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4.2 Extroversion analysis

4.2 Extroversion analysis
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The main objective of the priority setting phase in RIS3 is to identify areas that present a competitive advantage for the region in focus; this exercise will facilitate a highly targeted channelling of resources towards investments that present the highest potential for positive impact on smart specialisation at a later stage of the S3 strategy (Foray et al., 2012). To this end, extroversion analysis is a method applied in order to detect possible industry sections in which the region already presents increased extroversion and/or accordingly possesses increased future potential. Extroversion, in this exercise, refers to the characteristics and specifications of a region’s trade connections with other regions. On the Smart Specialisation Platform of the Joint Research Center (JRC) there already exists a tool for extroversion analysis, titled ‘S3 Inter-regional Trade and Competition Tool’. This tool has been jointly developed by the Joint Research Centre, the European Commission’s in-house science service and the PBL Netherlands Environmental Assessment Agency (Thissen and Gianelle, 2014).

Description of the method

Extroversion analysis is an important methodology for priority identification because it contributes to the definition of concrete and achievable objectives. It helps to determine the areas of present competitive advantage and regional excellence potential (Foray et al., 2012).

A well designed smart specialisation strategy/Extroversion Analysis will contribute to the selection of the few priorities that build on the specific strengths and opportunities of the region’s economy. This targeted selection with enable the development and advancement of economies of scale and scope, as well as local knowledge spillovers with regards to the selected sectors. The presented sectors/areas must not be too broad or generic (for. ex. agrifood, tourism or healthcare), but they should rather be more precise and, if applicable, cross-sectorial (Foray et al., 2012). A vis-à-vis comparison of the region with its other competitors, included in an extroversion analysis, is also very important, because it allows us to apprehend the potential of the region’s outward elements in absolute terms (Gianelle et al., 2014a, Gianelle et al., 2014b). Key questions addressed by the extroversion analysis method include (Thissen et al., 2013):

  • What are the links of regional economies with other regional economies in terms of global trade? Are there pronounced or underlying trade patterns?
  • Where are the products produced in one region consumed? In the same region, a nearby one, or a remote one?

It should be clarified here, that although all of the following methods represent the ‘external’ dimension of the region, Extroversion analys differs from value chain analysis and collaboration and networking analysis. It is also different from product-space modelling, as it refers to the current status of the region’s extroversion.

Extroversion Analysis should respect the key criteria for selecting a limited number of priority areas that demonstrate increased potential for smart specialization. According to the RIS3 Guide (Foray et al., 2012), these criteria include:

  • the existence of key assets and capabilities, including specialized skills and labor pools for the area selected, and ideally, a novel combination among these, for example cross-sectorial and cross-cluster
  • the diversification potential of these sectors, cross-sectors and domains
  • critical mass and/or critical potential within each sector
  • the international position of the region as a local node in global value chains

With regard to extroversion analysis, a key challenge to address is the absence of the necessary data to reflect bilateral relationships of regions (i.e. between regions) in terms of trade. Given the fact that extroversion analysis targets the characteristics and specifications of a region’s trade connections with other regions, there is a major information gap with respect to the data needed, hindering the ability to arrive to a comprehensive mapping of inter-regional flows of goods (as well as knowledge and human resources). In most cases, trade flow data are available only at the national level. Unilateral regional trade flow data are of no use either, if there is no data from other regions to compare it with. Some answers to the above challenges however, have been provided by researchers who have attempted to perform analyses related to extroversion using indirect methods for filling the information gap.

The research of Thissen et al. (2013) is predominantly built an analytical methodology for mapping regional trade in Europe, which is suitable for use in regional development analysis and predominantly smart specialization strategies. In detail, Thissen et al. (2013) developed a methodology for mapping interregional trade between 256 EU NUTS2 regions for the year 2000. Their methodology stays as close as possible to observed data, without imposing geographical patterns. For their methodology, they combined several different data sources (for example national accounts’ Supply and Use Tables), which they first regionalised and normalized to ensure consistency, credibility and sense-making. Their method provides results about regional trade flows (imports and imports), consistent with national accounts, regional accounts, and international trade flows, and is substantiated through interregional flows for 59 product categories including services.

A parallel methodology for identifying and comparing inter-regional trade networks of agricultural and processed food products in the EU25 (250 NUTS2 regions) was used by Gianelle et al. (2014a) and Gianelle et al. (2014b). The goal of their proposed methodology was to provide a basis for the analysis and comparative assessment of regional economies and their embeddedness in inter-regional and international trade networks. The tool (Thissen and Gianelle, 2014) determines not only the economic strength of a region with different EU markets, but it also allows the identification of competitor regions.

The Extroversion Analysis method can vary from simple to very sophisticated. Possible features to be calculated for a region include:

  • Competition, i.e. with which regions the region under focus competes, on what (i.e. which sector), and where it stands comparatively with its competitors (i.e. its rank)
  • Regional trade flows (imports and exports) between regions, and possibly distinguishing trade flows between regions of the same country and with other countries

Trade network features, for example the variety of export markets served, the magnitude of the region’s exports in the EU market, analogies of exports/import characteristics, the participation in trade clusters of specific characteristics.

Usability and impact

Extroversion analysis is an important methodology in the context of the RIS3 Step “identification of priorities” because it contributes to the definition of concrete and achievable objectives. It is a kind of analysis which is substantial for RIS3, as RIS3 by definition represents a highly targeted and selective type of research and development strategy for regional innovation (Gianelle et al., 2014a, Gianelle et al., 2014b). Hence extroversion analysis contributes substantially to the identification of areas of present and future competitive advantage and regional excellence potential (Foray et al., 2012).

