Regional Industrial Policy in the Western Balkans: Neither Specialization nor Spatialization?

ABSTRACT

This paper analyzes post-socialist industrial development and policy in the Western Balkans through the lens of its regional specialization and spatial concentration. Against a conceptual framework revolving around place-based industrial policy, and using the Concentration index (modified Herfindahl-Hirschman index) and location coefficients (Balassa index), a comparative analysis over three decades (1990–2020) highlights weak regional diversification and intra-regional integration of industrial activity. The findings offer a new industrial policy that transcends regional specialization and spatial concentration to address regional development, planning and governance. The concluding remarks reveal some basic paths toward effective and pro-European regional industrial policy in the Western Balkans.

KEYWORDS:

• Regional industrial development
• industrial policy
• regional specialization
• spatial concentration
• Western Balkans

Introduction

Several global crises in recent decades have placed European regional development in a critical position due to its weak economic and industrial growth facing large socio-economic challenges (EC 2014). Notably, Europe is faced with growing territorial inequalities between people and places (Rodríguez-Pose 2017), as most recently seen in unequal distribution of vulnerabilities linked to global developments: “the COVID-19 pandemic has exposed the fragility of global supply chains and a lack of resilience in several economic sectors and territories” (EU Ministers 2020, 11). However, even before global crises, deindustrialization had affected many European countries (Savić and Zeković 2004). Interestingly, countries with a large share of industry in their gross domestic product (GDP) seemed to be more resilient: contrary to ingrained neoliberal claims, markets are far from perfect and infallible, and without strong government intervention the economy may have failed; accordingly, industrial policy (IP) is of crucial importance for the expansion of technology, entrepreneurship and productivity (Mazzucato 2015). Although industry generated ~16% of GDP in Europe, its importance is much higher in the European Union (EU), as it has generated over 75% of exports and 25% of jobs (Eurostat 2016). The impact of the global financial crisis (2007–2009) and the subsequent European financial crisis (2010–2013) is reflected in the loss of 20 million jobs in industry over ten years (2007–2016), as well as in the decline of production and competitiveness (Dabrowski and Myachenkova 2018). The recent health pandemics have contributed to the loss of or failure to create almost 7 million jobs in the EU, thereby opening a new global crisis (CEDEFOP 2021).

However, the new EU Industrial Strategy (EC 2020a) creates conditions for the renewal of industrialization as a driver of economic growth and a major means for reducing disparities in competitiveness and territorial inequalities. Recent years have witnessed the development of “place-based,” “regional,” “innovative” and similar narratives related to industrial policy (Capello and Fratesi 2012; McCann and Ortega-Argilés 2015; EC 2020a). The place-based approach has gradually spread to the main fields and different levels of public policymaking, despite differences between countries (EU Ministers 2020). Across Europe, place-based thinking opens debates due to the divergence in the extent of development, education, research, and diffusion of innovation and industrial policy, as the essential drivers of regional growth. Even in developed areas, regional and industrial development has failed to occur due to limited mechanisms for spreading innovative industrial policy. It has caused growing differences in interregional productivity and other inequalities.

Looking again through a practical lens, similar to the EU, in South-Eastern Europe (SEE) deindustrialization took place in conditions of weak economic growth and a declining industrial gross value added (Hadžić and Zeković 2019). Much of the SEE region is considered to be the periphery of the EU in the geographical, economic and social sense (Gray and Jarosz 1995; Göler 2005). SEE countries introduced “shock therapy” market reforms (rapid changes toward a market economy). This approach to post-socialist reforms reflected mainstream ideological and political changes, based on the Washington Consensus, i.e., privatization, liberalization, and stabilization (Williamson 1990). At the end of the 1990s, shock therapy was abandoned in favor of a gradualist approach with a focus on institutional and legal frameworks. The inflow of foreign direct investment (FDI) was limited and insufficient in most SEE countries (Demekas et al. 2005) due to unadjusted institutions (Fabry and Zeghni 2010), lack of intra-regional integration and intangible resources, especially in Central and Eastern European (CEE) countries (Christie 2003). Although FDI is not the only channel through which technological diffusion can occur, the link between FDI and economic growth in developing countries has a theoretical background in the international diffusion of technology (Barro and Sala-i-Martin 1997), in the theory of endogenous growth (Romer 1990) as well as in the theory of neoclassical growth (Wang 1990). Mencinger (2003) argued that the connectivity between FDI and GDP growth was negative in eight CEE economies.

Briefly put, SEE transition countries face the challenges such as low levels of development; the impact of globalization on capital flows, markets and knowledge; the lack of leverages for new development cycles; and industrial lagging (Zeković and Vujošević 2015). Nevertheless, the most influential international actors have continuously been supporting European integration processes in the SEE region. In the early 2000s, the EU’s governing bodies coined the Western Balkans (WB) as a geopolitical term to designate countries aspiring the join the EU (Dabrowski and Myachenkova 2018). Originally, the WB region included seven countries: Albania, Bosnia and Herzegovina, Croatia, the Former Yugoslav Republic of Macedonia (FYROM), Kosovo*, Montenegro, and Serbia.¹ Croatia joined the EU in 2013, hence the region became known as the Western Balkans 6 (WB6), as adopted in several EU policies. Nevertheless, since this paper looks at changes in regional industrial development over the last three decades, WB here refers to the original seven countries, meaning the current WB6 and Croatia.

Even almost twenty years after designating WB as the largest European region outside of the EU, all of the region’s current states still follow (more or less successfully) paths to EU integration, meaning that sustainable development frameworks, effective governance, and inclusive institutions are far from the reality in any WB country and need much more improvement and adaptation (in any policy domain) according to EU standards. Just as a stable institutional and political framework is a necessary prerequisite for a successful implementation of IP, IP as a mechanism is often seen as a path to the collapse of developing economies and therefore has a poor reputation among policymakers. Keeping in mind the transitional and, thus, challenging context of the WB during the last thirty years, this paper examines the innovative nature of IP and its effect on industrial development to illustrate the latter’s regional distribution in the WB. More precisely, the paper focuses on the comparative analysis of industrial development in the WB, particularly considering the aspects of regional industrial concentration and industrial specialization in the post-socialist period. Finally, going beyond the traditional neoclassical approach, the analysis focusing on spatial concentration and specialization creates room for exploring improved regional planning and governance, considering the decrease of significant territorial differences, territorial fragmentation, and increased regional competitiveness to, ultimately, foster territorial cohesion.

The paper is structured as follows. After the introduction, the second section offers the theoretical background of the IP, industrial specialization and spatial concentration, as well as current debates on regional planning and governance. The next section presents the methods applied, followed by a structured comparative analysis of industrial development, regional industrial concentration and specialization in the WB. The discussion part highlights the main findings of the empirical research obtained through the analytical framework, pointing to the advantage of such a framework observed through the lenses of regional planning and governance. The concluding remarks offer some suggestions for improvements to IP in the WB region.

