Economic performance status

Gross Domestic Product (GDP) is commonly used as an indicator of the economic health of a country, as well as an economic indicator of quality of life to measure a country's standard of living. GDP per inhabitant can also be used to compare the productivity of various countries with a high degree of accuracy. The popularity of GDP as an economic indicator in part stems from its measuring of value added through economic processes. The volume index of GDP per capita in Purchasing Power Standards (PPS) is expressed in relation to the European Union (EU28) average set to equal 100.

GDP (PPS) per inhabitant in percentage of the EU average in Europe by IP delineations and EU regional typologies shows characterised distribution among the examined groups of NUTS 3 regions. All of IP delineations and EU regional typologies can be typified based on the economic productivity according to their position to the average of EU28 (100%), to their own national average and to each other (Figure 3.9).

If the unstandardized index of a country group is higher than 100, the group's level of GDP per head is higher than the EU28 average and vice versa. If the standardized index as percentages of national averages is higher than 100, the group's level of GDP per head is higher than the national average and vice versa. Only the group of Urban regions has higher average than 100% according to both unstandardized and nationally standardized data. All of the other NUTS 3 regions including groups of IP areas delineated in ESPON PROFECY, EU typologies and lagging regions reached lower level of development level than EU28 average and their national average.

The average GDP per inhabitant level within the groups of IP delineations varies between 79% and 93% compared to EU28 average, while it changes between 96% and 98.5%

compared to national averages. It is very hard to identify the best group regarding economic productivity among inner peripheries, because there are significant differences within each group. For instance, the highest arithmetic mean belongs to IP 3 regions (SGI access), but the highest level of maximum value can be found in IP 2 (interstitial). According to descriptive statistics significant differences can be also experienced within other groups of EU regional typologies based on high values of standard deviation and relative range (Table 3.8).

This differentiation can also be seen among regions identified as inner peripheries. The clearest differences among IPs can be observed between IP 4 (depleting) regions and the other three groups of inner peripheral areas (regarding lower levels of mean, median maximum and minimum values). This is not surprising, since the delineation of the former group (depleting IP) is partly based on handicaps considering economic performance.

Here are some examples. In general, the highest level of economic productivity belongs to IP 1 (regional centres) and IP 3 (SGI access) regions from Western (e.g. Luxemburg, the UK, Germany), Central (e.g. Austria) and Northern Europe (e.g. Norway). Besides these patterns, some regions defined as inner peripheries from Italy can be found among NUTS 3 regions with their highest level of GDP per capita. All of these Italian IP 1 regions are Urban regions.

All of those inner peripheral regions, which can be characterised by the lowest level of economic development – below 40% of EU28 average – are located in post-communist

Figure 3.9: GDP (PPS) per inhabitant in percentage of the EU average in Europe by IP delineations and EU regional typologies, 2015

A – unstandardized

B – standardized as percentages of national averages

In the group of IP 4 (depleting) there are also some Hungarian NUTS 3 regions (e.g. Nógrád, Szabolcs-Szatmár-Bereg, Jász-Nagykun-Szolnok) which are represented by low economic productivity. In comparison of IP regions to each other, it can be detected that the spatial distribution of inner peripheral regions is almost the same. Regions with higher productivity can be found in Western Europe (e.g. the UK, the Netherlands, Germany, France etc.), while lower productivity belong to East Central European countries (e.g. Bulgaria, Romania, Croatia, Poland, Hungary etc.).

Spatial distribution is moderately changing if we examine standardized data as percentages of national averages among inner peripheries identified by ESPON PROFECY project. In this case, some regions from East Central Europe (e.g. from Romania, Poland, Estonia) appear among regions with better economic productivity, because their GDP (PPS) per inhabitant is higher than the national average. The differences among IPs by standardized data as percentages of national averages show the followings. Firstly, the arithmetic mean is the highest – between 80% and 85% – in the group of IP 1 and IP 3, while it is lower – between 73% and 79% – in the group of IP 2 and IP 4. Secondly, differences can be seen among the groups of IPs but the scale of these differences is not very significant. Thirdly, nationally better or worse economic productivity differs wide range especially within each group among NUTS 3 regions.

Clear difference between IP regions – mostly identified by drawbacks in accessibility – and lagging regions – delineated by considering their lower development level – can be found according to both unstandardized and standardized data. On the one hand, inner peripheries have generally better positions based on their economic productivity compared them to lagging regions (<OnlyEU75%, <EUNAT75%, <OnlyNAT75%). On the other hand, especially between IP 1 and Lagging (<OnlyNAT75%) regions moderate similarity also appears.

Similarity is stronger than difference between IP regions and Rural, Intermediate areas. They appear as more compact groups, but it can be mentioned, that rural and intermediate areas have much more outlier regions.

