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Rinnakkaistallenteet Yhteiskuntatieteiden ja kauppatieteiden tiedekunta
2018
A social network analysis of
cooperation in forest, mining and tourism industries in the
Finnish-Russian cross-border region:
connectivity, hubs and robustness
Makkonen, Teemu
Informa UK Limited
Tieteelliset aikakauslehtiartikkelit
© Informa UK Limited, trading as Taylor & Francis Group All rights reserved
http://dx.doi.org/10.1080/15387216.2019.1593209
https://erepo.uef.fi/handle/123456789/7622
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A Social Network Analysis of Cooperation in Forest, Mining and Tourism Industries in the Finnish–Russian Cross-border Region: Connectivity, Hubs and Robustness
Teemu Makkonen1*, Timo J. Hokkanen2,Tatyana Morozova3, and Mihail Suharev4
1 Karelian Institute, University of Eastern Finland, Finland, teemu.makkonen@uef.fi
2 Centre for Economic Development, Transport and the Environment for North Karelia, Finland, timo.hokkanen@ely-keskus.fi
3 Institute of Economics, Karelian Research Centre of the Russian Academy of Sciences, Russia, morozova.ras@gmail.com
4 Institute of Economics, Karelian Research Centre of the Russian Academy of Sciences, Russia, suharev@narod.ru
* Corresponding author
Abstract: In this article the distribution and intensity of cross-border cooperation networks are tested with primary survey and interview data collected from the Finnish–Russian cross-border region. The study concentrates on the cross-border connectivity of three regionally significant and interlocking sectors (forest, mining and tourism industries together with associated research and administrative organisations) with varying strategies towards natural resource use. The collected survey and interview data were examined by applying social network analysis tools developed for cross-border contexts. The results depict varying landscapes of cross-border cooperation depending on the type (firms, research and administrative organisations) of the surveyed actors. Overall the studied cross-border cooperation network is weakly developed with low firm-level participation and low integration between the sectors – a weakness in the sustainable utilisation of natural resources. Cross-border cooperation is in its most intensive between local administrative organisations. Local administrative and research organisations are the network hubs in the studied cross-border region. As a positive note, the cross-border cooperation network is not solely reliant on these few network hubs but also consists of several moderately connected cross-border actors that increase its robustness against cross-border network failures. The emergence of new and the deepening of the existing cross-border links between the various industries would be pivotal for good policy design regarding local land-use and natural resource use policies.
Keywords: Cross-border cooperation; Finnish–Russian border; Forestry; Mining; Social network analysis; Tourism
1 Introduction
The facilitating role of cross-border cooperation (CBC) for striving towards sustainable socio- economic development in cross-border regions (CBRs) has been in the eye of the cohesion and development policies of the European Union (EU) for decades (e.g. European Commission 2012, 2017). This CBC creates and occurs in networks. Still, very few studies have actually taken up the task to examine and evaluate cooperation networks at cross-border settings (Leibenath and Knippschild 2005; Platonov and Bergman, 2011). Therefore, relatively little is known about CBC networks (Hassink et al. 1995; Koschatzky 2000) beyond the literature specialised on cross-border policy/governance networks (Svenson 2013; Walther and Reitel 2013; Sohn and Giffinger 2015; Dörry and Decoville 2016). Particularly, whereas CBC networks in EU’s internal CBRs have attracted some scholarly attention the external borders of the EU have clearly remained understudied (Turkina and Postnikov 2012). For example, in the case of the Russian Federation, despite some related work on the Finnish–Russian (Liikanen 2008; Scott and Laine 2012; Németh et al. 2014) and other Russian borders (Sagan 2010), little is known on how intensive is and who actually participates in this CBC. As such – and given the recognised need of CBC in transforming borders (both at internal, but particularly at external EU borders) into a possibility for development (Ministry for Foreign Affairs of Finland 2011) – it is vital to understand CBC networks better (who are the most important actors in such networks, how dependent are the networks on these few actors, etc.) to foster further collaboration (Svensson 2015).
The authors attempt to tackle this research gap by drawing conclusions on the distribution and intensity of CBC networks as it applies to the case of the Finnish–Russian CBR to shed more light on this interesting case for network analysis, specifically by concentrating on three regionally significant and interlocking sectors: 1) forest, 2) mining and 3) tourism industries.