Conceptual Background: Toward Place-based Industrial Policy

Classic IP is closely connected to import-substituting industries, emerging industries, clusters, the “state industrial champions” (Benner 2019), and protectionism. An innovative IP is considered a “true industrial policy,” export-orientated, and not the “forgotten hand of the state” (Cherif and Hasanov 2019). Chang (2009) further advocates vertical IP favoring specific industries/sectors or firms (even products). Several approaches are used to create an IP: a place-based approach, a multi-sectoral and a participatory approach, all emphasizing the participation between multiple levels of government and with different actors and stakeholders. The essence of modern place-based IPs is to value regional business ecosystems and use them for regional benefit (Bailey, Pitelis, and Tomlinson 2018). Therefore, IP is increasingly linked to innovation (Mazzucato 2011, 2015), by strengthening the entrepreneurial role of the state in innovation and initiating structural changes. More precisely, the structural features of a place, main actors, entrepreneurial networks, and institutions in charge of innovations become the key factors in defining the new approaches to IP (Grillitsch et al. 2022; Trippl, Grillitsch, and Isaksen 2018; Grillitsch and Sotarauta 2020). In other words, various combinations of innovative and institutional entrepreneurship, agents and place-based leadership should enable the new dynamics in the IP domain (Grillitsch and Sotarauta 2020; Mazzucato 2017; Bailey et al. 2010).

Over the last decade, the understanding of IP and regional policy in the EU has changed. This has included a shift from the traditional neoclassical approach (often spatially undefined and “blind”) toward a place-based approach. Notably, the European Framework of Research and Innovation proposed Strategies for Smart Specialization (RIS3) focuses on developing knowledge and innovations (EC 2020a). Due to its emphasis on structural changes, RIS3 qualifies as the EU’s IP (Radosevic 2017). Briefly put, smart specialization strategies (S3) focus on EU development strategies, with the aim of fostering regional growth around existing place-based capabilities (Barca 2009; Foray, David, and Hall 2009; Capello and Fratesi 2012; McCann and Ortega-Argilés 2015).

The goal of S3 is to identify hidden opportunities and generate a new platform around which regions can build comparative advantages in value-added activities. Furthermore, the S3 approach supports the institutional preconditions for experimentation and “entrepreneurial self-discovery” (Benner 2018; Kroll 2015). The S3 approach follows the territorial logic and focuses on the endogenous development of the region, while the traditional IP is characterized by spatially blind development logic. The new IP encourages the authorities to take a catalytic and facilitating role in innovation and economic growth. Mazzucato (2017) points to the complex nature of innovation and the mission-oriented development through institutionalized partnerships. Regional policy, based on a place-based approach, identifies sectors and fields, and encourages local innovations in specific areas (Camagni and Capello 2013). Regional actors should identify the capabilities and strengths that exist in the region in the collective process of entrepreneurial discovery (Foray, David, and Hall 2009; Foray 2019). Doloreux (2002) points to three types of RIS3: the organizationally weak RIS (lack of actors); fragmented RIS (lack of regional cooperation); and RIS that is “locked” (immanent to old industrial regions).

The S3 concept is integrated into the EU cohesion policy and regional cohesion programs, hence introducing a clear distinction to previous policies covering the topic of regional development (Foray 2018). Such a shift has contributed to abandoning the top-down “one-size-fits-all” policy at the national level, pursued by the EU rather than by particular regions (Asheim, Grillitsch, and Trippl 2016). The S3 implies establishing an innovation system for the implementation of mission-oriented research and development programs, and the improvement of institutional conditions for its operational application (Foray 2018). Although it considers specific regional needs and resources, S3 emphasizes local demand as the driver of innovation. Competitive advantage through S3 can be promoted in all types of industries (Asheim, Grillitsch, and Trippl 2016). Therefore, smart specialization is probably the largest attempt at an orchestrated supranational innovation strategy to promote a new development path and stimulate economic growth (Radosevic et al. 2017). S3 implies the identification of new development opportunities and regional economic diversification.

In territorial terms, the S3 concept is oriented toward the modernization of industrial activities in mature EU economies, as well as in developing economies (EU candidate countries), and it is binding for both groups. Nevertheless, its application also implies uncertain benefits in structurally weak geographical areas, especially in underdeveloped regions, such as the SEE, usually characterized by weaker institutional potential for supporting entrepreneurial talent, work skills and technological capacity to develop new specializations (Hassink and Gong 2019; Andrews, Criscuolo, and Gal 2016). Furthermore, S3 can deepen regional spatial inequalities as well as existing differences between the “advanced/leading” regions and “followers/following” regions. In this regard, Radosevic (2017) believes that the main challenges for the WB are the lack of leverages for growth, the focus on technology imports, and industrial backwardness. To sum, in addition to the practical obstacles, the S3 concept has been also criticized for its insufficient theoretical basis, as an example of policy running ahead of theory (Balland et al. 2019), a continuation of cluster policies rather than the brand-new policy instrument, and the questionability of the entrepreneurial discovery process as a tool to set in motion structural changes of a regional economy (Hassink and Gong 2019), the absence of an evidence base (Morgan 2015), and, finally, anecdotal evidence rather than the application of theoretically grounded methodologies (Iacobucci and Guzzini 2016).

However, S3 is rooted in an explicit place-based, place-specific and place-sensitive approach to regional innovation policy, emphasizing prioritization in policy by non-neutral (preferential) and vertical policies targeting diversified specialization (Hassink and Gong 2019). More precisely, S3 implies regional economic diversification, including diversified specialization (Asheim, Grillitsch, and Trippl 2016) and smart diversification (Balland et al. 2019). Lack of demarcation between specialization and diversification leads to confusion (Hassink and Gong 2019), as well as a lack of delimitation between specialization (cluster, smart, regional) and spatial concentration (Bickenbach, Eckhardt, and Krieger-Boden 2013; Dragan and Isaic-Maniu 2017; Van Egeraat et al. 2018). As such an approach intrinsically increases the dynamics of the regional industrial development (regardless of the specificity of a socio-spatial setting), attending to the domains of (industrial) specialization and spatial concentration (of industry) in the transitional context of the WB seems particularly important, as highlighted in the following subsection and elaborated in the central part of the paper.

Specialization and Spatial Concentration

According to the neoclassical economic approach, specialization can slow down growth and increase the risk of unemployment, while regional diversification of industrial structure reduces unemployment and supports growth (Krugman 1993). The proponents of neoclassical insights believe that external influences encourage the spillover of knowledge through specialized and geographically concentrated industrial growth (Porter 1990).

In recent years, regional policies have required more complex economic and geographical insights. This has stimulated discussion on identifying and integrating geographical and economic aspects of regional industrial development. Over the last decade, the regional economy has experienced a rise through a combination of a new economic geography and the theory of new (or endogenous) growth. Both approaches provide insight into spatial-economic phenomena, giving importance to evolutionary-institutional and economic-geographical concepts (McCann and van Oort 2019). The path-dependence approach, evolutionary-institutional approach, and new economic geography provide new insights into the nature of transition processes and the relationships between technology, development, diversification, specialization, and spatial concentration of industry (Barca, McCann, and Rodríguez-Pose 2012), as well as regional governance. Discussions have been opened on the role of regional diversification and specialization in the promotion of growth (De Groot, Poot, and Smit 2015).

The issue of spatial concentration has rarely been associated with regional industrial development, except in literature on geographic distribution that presents industrial concentration in some areas (Ellison and Glaeser 1999; Maurel and Sedillot 1999; Marcon and Puech 2010; Lehocký and Rusnák 2016; Van Egeraat et al. 2018; Stellian and Danna-Buitrago 2019). The analysis of spatial concentration is mainly based on locational and industrial specialization indices. Spatial concentration of industry can be defined as the extent to which employment in industry is directed toward some localities, regions or countries. It predominantly includes employment, and very rarely allocation of firms, incomes and the spatial spillover of their effects. Finally, spatial industrial concentration is under the influence of contextual factors and global, regional and local flows. Both aspects – specialization and spatial concentration – will be discussed on the WB case in the central section of the paper.

