European Regional Science Association 36th European Congress ETH Zurich, Switzerland 26-30 August 1996

Francesco Lissoni CESPRI, Università L. Bocconi Milano, [email protected]

DEINDUSTRIALIZATION IN : AN EMPIRICAL ASSESSMENT

ABSTRACT: The paper deals with deindustrialization both at a theoretical level and at an empirical one. On the theoretical side it provides a critical assessment of the existing literature, showing how the concepts of deindustrialization have been interpreted in different ways, according to the level of study (macroeconomic vs. geographical), to the role assigned to services in explaining the manufacturing growth or decline, and to the interpretation given to industry life cycle theories. At the empirical level it maps deindustrialization in the leading Italian region and it tries to assess the relative capability of the different theories in explaining the statistical evidence. No general process of deindustrialization has affected Lombardy in the past decade, although a few local economies have been hit hard. Overall, the tertiarization process has countered, rather than favoured, the deindustrialization one. 1. Introduction In the past twenty years, “deindustrialization” has become quite a popular word, often used to describe a series of intuitively correlated phenomena such as the decline of regions and metropolitan areas once famous for their manufacturing specialization, the apparent shrinking of the manufacturing base in many industrialized countries, and the related shift to services. Unfortunately, the more popular the word has become, the more meanings it has taken on, and the less precise. Nowadays ambiguities are so many, that economists like Bhagwati and Krugman feel the need to assert that the use of the word “deindustrialization” does not come from their own profession, but from journalists, politicians, and vulgarizers1. This article aims to put some order in the debate, by extracting a few key definitions of “deindustrialization” from the recent and less recent literature on the subject, and testing them against the development of Lombardy, the leading manufacturing region of Italy, in the past decade. In doing so, it refers to deindustrialization as a component of a more general process of structural change, and warns about the danger of reducing the former to the latter, even in local economies which have experienced an apparent loss of manufacturing power2. In particular, it selects and emphasizes two alternative views of deindustrialization: - a macroeconomic one, which identifies the latter with tertiarization, about which it often implies a negative judgement (section 2); - a geographical one, which sets deindustrialization in a life-cycle perspective; here deindustrialization is referred to local (in many cases metropolitan) areas, once specialized in a few traditional manufacturing activities, and nowadays threatened by plant closures and layoffs (section 3). The geographical view appears to be particularly complex and rich, since it hosts a variety of sub-themes. The article also discusses and tests two of them. The first one is the relative strength of the so-called filtering upward and filtering downward movements of economic activities, nowadays particularly relevant in the service sector, and their effects on deindustrialization. The second one is the role played by industrial districts in contrasting the expected decline of a few areas heavily specialized in traditional manufacturing activities. The combination of theoretical insights and empirical analysis leads to the conclusion that no general process of deindustrialization has been taking place in Lombardy (section 4.1). At a local level, on the contrary, deindustrialization seems to have hit hard, but only very few isolated areas, while the urban transformation taking place in the Milanese metropolitan area has left behind a few scars (section 4.3). At the same time, while small and medium enterprises have certainly played a key role in sustaining the regional industrial basis, no evidence of a distinctive contribution by industrial districts can be found (section 4.4). Indeed most of the re-distribution of both manufacturing and tertiary activities has clearly been the

1 result of contemporaneous filtering upward and filtering downward movements taking place within the regional boundaries.

2. The macroeconomic debate about deindustrialization, and the role of services Macroeconomic theories of deindustrialization have common roots in Nicholas Kaldor’s suggested association between productivity slowdown, relative decline of manufacturing (as measured by its GNP and employment shares, compared to those of services), and sluggish growth of GNP (KALDOR, 1966). Kaldor referred his analysis to a cross-country comparison, which addressed the relative weakness of growth rates in the UK when compared to more recently industrialized nations. Its contemporary version is on the contrary due to intertemporal comparisons, which reveal the same trends in all the major industrialized countries: such trends show a striking correlation between a relatively early decline of the manufacturing share of GNP, a slightly delayed decline of the manufacturing share of employees, and a more or less contemporary productivity and growth slowdown (PETIT, 1986). The basic intuition behind this view is that services could be held responsible for these trends, having them a lower productivity than manufacturing, and less perspectives for productivity growth. This conceptualization of services was originally due to the influence of Allyin Young’s theory of increasing returns in manufacturing, to be intended as the outcome of an endogenous relationship between market extension, division of labour, and technical change (YOUNG, 1928). Young’s theory referred to the manufacturing sector as a whole: the expansion of a single industry multiplied the opportunities for technological change in the others, as represented by process innovations to be received, and product innovations to be offered. Very few of these virtuous circles were thought possible in services, being them estimated as activities offering scarce room for technological change. Besides, the demand for services was seen more as a positive function of disposable income than of manufacturing growth. This implies that services were mainly thought as a mixture of personal services and retailing, rather than activities supporting production. This vision of manufacturing as the exclusive engine of growth was used by Kaldor to depict the progressive expansion of services, and the overall productivity decline, as the inevitable evolution of each industrial economy, which ought to be contrasted and possibly delayed by well engineered policies, rather than favoured by the myopic ones which he judged were followed in the UK at his time. This basic theory has been recalled and adapted more than once throughout the years, being again the UK the main country of reference (ROWTHORN AND

WELLS, 1987).

2 Since the late 1970s Kaldor’s emphasis on the origin of deindustrialization within the demand side has been often replaced by a supply side view. Deindustrialization has been identified with the loss of comparative advantages in manufacturing, either due to an unfavourable evolution of prices for raw materials and labour inputs, or (more often) to inappropriate industrial and trade policies. This version of deindustrialization theories has been quite popular in the US, where most efforts have been placed in the attempt to link the recent trends in international trade and the decline of a few metropolitan regions (BLUESTONE

