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Divergent Paths to Cohesion: The (Unintended) Consequences of a Place-Based Cohesion Policy

  • Stefan TelleEmail author
  • Martin Špaček
  • Daniela Crăciun
Open Access
Chapter
Part of the New Geographies of Europe book series (NGE)

Abstract

This chapter employs computer-assisted text analysis (CATA) to measure changes in EU Cohesion Policy (CP) objectives and to illustrate their place-based consequences. The method confirms a major shift in focus from employment and social objectives under the Lisbon Agenda to growth and innovation objectives under the Europe 2020 strategy. At the regional level, the same tendency has been found in German and Czech cross-border cooperation programmes in the last two programming periods. However, a more in-depth analysis reveals that these changes are significantly more pronounced in older member states. The chapter interprets its findings as evidence for a divergence between an innovation-driven path to cohesion in older member states and an inclusion-driven path to cohesion in the new member states and offer some tentative explanations.

1 Cohesion Policy to Which End?

Judging by its name, the objective of the European Union’s (EU) Cohesion Policy (CP) seems clear: to promote cohesion in the EU. But what do we mean when we talk about cohesion? Answering this question is complicated for two interrelated reasons.

First, the main legal basis for CP—Article 174 of the Treaty on the Functioning of the European Union—provides an exceedingly broad and vague definition of “economic, social and territorial cohesion”. Rather than contributing to a better understanding of the concept, this formulation points to the various domains it shall apply to. Moreover, it does not clarify at which level cohesion shall be promoted. From the perspective of this chapter, this scale-insensitivity is particularly problematic as shown by the polarising dynamic of simultaneous improvements of economic cohesion at the national level (Forgó and Jevčák 2015) and deterioration at the regional level within member states (Lang et al. 2015; Medve-Bálint 2014).

Second, as illustrated by the incessant debates surrounding it, the evolving political context of European integration continuously reshuffles the form and function of CP. To begin with, Manzella and Mendez (2009) suggest that CP was initially a side-payment for securing the support of the Mediterranean member states (MS) for further market integration. Subsequently, the eastern enlargements raised concerns as to whether a “Europe of Regions” actually provided a suitable framework for the effective and accountable implementation of the policy (cf. Bruszt 2008; Leonardi 2005; Molle 2007).

Enlargement also raised the question of whether CP was promoting an equality-oriented “Social Europe” or a neoliberal “Competitive Europe” (cf. Faludi 2010; Waterhout 2008). On the one hand, the influential Agenda for a Reformed Cohesion Policy (Barca 2009) proposed a set of liberal reforms, such as more efficient governance, a concentration of funds on core priorities, and a place-based approach to regional development. On the other hand, critics claim that the “Lisbonisation” of CP involved trading “more (place-based and conditional) growth” for “less redistribution and cohesion” (Avdikos and Chardas 2016).

The two observations are interrelated. In terms of Sartori’s (1970) typology of concept misformation, the first observation points to conceptual stretching. This problematic practice leads to “indefiniteness and elusiveness” because “the more we climb toward high-flown universals, the more tenuous the link with the empirical evidence” (p. 57). Consequently, the second observation illustrates that the lack of conceptual clarity surrounding cohesion lends itself to diverging interpretations about CP’s objectives, incites continuous debate, and limits the possibility of evaluating the policy’s actual achievements.

The present chapter addresses this issue. Section 2 suggests that the continuous reshaping of CP can be understood in terms of experimentalist governance theory. Section 3 introduces computer-assisted text analysis (CATA) as a method to quantify and clarify what we mean when we talk about cohesion in CP. Section 4 presents the findings. First, it compares the Lisbon Agenda and the Europe 2020 strategy to establish a baseline for changes in policy objectives. Second, it finds a corresponding tendency in regional-level cross-border cooperation programmes. However, an in-depth analysis reveals a divergence of programme objectives between old and new member states. These findings suggest that a more place-based CP may lead to different paths to cohesion. Section 5 uses data from roughly one hundred in-depth interviews to provide some tentative explanations for this regional-level variation. The last section concludes with a summary of the argument and policy implications.

2 Implications of Concept Misformation for Policy Change

The last section argued that the evolving political context of European integration drives continuous policy change. This section proposes that this is the case because the open-ended nature of integration builds the requirement for continuous adaptation into the process of EU policy-making. While this characteristic often leads to the problems described above, the theory of experimentalist governance emphasises the possibility of a more benign solution (De Búrca et al. 2014; Sabel and Zeitlin 2008, 2010, 2012; Zeitlin 2015).

