Navigating Turbulence to Ensure Sustainability: The Role of Economic Sectors in Shaping Regional Resilience in Greece Amid the Economic Crisis and COVID-19

Abstract

This study investigates the impact of two consecutive economic shocks—the 2009–2018 economic crisis and the 2019–2022 COVID-19 pandemic—on the resilience and recovery of Greece’s 13 NUTS II-level Administrative Regions. Using secondary data on employment per sector from the Greek Statistical Authority and Eurostat, resilience is assessed through resistance and recovery indices, complemented by shift-share analysis to identify sectoral and local influences on regional performance. Regions with robust agricultural bases displayed consistent resistance and recovery, while tourism-dependent regions transitioned from high to low resistance during the pandemic. Urban centres like Attiki showed enhanced resilience due to concentrated economic activity and local advantages, forming a high-resistance, fast-recovery cluster. Consequently, regions with local advantages are more resilient in times of crisis. Sectors developing locally due to these advantages strengthen economic resilience, whereas dynamic sectors at the national level relying on external environments are more vulnerable, recovering more slowly and impacting host regions. Strong concentration economies alongside local advantages further bolster resilience. Strengthening local economic structures and diversifying activities are crucial for mitigating disparities. This study underscores the importance of tailored sustainable policies to enhance resilience and ensure long-term regional sustainability.

1. Introduction

The most recent crises and shocks that communities have faced, such as the economic crisis and the pandemic, have affected their economies and developmental path. What is interesting is that regions and economies, in general, respond differently to these crises. According to [1] some regions were prepared against these shocks and managed to deal with them. Of course, others failed to deal with them, losing their developmental path. As [2] conclude, there is a heterogeneous pattern of resilience within countries and between the continental northern–central regions and the southern periphery.

The concepts of resilience and sustainability, while distinct, are increasingly recognised as complementary frameworks in addressing environmental and societal challenges. Sustainability is traditionally defined by the triple bottom line—encompassing environmental, social, and economic considerations—to ensure quality of life for current and future generations [3]. Resilience, on the other hand, focuses on the capacity of systems to prepare for, absorb, recover, and adapt to disruptions, whether from acute events like natural disasters or chronic stresses like climate change [4,5]. Although both concepts emphasise system survivability over time, they have a significant difference. Sustainability often operates on larger spatial and longer temporal scales, focusing on future generations [6]. Resilience, on the other hand, emphasises immediate and short-term system responses to threats [7]. Moreover, sustainability prioritises outcomes, such as reduced resource consumption, while resilience focuses on maintaining processes and functionality during disruptions [8,9,10]. According to [11], the association between these two concepts can be understood through three frameworks—more specifically, considering resilience as an element of sustainability, considering sustainability as an aspect of resilience, and considering resilience and sustainability as distinct objectives.

Researchers use the notion of resilience to describe how regions react to shocks. Engineering and ecology were the first fields in which resilience was used. Ref. [12] referred to resilience as the notion that defines how relationships within a system endure over time. It serves as an indicator of a system’s capacity to withstand shifts in state variables, driving factors, and parameters. According to [13] resilience refers to an economy’s capacity to foresee, prepare for, respond to, and recover from economic disruptions. In regional science, researchers used this notion to measure regions’ ability to resist shocks and recover from various crises. Ref. [14] defined resilience as a region’s ability to withstand or recover from economic, competitive, and environmental shocks by adapting its structures and institutions to sustain or transition to a sustainable growth path. Later, ref. [15] described resilience as a region’s response and recovery from shocks. Similarly, ref. [16] viewed it as an economy’s ability to recover from disruptions. Towards this end, ref. [17] defined resilience as a system’s capacity to absorb disturbances and reorganise while maintaining its core function and structure. Ref. [18] highlighted resilience as the ability to recover from, withstand, or avoid adversity.

While crises affect regions in different ways, their response varies based on their unique characteristics and economic structure. Furthermore, the levels of resilience vary, having regions with low or high resilience, depending on various factors. One of the critical questions regarding the regions’ development is why they respond differently to various shocks. More precisely, the question pertains to which characteristics of regions make them more resilient than others. Recent studies indicate that the level of the regions’ readiness varies and is influenced by multiple factors, including regional characteristics [2,19,20,21] and the region’s dominant industry [22,23]. Furthermore, regions demonstrate varying degrees of resilience. High resilience allows a region to recover and achieve higher development, while moderate resilience leads to eventual recovery, possibly at prior or slightly lower levels. In contrast, low or no resilience may cause a region to diverge from its development path.

