Unbalanced regional resilience to the economic crisis in Spain: a tale of specialisation and productivity

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1 Cambridge Journal of Regions, Economy and Society 26, 9, doi:.93/cjres/rsv34 Advance Access publication 29 January 26 Unbalanced regional resilience to the economic crisis in Spain: a tale of specialisation and productivity Juan R. Cuadrado-Roura a and Andrés Maroto a,b a Institute of Economic and Social Analysis (IAES), University of Alcalá, Plaza de la Victoria, 2, 2882 Alcalá de Henares, Madrid, Spain, jr.cuadrado@uah.es b Autonomous University of Madrid, Calle de Francisco Tomás y Valiente, 5 Cantoblanco, 2849 Madrid, Spain, andres.maroto@uah.es Received on March 9, 25; accepted on December, 25 The recent economic and financial crisis has generated significant and particularly adverse effects in Spain. All the regions were strongly affected, albeit with notable differences. The aim of this article is not to analyse the impact of the economic and financial crisis on the Spanish regions, but to contribute to an understanding of the resilient behaviour displayed by some regions by examining productive specialisation and its effects on regional productivity as explaining factors. The main conclusion is that the most resilient regions are those that had previously specialised in dynamic and productive industries, such as energy, some manufacturing and some advanced market services. Keywords: economic crisis, regional disparities, productive structures, Spanish economy JEL Classifications: E3, R, R2 Introduction Most European economies are still recovering from one of the deepest crises since the 93s (European Commission, 29). evertheless, the negative effects and the paths towards recovery differ substantially between countries. In the case of the Spanish economy, the effects of the crisis have been particularly severe. On the one hand, the positive economic trend experienced since the 99s masked some structural imbalances low productivity, a growing external imbalance and, among others, high household, corporate and public indebtedness. Activities that were strongly linked to construction and some less productive services such as tourism, real estate, retail commerce and some other final demand services led the previous expansion process and have also led the strong fall of the Spanish economy. Even before the international crisis, some macroeconomic imbalances in the Spanish economy already called for a stabilisation policy. However, the Government did not decide to control public expenditure increases and faced two additional restrictions: (i) a lack of control over monetary policy, decided by the ECB (low interest rates, easy credit to banks, etc.); and (ii) an inability to devaluate the currency (Euro) as a possible shock measure to stabilise an overheating economy (Cuadrado-Roura, 25). Moreover, once the The Author 26. Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved. For permissions, please journals.permissions@oup.com

2 Cuadrado-Roura and Maroto international crisis began, the Spanish authorities did not fully appreciate its severe consequences for the national economy. Starting with an erroneous diagnosis in 28, the Spanish authorities (both central and regional) applied Keynesian demand-side measures in 28, 29 and early 2, increasing public expenditure, social subsidies, etc. The European Commission subsequently pushed for this policy to be changed, in order to give way to the reforms and austerity measures applied from the end of 2. Obviously, in such a context all the Spanish regions were strongly affected, albeit with significant differences. In fact, all regions suffered the impact of the economic crisis but some figures suggest that a group of regions showed a resilience capacity to overcome the effects of the crisis. The aim of this article is not to analyse the impact of the economic and financial crisis on the Spanish regions, which will be briefly described, but to contribute to an understanding of the different behaviours exhibited by some regions compared to others, as well as the factors that may help to explain the positive reaction of those resilient regions. Before proceeding to the analysis, it is important to theoretically define the concept of resilience. This notion initially rose to prominence in several disciplines, from psychology and ecology to engineering, architecture et alia, to describe the effects of and reactions to exceptional and unexpected natural events. Resilience capacities and possibilities have also entered the political and policy discourse, as well as the fields of economic geography and regional studies. However, as pointed out by Simmie and Martin (2) and, more recently, by Martin and Sunley (25), the meaning and relevance of the concept are far from settled matters. From a general point of view, the term resilience is perceived as a positive attribute of an object, entity or system having the capacity to overcome or at least react to some kind of unexpected event. This is why it has been used to analyse how a community positively responds to dramatic natural events (Davies, 2) and/or economic shocks (Hill et al., 28). This idea has been recently extended to understand the reaction of regional economies to major recessionary shocks in the seminal paper by Martin (22). More recently, Martin and Sunley (25) suggested that there is as yet no theory of regional economic resilience as such, which helps us to know what determines the resilience of a region or local economy. They introduce a wider definition of regional economic resilience as: the capacity of a regional or local economy to withstand or recover from market, competitive and environmental shocks to its development growth path, if necessary by developing adaptive changes to its economic structures and its social and institutional arrangements, so as to maintain or restore its previous development path, or transit to a new sustainable path. Although this conceptual discussion could be extended and a good number of empirical works have been developed recently, 2 we take the definition by Martin and Sunley (25) as a starting point for our conceptual framework. Once the economic concept of resilience has been set out, the main objective is to clarify the factors that may help to estimate it. From this point of view, our motivation is to explore some (possible) factors explaining the differing reactions of regional economies. In doing so, we have chosen the relationships between regional specialisation patterns and productivity as an explaining factor. The main contribution of this article is to shed light on the resilience behaviours displayed by some Spanish regions since the last economic crisis in terms of specialisation and productivity, particularly from a sectoral point of view. The group of regions with a faster and stronger recovery are those that previously specialised and reinforced more productive industries, such as some manufacturing, energy and some advanced services. According to this, in the next section the article sets out the differences between the previous footprint and the impact of the economic 54