Regional Industrial Development: An Incentive for Regional Planning and Governance?

The system of regional planning and governance as an institutional technology performs the function of public control over spatial development in different ways. Planning affects land-use allocation, while regulations define the impact of planning on property rights, protecting all property rights and the public interest. Often, the intervention of the state against the influence of the global market in leading spatial development appears as a consequence of specific power relations between the state (as a regulator, investor and/or controller) and the market (e.g., various mechanisms, instruments, and interests of economic actors).

Against such feedback between the state and the market, the role of regional planning stems from the relationship between territorialized political power vs. the free movement of capital and the pressure of investor interests. In other words, in the planning of space (regions and cities), there is a constant conflict between two different understandings – the logic of capital and territorially established political power (Harvey 2005). Although territorial political power and the power of capital are tightly linked, the former is orchestrated by territorial borders. In contrast, the latter (the global movement of capital) generally has no boundaries and often breaks through already established ones.

In this “mainstream” framework, a significant challenge in regional planning is the connection (either tight or volatile) between the “territoriality” of political power and the movement and allocation of financial capital. In other words, the main challenge takes place between the territorial (political) logic of power and the logic of capital, which, however, is not territorially neutral. The logic of capital creates different territorial configurations at every time and context: on the macro level, it shapes “global cities,” regional distribution of work/economic activities, leading economic zones, etc., while, at the micro level, it creates various innovative urban structures, business structures, complexes and individual locational entities. Thus, the logic of capital is not space in itself (as opposed to fixed territorial political power that relies upon defined boundaries and governing power orchestrated within those boundaries) but the use of territorial capital (“hard,” i.e., physical, tangible; and “soft,” i.e., institutional, intangible) in order to extract its value and, subsequently, monetize it in the market.

Against such a background, the novel theoretical understandings about the future of regional planning are very heterogeneous, sometimes even contradictory. Some researchers believe that traditional forms of regional planning have died out (Harrison, Galland, and Tewdwr-Jones 2021a), that regional planning has lost its political significance and practical importance and is dying (Harrison, Galland, and Tewdwr-Jones 2021b), and that contemporary planning discussions are too focused on the institutional framework of planning offering a re-conceptualization of planning frameworks and content and repositioning planners in regional planning (Harrison, Galland, and Tewdwr-Jones 2021b; Davoudi, Galland, and Stead 2020). At the same time, others believe that regional planning is not dead (Smas and Schmitt 2021), so it is necessary to emphasize everything regional, including the fusion/blending of regional and urban planning that creates something completely new in space (Soja 2015), further identifying challenging topics for innovative critical and comparative regional research (Purkarthofer, Humer, and Mäntysalo 2021; Watson 2019; Sielker and Rauhut 2018). A comparative analysis of regional planning in different institutional frameworks in eight European countries challenges the view that regional planning has lost its political and practical importance in recent years (Smas and Schmitt 2021). On the contrary, authors of this analysis conclude that formal regional planning is needed, indicating that it is particularly important due to the “oversight” in regional research and empirical studies in which more attention is paid to informal (neoliberalized) forms of regional planning characterized by mostly “soft spaces” and “soft” governance arrangements (Allmendinger et al. 2015). Finally, Harrison, Galland, and Tewdwr-Jones (2021b) argues that planning remains an integral part of future regional research in a different form and with new approaches to the regional future, with an evident revival of regional design (Neuman and Zonneveld 2018). Hence, a key research question that needs to be carefully explored regarding the future of regional planning is how regional planning and practice should be improved in dynamic changing contextual factors, especially in new institutional and changing political conditions.

Research Methodology

Case Study: The Western Balkans Region

As briefly mentioned in the introductory section, the WB makes an interesting case for exploring the evolution of IP as the region has undergone significant changes over the last thirty years: economic (from a state-controlled economy to a market economy), political (from the one-party communist political environment toward pluralist democracy), and institutional (from hierarchical and exclusive expert-led institutional framework to (an attempt to achieve) a more participatory decision-making style). Though similar transformations may be relevant for other CEE and SEE countries (all once behind the so-called Iron Curtain), the WB as a part of SEE region still faces the ongoing transition in many policy domains, and, given the current global uncertainties, there is a solid concern about the foreseen deadline for its full transformation (e.g., the process of the WB’s integration into the EU has now lasted for more than two decades).

While, on the one hand, such an immature region faces inconsistency with EU regulations and standards, on the other hand the WB can be considered a testbed for developing innovative IP approaches – focused on specialization and spatial concentration underpinned by the place-based approach, hence fostering local spatial conditions without being burdened with supranational policy priorities. Therefore, the study of the WB’s regional industrial development over time, as well as the ways to improve its IP, may: 1) secure a prolific field for research; 2) facilitate and help create innovative policies, and 3) strengthen territorial cohesion within the region and, ideally, with the EU.

To address the latter, it is interesting to highlight that the critical contemporary policies relevant for the entire WB region only partially contain references to the innovative approaches in regional industrial development. For example, the Green Agenda for the Western Balkans (RCC 2020) supports improving the sustainability of the WB’s raw material production, expanding regional circular economy strategies, and creation of a sustainable development and innovation policy through the implementation of S3. However, the South East Europe Strategy 2030 (RCC 2021), adopted by all WB countries, ignores the new regional industrial policy and S3. Hence, the absence of a joint regional industrial policy and S3 as the national development strategy, weak implementation instruments and tools, and insufficient compliance with the broader (EU) framework all indicate a significant gap between stated goals and priorities, on the one hand, and the real conditions that help create regional industrial development, on the other. Nevertheless, attending to the regional specialization and spatial distribution could serve as a tool for better planning and governance of (regional) territorial capital.

Data Collection Methods

All data for the analysis of industrial development, regional specialization and spatial concentration of industries in the WB were collected from these countries’ national statistics and from other publicly available primary and secondary sources. The original data for each analyzed WB country can be found on official national websites, while the paper also used selected national data sets related to the WB published on the official websites of international institutions, such as Eurostat, World Bank, United Nations Industrial Development Organization (UNIDO), and European Bank for Reconstruction and Development (EBRD). Seven spatial levels of NUTS-1 (Albania, Bosnia and Herzegovina, Croatia, Kosovo*, Montenegro, North Macedonia, and Serbia) were analyzed based on data collected between 1990 to 2020.

Data Analysis Methods

This study of regional industrial development and industry distribution in the WB applied comparative analysis based on a consistent theoretical background and compatible analytical tools that critically examine the regional industrial development and its specialization and spatialization. This approach combines a qualitative ex-post analysis of the regional industrial development and quantitative research into its regional specialization and spatial concentration. To measure these aspects, several different locational indices can be used: the Gini index, the Herfindahl-Hirschman index (HHI), the Maurel-Sedillot index (Maurel and Sedillot 1999), the diversification index, Theil index, regional industrial specialization indices, and others. All these indices have various strengths and weaknesses; however, they are rarely applied in regional industrial development and planning. In this paper, industrial concentration and regional specialization were measured using a new concentration index (a modified Herfindahl-Hirschman index, according to Van Egeraat et al. 2018), and location coefficients.

