a University of Bío-Bío, Chile. Email: fgatica@ubiobio.cl
The author would like to thank the indispensable commentaries from the journal’s anonymous reviewers.
The opinions expressed in this paper are exclusively those of the author.
This paper seeks to determine the factors which explain differing levels of innovation in Chile at the regional level. Data used in the study was obtained from the Tenth Inquiry into Innovation in Businesses 2016, published in 2018 by the National Institute of Statistics (INE). Binary logistic regressions (Logit) were developed for each region, identifying the specific explanatory factors which determine the greatest likelihood of innovation among local businesses. The study concludes that the heterogeneities detected call for an adjustment in public policies in accordance with regional dynamics, which should be understood as subnational spaces.
INTRODUCTION
This article seeks to identify heterogeneities among the different Chilean regions, using as a foundation the innovation rates in each and the factors that explain these rates in each case. These differences oblige Chile to have specific public policies focused on stimulating regional competitiveness in the setting created by the new Ministry of Science, Technology, Knowledge, and Innovation.
To begin with, this work carries out a bibliographical review regarding the regional innovation systems and the productive clusters. It seeks to develop a variety of analytical elements which will allow the generation of a taxonomy for classifying the country’s different regions. Second, a methodology which delves into calculating the regional innovation rates and the reach of binary logistic regression models (Logit) is presented.
The field study entailed the analysis of different regional innovation rates with results from the regional models presented for each explicative variable proposed. Furthermore, each region is spatially identified in the quadrants derived from the innovation rate’s explicative factors and taxonomy. Finally, based on the results obtained, different public policy initiatives are proposed in the conclusions for each of the classifications.
2. REGIONAL CLASSIFICATIONS
The Chilean economy is lagging behind when it comes to R&D and the development of human resources, thereby limiting its growth model. Calderón and Castells (2016) venture that in the case of Chile, there is a “neoliberal mentality” which still pervades the Ministry of Economy (MINECON). The result of this is that technological modernization has been left up to the market’s forces and, as such, has ended up being inefficient depending on the sector, in addition to resulting in social disparity from a territorial point of view.
The current social and economic heterogeneity in Chile calls for the further empowerment of regional innovation systems, thanks mostly due to the R&D talent concentrated in the national capital. In that regard, according to the 7th National Survey of Personnel and Expenses in R&D (MINECON, 2018), in 2016, 70% of R&D spending happened in the capital, Santiago. This datum reveals the high level of centralization of private and public investments in factors facilitating innovation. As such it appears that the spatial inequity constitutes a factor in dire need of correcting so that a long-term sustainable growth model may be reached.
Facing this, it is important to have specific regional policies within the context of the recently enacted law 21.105, which for the first time, creates the Ministry of Science, Technology, Knowledge and Innovation, so that based on the acknowledgement of the different variables which explain innovation in businesses located in the region, a set of specific initiatives which favor the harmonious development of Chile can be generated.
This work outlines a study of the efficiency of regional innovation systems, generating a taxonomy based on the intensity of innovation in the regions. The traditional classifications are constructed based on different variables grouped into three categories: 1) the facilitating variables identifying human resources, financing, and support, 2) the company's own variables, which cover private investments, connections, and entrepreneurship, and 3) the parameters which reflect the results of innovative results and products and their economic effects (Molero, 2012).
In this context, it is the Regional Innovation Scoreboard (European Commission, 2017), where there is an annual comparative evaluation on the subject of research and performance in innovation for the member states of the union European Union, which is a great resource for targeting the different efforts. In this systematic comparative exercise, four large groups are identified according to their innovative performance: 1) regions leading in innovation (53), 2) strong innovators (60), 3) the moderately innovative (85), and 4) those classified as modestly innovative (22). This analysis allows us to identify the “pockets of excellence” inside every country.
Along the same lines, in the case of Spain there is the analysis of Buesa et al. (2015) which analyzes the efficiency of the Regional Innovation System with a Data Envelopment Analysis (DEA) combined with the factor analysis. Four factors are identified as configuring the innovation rate of systems: 1) the quantity of innovative businesses, 2) public administrations, 3) universities, and 4) the presence of scientific and technological policies incorporated in a “National R&D+i Plan.” As such, the variables of the results are: the number of patents, the number of utility models, and the quantity of scientific publications per region. The conclusion of the work is that in spite of strong differences between regions, in dynamic terms, there is greater convergence: the lagging regions display a relative improvement and the “border performance” shows stagnation stemming from the impact generated by the Spanish crisis.
