Call for Research Proposals. Innovation, R&D Investment and Productivity in Latin American & Caribbean Firms. The Chilean Case

Transcription

1 Latin American & Caribbean Research Network INTER-AMERICAN DEVELOPMENT BANK (IDB) Research Department (RES) Call for Research Proposals Innovation, R&D Investment and Productivity in Latin American & Caribbean Firms The Chilean Case INTELIS Department of Economics University of Chile -1-

2 MOTIVATION 3 The Global and Regional Context 3 The Chilean Case 4 OBJECTIVES 6 DATA 7 DESCRIPTION OF THE DATA 7 SUMMARY OF THE DATA 8 EIT QUESTIONNAIRE (See Annex-1 zip-file) 9 ENIA QUESTIONNAIRE (F1 & F3) (See Annex-1 zip-file) 9 METHODOLOGY 10 QUANTITATIVE ANALYSIS 10 CASE STUDY 15 CURRICULUM VITAE 18 CLAUDIO BRAVO-ORTEGA (clbravo@econ.uchile.cl) 18 JOSÉ MIGUEL BENAVENTE (jbenaven@econ.uchile.cl) 20 ROBERTO ALVAREZ (ralvarez@bcentral.cl) 22 LUCAS NAVARRO (lunavarr@uahurtado.cl) 24 CARMEN CONTRERAS (cacontre@fen.uchile.cl) 26 JOSÉ LUIS CONTRERAS (jose.contreras@fen.uchile.cl) 27 BUDGET (See Annex-2 pdf-file) 28 ANNEX A 29 REFERENCES 34-2-

3 I. MOTIVATION The relationship between productivity and R&D has been a topic of inquiry since the early work of Schultz (1953) and Griliches (1958). Since then, this area of research has produced a significant amount of empirical and subsequent theoretical work. While Zvi Griliches posed and approached most of the empirical questions, recent theoretical works credit a substantial role to R&D as an engine of productivity and hence economic growth. (see, for example Romer, 1990; Helpman and Grossman 1991; Rivera-Batiz and Romer, 1991; Aghion and Howitt, 1992). From an empirical perspective, the literature has found that close to one half of the income per capita and growth rates differentials across countries can be explained by differences in TFP (See Hall and Jones (1999)), but most importantly it has also found that R&D activities could explain up to seventy five percent of TFP growth rates, once externalities are considered (See Griliches (1995)). Our research proposal aims to contribute to understand the relationship between R&D and productivity in Latin America, by focusing in the Chilean experience. In the next paragraphs we first revise the regional and global context of R&D activities and then we develop our empirical and case study methodologies. The Global and Regional Context The rapid economic growth of East Asian economies has called into attention the role that R&D activities might have over the course of development. Let s take Korea as an example, while in the sixties its R&D over GDP ratio was closed to 0.35%, during the four subsequent decades this figure increased almost constantly around 2.4% during recent years. This increment has been credited as one of the causes of the significant TFP and GDP per capita growth experienced by Korea since the sixties. While TFP s yearly growth averaged 1.11% for the period , the income per capita grew over 6% each year during the same period 1. In contrast, Latin American and Caribbean countries have shown during the last decade a very modest rate of economic growth despite unusually favorable economic conditions. Unfortunately, this poor performance it is not new in the region. Indeed, during the last four decades of the twentieth century the income per capita in the region grew a 1.44% per year, while TFP did so in a modest 0.29%. Chile shadows when compared with East Asian countries over the same time period (See Table 1 in the Annex A). The previous figures on the poor economic performance of Latin America can be better understood by looking the R&D effort of the region compared with other regions of the world. (See Table 2 in the Annex A). Indeed, this indicator shows that the OECD s 1 See Bravo-Ortega and García (2007) -3-