For the regional economy of the Finnish–Russian CBR, it is important that all three sectors remain competitive. This can only be accomplished by coordination and cooperation for sustainable development led by administrative organisations and informed by state-of-the-art research. Therefore, not only firms but also related research organisations, within or active in these three fields, as well as administrative and non-governmental organisations (NGOs), promoting e.g. nature protection and regional development, were surveyed and interviewed. In short, the motivation behind the approach lies in the earlier literature, which has shown that CBC in natural resources use is important for sustainable economic growth on both sides of the border (Mayer et al. 2005; Terry et al. 2006; Hokkanen et al. 2007). However, despite these
notions, studies explicitly concentrating on the cooperation networks of industries and associated fields responsible for extracting natural resources and conserving nature have not been carried out before in the context of border regions. Similarly, earlier studies on CBC networks have concentrated on specific types of actors – firms (e.g. Koschatzky 2000), research (e.g. Makkonen 2015) or administrative organisations (e.g. Dörry and Decoville 2016) – but not on comparisons and the interplay between the different types of actors.
These notions lead us to the research questions of this paper, which can be summarised as follows:
1. In terms of cross-border connectivity, how developed is the CBC network of the actors engaged in forest, mining and tourism industries in the Finnish–Russian CBR?
2. Which actors act as the hubs (highly connected nodes) in the CBC network?
3. How robust is the CBR network (is it dependent only on few highly connected nodes)?
This paper is structured as follows. Firstly, a brief literature review on the Finnish–Russian CBR and the case industries is given followed by the introduction of the applied social network analysis (SNA) tools for depicting cross-border connectivity and the applied data collection procedures. The main findings of the conducted analyses are given in Section 4. Lastly, the paper concludes by summarising its main findings, by discussing their implications and by drawing potential directions for further studies.
2 Backgrounds to the Case Study Region and Industries 2.1 Cross-border Cooperation at the Finnish–Russian Border
The legacy of the Finnish–Russian border is distinctive: in the aftermath of WWII the formerly Finnish region of Karelia was divided between Finland and the Soviet Union resulting in differing development paths (Eskelinen and Jukarainen 2000; Antonsich 2005). Most of the ethnically Finnish population fled to Finland after the WWII, and the population in the Russian side of the border was “supplemented” by various settlers from other parts of the Soviet Union (Eskelinen and Kotilainen 2005). In economic terms, the gap between the more “affluent”
Finnish and “emerging” Russian sides of the border has persisted despite of the disintegration of the Soviet Union. This cross-border disparity (measured in GDP) has been described as one of the widest in the world (Alanen and Eskelinen 2000; Jukarainen 2002). Additionally, whereas Finland has improved the conditions of its peripheral border regions by means of
public income transfers, in Russia, the corresponding policies have been lacking (particularly in Soviet times the border zone was, in fact, deliberately de-populated and activities within it strictly regulated leading to a stagnation in economic development), which has caused evident deterioration of the infrastructure and living conditions at the Russian side of the border (Ahponen 2011). Concisely, the varying population and economic growth trajectories have led to a striking socio-economic and socio-cultural gap between the Finnish and Russian side of the border (see Table 2).
From the 1940s up until the collapse of the Soviet Union, the border was more or less closed and border crossing strictly regulated. Therefore, the neighbouring sides of the border grew largely in isolation from each other (Ahponen 2011). However, the cooperation in the border region gradually emerged, to a certain degree already in the Soviet period. This was visible, particularly, in the scientific CBC that took place between the local universities of the CBR and in the joint construction projects that were engineered on the Soviet side of the border (Jurczek and Vartiainen 2009; Joenniemi and Sergunin 2011). Still, it was the collapse of the Soviet Union that finally signalled the end of the strict dividing role of the border.
Today, visa requirements still apply, and traffic is permitted only through official border- crossing points, but crossing the border is still much easier than during the Soviet times. The opening up of the border has resulted in the growth of passenger traffic and cargo flows between the two countries (Tykkyläinen and Lehtonen 2008; Inkinen and Tapaninen 2009;
Finnish Border Guard 2019). This has led, in its part, to a higher interaction between the Finnish and Russian population and consequently to an increased integration between various actors on different sides of the border (Eskelinen et al. 2013). Evidently, whereas in the beginning of this century the level or speed of economic integration between Finland and Russia was still considered as sluggish at best (Rautio and Tykkyläinen 2001), today the importance of CBC and interaction are considered as vital for the future developments on both sides of the Finnish–
Russian border (Liikanen et al. 2007; Fritsch et al. 2015).