Ref. [24], for example, concluded that a strong and diverse knowledge stock can make a region more resilient than others, while ref. [2] supported that different types of regions (i.e., urban and rural regions) show different levels of resilience. These findings follow the findings of [25], stating that large urban regions with diversified high-value functions showed more resilience than small rural areas. Ref. [26] examined the economic resilience of rural and urban communities in Romania, finding that urban areas were hit earlier by the crisis, while rural areas recovered faster, though not uniformly. Contrary to expectations, rural communities demonstrated greater overall resilience than urban areas. Yet, variability exists within rural areas. Rural communities near major cities were high performers in terms of post-crisis growth, while isolated less-developed rural areas, though minimally affected by the crisis, experienced slower growth due to their agrarian self-sufficient economies and low global economic integration. Ref. [27] analysed the impact of related variety on industrial resilience in U.S. counties during the 2008 economic crisis. By examining employment data across industries, they assessed resilience based on the ability of counties to maintain or increase new industrial specialisations from 2009 to 2014 compared to 2002–2007. Their results revealed that metropolitan counties were more resilient than others, concluding that related variety positively influenced industrial resilience. Ref. [28] also found that counties with more diverse industry structures performed better during employment shocks, suggesting that the industrial mix of regions contributes to their resilience. Other researchers focus on specific sectors when examining resilience. For example, ref. [29] indicated that economies based on the tourism industry are generally more resilient, since tourism is more flexible and adaptive than other activities. This argument is also supported by other researchers who identified tourism as the most resilient sector of the economy [21,23,30,31]. Moreover, ref. [32] emphasised the fact that metropolitan regions, which are more manufacturing-oriented, seem to be less resilient, while others which are more tourism-oriented or with traditional agricultural production seem to be more resilient. In a similar way, ref. [33] argued that traditional industries tend to be more resilient, whereas modern industries, which are primarily located in large cities, appear more vulnerable to crises. For these urbanised areas, improving infrastructure is recommended to enhance their developmental environment. In contrast, for other regions, promoting a more balanced sectoral structure alongside strengthening human capital and innovative capacity is essential. Ref. [34] examined regional economic resilience in China during the 2008 financial crisis using data from 284 cities (2003–2018). They found that financial development boosted resilience, while government intervention hindered it, especially in smaller resource-dependent regions. Eastern and coastal areas showed higher resilience due to better innovation, human capital, and financial development. In another study, ref. [35] assessed resilience across 31 Chinese provinces, finding significant improvements over time. They identified factors like marketisation, industrial structure, and government intervention, noting regional variations in their effects. Ref. [36] carried out an extensive empirical study on the digital economy’s pivotal role in enhancing urban resilience. Their research showed that advancements in the digital economy strengthen resilience by improving industrial structures and adjusting energy systems. In a similar vein, ref. [37] highlighted how the development of the digital economy and technological innovation positively impact urban resilience, further supporting these findings. Ref. [38] examined the impact of the Innovative City Pilot Policy (ICPP) on urban resilience using a difference-in-differences approach. They found that the policy boosts resilience across economic, social, ecological, and infrastructural aspects, with key factors including increased R&D investment, tech talent attraction, green innovation, and improved information infrastructure.

Various approaches have been developed to assess and measure resilience. For instance, ref. [39] depicted a region’s development trajectory, showing how crises or shocks can disrupt it. Despite this, regions often have the ability to return to their pre-shock growth path, though responses vary depending on resilience levels. Also, the researcher outlined regional economic resilience as a process involving four interconnected aspects as follows: resistance, which refers to a region’s vulnerability to shocks; recovery, representing the time and extent of recovery; structural reorientation, indicating adjustments made by the region; and renewal, the extent to which the region can restore its previous development path. Regions exhibit varying resilience, from high, where recovery leads to even greater development, to low, where development diverges from its original trajectory.

The most recent references collectively emphasise the critical role of sustainability and resilience in mitigating the impacts of recent crises. Ref. [40] used a sustainability city index to analyse the impact of social and environmental dimensions on economic recovery in European cities. They found that cities with stronger sustainability performance in these areas showed better economic outcomes, highlighting the importance of sustainability for urban economic resilience. Ref. [41] highlighted the neglect of sustainable development despite its recognition over three decades ago. The economic branch of construction contributed significantly to carbon emissions and waste and exemplified profit-driven priorities over environmental protection. The study urged a shift from materialism to sustainable practises post-COVID-19. Ref. [42] evaluated the economic resilience of Poland’s provincial cities during the global financial crisis, focusing on labour markets. The study found a moderate negative correlation between employment structure diversification in 2009 and subsequent employment collapse, emphasising the importance of analysing and creating conditions for sustainable development to enhance resilience. Ref. [43] studied the sustainability of tourism start-ups, focusing on intangibles, economic, and financial sustainability across different sectors and regions. Their analysis showed that investment in intangibles and attention to economic and financial sustainability are crucial for resilience during economic crises, highlighting sectoral and regional differences Ref. [44] analysed retail areas in Seoul from January 2019 to August 2020, using sales data. They found time series clustering effectively classified retail areas based on sales changes during COVID-19, revealing patterns of decline and recovery useful for sustainable retail management and economic planning. Ref. [45] assessed Italian regions’ readiness for Industry 4.0 (I4.0) before COVID-19, highlighting disparities between the northern and southern regions. They found that “alert regions” were better positioned for resilience post-crisis, with measures focused on SMEs and collaboration critical for advancing I4.0 adoption and sustainability. Ref. [34] identified factors such as provincial trajectories, economic size, resources, innovation, and financial development as key influences, advocating for region-specific policies to promote sustainable resilience. Similarly, ref. [46] explored regional tourism ecosystems in Russia, emphasising the integration of innovations, including digital tools, for enhancing sustainability.

In order to quantify regional resilience, various indicators are used. For instance, refs. [2,47,48,49] used employment and employment growth rates. Ref. [32] developed an indicator based on socio-demographic, economic, and welfare data.

Similarly, this paper provides a comprehensive overview of regional sustainability and resilience in the face of crises, first during the financial crisis, which resulted in a recession, high unemployment rates, and strict fiscal austerity measures, and then during the COVID-19 pandemic, which further destabilised important economic sectors like tourism, manufacturing, and services, Greece has experienced significant economic disruptions. The necessity for a more thorough comprehension of regional resilience and the variables affecting stability and economic recovery is highlighted by these consecutive shocks. This study aims to address critical research questions that remain insufficiently explored in the literature:

• How do various economic sectors influence Greece’s regional resilience, especially in the face of many economic shocks?

• How does a region’s capacity to recover from crises depend on its sectoral dependencies, and how do these dependencies differ between administrative units?

• How have consecutive crises affected the capacity of Greek regions to recover and adapt, and what structural or policy-related factors contribute to their resilience or vulnerability?

The research aims to offer important insights into the processes of regional economic resilience by methodically examining these concerns. Additionally, by identifying sectoral strengths and vulnerabilities, it seeks to inform policy decisions and aid in the creation of focused initiatives that improve Greece’s long-term sustainability, adaptive capacity, and economic stability. The study examines the 13 Greek NUTS2 regions. Employment data by sector are used to measure the regions’ resilience and recovery. By utilising resilience indicators, the paper seeks to determine whether regions dependent on specific economic sectors exhibit varying levels of resilience. Additionally, shift-share analysis, with its key components—proportional shift and differential shift—will be employed to explain the performance of each cluster. The proportional shift reflects regional sectoral trends, while the differential shift highlights local influences.

