Volume 45 Number 176,
January-March 2014
The Impact of the Crisis on the Economic Development
of Mining Regions in Europe
Sergio A. Berumen *
Date received: April 18, 2013. Date accepted: September 3, 2013
Abstract

This work studies the effect of the crisis on the economic development of mining regions in European Union (eu) member countries where activities in this sector have been key to development. The methodological approach consisted of identifying the evolution of four socioeconomic variables in three representative years (2000, 2008 and 2011): 1) Consumption, 2) Foreign direct investment, 3) Public spending and 4) Net exports. The analytic hierarchy process (ahp) was used to conduct this study.

Keywords: Mining regions, socioeconomic variables, the economic crisis in Europe, ahp.
INTRODUCTION

In recent years, various historically competitive productive sectors have proved unable to adapt to the conditions currently dictated by globalized markets, especially since the outbreak of the crisis now afflicting European economies. Perhaps the two most representative cases would be the mining and shipbuilding industries. Throughout the nineteenth and twentieth centuries, both sectors were important poles of development for many countries in Europe. Regions specializing in mining exploitation and shipbuilding were effectively powerful motors driving local economic growth, especially when compared to areas specialized in agriculture and fishing, or the textile or services industries.

With an awareness of the importance of mining, eu institutions over the past two decades have granted greater subsidies, believing that maintaining these sectors would be fundamental to improving the quality of life for the region's residents and strengthening economies. Articles 268 to 280 of the Treaty on the European Community describe the activities and policies that shall be subject to financing. Mining aid was institutionalized in the framework of ecsc Decision 3632/93 on December 28, 1993.1 However, the development of mining regions in Europe has proceeded gunequally. There are examples of success among the set of community countries, but there are also rotund failures, which have depended on six principal factors. First, the specific capacity of each of the government teams to obtain community assistance. Second, the relative importance of mining for each of the governments, which in practical terms, has meant how governments orient the public resources of overall state budgets to items related to the mining sector, developing their regions and building infrastructure. Third, proper investment of resources in profitable projects and companies in the medium and long terms. Fourth, the value of minerals extracted in international markets. Fifth, production costs, including labor and technology requirements that companies need to operate. Finally, the concrete strategies that have allowed some companies to be more competitive.

With that in mind, this work seeks to study the effects of the current crisis on economic development in mining regions of community and member countries of the Euracom (Association of European Coal Mining Regions), where these activities have determined local development, as well as four socioeconomic indicators selected for three representative years: 2000, 2008 and 2011. This choice was due to four relevant arguments. First, the European economy experienced a significant phase of economic growth between 2000 and mid-2008. Second, between 2000 and 2008, mining regions received resources through eu aid. Third, the largest addition to the eu took place in 2004, as the union went from 15 to 25 members, and in 2007, the number rose again to 27, some of which have very important mining regions. Finally, 2007-2013 community budgets maintained aid and subsidies to regions specialized in mining, which were decisive in ensuring that mining regions would record significant growth. However, starting in the second quarter of 2008, Europe began to face one of the most severe crises of its history, and this has naturally had negative repercussions on all sectors of the economy. Obviously, the mining-intensive regions have been no exception.

In pursuit of the objective described above, this research has drawn on two major groups of works. The first include works from Goolsbee (2004), Kemfert and Diekmann (2006), Berumen and Llamazares (2007), Haftendorn and Holz (2008), Tavoni and Van der Zwaan (2009), Schreiber et al. (2010), Scott (2010), Llamazares and Berumen (2011) and Berumen (2012), among others. All of these focus on the evolution of the mining sector in Europe. The second group encompasses works by Mishra and Nayak (2005), Frondel et al. (2006), Barbu (2010) and Puppim de Oliveira and Ali. The latter set studies the effects of mining exploitation on the development of regions and locales in various countries.