4 decade-average during the period fluctuated between 1.87% and 2.25%. In the case of Scandinavian countries the R&D effort increased from 1.12% in the sixties to 2.71% in the nineties. In contrast, R&D expenditure in Latin America fluctuated between 0.36% and 0.52% of GDP during the same period of time. In theory, R&D expenditure could increase productivity through different channels. First, it makes it possible to produce new goods and services that bring with them more effective use of existing resources. Second, it may make it easier and faster to adapt the benefits of technological progress elsewhere in the world to local realities. Third, R&D activities elsewhere in the world may increase domestic productivity through learning embodied in new technologies and productive processes and the import of goods and services with technology incorporated. Notwithstanding this, the relationship between R&D and productivity does not seem so obvious in the Latin American countries. Thus, the unavoidable question that we will try to answer is: Why is the R&D effort in Latin America and in Chile particularly so low? So far, low private-sector investment in R&D+I in the Region has been explained by financial market failures, low human capital endowments, macro-economic volatility, insufficient provision of public goods, and shortcomings in the regulatory framework, among other reasons 2. The Chilean Case At first glance, the Chilean case does not deviate much from the historical pattern of Latin America. Although for some indicators Chile ranks on top regional positions during the nineties, this has not been necessarily true during the preceding decades. Furthermore, when compared to developed countries in most of the R&D measures Chile lags behind in a VERY significant manner. Table 3 in the Annex A shows the R&D expenditure over GDP and R&D per capita (in 1996 constant dollars). Although in recent years Chilean R&D expenditure has surpassed the 0.6% of GDP, still below the 0.84% that Brazil averaged for the nineties and very close to the 0.52% regional average during the same period. By the same token the average R&D per capita in the region was close to 33 dollars during the nineties and the Chilean average was 46 dollars, still below the 54 dollars that Brazil spent on average over the same period. With respect to the sources of funding for R&D and according to UNESCO by the year 2004 the main source was the private sector with 46% percent, the government and universities financed the rest. By the same token Chilean R&D capacity as measured by the number of researchers performs relatively well in the regional context, without deviating much of 2 Innovation Strategy, National Innovation for Competitiveness Council of Chile, Vol. I and Vol

5 the figures shown by Argentina or Brazil once you control by scale. However, when comparing Chilean capacity with those of developed countries the gap in R&D capacity turns out to be very significant. Let s take as an example the number of researchers (head count) per million inhabitants. According to UNESCO by the year 2004 Chile had 1,139 researchers per million inhabitants, Argentina had 1,203 and Brazil had 782. In contrast Korea had 4,908 researchers per million inhabitants and Japan even more with 6,492 researchers per million inhabitants. On the side of the R&D output there seems to be a very poor performance of the region when compared to leading countries and a very poor performance of Chile when compared with regional leading countries. We consider the number of patents granted in the US to Latin American researchers not living in the US a good indicator of this performance. Table 4 shows the regional performance at this regard. While an average of 858 patents were granted annually to Australian researchers during the period , Latin America as a whole obtained 258 patents. According to Bravo-Ortega and Garcia (2007) within Latin America Brazil obtained an average of 108 patents, Argentina 53.6 and Chile a modest 13 patents per year. Chile maintains a big gap in other indicators as the number of publications with respect to developed countries. For example, Australian researchers publish close to 1,000 articles per million inhabitants per year, similar figure is obtained in the case of Finland, whereas Chilean researchers publish 160 articles per million inhabitants a year 3. In the year 2005 and due to the poor indicators of innovative activity shown by Chile the government decided to modernize the national innovation system. Since then Chile has been designing and implementing a national strategy of innovation for competitiveness. In addition to what has been already mentioned. The general diagnosis is that the institutional setup for promoting R&D, innovation and related activities suffers of severe coordination problems between agents even within government agencies. Critical mass in some research fields and also at the productive level are almost non existent, and research in general is driven by curiosity from academics and scientists. As expected, the link between university and its demand from the productive sectors is very weak. Agencies like technology brokers, technology transfer offices or even technology extension offices are almost non existent in the country. These issues raise interesting questions for the case of Chile despite the fact that the country shows successful economic results at the aggregate level. It seems that more work has to be done in order to achieve the government goal of becoming a developed country in 15 years as suggested by the National Innovation for Competitiveness Council. In our study cases will focus in verifying this diagnosis and understanding better its origins and implications. 3 Figures collected by the National Innovation for Competitiveness Council of Chile. -5-

6 With the exception of a few studies, there is not much evidence on the impact of research activities on productivity in Chile (See Benavente (2006)). Previous results show that the link between both is very weak without getting into much detail on the causes of such weakness. In our empirical section we develop a methodology that will allow us to obtain the most up to date estimations of the link between R&D and productivity and its determinants in Chile. On the other hand, we observe several productive sectors that have performed quite well in recent years like salmon farming, pulp and paper, pork production and wine without having a clear idea about the role played by research on their performance. Moreover, despite the existence of public support in promoting research activities at the university level and partially at the firm level, evidence is not clear that these successful stories are due to or despite public support. Alternatively, it is not clear if this performance was directly or indirectly related with the creation and use of new knowledge at the firm level that gave these sectors a competitive advantage in the world markets. We will also cover these issues in the study cases we propose. II. OBJECTIVES In order to tackle our research questions including those suggested by the call for research proposals, our study will share the three main objectives suggested by the IADB team with a particular focus in policy design. Thus our main objectives will be: 1. To establish whether Chilean private investment in R&D+I has an impact on productivity growth, the extent and characteristics of that impact, and how and why such an impact is taking place; 2. To produce a thorough characterization of the innovative behavior of Chilean firms by exploiting the information provided in the national innovation surveys and other relevant sources; 3. To establish the main determinants of innovative behavior at the firm level, following a consistent methodology and a comparable analytical framework. 4. To accomplish the previous objectives providing useful directions for policy design. -6-

