Hyperatlas 3.0 Model Reports Annex to Deliverable 3 // December 2020

TOOLS & MAPS ACTIVITY // DRAFT HyperAtlas 3.0 Model Reports Annex to Deliverable 3 // December 2020

This Tools & Maps activity is conducted within the framework of the ESPON 2020 Cooperation Programme, partly financed by the European Regional Development Fund.

The ESPON EGTC is the Single Beneficiary of the ESPON 2020 Cooperation Programme. The Single Operation within the programme is implemented by the ESPON EGTC and co-financed by the European Regional Development Fund, the EU Member States and the Partner States, Iceland, Liechtenstein, Norway and Switzerland.

This delivery does not necessarily reflect the opinions of members of the ESPON 2020 Monitoring Committee.

Coordination Stephanie Kirchmayr-Novak

Authors Stephanie Kirchmayr-Novak, Roland Gaugitsch, Joanne Tordy, Chien-Hui Hsiung, ÖIR GmbH (Austria), Sergio Muñoz Gómez, Rubén Navarro, Laurentia Technologies (Spain)

Advisory group ESPON EGTC: Marjan van Herwijnen

Information on ESPON and its projects can be found at www.espon.eu. The website provides the possibility to download and examine the most recent documents produced by finalised and ongoing ESPON projects.

Layout and graphic design by BGRAPHIC, Denmark

Printing, reproduction or quotation is authorised provided the source is acknowledged and a copy is forwarded to the ESPON EGTC in Luxembourg.

Contact: [email protected]

TOOLS & MAPS ACTIVITY // HyperAtlas 3.0 Model Reports Annex to Deliverable 3 // December 2020

Disclaimer This document is a draft report.

The information contained herein is subject to change and does not commit the ESPON EGTC and the countries participating in the ESPON 2020 Cooperation Programme.

The final version of the report will be published as soon as approved.

TOOLS & MAPS ACTIVITY // HyperAtlas 3.0

Table of contents

Abbreviations ...... 7 1 Preface: Intention of the Model report ...... 8 2 Storyline A – Regional authority...... 9 2.1 Context ...... 9 2.2 Task description ...... 9 2.3 Use of HyperAtlas 3.0 ...... 10 2.3.1 Indicators useful for this task and availability of these indicators in the HyperAtlas3.0 ...... 10 2.3.2 Final settings in the HyperAtlas...... 10 2.4 Interpretation of the results ...... 10 2.4.1 Share of forest area and population of working age in Europe ...... 10 2.5 Summary on findings ...... 13 2.6 Limitations of the HyperAtlas – additional analysis useful for answering the task ...... 14 3 Storyline B ...... 15 4 Storyline C – national player/macro-region ...... 16 4.1 Context ...... 16 4.2 Task description ...... 16 4.3 Use of HyperAtlas 3.0 ...... 16 4.3.1 Indicators useful for this task and availability of these indicators in the HyperAtlas3.0 ...... 16 4.3.2 Final settings in the HyperAtlas...... 17 4.3.3 Methods and maps of the HyperAtlas useful to solve this task ...... 17 4.4 Interpretation of the results ...... 18 4.4.1 Map: Ratio –Areas with population at risk of poverty and social exclusion ...... 18 4.4.2 Map: Deviation to national average – Areas with population at risk of poverty and social exclusion ...... 19 4.4.3 Map: Ratio – economic performance ...... 20 4.4.4 Map: Deviation to macro-region’s average – economic performance ...... 21 4.4.5 Map: Deviation to national average – economic performance ...... 22 4.4.6 Map: Synthesis (deviation) – economic performance ...... 23 4.4.7 Lorenz curve on economic performance ...... 24 4.5 Summary on findings – Answers to the research questions ...... 24 4.6 Limitations of the HyperAtlas – additional analysis useful for answering the task ...... 26

