The Impacts of the Financial Crisis on Urban Competitiveness in Italy

Transcription

1 Journal of Economy, Business and Financing The Impacts of the Financial Crisis on Urban Competitiveness in Italy R. Papa, C. Gargiulo, S. Franco, L. Russo Department of Civil, Architectural and Environmental Engineering (DICEA) University of Naples Federico II Naples, Italy Abstract The financial crisis of that has spread from the U.S. to the whole world, initiated with the bankruptcy of Lehman Brothers bank, has affected a wide range of territories, from large metropolitan areas to small urban centers in industrialized Western nations. Now it is generally acknowledged that countries, regions and cities compete against each other to attract businesses, talents and skills (Camagni 2002). Thus, urban competitiveness can offer an interesting perspective to interpret the effects of the crisis. In this context, given that Italy has been suffering considerable financial and social difficulties since 2009, the paper 1 aims to understand if the economic breakdown has affected the level of urban competitiveness in Italy by investigating how the competitiveness of Italian provinces has changed in the last two decades comparing the 2011 scenario based on trend with the 2011 available data. Initial results of the analysis show that territories have responded differently to the strong economic recession: the economic and social systems of a large number of provinces have experienced a worsening of their competitiveness, some have been able to endure the consequences of the crisis preserving their level of competitiveness, while others have reacted succeeding in increasing it. Keywords - urban competitiveness; financial crisis; Italian provinces; Italian competitiveness I. INTRODUCTION The issue of territorial competitiveness has started being discussed in the second half of the 90s when authors such as Krugman and Lall challenged the use of the economic concept of competitiveness to the territorial dimension. 1 In particular, Krugman argued that countries do not compete with each other the way corporations do because the success of a nation does not imply the failure of another, as it happens for firms (Krugman 1994). Along a similar line of thoughts, Lall considered eventually significant countries competitiveness in specific activities and markets, but not countries competitiveness referred to the whole national economy, which he considered meaningless (Lall 2001). Afterwards, a different approach has emerged and it is now generally shared, mainly due to the contribution of Camagni; the Italian author contradicted Krugman s perspective stating that countries, regions and cities and not just firms compete with each other to attract business, talents and skills, especially in an era of globalization such as the current (Camagni 2002). 1 This paper describes a part of the research carried out by the Department of Civil, Architectural and Environmental Engineering (DICEA) of the University of Naples Federico II, within the Project Smart Energy Master for the energy management of territory, financed by PON 04a2_E R&C Axis II, from 2012 to Once this approach has become widely accepted, the theoretical debate on the issue of territorial competitiveness has continued with a large number of studies that have tried to formulate a definition of the concept, keeping in mind that theory ad empirical evidence suggest that many things matter for competitiveness (Porter et al. 2008). A more concrete line of research, whose objective was no longer defining territorial competitiveness, but measuring it, has then followed the discussion. Among these studies, many have focused on the country as a whole or on the world s major cities or larger urban zones (OECD 2006, Porter et al. 2008, World Economic Forum 201), while very little attention has been directed toward the competitiveness of the cities within a country (Bruneckiene 2010) (Papa et al. 2014). In the light of these considerations, sharing Camagni s point of view, we have used competitiveness as a significant measure of how territories have responded to the economic crisis. Therefore, this analysis aims to measure the change of Italian competitiveness between 1991 and 2011 in order to see if the economic events of have affected it. Why Italy? Because it is one of the European countries that is still suffering from the financial crisis of 2009, with both economic and social consequences. We have developed a synthetic Index of Competitiveness (CI) to provide a quantitative measure of competitiveness; then, the index has been assessed at different times (1991, 2001,