Furthermore, RIS3 emphasizes the outward dimension and external affairs of the region in focus, both in national and international frameworks. RIS3 specifically calls for the enforcement of a region’s position within interregional value chains, international collaboration and regional economic specialization in sections that are promising in terms of value added. Regional connectivity relates to, among others, transactions associated with trade, transportation and financial flows that start, pass through, and end in a region (McCann and Ortega-Argilés, 2015). In addition, by contributing to the appointment and exploitation of regional competitive advantages, Extroversion Analysis contributes to the prioritization and channelling of public investment in sectors that exhibit increased potential for innovation and regional cash flow generation. It enables insights in potential areas of technology diversification, and attraction of international investment (Gianelle et al., 2014a, Gianelle et al., 2014b).

Overall, Extroversion Analysis is a methodology which is useful for developing more successful RIS3, taking advantage of inherent potential, capitalising on synergies among sectors and regions, and ultimately enforcing the economic position of a region within the globalized web of interregional economies and connections.

Required data

The basic requirement in terms of data for the application of the Extroversion Analysis method is trade between regions on the EU (and global) level.

Although Eurostat publishes key regional statistics on a wide breadth of subjects, there is no published data on trade between regions. In addition, there is no data comprehensively describing interregional trade flows in terms of product categories, either. Although some regional trade data about specific sectors is available (for example agriculture), overall there is no comprehensive matrix representing all trade flows in Europe (Gianelle et al., 2014a, Gianelle et al., 2014b, Thissen et al., 2013).

Relevant data sources

Relevant data sources include:

  • International trade in goods based on the data collected by Feenstra et al. (2005)
  • Data on services based on Eurostat trade statistics taken from the balance of payments (Eurostat, 2009a)
  • National Supply and Use Tables, providing information on total imports and total exports, per product (Eurostat, 2009b)
  • COMTRADE database, United Nations Statistics Division (UNSD) – http://unstats.un.org/unsd/comtrade
  • Data on regional production, investment and consumption (Cambridge Econometrics, 2008)
  • Freight transport data from the Dutch Ministry of Infrastructure and the Environment (2007)
  • Business flight data from MIDT (2010)

Implementation roadmap

Step 1. Selection of Region to be analysed for extroversion. The user selects the EU Region to be analysed in terms of extroversion.

Step 2. Selection of year of reference. The user selects the reference year for which the Extroversion Analysis will be performed.

Step 3. Selection of extroversion-related business sector. The user selects one of the extroversion-related business sectors for which the extroversion analysis will be performed (for ex. regional production, food, agriculture, financial and business services, high-tech manufacturing, medium-tech manufacturing, low-tech manufacturing, chemicals, electrical devices, machinery)

Step 4. Selection of function to be performed in the framework of the analysis. The user selects one of the functions related to the Extroversion Analysis to be performed (for ex. Competition, Regional Trade flows -imports and/or exports-, Trade network features).

Step 5. Description and interpretation of Extroversion Analysis results. The user is asked to describe and critically interpret the results of the Extroversion Analysis with respect to each region, year, sector, function etc.

Step 6. Regional Extroversion overview. The user provides an integrated overview of the region’s extroversion, and points to overarching strengths, weaknesses, opportunities and threats. This step requires critical thinking and interpretation on the side of the RIS3 developer.

References
  • Cambridge Econometrics. 2008. Regional production, investment and consumption in Europe for year 2000 [Online].
  • 2009a. International trade in services. Data for years from 2000 to 2004 [Online].
  • 2009b. National use and supply tables. Data from year 2000 for Austria, Belgium, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, Hungary, Ireland, Italy, Lithuania, Luxembourg, Malta, the Netherlands, Norway, Poland, Portugal, Sweden, Slovenia, Slovakia, United Kingdom. Data from year 1998 for Greece and Latvia. [Online].
  • Feenstra, R. C., Lipsey, R. E., Deng, H., Ma, A. C. & Mo, H. 2005. World trade flows: 1962-2000. National Bureau of Economic Research.
  • Foray, D., Goddard, J., Goenaga, B. X., Landabaso, M., McCann, P., Morgan, K., Nauwelaers, C. & Ortega-Argilés, R. 2012. OK Guide to Research and Innovation Strategies for Smart Specialisation. European Commission.
  • Gianelle, C., Goenaga, X., Gonzalez, I. & Thissen, M. 2014a. Smart specialisation in the tangled web of European inter-regional trade. JRC Technical reports; S3 Working Paper Series. JRC – European Commission.
  • Gianelle, C., Goenaga, X., Gonzalez, I. & Thissen, M. 2014b. Smart specialisation in the tangled web of European inter-regional trade. European Journal of Innovation Management, 17, 472-491.
  • McCann, P. & Ortega-Argilés, R. 2015. Smart Specialization, Regional Growth and Applications to European Union Cohesion Policy. Regional Studies, 49, 1291-1302.
  • MIDT 2010. Regional flights, business and first class. Data for November 2000.
  • Ministry of Infrastructure and the Environment. 2007. Interregional freight. Data for years from 2000–2004 [Online].
  • Thissen, M., Diodato, D. & van Oort, F. 2013. Integrated Regional Europe: European Regional Trade Flows in 2000. PBL publication number: 1035, PBL Netherlands Environmental Assessment Agency, The Hague/Bilthoven.
  • Thissen, M. J. P. M. & Gianelle, C. 2014. S3 Inter-regional Trade and Competition Tool [Online]. European Commission, Joint Research Centre, Institute for Prospective Technological Studies and the PBL Netherlands Environmental Assessment Agency. Available: http://s3platform.jrc.ec.europa.eu/s3-trade-tool.