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List of figures

List of figures Figure 2.1: Groundwater wetness percentile (June 2020) ...... 9 Figure 2.2: Regional scope and selected indicators...... 10 Figure 2.3: Share of forest area on total area ...... 11 Figure 2.4: Share of population of working age...... 11 Figure 2.5: Deviation to EU 27+5, Share of forest area on total area ...... 11 Figure 2.6: Deviation to EU27+5, Share of population of working age ...... 11 Figure 2.7: Deviation to the national average, Share of forest area on total area ...... 12 Figure 2.8: Deviation to the national average, Share of population of working age ...... 12 Figure 2.9: Deviation to the neighbouring regions, Share of forest area ...... 12 Figure 2.10: Deviation to the neighbouring regions, Share of population of working age ...... 12 Figure 2.11: Regions performing over average regarding all three spatial contexts (share of forest area) ...... 14 Figure 2.12: Regions performing under average regarding all three spatial contexts (share of population of working age) ...... 14 Figure 4.1: Regional scope and selected indicators...... 17 Figure 4.2: Ratio – people at risk. (2014) ...... 18 Figure 4.3: Ratio – People at risk (2017) ...... 18 Figure 4.4: Deviation to the national average – people at risk (2014) ...... 19 Figure 4.5: Deviation to national average – people at risk, zoom at AT (2014) ...... 19 Figure 4.6: Deviation to the national average – people at risk (2017) ...... 19 Figure 4.7: Deviation to national average – people at risk, zoom at AT (2017) ...... 19 Figure 4.8: GDP/cap (PPS) (2010) ...... 20 Figure 4.9: GDP/cap (PPS) (2017) ...... 20 Figure 4.10: Deviation to the macro region’ average –GDP/cap (PPS) (2010) ...... 21 Figure 4.11: Deviation to the macro region’ average –GDP/cap (PPS), zoom at AT (2010) ...... 21 Figure 4.12: Deviation to the macro region’ average –GDP/cap (PPS) (2017) ...... 21 Figure 4.13: Deviation to the macro region’ average –GDP/cap (PPS), zoom at AT (2017) ...... 21 Figure 4.14: Deviation to the national average –GDP/cap (PPS) (2010) ...... 22 Figure 4.15: Deviation to national average –GDP/cap (PPS), zoom at AT (2010) ...... 22 Figure 4.16: Deviation to the national average –GDP/cap (PPS) (2017) ...... 22 Figure 4.17: Deviation to national average –GDP/cap (PPS), zoom at AT (2017) ...... 22 Figure 4.18: Synthesis – GDP/cap (PPS) (2010) ...... 23 Figure 4.19: Synthesis – GDP/cap (PPS), zoom at AT (2010) ...... 23 Figure 4.20: Synthesis – GDP/cap (PPS) (2017) ...... 23 Figure 4.21: Synthesis –GDP/cap (PPS), zoom at AT (2017) ...... 23 Figure 4.22: 3.4.7 Lorenc curve on the GDP/cap (PPS) ...... 24

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Abbreviations

EU European Union EUSDR EU Strategy of the Danube Region GDP Gross domestic product NUTS Nomenclature of Territorial Units for Statistics s PPS Purchasing Power Standard SME Small- and medium-sized enterprises

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1 Preface: Intention of the Model report

This document is made up of three different storylines, illustrating the ways the HyperAtlas 3.0 can be applied by different users to find answers to specific, typical questions. Following three storylines can be found in this report: (a) A regional authority benchmarking the region against its fellow regions (b) A city authority aiming at identifying socio-economic developments within its hinterland affecting the future territorial development of the city. (c) A national authority in process of strategic planning targeting SMEs Each storyline gives an example for a type of user, covering a particular geographical scale and challenge. Within this storyline, the report will give an example of ▪ the use of the tool

▪ the path of exploring the tool resulting in maps and figures ▪ how to interpret these maps and figures ▪ what conclusions can be deduced from these maps and figures Each storyline is produced following the same structure: ▪ Context and task description ▪ Methods and maps of the HyperAtlas useful to solve this task ▪ Indicators useful for this task and availability of these indicators in the HyperAtlas3.0 ▪ Interpretation of the results ▪ Summary and findings on findings and recommendations ▪ Limitations of the HyperAtlas – additional analysis useful for answering the task

By providing this hands-on examples for the use of the HyperAtlas 3.0, users shall be encouraged to use the HyperAtlas, read the results and disseminate them easily to all interested in the analysed topic.

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2 Storyline A – Regional authority

2.1 Context

The region Östliche Obersteiermark or Upper Styria East is located in the Austrian Alps. Styria is with 1 Mio. hectare of forest the region the most densely wooded area in Austria and the region of Upper Styria is even more rich on forest as 75% of its area are covered by wood. Nonetheless it is an important industrial region with an very competitive montane, paper and metal industry. The local wood processing industry is strong and includes a leading European wood-industry enterprise. The regional development is however hampered by a shortage of skilled professionals as well as climate changes and natural phenomena like strong winds or heavy snowfall which together with increasing bark beetle infestation in years with heat and dry periods.