AND HARRISON, 1982). Within macroeconomics, criticism to deindustrialization theories, and in particular to their negative consideration of services, has followed two main lines3. The first line refers to the need of a better distinction between services which are substitutes to goods, such as personal services, and services which fulfil a complementary role to production. The former compete with goods to enter families' consumption (substitute services), while the latter support manufacturing either as auxiliary services (i.e. infrastructure-like activities such as transport and retailing), and indirect services (to be considered just as production inputs, and ranging from the least productive ones, such as cleaning services or surveillance, to the most productive and advanced, such as R&D and training). Once this distinction is made, many observations arise, which challenge the deindustrialization theorists’ assumptions. First, time-series and input-output models have been used to demonstrate that the biggest contribution to the growth of tertiary activities in the last twenty years has come from indirect services (RAY, 1986; HOWE, 1986; MOMIGLIANO AND SINISCALCO, 1986). Second, it has been shown that the share of aggregate demand held by substitute services has been persistently declining in the post-war period, being their consumption often replaced by that of manufactured goods such as consumer durables (GERSHUNY AND MILES, 1983). Finally, cross-country data make clear that the weight of auxiliary and indirect services is positively correlated to the degree of industrialization. A key explanation for this regularity can be found in the restructuring process which affected manufacturing for an extraordinary long period, from the late 1970s to the mid-1980s. An optimistic view of this process would underline how tertiary activities are called in by manufacturing to support the implementation of organizational changes, the adoption of new process technologies, and the launch of many product innovations. This means that a big slice of modern services may be much more integrated into manufacturing, and into the potentially virtuous circle of increasing returns a la Allyin Young, than Kaldorian theories of deindustrialization used to assume (CARLSSON, 1989). A much less optimistic view stresses the existence, within indirect services, of a large number of ancillary activities, which, although connected to manufacturing, share many of the

3 characteristics of substitute services, such as low productivity, and scarce room for innovation opportunities (COHEN AND ZYSMAN, 1987). This pessimistic view is questioned by the second line of criticism to deindustrialization theories, which addresses the issue of opportunities for innovation and productivity growth in services. Q UINN ET AL. (1988) offer some evidence of the very high productivity and capital intensity of many auxiliary services. It is anyway very difficult to go further in the productivity assessment, being most statistics for services too aggregated, or based upon obsolete classifications. Besides, one should be able to show that not only services may increase their productivity as much as manufacturing, but also that they can influence positively the latter’s productivity growth. With this respect, positive evidence exists only for

R&D, and a few other activities directly supporting innovation (AUDRETSCH AND Y AMAWAKI, 1993). In conclusion, the macroeconomic debate is far from being settled, and no ultimate conclusion has been drawn from it. To our ends, its most valuable contribution to date has been the analysis it has promoted of the links between service activities and manufacturing growth or decline. Indeed such analysis lies at the core of the geographical approach. In addition, the Kaldorian definition of deindustrialization offers us a clear-cut hypothesis to test, which consists in the association of tertiarization and deindustrialization. Such a test will indeed open the empirical section of this article.

3. Deindustrialization in regional economics Industry life cycle theories offer a useful starting point to examine regional theories of deindustrialization. Taking for granted the existence of some evolutionary process which rules industrial dynamics, one can initially examine the spatial implications of each stage of the life cycle. Then, moving up to a more sophisticated theoretical level, the excessive determinism of this theory can be criticized from a regional viewpoint, thus emphasizing how the same industries can evolve differently, depending on where they are located, and on then way they are organized. Life cycles theories form a rather heterogeneous set. Some of them apply to narrowly defined markets for specific products, while others aim to describe the evolution of entire industries, usually defined by a set of technically related products (UTTERBACK AND SUAREZ,

1993; KLEPPER AND GRADDY, 1990). Applications to regional studies most often refer to the second variant (NORTON, 1986). Such applications identify three typical phenomena which may concur to cause deindustrialization.

4 1. Standardization, from which closure and relocation of local plants may occur. Standardization is the key consequence of the emergence, in the mid and late stages of the life cycle, of a dominant design for the products which define the industry. The loss of product variety emphasizes price competition, and it forces producers to place their best efforts in cutting costs. In this context, product standardization usually offers great help, and therefore is pursued vigorously. In parallel, routinization of production processes is very likely to take place. The more standardized (routinized) are the products (processes), the less effort is needed to monitor them tightly. Besides, less specialized workers and less technical and consultancy services are needed. In the absence of standardization, production plants would need physical proximity to the company headquarters (from where top management can intervene as often as necessary), to competitors’ and other own production plants (from where specialized workers can be recruited), and to main cities (where technical and consultancy services can be more easily found). On the contrary, when standardization (routinization) has taken place, no proximity of this kind is any more necessary, and production plants may become footloose. 2. Labour saving innovations, from which job losses inevitably occur. As the life cycle runs towards its late stages, process innovations tend to overweight product ones. They inevitably cause remarkable waves of layoffs, if not the substitution of old plant with new ones, whose location may also change. 3. Demand stagnation or contraction, from which selection effects occur. As the growth rates of demand become smaller, if not negative, and price competition becomes tighter, the failure rates of small and medium enterprises increase, which may deteriorate the local industrial basis. Large firms are less likely to fail abruptly, being subject to mergers or buyouts, which may result in the loss of proprietary control from within the region. Such a loss increases anyway the likelihood of heavy restructuring and relocation affecting the local manufacturing plants, whose distance from the headquarters of new owners may result in a loss of strategic relevance. These basic applications of the life cycle theory, although simplistic, allow us to comment upon the indicators used in regional studies of deindustrialization, and to introduce the key theoretical question which the same studies must answer to. As for the indicators, we notice that industry life cycle theories force students of regional deindustrialization phenomena to consider not the relative values of employees and value added in manufacturing (as in macroeconomic theories), but the absolute ones, with the addition of data on plant closures and proprietary control. As for the question, this can be formulated as such: does a regional life cycle exist, or do

5 industries shift along stable hierarchies of regions and cities (filtering down)? In other words: are regional specializations relatively sticky, so that regions rise and decline according to the industries they are specialized in, or is it the case that some regions recurrently nurture the new-born industries, and pass them, when mature, to other regions? A logical addendum is: if stable hierarchies do exist, which variables do affect the ranking? To answer these questions, one needs to refine the basic view of the life cycle we have given above, along three critical lines. First, the most recent theoretical and empirical work in life cycle theory remarks with great strength that the decline of technological opportunities for innovations does not affect all firms within an industry in the same way. Some firms clearly seem to be persistently more innovative than the average, and therefore less likely to compete only through price (GORT