2. Methodology

There are many approaches for measuring regional resilience, and several methodologies have been suggested by researchers [2,14,32,47,48,50,51].

This study utilises employment data for 13 Greek NUTS2 regions, obtained from the Greek Statistical Authority and Eurostat, covering the period between 2009 and 2022. The dataset encompasses ten economic sectors. For mathematical calculations, the Excel package from Office 2010 was used. For map design, the software GeoDA 1.22.0.2 (18 August 2023) was utilised.

The research follows a structured methodological framework. First, it identifies two major economic crises that have affected the Greek economy in recent years, namely the financial crisis and the COVID-19 pandemic. Second, it quantifies regional resilience by calculating the resistance index (βres) and recovery index (βrec) based on employment data. Third, it classifies the 13 Greek NUTS2 regions into four clusters according to their resistance and recovery performance. Fourth, it employs shift-share analysis, using proportional and differential shifts to assess regional economic performance. Finally, the study applies Boudeville’s regional classification to formulate policy recommendations.

As a first methodological step, using the approaches of [14,39,47,48], this paper measures the effect of a shock on a regional economy in two consecutive stages as follows:

• The first stage begins when the shock occurs;

• The second stage follows during the recovery process of the region.

Secondary data are used for the time period of 2009–2022. In particular, statistical data from official sources [52,53] provide information regarding regional employment in total and per economic activity. Furthermore, the periods covered are as follows:

• First shock—economic crisis:

• Resistance, which refers to the period of 2009–2015;

• Recovery, which refers to the period of 2016–2018.

• Second shock—COVID 19:

• Resistance, which refers to the period of 2019–2021;

• Recovery, which refers to the period of 2021–2022. (The recovery period should refer to the years 2022–2023 to be in line with previous shocks. However, as there are no published statistics regarding employment per region for the year 2023 yet (not even for the first quarter), the authors made the assumption that the period under study should be 2021–2022.)

Following this, the paper calculates two indices, the resistance index (βres) and recovery index (βrec), as they are estimated using Formulas (1) and (2) [21,39,47,48], applying them to regional employment (NUTS II level) from Eurostat.

(1)${\beta }_{res}={\displaystyle \frac{{\Delta \mathsf{E}}_R-{\Delta \mathsf{E}}_N}{\left|{\Delta \mathsf{E}}_N\right|}}$

(2)${\beta }_{rec}={\displaystyle \frac{{\Delta \mathsf{E}}_R}{{\Delta \mathsf{E}}_N}}$

where ${\Delta \mathsf{E}}_{\mathrm{R}}$ notes the percentage change in the value of variable E in region r. ${\Delta \mathsf{E}}_{\mathrm{N}}$ notes the percentage change in the value of variable E in the nation.

Positive values of βres suggest that the region shows higher “resistance” to a shock compared to the rest of the nation, while negative values imply the region is less resistant than the overall national economy. A zero value indicates that the response of the region to the shock is similar to the national effect.

Positive values of βrec imply that the region performs better than the nation following the recession, while negative values suggest weaker performance in comparison to the overall national economy. A zero value indicates no difference in the performance of the region relative to the national effect after the recession period.

After calculating the two indices, we classified the 13 Greek Administrative Units into four clusters, following the methodology of [2]:

Cluster I comprises regions that have low resistance and slow recovery, i.e., they have negative values in both indicators.

Cluster II comprises regions that have low resistance and fast recovery, i.e., they have negative values in the resistance indicator and positive values in the recovery indicator.

Cluster III comprises regions that have high resistance and slow recovery, i.e., they have positive values in the resistance indicator and negative values in the recovery indicator.

Cluster IV comprises regions that have high resistance and fast recovery, i.e., they have positive values in both indicators.

Then, the application of shift-share analysis will assist in explaining the performance of each cluster according to its sectoral structure and the changes occurred. It is a structural method used in urban and regional economics [33]. More specifically, shift-share analysis attempts to identify the causes of regional economic changes for a specific period of an economic variable for an economic branch or even within all branches of a regional economy (i.e., the productive structure of a region). The results of this method can help us to identify the economic branches where a region has local advantages or even disadvantages compared with the national economy. This in turn will help us to explore in a more systematic way the degree of resilience and recovery of the regions and, furthermore, to propose the appropriate policy means. The form of this method is split in three components [33,47,54] as follows:

(3)${NS}_r=A_{ro}\left({\displaystyle \frac{A_{nt}}{A_{no}}}\right)-A_{ro}$

(4)${PS}_r=\sum \left[\left({\displaystyle \frac{A_{int}}{A_{ino}}}-{\displaystyle \frac{A_{nt}}{A_{no}}}\right)A_{iro}\right]$

(5)${DS}_r=\sum \left[A_{irt}-A_{iro}\left({\displaystyle \frac{A_{int}}{A_{ino}}}\right)\right]$

A: the variable;

i: the economic branch;

r: the region;

n: the nation;

t: the last year of the period of analysis;

o: the first year of the period of analysis.

Based on the goal of the paper, the components of the shift-share analysis are as follows:

1. The National Share (NSr): Here, Greek regions are classified into four clusters based on their resistance and recovery indices, and the performance of regions is influenced by the national economic growth during crises;

2. The Proportional Shift (PSr): Again, Greek regions are classified into four clusters based on their resistance and recovery indices. That component identifies how much a region’s resistance and recovery is affected by specific economic branches. A positive PSr means that dynamic economic branches are located in the examined region of a specific cluster during crises;

3. The Differential Shift (DSr): Here, as in the previous components, Greek regions are classified into four clusters based on their resistance and recovery indices. It portrays how local advantages influence a region’s resistance and recovery. A positive DSr denotes local advantages for the examined region of a specific cluster during crises.