This analysis’ hypothesis is that the regions in Chile present different conditions or factors which stimulate innovation in businesses. As such, there is a need for specific public policies with a framework for regional incentives in order to reach a harmonious development in the country.
Next, I will develop the concept of resources, regional innovation systems and productive clusters in order to understand the behavior of business innovation in a specific territory.
Territorial Dimension of Innovation
Lundvall (1999) presents four forms of learning: 1) during production, 2) during use, 3) via interaction, 4) via R&D. This learning is most intense when there is geographical proximity (Dallasega et al., 2018). Present in each region are different degrees of clusterization of the innovative activities in the area due to the fact that businesses with greater technological content are found in those zones where there is greater stock of technological knowledge.
In this context are two theoretical models to explain the distribution of innovation in the area:
1) Territorial Innovation Systems
A group of actors which are interconnected and which carry out the activities of creation and distribution of new knowledge within a specific institutional and geographical framework in order to give way to innovations, primarily technological, upon which economic development rests (Buesa et al., 2015).
In this regard, three factors are identified in the system which can influence the direction and vigor of the innovative activities in the region (Tidd et al.,1999; Cimolli, 2000). These are: 1) the institutions given that their level of interconnectedness is important; the strategic mandates regarding research and development; the protection systems which ensure the appropriability of the benefits and policies geared towards qualifying the workforce, 2) the competencies learned and accrued over time, and 3) the incentives and pressures from the local market.
The possibility of producing and accumulating technological knowledge at the level of local businesses will depend upon the existence of an efficient regional innovation system. The case which has most inspired public policies in Latin America is that of Silicon Valley (Castells and Hall, 1994; Saxenian 2016), where it has been proven the importance of counting on an innovative medium with risk capital, a highly qualified workforce, emerging technologies, different local leaderships, and the presence of local networks which stimulate innovation
2) Productive Clusters
The concept was developed by Porter (1991 and 2009), inspired by the model of Italian Industrial Districts. A cluster is a group of businesses which are interconnected and find themselves densely localized in a set territory. Within this grouping of businesses arise processes of innovation and distribution which make participation in the cluster an attractive prospect.
A work which analyzes the clusters in creative industries is that of Gong and Hassink (2017). It determines three processes which reinforce the development of these clusters: 1) the economies of agglomeration, where we can essentially find in effect centripetal forces, the draw felt by businesses, the development in large metropolises, and access to specialized suppliers, 2) the development of spinoffs where parent companies play a key role, the proximity of universities, and the presence of leaders willing to take action, and 3) an institutional setting, where importance is given to protection mechanisms, normative frameworks for distribution, and public development agencies, the support provided by universities, incubators, qualified human capital, and institutional articulation at different levels.
Boix et al. (2015), Villareal and Flores (2015), and Seongsoo et al. (2017) open up the possibility of starting a subcluster in specific locations in regions and even in cities, as it is important to identify to what degree the business found in the subcluster accesses the various other subgroups, thereby increasing its innovative capabilities.
Both theoretical approaches, regional innovation systems and productive clusters, complement each other, thereby explaining the rate of innovation in the region. From these approaches arise five groups of parameters which explain the probability of innovation in regional businesses. These groups of variables, which are used in this study, are: 1) The accumulation of abilities or skills, 2) interactive learning, 3) human resources, 4) public policies, and 5) the path dependencies, which we will look at later on.
Need for a Regional Taxonomy
In order to identify a territorial heterogeneity with regards to innovation, from the meeting of two axes, a classification is proposed for the regions (which one should understand to mean as subnational spaces):
From the crossing of these axes arises a taxonomy for patterns of regional innovation in order to identify heterogeneity in subnational spaces at the moment of innovation. This turns out to be an important vector within the context of the new Ministry of Science, Technology, Knowledge and Innovation (Law 21.105, published Aug. 13 th, 2018).
From the aforementioned combinations one can identify the following four groups (see Chart 1):
Regarding Explicative Variables
This analysis works with five groupings of variables in order to explain the probability of innovation in regional businesses. In spite of the analysis model (Logit) appearing extensively throughout the methodology, and taking into consideration that the focus is the comparison between regions in order to identify heterogeneities, we will now look at some theoretical dimensions, which have been sorted by a grouping of skills, interactive learning, human resources, public policies, and path dependencies. This grouping of parameters based on the available data, was worked over at the moment of analyzing the Innovation Survey in a group of ICT companies (Gatica, 2018).