AND SINGSAMETTI, 1987). These firms will also be less likely to decline or fail, no matter which stage of the life cycle we are considering. More strikingly, firms which entered the industry first, and managed to survive the earliest selection stages, have been shown to be more likely to innovate, survive, and grow, even during the industry maturity and decline

(KLEPPER, 1995). Thus a stable set of “core” firms face a large and unstable peripheral belt, an observation which clearly has got some spatial counterpart, being regions were pioneering firms are located much less likely to suffer from plant closures than those hosting the peripheral oness. A second critique refers to the suggestion that firm size and concentration would necessary increase with time forcing small firms either to disappear, or to enter in some subcontracting arrangement with big producers, or to confine themselves to some low-margin and unstable market niches. This deterministic view has been challenged mainly by positing the possibility that spatial clusters of small firms would offer an alternative organizational form to large firms. The basic reference here is to industrial districts, which were first popularized, as a theoretical concept, by Italian economists trying to explain the unpredicted degree of resilience and vitality of a few local economies of north-eastern and central Italy, which are at the same time specialized in traditional manufacturing, and based upon a high number of small firms. The very high degree of labour division (which allows even the smallest firms to achieve relevant economies of scale), the rapid diffusion of innovations, and an institutional and cultural environment sustaining entrepreneurial natality and intra-firm labour mobility, explained much of the success of these districts. Later the debate has referred to the degree of generality this organizational form may assume, with some researchers spotting districts or small firms’ networks (a related concept) not only in the rest of Italy and in Europe, but also in the US and Japan (BECATTINI, 1989; AMIN, 1989). Going further, it has been observed that clusters of small firms are very common also in microelectronics and a few other science-based new-born industries. Based upon the 6 observation that these clusters share many characteristics with industrial districts, quite a controversial literature has started investigating the possibility that small firms’ networking will provide, in the future, a generally valid organizational alternative to vertical integration and big size, and a better guarantee against deindustrialization risks (SCOTT, 1986; STORPER,

1992; PHELPS, 1992). A third line of critique to simplistic applications of the life cycle approach states that local economies permanently differ in their ability to attract companies’ headquarters and main plants (which bring with them the companies’ strategic functions), and/or to favour entrepreneurial natality (a key ingredient for district-like manufacturing clusters). These differences are explained by the different distribution of auxiliary and indirect services to production (FRITSCH, 1992; MARSHALL AND WOOD, 1992; BEGG, 1993). Similarly, it has been shown that innovative activities in manufacturing are more concentrated than purely productive ones (BRESCHI, 1995). They tend to cluster up where services to production are more densely located (ANTONELLI, 1988), and where related innovative activities were previously started, thus giving birth to remarkable phenomena of increasing returns at the geographical level (JAFFE, TRAJTENBERG, AND HENDERSON, 1993). In addition, it has been shown that innovations which diffuse in areas dominated by branch plants, and small independent firms, are more likely to be process innovations, whose deleterious effects on employment we have already mentioned (GIBBS AND EDWARDS, 1985). Most of the agglomeration economies which explain local differences in innovativeness and protection from deindustrialization take place at the urban level. But size cannot entirely explain the position of a city in the ranking. First, the extent at which agglomeration economies can be exploited is limited from above; second, innovative spillovers are likely to be heavily path-dependent, thus leaving a few big urban centres at the bottom of the hierarchy, and pushing up some mid-size ones. The three lines of revision of life cycle theories we have summarized above do support the filtering down interpretation, although with three key qualifications. First, in many cases what differentiates lower rank areas from the higher rank ones is not their industrial specialization as such, but rather the higher exposure to the risks of plant closure and restructuring, which in turn is explained by the service endowment and the degree of proprietary control of local plants. Second, a filtering upward process may be also detected, which mark the very first stages of the life cycle of new-born manufacturing industries and indirect services. As long as demand is scarce and scattered, they can be provided along with traditional activities on an occasional basis, by unspecialized tertiary firms to be found in any city. But as soon as their demand grows, enough room for specialized firms appears; these will tend to cluster up in the top-of- the-hierarchy cities, where demand and specific inputs are more likely to be found

7 (CAPPELLIN, 1990). This implies that areas hosting cities which are net losers in this process may end up with a higher risk of deindustrialization; in other words, filtering upward processes may stretch the regional hierarchy along which the deindustrialization risk is distributed. Third, some degree of stickiness in local industrial specialization may exist, but it is more likely to emerge at the level of cities or metropolitan areas, rather than broad regions. Besides, this possibility does not apply to all manufacturing industries in the same way, being more likely the greater the amount of dedicated infrastructure which the industry requires, and technical indivisibility within individual firms. Some differences also exists between regional economies in Europe and in the US, the former being more complex and therefore less likely to be affected by generalized process of manufacturing decline (CHESHIRE, 1991).

4. Deindustrialization in Lombardy: filtering processes and a few isolated cases The empirical research is based on two main data sources: the regional time-series on value added by Istat (Istituto Centrale di Statistica), and the data set by ASPO (Archivio Statistico Provinciale sull’Occupazione) on employees and business units, by municipality. ASPO is quite a new and a very valuable statistical source. The data presented here derive from two censuses of business units (b.u.), in 1981 and 1991, with additional information on the number of units still active in 1981, but no more in 1991 (closed b.u.), and the number of units not yet founded in 1981, but active in 1991 (new b.u.). For each category of b.u. it is also known the total number of employees. It is therefore possible to measure not only the stock of firms and employees within the two census years, but also a sort of flow. This second measure is rather imperfect, because it misses all the b.u. founded and then closed within the time interval; besides, employment data referred to closed b.u. have not been registered in the closure year, but in 1981 (thus overestimating the loss of employees due to closures, which presumably have affected declining b.u.), while employment data referred to new b.u. have not been registered in the foundation year, but in 1991 (thus creating a bias of unknown sign, being possible either an increase or a decrease of employees in new b.u.). Despite this drawback, ASPO data represent one of the most valuable sources for regional economists interested to Lombardy.