However, in the analysis, only the proportional and differential shift components are utilised. These shifts provide a more focused measure of regional performance and adaptability by highlighting the relative changes in the regions’ economic structures during the examined period of the two crises. This approach offers better insights into regional resilience and the effectiveness of recovery strategies. Furthermore, the paper adopts Boudeville’s regional classification. The specific classification offers valuable guidance for shaping future regional economic policies. The key policy instruments associated with the various regional classification types, as defined by Boudeville, are summarised in Table 1. Additionally, ref. [55] refines this classification by recognising only six types of regions, merging types 1 and 2, as well as types 7 and 8 [56]. Furthermore, this classification can be simplified further in terms of policy instruments by combining types 4 and 5, as well as types 3 and 6 [33].

Figure 1 below presents the 13 Greek NUTS2 regions.

3. Results and Discussion

This section details the analysis. Data refer to the total and per sector employment of economic activity for Greece and the 13 Administrative Units.

The statistics for the employment of the 13 Administrative Units [53], show that Attiki faced a significant decline from 2010 to 2013 (years of the severe economic crisis) and then employment rises. This also applies to Kentriki Makedonia. These two regions have the two major metropolitan centres of the country (Athens, which is the capital of Greece, and Thessaloniki, respectively). On the other hand, in all other regions, employment declined till 2013, and then it has as upward trend, which is interrupted in 2019 due to the COVID-19 crisis.

Figure 2 presents the percentage change in employment during four periods. The first period refers to 2009–2015, during the economic crisis. The second period refers to 2016–2018, after the economic crisis. The third period refers to 2019–2021, during the COVID-19 crisis. Finally, the fourth period refers to 2021–2022, for the post-COVID-19 era. As anticipated, all regions saw a notable decline during the initial period of the economic crisis, though island regions like South and North Aegean experienced a smaller drop. In the third period, employment decreased in certain areas, while mainland regions such as Dytiki Ellada, Thessalia, Dytiki Makedonia, and Attiki recorded an increase.

Figure 3a–j presents the percentage change in employment per economic activity. During the first crisis (period of 2009–2015), there was a decline in employment in all regions in almost all economic activities, with some exceptions. More specifically, an increase in employment is noticeable in the following cases:

• Agriculture, forestry, and fishing in North Aegean, which indicates that this sector was flourishing;

• Industry in the Ionian Islands;

• Wholesale and retail trade, transport, accommodation, and food service activities in South Aegean, probably due to the fact that the dominant sector here is tourism;

• Information and communication in Crete and Peloponnisos;

• Professional, scientific, and technical activities, as well as administrative and support service activities in several regions;

• Public administration, defence, education, human health, and social work activities in South Aegean and the Ionian Islands;

• Arts, entertainment, and recreation, as well as other service activities and activities of households and extra-territorial organisations and bodies in the Ionian Islands.

Between 2016 and 2018, different sectors showed varied employment trends across regions. Sectors like wholesale and retail trade, transport, accommodation, food services, and public administration saw employment growth in most regions. However, some sectors continued to experience declines in specific areas. Overall, the employment trend remained positive.

During the COVID-19 crisis, i.e., the period of 2019–2021, there was no specific trend in employment per sector. Construction and wholesale and retail trade, transport, accommodation, and food service activities seem to be the ones most affected by the pandemic in most of the regions, which of course can be explained by the social distancing measures implemented.

Finally, after the pandemic, employment increased in all regions in wholesale and retail trade, transport, accommodation, and food service activities, and almost in all regions for the other sectors.

Figure 4 and Figure 5 present the classification [2] of the 13 Greek Administrative Units into the clusters described above during the first and second crises. By dividing the relationship into quadrants, this paper can identify the regions that were the most resistant to the recession and have also faced the fastest employment growth after the recession.

As has already been said, Cluster I comprises regions that have low resistance and slow recovery, Cluster II comprises regions that have low resistance and fast recovery, Cluster III comprises regions that have high resistance and slow recovery, and Cluster IV comprises regions that have high resistance and fast recovery.

What is noticeable is that for the first crisis, i.e., the economic crisis, all regions fall either in Cluster II or Cluster IV. This means that they all have positive recovery indicators. More specifically, 6 out of 13 regions belong to Cluster II, meaning they have low resistance and fast recovery. These are the regions of Attiki, Kriti, Dytiki Makedonia, Ipeiros, Thessalia and Dytiki Ellada. Except for Kriti (which has a resilience indicator close to zero, which is why we do not analyse it further), Attiki is the largest metropolitan region of Greece, with a strong tertiary sector. Dytiki Makedonia has made a significant contribution to the agriculture and mining sectors. Ipeiros and Dytiki Ellada are mainly agricultural regions. Finally, Thessalia provides a significant contribution to the agriculture and manufacturing sectors. The remaining seven regions have high resistance and fast recovery and therefore belong to Cluster IV. These are regions that, except for the agriculture sector, also make a significant contribution to tourism and services in general. For the case of the second crisis, i.e., the pandemic, 9 out of 13 regions fall into Cluster II (which means that they have low resistance and fast recovery), one into Cluster III (which means it has high resistance and slow recovery), and three into Cluster IV (which means that they have high resistance and fast recovery). More specifically, in Cluster II are the island regions of Notio Aigaio, Kriti, Ionia Nisia, and Voreio Aigaio, as well as Sterea Ellada, Kentriki Makedonia, Peloponnisos, and Anatoliki Makedonia and Thraki, regions with a significant agriculture sector. In Cluster III is Dytiki Makedonia. Finally, in Cluster IV are the regions of Attiki, Thessalia, and Dytiki Ellada, regions that have significant urban centres. It is noticeable that all island regions and regions whose leading economic branch is tourism belong to Cluster II.