Concentration Indices

The Herfindahl-Hirschman index (HHI) is a quantitative tool for the absolute measurement of the diversification and concentration of industrial employment. The value of this index between 0 and 1 indicates the level of spatial industrial concentration in one region. Higher index values indicate less product diversification. The HHI represents the absolute level of concentration or specialization expressed as the following formulas (Lehocký and Rusnák 2016):

(1) $\mathit{HH}{I}_j^k=\sum _{\mathit{i}=1}^n{\left(g_{i{j}^k}\right)}^2,\mathit{HH}{I}_i^s=\sum _{\mathit{i}=1}^m{\left(g_{i{j}^s}\right)}^2$(1)

(2) $g_{i{j}^k}=\frac{{\mathit{X}}_{\mathit{ij}}}{\sum _{\mathit{i}=1}^n{X}_{\mathit{ij}}}=\frac{{\mathit{X}}_{\mathit{ij}}}{X_j};g_{i{j}^s}=\frac{{\mathit{X}}_{\mathit{ij}}}{\sum _{\mathit{i}=1}^m{X}_{\mathit{ij}}}=\frac{{\mathit{X}}_{\mathit{ij}}}{X_i}$(2)

where is $\mathit{HH}{I}_j^k$ the Herfindahl-Hirschman concentration index; $\mathit{HH}{I}_j^s$ the Herfindahl-Hirschman specialization index; i region; j sector; $\mathit{X}$ number of employees; $\mathit{Xij}$ number of employees in a region i in sector j; x_j number of employees in sector j; x_i number of employees in a region i; $g_{i{j}^k}$ share of sector j in total value of a region i; $g_{i{j}^s}$ share of region i in total value of a country in sector j.

Maurel and Sedillot (1999) have modified a similar form of the HHI for the measurement of geographic concentration (by component G):

(3) $\mathit{G}=\frac{\sum _{1=1}^N{s}_i^2-\sum _{1=1}^M{x}_i^2}{1-\sum _{1=1}^M{x}_i^2}$(3)

where s_i is share of sector employment in geographic unit $\mathit{i}$, x_i is share of total industrial employment in unit $\mathit{i}$, and $\mathit{M}$ is the number of geographic units. A part of the Maurel-Sedillot index expresses the industrial concentration (by employment distribution):

(4) $\mathit{H}=\sum _{\mathit{i}=1}^N{Z}_i^2$(4)

where is z_i the share of industry j in total sector of employment (or share of company i on the market), and N is the number of companies. The value of H ranges from 1/N to 1. Its value <0.0 indicates a highly competitive industry; a value <0.15 indicates a non-concentrated industry; a value from 0.15 to 0.25 indicates moderate concentration, while a value >0.25 indicates high concentration. A low index value points to a competitive industry without a dominant player.

There also exists a normalized Herfindahl index ($H^{\ast }$) of industrial concentration modified by Van Egeraat et al. (2018). It is calculated using the number of companies in the market as H* = (H-1/N)/1-1/N for N > 1 and H* = 1 for N = 1, where is N the number of companies in the market, and H the standardized Herfindahl index. While the Herfindahl index ranges from 1/N to 1, the $H^{\ast }$ ranges from 0 to 1. The Herfindahl index can serve as a measure of equality of distribution, but it is less suitable for quantifying concentration.

The Concentration Index (CI) is a tool for measuring the spatial concentration of industry. Van Egeraat et al. (2018) have adapted and improved the Herfindahl-Hirschman index to identify significant industrial concentrations. A new modified CI indicator considers employment and number of enterprises as well as the distribution of the size/volume of concentration. The indicator also uses the share of a spatial unit’s surface relative to the country surface, i.e., physical size of a spatial unit. The relative size of the spatial unit is embedded as an exponent into formula (5) to obtain the expression for c_j, where a is the surface area and j is a specific spatial unit:

(5) $c_j={\left[\frac{2}{N}\right]}^{\frac{1}{1-\left(\frac{a_j}{\mathit{a}}-\frac{1}{N}\right)}}$(5)

A new Concentration Index (CI_ij) includes employment and firms in determining the industrial concentration in a region, as in the formula below (Van Egeraat et al. 2018):

(6) $\mathit{C}{I}_{\mathit{ij}}=\left(\frac{{\mathit{E}}_{\mathit{ij}}}{\mathit{c}{\hspace{0.17em}}_j^{\hspace{0.17em}}{E}_i^{\hspace{0.17em}}}\right)\left(\frac{{\mathit{F}}_{\mathit{ij}}}{\mathit{c}{\hspace{0.17em}}_j^{\hspace{0.17em}}{F}_i^{\hspace{0.17em}}}\right)$(6)

where CI is the concentration indicator, E employment, F number of firms, and i the specific industry. The separate terms for employment and number of firms are multiplied with each other, thus enabling the determination of spatial industrial concentration. According to Van Egeraat et al. (2018), the CI includes a customized form of the Herfindahl-Hirschman index, based on the sum of squared shares, where RCE denotes the spatial concentration of the employment industry, and RCF the spatial concentration of the firms (with values between 0 and 1), as expressed in (7) and (8):

(7) $\mathit{RC}{E}_i=\sum _j^N{\left(\frac{{\mathit{E}}_{\mathit{ij}}}{E_i}\right)}^2$(7)

(8) $\mathit{RC}{F}_i=\sum _j^N{\left(\frac{{\mathit{F}}_{\mathit{ij}}}{F_i}\right)}^2\mathit{\hspace{0.17em}}$(8)

By inserting the limit values for the $\mathit{RC}{E}_i$ and the $\mathit{RC}{F}_i$ into equation $(6)$, the final expression is obtained:

(9) $\mathit{C}{I}_{\mathit{ij}}=\left(\frac{{\mathit{E}}_{\mathit{ij}}}{\mathit{c}{e}_{\mathit{ij}}{E}_i}\right)\left(\frac{{\mathit{F}}_{\mathit{ij}}}{\mathit{c}{f}_{\mathit{ij}}{F}_i}\right)$(9)

where ce is the threshold for employment in which the RCE is used as a multiplier and the cf is the threshold for firms based in the RCF, i.e., ce_ij = cj(1+ RCE_i) and cf_ij = cj(1+ RCF_i).

Location Coefficients

Location coefficients are interpreted as indices of specialization, but there is no theoretical relationship between the degree of industrial specialization and the emergence of localization economies (Flegg and Webber 2000). Kemeny and Storper (2015) have addressed the issue whether it was better to be highly specialized or diversified; they also pointed to the ambiguity about whether specialization refers to the absolute or relative scale of an activity in the region and whether it has static or evolutionary effects.

In the comparative analysis of the spatial distribution of industry in the WB from 1990 to 2019, three methods were used to calculate the location coefficient.

1. The location coefficient (LQ1). It is expressed as the ratio of industrial and total employment of the state (NUTS-1) and the level of WB to the ratio of population of these spatial units, is calculated according to the general expression: LQ = ei:Ei/ s:S, where ei is the number of industrial workers in a smaller spatial unit, Ei the number of industrial workers in a larger spatial unit, s the number of inhabitants in a smaller spatial unit, and $\mathit{S}$ the number of inhabitants in a larger spatial unit. The numerical value of the indicator LQ1 served as a basis for the typology and classification of all national areas in relation to the regional level, where the value LQ = 1 indicates the average development of the industry. Values greater than LQ>1 indicate areas in which specialization in production has been achieved, while LQ<1 indicates low industrial development and a weak degree of its spatial concentration.