7 III. DATA A. DESCRIPTION OF THE DATA As we mentioned before we will use three main sources of information for the quantitative section, as well as personal interviews and ad hoc questionnaires for the case study section. The following paragraphs describe briefly each of them. A.1 ENIA This survey is available for every year since 1979 and is conducted by the Instituto Nacional de Estadísticas (INE). Observations come from the manufacturing plants according to ISIC. In our case we will use the main characteristics that come from Formulario Número 1 (Form 1, from now on F1) which captures economic and accounting information about the firm 4 such as (i) Labor inputs, (ii) Wages, (iii) Capital goods, (iv) Costs, and (v) Revenues. The information contained in Formulario Número 3 (Form 3, from now on F3) allows us to identify the specific goods that the plants produce and export. In particular F3 includes information on value and quantities of each of the produced and eventually exported plants' products following CPC 5 and other specific product codes 6. These two forms can be linked at the plant-level using the so called Número Único Identificador (Unique Identification Number, from now on NUI). The period that we will use is for the case of F1, and for for the case of F3. A.2 EIT This survey initially used as a sampling framework the ENIA, now also includes: (i) the mining sector, (ii) the electric sector, and (iii) others. The survey is taken every three years since The questionnaire follows the guidelines of the Frascati s Manual from OECD associated to R&D and Innovation. The main information that we can obtain from each survey consists of the last two periods of the firm. That is, for the 1995-survey we have the previous history of 1994 and 1993, and so on. The variables that we will use are associated with: (i) The kinds of 4 Almost 400 variables that describes the dynamics of the firm. 5 Which fulfills the same requirements than ISIC, but tit helps to identify raw materials used in the production as well as the main goods produced by the firm. 6 For detailed information on the mix of commodities used as plants inputs we may also need to use Form 4 from ENIA. -7-

8 innovations that the firm has done the past two years, (ii) The goals of those innovations, (iii) Property rights and Know-how, (iv) Difficulties to innovate, (v) Relevance of the innovation in the business, (vi) Innovative activities, (vii) Financing innovation, (viii) Expenditure in R&D, (ix) Labor skills, and (x) Perspectives concerning future innovations. In our case we have access to each of the surveys, that is: , , , , and In each case we can matched these observations with the F1 and F3 using for that the NUI code. B. SUMMARY OF THE DATA Table 6. ENIA SUMMARY (FORM 3, TOTAL) Year Plants Products ISIC7 ISIC6 ISIC Table 7. EIT SUMMARY Year Plants Variables

9 Table 8. ENIA SUMMARY (FORM 1) Year Plants C. EIT QUESTIONNAIRE (See Annex-1 zip-file) D. ENIA QUESTIONNAIRE (F1 & F3) (See Annex-1 zip-file) -9-

10 IV. METHODOLOGY This research will be structured in two parts. The first part will analyze the impact of several indicators of innovation on productivity. To do that we will use two different sources of information: (i) Encuesta Nacional Industrial Anual (ENIA), and (ii) Encuesta de Innovación Tecnológica (EIT). It is important to notice that these surveys will be matched at the plant-level using an identification number provided by the Instituto Nacional de Estadísticas 7 (INE). The second part of our research will focus on innovation and productivity using a case study methodology at the firm-level/clusters/ value of chains. We will proceed with these two parts sequentially due to the fact that our empirical section will certainly provide additional and relevant information for the study cases. Nonetheless, we have to mention that we aim to have by beginning of December of 2008 the methodology and the basic description for the firm-level/cluster/value chains that we will use in our case study. A. QUANTITATIVE ANALYSIS A.1 Introduction This part of the project will follow the empirical research line started by Crépon, Duguet and Mairesse (1998) about the impact of R&D on innovation and innovation on firms' productivity. It is based on a model which takes into account the whole process of innovation that includes decisions of firms to engage in R&D activities, the results of these efforts and their impact on productivity. There are several empirical studies analyzing the determinants of innovation in Chile using different numbers of the EIT. Crespi and Katz (1999) and Crespi (1999) have analyzed how industry and plant characteristics may explain differences in innovation using the first version of this survey. Benavente (2005) extends this analysis using three versions of the EIT. Alvarez (2001) and Alvarez and Roberson (2004) focus on trade-related variables as main drivers of innovation activity. There is, however, little evidence on the effects of innovation on productivity in the Chilean case 8. One exception is the work by Benavente (2006) and for this reason we will use his paper as our benchmark of analysis for the case of Chile. Following Crépon et al. (1998), Benavente (2006) finds that research and innovative activities are related with firm size and 7 National Institute of Statistics. 8 For Argentina there is a work by Chudnovsky, López, and Pupato (2006) -10-