By comparing the behaviour of the regions in the two examined crises (economic and COVID-19) regarding the above classification, we can conclude that a significant differentiation is observed.

Figure 6 presents a comparative classification of regions per type of crisis, and Table 2 presents a transition matrix of the regions between the first and second crisis. Based on the analysis of data and the classification, we can conclude the following:

• Most regions belonging to Cluster II during the first period under study moved to Cluster IV during the second period under study, showing an increase in resistance and recovery.

• All regions belonging to Cluster IV during the first period under study moved to Cluster II during the second period under study, showing a decrease in resistance.

• During the first period, island regions belonged to Cluster IV.

• During the second period, island regions belonged to Cluster II.

• During the second period, regions with significant urban cities belonged to Cluster IV.

According to Table 3, which refers to the shift-share results, four regions in the specific cluster (high resistance and fast recovery) exhibit a positive proportional shift component. However, all seven regions in the cluster during the economic crisis period have a positive differential shift component. The regions with both a positive total differential shift component and a positive total proportional shift component are the mainland regions Anatoliki Makedonia–Thraki and Kentriki Makedonia, as well as the insular regions Voreio Aigaio and Ionia Nisia. Economic branches such as “Agriculture”, “Industry”, “Trade and hospitality services”, “Professional and technical activities”, and “Public administration and social services” have a crucial role in driving the positive total proportional and total differential shift components in these regions.

In Table 4 below, the remaining six regions of Greece are in Cluster II (low resistance and fast recovery). Only the regions of Attiki and Thessalia exhibit a positive proportional shift component. This is influenced by the presence of dynamic branches such as “Trade and hospitality services”, “Professional and technical activities”, and “Public administration and social services”. Meanwhile, a positive total differential shift component is observed only in the island region of Kriti. Here, branches such as “Trade and hospitality services” and “Public administration and social services” benefit from the local advantages that region demonstrates during the economic crisis period.

Based on Table 5, only one of the three regions in this cluster (high resistance and fast recovery) shows a positive proportional shift component. However, all three regions in the cluster during the COVID-19 period exhibit a positive differential shift component. The sole region with both a positive total proportional shift component and a positive total differential shift component is the mainland region of Attiki, where the country’s capital, Athina, is located. Here, mainly economic branches from the tertiary sector play a key role in contributing to the positive total proportional and total differential shift components in this region.

In Table 6, nine regions of Greece belong to Cluster II (low resistance and fast recovery), while one region, Dytiki Makedonia, is part of Cluster III (high resistance and slow recovery). Among these, only two regions, Ipeiros and Dytiki Makedonia, exhibit the smallest positive proportional shift component in comparison with the regions in the previous clusters during the economic crisis. Additionally, a positive total differential shift component is observed in the regions of Kentriki Makedonia and Sterea Ellada. In Kentriki Makedonia, branches like “Trade and hospitality services” thrive, while in Sterea Ellada, branches such as “Agriculture” and “Arts, entertainment, and other services” benefit from the local advantages demonstrated during the COVID-19 period.

As shown in Figure 7 below, a few regions do not require any of the proposed regional policy means. Specifically, only four out of the thirteen regions during the economic crisis period and only one out of the thirteen during the COVID-19 period do not require such means.

The classification of these regions into the related clusters reveals that many need either infrastructure strengthening or sectoral restructuring or a combination of both of these proposed regional policy measures. According to Boudeville’s regional classification system (Table 7), the regions in Cluster IV demonstrate significant resilience during both the economic crisis and the COVID-19 period. Specifically, during the economic crisis, the regions of Voreio Aigaio, Anatoliki Makedonia–Thraki, Kentriki Makedonia, Ionia Nisia, Notio Aigaio, Sterea Ellada, and Peloponnisos showed notable resilience. However, their recovery requirements differ, reflecting varied structural capacities. Notio Aigaio, Sterea Ellada, and Peloponnisos require sectoral restructuring to adapt their economic structures to emerging demands and sustain their recovery trajectories. In contrast, regions from the remaining clusters face distinct challenges, highlighting the disparities in resilience and development needs. During the economic crisis, key regions such as Attiki and Thessalia required infrastructure strengthening, while Kriti focused on sectoral restructuring. Dytiki Makedonia, Ipeiros, and Dytiki Ellada face the dual challenge of needing both infrastructure improvements and sectoral restructuring to address their structural weaknesses and adapt to post-crisis demands. This illustrates their reliance on external interventions for recovery and underscores their lower resilience compared to Cluster IV regions. During the COVID-19 period, the dynamics shift slightly, with Attiki, the metropolitan region of Greece, demonstrating remarkable adaptability by requiring neither infrastructure strengthening nor sectoral restructuring. This indicates its robust economic framework and capacity to withstand disruptions. However, other Cluster IV regions like Thessalia and Dytiki Ellada show a need for sectoral restructuring, highlighting their reliance on targeted interventions to sustain recovery and growth. Regions from the rest of the clusters exhibit varied needs during the COVID-19 crisis. Dytiki Makedonia and Ipeiros continue to require infrastructure strengthening to enhance their economic capacity. Sterea Ellada and Kentriki Makedonia focus on sectoral restructuring to align with emerging economic opportunities. Several regions, including Voreio Aigaio, Notio Aigaio, Kriti, Anatoliki Makedonia–Thraki, Ionia Nisia, and Peloponnisos, face the dual requirement of both infrastructure strengthening and sectoral restructuring. This reflects significant structural vulnerabilities and the need for comprehensive development strategies to ensure long-term resilience and adaptability. In summary, the differentiated needs of these regions, as identified through Boudeville’s classification, underscore the importance of tailored policies that address both infrastructure deficits and sectoral realignments. This approach is essential for fostering balanced regional development and ensuring equitable recovery across Greece during periods of crisis.