A more schematic development of each explicative variable is presented in Chart 2 of this work.
3. METHODOLOGY
The 10 th Survey of Innovation in Businesses, 2016, from the National Statistics Institute (INE), published in 2017 was used in the study. This survey has national coverage and generated information by region.
In order to estimate the sample size, the INE considered two elements: random inclusion and forced inclusion, the latter being that which was applied when a selection had few units for sampling. In this context: sampled structure = 178,123 businesses; sample total = 5,500 businesses; forced inclusion = 1,858; random inclusion = 3,642 with a coefficient variation of 5.28%.
Based on the survey two supplementary analyses were created:
With this definition, the innovation rate was constructed for each region where:
Where j = specific region
15 Logit models were generated (one for each region of the country) which repeat the following structure:
Where the business is (i) in region (J)
For the binary logistic regressions (logit), the open source software for econometrics theory known as GRETL (see http://gretl.sourceforge.net/) was used. All the Logit models presented a rate of “predicted cases” above 92%. Furthermore, multicollinearity presenting a variance inflation factor (VIF) under 10 was written off. Finally, the McFadden R-Squared was above 0.52 for all the models.
4. RESULTS FROM FIELD STUDY
The results of the interregional distribution of innovative businesses will now be presented. In the following section the factors which explain business innovation from an interregional point of view will be analyzed. Finally, the interregional similarities or differences based on the rate of innovation and explicative factors will be developed. For the analysis of the similarities an initial identification of the territories using all the regions of the country will first be considered and from the results a second analysis will be generated, excluding the national capital (Metropolitan region, Santiago de Chile).
Interregional distribution of innovative businesses
Upon analyzing the relationship between regional innovation rates and the distribution of businesses surveyed, it was proven that:
Explicative factors for the probability of innovation in businesses
Table 2 summarizes the frequency of occurrence for significant parameters at the moment of explaining innovation in regional businesses and which is a product of the Logit models in different regions (15).
From the analysis, the following was concluded:
To be sure, the expected parameters with greater importance are related to the size of R&D policies, and in particular when they are decentralized from the path dependencies and the variety of research sources. As such, the reader will be able to see that more traditional variables (sales, total workforce, exports, among others) do not have a great explicative potential.
Global interregional similarities
For the purpose of identifying similarities and distances between regions, a visual representation of where the two axes cross was generated: on the one hand, we have the innovation rate and, on the other, the quantity of variables which are significant at the moment of explaining the probability of innovation. Four regional innovation patterns were identified (see Figure 1):
1) Competitive territory
Where there is a confirmed greater rate of innovation and a greater number of significant variables. In this group, the metropolitan region (Santiago) can be found exclusively. No more regions were identified within this quadrant, which proves a great disparity with the rest of subnational units. It is a territory which presents greater efficiency in its innovation system and good locational advantages for businesses with greater complexity. It is interesting that in Santiago (the national capital) local businesses show a greater probability to innovate when their R&D expenses are actually in other regions of the country.
2) Territories with innovation poles
In this case we have the region of Antofagasta with only two significant variables (R&D in regions and R&D in the metropolitan region). It is a mining territory which is strong in investment at a national level and which presents a “pole” style development in its environment. In this group, one can find the Valparaiso region which only has two significant parameters: R&D in regions and the possibility for future innovation. Finally, the region of Aysén, which presents a high percentage of innovative businesses with a low quantity of significant parameters. These territories present a greater rate of innovation which is not necessarily explained by local synergies.
3) Territory with a low efficiency
The region has parameters which could be significant at the moment of innovation but the efficiency rate of these efforts is relatively low. In this case the Tarapacá region stands out due to the diversity of innovative sources and R&D in regions. It is interesting that the presence of a workforce with postgraduate studies presents a negative relationship, which could be explained by a low critical mass or by a skills gap between the supply and demand for qualified workforce.
In this classification, one can also find the regions of Atacama and Maule, in spite of having as significant variables R&D in regions and the possibility of future innovation. In the case of Atacama, the support from public mechanisms appears as a negative and as positive the diversity of innovative sources. In the case of Maule there appears to be a negative relationship between the rate of a workforce with postgraduate studies. Also in this category, we can find the regions of Biobio with the second most important conurbation in the country, and that of Los Lagos. Finally, there is Araucanía where age as an explicative element for the probability for innovation stands out.