4.1 Sectorial trends Table 1 summarizes the evolution of value added in Lombardy between 1981 and 1991, by 1- and 2-digit sectors of activity (Ateco 81 classification). As for 1-digit data, they clearly show how a tertiarization process occurred in Lombardy, which, although impressive, cannot be explained by an overall decline in manufacturing. It rather appears that the growing weight

8 of services has had a counterpart in a striking decline of the energetic and building industries. The rate of manufacturing growth has been quite a satisfactory one, not very far from saleable services, and well beyond non-saleable ones. Table 1. Value added in Lombardy, by industry: average growth rates, 1981-1991, and shares growth 1 share ‘812 share ‘912 Agricolture 2.2 2.0 2.0 Energy and water -1.1 4.0 2.7 Manufacturing 2.3 35.2 35.5 - Metallic minerals, mining and transformation -1.7 5.8 3.6 - Other minerals, mining and products 1.0 3.4 2.9 - Chemicals and pharmaceuticals 5.8 8.8 12.5 - Metal products; electrical and non-electrical machinery and appliances; electronics 2.3 35.3 35.4 - Means of transport 1.1 5.3 4.4 - Food, beverages, and tobacco 3.1 7.1 7.6 - Textiles, clothes, leather, and shoes 1.6 17.8 16.5 - Paper and publishing 3.2 6.9 7.5 - Woodworks, rubber, and other manufacturing 2.3 9.6 9.5 Construction -17.6 6.6 5.4 Market services 2.9 44.4 47.5 - Trade and hotels 2.1 41.9 38.8 - Transportation and communications 3.6 10.2 11.1 - Financial services 2.8 12.1 12.0 - Other market services (incl. Business services) 3.5 35.7 38.1 Non-market services 0.9 7.9 6.8 TOTAL 2.2 100 100 1 average of the yearly growth rates, 1981-91; 2 percentage values; source: ISTAT

Passing to 2-digit data one notices immediately how the various manufacturing classes have registered extremely different rates of growth. “Chemicals” have grown impressively, and the growth rates of “Food, Beverage and Tobacco”, and “Paper and Publishing” have not been much inferior to those of the most dynamic services (“Transport and Communications”, and “Other manufacturing”; the latter encompasses most of the indirect services to production). On the other hand, “Means of Transport” and “Non-Metalliferous Minerals” have grown very sluggishly, while “Metalliferous Minerals” have indeed declined in absolute value. As for the two biggest industries of the region, we notice that “Metal products” have grown enough not to lose any weight on the regional value added, while “Textiles, Clothes, Leather and Shoes” have gone the opposite way. Unfortunately, data on regional value added come under a very aggregate and obsolete classification, so that “Metal products” encompass also machinery, electric appliances and electronics. To say more on these industries we need to refer on employment data, which come with a better classification system. Employment data do confirm the extent of the tertiarization process, as well as the heterogeneity of trends within manufacturing (tables 2 and 3). They also make clear where many claims of deindustrialization in Lombardy come from: the region has indeed registered impressive job losses in manufacturing, whatever industry is considered (the residual class

9 “Other manufacturing” being the only exception). At the same time tertiary employment has boomed, particularly in “Financial and Business Services”, up to a level which has allowed a compensation for job losses in manufacturing and a positive rate of growth for overall employment (table 2).

Table 2. Variations of business units and employees in Lombardy, by 1-digit industry; 1981-91 Business units Employment Net natality Weighted net growth 1 growth 2 rates .3 nat. rates 4 Agricolture 2.1 25.4 2.2 12.8 Energy and water 8.5 -9.0 8.2 8.1 Bulk manufacturing 4.3 -18.8 4.7 -8.1 Durable goods manufacturing 14.4 -9.9 14.4 -1.6 Traditional manufacturing -1.0 -12.2 -1.0 -7.5 Construction 22.6 17.7 22.4 66.2 Trade and hotels 3.6 9.1 3.6 7.1 Transportation and communications 14.3 11.6 14.5 11.2 Financial and business services 87.4 60.1 87.2 55.3 P.A., altri servizi 15.1 27.0 15.4 26.3 TOTAL 11.8 2.2 11.8 8.8 1 (B.u. 1991-B.u. 1981)/B.u.1981; percentage values; 2 (Employees1991-Employees1981)/Employees1981; percentage values 3 (New b.u. - Closed b.u.)/ B.u.1981; percentage values; 4 (Employees in new b.u. - Employees in closed b.u.)/ /Employees1981; percentage values source: elaborations on ASPO

Thus the 1980s have marked an impressive growth in productivity for manufacturing, whose trends for employment and value added appear to be at odds. At the same time, the class of services which has grown most, either in value added and in employees, has been the most productive one, and the most closely related to production (rather than consumption) activties, i.e. Financial and Business services. Trying to understand whether restructuring is due more to plant closures than to rationalization and innovation processes within existing firms is not an easy task, but a quick overview of trends in net natality rates may suggest a few basic observations. Net natality rates refer to counts of new vs. closed business units; weighted net natality rates refer to employees in the two categories of b.u.. In “Traditional manufacturing” both rates are negative: this means that new-born firms are less than the closed ones, and that their average size is smaller. The observation about size suggests that most of the new firms have had few market opportunities to exploit, and little room for growth. In any case they have been unable to counter the reduction in the number of firms, which means that plant closures must have been so many to be considered as the main cause for the overall employment reduction. The loss of employees by surviving firms has probably played a relatively minor role. The 2-digit traditional industries which have suffered most of this trend are the main ones, that is “Textiles”, and “Shoes and Clothing”. This sounds coherent with the sluggish growth reported by value added data. The demographic trends in “Durable goods” are much less alarming. The number of b.u.