This work is structured into two sections. The first describes the research units and data analysis, showing the European mining regions studied and justifying the four variables of interest. The second part is centered on the empirical work; the analytic hierarchy process (ahp) was used for this purpose, which allowed each region to be assigned a value that corresponded to its behavior with regards to the scale chosen. The program used to apply the ahp was Expert Choice. The results allowed the author to determine the socioeconomic position of the regions studied and rate the number of alternatives used to evaluate each region.

DESCRIPTION OF RESEARCH UNITS AND DATA ANALYSIS

Table 1 and Map 1 show the countries and regions that were considered for this research. These countries were selected because they are considered suitable for this work, in accordance with the provisions of the Euracom.

The regions selected were places where mining activities have been a determining factor for local economic development: 1. Dortmund, located in the federated state of North Rhine-Westphalia, in Germany; 2. Bleiberg, a region belonging to the federated state of Carinthia, in Austria; 3. The mining area Mons-Charleroi in the Belgian region of Wallonia, a province of Hainaut; 4. The North Bohemia mining region and the Sokolov region, in Czech Republic; 5. The mining areas of Castile and León and Asturias, in Spain; 6. The regions of Kemi and Pyhäsalmi, in Finland; 7. The mining region Nord-Pas-de-Calais, in France; 8. The mining area including Salgótarján, Tatabánya and Pécs, in Hungary; 9. The island of Sardinia, in Italy; 10. The mining area of Silesia, Poland; 11. The country of Blaenau Gwent, in Wales, United Kingdom; 12. The mining region of Târgu Jiu, near Petrosani and Lupeni, in Romania, and 13. The Kiruna region, in northern Sweden. The rest of the community countries, Bulgaria, Cyprus, Denmark, Slovakia, Slovenia, Estonia, Greece, Ireland, Latvia, Lithuania, Luxembourg, Malta, the Netherlands and Portugal, do not have strong mining sectors, and mineral exploitation has not led to significant economic development in these regions. On July 1, 2013, Croatia became an eu member. The country has a relatively important mining sector, but it was not included in the study because its data has yet to be collected by the Euracom.

Aiming to measure the effects of the mining sector on the development of European regions, the methodology proposed involved identifying how four socioeconomic variables evolved in three representative years: 2000, 2008 and 2011. These were: a) consumption, b) foreign direct investment, c) public spending and d) net exports. The analysis was unable to include statistical information regarding coal, wulfenite, chromium, copper, zinc, granite and iron reserves, because data for Austria, Belgium, Finland, France, Italy and Sweden was unavailable.2 Regarding the years chosen for the study, 2000 was important because it was the year in which the European Commission determined that the development of mining regions would be cataloged as objectives 1 and 2. As a result, assistance would take on new importance. The second year of analysis, 2008, is relevant as it was the year in which the current financial crisis broke out. Finally, 2011 was chosen because this is the year for which the most recent data is available, which allowed the researcher to clearly identify the negative evolution of the four variables used in the study. This work assumes that there is a correlation underlying the crisis and the deterioration of the socioeconomic variables of the regions studied. Certainly, the crisis has had more severe repercussions for these regions, because, unlike other areas that took better advantage of economic cycles, these economies were already impaired starting from years back.

The empirical side of this study used data from Euracom. The analytic hierarchy process (ahp) was used in this research. This method is especially useful for simplifying a broad range of alternatives into a few variables, to ease a later analysis, generate a numeric measuring scale and, based on that, draw conclusive explanations. Once the model was evaluated, the relative measure classified the alternatives from best to worst. The results allowed the researcher to identify the comparative levels of the regions studies in the years indicated.

Consumption-gdp

In this analysis, consumption is a positive variable, from which it can be deduced that increased consumption leads to greater gdp, while negative growth causes the gdp to fall. Table 2 shows consumption data as percentages of gdp in 2000, 2008 and 2011, in absolute terms in the principal mining regions of Europe. Purchasing power parity was taken into account here, which is especially relevant as these countries are rather heterogeneous.