2. Location coefficient of industrial specialization LQ2 (Balassa Index) or Revealed Comparative Advantage/RCA index. One of the premises of neoliberal teaching on economic development is based on the prediction that political and economic integration, deregulation, and the reduction of barriers will affect the increase of regional specialization (Krugman 1991). The degree of industrial specialization can be measured by the location coefficient, which identifies the relative disproportion between the share of industrial employment in the total employment of a smaller spatial unit and the share of industrial employment in the total employment of a wider area. In addition to identifying general economic and industrial advantages, LQ includes location comparative advantages. The same mathematical measure (LQ) is used in spatial economics to measure the “discovered location advantage” of certain countries for industrial allocation, according to the expression: $\mathit{LQ}2=\mathit{ei}:\mathit{e}/\mathit{Ei}:\mathit{E}$, where $\mathit{ei}$ is the industrial employment of a smaller spatial unit, $\mathit{e}$ the total employment of a smaller spatial unit, $\mathit{Ei}$ the industrial employment in a wider area, and $\mathit{E}$ the total employment in a wider area. Regional or national industrial specialization, measured by LQ or RCA, will inevitably lead to export specialization and vice versa. RCA is an index used for calculating the relative advantage or disadvantage of some country in certain services. It most commonly refers to the Balassa index, introduced by Balassa (1965). Analogously, the general term for the RCA index is defined as the ratio of two shares:

(10) $\mathit{RC}{A}_{\mathit{ij}}=\frac{\frac{X_{ij}^{\hspace{0.17em}}}{\sum _i^{\mathit{\hspace{0.17em}}}{X}_{ij}^{\hspace{0.17em}}}}{\frac{\sum _{\mathit{j}}^{\mathit{\hspace{0.17em}}}{X}_{ij}^{\hspace{0.17em}}}{\sum _i^{\mathit{\hspace{0.17em}}}\mathit{\hspace{0.17em}}\sum _j^{\mathit{\hspace{0.17em}}}{X}_{ij}^{\hspace{0.17em}}}}$(10)

• where x_ij represents the country i’s export of product j. The RCA_ij>1 indicates that the country i has a comparative advantage in production of j; the greater the index the stronger the advantages. There is no real consensus on the unique method for calculating RCA (Stellian and Danna-Buitrago 2019).

• (3) Dynamic LQ or the so-called “regional factor” (RF). The RF shows changes in the industrial employment index of individual NUTS-1 compared to the WB in the analyzed period. It provides insight into the industrialization dynamics in individual countries. The RF of industrial distribution is expressed as an expression analogous to LQ2: RF = ei^t:ei^ts/Ei^t:Ei^ts, where ei^t is the number of industrial workers for the last year in a smaller area; ei^ts is the number of industrial workers for the initial year in a smaller area; Ei^t is the number of industrial workers for the last year in a wider area; Ei^ts is the number of industrial workers in the initial year in a wider area.

Industrial Development in the Western Balkans: A Comparative Analysis

The results of comparative research are discussed keeping in mind the specific context of the WB. After the collapse of socialism in CEE and SEE, especially in the former Yugoslavia, the countries that emerged began their renewal by transitioning to a neoliberal economy. The regional development of the WB in the post-socialist period is based on the classical model of regional economic integration developed by Balassa (1965), which also corresponds to the approach to regional integration in the EU (Telo 2014). The model promotes formal cooperation between countries, the transition from a free trade zone to a customs union, a common market, monetary union, and overall economic integration. Observed through the lens of EU integration, the first joint steps in the WB region were the adoption of the Energy Community Treaty (2006/500/EC) and amendment and broadening of the scope of the Central European Free Trade Agreement/CEFTA.

The early transitional years in the WB were accompanied by low regional development, low economic growth, declining competitiveness, high unemployment, an informal economy, inadequate institutional frameworks for new development, poor technical infrastructure, poverty, refugees, and continued lagging behind EU economies. The main problems of economic and industrial development in the WB appear mainly as the consequences of the transitional recession and global changes. The introduction of the neoliberal concept of development in the WB in the 1990s induced a strong process of deindustrialization, emerging due to the applied economic changes and the collapse of industrial development. Its negative consequences continue to exacerbate preexisting territorial disparities in industrial development within the WB. The industrialization-based development strategies – pursued for decades in the WB – should be transformed in the future (Sanfey 2011; Mencinger 2003): economic recovery in the WB is not possible without new approaches to industrial development (Gligorov 2013).

The WB countries have adopted both the South East Europe Strategy 2020 (RCC 2013) and the South East Europe Strategy 2030 (RCC 2021) that focused on fostering innovation, skills and trade integration, i.e., integrated, smart, sustainable, and inclusive growth. The strategies call for SEE to change the current growth model by accelerating socio-economic reform, speeding up measures to modernize the economy, creating more new jobs and improving living standards; however, they do not tackle the new regional IP. The strategies envision trade liberalization as a key policy that would increase exports and FDI, and accelerate SEE integration into the EU, however, not counting on remittances and savings as sources for the WB development. In 2020, the average share of remittances reached 8.75% and savings 12.1% of GDP, while the share of FDI in regional GDP was 5.02% (Table 1).

Table 1. Economic indicators in WB.

Country		GDP growth %	FDI (million $)	FDI in GDP (%)	GDP p.c. ($)	GVA Manufac. (in % total GVA)	Remittances (% GDP)	Share of industry in GDP (%)	GDP (million EUR)	GVA (million EUR)
Albania	2.24		1,200	7.86	5,350	13.2	9.63	20.06	13.64	11.90
Bosnia and Herzegovina (BiH)	2.68		390.2	1.94	6,110	23.3	11.21	23.74	18.05	15.39
Croatia	2.86		1,200	1.93	14,940	19.2	6.20	20.17	54.23	44.44
Kosovo*	4.94		325.5	3.82	3,986	14.4	11.98	11.70	7.93	5.74
N. Macedonia	0.02		430.7	3.80	5,440	21.4	2.77	23.95	11.21	9.72
Montenegro	4.06		418.2	7.55	8,910	12.5	10.70	15.93	4.96	4.02
Serbia	4.25		4,300	8.29	7,410	24.0	8.78	25.65	45.98	38.02
WB	3.00		8,264.6	5.02	7,449.4	18.93	8.75	20.17	156.0	129.23
EU-27	1.60				35,623	19.70		24.50	16,491.9	14,738.63

Source: Compiled from the National statistics (2021); World Bank (2021); Eurostat (2021)

The development challenges in the WB stemmed from an insufficiently competitive economy, especially the industry, an untransformed structure, and a transition process. The reforms led to a further reduction in economic performance, a dramatic reduction in industrial employment between 1989 and 2012 by 1.33 million employees (of which 700,810 were Serbian manufacturing workers), as well as a decline in the share of manufacturing in GDP from 44.5% to 18.43% (Zeković and Vujošević 2015). The implementation of CEFTA, the launch of regional rules in the energy sector and infrastructure, trade agreements and duty-free exports to Russia, and the “mini-Schengen” – a new regional economic cooperation initiative between Serbia, Albania and North Macedonia based on a free trade zone – have fostered industrial renewal. In 2020, total employment increased by 1.36 million compared to 2012, about 235,000 of which in manufacturing. The industrial sector in the WB employs 1.24 million workers or 17.63% of the total number of employees, with manufacturing enjoying a share of 20.7% in regional GDP, and 18.9% in GVA in 2019 (Table 1). The share of the regional GDP fell to only 0.94% of total EU-28 GDP, with a dramatic decrease in the average regional GDP p.c. from 36% in 2010 to 20.9% of the EU average in 2019 (Table 1).