10 increases in all the 2-digit sectors, and the net natality rates are positive. This suggests a much lighter impact of plant closures than in traditional manufacturing. As for the nature of the new firms, a clear-cut difference separates the two main mature industries (“Metal Products” and “Machinery and Mechanical Equipment”) from the relatively younger ones (“Electrical and Electronic Supplies” and “Scientific and Precision Instruments”), being the weighted net natality rates negative for the former, and positive for the latter. This suggests that in less mature industries a relatively high number of new firms may have been founded to enter new market niches, thus adding some fast growing firms to the those founded on a pure self- employment basis by laid-off workers. Table 3. Variations of b. u. and employees in Lombardy, by 2-digit manufactring industry; 1981-91 Business units Employment Net natality Weighted net growth 1 growth 2 rates .3 nat. rates 4 Bulk manufacturing 4.3 -18.8 4.7 -8.1 Metallic minerals, mining and preparation 0.0 -38.2 2.7 -39.8 Metallic minerals, transformation -0.5 -33.4 0.3 -13.6 Non-metallic minerals, mining -12.2 -6.3 -11.7 -9.4 Non-metallic minerals, products 6.1 -19.4 6.3 -11.3 Chemicals 9.1 -8.9 9.2 -2.9 Artificial fibres 3.9 -42.0 9.1 -12.4 Durable goods manufacturing 14.4 -9.9 14.4 -1.6 Metal products 4.2 -3.9 4.2 -1.1 Machinery and mechanical equipment 20.0 -13.3 20.3 -4.7 Office machinery and EDP equipment 90.8 -17.6 91.2 -21.2 Electrical and electronic supplies 21.0 -6.0 21.2 2.8 Motor vehicles 13.5 -40.8 14.8 -3.9 Other means of transport 9.6 -5.2 11.4 -6.0 Scientific and precision instruments 47.1 -1.1 46.2 4.7 Traditional manufacturing -1.0 -12.2 -1.0 -7.5 Food (basic) 3.0 -13.4 3.1 -15.4 Sugar, tobacco, and food n.e.c. 8.6 -26.6 8.9 -18.4 Textiles -8.4 -18.2 -8.3 -11.8 Leather, and products thereof -2.5 -16.5 -2.4 -6.5 Shoes and clothing -4.8 -12.0 -5.2 -8.4 Woodworks, and wood furniture -9.8 -10.6 -9.5 -8.3 Paper and publishing 13.9 -5.4 14.0 0.6 Rubber and plastic products 14.7 -3.9 14.7 1.7 Other manufacturing 29.0 1.6 29.2 6.2 1 (B.u. 1991-B.u. 1981)/B.u.1981; percentage values; 2 (Employees1991-Employees1981)/Employees1981; percentage values 3 (New b.u. - Closed b.u.)/ B.u.1981; percentage values; 4 (Employees in new b.u. - Employees in closed b.u.)/ /Employees1981; percentage values source: elaborations on ASPO

Bulk manufacturing industries are the most heterogeneous group. The clearest trend it emerges is the dramatic decline of the two industries linked to mining, and of the one linked to steel production (“Metallic minerals transformation”), both mirrored by the value added data. 4.2 Local trends: methodology Data on local trends are available only for employment and business units (ASPO data set). A preliminary step in data treatment has consisted in summing up the original figures, 11 originally disaggregated by municipality, into 78 areas, corresponding to the local units for the administration of health and social services (USSL). This aggregation smooths down the local rates of employment growth and decline, and makes the computational work easier; at the same time it preserves the most meaningful local trends. Figure 1. Employment distribution in Lombardy, by USSL (% values)

source: elaborations on ASPO Figure 1 is the reference map for employment density in Lombardy. It clearly shows the very high density corresponding to the boundaries of municipality, surrounded by a few high density areas (Rho, , and , respectively to the West, the North, and the east of the city) and many medium-density ones (the most important being, to the North: , Desio and , sites of historical manufacturing plants; to the East: Vimercate; to the South: ). The other high density areas correspond, from East to West, to the main cities of Lombardy beside Milan, i.e. Brescia and Bergamo, and to Gallarate, one of the oldest manufacturing areas, still nowadays hosting an important and large industrial district. Also notable is the area surrounding the lake in between Bergamo and Gallarate, which hosts the two cities of Como and Lecco, whose manufacturing traditions are as old as those of the other areas we have mentioned above. In order to identify as clearly as possible the areas which could have been hit hardest by the restructuring process, we have chosen to bring to the surface the most extreme local cases of deindustrialization and tertiarization. In addition, we have focussed our attention on the behaviour of cities and industrial districts. The identification of the local cases has been based on a cluster analysis aiming to group USSLs according to three variables:

12 - INDX, representing the variation in manufacturing jobs within the USSL, relative to the regional variation; being the latter negative, INDX takes negative values for the few USSLs whose manufacturing employment has grown, values between 0 and 1 for USSLs which have lost relatively less jobs than the region, and values greater than 1 for the others; - SERX, representing the growth of jobs in services within the USSL, relative to the regional growth; being the latter positive, it indicates a higher than average growth if greater than 1, a positive, but smaller than average growth if comprised between 0 and 1, and an loss of employees if negative; - AREAX reflects the combined effects of the two previous indexes. Being the regional variation of employees in all industries positive, its interpretation is the same as that of SERX. The method chosen for the cluster analysis has been the centroid one, one of the simplest, which more than others helps in identifying the outliers. Its main drawback consists in leaving a large intermediate cluster whose interpretation may be doubtful. To compensate for this drawback we have followed two steps. The first step has been based on variables SERX and INDX, and all the USSLs in Lombardy. This exercise has allowed us to identify five clusters: four represent the extreme cases we were looking for, while the remaining (“intermediate”) one is the central cluster which collects all the other USSLs. In the second step we have focussed on the intermediate cluster, trying to break it as much as possible. In order to do this we have considered only the USSls whose manufacturing specialization index in 1981 was greater than one. Adding AREAX to INDX and SERX we have re-iterated the cluster analysis, thus obtaining three more clusters, respectively characterized, above all, by stable, increasing, and decreasing employment levels. After completing the cluster analysis we have explored the causes of the different trends, in order to identify which sectors within each USSL revealed a good or bad performance, and whether this performance supported or damaged the local employment rates. To do this we have performed twice a two-component shift-share analysis of local trends. In the first application, the first component represents the expected employment change, on the basis of the regional trend (SA), so that the second component, i.e. the residual one, can be considered as the competitive one (C). In the second application, the first component represents the expected change within each local sector, on the basis of the overall trend of local employment (T), with the residual component thus representing the differential performance of the specific sector vs. the local average (sectorial component, S)4. Components S and C are those which call for our attention. They have been combined in four classes, from 1 to 4. Classes 1 and 4 are most easily interpreted, since they mark those industries which, at the local level, have respectively:

13 - hampered the employment growth (or worsened the employment loss; S<0), and performed worse than the regional average (C<0); - sustained the employment growth (or limited the employment loss; S>0), and performed better than the regional average (C<0). Class 2 refers to industries which at the local level have outperformed the region as a whole (C>0), but without being able to contribute positively to the local employment trend (S<0). Class 3 refers to the remaining combination.