Experimentalist governance suggests that a virtuous feedback loop of shared policy design, place-based implementation, and results pooling can reduce the gap between policy inclusiveness and policy efficiency. In other words, this approach highlights continuous adaptation as an opportunity to simultaneously boost input and output legitimacy (Scharpf 1997).

The experimentalist governance process proceeds in four steps. First, higher and lower level units engage in a deliberative policy-design process, at the end of which they agree on a set of general framework objectives as well as on measures to monitor their attainment. Second, lower level units are given sufficient autonomy to engage in experimentation so as to devise creative and place-based solutions to the framework objectives. Third, their progress is closely monitored and all individual experiences are pooled and shared to allow peer review and mutual learning. Fourth, based on these learning processes, institutional structures and framework goals are periodically subjected to scrutiny and recalibrated to reflect changing internal and external conditions. The repetition of this cycle supports the dissemination and connection of distributed knowledge (Newell 2005), the mainstreaming of best practices, and the naming and shaming of underperformers.

CP has several of the characteristics associated with experimentalist governance: it is the essential multi-level policy of the EU (Hooghe 1996), it relies on implementing broad framework objectives (European Parliament and Council 2013) through an increasingly place-based approach (Avdikos and Chardas 2016; Barca 2009), the role of evidence-based learning is becoming more central (Leonardi 2005, Ch. 3; Rodríguez-Pose and Novak 2013; Neacsu and Petzold 2015), and periodic framework revisions have led to wide-ranging changes in policy substance and structure (Bachtler and Mendez 2007; Molle 2007). Moreover, the partnership principle (Demidov 2015) aims at the involvement of national, regional, and local actors in determining programme priorities. Finally, initiatives like ESPON and INTERACT are specifically intended to promote evidence-based decision-making by enabling the gathering and dissemination of knowledge acquired through the implementation of CP (Faludi 2008).

Considering these features, it is understandable that Mendez (2011) asked whether the “Lisbonisation” of CP constitutes “a successful case of experimentalist governance”. However, he concluded that “the effects on mutual learning - a core feature of experimentalist governance - have been limited or non-existent and are likely to remain so in the immediate future” (ibid. 534). Importantly, he highlighted that the experimentalist dynamics “are handicapped by a lack of clarity and prioritization over EU Cohesion objectives, the lack of political commitment to assessment indicators and targets[,] and uneven performance reporting which focuses too much on financial implementation” (ibid. 534).

In sum, the first two sections of the chapter established concept misformation as an important impediment to the operation of a virtuous feedback loop in CP. To address this issue, CATA provides an easy-to-use and reproducible way for disentangling the diverse policy objectives (Craciun 2018) subsumed under cohesion. Therefore, from an experimentalist governance perspective, it has the potential to contribute to more constructive policy debates and to virtuous policy change.

3 Methodology

The analysis uses an extended methodological framework to trace changes in policy objectives. It uses a quantitative method (CATA) for gauging changes in policy objectives and a qualitative method (semi-structured interviews) for explaining the variation identified by CATA.

Text analysis represents a scientific method for “making replicative and valid inferences from data to their context” (Krippendorff 1980, 21). CATA is a form of text analysis that uses computers, rather than human coders, to analyse texts. It is used to automatically quantify the existence of certain concepts of interest as well as their embeddedness in the broader institutional context in which they are used (Berg 2001).

The chapter utilises CATA to analyse an original set of documents. The analysis focused on the following key policy concepts: social, employment, cohesion, growth, innovation, competitiveness. The first three concepts broadly reflect the Social Europe discourse, while the last three reflect the Competitive Europe discourse (see Sect. 1). Additionally, the analysis includes development to highlight changes in the meaning of core objectives, as well as priority to indicate the influence of the 2013 CP reform.

The analysis proceeds in three steps. First, the Lisbon Agenda and Europe 2020 strategy are examined to identify changes in the EU’s overarching policy objectives. Second, all Czech and German cross-border operational programmes (OPs) are analysed for the 2007–2013 and 2014–2020 programming periods to see how the macro objectives were translated into regional-level objectives. The principle of multi-annual programming ensures that the former period reflects the objectives of the Lisbon Agenda, while the latter period reflects those of the Europe 2020 strategy. The focus on cross-border OPs highlights the significance of regional-level conditions and agency in translating the macro strategies as it allows variation within MS to be studied.