4. Discussion

The findings of this study align with and extend the existing literature on regional resilience, shedding light on how structural characteristics influence the response to economic shocks. Consistent with [2], this study confirms the heterogeneous resilience across Greek regions, with significant variation in their resistance and recovery capacities during the economic crisis and the COVID-19 pandemic. The unique responses of island regions showed stronger resistance during the economic crisis but diminished resilience during the pandemic; during the first period, island regions belonged to Cluster IV, while during the second period, island regions belonged to Cluster II. This echoes the observations of [33], who highlighted the vulnerability of tourism-dependent regions. Furthermore, the challenges faced by tourism-dependent regions during the pandemic support [29] argument that tourism industries, while flexible, are not immune to shocks of this magnitude. Similarly, the superior recovery of regions with urban centres like Attiki underscores the findings of [25], stating that large diversified urban economies are more resilient than smaller rural ones; during the second period, regions with significant urban cities belonged to Cluster IV.

The shift-share analysis revealed intriguing patterns in resilience influenced by sectoral compositions. The positive proportional and differential shifts observed in some regions during the economic crisis, driven by a number of dynamic economic branches, mirror the findings of [27], who demonstrated the role of industrial diversity in bolstering resilience. More specifically, economic branches such as “Agriculture”, “Industry”, “Trade and hospitality services”, “Professional and technical activities”, and “Public administration and social services” have a crucial impact in driving the positive total proportional and total differential shift components in these regions of Cluster IV during the economic crisis, while the sole region with both a positive total proportional shift component and a positive total differential shift component is the mainland region of Attiki during COVID-19. Here, economic branches from the tertiary sector are pivotal in contributing to the positive total proportional and total differential shift components in this region.

These results reaffirm the need for infrastructure development and human capital strengthening, as suggested by [19,43] to promote balanced sustainable regional development. Future research should delve into finer spatial dynamics and integrate sustainability indicators, enabling tailored policies to foster resilience. By addressing these gaps, policymakers can better prepare regions for future shocks while promoting sustainable economic growth.

5. Conclusions

This study assessed the resilience of Greek regions to consecutive economic shocks, focusing on the economic crisis and the COVID-19 pandemic. The findings highlighted that regional responses varied significantly, driven by structural differences and dominant economic sectors. Insular regions initially showed greater resistance during the economic crisis but struggled during the pandemic, whereas regions with urban centres such as Attiki demonstrated robust resilience, underscoring the importance of sectoral alignment and adaptability. Shift-share analysis revealed the differential contributions of agriculture, industry, and services, with certain regions exhibiting strong recovery capabilities without substantial policy interventions. In contrast, other regions, particularly those in Cluster II, faced structural vulnerabilities requiring targeted interventions, such as infrastructure investments and sectoral diversification.

In conclusion, regions with local advantages seem to be more resilient in times of crisis. That is, sectors that develop locally due to local advantages strengthen economic regional resilience, while dynamic sectors at the national level that depend more on the external (outside the region) development environment are more vulnerable and recover more slowly, affecting the regions that host them. Also, the existence of strong concentration economies, in parallel with the existence of local advantages, strengthens regional resilience. The need for balanced regional development became evident, aligning with the principles of sustainable development to ensure equitable recovery and long-term economic stability.

The research findings offer critical insights into the resilience of Greek regions to consecutive economic shocks, with broader implications for countries facing similar crises. Understanding the differential regional responses to the economic crisis and the COVID-19 pandemic emphasises the need for strategies that consider local economic structures, sectoral composition, and vulnerabilities. These insights can be used by policymakers to apply focused interventions that support long-term economic stability and sustainable regional development. Policymakers should focus on a balanced approach that promotes sectoral diversification, infrastructure development, and sustainability while addressing regional disparities.

Based on the research findings, several key recommendations emerge for policymakers as follows:

• Promote economic diversification to reduce dependency on a particular industry and mitigate vulnerability to future shocks;

• Encourage investments in infrastructure that has the potential to improve regional resilience, such as transportation, digital networks, and renewable energy;

• Make use of local assets, such as agricultural resources or special geographic features, to boost resilience and guarantee self-sustaining economic growth.

The study’s limitations, including the absence of recent post-pandemic data and a lack of sector-specific orientation analysis, present opportunities for future research. Expanding the analysis to a finer spatial resolution and integrating sustainability indicators would provide actionable insights for policy design. Future studies could focus on intra-regional disparities and sector-specific resilience to develop tailored strategies that promote sustainable economic growth. This approach is essential to address regional disparities effectively and build resilient economies capable of withstanding future shocks while adhering to sustainable development principles.

Author Contributions

Conceptualization, M.C.; Methodology, E.G., M.C. and A.G.; Software, A.G.; Formal analysis, E.G. and A.G.; Investigation, E.G. and A.G.; Resources, A.G.; Writing—original draft, E.G. and A.G.; Writing—review & editing, M.C.; Supervision, M.C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

We obtained the data used in our article calculations from the official statistical sources of Eurostat (https://ec.europa.eu/eurostat, accessed on 8 May 2023), and Hellenic Statistical Authority (https://www.statistics.gr, accessed on 14 May 2023). The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Greek NUTS II regions.

Figure 2. Percentage change in employment per region. Source: [53].

Figure 3. Percentage change in employment per economic sector. Source: [53]. (a) Agriculture, forestry, and fishing. (b) Industry (except construction). (c) Construction. (d) Wholesale and retail trade, transport, accommodation, and food service activities. (e) Information and communication. (f) Financial and insurance activities. (g) Real estate activities. (h) Professional, scientific, and technical activities; administrative and support service activities. (i) Public administration, defence, education, human health, and social work activities. (j) Arts, entertainment, and recreation; other service activities; activities of households and extra-territorial organisations and bodies.

Figure 3. Percentage change in employment per economic sector. Source: [53]. (a) Agriculture, forestry, and fishing. (b) Industry (except construction). (c) Construction. (d) Wholesale and retail trade, transport, accommodation, and food service activities. (e) Information and communication. (f) Financial and insurance activities. (g) Real estate activities. (h) Professional, scientific, and technical activities; administrative and support service activities. (i) Public administration, defence, education, human health, and social work activities. (j) Arts, entertainment, and recreation; other service activities; activities of households and extra-territorial organisations and bodies.