4.3 Local trends: analysis Figures 2 and 3 map the results of the first step of the cluster analysis, while table 4 provides the values of INDX, SERX and AREAX for each cluster (for the intermediate cluster and its sub-clusters we provide average values only). They clearly show the high degree of heterogeneity of structural change within local economies. Figure 2. Cluster analysis: first step.

1 Industrialization; 2 Tertiarization; 3 Intermediate cluster (USSLs with industrial specialization); 4 Deindustrialization and tertiarization; 5 Deindustrialization; source: elaborations on ASPO

Cluster 1 collects all the USSLs which have been subject to what clearly is a positive “industrialization” process, being INDX negative and AREAX positive. Two of these USSLs are very marginal ones, being localized in the northern part of the region, where population density is scarce, as well as any manufacturing activity. More interesting are the Southern and Centre-Eastern areas, which, although marked by quite a low specialization index in manufacturing, are placed around small districts, which the following analysis will reveal to be very lively; besides, they belong to larger areas where small firms do play a very active

14 role. Finally, a single USSL (Cassano d’Adda) stands right in the centre of the region, between the two cities of Milan and Bergamo. All areas in the cluster have relatively modest values for SERX, that is do not owe very much of their growth to the service sector, the only notable exceptions being Cassano d’Adda and Seriate, both being part of metropolitan systems (Milano and Bergamo respectively). Cluster 2 is extremely compact from the geographical point of view. It takes all the south- eastern belt of the Milanese metropolitan area, and it is characterized by extremely high values of SERX, which explains the definition of “tertiarization” given to the process of structural change which affects them. This process is a beneficial one, being values of AREAX extremely high. Apart from one case, the original specialization in manufacturing was already low, and it has been completely lost in 1991. Tertiarization in these areas is explained by the contemporaneous processes of filtering upward of Business Services to Milan, and by the relocation of the most standardized among them and of many Transport and Communication services from within the overcrowded city centre to its outskirts. Being these areas relatively free from pre-existing manufacturing plants and connected infrastructures, the net effect has been one of employment growth, in the absence of remarkable job losses in manufacturing. Figure 3. Cluster analysis: second step (breack-up of cluster 3).

1 Stability; 2 Development; 3 Crisis; source: elaborations on ASPO

Cluster 4 collects the most notable cases of “tertiarization with deindustrialization”. Apart from Laveno Mombello, these areas belong to the traditional manufacturing core of Milan, being placed within the north-west metropolitan belt. They were (and still are) very densely

15 populated and used to host the biggest manufacturing plants of the region, with specializations in Steel, Means of transport, Metal products and Machinery. Along with them a great number of small firms were present, being active both as contractors of the large plants, and in a wide range of traditional manufacturing sectors. It is clearly seen that the creation of service jobs has been much less remarkable than in the “tertiarization” areas, being therefore unable to compensate for the decline of manufacturing5. Finally, Laveno Mombello is a very small area in the North of the region. Its crisis is largely induced by the closure of a single plant, which accounted for most of the manufacturing jobs. Its case is indeed more similar to those of cluster 5. Table 4. Cluster analysis, steps 1 and 2; values for INDX 1, SERX 2, AREAX 3 and the industrial specialization index 4, 1981 and 1991

Industrial Industrial Località (nome USSL) Province INDX SERX AREAX specialization specialization index, 1981 index, 1991 Cluster 1 - Industrialization Costa Volpino (Bergamo) -0.87 1.42 7.94 1.11 1.21 Romano di Lombardia (Bergamo) -1.59 1.43 11.8 0.91 1.01 Seriate (Bergamo) -2.06 2.72 13.4 1.15 1.3 Montichiari (Brescia) -0.69 0.94 4.64 1.13 1.29 Cassano Adda (Milano) -1.32 2.14 12.34 1.11 1.18 Asola (Mantova) -2.82 0.75 10.59 1.12 1.42 Castiglione delle Stiviere (Mantova) -1.79 1.00 8.12 1.03 1.24 Morbegno (Sondrio) -0.96 1.15 7.48 0.89 0.99 Tirano (Sondrio) -1.28 0.87 5.18 0.45 0.54 Cluster 2 - Tertiarization (Milano) -0.17 3.31 12.88 1.06 0.98 Pioltello (Milano) 1.16 3.21 7.42 1.12 0.96 (Milano) 0.64 4.07 12.04 1.15 0.98 S.Donato Milanese (Milano) 1.13 2.87 6.02 0.8 0.71 Vimercate (Milano) 1.07 5.72 4.89 1.42 1.29 Cluster 3 - Intermediate 3.1 - Stability * 0.77 1.30 0.64 1.22 1.26 3.2 - Development * 0.23 1.44 3.20 1.19 1.26 3.3 - Crisis * 0.91 1.03 -1.58 1.24 1.32 Cluster 4 - Deindustrialization and tertiarization Cinisello B. (Milano) 1.80 0.86 -0.76 1.06 0.98 (Milano) 2.18 1.21 -5.19 1.26 1.22 Rho (Milano) 2.32 2.21 -4.94 1.34 1.25 Sesto S.G. (Milano) 2.13 1.70 -1.49 1.16 1.04 Laveno Mombello (Varese) 1.60 1.06 -4.69 1.33 1.39 Cluster 5 - Deindustrialization Dongo (Como) 3.13 0.34 -7.87 1.06 0.93 Vigevano (Pavia) 2.28 -0.11 -7.57 1.08 1.09 1 2 Employment variation in manufaturing: Local/Regional; Employment variation in services: Local/Regional; 3 4 Total employment variation: Local/Regional; Share of manufacturing on total jobs: Local/Regional * Average values, source: elaborations on ASPO