Starting 1995, after Finland became a member of the EU, CBC (including scientific research, infrastructure projects, etc.) has increasingly been coordinated through different EU-funded programmes (Fritsch et al. 2015). The EU is not the sole source of funding for cross-border projects, but its role in facilitating and funding CBC in the Finnish–Russian CBR has been highly influential (Eskelinen and Kotilainen 2005; Makkonen et al. 2018). Recently, Russia’s
central government has also taken a close interest in funding these CBC programmes and individual projects in order to act as an equal partner to the EU (Fritsch et al. 2015) and, as Yarovoy (2010) has stated, to exert a certain degree of control and scrutiny over the regional administration. The importance given to CBC has not been severely affected by the current political climate; CBC has not been included in the sanctions/countersanctions list of the EU nor Russia (Fritsch et al. 2015; Koch 2017).Notwithstanding, CBC networks can be vulnerable towards such macro-political changes (and their economic consequences) that affect foreign relations between countries (Koch 2018; Makkonen et al. 2018).Despite the interest on CBC networks at the Finnish–Russian border, these networks have not been formally investigated with SNA tools: an issue discussed here through the case of forest, mining and tourism industries.
2.2 Forest Mining, and Tourism Industries at the Finnish–Russian Cross-border Region To participate in the discussion on CBC networks, a decision to concentrate on firms operating in resource intensive extractive industries (forest and mining industries) and fields heavily affected by natural resource use (tourism) together with related NGO’s (engaged with nature protection) as well as research and administrative organisations (active in the fields discussed in this paper) in the Finnish–Russian CBR was made. More specifically this choice is motivated by the following notions:
• Forest industry: Even though the economic crisis of 2008 has impacted the wood processing industry in Finland and, thus, also the export-oriented forestry sector of the Russian side of the border (Yarovoy 2010), the role and importance of forest industry as a tool for rural development and economic growth in both sides of the Finnish–Russian border is still decisive (Halonen et al. 2015; Saveliev et al. 2015).
• Mining industry: Along with the forest industry, mining industry has traditionally been essential for the economy of the Russian side of the border (Saveliev et al.
2015), while the recent upsurge in mining claims has heightened the prominence of this sector also in the Finnish side of the border (Tuusjärvi et al. 2014).
• Tourism industry: In the EU’s external border policies, tourism has been depicted as a contributor to sustainable regional development (Izotov and Laine 2013), while in the academic literature, it has been hailed as one of the few sources of livelihood with credible growth potential in peripheral (border) regions (Saarinen 2003;
Saveliev et al. 2015; Makkonen et al. 2018).
These sectors are extremely important for the economy of the Finnish–Russian CBR. Their trade patterns are inter-linked across the Finnish–Russian border (Ollus and Simola 2006) and consequently they “cohabit” the border region with (roughly) equal economic importance, but with very different strategies towards natural resources. For nature-based tourism, natural resources are an economic asset in themselves. For forest and mining industries natural resources can basically be turned to products only after extracting them. Therefore, what can benefit the tourism sector can consequently damage the operational environments of forest and mining industries and vice versa (Kniivilä et al. 2002; Leppänen et al. 2005; Rinne and Saastamoinen 2005; Newell and Henry 2016). CBC networks for coordinating land-use and natural resource use are thus needed to balance the sectors in a way that would ensure their competitiveness benefiting the economy in both sides of the Finnish–Russian CBR.However, there are no existing studies (in the Finnish–Russian or other CBRs) that would have explicitly concentrated on examining CBC networks of industries and associated fields responsible for extracting natural resources and conserving nature.
What is known is that the studied sectors are different in structure, scope and strategic importance for the society. On the one hand, the status of the regional forest and mining industries on both sides of the border is similar; their key function has been to provide resources [i.e. function as resource peripheries (e.g. Kortelainen and Rannikko 2015; Tykkyläinen et al.
2017)]. As the key forestry and mining firms are large international corporations (or formally independent enterprises established by these large corporations), the decisions concerning actions in the Finnish–Russian CBR are most often done on national or international level:
local directors only manage e.g. timber logging and sending it to the addresses indicated by the headquarters, whereas the wood processing industry is not interested in from where exactly its timber is sourced. The regional units are, thus, often implementing the decisions from headquarters according to their role as a source of resources and, therefore, do not (necessarily) need well-developed (cross-border) regional networks for running successful day-to-day operations. Tourism in the study area, on the other hand, is structurally very different from forest and mining industries, there are hundreds of small, local, independent businesses and only very few national or international actors. Therefore, various regional decisions, actions and networks (particularly involving central travel agencies) are more important for facilitating the regional tourism than in the case of forest and mining industries. However, since establishing direct international contacts are not easy for small firms (Jørgensen 2014), the
regional tourism industry is also likely to be relatively weakly connected across the border.
Thus, it is hypothesised that:
H1 The examined CBC network is weakly developed.