Third, the comparison of Czech and German cross-border OPs works as a natural experiment (cf. Dunning 2017) which allows evaluating the impact of the old/new member state cleavage on the translation process. Accordingly, the chapter divides cross-border OPs into three categories (Table 1): OPs of two old member states [OMS/OMS], OPs of old and new member states [OMS/NMS], and OPs of two new member states [NMS/NMS].
Table 1

Analysed Czech and German cross-border cooperation OPs

Category

Germany

Czech Republic

[OMS/OMS]

1) AustriaGermany/Bavaria

2) Belgium–Germany–The Netherlands

3) France–Belgium–Germany–Luxembourg

4) France–Germany–Switzerland

5) Germany–Austria–Switzerland–Liechtenstein

6) Germany–Denmark

7) Germany–The Netherlands

 

[OMS/NMS]

8) Germany/BavariaCzech Republic

9) Germany/SaxonyCzech Republic

10) Germany/Brandenburg–Poland

11) Germany/Mecklenburg–Vorpommern–Brandenburg–Poland

12) Poland–Germany/Saxony

1) Germany/BavariaCzech Republic

2) Germany/SaxonyCzech Republic

3) AustriaCzech Republic

[NMS/NMS]

 

4) Czech Republic–Poland

5) SlovakiaCzech Republic

Source Own elaboration

The OPs analysed are shown in italics.

Some caveats need to be mentioned. First, cross-border OPs are not distributed equally across countries and categories. As such, the comparatively lower number of Czech OPs runs the risk of skewing the analysis if one of the OPs is an outlier in terms of the objectives under analysis. Second, not all the OPs fall neatly into the categorisation. On the one hand, Switzerland and Lichtenstein are non-EU countries. On the other hand, Austria and Sweden joined the EU only in 1995. Insofar as the former are concerned, the respective OPs always involve at least two EU MS, whereas insofar as the latter, both countries are commonly considered to be EU-15 countries. Moreover, the Poland–Denmark–Germany–Lithuania–Sweden (South Baltic) cross-border OP cannot be included in the analysis for methodological reasons as the programme document was not available in German. Third, the number of German OPs changed between programming periods due to the merger of the Syddanmark–Schleswig K.E.R.N. OP and the Fehmarnbelt Region OP into one Germany–Denmark OP. The analysis includes both cross-border OPs of the 2007–2013 period.

Before using CATA techniques to reduce the complexity of the documents, the texts needed to be pre-processed (Craciun 2018). In practice, this entailed (1) transforming all the documents into .txt format, (2) cleaning the text by removing the parts that are not directly relevant to answering the research question, and (3) taking out stop words (i.e. common words for each language that appear often in the structure of the sentence but do not provide any content).

The web application Voyant Tools (Sinclair and Rockwell 2017) was used to conduct three different kinds of analysis. Trend analysis provides a line graph of word frequencies in different policy documents and enables researchers to measure and compare the relative importance of policy objectives. Collocation analysis reveals a network graph of highest frequency terms that appear in proximity with each other within the documents (cf. Lehecka 2015). For the purpose of this chapter, the tool was used to show how the meaning of the policy objectives has changed over time. Finally, correspondence analysis, a type of cluster analysis, was used to illustrate the proximity of individual OPs in terms of the policy objectives (cf. Greenacre 2010). This technique is used to visualise the OMS/NMS divide.

In a final step, the findings obtained via CATA were contrasted with qualitative evidence from six of the analysed OPs (italics in Table 1). As these six OPs cover all three categories ([OMS/OMS], [OMS/NMS], [NMS/NMS]), their analysis allows us to develop explanations for the variation across these categories. The evidence was gathered in 104 semi-structured interviews with representatives of regional and national administrative authorities, OP programme administrations, programme audits, Euroregion representatives, local politicians, and representatives of local non-governmental organisations and municipalities (Table 2). They took place between January 2016 and April 2017 and had an average duration of one hour. The questions focused on the achievements and challenges of cross-border cooperation in the border region, paying attention to issues related to the implementation of the respective INTERREG programmes.
Table 2

Distribution of interviews

Cross-border cooperation OP

Category

Number of interviews

Germany/Bavaria–Austria

[OMS/OMS]

5

Germany/Bavaria–Czech Republic

[OMS/NMS]

24

Germany/Saxony–Czech Republic

[OMS/NMS]