The indicator of GDP (PPS) per inhabitant in percentage of the EU average and its spatial distribution among the examined groups of NUTS 3 regions in comparison with other indicators representing economic performance status can show strong similarity between GDP per inhabitant and GVA per inhabitants. This similarity is significantly based on relative position of the examined regions to each other measured to national averages.

In summary, inner peripheries have multiple potential risk factors of becoming more

Table 3.8: Descriptive statistics related to GDP per inhabitant data A – unstandardized (compared to EU28 average)

Mean

B – standardized as percentages of national averages Mean

Besides GDP, labour productivity can also be measured by gross value added per employed person with the result of a strong connection between these output measures¹⁷. Gross value added per employed person and its distribution in Europe by IP delineations and other EU regional typologies show very similar pattern to the distribution of GDP (PPS) per inhabitant.

Similarities between these two examined economic indicators come from their position to the average level of EU28 and national levels, and of course, to each other.

It is worth discovering a unique position of islands in Europe: their economic productivity by GDP (PPS) almost equals with IP 1 regions (low access to regional centres), but their productivity by gross value added per employed person is the second best – but lower than the EU28 average – among the other groups of regions. The most explanatory factor of this situation is based on higher productivity of prosperous tourism and catering which is overrepresented in islands in the Mediterranean area, but on the other hand, metropolitan and urban regions (e.g. Dublin, Belfast) defined as Island regions can also increase the average level of island areas regarding this performance indicator. Better position of islands can be seen according to unstandardized and standardized data too (Figure 3.10).

One of the similarities between distribution characteristics of groups of inner periphery delineations and other EU regional typologies is based on that all of the examined groups of NUTS 3 regions – except for urban areas – have more or less disadvantaged position in relation to their national averages.

Only one exception is the group of Urban regions with its highest value of arithmetic mean and the outstanding maximum values of e.g. in Camden & City of London, Milton Keynes, Dublin, Munich, Paris, Oslo etc. From the aspect of economic productivity urban areas stand out with their higher rates of highly qualified, active working age population and share of employed persons in tertiary and quaternary sector of the economy.

The other similarity between areas defined as inner peripheries and other European regions can be observed in the minimum values. Firstly, these minimum values – less than 10 Thousand Euro – really represent those regions from across Europe which have multiple socio-economic problems. Secondly, the more unfavourable position of them is also underlined by nationally standardized data: majority of examined groups of regions have minimum values less than 60% compared to national averages. Thirdly, these regions with socio-economic handicaps and their territorial distribution show typical spatial patterns in Europe: majority of them can be found in East Central Europe, but many others are located in the Mediterranean part of Europe. In all four groups of delineated inner peripheries – the highest GVA values are occurred in the Benelux countries, Scandinavian nations, the UK, Germany and Austria.

Among the groups of IPs there is no significant difference if we examine their position by GVA per inhabitants based on standardized data as percentages of national averages. Their arithmetic mean changes between 85.3% and 88.6% which is lower than their national averages, but this gap is not large-scale. In majority of IP groups minimum values are

Figure 3.10: Gross value added per employed person in Europe by IP delineations and EU regional typologies, 2014

A – unstandardized

B – standardized as percentages of national averages

Generally, a little bit worse position of IP 2 regions (interstitial) is detected compared to other territories identified as inner peripheries. Among IP 1 (regional centres) and IP 3 (SGI access) regions Luxembourg has the highest level of gross value added per person employed. Among IP 2 (interstitial) and IP 4 (depleting) regions from the UK have the highest level of gross value added per person employed.

Table 3.9: Descriptive statistics related to GVA per inhabitant data A – unstandardized

B – standardized as percentages of national averages Mean

delineated by ESPON PROFECY project and lagging regions (defined by the level of GDP per capita). It is also confirmed by the finding that the average GVA per capita level of Lagging (<OnlyEU75%) regions (economic performance is lower than 75% of EU average, but higher than 75% of national averages) only can reach 50% of the average level of inner peripheries regarding the four groups of delineations. Among lagging areas, Lagging (<OnlyNAT75%) regions (considered to be lagging only in national contexts) have the most favourable position according to descriptive statistics (Table 3.9).

In summary, inner peripheries identified by delineation processes of the project show better economic productivity than labour productivity when considering nationally standardized data.

However, the gap among regions may be narrower when analysing labour productivity than when analysing GDP per capita¹⁸.

Employment in manufacturing industry

The manufacturing sector includes a vast range of activities and production techniques, from small-scale enterprises using traditional production techniques to very large enterprises sitting atop a high and broad pyramid of parts and components suppliers collectively manufacturing complex products. The manufacturing sector is probably the most varied activity within the non-financial business economy at the NACE section level of detail¹⁹.