Through tourism, the visibility (particularly now in the age of the Internet) of the region can be extensive, and the nature-friendly image can help the development of other fields, especially those that are in concordance with tourism. However, forestry and mining operations create frequent environmental worries (such as clear cuts, open-pit mines, etc.), which are contradictory with tourism. Cooperation between these sectors therefore requires serious efforts. However, this is very complicated at the present, simply because the strategically more important sectors (forest and mining industries) dominate the discussions (see e.g. Kortelainen and Rannikko 2015; Jartti et al. 2017) and subsequent land-use planning in the Finnish–Russian CBR, while nature tourism sector has to manage with the “leftover natural environments”.
In Russia, nature parks and reserves are extensive even though there are only a few of them, whereas in Finland, they are numerous but in general smaller and fragmented, giving few opportunities for decent development of nature tourism in the CBR. As such, the long-term cross-border relationships and networks between administrations, research and nature protection offer an option to alleviate the situation between sectors. Regional administrations and research units can thus be seen as important mediating organisations with the opportunity to involve different actors into the network(s) to combine the interests of these various sectors (Hokkanen et al. 2007). This discussion leads us to hypothesise that:
H2 Administrative and research organisations act as the hubs of the studied CBC network.
H3 CBC in the studied network is to a great extent dependent on these hubs.
3 Analysing Cross-border Cooperation Networks
3.1 Cross-border Cooperation Networks and Social Network Analysis
Networks make up an effective structure for it members to be a part of various complex operations for gaining access to resources (e.g. knowledge) they do not possess themselves (Huggins 2010). Therefore, the role of these networks in information sharing has been underlined as decisive for successful economic outcomes. This applies especially in cross- border settings, where the cooperation amid different actors across the border has been described as extremely cumbersome and characterised by uncertainty (Blatter 2004; Leibenath
and Knippschild 2005), but at the same time, due to the possibilities for new combinations of varying knowledge, skills and expertise present in adjacent sides of the border, as potentially lucrative and highly innovative (Lundquist and Trippl 2013).
Various tools of SNA have been positioned as extremely helpful in disentangling the structures and evolution of cross-regional cooperation networks and interaction (Glückler 2007; Ter Wal and Boschma 2009; Javakhishvili-Larsen et al. 2018). Therefore, some general conclusions concerning CBC networks have been drawn in the earlier literature. Firstly, the existing firm- level evidence seems to point towards a conclusion that firms collaborate and network mainly with domestic partners (Hassink et al. 1995; Koschatzky 2000; Turkina and Postnikov 2012).
Secondly, studies on scientific CBC have shown how the adjacent side of the border is commonly by-passed. Instead international partners are typically sought from global science hubs (Coenen et al. 2004; Hansen 2013; Makkonen 2015). Thirdly, recent analyses on cross- border policy networks point towards a conclusion that administrative organisations tend to collaborate with similar domestic partners: a trend that is commonly termed as “homophily”
(Dörry and Walther 2015; Sohn and Giffinger 2015). However, when institutional similarity or similarity in organisational backgrounds (firm-firm, academic-academic or administrative- administrative collaboration) between two actors is high, the importance of them being located in the same region for successful collaborative outcomes diminishes (Ponds et al. 2007).
Similarly, actors with similar status (or prestige) are more likely to share information with each other (Godart 2015). Institutional, organisational and status-based similarity can, thus, facilitate CBC.
As stated in earlier literature (Durand and Nelles 2014), assuming that links exist in both ways without confirming them as reciprocal (called symmetrisation of the data) can lead to the overestimation of actual number of ties. Therefore, when assessing which actors act as the hubs in networks, it is more productive to concentrate on “in-degree links” (the standing of an actor as seen by the other actors in the network) than “out-degree links” (the standing of an actor in the network as seen by the actor itself). In practice, in-degree centrality equals to the number and intensity that the other actors in the network have stated about their collaboration with the organisation in question (also termed as “prestige centrality”).
When one is interested on networks in cross-border contexts, the commonly applied SNA indicators do not necessary fit the purposes of the posed research questions. Therefore, earlier
works on CBC networks have proposed measures for depicting the distribution and intensity of cross-border links and the connectivity between actors across the border. These measures include: 1) the External-Internal (E-I) index as well as 2) cross-border connectivity and 3) cross-border overfitting measures. Moreover, we introduce a novel test for cross-border network robustness.