Figure 2.1: Groundwater wetness percentile (June 2020) The map on groundwater wetness shows the ex- posure of agriculture and forestry to draughts lined with Europe’s warmest winter in 2019/20 and a historic heat wave in the middle of May. Ground- water reserves are an important source for vege- tation, both for agriculture and forestry and impact the vulnerability of forest against other damage, e.g. bark beetles. The map shows that especially Central Europe has experienced a series of droughts. The forest regions in these regions – be it large areas at the European scale or important “green island” within more densely populated areas thus need to be cultivated and protected in order to ensure a sustainable development for whole Europe.

Source: Lauren Dauphin, NASA Earth Observatory, June 22, 2020 The regional management of Upper Styria East thus wants to investigate which regions in Europe are likewise densely forested in order to arrange for a cluster meeting discussing measures to strengthen the forests, discuss options to protect forest areas in regions of increasing settlement development as well as measures to counter-act the increasing shortage of skilled professionals.

2.2 Task description

The main aim of the task is to identify regions within Europe which are likewise densely wooded and might also have a similar demographic structure as Upper Styria East.

The HyperAltlas 3.0 is used to answer the following research questions: ▪ Which regions in Europe have a similar density of wooded areas respectively a similar demographic structure? ▪ Which regions are in a likewise leading/exposed position as regards the comparison to the national average? ▪ Which regions within 100 km could be activated for closer cooperation? Lastly, the HyperAtlas 3.0 is used to produce expressive maps which support the demonstration of results.

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2.3 Use of HyperAtlas 3.0

2.3.1 Indicators useful for this task and availability of these indicators in the HyperAtlas3.0

The following indicators are seen as useful to answer the research questions. Their availability in the Hyper- Atlas 3.0 and possible limitations are listed in the table below. Information on the indicators included and their limitations can be derived from the table in the User Guide. Data on the forest area and total area are available for all NUTS-3 regions in Europe and this data are included in the HyperAtlas 3.0

Useful indicator Availability in HyperAtlas 3.0 Limitations of data in HyperAtlas 3.0 Forest area (km)/Total area (km) Forest area (ha)/Total area (km) Share of population being of Population aged 15-64 years Data available at NUTS-3 level for all working age on total population Total population regions in ESPON space

2.3.2 Final settings in the HyperAtlas

In order to answer the above research questions, the following settings were done in the HyperAtlas 3.0.

Figure 2.2: Regional scope and selected indicators Setting for Indicator: Forest area/total area Population aged 15-64 years/total population

2.4 Interpretation of the results

2.4.1 Share of forest area and population of working age in Europe

Focusing on the forested area, the Upper Styria East is clearly visible in the North of the Austrian Alpine Area. Most interestingly, equally densely forested areas are to be found in Slovenia and Slovakia as well as in Sweden, Finland and Estonia and Romania. As regards the share of population of working age, Upper Styria is highlighted with share of 63% in population of working age in the “center” of Austria. Similar shares can be detected in the East of Romania, with likewise large forest areas. The forested areas in Sweden have even lower shares of 59% to 60%. Same holds true for Eastern Germany.

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Figure 2.3: Share of forest area on total area Figure 2.4: Share of population of working age

The above findings become even more profound if one compares the regions to the European average (EU27+5). Again the Austrian Alpine Regions as well as Slovenia, Croatia and Slovakia are visible, together with Slovakia and the large forest areas in the East of Norway, Sweden and Finland. As regards the share of population of working age, the Scandinavian Countries fall in the same category as Upper Styria East. The forested areas of Slovakia, Slovenia and Croatia in contrast have higher shares regarding the population of working age than Upper Styria.

Figure 2.5: Deviation to EU 27+5, Figure 2.6: Deviation to EU27+5, Share of forest area on total area Share of population of working age

At the national scale Austria is quite divers as regards the density of forested areas, which are primarily found in the Alpine Regions and Styria is clearly rich of forest as compared to the other regions. The deviations at the national scale illustrate the low share of population of working age in especially these forested areas in Austria. Upper Styria East is displayed as below average, while the more densely populated areas of the agglomerations Vienna, Graz and Innsbruck have a better demographic structure. Further East- ern Germany is dominantly highlighted in this map, given that Eastern Germany also hosts forested regions at the border to Poland.

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Comparing both maps the Polish regions of Gorzovski and Zielonogorski become evident, which have a strong share of forested area at European and National Scale, but a higher share of population of working age. They could be an interesting interview partner for a workshop.

