Selection of regional typologies used in the analysis

Among typologies available at NUTS 3 level in the NUTS 2013 system not all region types are used in the analysis built on the comparison of geographical patterns and socio-economic status of inner peripheries and other regions with geographical specificities or economic performance characteristics. The selection principles of typologies used served the goal of making the comparison of different datasets (IP regions and other areas) more meaningful for the research.

Several typologies for potential use include a large number of regions, covering even the third or more of the 1400–1500 NUTS 3 units considered. When analysing group specificities or distribution characteristics of these region types, it is hard to interpret them, because of the great variety of these areas, including more disadvantaged or more developed territories as well at the same time. On the contrary, a more limited number of regions within a typology category might show more significant and specific information on group characteristics.

In this way, from Urban-Rural typology of DG AGRI and DG REGIO, the category of

‘Intermediate’ regions are considered to be challenging to be interpreted, but was kept for these analyses (Table 1.1). This category consists of a large number of areas, which might be transitional in their socio-economic characteristics, not only by taking into account their geographical status between urban and rural regions. While ‘Urban’ and ‘Rural’ classes, however also contain numerous areas, they might show more specificities in this sense as separate groups. Similarly to intermediate regions, elements of Metropolitan region typology were also considered to be hard to interpret as a coherent groups. This typology covers metropolises and their hinterlands, consisting of urban, intermediate or rural regions too, with potentially very diverse socio-economic characteristics. Regarding these potential drawbacks and the strong similarity considering group characteristics with urban regions, metropolitan areas were excluded from some of the analyses.

Table 1.1: Selection of regional typologies used in analyses

Typology name Elements Source Used in

analyses Name in analyses Urban-Rural typology

DG AGRI and DG REGIO

Predominantly urban Yes Urban

Intermediate Yes

Predominantly rural Yes Rural

Typology on mountain areas

DG Regio

> 50 % of population

live in mountain areas No

> 50 % of surface are in

mountain areas No

> 50 % of population and > 50 % of surface

are in mountain areas Yes Mountain

Typology of NUTS 3 regions entirely composed of islands island with >= 1 million inhabitants

national average but not Yes Lagging

From different categories of mountain area typology, only those areas were processed into the analyses, which covers regions with >50 % of population and >50 % of surface is in mountain areas. These criteria might ensure to be more focused when considering mountain region characteristics of distribution of population and economic activities, since they exclude those mountain areas where the majority of population resides in lower elevated areas.

Contrary to that, all categories of ‘Island’ typology are considered to be kept for the analysis, since several socio-economic specificities related to island positions (demographic characteristics, industries, accessibility conditions etc.) might affect this entire group independently from the size-factor, at least in some extent.

The definition of lagging areas is a relative and open issue, since it is not a fixed and permanent category and significantly depends on both the level of comparison (lagging compared to what, at what regional level) and the purpose of classification. In academic papers researchers might have more space to formulate and develop complex ways of identifying with a potentially better targeting ability. Complex and innovative options for defining lagging regions in European-level policy oriented researches, such as the classification of DG Internal Policies analysis¹ or the ESPON EDORA typology (A–D type)² provide interesting insights on how to define socio-economically disadvantaged areas without restricting it to one or two underlined aspects, and be comprehensive at the European level.

These experiments rarely build into actual policy practices. From the viewpoint of EU-level classifications, such methodologies are more favourited, which are reproducible, traceable and available (regarding data needs) for a continent-wide coverage. This might indicate the advantage of using simple indicators for policy purposes. Nevertheless, these can only have restricted facilities in interpreting disadvantages of regions in a complex way, so their connection to the phenomena to be identified should be clear for a reliable usage.

The European Commission use the simple GDP per inhabitant value-based classification in determining the eligibility of NUTS 2 regions for accessing EU Structural Funds (for lagging regions). It emphasises the role of economic performance in disadvantaged status of regions by implying that those areas might be lagging, which lack economic capacities to perform (better). This definition compares GDP/capita (PPS) values of regions to the EU average.^3,4. Three classes are formed by this method: less developed (GDP/capita < 75% of EU average), transition (GDP/capita = 75–90% of EU average), more developed (GDP/capita > 90% of EU average).

The categorisation provides an acknowledged and well-grounded regional typology of economic performance, and is selected to be used in analyses of ESPON PROFECY project for identifying lagging areas. Nevertheless, some drawbacks affect this mode of defining lagging regions that one needs to have in sight. These shortcomings of this categorisation might be overcome by different ways of fine tuning. Current categorisation of NUTS 2 regions eligible for subsidy can be updated by the most recent data on regional performance

(GDP/capita), which is based on three years average values (most current data cover years 2013–2015).

It is also possible to keep the criterion of ‘GDP/capita below 75% of EU average’ as a form of identification of less developed regions, but do the calculations directly at NUTS 3 level. This might have an impact on the accuracy of targeting, since many NUTS 2 regions consist of different types of areas regarding economic performance, which might cover the presence of several disadvantaged areas, while identifying others which are less affected by these handicaps.

Another option for fine tuning this methodology might be using the ‘below 75%’ criterion not just by comparing regional performance values to the EU average, but reflecting on within country differences too. From policy-oriented aspects, this option might gain importance, since it puts emphasis not only on lagging regions measured at the European level, but the presence of territorial inequalities at national levels too. On the one hand, it might represent multiply disadvantaged regions by the coincidence of certain degrees of deviation both from EU and national average, on the other hand, it helps to fine tune this type of measure to outline areas identified as lagging in national context in more developed countries.

Figure 1.1: Lagging region typologies used in ESPON PROFECY project

During the identification of lagging areas (for comparing them to the status of inner peripheries) in PROFECY analyses these options were taken into consideration.

regions, whose GDP per capita value do not reach the 75% of EU28 average but their economic performance is above the 75% level of their national average. Another group consists of the less developed regions in the comparison, lagging behind the 75% of both the EU and national GDP per capita averages. While the last two groups of lagging areas cover those regions, which were identified as disadvantaged by considering their GDP per inhabitant level compared to national averages (including disadvantaged areas at the EU level too) or only regarding their GDP per inhabitant level compared to national averages. In some parts of the analyses joint categories representing all regions lagging in the EU context or outlining every economically disadvantaged area from national aspects are not listed (see for instance Chapter 3).