11 market power. Also he finds that firm s productivity is not affected by innovative results nor by research expenditures in the short run. On the first hand, we may replicate the results previously obtained and significantly extend the analysis by the use of new and more detailed information. To do that, we have two main information sources: (i) ENIA plants panel data (Form 1, ), and ENIA plants and products panel data (Form 3, ). The last data set includes detailed information on the mix of products produced and exported by plants as documented by Navarro (2008), and (ii) 5 versions of the EIT (1995, 1998, 2001, 2005 and 2007) One strength of this information is that we can analyze not only the impact of innovation on current productivity, but also whether there are lagged effects. Since ENIA only covers industries in manufacturing, the relationship between innovation and productivity will be constrained to the manufacturing sector. On the second hand, given that we have information on quantity and values for the plants' product mix, we may improve the procedure to measure firms productivity taking into account firms decisions about which goods to produce. This is an emerging issue in the literature as pointed out by Bernard, Redding and Schott (2005) and De Loecker (2005). Decisions about which product markets to enter are often made at a more disaggregate level than the data normally used for econometric studies. This mismatch is important for the measurement of productivity because the products that firms switch into may utilize different production techniques than the products firms leave behind. As a result, variation in firms observed performance may be driven by unobserved product choices in addition to underlying (and observable) firm characteristics. In order to control for this sort of problems and other traditional ones in the measurement of productivity, we will follow recent work done by Levinsohn and Petrin (2000), Levinsohn and Melitz (2002), Katayama, Lu and Tybout (2003), De Loecker (2005), Martin (2005), and Bernard, Redding and Schott (2005). Given the data available, we aim to test most of the traditional hypotheses related with industrial innovation. Many studies stressed that large firms are in a favorable position for innovation due to their size, market share and market power. Market concentration allows the existence of monopolistic rents that enable firms within the industry to finance R&D projects. In a perfectly competitive world private investment in R&D would not be feasible. Additionally, when imperfections in capital markets are recognized, large firms tend to have greater capabilities of securing the necessary resources to finance R&D projects baring uninsurable risk. Moreover, the potential total impact of the results of an R&D project can be significantly greater in large firms given larger sale volumes. However, there exist several external forces which stimulate firms to innovate despite their size and market power. The first of such forces is based on demand factors, such as market growth. On the other side, the role of scientific advancements in stimulating industrial innovative efforts may influence the path and rate of technology advance. -11-

12 At the same time we will incorporate in our analysis other aspects which determine firms to innovate. Those include mainly buying vs. originating know-how; adopting vs. creating technologies; formal vs. informal innovation activities. These issues are generally associated to the innovation costs and the time it takes to perceive the returns of innovation, especially for SMEs. In these cases the EIT survey will be a key input in our analysis which hopefully will give some light on these issues. The empirical estimation procedure aims to capture the effect of some of these variables over the research and innovation effort by firms. We will estimated a four-equation model to explain the determinants of research and development, innovation, and finally productivity. Given the design of the surveys some restrictions need to be imposed on the data requiring a different econometric treatment. A.2 The Model As in Benavente (2006) and Crépon et al. (1998), we will empirically model the whole innovation process. We will assume that firm characteristics, market structure and technological variables affect not only investments in R&D but also the successful introduction of new products and processes into markets. The baseline model consists of four equations 9, two for research, one for innovation and one for productivity, each of them requiring a different econometric treatment. We will rely on a Generalized Tobit framework to model the decision to invest and the amount invested in research activities. It consists of two separate equations: (i) one related to the decision to invest in R&D, and (ii) the other to the amount of resources involved. More precisely, we assume that there exists a latent dependent variable g i for the firm i given the following equation, g i = x i0 b 0 + u i0 (1) where x i0 is a vector of explanatory variables 10, b 0 the associated coefficient vector and u i0 an error term, where represent some decision criterion like the expected present benefits associated with a research project. The econometrician observes that resources are invested in R&D activities if gi is positive or bigger than a given threshold. 9 Here we follow the expressions of Crépon, Duguet and Mairesse (1998). 10 In our case we will use variables such as: employment, average market share, number of industry segments, demand pull and technology push dummies. -12-

