Template-Type: ReDIF-Article 1.0
Author-Name: Tomás Mancha Navarro
Author-Name: Fabio Mosocoso Durán
Author-Name: Juan Luis Santos
Title:  Un índice de competitividad regional para España
Abstract: Resumen:El concepto de competitividad es impreciso referido a países y regiones y su medición a escala regional es compleja sin que exista una metodología clara para evaluarla de manera unívoca. En este artículo repasamos algunos intentos previos para su medición a escala regional y de acuerdo con el marco teórico planteado elaboramos un índice de competitividad global de periodicidad anual para el periodo 2001-2014 basado en tres entornos clave (capital productivo, capital humano y capital público), que se desagregan en 63 variables, que se reducen posteriormente mediante un análisis clúster para construir un índice de competitividad robusto.Abstract:The concept of competitiveness is increasingly to compare different economies at national and regional levels. It is a concept that firstly was applied at microeconomic level. However, its definition is not clear when it is applied to regions and countries. Therefore, its clarification is imperative in order to measure it at regional level and establish meaningful comparisons.
One of the key approaches to the study of competitiveness is the World Economic Forum. This institution began developing the Global Competitiveness Index in 2004. Their methodology, with an important contribution of Sala i Martin and his team, is centered in identifying the pillars of competitiveness and the key variables in each of the pillar. Their aim is to obtain a more complete picture of the real development of the competitiveness of an economy, disaggregated into a comprehensive set of components that properly quantify this development. This makes it possible to know the situation and the evolution of global competitiveness.
In this study the methodology is adapted for the 17 Spanish regions selecting 15 pillars: Business culture, Regional concentration/specialization, Internationalization, Innovation, Favorable industrial environment, Availability of capital, Institutional factors, Labor availability, Highly qualified workforce, Entrepreneurs and managers qualifications, Post-educational level, Knowledge infrastructures, Traditional basic infrastructures, Technological infrastructures and Region image.
Business culture pillar consists of two basic indicators: the existence of barriers to entry and exit and the existence of risk culture. Each is analyzed through two variables. The existence of barriers to enter and exit is approximated by the tax burden on companies, which is a significant barrier to entry for companies and regulations. The second indicator is studied through two variables: the rate of entrepreneurial activity and the number of self-employed.
Regional concentration/specialization is analyzed through four indicators: the concentration of production by sectors, the concentration of employment by sectors, the importance of high value-added activities, and the contribution of advanced services in the GDP. 
Internationalization is analyzed using three indicators: The share of exports in total regional GDP, the percentage of foreign direct investment in regional GDP and the new businesses created in the year divided into the population, as new firms tend to export a greater extent than older ones with the same size.
Innovation is quantified through four indicators per population: registered patents, total expenditure on R&D, private spending on R&D, and expenditure on technological innovation.
A favorable industrial environment allows the existence of a dynamic network of small and medium enterprises, as well as a low level of unemployment. To measure this pillar, the existence of SMEs is studied in two complementary ways; first the percentage of microSMEs (1 to 10 employees) and the percentage of medium-sized companies (50-250 employees). The first group has a size too small to optimize processes and thus must be considered a negative indicator of a favorable industrial environment, since in many cases they lack access to finance. By contrast, companies between 50 and 250 workers have a sufficient size to be able to export, improve the value chain, innovate and specialize. Additionally, the number of strikes and number of lost working days per year are studied.
The availability of capital is measured through two variables: the savings as a percentage of regional GDP and the gross fixed capital formation in the region.
Institutional factors are analyzed with three different variables: regional and local expenditure per capita, the crime rate, and cultural facilities such as the number of golf courses, cinemas and theaters per number of inhabitants.
Labor availability is measured with the variation of the working population each year, the number of public vocational training centers, and the annual change in the number of foreign employees.
Highly qualified labor force is studied with three indicators: The level of training of workers as the percentage of employed persons with secondary or tertiary education, the apparent labor productivity as the ratio of the regional GDP into the number of employees, and the number of university students as a percentage of the population.
The pillar of qualification of entrepreneurs and manager is analyzed by two indicators: The level of professionalism and the level of efficiency. The first one can be measured by the percentage of entrepreneurs and managers of the total employees, while the ratio of GDP into the number of entrepreneurs and managers can be used to measure the level of efficiency.
The post-educational level is studied through the analysis of two variables that provide insight into people who have completed non-compulsory education:  the percentage of population with a university degree or a vocational training degree.
The knowledge infrastructures included in the analysis are universities and R&D centers. They can be counted, but due to the great disparity in size is preferable to analyze homogeneous variables such as the spending on university education and number of full-time equivalent employees in R&D departments.
Transport and communications infrastructures are essential for a competitive region. Kilometers of both roads and highways, airport activity (passenger traffic and freight transport) and the passenger numbers of middle- and long-distance trains are included in this pillar.
Technological infrastructure is studied from the perspective of businesses and households through the calculation of the following indicators: percentage of companies with a website, companies using web-based services to communicate with public administrations, companies that sell their products or services on the internet and companies that have a local area network. Technological infrastructures of individuals and households are measured with the percentage of households with broadband internet access, percentage of the population who frequently use the internet, who contact electronically with public administrations and who buy often through websites.
The last pillar is region image. Its focus is diverse in order to capture a number of very different dimensions. The indicators related to education are the number of students per teacher and the ratio of classrooms per student. The number of medium-sized cities, which according to the OECD definition correspond to municipalities between 100,000 to 250,000 inhabitants, is also an indicator of a good quality of life. Affordable housing price is also taken into account. It is measured by the number of real estate transactions divided by the population. In addition, natural conditions are a factor to consider, because more attractive regions typically have a higher number of hours of sunshine per year, more kilometers of coastline and lower annual rainfall. Finally, health related variables such as the number of hospitals, hospital beds and doctors per capita are computed in this pillar.
Each of the pillars is included in one of the three environments that incorporate information about the level of competitiveness: productive environment (pillars 1 to 7), human capital environment (pillars 8 to 11) and public capital environment (pillars 12 to 15). In each pillar there are a set of variables that allow measuring the annual value of the pillar index. These indexes take values between zero and one. A higher value represents a higher level of competitiveness. With the averages of the pillar indexes we compute the indexes of the three environments. In turn, the aggregate index of regional competitiveness is the average of the three indexes of the environments.
In a second stage, we perform a cluster analysis in order to arrive to the robust index of regional competitiveness. Out of the 63 variables incorporated in the 15 pillars we select the 34 that are located in the cluster that includes the aggregate index of regional competitiveness. Making use of these 34 variables it is possible to calculate a robust index that does not present important differences with respect to the order of regions. However, if the less significant indicators are excluded the difference among regions increases.
In the period 2001-2014 there are no significant changes in the index of competitiveness for any region. Therefore, Madrid is the most competitive region in all the years analyzed. The Basque Country, Navarra and Catalonia are also competitive regions and their high competitiveness level is similar. Aragon and La Rioja have less competitiveness but are in a better position than the group that includes most of the Spanish regions. Finally, the three least competitive regions are Canarias, Castile la-Mancha and Extremadura.
Classification-JEL: R1
Keywords: Competitividad, Competitividad Regional, Entorno de Capital Productivo, Entorno de Capital Humano, Entorno de Capital Público, Competitiveness, Regional Competitiveness, Production Environment, Human Capital Environment, Public Capital Environment
Pages: 67-94
Volume: 2
Year: 2017
File-URL: http://www.revistaestudiosregionales.com/documentos/articulos/pdf-articulo-2520.pdf
File-Format: Application/pdf
Handle: RePEc:rer:articu:v:2:y:2017:p:67-94