3 Unbalanced regional resilience to the economic crisis in Spain crisis in the Spanish UTS2 regions, from the mid-99s until the most recent data points. This first section aims to provide an empirically supported answer to the following two questions: (i) how have different regions been affected by the crisis?; and (ii) which regions are proving to be apparently more resilient? From this starting point, our analysis delves particularly 3 deeper into specialisation and productivity (see below). The aim of this latter part of the article is to test the following simple research hypothesis: Resilient regions are those which specialised in more productive sectors before the crisis. This has helped them to make a faster and stronger recovery from the recessionary shock. This question is related to the economic literature relating to productivity and regional resilience using Friedman s so-called plucking model of business cycles (Friedman, 993). According to this model, the path of an economy s growth can be likened to a string attached to the underside of an upward-sloping board, which is plucked downward at irregular intervals by recessionary shocks (Fingleton et al., 22). The board represents a slowly rising upper limit or ceiling set by an economy s productivity. The path is assumed to rebound in each case to the (upward-sloping) ceiling level. The higher the productivity levels displayed by an economy, the higher the upper ceiling reaches, the quicker the recovery from recession shocks will be and the more likely this economy will display resilient behaviour. The final section of the article presents a summary of our conclusions, paying particular attention to the capacity of the analysis carried out, in order to provide a better understanding of the case studied and its possible contribution to the theory and practice of regional economic resilience. The effects of the economic crisis in Spain: uneven regional effects and resilience Obviously, the set of effects deriving from the crisis has significantly influenced interregional disparities in the European Union (EU) and within various member states. Output and employment are the most commonly used indicators for quantifying growth and downturn of economic activity and it seems clear that the crisis has ended a long period in which interregional differences in GDP per capita (in PPS) and unemployment were falling. As can be seen in Figure, part of the 8th Progress Report on Economic and Social Cohesion (European Commission, 23), interregional differences in GDP pc were falling on a regular basis between 2 and 28, but this trend stabilised in 28 and clearly reversed direction in 29 and the following three financial years. As this report lays out, two-thirds of EU regions registered a fall in GDP pc of up to 6% annually between 27 and 2. A similar situation, although even more dramatic, took place in the area of unemployment. From 2 to 27, disparity in the unemployment rate had fallen by around 4 points (index 2 = ). However, as shown in Figure, from 27 onwards, the differences began to increase at a faster rate, so that by the end of 22 the coefficient of variation was higher than in 22. This is one of the worst consequences of the crisis. In fact, four out of every five EU regions recorded unemployment increases from 28 onwards, although some improvements were observed from 2/2 onwards. In Spain, the crisis showed particular traits that the country is very familiar with. The eruption of the international economic crisis and the lack of confidence it generated in the Spanish economy precipitated, from the second quarter of 28, a sharp and severe fall in the economy. The Spanish GDP pc level related to the EU average returned to values last seen 6 years ago. The convergence path stalled in 27 and dropped off until 23. The crisis caused a -point drop in GDP, settling at below 95 at the end of 23. Additionally, from 28 onwards, the unemployment rate began an uninterrupted increase that by the start of 24 reached 25.7% of the active population. 55

4 Cuadrado-Roura and Maroto Figure. The crisis and regional convergence in the EU, Source: Data from the Regional Economic Accounts (ESA2) provided by Eurostat. Own elaborations. Although it initially began in the financial sector, it rapidly extended to all sectors, particularly construction and the numerous related industrial and services activities. A severe economic recession resulted from these factors, reaching a climax in 29 and lasting until almost the end of 23, except for a brief lull in 2. The effects of the macro variables worked quickly: solvency issues in some banking entities and saving banks, businesses closing, a sharp increase in unemployment (mainly unqualified labourers in the construction sector, related industries and routine services), a fall in consumption and private investment, a significant increase of the risk premium, as well as the tax deficit and sovereign debt, etc. All this was exacerbated by a tardy financial and economic policy (supposedly Keynesian) that was the opposite of what the situation required. Only in the middle of 2, as a result of pressure from the European Commission, the IMF and the ECB were the first stabilising measures adopted, followed by, at the end of 2, a cutback and reform policy at the hands of the new Partido Popular government. These policies helped deepen the economic depression in 22 and almost all of 23, although they have helped to stabilise the economy and have allowed some positive economic data to emerge in The most positive aspect of the period, and of course the most important, is the increase in Spanish exports to Europe and the rest of the world, thanks to the recovery of competitiveness and productivity. 5 More than four years of recession and the subsequent adjustment policies applied from 2 have had a very negative effect on the Spanish economy: a considerable number of people have been out of work (5.9 million unemployed at the end of 23, equivalent to a rate of 25%) to the extent that the nearly 3 million jobs created between 2 and 27 have all disappeared. To this we can add the fall in average earnings per Spanish resident, the increase in long-term and youth unemployment, significant cutbacks in social provisions and public investment, cuts of public salaries and a high tax revision (VAT and income tax), among other changes. From a regional point of view, the effects of the crisis in Spain materialised, first, in a very negative turn in interregional GDP pc convergence. Figure 2 shows the results of sigma 6 convergence since 2 onwards (sigma convergence here is defined as the standard deviation of the regional GDP per capita). There 56

