stimulate the demand for innovative productsnor to stimulate transfer, assimilation and improvement of existent technologies.(Dutrénit et al, 2010) In other words, the design has focused more on the increase andmould of existent capabilities, without much learning, than on the creation of a criticalmass to generate endogenous processes. Final commentsThis paper is a first approach to discuss the concept of critical mass in the STI arenasand to measure them in the context of emerging economies. Particular attention wasgiven to how far off Mexico is in reaching critical masses in STI, and whether thedesign 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 criticalmasses in the STI arenas, the problems with its measurement, a proposal on how tomeasure them, an application of such a proposal taking into consideration the structuraldifferences between the countries that are considered as benchmark, and a considerationof the distance that separates Mexico from the critical masses necessary for a sustainedco-evolution within its NSI.1 4 56+
  • #In conceptual terms the adopted definition follows others that have been done inEconomics and in Social Analysis. Its specificity lies in that it is consistent with theevolutionary analysis applied to the NSI and, in particular, with a co-evolutionaryapproach of development.The proposed measurement takes into consideration the difficulties of measuringcapabilities, of doing so when it regards their evolution and of using references fromother systems’ trajectories. Thus, the measurements for nine economies –five of themdeveloped or recently industrialized and four of them emergent- are extremelyprovisional. This is so because the construction of indicators for the defined concept,and with the noted difficulties for measurement, supposes an analysis of statisticalnational sources more detailed than the one that has been done from internationalsystemized sources.Nevertheless, the profiles obtained for the NSI and their linkages to the structuralcharacteristics of the countries authorize the relative positioning and the comparisonsthat have been made. At the same time, these measurements are consistent with theanalysis of results of STI policies that aim towards creating critical masses. In this sensethe analysis made for Mexico rightfully aims towards alternative formulations of suchpolicies.Since 2000 there has been an effort to redirect the STI building to the construction ofthe NSI, the institutional and legal changes and STI programs aimed towards this goal.The policy mix has improved from identifying successful programs and introducingnew programs to filling the gaps that have emerged. However, policy learning has beenslow, 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 ofindicators of inputs and outputs with those observed in countries that have similaritiesin terms of size, the initial conditions that showed up in the 1960s-1970s, ordevelopment models that they have followed, like Korea and Brazil. It seems thatMexico has not exceeded the threshold of STI capabilities that can generate anendogenous dynamic allowing the NSI to develop, as perceived in some of thesecountries.STI policy is called upon to accelerate the building of this critical mass of STIcapabilities, but this requires a systemic/evolutionary approach to STI policy, whichlooks to the system- the NSI, focuses on the generation and absorption of knowledge asnonlinear dynamic models, and on systemic failures, not only on market andgovernment failures. For this approach, learning, accumulated capabilities and timematter, institutions mediate between agents, and there is an increasing concern for theregional 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 thissystemic/evolutionary approach, and following Dutrénit, Puchet and Teubal (2011), thispaper 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 stillbelow the critical masses, and these ST capabilities are also needed for knowledgegeneration, technology transfer and human resources formation. In addition, the existent
  • *knowledge base may be enough today, but new knowledge based on higher STcapabilities 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 moreefficient allocation of resources and greater budgets spent on STI activities. This isrequired 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 anefficient retention process. Increased budget is required to allocate additional resourcesto new demands, lacking additional resources it is very difficult to generate a structuralchange in the economy without the emergence of governance problems.Several questions remain pending to be answered; this paper would like to highlight twokey questions. First, the analytical framework used for the STI policy design wasconceived on the bases of countries with different initial conditions -the centraleconomies; to what extent is this framework useful to be applied in economies withdifferent initial conditions, like developing countries? Second, if the idea of a thresholdis useful and a critical mass has to be achieved, how can the level of a critical mass beidentified? These questions require further research.
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