Figure 2.7: Deviation to the national average, Figure 2.8: Deviation to the national average, Share of forest area on total area Share of population of working age

Finally, the situation of the regions is compared to their neighbourhood region reachable within 100 km. In terms of woodland, Upper Styria East is not in such focus anymore as it is linked to other regions with large forest areas in Styria. It’s neighbor region Niederösterreich-Süd is however marked as above average, as it is bordering more densely populated areas of the Viennese suburbs. This highlights this area however as an important habitat for different species, which would be a good foundation for further discussions on how to develop sustainable forestry in Austria. Other, similar regions in the neighbouring countries of Austria are Garmisch Partenkirchen and the Italian regions of Como, Bergamo, Ticino, Verbano-Cusio-Osola.

Figure 2.9: Deviation to the neighbouring Figure 2.10: Deviation to the neighbouring regions, Share of forest area regions, Share of population of working age

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2.5 Summary on findings

The synthesis maps however present an overview on the situation regarding both indicators.

Upper Styria East is one of the most densely forested areas in Europe

The map on the left-hand side highlights all regions in Europe that are over-performing regarding their share of forest area in all three spatial contexts, i.e. the European scale, the National scale and the neighborhood level (100 km radius). The share of forested area in Upper Styria East exceeds average levels of all three spatial contexts. Over Europe, the map shows a scattered pattern, not as easy to interpret. The Carpathian Mountains are however visible, interrupted by the region in the Ukraine without data.

High number and scattered distribution of forest areas in Europe

There are however in total 335 other regions1 which perform over-average in all three spatial contexts, that is 25% of all NUTS-3 regions. Judging from the previous work, regions in Sweden, Slovakia, the North of Italy and Romania seem to be good sparing partners regarding an exchange on the further development and protection of forests in Europe. Further, French regions should take part on these discussions, as the East of France was already hit by the draughts following the heat period of 2003 and these regions might already have more insights on potential counter-measures.

Eastern European Countries have more population of working age than other regions in Europe

The demographic situation of the Western and Central European Countries with a high share of forest area is quite similar. The map on the right-hand side illustrates the regions which are below average as regards the share of population of working age. Here again, Upper Styria East is highlighted, together with the West- ern and Central European Countries, often in regions with strong forestation. The demographic situation is however different in wide parts of Eastern Europe, except Romania and Greece. Additionally, Estonia is more likely in the group of the Scandinavian countries.

Potential partners for sustainable forests and demographic development in Sweden, Fin- land, Eastern Germany and the North of Italy

Based on this first analysis, potential partners for the future work are foremost regions of Sweden, Finland, Eastern Germany and the North of Italy. It will be however interesting to invite North Romanian regions as well as French regions to join the discussion.

1 Figure from the Histogram of HyperAtlas for the respective map.

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Figure 2.11: Regions performing over Figure 2.12: Regions performing under average regarding all three spatial contexts average regarding all three spatial contexts (share of forest area) (share of population of working age)

2.6 Limitations of the HyperAtlas – additional analysis useful for answering the task

In order to more thoroughly analyse the study question at hand, further information on the vulnerability of the regions would be helpful. In another step, inserting custom data on groundwater levels or vulnerability to draught would be interesting to add.

Further, the extraction and analysis of the generated data could be feed into a more profound cluster analysis, grouping the regions in more homogeneous groups and adding other parameters to the analysis. Over- all, the results of the HyperAtlas allowed for quickly exploring the situation of Upper Styria East within the wider context of Europe, the national scale as well as its neighbourhood and to create new insights.

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3 Storyline B

A city authority aiming at identifying socio-economic developments within its hinterland affecting the future territorial development of the city will be added in the final report. The report will be aligned along the following structure: 1. Context and task description 2. Methods and maps of the HyperAtlas useful to solve this task 3. Indicators useful for this task and availability of these indicators in the HyperAtlas3.0 4. Interpretation of the results 5. Summary and findings on findings and recommendations 6. Limitations of the HyperAtlas – additional analysis useful for answering the task

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4 Storyline C – national player/macro- region

4.1 Context

Macro-regional strategies were endorsed by the European Council to address common challenges faced by a defined geographical area and encourage strategic cooperation. 19 EU Member States participate in one or more of the four macro-regional strategies. Although different in their thematic focus, they all aim at con- tributing to achieve economic, social and territorial cohesion within their realm. The EU-strategy of the Danube Region was endorsed in 2011, including regions of EU and Non-EU countries. The Macro Region has a complex governance structure, with a political, strategic and operational sphere. Within the operational spheres, priority areas are defined and respective boards established to ensure the implementation of the EUSDR. Austria is among others responsible for the overall coordination and the priority area focusing on “investing in people and skills”.