5 Unbalanced regional resilience to the economic crisis in Spain (Sigma convergence, regional GDP per capita) Figure 2. Regional convergence in Spain, 2 23 (sigma convergence, regional GDP per capita). Source: Data from the Spanish Regional Accounts (SRA), Rel. 2, provided by the IE. Own elaborations. was interregional convergence until the middle of the decade, but from then on, it stalled and from 28 onwards, reversed direction. The confirmation of a U-shaped curve before and after the crisis, along with the accompanying data, indicates that the differences between regions increased. A deeper analysis of the evolution of individual regional figures in terms of GDP pc and unemployment, which we do not consider it necessary to review in detail here, shows a clear increase of income disparities between regions. As an example, between 28 and 22, the Basque Country (the region with the highest per capita income) increased its difference in income per inhabitant with Extremadura (the region with lowest per capita income) by more than six points. A similar evolution can be observed if we compare the income level of the Basque Country with Andalusia and Castilla-La Mancha at the end of the same period. In any case, it must be pointed out that the economic crisis has also had important negative effects on the Basque Country s economy and other rich Spanish regions, both in terms of negative rate of growth and increasing unemployment. The effect of the crisis on regional unemployment levels has been even more dramatic, albeit uneven. Regions such as Andalusia, traditionally one of the comparatively poorest regions, have tripled their unemployment rate between 27 and 23. The same happened in Aragon, Valencia, Murcia and other less-prosperous regions, especially those worst affected by the construction crisis. Extremadura, which already had the country s highest unemployment rate in 22 (9.2%), reached a high of 32.% in 24. Meanwhile, increases seen in the Basque Country, avarre and La Rioja, richer regions that already had lower unemployment rates, were more contained. This was also the case in Cantabria, Castilla y León, Catalonia and Galicia, although there were some differences that we will not go into here. In general, the regions that suffered the least were the richest, which led to greater disparity in GDP pc, as already noted. Identifying the resilient regions After describing the main national and regional effects of the recent economic crisis in Spain, 57

6 Cuadrado-Roura and Maroto in this section, we are going to identify those regions that have navigated it best and displayed resilient behaviour. As can be seen in Figure 3a and 3b, in the 2 27 period where convergence was experienced, the majority of the poorer regions (or those with lower GDP pc) reached higher levels of growth than richer regions, with the exception of the Basque Country, a region that can be classified as richer, but that experienced rates of GDP pc growth above the Spanish average. Later, between 28 and 23, this behaviour reversed significantly: regions with lower GDP pc showed more negative changes than the average, while the richer regions (although not all of them) performed more positively, among them the Basque Country, avarre, La Rioja and Catalonia. Taking this data into account, more specifically the data regarding GDP pc change, we believe that it is possible to distinguish two Figure 3. Regional GDP per capita and annual growth rate in Spain. ote: AD = Andalusia; ARA = Aragon; AST = Asturias; BAL = Balearic Islands; CA = Canary Islands; CAB = Cantabria; CLE = Castilla y León; CMA = Castilla-La Mancha; CAT = Catalonia; CVA = Com. Valenciana; EXT = Extremadura; GAL = Galicia; MAD = Madrid; MUR = Murcia; AV = avarra; PVA = Basque Country; and RIO = La Rioja. Source: Data from the Spanish Regional Accounts (SRA), Rel. 2, provided by the IE. Own elaborations. 58