In a new regional IP, the support from the most influential international actors can be an advantage in the future WB development. For example, A Credible Enlargement Perspective for and Enhanced EU Engagement with the Western Balkans (EC 2018) provides impetus to the perspective of regional integration into the EU. Additionally, some of the objectives of A New EU-US Agenda for Global Change (EC 2020b) relate to the reforms in the WB, especially targeting the restructuring of the economy, strengthening democratic and market institutions, mechanisms for coordinating program aid, donations and its integration into the EU. The European Commission (EC) has adopted An Economic and Investment Plan for the Western Balkans to 2024 (EC 2020c), with the aim of promoting long-term recovery of the region, green and digital transition, economic regional cooperation, economic growth, and support for reforms in the region that would lead to progress and EU integration. The EC supports the economic convergence of the WB and the EU through investments for competitiveness, inclusive growth, sustainability, and green and digital transition. That implies the regions’ integration into the EU market and its industrial ecosystems that are being transformed according to the European Green Deal (“green” modernization of enterprises, industrial innovation, FDI, export and skills development according to national S3). The obligation to develop S3 as a new IP was introduced in the EU candidate countries in 2018. S3 was brought in by Croatia, Serbia and North Macedonia, while it is still in preparation in Albania, Bosnia and Herzegovina and Montenegro.

A comparative analysis of the industrial development indicators in the WB points to the dynamics of change and level of development (Table 2). The empirical comparison is based on the several composite indicators of industrial development, regional specialization and industrial spatial concentration in the WB. The Industrialization Intensity Index applied is measured by a simple average of the share of manufacturing value added (MVA) in GDP and the share of medium and high-tech industries in MVA (UNIDO 2020). The first share implies the role of industrial production in the economy, and the second its technological complexity. Therefore, when this index is higher, it is considered that a relatively large proportion of the industry is associated with the growth of its share in GDP and a higher technological level. A low index value indicates a low industrial level, weak industrial competitiveness, and poor integration into the global economy. In addition, the dynamics of index decline and its low value in the WB from 1990 to 2018 indicates deindustrialization and a low industrial share in regional specialization (Table 2). The comparison of the industrialization intensity indices points to existing large differences in the level of industrialization of the WB (up to 4 times), as well as to a reduced role of manufacturing compared to 1990.

Table 2. Comparison of industrial indicators in WB (2018–2019).

	Industrialization intensity index	Competitive industrial performance index	Share of manuf. value added in GDP index	Share of medium& high-tech industries in total manuf. value added index	Share of indust. in GDP (%)	GVA indus. in total GVA (%)	Share of industrial employ. (in %)
Albania	0.11	0.01	0.17	0.06	20.06	13.20	17.90
BiH	0.28	0.03	0.34	0.22	23.74	23.30	19.80
Croatia	0.34	0.04	0.35	0.34	20.17	19.20	14.00
N. Macedonia	0.34	0.03	0.32	0.37	23.95	21.40	16.90
Montenegro	0.14	0.01	0.10	0.18	15.93	12.50	5.60
Serbia	0.38	0.04	0.44	0.32	25.65	24.00	21.20
Kosovo*	-	-	-	-	11.70	14.40	15.30
WB	0.265	0.028	0.286	0.248	20.17	18.93	17.63

Source: UNIDO (2020); World Bank (2021)

The Competitive Industrial Performance (CIP) index as a composite indicator considers the countries’ productive capacities, intensity of industrialization and their impact on the market as major components of industrial performance (UNIDO 2020). The CIP index value represents a composite measure of a country’s competitive industrial performance. The quantitative results of the regional CIP index indicate deep differences in the WB (up to 5 times, Table 3). Several basic indicators also show the role and dynamics of the industry in WB (Table 2). It has been proven that the dynamics of both the indices in the WB indicate a lower economic development level and a possible technological gap in comparison with developed economies (Tables 2 and 3).

Table 3. Dynamics of industrialization intensity index and CIP index in WB.

Country	Industrialization intensity index				Competitive industrial performance Index
	1990	2000	2010	2018	1990	2000	2010	2018	Level from 1990 to 2018
Albania	0.14	0.08	0.12	0.11	0.04	0.03	0.04	0.04	Bottom to lower middle
BiH	0.31	0.29	0.12	0.28	0.04	0.03	0.04	0.04	Lower middle to middle
Croatia	0.49	0.37	0.36	0.34	0.07	0.06	0.05	0.05	Upper middle
Kosovo*	-	-	-	-	-	-	-	-	-
N. Macedonia	0.33	0.20	0.26	0.34	0.02	0.02	0.02	0.03	Lower middle to bottom
Montenegro	0.24	0.23	0.15	0.14	0.01	0.01	0.01	0.01	Middle
Serbia	0.43	0.43	0.31	0.38	0.05	0.03	0.04	0.04	Upper middle to middle
WB	0.34	0.274	0.234	0.265	0.038	0.030	0.033	0.035

Source: UNIDO (2020); World Bank (2021)

Spatial Concentration and Regional Specialization of Industry in the Western Balkans

In the comparative analysis of the spatial industrial concentration and industrial specialization in the WB, several measuring tools were applied, such as: the concentration index (CI) based on the modified Herfindahl-Hirschman index, location coefficient and industrial specialization (Balassa index).

To measure spatial industrial concentration, all the applied tools are compared through the pattern of national employment regarding the aggregated values in all countries, though the key tool applied is a new concentration index (CI) (Van Egeraat et al. 2018). Table 4 summarizes the results of regional industrial analysis, namely CI, RCE (spatial concentration of industrial employment index) and RCF (spatial concentration index of industrial enterprises) at the country level (NUTS-1). The values of RCE and RCF (<0.15) indicate an unconcentrated industry in all WB countries, while the value of RCF indicates moderate concentration regarding industrial enterprises only in North Macedonia. The data of the CI index (Table 4) are applied at the NUTS-1 level and sometimes contrasted with the results of LQ analysis. The results of the comparative analysis of industrial spatial concentration dynamics (LQ1) in the WB from 1990 to 2019 are shown in Table 5. In addition, the classification of these areas (NUTS-1) according to LQ1 values using a quantitative scale of values for their typology (Puljiz 2009) is shown in Table 6.

Table 4. A modified HHI indices of spatial concentration of industries in WB (2019).

Country	CI index	RCE	RCF	Number of industrial employ.	Number of manufac. firms (NACE, Rev.2, Sec.C)	Total number of firms	Country surface (km^2)	a (share in surface (%)
Albania	4.24	0.0311	0.0143	220,000	7,961	104,090	28,748	10.44
BiH	2.45	0.0171	0.0086	163,012	6,199	60,794	51,197	18.59
Croatia	9.92	0.0336	0.0972	228,668	20,756	64,043	56,594	20.55
Kosovo*	0.29	0.0004	0.0054	27,125	4,930	35,540	10,887	3.95
N. Macedonia	2.71	0.0117	0.1578	134,863	8,362	75,914	25,713	9.34
Montenegro	0.11	0.0001	0.0014	13,022	2,522	34,707	13,812	5.01
Serbia	29.77	0.1240	0.0565	439,000	15,831	87,407	88,361	32.09
WB	1.0			1,246,337	66,561	462,495	275,312	100.0

Source: Authors’ calculations according to modified HHI concentration index (Van Egeraat et al. 2018), based on publicly available data of the national statistical institutions in the WB (National statistics 2021)

Table 5. Dynamics of the location coefficient, the index of industrial specialization and “regional factor” in WB (1990–2019).