Basically, in cross-border contexts, the E-I index can be calculated as the difference between foreign (External = E) and domestic (Internal = I) ties of the observation units (Dörry and Walther 2015). Negative values on the E-I index indicate that the actors tend to collaborate more with domestic (i.e. the homophily effect), whereas positive values imply that the actors tend to collaborate more with foreign partners (Walther and Reitel 2014). Another measure for looking at the cross-border links is that of cross-border connectivity, which measures the share of actors with cross-border connections. The cross-border overfitting measure indicates the share of ties that are not necessary in order to maintain a given cross-border connectivity (Svensson and Nordlund 2015). The latter two measures were calculated by utilising the
“CrossborderBlocker” software package(http://cnslabs.ceu.hu/software.php).
A final SNA test applied in this study is the cross-border network’s robustness (or durability) to network failures. Network failure has also been defined as the set of problems that inhibit network governance and coordination (Schrank and Whitford 2011; Moretti and Zirpoli 2016).
In this paper it is defined simply as the loss of cross-border connectivity. Basically, we wanted to find out what happens to cross-border connectivity if some of the most connected cross- border hubs would “disappear”. Following Casper (2007) this is tested by removing the most highly connected cross-border hubs from the network, calculating the cross-border connectivity measure for this “new” network and comparing it to the original CBC network.
To the best of the authors’ knowledge, earlier SNA literature has not tackled the issue of cross- border network failures before.
3.2 Study Design
The data applied here was collected through the so-called “recall methodology”. In essence, this methodology involves using surveys and interviews where the respondents are asked to recall and indicate the relationship between them and the principal actors they have relations with. The advantages of the recall methodology include: 1) it facilitates the respondents to indicate linkages unknown to the researchers and 2) it makes it possible to enquire about the
characteristics of the interaction (e.g. duration, frequency, importance and intensity). However, the methodology also suffers from shortcomings, mainly: the data collection processes are demanding for the respondents. Therefore, the results remain somewhat subjective, as the respondents one-sidedly decide which collaboration partners to report (and potentially not to report), rather static, since it is unfeasible to ask the respondents about their collaboration partners in the remote past, and potentially biased, since it is likely that not all collaboration partners are recalled (Ter Wal and Boschma 2009). Furthermore, since CBC is commonly driven by individuals inside the organisation (see e.g. Makkonen et al. 2018), the results presented here are likely to be influenced by personal perspectives (interviewing two persons from one organisation might not lead to a similar result). Thus, some uncertainty related to reliability and reciprocity necessarily remains in how accurate the data collected with the recall methodology are. Still, since the emphasis is on relatively weakly-known CBC networks – if the actors in the network are known beforehand, one could apply the so-called “roster methodology”, where the respondents are asked to pick their cooperation partners from a pre- defined set (Ter Wal and Boschma 2009) – the advantages outweigh the limitations for the purposes of this paper.
A questionnaire applying the recall methodology (Table 1) was distributed to the expected population of principal research and administrative organisations, NGOs and firms that were identified by a panel of experts. These actors are all operating in the Finnish–Russian CBR within the selected fields: 1) forest, 2) mining (incl. forestry and bioenergy) and 3) tourism industries or relatedly within 4) research or 5) administration (nature protection and regional development). The Finnish–Russian CBR naturally stretches from the Baltic Sea in the south (almost) to the Arctic Ocean in the north. However, for practical purposes (i.e. data collection) the firms and organisations surveyed were selected from the Finnish provinces of Kainuu and North Karelia, and from the Russian Republic of Karelia (Figure 1). Thus, in this paper these regions constitute the delineated exemplary case study setting. Basic physical, demographic and socio-economic characteristics of the selected regions are presented in Table 2.
[Table 1 about here]
[Figure 1 about here]
[Table 2 about here]
The questionnaire was sent to persons in positions in the firms and organisations that can be deemed as elite informants (CEOs, heads of organisations, etc.) capable of providing the necessary and valid information concerning their firm or organisation for the purpose of this study (Rice 2010). Supplementary interviews following the structure of the questionnaire were collected to increase the number of cases by applying the so-called snowballing technique. That is, firms and organisations that had emerged as apparent key actors in the local CBC network through the questionnaire were approached. These firms and organisations had to be active in the sectors under scrutiny here, and needed at least two mentions with significant weights (the assignment of weights was explained in Section 3.1) in the data collected via the survey.
Subsequently, altogether 44 (23 from the Finnish and 21 from the Russian side of the border;
eleven from forest, five from mining and seven from tourism industries, eight from research and thirteen from administrative organisations) usable answers were acquired either through the questionnaire or the interview resulting in a response rate of 77% of the targeted survey population of firms and organisations. The data was collected between 2013 and 2014.