7 Unbalanced regional resilience to the economic crisis in Spain large regional groups, although there are of course some internal differences. The first group includes the regions that overcame the economic crisis earlier and became stronger for the experience, and that already had per capita income levels above the Spanish average. This group, as shown in Figure 3a and 3b, includes the regions around the Ebro River (Basque Country, La Rioja, avarre, Aragon and Catalonia) that experienced above-average growth since 27, as well as Madrid and Baleares, 7 which, although they have experienced below-average growth levels in recent years, still show a difference in terms of per capita income in comparison to the remaining Spanish regions. Additionally, these regions also scored a higher competitiveness index 8 according to the regional indicator estimated by Annoni and Dijkstra (23). Hereafter, we refer to this group of regions as resilient ones. The remaining Spanish regions, already experiencing lower per capita income levels before the crisis and unable to catch up with the leading regions, remained below the national average in 23. Within this second group, which we will identify as non-resilient below, differences can also be observed. On one hand, regions like Extremadura, Cantabria, Asturias and, particularly, Galicia and Castilla y León, have shown levels of GDP pc growth above the national average after the crisis, but even so still register levels below the Spanish average. On the other hand, the remaining regions in less favourable positions at the end of the 99s have not only failed to converge but have even diverged from the rest in the intervening years. Thus, the conclusion to the first question introduced at the beginning of the article is that the crisis has clearly affected Spanish regions to a varying extent. There is a small group of resilient regions that have shown a better and more flexible response to the general negative economic conditions. But the next question is: why this uneven regional behaviour? In order to answer this, the following section will analyse the role of production specialisation, structural changes and their impact on regional productivity, focusing on the group of resilient regions. The final aim is, first, to establish if these items can explain along with other factors that have not been analysed here the divergent process observed at a regional level in Spain during the crisis, and second, to find out if the characterisation in terms of these differences has been significantly different between the two groups of regions, something that could also help to explain the differences observed between the two groups in their recovery from the crisis. Productive specialisation as a source of regional resilience We began by discussing the effects of the recent economic-financial crisis on a regional level in Europe and then delved deeper into the Spanish case. The main conclusion that we can extract is that neither the consequences of the crisis nor performance prior to it were homogeneous across regions. The regional convergence in terms of GDP pc experienced during the years of economic expansion drew to a halt from 27 onwards, and the majority of the most resilient and competitive regions displayed the best and quickest recovery in the aftermath of the crisis. The reasons explaining this dichotomous panorama might be varied. They include factors ranging from those related to the need for fiscal consolidation in each region s public sector to the level of leverage in the private sector, as well as factors underlying the positive performance of exports in the resilient regions. However, this article will focus on another possible cause of this phenomenon: the different productive structures and the patterns of regional specialisation and their effects on productivity in the Spanish regions. The conclusion drawn by most studies is that the analysis of productive structures, regional specialisation and their differences in relation to the national average can explain regional economic growth and, naturally, in relation to the possible 59

8 Cuadrado-Roura and Maroto convergence or divergence of the regions in a country in terms of productivity and per capita income. Additionally, the effects of a crisis can differ substantially across industries. Some manufacturing, mining and energy and construction sectors, for instance, tend to be much more affected by business cycles than service sectors (Maroto, 22). Thus, when a region has a relatively large share of industries with a higher-than-average sensitivity to the business cycle, they can ceteris paribus be expected to be significantly affected during a recession (De Groot et al., 2). With this in mind, this section will analyse the specialisation patterns in Spanish regions before and after the crisis. In doing so, it will be important to bear in mind the changes (or absence thereof) observed since 26, as well as how the resilient regions were faring at the beginning of the crisis, in order to favour a quicker and more positive recovery. The aim is to answer the following questions: (i) are there significant differences in productive specialisation patterns between the two groups highlighted in the previous section?; and (ii) have the resilient regions maintained their pre-crisis productive specialisation characterised by higher productivity and less sensitivity to the economic cycle? In order to carry out the analysis, we have used information from a database created by BBVA Research reflecting production data (GDP and value added to current and constant prices), employment and population at a regional level in various economic sectors from 99 until 23. This database links and corrects various errors in the official databases provided by the Spanish ational Statistics Institute s (IE) Regional Accounting. The use of this source also allowed us to work with a wider time frame (99 23), a necessary requisite when studying the role of structural changes, as we will do in the following section. However, this database has one drawback: it does not include sufficient sectoral segregation, splitting the economy into just seven sectors: agriculture, livestock and fishing (AGR); mining, extraction and energy (EEXT); manufacturing (MA); construction (CO); commerce, tourism, transport and communications (CHTC); financial and business services (FIBUS); and finally, non-commercial services, like Public Administration, health and education (SM). Some of the conclusions made here should bear these limitations in mind, while some of the analytical techniques that have been used are not as powerful as they could have been if the database were better disaggregated. Methodologically, the productive specialisation analysis carried out was based on the known specialisation coefficients, which compared the relative weight of a sector within a region with the percentage participation of that sector on a national level. A generic expression of this index would be: IE irt = ir m ir i r= ir () ir r t= t k where i is the sector in question, r the regional indicator and the analysed variable calculated in terms of a specific year t k. This IE irt is always positive. When it exceeds the unit value, we can confirm that region r shows specialisation in sector i for year t. In our case, the variable used to calculate these indicators was employment, segregating for the different Spanish UTS2 regions. Table shows the specialisation coefficients (Equation ) related to total employment for the two regional groups analysed in 99, 26 and 23, as well as the cumulative variation of these coefficients during the reference period. The values shown in Table demonstrate that regional productive specialisation patterns in Spain were already clearly dichotomous at the beginning of the 99s. While resilient regions specialised in areas such as manufacturing and market services, the rest of the regions specialised in construction, non-commercial services and, particularly, extraction, energy and primary sector activities. These industries have higher sensitivity to business cycles and therefore suffered the most during 6