2 Issn:19-72, volume 2, issue 2, ) and, by using a statistical analysis, the impacts of the breakdown have been evaluated and discussed. The CI represents a useful tool for measuring the level of competitiveness of a territory and it enables decision-makers to plan specific policies depending on different competitive aspects; it is simple to calculate, flexible and immediately comprehensible, so it can be easily used and proposed in different context. In order to achieve the aim of this work, the analysis has been divided into the following 5 steps: choice of the territorial scale of reference; selection of variables to be considered for building the CI; collection of data and development of the Competitiveness Index; multivariate statistical analysis; interpretation of results and discussion. The first part of the article describes the procedure used to build the Competitiveness Index (CI) and the statistical analysis that strengthens the results of the investigation (steps 1 to 4). The second part of the paper focuses on the comparison between the province s competitive performances at 1991, 2001 and 2011 (step 5). Furthermore, one more index has been developed (CI * ), which considers the scenario at 2011, calculated on the geometric projection from 1991 to By comparing expected values at 2011 with the real ones, we have tried to understand if the financial crisis of has affected the competitiveness of Italian provinces and how the different territories have responded to it. II. DATA AND METHODS Before proceeding to the selection of the variables for developing the CI, it has been necessary to define the territorial scale of reference: the provincial level corresponding to the European Union NUTS seemed to represent a good balance between territorial homogeneity and reliability of data. Afterwards, in order to build the CI and rank Italian provinces at different times, 12 variables have been selected. The variables that populate the dataset correspond to the 12 determinants identified in the analysis The Evolution of Urban Competitiveness in Italy from 1995 to 201 (Papa et al. 2014) to measure the level of competitiveness of Italian provinces on an international scale (Tab 1). For each variable data have been collected at 1991, 2001 and 2011 so to build three matrixes of data (10x12), where 10 is the number of provinces at Since that between 2001 and 2011 seven new provinces have been founded, the initial 2011 data matrix (110x12) has been transformed in a 10x12 matrix by merging the data referred to the new provinces with the old ones of reference 2. After collecting all needed data, the following operating procedure has enabled to build the Competitiveness Index, based on the analysis of the Handbook on Constructing Composite Indicators (OECD 2008) and on the method provided by Del Colle (Del Colle 2006): TABLE I. Normalization: data have been normalized in order to consider the demographic weight of each province. Standardization: normalized data have been standardized to make the different scales comparable, using the Min-Max method: z ij = Development of the CI: for each province the 12 standardized values have been combined using the Equal Weighting method (EW) to develop the synthetic index of competitivenss based on the forumla of Euclidean distance: CI i = DESCRIPTION OF VARIABLES AND DATA SOURCES Variable Years Source International airports 1991; 2001; 2011 Istat census Passengers on international flights 1991; 2001; 2011 Istat census Networks for telephony and telematics 1991; 2001; 2011 Tagliacarne High-speed connections 1991; 2001; 2011 Trenitalia/NTV Population 1991; 2001; 2011 Tuttitalia Population of the provincial capital 1991; 2001; 2011 Tuttitalia Income per capita 1991; 2001; 2011 Tagliacarne Number of bank branches 1991; 2001; 2011 Tagliacarne Universities 1991; 2001; 2011 Cestor (Study Center Orientation) Number of patents 1991; 2001; 2011 Ministry of Economic Development Number of firms 1991; 2001; 2011 Istat census Exhibitions 1991; 2001; 2011 Italian Trade Fair System 2 Barletta-Andria-Trani, Monza e della Brianza, Fermo, Medio Campidano, Carbonia-Iglesias, Ogliastra and Olbia-Tempio are the seven provinces born after 2001; all their data have been added to those of the provinces they were part before (Bari, Milan, Ascoli Piceno, Cagliari, Nuoro and Sassari) except for the Income per capita for which an average value has been used. Each variable has been divided by the total population of the province of reference except for the Income per capita

3 Journal of Economy, Business and Financing TABLE II. VALUES OF THE PARAMETERS FOR SOME SIGNIFICANT CASES Competitiveness Index (CI) Province m b CI * 2011 R 2 Δ CI δ δ Agrigento 0,166 0,146 0,114-0,002 4,127 0,126 0,98-0, Ancona 0,29 0,21 0,79-0,001 1,868 0,1 0,66 0, Avellino 0,099 0,078 0,091-0,002 4,242 0,057 0, ,12% 17,14% Belluno 0,4 0,61 0,52 0,00-5,144 0,89 0,49-8,25% -2,47% Firenze 0,48 0,467 0,447-0,002,58 0,452 0,99-0, Lecco 0,24 0,294 0,27-0,00 6,1 0,264 0,988 0, with i = 1, 2,,, 10 and j = 1, 2,,, 12. The numerator represents the distance between the value of the individual province and that of the worst one divided by the distance between the best and worst performance within the single variable. This aggregation method is inspired by the taxonomic method and has been already used in other researches (Del Colle 2006, Mazziotta 1998). The Index thus obtained varies from 0 to 1; these values represent the domain of all possible performances a province can achieve respect the totality of variables. With the development of the CI three rankings of Italian provinces respect the level of competitiveness have been created, one at 1991, one at 2001 and the last at For each of the 10 Italian provinces the three values of the CI allowed estimating a linear regression line and, based on the obtained coefficient of determination R 2, the cases have been split into two groups, assuming R 2 = 0.5 as a discriminant value (Tab. 2): R for these cases we consider the representation significant, meaning that the phenomenon is reliable and it evolves following a linear logic; R 2 < 0.5 the values of the regression line cannot be considered reliable. For the first group of provinces, those with R (5 out of 10), we have calculated the gradient (m) and the intercept (b) of the line defined by the following two points: (x = 1991; y = CI ) ; (x = 2001; y = CI ) then, we have used m and b to identify the ordinate corresponding to x = 2011: y = m x + b This value represents the projection of CI at 2011 (CI* 2011i ). The next step has been the comparison of CI* 2011i with the Index calculated at 2011 using the effective values, i.e. CI 2011i. The comparison allowed defining the variation between these two values: Δ = CI CI Δ assumes positive or negative values depending on the actual competitive performance of the province respect the projected one: if the real value at 2011 is greater than the expected one, Δ is positive. Furthermore, for values of Δ with two decimal zeros (both positive and negative), we have considered the variation not significant. For the second group of provinces, those with R 2 < 0.5 (50 out of 10), we have used the following formula to calculate the variation (%) between the three values of CI: δ = CI CI CI δ = CI CI CI These δ show how the Index of Competitiveness has changed in the period of reference. The approach described above has provided interesting findings for answering the question: did the financial crisis of affected Italian competitiveness? III. RESULTS AND DISCUSSION Data analysis has been different for the two groups in which the 10 Italian provinces have been split. For the first group of 5 provinces, whose CI s trend fits into a linear model (R 2 0.5), we have analyzed and interpreted the geographical distribution of the Δ, comparing the actual CI at 2011 with that projected, CI*. For the second group of 50 provinces (R 2 < 0.5), a different approach has been used because we could not reliably calculate the expected value of CI at 2011 since that CI s trend does not fit into a linear model. In this case, we have compared the measured values of the three CIs in order to verify the changing between 1991, 2001 and Considering the first group of 5 cases, Figure 1 shows the territorial distribution of Δs: those provinces with a negative Δ whose economic and social systems have been adversely affected by the economic crisis (CI < CI ) are 21 out of 5 and appear in two colors, orange (11) and red (10). Those in red are the provinces whose CI at 2011 is not just worse than expected, but it is even lower than the one at 2001; the 15 cases in yellow represent the provinces whose competitiveness has