Figure 4. Employment resistance and recovery indices for the first crisis. Source: [53], own elaboration.

Figure 5. Employment resistance and recovery indices for the second crisis. Source: [53], own elaboration.

Figure 6. Classification of regions per type of crisis.

Figure 7. Resilient clusters in Greek regions during economic crisis and COVID-19 period. Source: [53], own elaboration.

Table 1

Regional types according to the classification of Boudeville.

Regional Classification Type	Relative Change (Relation Between PS and DS)				Means of Regional Policy
1	PS > 0;	DS > 0	and	|PS| > |DS|
2	PS > 0;	DS > 0	and	|PS| < |DS|
3	PS > 0;	DS < 0	and	|PS| > |DS|	infrastructure strengthening
4	PS < 0;	DS > 0	and	|PS| < |DS|	sectoral restructuring
5	PS < 0;	DS > 0	and	|PS| > |DS|	sectoral restructuring
6	PS > 0;	DS < 0	and	|PS| < |DS|	infrastructure strengthening
7	PS < 0;	DS < 0	and	|PS| > |DS|	sectoral restructuring, infrastructure strengthening
8	PS < 0;	DS < 0	and	|PS| < |DS|	infrastructure strengthening, sectoral restructuring

Table 2

Transition matrix of the regions between the first and second crisis.

		2nd Crisis—COVID-19
Number of Regions		Cluster I	Cluster II	Cluster III	Cluster IV	Sum
1st crisis—Economic	Cluster I
	Cluster II		2	1	3	6
	Cluster III
	Cluster IV		7			7
	Sum		9	1	3	13

Table 3

Proportional and differential shift in Cluster IV during economic crisis.

Proportional Shift
	A	B	C	D	E	F	G	H	I	J	Total
Anatoliki Makedonia–Thraki	2.78	−2.80	−6.75	3.67	0.37	−0.32	0.00	1.24	5.03	−1.02	2.20
Voreio Aigaio	0.42	−0.59	−2.28	1.44	0.00	−0.14	0.00	0.38	1.95	−0.38	0.80
Kentriki Makedonia	4.24	−11.79	−23.64	14.87	2.78	−1.15	−0.54	6.98	14.04	−5.02	0.75
Ionia Nisia	0.70	−0.45	−4.04	2.45	0.00	0.00	0.00	0.72	1.10	−0.45	0.02
Peloponnisos	3.22	−2.37	−8.81	3.93	0.34	−0.26	0.00	1.14	3.61	−1.35	−0.53
Notio Aigaio	0.42	−1.41	−6.45	3.89	0.00	0.00	0.00	1.03	2.24	−0.74	−1.01
Sterea Ellada	1.82	−4.47	−9.15	3.45	0.00	−0.31	0.00	1.21	3.46	−0.97	−4.97
Differential Shift
	A	B	C	D	E	F	G	H	I	J	Total
Kentriki Makedonia	11.39	8.16	−2.49	−1.21	0.72	0.04	−0.80	−7.19	9.58	0.03	18.22
Sterea Ellada	4.32	4.27	−1.41	7.91	0.00	−0.02	0.00	2.88	−0.19	0.32	18.08
Notio Aigaio	−2.81	−0.74	2.67	14.45	0.00	0.00	0.00	2.75	1.66	−0.39	17.59
Anatoliki Makedonia. Thraki	−1.10	4.09	−1.02	5.13	−1.54	−0.50	0.00	3.58	3.00	1.31	12.96
Peloponnisos	−8.24	2.03	2.22	3.80	−1.43	−0.37	0.00	1.66	4.82	5.23	9.71
Voreio Aigaio	3.30	1.14	0.93	3.06	0.00	−1.20	0.00	1.55	0.06	−0.46	8.38
Ionia Nisia	−6.84	0.12	0.66	6.69	0.00	0.00	0.00	−0.30	2.27	0.59	3.18

A: Agriculture, forestry and fishing; B: industry (except construction); C: construction; D: wholesale and retail trade, transport, accommodation, and food service activities; E: information and communication; F: financial and insurance activities; G: real estate activities; H: professional, scientific, and technical activities; administrative and support service; I: public administration, defence, education, human health and social work activities; J: arts, entertainment, and recreation; other service activities; activities of households and extra-territorial organisations and bodies.

Table 4

Proportional and differential shift in remaining clusters during economic crisis.

Proportional Shift
	A	B	C	D	E	F	G	H	I	J	Total
Attiki	0.71	−26.98	−55.10	36.14	16.74	−6.41	−1.49	22.98	36.29	−16.40	6.47
Thessalia	2.63	−4.61	−8.94	4.86	0.40	−0.35	0.00	2.10	6.67	−1.44	1.32
Dytiki Ellada	2.68	−2.80	−10.44	4.94	0.58	−0.47	0.00	1.61	5.08	−1.33	−0.16
Kriti	1.96	−2.69	−12.34	5.78	0.61	−0.37	0.00	2.31	4.45	−1.21	−1.49
Ipeiros	1.12	−1.59	−6.06	2.30	0.00	−0.17	0.00	0.75	2.92	−0.77	−1.50
Dytiki Makedonia	0.90	−2.55	−3.91	1.61	0.00	0.00	0.00	0.47	2.03	−0.44	−1.90
Differential Shift
	A	B	C	D	E	F	G	H	I	J	Total
Kriti	5.47	−0.29	0.47	12.33	0.86	−0.27	0.00	−0.87	7.40	1.23	26.34
Ipeiros	−7.43	−2.11	0.44	3.67	0.00	0.48	0.00	1.21	2.43	0.07	−1.24
Dytiki Makedonia	0.05	0.36	−0.62	−3.01	0.00	0.00	0.00	0.07	0.81	0.16	−2.18
Thessalia	−3.95	−4.58	1.30	4.53	−0.05	1.18	0.00	0.30	−3.46	1.14	−3.59
Dytiki Ellada	6.17	−1.01	−0.33	−2.96	−0.33	−1.10	0.00	−3.27	−3.26	−0.60	−6.69
Attiki	−0.31	−11.44	−2.81	−54.40	1.78	1.76	0.80	−2.38	−25.13	−8.61	−100.73

Table 5

Proportional and differential shift in Cluster IV during COVID-19 period.