9 Unbalanced regional resilience to the economic crisis in Spain Table. Regional productive specialisation in Spain (IE irt in terms of employment). IE 99 IE 26 IE 23 Variation Variation Inertia a Resilient regions Agriculture and fishing Extraction industry and energy Manufacturing industry Attenuated before the crisis but reinforced after it Construction Specialisation after crisis Commerce and hospitality + transport and communications Other market services De-especialisation before the crisis oncommercial services Rest Agriculture and fishing Extraction industry and energy Manufacturing industry Reinforced before the crisis but attenuated after it Reinforced specialisation Specialisation after the crisis Construction Reinforced Commerce and hospitality + transport and communications Other market services on-commercial services specialisation Specialisation before the crisis and reinforced after it otes: a A specific region is said to reinforce its specialisation when, beginning from a coefficient greater than, its growth rate is positive; it attenuates when, beginning from a coefficient greater than, it is negative. A region specialises when it goes from having coefficients less than to greater than, and it despecialises when the change is the opposite. Source: Data from the Regional Statistics provided by the BBVA Research. Own elaborations. Spain s economic downturn since 27. Thus, the most resilient regions, such as Madrid, the Basque Country, avarre and Catalonia, were already specialised in more dynamic and less sensitive sectors. Additionally, these resilient regions have steadily maintained their specialisation patterns, 6

10 Cuadrado-Roura and Maroto as Bristow (2) suggested, and reinforced their competitive advantages during and after the crisis. This fact has led to a regional divergence since 27. Regarding the inertia in productive specialisation, significant differences can be seen between the two groups of regions. While post-crisis differences are barely noticeable in the richer regions with the single exception of an increase in specialisation in construction and the reinforcement of the manufacturing industry less developed regions saw greater changes. Specialisation in the primary sector, mining and extraction continued to strengthen and, in parallel, there was an increase in specialisation in non-commercial industries that began just before the economic crisis. However, two factors must be kept in mind. First, as mentioned above, the low segregation level applied when performing these calculations: in order to better register these changes, data-sets would be required that segregated branches of activity to a greater extent. This is especially relevant in the services sector, as it comprises many and diverse branches of activity. Second, it is worth noting that this analysis was carried out in relative terms. This means that a given region increases or decreases its level of specialisation in the context of what the remaining regions do, and not in the context of whether or not the economic activity is increasing or decreasing within that specific region. As a result, although it is useful for carrying out statistical comparisons, it can become problematic when analysing time changes, as it can mask smaller, interesting variations that do not manage to constitute trends in themselves. Changes in productive structures and competitive advantages at a regional level as a source of differentiation in Spain The results obtained in the previous section suggest that equality among the productive structures of the regions did not increase during the crisis. Instead, it seems that the changes registered left the regions in similar positions to the ones they were in at the beginning of the studied period. evertheless, it is important to confirm or disprove this assumption. The relative importance of each economic sector in each regional economy can result in a higher level of inequality if those activities become more relevant over time. Therefore, the question that arises is whether the observed change processes allow us to say that equality among the productive structures of Spanish regions has increased or not. In order to do so, we have used a Florencetype geographical association index, which quantifies the difference or similarity in the productive structures of each region, comparing them to the national mean. Its expression is the following: IF r = 2 m ir ir m r= i ir ir r (2) where r indicates the region, i the sector and the studied variable (in this case, employment). The first brackets identify the weight (in terms of employment) of a given sector i over the total of the region, while the second brackets do the same for the entire country. Values approaching zero indicate similar sectoral structures between the region and the nation, whereas values approaching one reflect highly differing sectoral structures. The results of this index (Equation 2) are shown in Figure 4. The solid intermediate line in the figure indicates that, in terms of employment, the productive structure of Spanish regions became progressively less homogeneous after the crisis. The progress towards greater relative homogeneity that had characterised Spanish regions in the 98s practically came to a standstill in the following years, and it was even possible to see a trend towards divergence of regional structures that became more pronounced with the onset of the crisis. The explanation for the events of those years lies, essentially, in the 62