4) Lagging territories
Here we can find the regions of Arica and Magallanes, found at opposite ends of Chile. The prior only has one significant variable which is regional R&D. In the latter, the only variable is possible future innovation. In this category we also find the region of O'Higgins which, in spite of presenting significant variables such as regional R&D and R&D carried out at the national capital, has a low rate of innovation. A similar situation is present in Coquimbo, where we have as significant variables R&D in the region, diverse sources for innovation and the possibility for future innovation. Lastly, we have the region of Los Rios, which has as a significant factor the presence of a professional workforce, the realization of R&D and the possibility of future innovation. In these territories. We find a low rate of innovative businesses and a low regional capacity for generating synergies which could improve the efficiency of the local innovation system.
Interregional similarities excluding the national capital
The previous analysis proves the role played by the leadership which the capital represents in a national context. With the aim of seeing the interregional differences clearly a new positional analysis is generated where the metropolitan region is excluded. It is important to note that excluding the national capital does not imply recalculating the different binary logistic regression models (Logit) as they were carried out individually for each region. Furthermore, the determination of regional innovation rates does not present any variances due to the fact that they were calculated individually.
Nevertheless, the exclusion of the national capital changes the average value for the innovation rate, going from 23.6% to 21.8% and the average rate for significant variables shifts from 3.5 to 3.2%.
We have the following positional analysis (see Figure 2), excluding from different quadrants the national capital.
The positional changes seen in the different regions are explained primarily by a reclassification of some of the territories due to a change in the average innovation rate from 23.6% to 21.8%.
5. CONCLUSIONS
The approach used did not focus on innovative efforts, but rather on the environs where they are currently innovating. With this definition, it was proven that 23.6% of the surveyed businesses are innovative at a national level, which is above the expected percentage as Chile is the country from the OECD which spends the least on R&D, investing only .36% of its GDP, while the average is 2.34% (MINECON, 2018).
From the interregional similarity analysis (global and excluding the national capital). The hypothesis of this work was proven: the regions present different conditions and factors which stimulate innovation in businesses. As such, specific public policies are required with regional incentive frameworks in order to reach a harmonious development for the country.
It is interesting the major role that regional R&D has at the moment of explaining the probability that a local business will be innovative. This precedent is important at the moment of decentralizing investments in R&D+i and proposes a new line of work for the new Ministry of Science, Technology, Knowledge, and Innovation (Law 21.105, published 13/08/2018).
It was also proven how insignificant variables associated with scales of production actually are. In few regions do we find significant the following parameters: age, sales, volume of exports, and the quantity of workers, belonging to a business group, among others. The traditional criteria and instruments for classification are not pertinent in order to focus public spending on R&D+i in the territory.
Nevertheless, of significance are the diversity of sources and the possibility for innovation in the future. These variables are mezzoeconomic (intermediate) elements, in particular due to the generation of varied and complex territorial networks, which stimulate innovation. This marks a key methodological aspect at the moment of operating the Ministry of Science, Technology, Knowledge and Technology in the territory, focusing public investments not only in key locations where instruments are found, but rather on incorporating a systemic and more complex logic in the actions of the new Ministry, pointing it towards the articulation of actors and the identification of paths to innovation in regional businesses.
Regarding the low impact of support from the State, there could be two complementary explanations: a temporal discrepancy between the moment of public investment and the innovative result where the state support is at a determined moment (t), which translates into business innovation in the future (t+1) and a misalignment in the criteria for focusing and intervention methodologies.
It is certain that the evidence proves the diversity of situations at a regional level in regards to innovation. As such, it turns out that generating public policies which adapt to the different territories is key, with the following emphasis:
Based on the comparison of interregional similarities, it is proven the clear advantage that the Metropolitan Region (Santiago) has in regards to the innovation rate and the quantity of significant variables. It should be noted that 70% of R&D spending is concentrated in the national capital (MINECON, 2018). It turns out that it is of the utmost importance to have more regions which could assume a position of leadership, passing into the classification of competitive territory. As such, it is urgent to decentralize public spending on R&D at a national level.
The new Ministry should open up the possibility of mixing new approaches and mechanisms for stimulating innovation and promoting productivity. The feasibility of generating innovation based on market stimuli, in the context of a productive cluster (for example: based on export chains) is greater when there are policies which strengthen regional systems by developing new decentralize skills for R&D, concentrating local actor networks in the context of public, private, university, and general partnerships.
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