4 Issn:19-72, volume 2, issue 2, 2014 not suffer serious consequences from the events (CI CI ); furthermore, the provinces in green, 17 out of 5, correspond to those territorial systems that have been able to react at the breakdown and even exceed the projected value at 2011 (CI > CI ). Looking at the map, it is possible to do some considerations. The provinces that have suffered the greatest effects, those in red, are concentrated in North-Central Italy (9 out of 11), and the same goes for the orange provinces, with just cases in the South part of Italy, including Sardinia and Sicily. A different distribution is that of the territories showing the best relative performances, which are concentrated principally in the Southern regions (9 out of 17), mainly in Basilicata and Calabria. Furthermore, looking at those provinces with Δ equals to zero, they appear to be concentrated in Central Italy, especially in Tuscany, where 6 out of 10 provinces did not significantly suffered the impacts of the crisis. These findings can be explained if we consider the difference between the economic-social-productive textures that structure Italian provinces: those territories that have suffered the most are characterized by a stronger and dynamic economic and productive system (Milan, Bologna, Aosta, Asti, Alessandria and Como); the impact has been not significant where entrepreneurship is in small part financially supported by public action (Firenze, Arezzo, Prato, Pistoia, Lucca, Livorno and Perugia); in the South part of the country (Naples, Lecce, Potenza, Matera, Catanzaro, Cosenza, Crotone, Ragusa and Nuoro), instead, the weak productive structure, which depends in large part by state aid, has not been negatively Figure 1. Italian provinces grouped by Δ values (R2 0.5) Figure 2. Italian provinces grouped by δ values (R2 < 0.5) affected by the economic crisis, mainly because public investments have not been hit as much and rapidly as private ones, in the first phase of the financial breakdown. Considering the second group of 50 Italian provinces, another map helps us evaluating the results of the analysis (Fig. 2). The 4 cases colored in blue represent those provinces with the CI at 2001 higher than that at 1991 and, at the same time, the CI at 2011 lower than that at 2001; in the 50% of these cases (17 out of 4) the value of CI at 2011 is even lower than that at For this latter group of provinces, mainly concentrated in the Northeast part of the country (Trento, Gorizia, Pordenone, Vicenza, Modena, Ferrara and Parma), we could say that competitiveness has been strongly affected by the crisis, which has even managed to frustrate the progress achieved between 1991 and Different fate befell the 16 provinces in green, which experienced a worsening of their level of competitiveness between 1991 and 2001 and an improvement between 2001 and At 2011, the economic and social systems of these provinces mainly concentrated in the South (Benevento, Avellino, Taranto, Brindisi, Bari, Reggio Calabria, Messina, Palermo and Cagliari) have not been negatively influenced by the crisis and, 11 out of 16 cases have managed to recover the decreasing of competitiveness from 1991 to 2001 and exceed the value of CI at These results do not differ from those previously described, i.e. those referred to the first group of provinces. Again, a great number of North provinces, where most of the small and medium firms are concentrated, experienced the strongest effects due to the dramatic event of , while the South