23

Austria–Slovakia

[OMS/NMS]

20

Austria–Czech Republic

[OMS/NMS]

16

Slovakia–Czech Republic

[NMS/NMS]

16

Total

 

104

Source Own elaboration

The OPs analysed are shown in italics

4 Analysis

The analysis proceeds in three steps. First, a comparison of the Lisbon Agenda and the Europe 2020 strategy establishes the evolution of the EU’s macro objectives. Second, the Czech and German cross-border OPs of the 2007–2013 and 2014–2020 programming period are scrutinised to illustrate how the changes in the macro strategies translate into regional-level OPs. In a third step, the distinction between OPs according to the three categories is used to study the context-dependent implementation of the CP, suggesting OMS and NMS cleavage as an explanatory factor.

4.1 Macro Strategies: Lisbon Agenda and Europe 2020 Strategy

The trend analysis of the Lisbon Agenda and the Europe 2020 strategy is based on two key policy framing documents: the Presidency Conclusions of the Lisbon European Council (European Council 2000) and the Communication from the Commission EUROPE 2020. A strategy for smart, sustainable and inclusive growth (European Commission 2010).

The most significant findings include a substantial decrease of the relative word frequency of the concepts social and employment and a simultaneous increase in the frequency of the concepts of growth and innovation (Fig. 1). This development resulted in an alteration of the ranking of the first three most frequent policy concepts from the Lisbon Agenda (social, employment, growth) to the Europe 2020 strategy (growth, social, innovation).
Fig. 1

Trend analysis of policy concepts in the Lisbon Agenda and the Europe 2020 strategy

(Source Own elaboration based on Sinclair and Rockwell 2017)

The change in focus may be explained by the macro political context at the time of drafting the two documents. On the one hand, the Lisbon Agenda reflects the expected effects of impending Eastern enlargements, putting questions of social inclusion and employment at centre stage. On the other hand, the Europe 2020 strategy was drafted during the aftermath of the financial and sovereign debt crisis of the late 2000s and reflects a greater concern with innovation-driven economic growth.

While word frequencies provide a picture of the change in the EU’s macro objectives, a more detailed understanding can be gained by conducting collocation analysis to uncover shifts in the meanings attached to these objectives (Fig. 2). At the document level, collocation analysis of the Lisbon Agenda and Europe 2020 strategy confirms the general trend from social towards growth-related issues. Specifically, the term social not only features prominently in the Lisbon Agenda, but is closely related to employment, protection, and exclusion. In contrast, the Europe 2020 strategy is alluding to a variety of terms referring to Europe, ostensibly demonstrating the intention of signalling supranational political unity during the financial crisis. Moreover, the latter document is clearly identified as a strategy for growth, based on goals and measurable targets.
Fig. 2

Collocation analysis of the Lisbon Agenda and the Europe 2020 strategy

(Source Own elaboration based on Sinclair and Rockwell 2017)

Turning to the concepts development and cohesion, additional significant differences can be observed. While development in the Lisbon Agenda is related to employment and human, the Europe 2020 strategy links development closely to the structural funds as a key delivery framework. The link to research is present in both documents, but the Europe 2020 strategy also highlights innovation.

In both documents, cohesion is related to employment. However, while the Lisbon Agenda refers to employment growth, the Europe 2020 strategy refers to productivity growth. At the same time, the Europe 2020 strategy frames cohesion in both social and economic terms, while in the Lisbon Agenda, it has a purely economic focus. Having established the evolution of the EU’s macro objectives, the analysis now turns to regional-level OPs.

4.2 Cohesion Policy Implementation: Cross-Border Cooperation Programmes

To answer the question of how changes in the macro strategies translate into regional-level OPs, trend analysis was conducted for all cross-border OPs with Czech and German participation. This moves the focus of the analysis from the supranational to the regional level and from the strategic to the policy implementation phase.

To this end, the cross-border OPs of the 2007–2013 programming period were aggregated and compared to the OPs of the 2014–2020 programming period for both countries separately. Trend analysis of the German OPs found that development was by far the most frequent concept in both programming periods. Moreover, the introduction of thematic priorities in the 2014–2020 programming period was obvious in the documents. Similar results were obtained in the trend analysis of the Czech OPs, where the term development was the most frequent concept in the programming period 2007–2013. In the following period, it was superseded by the term priority.