13 We then assume that a latent or true intensity of research k i equation: for firm i is determined by a second k i = x i1 b 1 + u i1 (2) where k i = k i the current R&D expenditure of firm i when it is engaged in research activities i.e. when g i is bigger than the minimum industrial threshold. Here k i and k i are both expressed in logarithms, x i1 is a vector of the explanatory variables, b 1 the associated coefficient vector and u i1 an error term that summarizes omitted determinants and other sources of unobserved heterogeneity. In general, it is assumed that innovative output is related to enhancements in the productivity capacity of the firm. However, in empirical work there exist several ways to proxy innovation output. More common proxies are the number of patents 11 or share of innovative sales. The EIT only provides information on the later. In that case the firm is asked what is the percentage share of its sales coming from products launched in the last three years and/or what are the results of the implementation of absolutely new production techniques during the same period 12. In our case we may also use as another proxy some questions associated to product innovations as well as process innovation from F3 (ENIA). Hence, we will assume that the underlying (unobserved) true share of sales t i can be viewed as an innovation intensity variable where innovation may be measured: (i) by the number of sales weighted innovations using for that EIT, or (ii) by the share of the new products/processes made by firm, whether it is new for the market or for the firm only, using for that F3. We can also use the plants-products information of F3 to obtain another measure of innovation at annual frequency. Potential measures involve number of products per plant, product entry by plant, or alternatively product churning, i.e. the simultaneous add and drop of products by a plant. Navarro (2008) presents information on the prevalence of these activities in the sample period. In addition to this, we can consider the creation and destruction of products not only at the plant level but also at the market level. That is, we would be able to identify the dynamics of product lives not only at the plant level but also at the aggregate level. This would allow us to obtain more detailed information on the product innovation process by plants. Since the share is only known by intervals, we can specify the innovative sales equation as an Ordered/Simple Probit model: t i = α K k i + x i2 b 2 + u i2 (3) 11 EIT mention about number of patents, but only on average a 10% of the sample respond positively. 12 The firm has to answer on a four-point scale: 0-10%,10-30%, 30-70%, and %. -13-

14 where t i is the underlying (unobservable) true share expressed as a logarithm, x i2 is a vector of explanatory variables 13, b 2 the associated coefficient vector and u i2 an error term which we will assume to be normally distributed with mean zero and variance σ 2 2. The coefficient α K is the elasticity of the percentage share of innovative sales relative to research expenditure, a measure of the return or impact of research on innovation output, x i2 is a vector of exogenous variables. The last equation of the system is that for productivity. Following the above mentioned literature we have: q i = α I t i + x i3 b 3 + u i3 (4) where q i stands for productivity measured as a logarithm, x 3i is a vector of explanatory variables 14. The coefficient α I is the elasticity of total factor productivity with respect to innovation output, and b 3 is a vector of coefficients related to the elasticity of scale, that of physical capital and the skill composition parameters, reflecting percentage differences in efficiency of skilled labour relative to unskilled labour respectively. As we mentioned above, one strength of the information available is that we may capture lagged effects of innovation using productivity measured at lead years. As suggested, despite the fact that the essence of the empirical strategy follows closely previous work done by participants of the proposal, there are several new issues that we want to explore in the estimation strategy, among them: (i) new information from the Form 3 of ENIA which now includes a interesting product disaggregation that may enrich the definition of innovation by its results, and at the same time be matched at the plant level with new information related with innovation practices and procedures which will permit building a panel structure database following firms from almost a decade. (ii)we may explore biases in productivity looking at multi-product plants versus one-product plants trying to correct for non productivity biases like price movements, quality improvements or the like. 13 In our case we will use variables such as: employment, average market share, number of industry segments, demand pull and technology push dummies. 14 In our case we will use variables such as: physical capital per employee, shares of skill labor and unskilled. -14-

15 B. CASE STUDY Case studies may enrich and complement the analysis made from quantitative data because it allows to check the main findings of the previous analysis. Notwithstanding this, we are going to additionally study some sectors that do not appear in the data. Thus, we will have the opportunity not just to analyze but also complement our research on the impact on productivity in relevant sectors beyond the scope that the survey have considered so far. In this project we would like to realize in-depth interviews in at least three main areas or sectors of interest. The main objective of this section will be to understand the role of innovation for the growth and the development process of successful sectors, checking whether the econometric finding are considered valid by the firms. Since mid-70s a significant number of new exporting sectors have emerged in Chile (See Agosin and Bravo-Ortega (2007)). These new activities have had a significant role in economic growth in Chile (See Meller (1995)). The emergence of these sectors makes Chile an interesting country to look for study cases due to the fact that they are dynamic sectors that demand constant improvements, associated to innovation, as well as having strong exposition to shocks commonly associated to trade. Some important factors that have been studied in the literature are: (i) the impact of trade liberalization, (ii) the effect of exchange depreciation, (iii) the entrepreneurial activity of the state, (iv) state-sponsored accumulation of factors of production, and (v) the encouragement of FDI where there was potential comparative advantages. These dynamic sectors have arisen almost entirely in segments of the food and forestry, such as: (i) pulp and paper, (ii) fruits and vegetables, (iii) fishmeal, (iv) other marine products, (v) cultivated salmon, (vi) pork meat, (vii) poultry, (viii) milk products, and (ix) wine 15. Besides that these sectors have been successful in terms of growth, economic relevance or world positioning, there are also new sectors that have appeared associated to technologies/services mainly due to government policies through public institutions 16. The first sector to be studied requires permanent product (or services) innovation in order to growth, the IT sector. Firms to be interviewed show significant sales growth during the last five years, both at national and international level. The second sector to be analyzed will be the Salmon cluster. This sector, based in natural resources, has been growing since beginning of 1980 s, and it has always generated a demand for innovation, especially in technology adaptation and in their processes. Additionally, the government has played a significant role in its development, especially government agencies like CORFO and Fundación Chile, which makes this sector particularly relevant for a case study. 15 Agosin and Bravo-Ortega (2007). 16 Towards an Innovation National Strategy. Vol II. National Innovation for Competitiveness Council of Chile. -15-