Cluster 5 accounts for two USSLs only, whose employment losses come from manufacturing, and have not been compensated by any significative growth in services. These, as well as Laveno Mombello, are cases of “endogenous” deindustrialization, being their crisis not related to any process of regional relevance, but simply to local causes. In 16 Dongo these can be summarized in the closure of the only big manufacturing plant, while in Vigevano they consist in the sudden decline of the local industrial district. With regard to cluster 3, the second-step application of cluster analysis reveals how the central areas within cluster 1 are surrounded by other USSls whose overall employment performance has been remarkably good, and largely due to higher-than-average values for SERX, and lower-than-average values for INDX (cluster 3.2: “development”). Other USSLs of the same kind are scattered in the Western part of the region. Two USSLs seem to suffer of local problems (cluster 3.3: “crisis”), and are both localized to the East. The remaining areas have had a stable employment (cluster 3.1: “stability”). The identification of the industries which are responsible for the local trends we have just seen is a very time consuming exercise. We have summarized the most relevant information in a few maps, which refer to the most important 2-digit industries. Industry by industry the maps report the four classes obtained by the shift-share analysis, for all the USSLs which in 1981 were specialized or nearly-specialized in them. Figure 4. Business services: shift-share analysis (USSL with specialization index > 0.9)

1 S<0, C<0; 2 S<0, C>0; 3 S>0, C<0; 4 S>0, C>0; source: elaborations on ASPO

Figure 4 confirms the filtering upward thesis regarding Business services. Their geographical concentration is remarkable; besides, they support the employment performance of Milan and its surroundings, where they also grow more than anywhere else in the region. Bergamo, which lies at the centre of the widest cluster undergoing industrialization, share these features. Employment in Brescia, Como and Varese also owes very much to this sector, which anyway it develops there less than in the capital.

17 Figure 5. Electrical and electronic supplies: shift-share analysis (USSL with specialization index > 0.9)

1 S<0, C<0; 2 S<0, C>0; 3 S>0, C<0; 4 S>0, C>0; source: elaborations on ASPO

Similarly, but to a lesser degree, the Electrical and Electronic industry tends to concentrate around Milan (figure 5). There it counters the deindustrialization process of Legnano and Rho (which belong to cluster 4), and, most of all, supports the employment growth in the areas belonging to cluster 2. It also explains part of the good performance of Vimercate (cluster 1), and Crema (cluster 3.2). Figure 6. Metal products: shift-share analysis (USSL with specialization index > 0.9)

1 S<0, C<0; 2 S<0, C>0; 3 S>0, C<0; 4 S>0, C>0, source: elaborations on ASPO

18 The other industries are much less concentrated. “Metal Products” have a positive competitive component (C>0) in all clusters of type 1, 3.1 and 3.2 surrounding Bergamo (figure 6). There it also contributes, in all cases but two, to the employment growth (S>0). Metal products play the same role in two out of the other three USSLs belonging to cluster 1. On the contrary, its contribution to USSLs in clusters 3 and 4 is most often negative (S<0), and sometimes worse than for the regional average (C<0). The “Machinery and Mechanical Equipment” industry most notably counters the deindustrialization of Vigevano and Rho, while it contributes to the deepening of the same process in the rest of clusters 3 and 4 (no figure). At the same time, it explains part of the success of a few areas within cluster 3.2. Figure 7. Textiles: shift-share analysis (USSL with specialization index > 0.9)

1 S<0, C<0; 2 S<0, C>0; 3 S>0, C<0; 4 S>0, C>0; source: elaborations on ASPO “Textiles” (figure 7) and “Shoes and clothing” (no figure) contribute positively to the performance of areas in cluster 1. The former is particularly relevant for the most southern USSLs within the cluster, while the latter largely overlaps the effects of “Metal Products” within the areas of cluster 1 surrounding Bergamo. At the same time they enter the deindustrialization process within Vigevano as a key determinant, and present negative values for both C and S in almost all the western part of the region. Overall, these trends seem to suggest a filtering downward process of the main traditional industries, which favours the eastern and southern parts of the region, where most USSLs belonging to clusters 1 and 3.2 may be found. On the contrary, the Electronic industry and the Business services go hand in hand through a filtering upward process, which explains the good performance of the Milanese southern belt. Finally, the mechanical and metalworking

19 industries, while geographically scattered, do not seem to play any clear cut role across the clusters, being largely ruled by local dynamics.

4.4 Any role for the industrial districts? Table 5 summarizes the key evidence which ASPO data provide with regard to industrial districts in Lombardy. The 20 districts described by the table are those recognized, for purposes of industrial policy, by the Regional Council of Lombardy (Regional Law n.317/91). Table 5. Industrial districts: growth of employment and b.u. shares, pure and weighted net natality rates, and competitive component in employment variation Industrial specialization Net Weighted Competitive District * Province Cluster ** Share of ∆share2 natality net natality component5 employees1 rate3 rate4 Metal products Camuno sebino (Brescia) - 0.6 0.12 10 5.2 282 Valtrompia (Brescia) 3.1 6.7 -0.15 -6.7 -5.3 -339 Metal products; Machinery e mechanical equipment Trevigliese (Bergamo) 3.1 3.7 0.07 17.6 4 280 Lecchese (Como) - 6.6 -0.13 4.5 -7.3 -495 Besana in Brianza (Milano) - 1.2 0.12 18.6 6.7 480 Belgioioso (Pavia) - 0.3 -0.05 7.3 -23.8 -197 Textiles Comasco (Como) 3.2 10.5 1.52 8.4 -7.4 2220 Castelgoffredo (Mantova) 1 2.9 3.05 1.7 35.5 4443 Shoes and clothing Bassa bresciana (Brescia) 1 6.5 0.32 9.4 -7.7 356 Textiles; Shoes and clothing Valseriana (Bergamo) 3.2 6.0 0.33 -0.6 -2.9 564 Palazzolo sull'Oglio (Brescia) 3.3 3.7 0.31 16.7 5.7 535 Oltrepò mantovano (Mantova) - 0.4 0.13 -23.8 13.5 216 Lomellina (Pavia) 5 1.8 -0.6 -29 -25.7 -1032 Asse sempione (Varese) 3.1 28.3 -1.56 -8.5 -14.3 -2686 Woodworks, and wood furniture Valbrembana (Bergamo) 3.2 0.4 -0.06 -9.8 -10.7 -231 Brianza (Como-Milano) 3.1 39.3 -3.95 -19.3 -14.5 -3359 Casalasco viadanese (Cremona- - 2.7 -0.05 -24.1 -18.6 -41 Mantova) Rubber and plastic products Sebino bergamasco (Bergamo) 1 1.9 1.71 72.7 72.3 1255 Other manufacturing(toys) Canneto sull'Oglio (Mantova) 1 4.0 -1.29 -25 -20.1 -339 Shoes and clothing; Machinery and mechanical equipment Vigevanese (Pavia) 5 4.7 -1.42 -27 -34.2 -3859 * Districts have been obtained according to the Regional Law n.317/91. ** Cluster where the USSL of the district is placed 1 Employees in the district / Employees in the region, for the mentioned industries; % values; 2 Percentage change of (1), from 1981 to 1991 3 4 (New b.u. - Closed b.u.)/ B.u.1981, in the mentioned industries; % values; (Empl. new b.u. - Empl. closed b.u.)/Empl.1981, in the mentioned ind.; % values 5 Competitive component of the district in the employment variation, as obtained from a shift-share analysis. source: elaborations on ASPO