The data indicates that family spending in mining regions of the most powerful economies, such as Germany, France, Italy and the United Kingdom, was 15 percent lower than what it was for families living in regions specialized in manufactures and services. This makes it clear that choices regarding consumption are closely linked to how factors related to purchasing power vary in an economy. Moreover, in 2009, the mining regions of Belgium, the Czech Republic, Spain, Finland, France, Hungary and Sweden showed slight growth in consumption, which oscillated between 7 and 10 percent relative to data from 2000. Data from 2011 revealed that family spending in all countries fell on the order of 7 to 12 percent, relative to 2008 data, from which it can be deduced that the current crisis in Europe is especially evident in the reduced consumption of these regions. It can be concluded that in the regions analyzed, consumption has fallen overall since 2008. The data confirms that this evidence is very strong for the mining regions in Spain and Italy.

Foreign Direct Investment-gdp

The countries analyzed have close ties through the European Common Market to which they belong. As a result, foreign direct investment (fdi) is constantly flowing among these nations. Table 3 is made up of two parts; the first shows gdp per capita in millions of euros at constant prices and the second shows the volume of fdi received by each of the regions studied, for the years 2000, 2008 and 2011 in both cases. Based on this data, a comparison can be drawn between the gdp in millions of euros of these regions and how much money was received from foreign investments. The results shed light on the significant disparities between regions, both in terms of gdp per capita and the amount of fdi received.3

According to this data, 2008 was a bad year for attracting fdi, as the volume of transactions performed was scarce (although the data shows positive evolution in Spanish regions). In overall terms, the growth between 2000 and mid-2008 was rather strong, although the data turned negative between the end of 2008 and 2011. For the percentage of the origin of incoming fdi, an overwhelming majority came from Euracom member countries. In any event, as can be seen in the table, these variables grew over time. The gdp could perhaps be described as less linear, but this is likely due to the influence of other variables besides fdi (and it also makes clear the close correlation between the two variables).

Public Spending-gdp

When a State invests in its economy, there is an added value to growth. However, in recent years, the eu has imposed a policy of budgetary austerity. Table 4 shows the public spending of the regions studied.

Net Exports-gdp

The fourth and final variable studied was net exports of mining regions, because the benefits linked to this variable have an influence on economic growth, by including both static and dynamic trade revenue, such as a more efficient use of resources, the increased competitiveness of productive agents, an increase in the flow of knowledge, the capital accumulation rate and technological progress, among other factors. In addition, commercial opening and fdi are strongly correlated. As such, a greater degree of openness increases production, exchange and potential for consumption, which allows residents to achieve a greater standard of living. Looking at the data shown in Table 3, the number of mining regions with a trade deficit is significant.4

THE EFFECT OF THE CRISIS ON THE DEVELOPMENT OF MINING REGIONS IN EUROPE

The empirical study began by recognizing an intensity scale to establish criteria and assign each region a value that matched its behavior with respect to the scale. To understand the territorial organization of countries, it is increasingly necessary to study the socioeconomic environments of the regions. To do so, multivariable analysis techniques can be used to identify the socioeconomic positions of the regions. Factor and cluster analyses complement the ahp methodology (using social and economic indicators). In recent years, ahp has been used frequently in studies on regional development, both in general terms and for specific studies of mining regions in Europe.

The ahp method allows researchers to incorporate qualitative and quantitative factors to provide a solution to any problem that is proposed and later determine preferences, which ahp represents on a measurement scale. The commercial program used to apply ahp was Expert Choice, which works in Windows, is easy to use and serves as a mechanism resulting from participatory consensus. The data was introduced to find the weights resulting from its comparison. This technology tool is extremely useful for two reasons: 1. Its precision in determining the socioeconomic ranking of the regions (from greatest to lowest, as a function of their attributes and factors) and 2. Because it has a ratings module to evaluate the number of alternatives. The ratings were provided in a rating table (in other words, with the values of the intensity scale) that was used to evaluate each region.