In fact, these two concepts were so prevalent as to hide the degree of change in the frequency of other core concepts. Hence, they were excluded in the next step of the analysis, both for Germany and for the Czech Republic (Fig. 3). This procedure revealed that the use of the terms innovation and growth has increased significantly, while the terms social, employment, cohesion, and competitiveness have become less frequent or stayed almost unchanged. The lower frequency of the term competitiveness can be interpreted as a shift in terminology towards the more inclusive term of growth. The trend in Czech cross-border OPs broadly corresponds to what has been observed in the German case. It shows a rise in the frequency of the term innovation and (to a lesser extent) growth and a simultaneous decline in the frequency of the term social.
Fig. 3

Trend analysis of German and Czech cross-border OPs (2007–2013 vs. 2014–2020)

(Source Own elaboration based on Sinclair and Rockwell 2017)

These findings corroborate the argument that the transition from the Lisbon Agenda to the Europe 2020 strategy involved a broad reorientation of the CP towards more “liberal” ideas of welfare creation. The greater focus on thematic priorities, as well as the increased importance of growth, confirm Avdikos and Chardas’ (2016) critical assertion that post-2014 CP means “more (place-based and conditional) growth”. The next section turns to the differences between the three categories of OPs.

4.3 Context-Dependent Implementation: The Old/New Member State Cleavage

The final section analyses whether the OPs show variation along the OMS/NMS cleavage. To this end, the German cross-border OPs were divided into [OMS/OMS] and [NMS/OMS] and the Czech OPs were divided into [OMS/NMS] and [NMS/NMS]. The analysis first compares the categories in each programming period and subsequently highlights the change over time in each category.

In the 2007–2013 programming period, social was the most frequent term both in [OMS/OMS] and in [OMS/NMS] cross-border OPs. However, while [OMS/NMS] OPs were dominated by this term, [OMS/OMS] OPs were more diversified. Here, innovation was as frequent as social and employment and growth also featured centrally. These findings can be interpreted as illustrating a preference of the NMS for social cohesion via redistribution and solidarity, while the OMS appear to be influenced by liberal ideas of creating equality through empowerment of the individual.

Importantly, the divide between [OMS/OMS] and [OMS/NMS] OPs has become more pronounced in the 2014–2020 programming period. The two most significant developments are a decline in the use of social in [OMS/NMS] OPs and a dramatic rise in the use of innovation in [OMS/OMS] OPs.

Analysis of the three categories over time reveals that the increased frequency of the term innovation for all German OPs (Fig. 3, left side) derives exclusively from [OMS/OMS] OPs. This is the case, because over the two programming periods, the frequency of innovation rises steeply in [OMS/OMS] OPs (Fig. 4), but declines in [OMS/NMS] OPs (Fig. 5).
Fig. 4

Trend analysis of [OMS/OMS] OPs across the 2007–2013 and the 2014–2020 period

(Source Own elaboration based on Sinclair and Rockwell 2017)

Fig. 5

Trend analysis of [OMS/NMS] OPs across the 2007–2013 and the 2014–2020 period

(Source Own elaboration based on Sinclair and Rockwell 2017)

Moreover, when it comes to [OMS/NMS] OPs (Fig. 5), only the concept of growth became more frequent in the later period. In fact, while growth was one of the least frequent concepts under investigation in the 2007–2013 programming period, in the 2014–2020 programming period, it became the second most frequent. Simultaneously a notable decline in the use of the term cohesion could be observed. Nevertheless, despite declining frequency, social remained the most frequently used term in [OMS/NMS] OPs.

The analysis of [NMS/NMS] OPs shows that while the concepts growth and employment have become more frequent in [NMS/NMS] OPs, cohesion has become less so (Fig. 6). Moreover, the term innovation has become only slightly more prevalent in the 2014–2020 programming period. This means that almost the entire increase in the use of innovation across all Czech OPs (Fig. 3, right side) derives from [OMS/NMS] OPs. Considering that [OMS/NMS] OPs referred to innovation less frequently than [OMS/OMS] OPs in the 2007–2013 period, a clear ordering of the three categories has emerged in the 2014–2020 period for the term innovation: [OMS/OMS] > [OMS/NMS] > [NMS/NMS].
Fig. 6

Trend analysis of [NMS/NMS] OPs across the 2007–2013 and the 2014–2020 period

(Source Own elaboration based on Sinclair and Rockwell 2017)

However, comparing Figs. 5 and 6 does not confirm this conclusion. If the [OMS/NMS] and [NMS/NMS] categories feature lower frequencies of innovation than the [OMS/OMS] category, it is surprising that German OPs have lower frequencies than Czech OPs (Fig. 3). These findings are, however, explained by further sub-dividing the [OMS/NMS] category into OPs involving the old German Länder (states) and OPs involving the new German Länder. Additional analysis suggests that OPs involving the new German Länder closely resemble the [NMS/NMS] category, while those involving the old German Länder closely resemble the [OMS/OMS] category.