4.2 Task description

The overall research question asks for the extent of which the overall goal of cohesion has been achieved. The focus lies on the Austrian regions, which are benchmarked against its fellow regions participating in the macro-regional strategy in terms of territorial inequality and non-exclusive growth. The HyperAltlas 3.0 is used to answer the following research questions: ▪ Are there significant variations in terms of territorial inequality and non-exclusive growth within the administrative boundaries of Austria, and within the macro-region? ▪ What changes can be observed since the creation of the EUSDR in 2011? ▪ Are the chosen indicators suitable for answering the research questions? Is the data quality and availability in the ESPON database and EuroStat satisfactory? Lastly, the HyperAtlas 3.0 is used to produce expressive maps which support the demonstration of results.

4.3 Use of HyperAtlas 3.0

4.3.1 Indicators useful for this task and availability of these indicators in the HyperAtlas3.0

For the questions at hand, following indicators are useful, respectively available in in the HyperAtlas 3.0. Information on the indicators included and their limitations can be derived from the table in the User Guide.

Useful indicator Availability in HyperAtlas 3.0 Limitations of data in HyperAtlas 3.0 People at risk of poverty or so- People at risk of poverty or so- NUTS 2010/13, NUTS2, Data gaps in cial exclusion (2011-2020), cial exclusion (% of total popula- RO, generally available on NUTS 2, In NUTS 3 tion NUMBER) (2005 – 2019) some countries only in NUTS 1 or 0 NUTS 2 (e.g. Germany: NUTS 0)

Gross domestic product (GDP) Gross domestic product (GDP) Data gaps for 2018, at current market prices by Pur- at current market prices by Pur- chasing Power Standard per in- chasing Power Standard per in- habitant (2011-2020), NUTS 3 habitant (2005-2018), NUTS 3

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4.3.2 Final settings in the HyperAtlas

To ensure replicability, this sub-chapter shares the selected settings.

Figure 4.1: Regional scope and selected indicators

Setting for Indicator: Risk at poverty & soc. excl Setting for Indicator GDP (PPP)

No change was made to the colour settings and the synthesis thresholds.

4.3.3 Methods and maps of the HyperAtlas useful to solve this task

The HyperAtlas 3.0 offers a wide range of tools which inform parts of the research interest. ▪ RATIO map: a map on the geographical distribution of the ratio selected in the Danube region. ▪ DEVIATION (Large-scale reference area): a map on the deviation of the chosen ratio compared to the other regions participating in the macro-strategy ▪ DEVIATION (Aggregated NUTS level): Map on the deviation of the chosen ratio within the participating countries in the macro-region. ▪ SYNTHESIS: Map overlaying the results of the above mentioned two deviation maps.

▪ LORENZ CURVE: a graphical representation of inequalities in the Danube region. ▪ The tool can produce maps on the topic for different points of time which allow the user to interpret the results as development over time.

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4.4 Interpretation of the results

4.4.1 Map: Ratio –Areas with population at risk of poverty and social exclusion

Figure 4.2: Ratio – people at risk. (2014) Figure 4.3: Ratio – People at risk (2017)

The maps show the ratio of the population at risk of poverty or social exclusion per total population. They depict those NUTS 2 regions which have a higher rate of people at risk of poverty and social exclusion, situated mostly in the South and East of the macro-region. Especially most regions in Bulgaria, Romania and Croatia show a high risk, but also some regions in Austria, Slovenia and Hungary. On the opposite, the regions with the lowest risk of poverty and social exclusion are to be found mostly in the northern and central parts and Bucharest. Interesting inner-state heterogeneity is visible a number of countries: in Czech Republic, the northern NUTS 2 Regions look worse (but still good in comparison to the macro-region), in Slovenia and Hungary and more so in Slovakia a strong west-east divide is obvious. These results hold true over time. There are no significant changes over the 3-year time period under con- sideration. Three years is usually not a period in which social-economic factors change substantially, especially if there is no social upheaval. One can argue that the EUSDR came into being in 2011 and hence it would be worthwhile to assess the differences almost one decade after its coming to existence. However, in this case the data availability is a limiting factor. The earliest data entry that covers most of the area is in 2014 and the most recent one in 2017. Hence the comparison of those two points in time does not supple- ment the information.