4 Social Network Analysis:Connectivity, Hubs and Robustness 4.1 Empirical Illustrations of Cross-Border Cooperation Network
Figures 2–3 depict the schematic illustrations of the (ego) networks of selected Russian and Finnish firms and organisations situated in the Finnish–Russian CBR. The weight (intensity) or “width” of the collaborative links were assigned based on the questionnaire items (Table 1):
the more people involved, the more frequent and the longer the duration of the collaboration the more weight was assigned (from 3 = weak, to 12 = intense collaboration). The drawn figures represent the most important collaboration partners of the firms and organisations (that is, out- degree links), as identified by the key informants (CEOs, heads of organisations, etc.) who replied to the questionnaire or were interviewed.
[Figure 2 about here]
[Figure 3 about here]
The illustrations in Figure 2 clearly show that firms rarely cooperate across the border. Rather, most of the interviewees/respondents reported only local or national collaboration partners.
However, for many Russian firms, it is common to have contacts abroad through local offices
of foreign firms (predominantly from Finland or Sweden). These local offices, thus, bridge actors in the Russian side of the border to their headquarters (abroad).On the contrary, Finnish firms are engaged more directly with their foreign counterparts, since foreign firms rarely have local offices in the Finnish border region (if they do have local offices they are more commonly located in larger cities, such as the Finnish capital of Helsinki, situated outside the Finnish–
Russian CBR). Nonetheless, the picture is quite different for nature protection, research and administrative organisations (Figure 3). For example, research organisations collaborate actively with partners from the other side of the border. However, the illustrations pinpoint that the immediate neighbours are also frequently by-passed and collaboration partners sought from farther away international research hubs. The cooperation within the administrative organisations (engaged with nature protection or regional development) is also extensive across the Finnish–Russian border. This cooperation is directed both to the immediate border region and to other parts of Finland/Russia. However, CBC mainly occurs only between similar types of actors (i.e. Finnish universities with Russian universities, Finnish regional administration with Russian regional administration, etc.).
4.2 Network Measures of Cross-Border Cooperation Network
For the purposes of the SNA only collaborations occurring in the case study CBR were included to focus the research on CBC networks and to simplify the network presentation. Furthermore, as discussed in Section 3.1, only in-degree links (or prestige centrality) were taken into account.
Already from Figure 4 (the width of the link indicates the intensity of the cooperation in terms of its frequency, number of people involved and duration), one can see that local domestic networks are much denser than the overall network crossing the border. Density scores (the number of actual links divided by the number of all possible links) for the different parts of the network verify this image (and H1): 1% for cross-border links, 3% for the Russian side and 4%
for the Finnish side. Thus, it can be stated that overall the network is weakly connected on both sides of the border and particularly across the border. It indicates that the sectors discussed are rather isolated from each other in terms of cooperation. However, the low density scores (cf.
e.g. Javakhishvili-Larsen et al. 2018) also reflect the fact that out of the 166 firms and organisations mentioned in the answers (and, thus, included in the network) only 44 were surveyed by using the recall-methodology, which does not set a pre-defined list for the interviewees to choose from. Therefore, the methodology applied here gives a more realistic picture of the density of CBC networks compared to studies which have concentrated their
attention to very narrowly defined lists of actors (e.g. within a very specific branch of administration).
[Figure 4 about here]
This picture of low cross-border connectivity is enforced when looking at the E-I index scores of the most connected actors (Table 3). In the Finnish side of the border, there is not even a single firm or organisation in the list that would score positively on the E-I index. In the Russian side of the border, there are a couple of highly connected hub organisations (Petrozavodsk State University and Kostomuksha Nature Reserve) that exhibit a slight tendency to collaborate more intensively with Finnish than local Russian partners. In addition, there are two organisations (Petrozavodsk City Administration and Tourist Info Centre) that were mentioned as collaboration partners only by Finnish organisations; these organisations seem to be well connected to the Finnish side of the border but lack a hub status within the local domestic network. From Table 3 (confirming H2), it can be stated that research and administrative organisations (e.g. local development companies that are owned by local municipalities, which are commonly responsible for regional business development, business counselling, marketing of the region, creating co-operation channels etc.) are the hubs of the network in the Finnish side of the border. Likewise, in the Russian side of the border, research organisation, and administrative organisations (ministries, district administrationand organisations engaged with nature protection) play an important part in the network.