11 Unbalanced regional resilience to the economic crisis in Spain considerable destruction of employment in some highly productive sectors, such as construction and manufacturing. This destruction was more intense in non-resilient regions, thus contributing to explain the evolution of sigma divergence in terms of per capita income analysed in the previous lines. The evolution shown in Figure 4 enables us to highlight other stylised facts. The figure presents some descriptive statistics of the Florence association index. This index quantifies the difference (values closer to ) or similarity (values closer to ) in the productive structure (in terms of employment) of each region, compared to the national average. Thus, the figure illustrates the existence of a large discrepancy between the extreme values: there was a difference of.5 in their respective IF j between the regions that, in 99, had the closest productive structure to the national average (Aragon) and that furthest away from it (Galicia). The coefficient decreases throughout the studied period until it reaches a mean value of.9 in 23, when the least and most unequal regions were Asturias and La Rioja, respectively. Moreover, in most cases, the Florence index estimated for each region decreased from 99 until 26. This indicates that the sectoral productive structures have come gradually closer over time. Only the regions of Murcia and avarre and, to a lesser extent, Castilla-La Mancha have registered an increase in their indices, thus growing farther from the mean overall structure of Spain. However, this process of convergence was slowed down by the onset of the crisis, when all the indices could be seen to start increasing once again. In other words, Spanish regions have faced the economic crisis by differentiating their productive structures and attempting to capitalise on their specialisation advantages, as we will see in the next point. As a result, their behaviour has not been homogeneous and those regions with a productive fabric that was already specialised in more productive, dynamic and open to exterior activities before the crisis have been able to overcome its negative effects sooner. Following the reasoning introduced in this section, our analysis of the change in productive structures and its effect on advantages and disadvantages for Spanish regions concludes with a Figure 4. Florence index of the Spanish regional productive structure (in terms of total employment). ote: They are shown the average of the 7 Florence indices corresponding to the Spanish UTS2 regions since (labelled as average ), both the minimum and maximum of these indices ( min and max ), both percentiles and 9 ( % and 9% ) and, finally, both quartiles and 3 ( 25% and 75% ). Source: Data from the Regional Statistics provided by the BBVA Research. Own elaborations. 63

12 Cuadrado-Roura and Maroto shift-share analysis that breaks down the regional growth of a specific variable in our case, sectoral occupation, which determines the specialisation patterns seen in the previous section following a multiplicative model. Each index or effect can be greater than one (if the region has grown above the national average) or lower than one (if otherwise). The mathematical expression in the analysis of production and employment is as follows: T r = = RE = i i r i i r irt, r r T T irt, r r i r T = T i r i r T i r T r r T = S RE = IM RS (3) (4) where represents the variable analysed (occupation in this case), i represents productive sectors, r corresponds to the regions considered and T and are the final and base reference years, respectively. The national component (S) in Equation (3) measures which part of the total growth of employment in a sector and region can be explained through the aggregate growth of the country it belongs to over the studied period. Meanwhile, the structural component (IM) in Equation (4) identifies the productive sectors of a region with a faster or slower growth rate than the national average. Thus, a region with a percentage that is above the average for dynamic sectors should grow faster compared to another in which low-growth sectors are predominant. The sum of these two components (S + IM) is the growth expected for a specific sector i in a region r. Finally, the regional component or regional competitive advantage (RS) in Equation (4) will be the difference between real and expected growth. That is, it measures the competitive advantage of a specific sector i in a region r, allowing us to identify the leading activity sectors (when the sector in region r grows faster than the national average) compared to other, slower sectors (when the sector in region r grows at a slower rate than the national average). The sum of the structural and regional components (IM + RS) is defined as the aggregate regional effect (RE). Figure 5 summarises the results obtained for Spanish regions in We differentiate between the structural (IM) and regional component (RS) of the total regional effect (RE) that has driven growth in those regions. Likewise, and following the objective of this article, we distinguish between the period before (Figure 5a) and after the crisis (Figure 5b). Tables A2. 3 in the Annex 2 show the average regional numbers when specific sectors are analyzed. In the period before the crisis, the greatest structural changes were observed in the Mediterranean regions, the two archipelago regions (Balearic and Canary) and Madrid (including all the resilient regions). On the other hand, the effects had values lower than one in the central and north-western regions. After the onset of the crisis, those effects became more 64

13 Unbalanced regional resilience to the economic crisis in Spain with some specific regions, such as the Balearic Islands and La Rioja, standing out. This has helped the latter regions to become part of the group that have overcome the crisis sooner. Lastly, if we only compare the two control groups mentioned in the work, the differences in terms of growth and components become evident. On Figure 5. Role of structural changes and regional effects in Spain, (regions clustered by percentiles). Source: Data from the Regional Statistics provided by the BBVA Research. Own elaborations. 65 notable in some resilient regions, such as Madrid, the Basque Country and Catalonia, although they were also higher (albeit, without reaching a unit value) in the central and north-western regions. A significant change has also taken place since the economic crisis, going from a centreperiphery model to a more homogeneous one