Limitations

The maps show the ratio of the population at risk of poverty or social exclusion per total population in the Danube macro region, excluding the non-EU areas. The dark grey areas indicate the missing data for the German regions. This entails a caveat: all calculated averages and deviations do not include data from Germany and non-EU countries participating in the EU strategy for the Danube region.

The missing data availability over a longer period of time limits the explanatory power of a comparison over time. The mapping of the results should also make the onlooker aware of the possible differences in compiling the data in the respective Member States. It is not without doubt, e.g. that Austrian population is in general more at risk of poverty and social exclusion that it is in Czech Republic.

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4.4.2 Map: Deviation to national average – Areas with population at risk of poverty and social exclusion

Figure 4.4: Deviation to the national average Figure 4.5: Deviation to national average – – people at risk (2014) people at risk, zoom at AT (2014)

Figure 4.6: Deviation to the national average Figure 4.7: Deviation to national average – – people at risk (2017) people at risk, zoom at AT (2017)

The deviation maps on the aggregated NUTS level show how the NUTS 2 regions are performing in contrast to the national average. In Slovenia and Romania, but to a lesser extent also Praha, Budapest and the NUTS 2 region including Sofia, the capital regions stand out as those regions with by far the lowest poverty risk for its population while in Austria the situation is reversed: it is the capital city which shows the highest risk for poverty and social exclusion. To a lesser extent this situation is also true for Ljubljana’s and Zagreb’s NUTS 2 Region. In Bratislava this situation grew even more pronounced over the 3 years observation period, whereas in all other involved capitals/capital regions the deviation to national average grew smaller. This decrease in inner-national deviation points to a balancing development within the countries. In the Austrian context, the capital of Vienna and the most western provinces of Vorarlberg and Tyrol stand out as those regions in which the risk of poverty proportional to the total population is the highest within Austria. While in Vienna (in 2017) the risk is about 50% (deviation: 1,48) higher, the lowest risk face inhabitants of the province surrounding the capital: Lower Austria lies one third below the average risk facing poverty. This situation slightly increases between 2014 and 2017. This can point to a rather rich sub-urban

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population living in Lower Austria, while population at risk tend to move/stay in the capital due to the better social welfare system. Limitations: As the indicator chosen as nominator describes a negative connotated situation (being at risk of being poor or socially excluded) the colour coding of the map is slightly misleading. The redder the regions the more minor the risk, the darker the blue the more the population is at risk to suffer poverty.

4.4.3 Map: Ratio – economic performance

Figure 4.8: GDP/cap (PPS) (2010) Figure 4.9: GDP/cap (PPS) (2017)

The maps clearly depict two results: First, there is an undeniable west-east divide. The largest concentration of well performing regions in an economic sense are situated in Bavaria and Austria and the lowest in the most south-eastern regions. Second, the capital regions clearly stick out as the best performing regions.

The observation period shows generally positive developments in the entire Danube region with an overall increase of the GDP per capita.

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4.4.4 Map: Deviation to macro-region’s average – economic performance

Figure 4.10: Deviation to the macro region’ Figure 4.11: Deviation to the macro region’ average –GDP/cap (PPS) (2010) average –GDP/cap (PPS), zoom at AT (2010)

Figure 4.12: Deviation to the macro region’ Figure 4.13: Deviation to the macro region’ average –GDP/cap (PPS) (2017) average –GDP/cap (PPS), zoom at AT (2017)

These maps depict the deviation of each region to the macro-region’s average in terms of their economic performance in two points of time: 2010 and 2017. Yellowed regions depict those at a national average. The more pronounced colour red or blue gets, the higher is the deviation from the average.

The maps show a significant west-east divide, with the exception of the capital regions, which perform well regardless of their location. The West-East divide slightly decreases within the observation period of 7 years, indicating that the eastern regions are catching up slightly. In Austria, there are only small changes visible over time. In comparison to the average of the Danube-region Austrian regions perform above-average in both points of time. Only 5 regions in 2010 and 8 regions in 2017 – all situated at the Austrian periphery - score below 98% of the average, with Weinviertel AT124 lagging behind the most with 76% (2010) and 74% (2017) of the macro-regions average.