In this way, instead of defining the diverse regions visible in the model, specific scales were created for each of the criteria (against which the regions would be contrasted). Using each of the four study variables, a scale was composed of five levels: 1. High, 2. Medium-High, 3. Medium, 4. Medium-Low, 5. Low. The next step was to define the intensities for the criteria, which refers to the meaning of the five levels, in accordance with the (centroid) values obtained from the cluster analysis.5

Table 6 shows the rating given to each region based on the intensity scale in the year 2000. In this case, the data for the intensity scale came from the results obtained from the previous cluster analysis, which created five groups, so that they could be made to match the intensity scale (also with five levels), making it easier to introduce and interpret the data. In each cell, the value of the selection from the intensity scale appears, while the total column contains the weighted sum of the values for the alternative at all levels of the hierarchy, which allowed the researcher to obtain the resulting vector for the entire process.

Figure 1 shows the results of the study and indicates that in the year 2000, the top-ranked region was Germany, because it met the criteria considered. The second, third and fourth places were occupied by Sweden, Finland and Austria. At the other end, the three lowest-ranked regions were Romania, followed by Hungary and the Czech Republic, at a relative distance.

Figure 1. The Socioeconomic Ranking of the Mining Regions of the Countries in 2000
(Percent of Priority Reached Over Maximum Value)


Source: Prepared by the author based on the results obtained from the Expert Choice program.


This figure reveals few surprises. The top regions have historical advantages, as they are highly industrialized and developed countries. In addition, all of these regions were net recipients of mining aid in the two decades prior. In other words, in all cases, these mining regions received aid and subsidies from the European Regional Development Fund (erdf), Cohesion Funds (cf) and the European Social Fund (esf).6 This may lead to a belief that the mining regions of Germany, Sweden and Finland were those that made the best use of community resources, while Austria, the United Kingdom and France performed relatively worse. Farther off would be the performance of Belgium, Italy and Spain. The underlying socioeconomic gap between Italian and Spanish mining regions relative to those in Germany or Sweden is around 30 percent, undoubtedly significant.

Mining regions in Poland, the Czech Republic, Hungary and Romania had rather low performance, although it matches the expected results. The former three joined the eu on May 1, 2004, while Romania joined on January 1, 2007. In any event, these countries started receiving community funds from the moment they joined. The socioeconomic situation between Germany and the best situated of all of the countries, Poland, is 48 percent, and increases to 66 percent when compared to Romania (Table 7 shows the same procedure carried out for the year 2008).

In all cases, the socioeconomic situations of mining regions improved over the year 2000. The most notable specifics are as follows: a) the Swedish region surpassed Germany and b) the Spaniards overtook the Italians. The increased socioeconomic position of the regions of all countries is in accordance with the conditions of the economic boom cycle that took place between 2000 and mid-2008. If the intensities of the ratings are analyzed, it becomes clear that in the majority of cases, the variable improving is consumption, while public spending and fdi increase to a lesser extent, and exports the least. This can be explained due to the fact that the majority of governmental policies were focused on growth oriented towards domestic consumption, and less on exports (except in Germany, Sweden, Finland, Austria and the United Kingdom, and farther off, France).



Figure 2. The Socioeconomic Ranking of the Mining Regions of the Countries in 2008
(Percent of Priority Reached Over Maximum Value)

Source: Prepared by the author based on the results obtained from the Expert Choice program.

Finally, the results shown in Table 8 and Figure 3 are rather revealing. If we focus on the intensity scale of the ratings, the consumption indicator fell significantly for the majority of countries, and Italy and Spain were especially impaired. Secondly, the regions of France, Belgium, Italy, Spain, Poland, the Czech Republic, Hungary and Romania saw the public spending indicator fall significantly. However, net exports suffered relatively modest negative behavior. Even so, if we contrast the results obtained before the second quarter of 2008 (the zenith of growth) with those in 2011 (one of the worst periods in the historical series), there is a notable gap between the indicators measuring socioeconomic circumstances in European mining regions.

Figure 3. The Socioeconomic Ranking of the Mining Regions of the Countries in 2011
(Percent of Priority Reached Over Maximum Value)


Source: Prepared by the author based on the results obtained from the Expert Choice program.