Ratio of employed persons working in manufacturing industry (NACE Rev.2 C) in Europe by IP delineations and other EU regional typologies show quite concentrated pattern among the European NUTS 3 regions (Figure 3.11). The EU28 average is 13.9%. Island regions are recording very low shares of manufacturing employment, while other groups of regions have much higher level of manufacturing employment, including inner peripheries. The concentration of people employed in manufacturing industry is also relatively low in most of the urban regions in Europe, because these areas are particularly concentrating high-quality, knowledge-intensive activities, tertiary jobs or labour market opportunities related to R&D&I sector. At the same time, it is worth declaring that Urban regions were also hit seriously by the latest economic crisis and suffered temporary recession in e.g. manufacturing activities with the result of decreasing number and rate of workers in manufacturing industry. Thus, the arithmetic mean and median, or the maximum and minimum values of Urban regions are the second lowest among the examined regions by this economic indicator.

Among IP delineations, similarities are much stronger than differences. IP regions are representing higher level of employed persons working in manufacturing industry across all over Europe. Among IP regions, only IP 4 (depleting) regions are standing with lower level of this labour market indicator. The average level of inner peripheries (arithmetic mean) varies between 14.1% and 19.2%, while they are highly above their national average, except for the group of depleting inner peripheries (IP 4), which equals with it.

Figure 3.11: Ratio of employed persons working in manufacturing industry (NACE Rev.2 C) in Europe by IP delineations and EU regional typologies, 2014

A – unstandardized

B – standardized as percentages of national averages

If we examine the standardized data as percentages of national averages, similar patterns can be seen, but besides the urban and island areas, Lagging (<EUNAT75%) regions have also lower level of employed persons working in manufacturing industry than averages measured at national levels. In other groups of NUTS 3 regions – also including inner peripheries – there are higher rates of workers in manufacturing industry than their national average. The highest arithmetic mean can be experienced just in IP 3 regions (123.6%), while the group of IP 4 regions almost equals with the national average (100.8%). This fact may indicate the differences among inner peripheries based on standardized data as percentages of national averages. It means the best position for IP 4 regions, the worst situation for IP 3 regions, while IP 1 and IP 2 regions have a medium position among IPs. We can conclude that accessibility-based inner peripheries (mainly IP 3 regions) are in strong connection with higher rate of employed persons working in manufacturing industry.

The closest connection can be experienced firstly, between IP and different types of lagging regions (especially between IP 3 and Lagging [<OnlyNAT75%]). Secondly, similarities might occur for example, between the group of IP 2 (interstitial) and the groups of Rural and Intermediate regions, according to descriptive statistics (Table 3.10). Thirdly, similarities among IPs might be detected between IP 1 and IP 3 regions based on their arithmetic mean, median, maximum and minimum values.

The clear difference is mainly appeared between Island regions and other typologies with the result of the lowest level of manufacturing employment in islands.

At the top end of the scale among regions defined as inner peripheries, areas from both Western European and East Central European countries might record a more than 30% share of employed persons working in manufacturing industry. For instance, majority of these Western European regions can be found in the Northern part of Italy (e.g. Reggio nell’Emilia) or in Bavaria of Germany, while East Central European inner peripheries with higher share in manufacturing industry are located in the Czech Republic, Poland, Slovenia, Bulgaria or Romania. At the other end of the scale, the IP regions with the lowest share (10% or less) of employers in the manufacturing sector are in Southern European states, Benelux countries, the United Kingdom or in Scandinavia.

The relative importance of manufacturing industry in the economy is based on its specialisation which differs within the groups of inner peripheries. It is worth analysing the faces of the specialisation at the subsector level of manufacturing industry according to countries with the highest shares of employed people working in manufacturing industry. For example, Italy is the largest in the specialisation in manufacturing of the textiles, wearing apparel, and leather products; the German specialisation is for the manufacture of machinery and equipment; high specialisation ratios are recorded in Slovenia for the manufacture of fabricated metal products and basic pharmaceutical products and preparations¹⁹.

Table 3.10: Descriptive statistics related to manufacturing employment data

B – standardized as percentages of national averages Mean

The indicator of ratio of population (25–64) with low qualification (ISCED 0–2) and its distribution among groups of regions by IP delineations and EU regional typologies draws attention a typical connection with the rate of employed persons working in manufacturing

of manufacturing industry employees combines with lower level of low qualification in inner peripheries implies that most likely higher qualified employees work in manufacturing industry.

In summary, most of similarities between IP delineations and especially lagging regions can be observed by the ratio of employed persons working in manufacturing industry (NACE Rev.2 C) compared to other examined economic or demographic indicators.

To conclude the results and experiences related to demographic, labour market and economic factors, generally inner peripheries are defined by socio-economic rather than geographic characteristics, or distance from centres of economic activity²¹. Often, they are affected by economic restructuring; the loss of a key industry and high unemployment. Unlike true geographic specificities they are mutable or transient rather than permanent²². Perhaps this transient position can be an explanatory factor not to appear inner peripheries as the most disadvantaged regions among other EU typologies.

3.3.4 Entrepreneurship status