5 Journal of Economy, Business and Financing TABLE III. has endured better, especially thanks to the support of public investments. However, these findings should be analyzed also considering the absolute value of CI and not just its variation, in fact, despite Southern provinces show a better reaction to the economic crisis than Northern ones, in the Italian ranking of CIs at 1991, 2001 and 2011 the North-South divide remains wide, with a leader North and a laggard South: Table shows that only Northern territories occupy the first 7 positions and, at the same time, only Southern ones rank at the bottom of the lists, for the three years of reference. Furthermore, a more general analysis has followed the previous one and it has concerned the maximum and minimum values assumed by the CI in the three years of reference (Tab. 4). Data demonstrate a decline in the level of competitiveness of Italian provinces during the last twenty years, substantiating the finding of a previous study (Papa et al. 2014): from 1991 to 2001, Milan and Caserta keep their role of provinces with the highest and lowest CI, but while Milan shows a decreasing of its CI (-.07%), Caserta records an improvement (+.74%); from 2001 to 2011 the maximum value of CI decreases further and Milan falls from the podium, giving way to Rimini, while Caserta is still bringing up the rear of the country, this time experiencing a strong worsening (-46.56%). IV. PROVINCES ORDERED BY CIS Milano 0,665 1 Milano 0,645 1 Rimini 0,608 2 Bologna 0,601 2 Aosta 0,628 2 Trieste 0,605 Trieste 0,58 Bologna 0,610 Aosta 0,598 4 Forlì 0,529 4 Rimini 0,58 4 Pisa 0,556 5 Prato 0,506 5 Trieste 0,568 5 Bologna 0,547 6 Roma 0,50 6 Prato 0,500 6 Milano 0,544 7 Pisa 0,492 7 Roma 0,499 7 Roma 0, Avellino 0, Salerno 0, Agrigento 0, Lecce 0, Lecce 0, Siracusa 0, Enna 0,07 99 Caltanissetta 0,08 99 Cosenza 0, Cosenza 0, Avellino 0, Avellino 0, Caltanissetta 0, Cosenza 0, Enna 0, Vibo Val. 0, Vibo Val. 0, Vibo Val. 0, Caserta 0, Caserta 0,07 10 Caserta 0,020 CONCLUSIONS If we consider the whole country system, the results of the analysis show that the economic breakdown initiated in TABLE IV. MINIMUM AND MAXIMUM VALUES OF CI BY YEAR year CImin CImax (Caserta) (Milan) (Caserta) (Milan) (Caserta) (Rimini) 09 has deteriorated the level of Italian competitiveness, confirming what has been found and described in a previous paper (Papa et al. 2014). Despite the country has suffered this general negative trend, within Italy, territories have responded differently to the strong economic recession, with the economic and social systems of a large number of provinces that have experienced a worsening of competitiveness, some that have been able to endure the consequences of the crisis and some others that have reacted succeeding in increasing it. Mapping these findings, once again, the break between North and South emerges significantly: Northern territories have suffered a more considerable worsening than Southern ones. Nevertheless, this not necessarily means that Northern provinces have not been able to deal with the crisis unlike those in the South, but most probably it depends on the different economic and social systems shaping their competitiveness. Our results are substantiated by those achieved by M. C. Cohen in his study Cities in Times of Crisis (2011), where the author analyzed the impacts of the economic events of on different regions across the world and founded out that the devastating effect of this phenomenon differs considerably among large regions, countries, cities and neighborhoods and this variety of results mainly depends on the different economical structures and financial situations of the territory considered. Another analysis that supports our results is that by the Sole 24 Ore (2014) the main financial newspaper published in Italy which has recently investigated the crisis response capability of Italian provinces, showing that the Padania 4 of industrial districts has been the most affected Italian territory, where many small companies have failed and the middles class has experienced the deepest wound. The findings described above provide useful information for decision makers in order to identify different strategies to be implemented for increasing the level of competitiveness of the different provinces. Furthermore, the development of the Competitiveness Index represents a helpful tool for measuring territorial competitiveness. The methodology is very simple to repeat because it is only based on an Excel sheet, moreover, it is possible to add new variables or delete old ones without changing the entire structure of the Index. Nevertheless, the present analysis describes only the first results of a wider research aiming at identifying how the economical crisis has affected Italian competitiveness; we will present other results in future articles, increasing the number of variables used for developing the CI. Another interesting future research could focus on the measurement of countries level of competitiveness using the methodology developed for the CI, changing the type of variables selected and the geographical scale of reference. 4 Northeast part of Italy, including part of Piemonte, Lombardia, Emilia Romangna and Veneto

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