CONCLUSIONS

Among the variety of factors that influence the socioeconomic development of the regions, four key variables were selected for study. The empirical study allowed the researcher to demonstrate the positive dependence underlying the variables of consumption, fdi, public spending and exports in the development of the principal mining regions in eu community countries.

In addition, in response to the originally formulated proposal as to studying the effect of the crisis on the economic development of these regions in the years 2000, 2008 and 2011, there are various relevant points. The first is that economic growth in the European economy between 2000 and mid-2008 was positive for mining regions. Second, the regions of countries that most recently joined the community integration process also grew, but less so between 2004 and 2008, although they suffered severely in the recession starting in mid-2008 and through 2011.

The results for the mining regions of Spain and Italy were deficient because the State was required to reduce public spending in these cases, among other reasons. Added to that is a drop in consumption, which means it is likely that socioeconomic variables will fall significantly in the years to come. By contrast, mining regions with better prospects are found in Sweden, Germany, Finland and Austria. In these four nations, besides mining, there are other sectors that could function as alternatives, if not to improve the socioeconomic conditions of residents than at least to maintain them. Additionally, the mining sectors in these countries have developed highly specialized R&D programs, especially in Finland and Austria, focused on developing and exporting high technology applied to mining. The main mineral exploited in Austria is wulfenite, with little scientific or industrial interest, which motivates companies (instead of being more labor intensive) to specialize in developing granite drilling, an extremely profitable activity. Likewise, regions in Sweden are positioned in the best scenario possible, because this nation exploits the highest quality iron in the world. Once processed, it is converted into steel (iron alloy that contains less than 2.1% carbon is turned into steel).

The future of mining regions in France, Belgium and the United Kingdom is rather dismal. The data indicates that these three nations suffered significant decreases in the four variables between 2008 and 2011. In the past, these regions were the nerve centers of development, especially in Wales (Cardiff was one of the major urban centers during the Industrial Revolution), but nowadays, the circumstances have changed.

In general terms, it is of note that mining activity in Europe specialized in exploiting coal is currently on track to disappear. There are still thermoelectric plants that consume coal, but the resource is increasingly expensive. Quality coal is found at depths where extraction is not profitable, which is why it is preferable to import it from Russia, South Africa, Ukraine, Kazakhstan or other developing countries. This factor, added to cuts in community aid, means that overall, European mining regions must urgently undertake a process of industrial reconversion. Is the currently dismal overall state of mining regions in Europe due to the fact that the sector has been unable to adapt to the prevailing conditions of globalized markets, or is it rather that the resources are not sufficient? In response, it is clear that resources are more abundant in Germany than in the rest of countries, but this problem is not based on the amount of reserves, but rather on the prices derived from extraction. Consequently, everything would seem to indicate that, at least in the case of coal, it will be more profitable to import than extract in the coming years. The problem effectively resides in the fact that the sector and regions have been unable to adapt to prevailing conditions in the majority of cases.

With that said, it must be recognized that the contributions of this empirical study are subject to a series of rather important methodological limitations. The first is related to the use of regions as a unit of analysis, which in many cases are heterogeneous. In addition, it must be assumed that the differences in productive capacities among the countries analyzed may be very large. For example, there are immense differences between the German region of Dortmund and the Romanian region of Târgu Jiu. Among other factors separating them, the former is located 82 kilometers from Dusseldorf, the most important industrial center of the federated state of North Rhine-Westphalia, while Târgu Jiu is located 288 kilometers from Bucharest, the capital of the country. For this reason, a second stage of this research will focus on comparing the most similar mining regions, which will allow for the formation of homogenous groups in order to go further into depth as to the underlying features and differences.

In addition, this research has not taken into account factors such as the size of the territory of each region, the number of inhabitants, secondary productive activities, population aging, the emigration of inhabitants to large cities and climate conditions (which are undoubtedly very relevant in Sweden, as it is located so close to the Arctic circle), among others. All of these variables are very important, but taking them into account would have meant including an indeterminate number of externalities, which certainly would have complicated the study. Consequently, because the only objective was to identify how the four selected socioeconomic variables evolved, the choice was made to sacrifice the breadth of the study to see if it was possible to obtain more precise conclusions.