14 Cuadrado-Roura and Maroto Figure 5. Continued the one hand, in the period before the crisis, the resilient regions behaved as dynamic regions, with structural (.) and regional (.37) components greater than one. This aggregate effect was greater during the 99s since, after the year 2, it dropped to a value slightly lower than one (.972). On the other hand, during the same period, the non-resilient regions behaved as delayed regions, since both their structural (.967) and regional (.968) effects showed values lower than one. If we only look at the structural effects (IM), we can see that there are no large differences between the two groups of regions in terms of the sign of the effect (although there are differences in their values). Thus, regardless of the regional group, we can refer to non-market services, finance and businesses services as dynamic sectors in terms of job creation, whereas the remaining market services, construction and manufacturing 66

15 Unbalanced regional resilience to the economic crisis in Spain behaved as low-growth sectors throughout the entire period analysed. The main changes were observed in energy and extraction activities, as well as the primary sector, which ceased to be slow-growing sectors after the onset of the crisis, although their positive structural effect has been practically non-existent in recent years. Likewise, in addition to differences between both control groups, after 27 it is also possible to observe significant differences regarding their past behaviour. Thus, after the crisis, resilient regions started to gain localisation advantages, since the value of their total regional effect was greater than one (.5), but only due to the role of the specific regional effect (.2). On the other hand, nonresilient regions suffered productive specialisation problems, as their aggregate regional effect was lower than one (.988) despite their specific regional effect being greater than one. The combination of these two trajectories caused resilient regions to distance themselves from the others in terms of growth, which contributes to explain the diverging trend observed in the section above. These data allow us to conclude that resilient regions have been better able to overcome the crisis because their productive specialisation pattern before the crisis was based on sectors with a low relative employment growth (IM < ), so the considerable loss of jobs suffered by the entire Spanish economy since 26 has not affected these regions to the same extent as those specialising in sectors with a rapid growth of employment. Furthermore, another reason that helps to explain this divergence of rich regions in recent years are the competitive advantages in the mentioned sectors (RS > ) that they displayed before the crisis, as well as their capacity to adapt their productive structure as shown previously to those sectors where they could capitalise on greater regional advantages since 27. Productivity as a key explaining factor of the resilience after the crisis in Spain We must not forget that the relationships be - tween regional specialisation and the capacity of regional economies to face the crisis analysed in the previous section may be mostly explained through the within-productivity of those sectors in which each region specialises, since only by directing productive structures towards highly productive activities is it possible to transform competitive advantages into improvements in the quality of life within a region. To analyse it we will use a shift-share decomposition relying on previous works, 9 although considering regions as the economic decision unit instead countries. This technique provides a convenient tool to investigate how aggregate growth is mechanically linked to differential growth of labour productivity and the reallocation of labour between industries. It breaks down overall productivity growth into two effects: structural changes (SCE) and the within-sector productivity growth (ISE). The first structural component is calculated as the sum of relative changes in the allocation of labour across industries between the final year and the base year, weighted by the value of the sector s labour productivity during the base year. This component is called the static shift effect (SSE in Equation (6)). It is positive/negative if industries with high levels of productivity (and usually also high capital intensity) attract more/less resources and hence increase/ decrease their share of total employment. The standard structural bonus hypothesis postulates a positive relationship between structural change and economic growth as economies upgrade from low to higher productivity industries. It would correspond to an expected SSE >. Second, dynamic shift effects (SDE) are captured by the sum of interactions of changes in employment shares and productivity of individual industries. The interaction term becomes larger, the more resources move towards industries with fast productivity growth. However, the interaction effect is negative if industries with fast-growing productivity cannot maintain their shares in total employment. Thus, the interaction term can be used to evaluate 67