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4.4.5 Map: Deviation to national average – economic performance

Figure 4.14: Deviation to the national average Figure 4.15: Deviation to national average – –GDP/cap (PPS) (2010) GDP/cap (PPS), zoom at AT (2010)

Figure 4.16: Deviation to the national average Figure 4.17: Deviation to national average – –GDP/cap (PPS) (2017) GDP/cap (PPS), zoom at AT (2017)

These maps depict the deviation of each region to the national average in terms of their economic performance in two points of time: 2010 and 2017. Yellowed regions depict those at a national average. The more pronounced colour red or blue gets, the higher is the deviation from the average. All in all, the maps depict a rather homogenous distribution of economic performance with highlights in the capital cities. Again as in the case of the Ratio-map, the capital cities stick out in both points of time as those regions with the best economic performance. In Bavaria, Hungary, Bulgaria and Romania one regions is found in each case which perform far below the national average in 2010. Seven years later, the Bavarian region has caught up, whereas in Bulgaria and Romania a second region fell below 50% of the national average. In Austria the economic performance is rather equally distributed. There are not regions falling far off either side of the national average.

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4.4.6 Map: Synthesis (deviation) – economic performance

Figure 4.18: Synthesis – GDP/cap (PPS) Figure 4.19: Synthesis – GDP/cap (PPS), (2010) zoom at AT (2010)

Figure 4.20: Synthesis – GDP/cap (PPS) Figure 4.21: Synthesis –GDP/cap (PPS), zoom (2017) at AT (2017)

The synthesis maps join the results of the deviation maps at the different spatial contexts. “1” relates to the macro-regional context, “2” to the national context and “3” to the neighbouring regions. For the task at hand, the third spatial context was of no interest which is why the third column (high performing regions are coloured in green) can be ignored. Red and pink indicate those regions, that have an economic performance above the average regarding both analysed spatial contexts (macro-regional and national), whereas the orange-coloured regions perform above-average in the macro-regional context and the yellow-coloured one in the national context. Summarizing the two outputs from before, the synthesis map highlights the capital regions and economically potent regions in the western part of the Danube regions as “winning regions”. Considering only the macro- regional context then the concentration of winning regions in the west increases substantially. Putting the national context as analysing frame, a couple of well performing regions in each country join the league of the “winning regions”. No significant changes can be observed between 2010 and 2017.

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4.4.7 Lorenz curve on economic performance

The Lorenz curve displays inequalities in the distribution of the indicator data at hand. While the black curve depicts the theoretical equal distribution, the red line shows the actual distribution within the Danube macro- region (LRA). The curve can be interpreted as rather positive sign, since it shows a rather equal distribution of risk within the total population (the red line does deviate significantly from the black line). Strengthening this fact is that the Gini-coefficient is 0,17 (ranging between 0 [perfect equality] and 1 [maximal concentration], hence pointing to a relatively equal risk distribution.2 Figure 4.22: Lorenz curve on the GDP/cap (PPS)

4.5 Summary on findings – Answers to the research questions

1. Are there significant variations in terms of territorial inequality and non-exclusive growth within the administrative boundaries of Austria, and within the macro-region? The chosen indicators (people at risk of poverty and social exclusion; GSP/cap (PPS)) suggest that there are significant territorial inequalities. Capital versus non-capital regions ▪ The most pronounced is to be found between capital cities in contrast to all other regions in term of their economic performance. ▪ Considering the people at risk of poverty and social exclusion the result also points to a special role of cities. In this case however, a two-fold situation becomes visible: In some countries the capitals tend to be home to the highest share of people at risk than the national average, while in others the capitals are those regions in the country with the lowest share of people at risk.

▪ In all capitals/capital regions (excluding Bratislava) the deviation to the national deviation grew smaller over the 3 years observation period possible with the chosen indicator. This decrease in inner-national deviation points to a balancing development within the countries.

2 In this graph it seems that the Lorenz curve is above the line of equal distribution. This is a display problem and will be solved in the next version.

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▪ In the Austrian context, the result of the capital of Vienna in comparison to its surrounding province Lower Austria is worth noting. Vienna shows the highest deviation from the national average on the negative spectrum (the risk is 50% higher) while the lowest risk face inhabitants of the province surrounding the capital (one third below the average risk facing poverty). This situation slightly increases between 2014 and 2017. This can point to a rather rich sub-urban population living in Lower Austria, while population at risk tend to move/stay in the capital due to the better social welfare system. West-East divide ▪ There is a noticeable west-east divide, particularly considering the economic performance. The largest concentration of well performing regions in an economic sense are situated in Bavaria and Austria and the lowest in the most south-eastern regions. ▪ In Austria the economic performance is rather equally distributed. There are not regions falling far off either side of the national average ▪ Considering the share of population at risk of poverty and social exclusion the divide is not as apparent but still visible. Regions which have a higher rate of people at risk are situated mostly in the South and East of the macro-region. Especially most regions in Bulgaria, Romania and Croatia show a high risk, but also some regions in Austria, Slovenia and Hungary. Regions with the lowest risk of poverty and social exclusion are to be found mostly in the northern and central parts of the Danube Region and Bucharest. ▪ Apart from the capital versus non-capital dynamic, some countries show a big heterogeneity within its territory: in Czech Republic, the northern NUTS 2 Regions show a higher share of people at risk than they southern regions (but still good in comparison to the macro-region), in Slovenia and Hungary and more so in Slovakia a strong west-east divide is obvious. 2. What changes can be observed since the creation of the EUSDR in 2011? ▪ The indicator on the people at risk of poverty and social exclusion is not available for most of Danube- region bevore 2014 and not later than 2017. This entails a rather short observation period of 3 years, in which no significant changes could be found. ▪ Regarding the economic indicator (GDP/cap (PPS)) a slight decrease in the west-east divide is notable. This could be interpreted as the start of a catching-up process of the eastern regions.