Experts agree that the future of mining in community countries is dismal. With a few very specific exceptions, such as the Swedish region of Kiruna, the regions are headed towards an immense effort at industrial reconversion, especially regions specialized in coal exploitation. In any case, this work may be of interest for future research, both for studies focused on overall productive sectors as well as those centered on the regional and local economies of community countries.

Two concrete reflections arise concerning the utility of this work for the mining regions of Latin America:

  • The European mining sector deteriorated gradually over various decades. In its best year (1957), the sector generated 607,000 jobs, while in its worst, it destroyed 166,000 (ewg, 2013). Latin American countries should be mindful of this fact, especially for those that have benefited from an increased demand for minerals around the world over the past ten years, particularly copper (like Chile and Peru). Concentrating a significant portion of the development of a region or country in a single sector evidently implies an enormous risk. It is very likely that as long as the demand for minerals continues to rise, Latin American countries will benefit, but if the trend changes, the situation of the regions studied here could be repeated. Consequently, the solution will be found in diversification, regardless of whether or not there is a natural tendency towards mineral exploitation.
  • It was previously ascertained that, at least for coal, everything would seem to indicate that it will be more profitable for Europe to import rather than extract in the coming years. As such, it may be a great opportunity for coal mining regions in Latin America to become poles of attraction for industries that may emigrate as community aid for coal runs out in 2018. If this scenario happens, the mining companies that relocate (especially German companies) will have technology and a variety of technical and technological resources that may be very attractive to the recipient mining regions. In addition, the internationalization of European companies will open a window of opportunity to develop Latin America mining regions. The recommendation is therefore to ensure that when the time comes, these regions have the proper legal and institutional framework, infrastructure and specialized personnel to take full advantage of the options that are likely to arise.

Finally, this work was carried out in the framework of a research group specialized in the development of mining regions and areas. One of its principal areas of interest is to study collaboration between companies and regions. Among other projects under way is one that is precisely oriented towards evaluating the advantages of Latin American mining regions in general, and Mexico and Colombia, specifically.

ACKNOWLEDGEMENTS

This work was carried out with the support of the Mining and Regional Economic Development (M&Red) research group. I would like to express my deep appreciation to professors Petra Hegemann, Francisco Llamazares Redondo, Matthias Engström, Guillermo Vázquez-Vicente and Kajsa Gunnarson, all members of the group, for their incalculable assistance.

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*Rey Juan Carlos University, Madrid, Spain. sergio.berumen@urjc.es



1 This document was made operational through five instruments: 1) The Coal Mining Restructuring Program (1990-1993), 2) The Modernization, Rationalization and Restructuring Plan for Coal Industry Activities (1994-1997); 3) The Coal Plan (1998-2005, also known as the Plan for Coal Mining and the Alternative Development of Mining Regions), 4) The National Plan for Strategic Coal Reserves (2006-2012) and the New Model of Comprehensive and Sustainable Development of Coal Mining Regions and 5) the eu 2000-2006 Structural Funds.

2 Information regarding coal reserves for the top 30 producing countries is available in Europe’s Energy Portal.

3 In countries where more than one region was considered, such as Spain, Czech Republic, Finland and Hungary, the average of the regions was used.

4 In fact, the degree of openness is lower when compared with that of other regions with a trade surplus. For the years in this study, the negative commercial balance had a regressive influence on the gross domestic product.

5 Because there was an absolute scale, the intensities did not have to be weighted by comparison of pairs, and as such, the same scale was used for the four variables, each with its own values.

6 The other two sources are the European Agricultural Guarantee Fund (eagf)

and the Financial Instrument for Fisheries Guidance (fifg).

Published in Mexico, 2012-2017 © D.R. Universidad Nacional Autónoma de México (UNAM).
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