Proportional Shift
	A	B	C	D	E	F	G	H	I	J	Total
Attiki	−0.56	5.12	−2.79	−21.32	0.31	−9.58	6.50	12.86	15.81	4.68	11.03
Thessalia	−1.82	0.73	−0.56	−3.15	0.00	−0.68	0.00	1.13	3.00	0.48	−0.87
Dytiki Ellada	−2.18	0.53	−0.64	−2.74	0.01	−0.35	0.00	0.56	2.17	0.38	−2.27
Differential Shift
	A	B	C	D	E	F	G	H	I	J	Total
Attiki	−0.29	−11.13	0.87	8.15	−0.69	5.72	−2.30	2.98	33.50	−1.12	35.68
Dytiki Ellada	−1.84	−0.76	−2.18	9.79	−0.87	0.34	0.00	8.08	−0.83	2.59	14.32
Thessalia	−7.82	12.18	−1.79	1.22	2.40	−1.73	0.00	−0.67	−2.74	1.10	2.15

Table 6

Proportional and differential shift in remaining clusters during COVID-19 period.

Proportional Shift
	A	B	C	D	E	F	G	H	I	J	Total
Ipeiros	−0.63	0.19	−0.31	−1.57	0.00	0.00	0.00	0.71	1.48	0.28	0.16
Dytiki Makedonia	−0.66	0.49	−0.25	−0.87	0.00	0.00	0.00	0.33	0.93	0.19	0.16
Voreio Aigaio	−0.45	0.10	−0.13	−1.13	0.00	0.00	0.00	0.39	1.00	0.12	−0.10
Anatoliki Makedonia–Thraki	−1.73	0.81	−0.24	−2.68	0.01	−0.28	0.00	1.00	2.51	0.33	−0.28
Kentriki Makedonia	−3.32	2.96	−0.97	−9.17	0.06	−1.51	0.00	3.77	6.46	1.33	−0.40
Ionia Nisia	−0.24	0.08	−0.25	−1.61	0.00	0.00	0.00	0.50	0.67	0.19	−0.66
Sterea Ellada	−1.45	1.23	−0.35	−2.57	0.00	−0.49	0.00	0.81	1.51	0.23	−1.08
Peloponnisos	−1.98	0.54	−0.42	−2.81	0.01	−0.42	0.00	0.74	1.90	0.78	−1.66
Notio Aigaio	−0.25	0.22	−0.47	−3.22	0.00	0.00	0.00	0.74	0.99	0.23	−1.77
Kriti	−1.55	0.63	−0.66	−4.30	0.01	−0.44	0.00	1.16	2.41	0.48	−2.26
Differential Shift
	A	B	C	D	E	F	G	H	I	J	Total
Kentriki Makedonia	−2.63	0.94	0.32	11.50	−1.02	−1.55	2.30	−6.74	−1.09	0.79	2.82
Sterea Ellada	7.21	−6.29	1.61	−0.98	0.00	−2.46	0.00	−2.04	0.11	5.07	2.23
Ipeiros	3.19	8.17	−1.09	−1.89	1.50	0.00	0.00	−3.68	−4.53	−1.84	−0.17
Dytiki Makedonia	2.48	−4.76	−1.69	2.64	0.00	0.00	0.00	−1.34	−2.42	2.13	−2.97
Voreio Aigaio	0.05	−0.18	0.40	−0.41	0.00	0.00	0.00	−0.45	−4.38	0.58	−4.39
Ionia Nisia	0.22	0.59	2.21	−0.90	0.00	0.00	0.00	−0.73	−6.17	−0.37	−5.15
Peloponnisos	−1.67	1.50	1.31	0.58	−1.59	−0.11	0.00	3.48	−4.35	−7.46	−8.31
Notio Aigaio	4.91	−0.21	−0.49	−13.60	0.00	0.00	0.00	−1.02	2.34	−1.53	−9.59
Anatoliki Makedonia. Thraki	−3.69	−2.63	0.61	−5.90	0.32	0.49	0.00	0.75	−1.97	−0.32	−12.33
Kriti	−0.12	2.57	−0.08	−10.20	−0.05	−0.70	0.00	1.38	−7.45	0.39	−14.27

Table 7

Regional policy means between the two examined periods for each cluster.

Cluster IV	Economic Crisis		Cluster IV	COVID-19
	Infrastructure Strengthening	Sectoral Restructuring		Infrastructure Strengthening	Sectoral Restructuring
Voreio Aigaio			Attiki
Anatoliki Makedonia, Thraki			Thessalia		√
Kentriki Makedonia			Dytiki Ellada		√
Ionia Nisia			Remaining Clusters	Infrastructure Strengthening	Sectoral Restructuring
Notio Aigaio		√
Sterea Ellada		√	Dytiki Makedonia	√
Peloponnisos		√	Ipeiros	√
Rest Clusters	Infrastructure Strengthening	Sectoral Restructuring	Sterea Ellada		√
			Kentriki Makedonia		√
Attiki	√		Voreio Aigaio	√	√
Thessalia	√		Notio Aigaio	√	√
Kriti		√	Kriti	√	√
Dytiki Makedonia	√	√	Anatoliki Makedonia, Thraki	√	√
Ipeiros	√	√	Ionia Nisia	√	√
Dytiki Ellada	√	√	Peloponnisos	√	√

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