▪ In Austria, there are only small changes visible over time. In comparison to the average of the Danube- region Austrian regions perform above-average in both points of time. 3. Are the chosen indicators suitable for answering the research questions? Is the data quality and availability in the ESPON database and Eurostat satisfactory? ▪ The topic of territorial inequality and non-exclusive growth is a very comprehensive field. The two chosen indicators put the spotlight on two important issues: the economic performance, and the people at risk of poverty and social exclusion. However, the analysis of those two indicators can only act as a starting point of a more in-depth assessment including a broader range of indicators. E.g., any GDP/capita indicator comes with known limitations, especially if it is used (as was done in this case) as a proxy to social progress: It does not take into account the distribution of income among the residents, or the value of unpaid work. Furthermore, an increase in GDP does not necessarily lead to a higher standard of living, particularly in areas such as healthcare and education. ▪ Data quality and availability: People at risk of poverty and social exclusion: The dataset has multiple flaws. The most obvious one is the missing data for the German regions participating in the macro- region. As Germany provides data only on NUTS 0 level, there was no possibility to include appropriate regional data in the analysis. The second flaw is the general availability on NUTS 2 level only. Detailed regional analysis asks for detailed regional data. E.g. Sofia and Ljubljana are no separate NUTS 2 region which is why the respective statements on the situations in capital regions are to be treated with caution. Third, the dataset is only available for a very limited timeseries. Forth, the results displayed in the map exhibit probable differences in compiling the data in the respective Member States. For the purpose of the present task the indicator however fulfils its purpose sufficiently. ▪ Data quality and availability: Economic performance: the GDP indicator shows a very high data quality and availability over a long period of time.

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▪ A general limitation when working with a geographical scope that includes countries which are not part of the EU or ESPON Space is the availability of data. The discussed macro-region includes non-EU countries whose data is not included in the datasets which biases the analysis-

4.6 Limitations of the HyperAtlas – additional analysis useful for answering the task

The HyperAtlas provides an overview of the performances of regions and in contrast to different spatial contexts. It also enables a quick check of the data quality and availability. But the HyperAtlas has a couple of limitations: ▪ The results of the analysis and its visualisation in form of maps are only as good as the data fed into the tool. Hence, the limitations of the ESPON and Eurostat database are equally limiting the HyperAtlas ▪ Although some datasets are available for Non-EU countries in the ESPON and Eurostat database, they are not part of the calculations in the HyperAtlas by default. This leads to biased results when setting a mapping frame that expands over the EU- or ESPON-space.

▪ The mapping of the deviation to the large-scale reference area (in this case the Danube region) does not work, if data is missing for any included region (in this case: German regions). ▪ Hence: all calculated averages and deviations do not include data from Germany and non-EU countries participating in the EU strategy for the Danube region. ▪ If indicator chosen as nominator describes a negative connotated situation (being at risk of being poor or socially excluded) the colour coding of the map is slightly misleading. The redder the regions the more minor the risk, the darker the blue the more the population is at risk to suffer poverty. If this is too misleading for the onlooker, the colour settings can be changed according to the needs. As already stated above, the topic of territorial inequality and non-exclusive growth is a very comprehensive field. Additional analysis could include more indicators, some found in the database other not (like e.g.: level of education, number early leavers, life expectancy, level of availability of services of general economic interest, number of hospital beds, etc) or a counter-factual analysis assuming the EUSDR did not enter into force.

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ESPON 2020

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Disclaimer This delivery does not necessarily reflect the opinion of the members of the ESPON 2020 Monitoring Committee.

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