Country	Location coefficient (LQ1)			Index of industrial specialization (Balassa)			RF (Balassa)	Dynamics of changes (Balassa)
	1990	2012	2019	1990	2012	2019	2019/1990	2019/1990
Albania	-	1.283	1.340	-	0.977	1.015	-	-
BiH	1.130	0.749	0.858	1.118	1.062	1.126	0.649	0.008
Croatia	1.249	1.149	0.975	0.900	0.892	0.796	0.758	−0.104
Kosovo*	0.464	0.405	0.262	0.911	1.608	0.866	0.591	−0.045
N. Macedonia	1.127	1.202	1.125	1.035	0.939	0.958	1.186	−0.077
Montenegro	0.948	0.640	0.379	0.833	0.585	0.318	0.448	−0.515
Serbia	1.438	1.040	1.151	1.015	1.108	1.202	0.834	0.187
WB	1.0	1.0	1.0	1.0	1.0	1.0	0.744

Source: Authors’ calculations

Table 6. Typology of NUTS-1 units according to the spatial industrial concentration (LQ1) in WB (1990–2019).

Type of NUTS-1**	Location coefficient LQ1	LQ1 (1990)	LQ1 (2019)
The industry at the beginning	≤ 0.399	-	Montenegro, Kosovo*
A poorly developed industry	0.400‐0.699	Kosovo*	-
Industrial development close to the WB average	0.700‐0.999	Montenegro	Croatia, BiH
Industrial development slightly above WB Average	1.000‐1.300	Croatia, BiH, N. Macedonia	Serbia, N. Macedonia
Industry as main branch	1.601‐2.000	-	-
Strongly developed industry	≥ 2.000	-	-

Source: Authors’ calculations

** Scale toward Puljiz (2009)

In measuring regional specialization, this paper applies the Balassa index (Balassa 1965). It is measured by the relative share of industrial employment in the total employment in relation to the wider territory by location coefficient. The empirical analysis of industrial specialization (using the Balassa Index, LQ2) for the WB indicates the dynamics of regional specialization and territorial differences according to the development of industrial and total employment at the national and regional levels from 1990 to 2019 (Table 5). The results show that the growth of regional specialization was achieved only in Serbia; stagnation of specialization occurred in Bosnia and Herzegovina, Kosovo*, and North Macedonia, while the decline of specialization was a characteristic of Montenegro and Croatia (Table 7). The value of RCA≥1 indicates that a country has reached a higher share of industry, so in this respect it is characterized as a “discovered comparative advantage.” A greater RCA implies stronger advantages. An RCA≤1 implies a detected comparative absence of industry. According to the Balassa Index, the lowest level of RCA and industrial concentration characterize North Macedonia, Kosovo*, Croatia, and Montenegro (Table 8), with the highest level of RCA and spatial concentration of industry in Serbia, Bosnia and Herzegovina, and Albania (Table 5, 7 and 8). A weakness of the regional specialization index is seen in providing an insight into an aggregate state yet with a limited possibility of understanding the industrial or economic structure. Also, in the empirical analysis of the WB, it was impossible to take into account the industrial connectivity, primarily due to insufficient or poorly available input data at the national level statistical sources.

Table 7. The changes in regional specialization of industry (LQ2-Balassa index) in WB, 1990–2019. (t‐2019, ts-1990).

NUTS-1 areas that characterize	Level of LQ2 changes (2019–1990)	NUTS-1
Growth of specialization	LQt‐LQt‐ts>0.1	Serbia
Stagnation of specialization	‐0.1< LQt‐LQts>0.1	BiH, Kosovo*, N. Macedonia
Decline in specialization	LQt‐LQts<‐ 0.1	Montenegro, Croatia

Source: Authors’ calculations

Table 8. Categorization of NUTS-1 areas in WB according to RCA based on LQ2 and RF (1990–2019).

Categorization of type NUTS-1	Scope of RCA**	RCA according to LQ2 (1990)	RCA according to LQ2 (2019)
Industries with a comparative disadvantage	0< RCA< 1	Kosovo*, Croatia, Montenegro, Albania	N. Macedonia, Kosovo*, Croatia, Montenegro
Industries with low comparative advantage	1< RCA< 2	BiH, N. Macedonia, Serbia	Serbia, BiH, Albania
Industries with medium comparative advantage	2< RCA< 4	-	-
Industries with strong comparative advantages	4< RCA	-	-

Source: Authors’ calculations

**According to Hinloopen and van Marrewijk (2001)

Dynamic LQ or the so-called “regional factor” (RF) shows changes in the industrial employment index of individual NUTS-1 compared to the WB in the analyzed period (Tables 5 and 8). It provides insight into the industrialization dynamics in individual countries. Finally, the results of the comparative analysis at NUTS-1 scale are summarized in Table 9. The results show the leading importance of Serbia in the spatial industrial concentration and its regional specialization in the WB.

Table 9. Comparison of the spatial concentration and industrial specialization in WB (2019).

	Concentration index (CI)	Location coefficient (LQ1)	Index of industrial specialization (Balassa index or LQ2)
Albania	4.24	1.340	1.015
BiH	2.45	0.858	1.126
Croatia	9.92	0.975	0.796
N. Macedonia	2.71	1.125	0.958
Montenegro	0.11	0.379	0.318
Serbia	0.307	1.151	1.202
Kosovo*	0.290	0.262	0.866
WB	-	1.0	1.0

Source: Authors’ calculations

Discussion: Fostering the Territorial Dimension of Regional Development, Planning and Governance

Transitional changes and the economic recessions, both globally and in Europe, have highlighted the long-standing structural weaknesses associated with the shrinking industrial base in the WB. The factors behind this development have been the low share of industry, the absence of industrial policy in both the South East Europe Strategy 2020 (RCC 2013) and South East Europe Strategy 2030 (RCC 2021), slow economic growth, and the transitional industrial collapse of SEE.

The quantitative results of a comparative analysis of spatial industrial concentration in the WB based on applying a modified HHI concentration index, location coefficient, and regional industrial specialization indicate a general deterioration of all analyzed indicators from 1990 to 2019. The exception is Serbia as the only country to have increased its regional industrial specialization in the post-socialist period, while Montenegro and Croatia faced a decline in industrial specialization. The stagnation of industrial specialization has characterized Bosnia and Herzegovina, Kosovo*, and North Macedonia.

According to the Balassa Index, industries with comparative disadvantages are present in North Macedonia, Kosovo*, Croatia, and Montenegro. Industries with low comparative advantages exist only in Serbia, Bosnia and Herzegovina, and Albania. Also, there are no industries with medium or strong comparative advantages in the WB. The highest level of spatial concentration of industry was found/measured in Serbia, Bosnia and Herzegovina, and Albania.

Finally, the relevance of the above comparative analysis, and mainly the analytical framework applied, based on two aspects related to the place-based IP, lies not only in its concise and convincing overview of the historical development and current trends in the industrial development of the WB region. By contrast, the framework emphasizes the so-called hybrid domain, which shapes the base for better integration of territorial dimensions into decision-making, planning and governance of the industrial development.