These findings are also corroborated by correspondence analysis of all German OPs of the 2014–2020 programming period (Fig. 7). First, analysis shows that [OMS/OMS] OPs form a cluster on the left side towards the term innovation. [OMS/NMS] OPs form a cluster on the right side towards the term social. Second, the only exception to this pattern is the Czech–Bavarian OP, which is highly similar to the Austrian–Bavarian OP. Moreover, the same analysis for the 2007–2013 programming period revealed that the Czech–Bavarian OP used to be much more clearly aligned with the other [OMS/NMS] OPs. This drift suggests that the Bavarian side largely succeeded in determining the content of the Czech–Bavarian OP in the 2014–2020 programming period. Correspondence analysis, therefore, confirms that there is a significant and growing difference between the old and the new German Länder in terms of OP objectives.
Fig. 7

Correspondence analysis for all German cross-border OPs (2014–2020)

(Source Own elaboration based on Sinclair and Rockwell 2017)

In summary, the analysis found significant changes between the Lisbon Agenda (social and employment) and the Europe 2020 strategy (growth and innovation) and showed that these changes are also visible at the regional level. However, it also highlighted that there are significant differences among the three categories of OPs, which point towards the salience of OMS/NMS cleavage. The chapter now turns to these differences and provides a tentative explanation.

5 Divergent Paths to Cohesion in Old and New Member States?

The findings suggest an interesting conclusion about the pathways towards cohesion: at the level of macro strategies, the shift towards innovation and growth suggests that enhancing labour productivity is seen as a core strategy towards greater cohesion. This strategy corresponds to the neoliberal “Competitive Europe” discourse.

However, analysis of the OPs suggests that change in the macro objectives has led to a divergence between OMS and NMS. While the OMS appear to be in line with the liberal ideas of the Europe 2020 strategy, the NMS appear to pursue a different trajectory. In fact, the significant rise in growth and employment in combination with a decline of social in [NMS/NMS] cross-border OPs suggests that labour market participation is a preferred strategy towards cohesion. This strategy corresponds more to the “Social Europe” discourse.

These findings can be interpreted as evidence for the dominance of OMS in framing an increasingly liberal CP paradigm, which NMS are reluctant to adopt. To give a tentative explanation for these different trajectories, this section presents local-level evidence from six cross-border OPs, spanning all three categories.

First, the interview data confirm that differences in the level of economic development and infrastructure endowment are a major obstacle to determining common OP objectives in [OMS/NMS] programmes. Whereas the Czech and Slovak respondents (NMS) tended to emphasise the lack of public infrastructure on their side of the border, German and Austrian (OMS) respondents tended to highlight the importance of “soft” factors as preconditions for knowledge-based growth. In particular, regional capacities for innovation-driven growth, such as institutions of higher education or high-tech enterprises, exist outside urban agglomerations in federal Germany and Austria, but only to a lesser degree in the more centralised Czech Republic and Slovakia.

Second, there are significant differences in the levels of administrative capacity and autonomy with regard to designing and implementing OPs. For example, German and Austrian federal states are more autonomous and better endowed with financial and human resources than the recently created Czech and Slovak self-governing regions. In the latter countries, key decisions regarding the CP are usually taken in national ministries. Correspondingly, the interviews suggest that the design and implementation of OPs in the Czech Republic and Slovakia tend to reflect the preferences of national ministries or governments, rather than the conditions in the border region. Moreover, frequent changes of administrative staff in the Czech Republic and Slovakia, often in the aftermath of elections, were associated with a limited capacity to build and retain operational knowledge within the institution responsible for OP administration. This situation supposedly sustains a culture of ad hoc decision-making that is seen as detrimental to the coherent translation of EU-level regulations into national and sub-national policy.