16 Cuadrado-Roura and Maroto the hypothesis of a structural burden of labour reallocation (Baumol, 967). Finally, the within growth effect (ISE) corresponds to growth in productivity under the assumption that no structural shifts have ever taken place. Formally, the method applied here may be derived as follows: π π = r π π rt, r, r, = + + π ( π s ( s s ) T π r, π )( ) s T s T r, ( π π ) T π r, π (5) π r = SSE r + SDE r + ISE r = SCE r + ISE r (6) where π is the labour productivity; is the initial year; T is the final year; i corresponds to each economic sector; r to regions; and s is the sector weight in terms of employment S i = L i /L. The results, according to Equation (5), are displayed in Table 2, broken down into the individual contributions of the seven economic sectors analysed. In line with Equation (6) for the breakdown of overall productivity, the sum of the static and dynamic structural effects (SCE r = SSE r + DSE r ), as well as the intra-sectoral effect (ISE r ), is equal to the average growth rate of labour productivity in the corresponding aggregate (first cell in each sub-table). This is how the data adds up horizontally. Vertically, for each of the three components, the contributions of each sector also adds up to the corresponding figure in the first line of each sub-table. As additional information, the number in brackets shows the average growth of labour productivity within individual sectors or service industries and does not add up either in the horizontal or in the vertical dimensions. The figures allow us to identify whether there are any regular patterns of differential productivity growth between industries. Supported by data from Table 2, some stylised facts can be underlined. First of all, consistent with results obtained by some authors referring to other economic areas and time horizons, the structural components (SCE r ) seem to be generally dominated by the withineffects of productivity growth (ISE r ) both during the entire period (99 23) and after the crisis. This means that, in aggregate terms, the reallocation of labour among those sectors with low and high productivity had a positive role on overall growth in the period before the crisis (SCE = SSE + DSE > ). However, this role seems to disappear after 27 as the SCE components become negative. Secondly, the structural burden of resource reallocation seems to be robust in our case, where the DSE is negative for the broad sevensector breakdown (.8 in average terms), although this effect is the opposite for the years after the crisis in some sectors such as agriculture, mining and extraction and non-market services. Finally, if we analyse the performance by sectors, most of the effects on the overall productivity growth come from non-tertiary activities, especially some manufacturing industries and energy activities. These industries are those with the highest productivity levels in Spain. Also, primary industries showed relatively high intra-sectoral growth rates, although in this case, those increases in productivity represent little more than labour shedding. After the crisis there is evidence of significant within-productivity effects in construction and some market services, such as transport and communications. These are some of the dynamic industries with productivity levels above the national average. Lastly, we performed an in-depth analysis of the differences between resilient and nonresilient regions (Tables 3 and 4, respectively), which was the core aim of this article. On one hand, in non-resilient regions, structural effects played an even more prominent role in the period before the crisis than sectoral internal productivity growth (.7 versus.5 for a mean regional growth of.2 between 99 68

17 Unbalanced regional resilience to the economic crisis in Spain Table 2. Shift share analysis of productivity growth, (average annual growth, 7 Spanish regions). Labour productivity growth Static structural effect (SSE) Dynamic structural effect (DSE) Intra-sectoral effect (ISE) TOTAL.2 = = = = Agriculture (.2) Mining and energy (.39)... Manufacturing (.59).7.4. Construction (.2).5..3 Trade, tourism, transport (.23)...5 and communications Finance and business (.4) services on-market services (.9) pre-crisis TOTAL.7 =.5.9. = = = Agriculture (.9) Mining and energy (.24)... Manufacturing (.32).5..5 Construction (.8).5..2 Trade, tourism, transport (.2).2.. and communications Finance and business (.34) services on-market services (.8) post-crisis TOTAL.2 = = = = Agriculture (.2)... Mining and energy (.3)... Manufacturing (.7).2..2 Construction (.54) Trade, tourism, transport (.22)...5 and communications Finance and business (.).8..2 services on-market services (.8).3..2 Source: Data from the Regional Statistics provided by the BBVA Research. Own elaborations. and 26). However, after the start of the crisis, these gains from structural changes disappeared and most of the mean growth in these regions between 27 and 23 (.3) came from a productivity increase in sectors like energy, manufacturing and some market services, in which these regions previously specialised and that presented not only strong productivity growth rates but also higher levels. These industries are the ones with higher 69

18 Cuadrado-Roura and Maroto Table 3. Shift share analysis of productivity growth, (average annual growth, resilient regions). Labour productivity growth Static structural effect (SSE) Dynamic structural effect (DSE) Intra-sectorial effect (ISE) AVERAGE.28 = Aragon (.3) Baleares (.)..9.2 Cataluña (.3) Madrid (.) avarra (.24) País Vasco (.23) La Rioja (.33) pre-crisis AVERAGE.3 =.9..5 Aragon (.4)..7. Baleares (.2) Cataluña (.)..9. Madrid (.)..7.4 avarra (.6) País Vasco (.8) La Rioja (.5) post-crisis AVERAGE.3 = Aragon (.2)..3.4 Baleares (.2) Cataluña (.) Madrid (.9).8.9. avarra (.6) País Vasco (.2)..4.5 La Rioja (.3) Source: Data from the Regional Statistics provided by the BBVA Research. Own elaborations. capital productivity and efficiency (or improvements in their multifactor productivity). The figures shown in Tables 2 and 3 suggest one of the main conclusions of this article. The Spanish regions that have recovered faster and better from the recent economic and financial crisis are those that had previously specialised in more dynamic industries and sectors. This competitive advantage has allowed resilient regions to exploit their higher productivity potential during the period after the crisis, which has widened the regional productivity gap until the present day (see Figure 6). Final remarks Within the EU, the Spanish economy has been one of the most dramatically affected by the recent international economic and financial crisis. Obviously, the impact of the crisis plus the effects of the austerity policies applied have had a clear impact at the regional level. As pointed out in the second section, one 7