16 Finally, the third, forestry, is very relevant for the country exports, while the fourth sector, pork farming, has developed quite strongly during recent years. The growth of these two sectors apparently has not been necessarily related to innovation. Apparently, during the first years of their development most of the innovations within those sectors are related to a management strategies as well as marketing strategies 17. For example, the take off of the pork industry came with the arrival of Nippon Meat 18, which started to export frozen pork meat to the Japanese and South Korean market 19. In economic terms pork meat has been very successful since mid-1990s. Before that, there were some exports of this product since the late 1980s mainly to Latin American markets, obtaining at that time revenues for US$ 6.6 million (in 2000 US dollars). In recent years the exports of this sector has reached US$ million (in 2000 dollars). In recent years the pork and forestry sectors appear to be increasing their investments on R&D. For example, the two main forestry companies established in 2004 a technological consortium. This behavior is certainly a good example of the limits in productivity growth that might have taken place without investing in R&D, and that could have driven the decision to change their innovative behavior. We will focus our case study in the biggest companies in terms of exports, within each sector. Expected in-depth interviews will consider firms detail on Table 5 (See Annex A). The methodology for the case studies will consist of doing in-depth interviews, using as criteria Chilean ownership of the firms and relevant amount of exports within the sector. In case that it is not possible to materialize any interview we will opt by changing the sector rather than looking for new companies. We will contact at least one relevant person of each company (CEO or Plant Manager), who should know the history and the main changes made by the firm through the years. However, previous experiences had taught us that to obtain these types of interviews is not an easy task. Mainly because managers lack of time and also since there are many studies and surveys related with innovation in the country at the moment, which implies that managers disposition to answer long interviews is reduced 20. Additionally we will obtain information from other sources (for example, relevant institutions), interviewing other experts in those sectors like researchers or government employees in charge of national programs aimed to encourage innovation and production (CORFO, Fundación Chile, INNOVA, etc.). 17 The Emergence of New Successful Export Activities in Latin America: The Case of Chile, Agosin and Bravo- Ortega (2007). 18 A subsudiary of a major Japanese multinational. Source, Agosin and Bravo-Ortega (2007). 19 Japan and South Korea have represented on average during this 10 years of exportation growth almost 80% of the exports. 20 in order to increase our chances to interview we may request the IADB support for sending formal letters to the companies. -16-

17 Topics to be treated during the interviews will be contrasted with results for these sectors from ENIA and EIT where applicable. Main topics will be: 1. The local form of industrial organization prevailing within each unit of analysis; 2. How firms/clusters/value chains relate to the country s innovation systems which seek to enhance enterprise learning and innovation strategies (local/regional/sectorial); 3. The role played by specific actors/agents/organizations in stimulating or inhibiting innovation (This will be contrasted with data from EIT, section 9 and 10); 4. The technology sourcing strategies of firms and how governance patterns in value chains determine technology diffusion and absorption among firms (This will be contrasted with data from EIT, section 3, 4, 5, 7, 8 and 11); 5. The role played by the dynamics of the technological frontier at the world and country level, that is, whether firms/clusters/value chains are in sectors that around the world or in the country experience a rapid expansion in the technological frontier; 6. The role played by comparative advantages that is, whether these are industries/activities in which the country is a main player; 7. The role of coordination failures in systematically hindering innovative behavior and how these failures affect each unit of analysis differently (This will be contrasted with data from EIT, section 3 and 6); 8. The role of financial and other market failures and how they impose different constraints on each group of firms/cluster/value chain (This will be contrasted with data from EIT, section 6); 9. The role of the regulatory and overall business environment and how it constrains each group of firms/cluster/value chain (This will be contrasted with data from EIT, section 3 and 6). -17-