[Table 3 about here]
Cross-border connectivity measures show similar findings (Table 4). The network as a whole is not that connected across the border; only about 26% of the actors in the network have cross- border ties, whereas the majority do not engage in cross-border collaboration within the studied CBR (giving further evidence supporting H1). These figures are low compared to, for example, the figures from Euroregions provided by Svensson and Nordlund (2015, 379). The cross- border overfitting measure (Table 4) provides further evidence that despite a number of connections visible from Figure 4, a large share of these links do not raise the number of actors with cross-border ties since the actors having cross-border links in the first place commonly have several collaboration partners from the other side of the border. This finding casts doubts on the robustness of the CBC network indicating that the removal of a few highly connected
cross-border hubs, could lead to a cross-border network failure. This was formally tested by removing the most connected (based on Table 3) Finnish (University of Eastern Finland) and the most connected Russian (Karelian Research Centre of the Russian Academy of Sciences) cross-border hubs from the data. The loss of cross-border connectivity, however, was not as catastrophic as initially considered (cf. Casper 2007) sincethe removal of the two most well- connected cross-border hubs would lead to a decrease of 9% in the overall cross-border connectivity measure (contrary to H3). As such, there are several other moderately connected actors that maintain the connectivity of the CBC network even if the most connected cross- border hubs would be removed. Notwithstanding, the SNA measuresdraw a conclusion that the CBC network of the case study industries and region is relatively weakly connected.
[Table 4 about here]
5. DISCUSSION AND CONCLUSIONS
To answer to the first research questions: CBC in the studied sectors (forest, mining and tourism industries) of the Finnish–Russian CBR is weakly developed. However, these results vary according to the type of actors surveyed:
1) Firms engaged in forest, mining and tourism industries mainly cooperate domestically and only rarely across the border. These notions are in line with the earlier studies on firm-level CBC (Hassink et al. 1995; Koschatzky 2000; Turkina and Postnikov 2012). For some firms, this might be caused by the fact that they do not (or feel that they do not) have prospective customers or cooperation partners in the other side of the border and, thus, deem collaboration as unnecessary. However, roundwood exports from Russia to Finland constitute one of the largest international roundwood trade flows within Europe (Mutanen and Toppinen 2007), while Russian travellers form the largest group of foreign visitors in Finland, and Finnish tourists are among the most frequent incoming tourists in Russia (Stepanova 2014; Makkonen et al. 2018).
Therefore, there is ample potential for the firms to develop their CBC networks in the future.
2) Research organisations cooperate both with international actors in the immediate border region, but at least as frequently with partners from more faraway locations.
These findings are in line with studies demonstrating the level and impact of Finnish and Russian researchers’ increasing participation in international collaboration networks (Pislyakov and Shukshina 2014; Puuska et al. 2014). Part of this work has
been engendered by CBC programmes, jointly funded by the EU and Russia, contributing to the overall number of cross-border links. The results also confirm the findings of earlier studies on scientific CBC (Hansen 2013; Makkonen 2015), which have shown that the immediate border region can be frequently by-passed and foreign collaboration partners sought from international research hubs, since there are not many research institutes or universities close to the border to cooperate with.
3) CBC is its most intensive between the administrative organisations. This might be due to the fact, that administrative organisations have a multitude of formal bi-national agreements and arrangements, and thus several CBC partnerships with frequent interaction, for coordinating e.g. fire and rescue services (Princen et al. 2016). The results, thus, also corroborate the statements concerning institutional, organisational and status-based similarity: actors with similar roles in the adjacent sides of the border cooperate intensively with each other (cf. Ponds et al. 2007; Godart 2015).
These results are likely to apply to a range of other CBRs besides the one analysed here.
To answer to the second research question, research and administrative organisations have taken the lead in the Finnish–Russian CBC in the region and act as the network hubs, but the sectors discussed here seem to be weakly integrated with one another in terms of (cross-border) cooperation. Finally, to answer to the third research question, even in situations where the most connected actors in the Finnish–Russian border region would stop cooperating across the border, the CBC network would not be seriously affected. As such, the network is not dense, but it has several moderately connected actors that link the network across the border, which increases its robustness against cross-border network failures. It is recommended that further studies on CBC networks should not concentrate merely on the contemporary connectivity of the studied network, but also consider its robustness such as if a CBC network is vulnerable to cross-border network failures, i.e. heavily dependent on just a few organisations connecting it across the border, which is a potential cause of concern for the durability of the network.
For successful land-use and natural resource use policies, intensifying CBC is recommended to take into account the varying interests of the sectors. We highlight the important role, and thus the “responsibility”, of regional administrations, development companies and research units in driving this sustainable development (also) in the future.Development of cross-border infrastructure, such as border crossing points, can give a certain impetus to this development.