Third, while Austrian respondents repeatedly stressed that CP funds are “expensive money” which demands efficient and accountable spending, Czech and Slovak respondents regularly depicted the OP funds as a way to prop up local budgets. The introduction of thematic priorities in the 2014–2020 programming period was received with scepticism, especially in NMS, where respondents criticised that local conditions and developmental potential are not properly reflected in the thematic priorities (especially with regard to the economic potential of tourism and infrastructure projects). To sum up, different levels of economic development and institutional capacity, and different positionalities as net-contributing or net-receiving MS can partially explain the divergent strategies towards cohesion in OMS and NMS.

6 Conclusion

The chapter employed an extended methodological framework to address the conceptual ambiguity of cohesion. Building on experimentalist governance theory, it was argued that conceptual clarity is an important precondition for unleashing the CP’s virtuous feedback loop. CATA was applied to the EU’s macro strategies as well as cross-border OPs to quantify and clarify what lies underneath the conceptual “veil” of cohesion.

The analysis has shown, first, that the EU’s macro political objectives evolved from social and employment-related issues in the Lisbon Agenda to growth and innovation-related issues in the Europe 2020 strategy. Second, scrutinising German and Czech cross-border OPs of the 2007–2013 and 2014–2020 programming periods, the chapter confirmed the trend towards growth and innovation objectives at the regional level. The third part of the analysis revealed a divergent trend among three categories of OPs. While [OMS/OMS] OPs clearly shifted towards an innovation-driven cohesion strategy, this trend was less pronounced in [OMS/NMS] OPs and in [NMS/NMS] OPs. By contrast, growth and employment objectives became relatively more important in [OMS/NMS] OPs and, especially, in [NMS/NMS] OPs. Subsequently, interview-based evidence from six cross-border OPs was used to suggest potential explanations for this divergence.

We believe that observation of the divergent paths to cohesion raises important questions about their respective long-term trajectories. In terms of economic cohesion, does a place-based CP lead to efficient specialisation or to a polarisation of productivity levels? In terms of social cohesion, will the greater workfare focus in OMS and the greater welfare focus in NMS balance the size of the welfare state and lead to the emergence of a shared European Social Model? In terms of territorial cohesion, what are the implications of the innovation focus in OMS and the social focus in NMS for the relative socio-economic position of urban and rural regions?

Against this background, we suggest a rethinking of spatial policies in Europe. First, future rounds of CP reforms need to take the “divergent paths” into account and acknowledge the different policy preferences between OMS and NMS regarding cohesion. Second, considering that especially [OMS/NMS] programmes struggle to determine shared cross-border objectives, the legal instrument of European Groupings of Territorial Cooperation should be mainstreamed as a solution to the problem of political bargaining that surrounds national funding envelopes. Third, the recent proposal of the EU Commission to concentrate future CP funding exclusively in below-average GDP MS should be combined with a stronger focus on the promotion of good governance and systematic institutional capacity building.

While the chapter has presented an innovative methodology for unveiling divergent paths between OMS and NMS, the analysis was restricted to a limited number of MS as well as to cross-border OPs. Future research can build on the methodological foundations presented in this chapter, to test the validity of the presented results with a broader corpus of policy documents. Moreover, regarding experimentalist governance theory, the methodology can be further developed to allow an analysis of whether and how the pooling and sharing of local-level experiences impact supranational CP reforms.

Recommendations for Further Readings

  1. Krippendorff, Klaus. 1980. Content Analysis: An Introduction to Its Methodology. London: Sage.Google Scholar
  2. Leonardi, Robert. 2005. Cohesion Policy in the European Union: The Building of Europe. Basingstoke: Palgrave Macmillan.CrossRefGoogle Scholar
  3. Rodríguez-Pose, Andrés, and Katja Novak. 2013. “Learning Processes and Economic Returns in European Cohesion Policy.” Investigaciones Regionales 25: 7–26.Google Scholar
  4. Sabel, Charles F., and Jonathan Zeitlin. 2008. “Learning from Difference: The New Architecture of Experimentalist Governance in the EU.” European Law Journal 14 (3): 271–327.  https://doi.org/10.1111/j.1468-0386.2008.00415.x.CrossRefGoogle Scholar
  5. Zeitlin, Jonathan. 2015. Extending Experimentalist Governance: The European Union and Transnational Regulation. Oxford: Oxford University Press.Google Scholar

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Authors and Affiliations

  • Stefan Telle
    • 1
    Email author
  • Martin Špaček
    • 2
  • Daniela Crăciun
    • 1
  1. 1.Central European UniversityBudapestHungary
  2. 2.Comenius UniversityBratislavaSlovakia

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