18 V. CURRICULUM VITAE A. CLAUDIO BRAVO-ORTEGA MAIN FIELDS OF RESEARCH. -Economics of Innovation and Growth, International Economics, Development Economics. -Professional and academic references: Maurice Obstfeld (UC at Berkeley), Daniel Lederman (World Bank), Daniel Hojman (Harvard). PRE AND GRADUATE STUDIES DEGREE DATE FIELD -UC at Berkeley Ph.D Economics -Universidad de Chile M.Sc Industrial Engineering (Max. Distinctions) -Universidad de Chile Engineering 1998 Industrial (Maximum Distinctions) -Universidad de Chile B.Sc Engineering Sciences (Distinction). PROFESSIONAL EXPERIENCE, RESEARCH & CONSULTING -Visiting Professor, Science and Technology Policy Research (SPRU), University of Sussex. September up to date. -Assistant Professor, Department of Economics, Universidad de Chile. July 2004-up to date. -Consultant World Bank, June 2007-November Topic: Governance in the Chilean NIS. -Consultant Korean Development Institute, Spring Topic: Free Trade Liberalization in Chile. -Consultant, Inter American Development Bank, March 2006-March Topic: The Emergence of New Successful Export Activities in Latin America: The Case of Chile. (Sectors Studied: Wine, Blueberries, Pork) -Consultant, Pro-Chile (Export Promotion Agency), Ministry of Foreign Affairs, March 2005-March Topic: Estimation of Direct and Indirect Employment in Exporting Sectors. (Sectors: Wine, Salmon, Pulp). -Consultant, World Bank, Chief Economist Office for Latin America and the Caribbean region. Research Topics: International Economics, Development and Innovation. June (several occasions), based in Washington DC-USA and Santiago- Chile. -Summer Internship, World Bank, Chief Economist Office for Latin America and the Caribbean region. Research Topic: Wages, Trade and Geography in Brazil. (Supervisor Daniel Lederman), Summer Summer Internship, Chilean Government. Advisor of the Minister of Economics, Mining and Energy, Mr. Jose De Gregorio. Topic: Modeling the impact of Natural Resources on Economic Growth and Human Capital Accumulation, Summer Researcher, Center for Applied Economics at the Universidad de Chile, 1998 June Research Assistant, CIEPLAN (Corp. de Investigación Económica para Latinoamérica), TEACHING -Professor Master and Ph.D. course on Trade, Innovation and Growth (with Roberto Alvarez, CBCh), Universidad de Chile, Spring Professor Master and Ph.D. course on Macroeconomic Theory II, Universidad de Chile, Spring Professor of undergraduate International Trade Theory, Universidad de Chile, Fall and Spring Professor of Macroeconomics, MBA Universidad de Chile, Teacher Assistant of Macroeconomics, Department of Economics, UC at Berkeley, Fall Teacher Assistant of Public Finance, Department of Economics, UC at Berkeley, Spring

19 PUBLICATIONS AND WORKING PAPERS Exploring the Relationship between R&D and Productivity: A Country-Level Study (with Alvaro García). Documento de Trabajo Nº 282, Departamento de Economía de la Universidad de Chile Intellectual Property Rights, Human Capital and the Incidence Of R&D Expenditures (with Daniel Lederman). Documento de Trabajo Nº 277, Departamento de Economía de la Universidad de Chile Agriculture and National Welfare Around the World: Causality and International Heterogeneity since 1960 (with Daniel Lederman). Forthcoming Trimestre Económico. (World Bank working paper 3499, February 2005) Trade Liberalization in Chile: A historical Perspective. In FTA and Structural Changes: Experiences and Implications. Edited by MoonJoong Tcha. Korean Development Institute, Seoul Korea Cerrando la brecha innovativa latinoamericana: Qué podemos aprender de Corea, Israel y Finlandia? Serie Estudios Socio Económicos Nº35, CIEPLAN, Santiago, Chile Remoteness and Real Exchange Rate Volatility (with Julian di Giovanni) IMF Staff Papers, September. (IMF working paper 05/01, January 2005) -2006, "The Relative Richness of the Poor? Natural Resources, Human Capital and Economic Growth." in Neither Curse Nor Destiny: Natural Resources and Development. Edited by Daniel Lederman and William F. Maloney. Stanford University Press.(with Jose De Gregorio). (World Bank working paper 3484, January 2005) Agricultural Productivity and its Determinants: Revisiting International Experiences (With Daniel Lederman). Estudios de Economia 31(2): Does Asymmetric Information Cause the Home Equity Bias?. World Bank working paper Trade Costs and Real Exchange Rate Volatility: The Role of Ricardian Comparative Advantage (with Julian di Giovanni) IMF working paper No. 05/5r Macroeconomic Dimensions of Integration and Trade: A comment, in Issues Papers Second Annual Conference, Euro-Latin Study Network on Integration and Trade. Published by INTAL, ITD, and SOE of the Inter American Development Bank Multinationals and Linkages. An Empirical Investigation: A comment," in Economía, LACEA and Brookings Institution eds., Spring, volume 4 number 2.. FELLOWSHIPS, AWARDS AND GRANTS Fondecyt Grant Number Topic of research: Heterogeneity in the Returns to R&D (joint with Jose Miguel Benavente) Fondecyt Grant Number Topic of research: Export Diversification and Economic Growth (joint with Manuel Agosin and Roberto Alvarez) Fondecyt Grant Number Topic of research: Institutions, Imitation and R&D Best Economic Professor Award, Department of Economics, Universidad de Chile (undergraduate level) Dean s Normative Time Fellowship, UC Berkeley , Fulbright Commission Fellowship , President of the Republic of Chile, Fellowship. -19-