The role of the EU as a main funder, without which the CBR network would be even less-
developed (cf. Makkonen et al. 2018), of cross-border activities in the Finnish–Russian CBR is pivotal. For example, the Karelia CBC (www.kareliacbc.fi) funding programme operating in the case study region funds a multitude of projects for international networking and cooperation. It is thus among the greatest efforts and opportunities in the Finnish–Russian CBR to develop CBC for the benefit of the local economies and the society; particularly via its role in helping firms (and organisations) understand the institutional contexts of doing business across the border and with foreign firms. Nature and environmental issues are, however, still often seen as an obstacle for economic development. Therefore, continued support and further integrating and deepening of the sustainable development aspects from rhetoric into concrete actions – in the design of CBC programmes and when making funding decisions – is necessary for achieving local and global environmental goals.
Due to the limitations in the data collection (recall-methodology), the findings have to be interpreted with some caution. Still, the interpretations presented here are consistent in both sides of the border and within similar types of actors. As discussed above, the results also conform to several notions on CBC networks made in the earlier literature underlining the validity of the approach. Therefore, methodologically the combination of the (cross-border) SNA measures presented here offers tools for other CBRs to analyse the connectivity and robustness of their CBC networks. This would further our understanding on the specifies of cooperation in various other sectoral and cross-border contexts.
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Figure Captions
Figure 1. The map of the study region: provinces of Kainuu and North Karelia (Finland) and the Republic of Karelia (Russia).
Figure 2. The cooperation networks of selected firms situated in the Finnish–Russian cross-border region.
Figure 3. The cooperation networks of selected nature protection, research and administrative (promoting regional development) organisations situated in the Finnish–Russian cross-border region.
Figure 4. Finnish–Russian cross-border cooperation network (as identified in the sample) within forest, mining and tourism industries.
Tables
Table 1. The questionnaire/interview framework for collecting the network data.
DOMESTIC NETWORK/INTERNATIONAL NETWORK:
Name your most important domestic and international collaboration partners
Firm/Organisation Persons in cooperation Frequency of contacts Duration of cooperation Name and
location Sector, industry or field
1 = 1–3 persons 2 = 4–10 persons 3 = over 10 persons
1 = daily
2 = 2–3 times per week 3 = weekly
4 = monthly 5 = more seldom
1 = less than a month 2 = less than a year 3 = 1–3 years 4 = over 3 years
Table 2. Selected physical, demographic and socio-economic characteristics of the case study regions in 2018.
* USD per head, current prices, current PPP, 2014
** Calculated based on information provided by Natural Resources Institute (LUKE) Sources: www.euregiokarelia.com; https://stats.oecd.org/
Area Forest cover Population GDP*
Finland Kainuu
North Karelia 22 687 km²
21 584 km2 90% **
70 % 73 100
162 300 29 945 $
32 785 $ Russia
Republic of Karelia 180 520 km² 50 % 687 500 13 945 $
Table 3. Weighted in-degree links and E-I index scores of the most prominent firms and organisations (as identified in the sample) in the Finnish–Russian cross-border cooperation network within forest,
mining and tourism industries.
FINLAND Internal External E-I
University of Eastern Finland (UEF) 112 42 -70
Finnish Forest Research Institute (METLA) 40 16 -24
North Karelia ELY-Centre* 51 0 -51
Pielinen Karelia Development Centre (PIKES) 47 0 -47
Regional Council of North Karelia 43 0 -43
Joensuu Regional Development Company (JOSEK) 37 0 -37
Central Karelia Development Company (KETI) 37 0 -37
Karelia University of Applied Sciences 31 0 -31
Regional Council of Kainuu 20 9 -11
Joensuu Science Park 28 0 -28
Natural Resources Institute (LUKE) 28 0 -28
RUSSIA
Ministry of Natural Resources of the Republic of Karelia 122 6 -116 Karelian Research Centre of the Russian Academy of Sciences 39 33 -6
Petrozavodsk State University 29 31 2
Muezersky District 43 0 -43
Ministry of Economic Development of the Republic of Karelia 30 12 -18
Russian Railways (Petrozavodsk/Moscow) 34 0 -34
Ministry of Internal Affairs of the Republic of Karelia 30 0 -30 State Committee of the Republic of Karelia for Tourism 22 7 -15
Karelian Wood Company** 27 0 -27
Kostomuksha Nature Reserve 8 17 9
...
Petrozavodsk City Administration 0 14 14
Petrozavodsk Tourist Info Centre 0 13 13
* Centre for Economic Development, Transport and the Environment
** Formerly Swedwood Karelia
Table 4. Cross-border measures.
MEASURE SCORE (%)
Cross-border connectivity 25.9
Cross-border connectivity (Finland) 25.7 Cross-border connectivity (Russia) 26.1
Cross-border overfitting 45.5