We attribute to each district the business units and employees of the municipalities which concur to form the district itself, for the 2-digit industries which the latter is specialized in. By doing so we probably overestimate the size and product extension of many districts, being the market niches they cover much more narrowly defined than any 2-digit classification. In any

20 case, this is the nearest one can go by using ordinary general-purpose statistical sources such as ASPO. There is no evidence of any peculiar capability of industrial districts to counter the risks of deindustrialization, or to go against the general trend of the geographical area and the specific industry they belong to. A quick look at the data on the employment share variation and at the competitive component of the shift-share analysis shows that districts more or less conform to the overall trend of the province they belong to. All districts in Bergamo province outperform the region as a whole, with the only exception of the Valbrembana district, specialized in Woodwork and Furniture (all districts in this sector behave worse than the region); all of them belong to USSLs we have been able to classify by means of the cluster analysis, and perform accordingly to the cluster they belong to (the only exception being again the Valbrembana district). At the other end, all districts in Pavia province follow the local trend, which is negative one; not surprisingly two of them belong to the Vigevano USSL, that is to cluster 5. Even worse, the two biggest districts (Asse Sempione, specialized in Textiles and Clothing, and Brianza, specialized in Furniture) both placed in the western part of Lombardy, contribute negatively to the performance of the clusters they belong to. Evidence about the districts placed in Mantova and Brescia provinces is more confusing. A cross-industry examination of the districts confirms the absence of any particular strength. In Textiles and Clothing, as expected, all Eastern districts perform well, often registering positive net natality rates (both pure and weighted); on the contrary, western ones register negative rates. Districts specialized in Woodwork and Furniture behave as the regional average, no matter where they are placed. The evidence for districts specialized in Metal products and Mechanical equipment is more scattered, but this does not come as a surprise, having we observed that the same is true for all USSLs in the region. The Vigevanese district deserves a special mention, since our data clearly show that its nature has been radically altered by the events occurred in the past decade. Originally born as a vertically integrated district (Shoes plus specialized Machinery and Equipment), it has now completely lost the bottom end of its specialization: a quick look back to figures 7 and 9 will confirm that all of its bad performance is due to the Shoes industry, while the mechanical one has been the only barrier to an even worse deindustrialization of the area.

21 5. Conclusions In this article we have shown how, despite many worries, no general process of deindustrialization has affected Lombardy in the past decade. At a general level the impressive loss of manufacturing jobs has been compensated by the growth of services. At the local level, deindustrialization has hit hard a few local economies, either because of endogenous causes, or because of a generalized reshuffling of industrial localization, due to contemporaneous filtering upward and filtering downward processes. At the same time, these processes have helped the core area of the region to keep up its employment levels, and a few Eastern and Southern areas to increase theirs. Overall, the tertiarization process occurred in Lombardy has countered, rather than favoured, the deindustrialization one. This is certainly true for the core metropolitan area (Milan), where the employment growth in services has compensated the employment loss in manufacturing, but also for the fast growing areas of the East, whose main cities, and particularly Bergamo, have considerably increased their service endowment. Industrial districts identified by the Regional Council have shown no special strength when compared to the rest of the region, having them fully shared the same destiny of similarly specialized areas. Provided that some priorities in the Regional policies have to be identified, these results clearly seem to suggest that most care should placed in nurturing the tertiarization process, rather than directly countering the deindustrialization one, with relief measures. In addition, one should ask whether the same filtering upward processes which have taken place within Lombardy are currently passing through Lombardy itself and the other European regions. If this is the case, one should also ask whether such a process is beneficial or not. With this respect, a thorough analysis of the innovative content of the service activities of Lombardy would be extremely helpful. The few researches which have been carried out on this subject have indeed revealed some disquieting truths, such as the relatively low tradability and technological content of service activities in the Milanese area, when compared to other urban agglomerations within the leading European local economies (IRER, 1988; OETAMM, 1993). This relative backwardness seems to be in line with a more general technological backwardness of the region, which patent-based inter-regional comparisons on a European scale have also revealed (MALERBA, 1993). If these worries were confirmed by further research work, the conclusion would be that Lombardy’s position, although high in the Italian hierarchy, is at risk in the European one. The policy recommendation of strengthening the regional service infrastructure would therefore deserve further attention.

22

NOTES 1 See pages 62-65, and 110-114 in BHAGWATI, 1989, and chapter 10 in KRUGMAN, 1994. See also LAWRENCE, 1987. 2 In doing so, it follows mainly KUTSCHER AND PERSONICK, 1986, MASI, 1989, and CAMAGNI, 1991. 3 A third key line of debate has referred to the international tradeability of services, which we cannot discuss for lack of space. For a reference, see BHAGWATI, 1987. 4 For the methodology see BIFFIGNANDI, 1993. 5 The northest area of these four (Legnano) had anyway some greater degree of indepedence from the others. Besides, Cinisello Balsamo seems to have been relatively unaffected by the tertiarization process, although we know it underwent it before the others (in the 1970s).

ACKNOWLEDGEMENTS This article is part of a wider research project, sponsored by the Regional Council of Lombardy and coordinated by IReR (Regional Institute of Research). I am indebted to Flavio Boscacci and Adolfo Carvelli for their helpful support to the background research work, and to Stefano Breschi, Roberta Capello, Antonio Guarino, Franco Malerba, and Marco Vivarelli for their comments to a previous draft of the article. The usual disclaimers apply.

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26