20 B. JOSÉ MIGUEL BENAVENTE Posición Actual. Profesor Asistente, Jornada Completa. Departamento de Economía. Universidad de Chile. Educación. -Licenciado en Ciencias de la Ingeniería, Universidad Católica de Valparaíso Ingeniero Civil Industrial, Universidad Católica de Valparaíso Magister en Economía, Universidad de Chile Master of Science in Economics, University of Oxford Doctor of Philosophy in Economics, University of Oxford Areas de interés. -Microeconometría Teórica y Aplicada, Economía de la Innovación y Política Tecnológica, Evaluación de Programas Públicos, PYMEs, Criminalidad y Emprendimiento. Artículos Publicados con Comité Editorial -Benavente J.M. y R. Lauterbach (2008) Technological Innovation and Employment: Complements or Substitutes?. European Journal of Development Research 20 (2) : Junio -Benavente J.M. (2006) The Role of Research and Innovation in Promoting Productivity in Chile. Economics of Innovation and New Technology 15 (4-5) : Junio. -Benavente J.M., Galetovic, A, Sanhueza, R y P. Serra (2005) El costo de falla residencial en Chile : una estimación usando la curva de demanda. Análisis Económico 20 (2): Diciembre. -Benavente J.M. (2005) Self-Discovery in a Development Strategy for El Salvador. A Comment. Economia The Journal of the Latin American and Caribbean Economic Association, 6 (1) : Otoño. -Benavente J.M. (2005) Investigación y Desarrollo, Innovación y Productividad : Un Análisis Econométrico a nivel de la Firma. Estudios de Economía 32 (1): Junio. -Benavente J.M., Galetovic, A. y R. Sanhueza (2005) La Dinámica Industrial y la Financiación de las PYMEs, Trimestre Económico, LXXII (2) : Abril-Junio. -Benavente J.M., Galetovic, A, Sanhueza, R y P. Serra (2005) Estimando la demanda residencial por electricidad en Chile: El Consumo es Sensible al Precio. Cuadernos de Economía (42): Mayo. -Benavente J.M. (2005) Innovación Tecnológica en Chile, Dónde Estamos y Qué se Puede Hacer. Economía Chilena. (8): Abril. Banco Central de Chile. -Benavente J.M. (2005) The Free Market Innovation Machine por W. Baumol. Comentario de libro. Economía Chilena. (8): Abril. Banco Central de Chile. -Benavente J.M. (2004) Empresario Chileno durante los Noventa : a self-made man?, Economía y Administración (148): Benavente J.M., Johnson C. y F. Morandé (2003) Debt composition and balance sheet effects of exchange rate depreciations : a firm-level analysis for Chile, Emerging Markets Review (4): Benavente J.M., Crespi G., Katz J. y Stumpo G. (1997) New Problems and Opportunities for Industrial Development in Latin America, Oxford Development Studies 25 (3): Contribuciones en Libros -Benavente J.M. (2008) Dinámica Empresarial Chilena Ministerio de Economía, INTELIS, FUNDES y Foro Pro Innovación. Santiago. -Arellano S. y J.M. Benavente (2008) Sustitución entre Telefonía Fija y Móvil en Chile en A. González (ed.) Telecomunicaciones : Convergencia y Nuevos Desafíos. Subsecretaría de Telecomunicaciones y Facultad de Economía y Negocios, Universidad de Chile. -Benavente J.M. (2006) Wine Production in Chile en V. Chandra (ed) Technology, Adaptation, and Exports. How developing countries got it right. Banco Mundial. Washington. -20-