Reflecting upon some of the conceptual debates against and in favor of regional planning (as briefly presented in the conceptual overview, Section 2), the given framework, underpinned by the regional specialization and spatial concentration, in its essence, contains the territorial dimension, hence being a kind of compromise between the previously described two opposing approaches. The framework seems equally suitable for application in more rigid formal regional planning and a “softer” neoliberalized approach (informal regional planning). At the core of our proposed framework is “regional territoriality,” which importance has already been elaborated (Harvey 2005; Allmendinger et al. 2015), considering that territoriality has a significant place in regional planning. Also, it seems that global policies, e.g., the New Urban Agenda (UN 2017) and planning guidelines e.g., Transforming our world: the 2030 Agenda for Sustainable Development (UN 2015) initiate the renewal of regional planning by, for example, strengthening the old concept of city-region relations (Watson 2019), as it has strong implications for development policy (Rodríguez-Pose 2008), especially in the mainstream discourse.

From a more operational perspective, the framework includes several pillars: strategic regional industrial allocation, selection of macro-and-micro-location, territorial capital and preconditions, and possible participation of diverse stakeholders and actors, usually originating from a variety of backgrounds (business, academia, policy arena) and geographies, thus going beyond conventional thinking on different markets, countries, and society. Aimed at generating new sustainable solutions of industrial development, such a framework mainly requires a “bottom up” approach to regional industrial development at the strategic and local levels and the particular investment level. This is especially true for improving the traditional approach to making expertise, e.g., choosing strategic options (regional diversification) and territorial allocation/distribution.

Finally, the practical application of the framework would lead to the following benefits: greater flexibility or cohesion in the regional policy of industrial development and planning of territorial allocation at multi-scalar level; improved basis for a new frame of regional industrial development around the territorial dimension; better territorial cohesion, spatial distribution, and reduce spatial imbalances; new value creation anchored in line with available territorial capital; and better regional balance.

Concluding Remarks

Over the last decade, IP and S3 in Europe have faced various market challenges and failures (Mazzucato 2015), however, some key policies, e.g., the new EU Industrial Strategy (EC 2020a), and the South East Europe Strategy 2030 (RCC 2021) list the following priorities: increased competitiveness, reduced spatial inequality, and a new type of governance focused on the transition of industrial development toward climate-neutral production until 2050 and digital leadership. In addition, a new Just Transition Platform mechanism created to ensure the transition of carbon-intensive regions, also addressing the SEE, includes measures for energy-intensive, sustainable and smart industries.

With this in mind and relying upon the previous analysis of the WB countries’ IP, some essential suggestions for improving regional IP in the WB can be defined. Firstly, although the preparation of S3 has just begun in the WB, the role of S3 should be to foster regional and sectoral benefits by upgrading existing technologies and promoting new ones.

Secondly, aligned to place-based strategies aimed at developing the current advantages in the regions and boosting their territorial capital, place-based measures in future WB IP should encourage a more territorially balanced and cohesive regional growth, such as: using public investments (e.g., in infrastructure) to overcome regional gaps; improvement of innovations and business ecosystems in the lagging regions; the cooperation between more advanced and backward regions; etc.

Thirdly, as industrial production in the WB has been sluggish compared to the EU, the IPs should include measures such as: supporting competitiveness, innovations, productivity, and export; further restructuring; achievement of sustainable/circular production in the most regional industry; supporting an increase of regional industrial specialization and better spatial distribution; improvement of institutional framework; creation of new jobs; more balanced regional territorial development and governance; and development of innovative infrastructure (industrial zones, industrial parks, etc.).

Finally, the regional specialization and spatial concentration as the critical dimensions of the IP emphasize the notion of “territoriality” and, as such, inevitably contribute to elucidating the specific, local aspects of (physical) space, contrary to the traditional approaches that neglected the spatial and/or territorial aspects of regional industrial development. Such a novel understanding of IP contributes to industrial development and, more substantially, to regional planning and governance. In other words, industrial development highlighting the territorial dimension can be seen as a hybrid tool to be implemented in the formal regional planning framework and more informal (neoliberal) planning. Ultimately, such an instrument builds the foundation for elaborating the current spatial processes, offers room for studying novel territory-oriented developmental options, and opens the debate on perspectives of regional industrial development seen in the light of the new European IP (specifically S3).

In a nutshell, under the circumstances of limited opportunities for the development of innovations and bleak prospects for growth not only of high-tech industry but general industrial growth, the WB needs a proactive, comprehensive, and tailor-made IP to achieve faster industrial and economic development. Only in this way can WB hope for industrial specialization and spatialization – if IP becomes a panacea or performs just as a placebo, it remains to be seen. Nevertheless, the framework applied and the empirical results induce various developmental aspects and concepts relevant to post-socialist social, spatial and economic transformation. Finally, the specific and detailed approach may serve as an instrument to explain the overall regional territorial development and study new developmental paths not only in post-socialist countries but also in other developing and developed societies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Statistical sources:

National statistics. 2021. Data from National Statistical Institutions in the WB available at:

http://www.instat.gov.al/media/8006/asn-2019.pdf

http://fzs.ba/wp-content/uploads/2020/12/Godisnjak2020.pdf

https://publikacije.stat.gov.rs/G2020/Pdf/G20202053.pdf

https://www.dzs.hr/

https://www.stat.gov.mk/Publikacii/SG2020/SG2020-Pdf/12-DelovniSubj-BusinessEnt.pdf

https://ask.rks-gov.net/media/5641/vjetari-2020-final-per-web-ang.pdf

https://ask.rks-gov.net/en/Kosovo-agency-of-statistics/economy/national-accounts-gdp

https://www.monstat.org/userfiles/file/biznis%20registar/BROJ%20I%20STRUKTURA%20POSLOVNIH%20SUBJEKATA%20u%20%202,019%20god.pdf

International statistics/World Bank. 2021. Datasets available at:

https://tcdata360.worldbank.org/countries

https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG

https://tcdata360.worldbank.org/indicators/mva.ind.int?country=BRA&indicator=3793&viz=line_chart&years=1990,2014

https://tcdata360.worldbank.org/indicators/h27e52df8?country=BRA&indicator=3787&viz=line_chart&years=1990,2014#comparison-link

https://tcdata360.worldbank.org/indicators/hbe238413?country=BRA&indicator=3794&countries=ALB&viz=line_chart&years=1990,2014

Additional information

Funding

This work was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, grant numbers 451-03-68/2023-14/200006 and 451-03-68/2023-14/200090.

Notes on contributors

Slavka Zeković

Slavka Zeković is Principal Research Fellow at the Institute of Architecture and Urban & Spatial Planning of Serbia, Belgrade.

Ana Perić

Ana Perić is Assistant Professor in the UCD School of Architecture, Planning and Environmental Policy, Senior Researcher at the Institute for Spatial and Landscape Development of ETH Zurich and Senior Research Associate at the Department of Urban Planning, Faculty of Architecture, University of Belgrade.

Notes

1. For Kosovo*, the asterisk is intentionally used throughout this paper to denote the conventional disclaimer: “Designation is without prejudice to positions on status (under UNSCR 1244/99)”. In 2019, FYROM resolved the conflict with Greece over its name and officially gained recognition as North Macedonia.