Local Industrial Systems and the Location of FDI in Italy Lisa De Propris, Nigel Driffield and Stefano Menghinello Abstract This paper investigates the local determinants of FDI location across Italian manufacturing industries. Specifically it examines the importance of industry-specific local industrial systems as potential catalysts for attracting FDI. The paper develops a model of FDI location choice using a unique FDI database, stratified by industry and province. This extends previous analysis beyond the mere density of activity, to analyse the specific nature of agglomerations and their importance for attracting inward investment. The results also suggest that the importance of agglomeration differs between industries, and offers some explanation for this. Keywords: Local industrial systems, knowledge sourcing, agglomeration, count data econometrics. JEL: F23, R12 Lisa DePropris
Nigel Driffield
Stefano Menghinello
Birmingham Business School The University of Birmingham, Edgbaston, Birmingham B15 2TT
[email protected]
Aston Business School, Aston University, Birmingham B4 7ET
[email protected]
ISTAT, Italy and Birmingham Business School The University of Birmingham, Birmingham B15 2TT
[email protected]
∗
Lisa DePropris and Nigel Driffield gratefully acknowledge the financial support of the Nuffield Foundation for the funding of this research (Grant number: SGS/00741/A). Thanks are also due to participants at the EUNIP 2003 annual conference and l’institute-Bimingham Seminar Series for comments on earlier drafts of this paper.
1
1.
Introduction
Traditionally, Italy has few policy initiatives designed to attract inward foreign direct investment (FDI). Many countries in the developed world have come to see the attraction of inward investment as synonymous with regional development, a position that is extended by the various EU level policy initiatives designed to attract internationally mobile capital to certain regions. Recently the stance of the Italian government appears to have changed, with the strengthening of regional agencies seeking to promote local development by attracting inward FDI. The extent to which such policies have been effective is however yet to be tested. As a result of this apparent ambivalence, Italy has received much smaller levels of foreign investments compared with the other members of the EU, and Italy ranks 109th in terms of potential foreign investment attractiveness (UNCTAD, 2003). Initial analysis suggests that foreign owned firms in Italy are concentrated in sectors with significant scale economies. Such industries account for 42.8 per cent of the total FDI. Industries that are more specialised than average attract 24.4%, while R&D intensive industries attract very little. Foreign firms in R&D intensive industries are mainly located in metropolitan areas, while foreign-owned firms in older traditional industries are mostly located in peripheral areas. In the following analysis we extend this by considering Pavitt’s taxonomy (1984) in order to examine the determinants of inward investment in Italy across industries. This therefore extends the work of Piscitello-Mutinelli (1994) and Basile (2003) who argue that variation in inward FDI in Italy essentially an industry level phenomenon. The dominant model of the motivation for a firm to enter a foreign market through FDI has changed little since the seminal work of Dunning (1958) and Vernon (1966). The basic framework has been one which envisages the firm generating certain firm specific assets in its home country, then seeking to exploit these further by creating income generating assets abroad. The importance of locational or “pull” factors was very much secondary to this, in explaining which host countries the MNE chose to locate. There is a relatively large literature that seeks to relate location specific factors to the determinants of inward investment across countries, regions or industries, often based on the importance of agglomeration or the possible links between domestic sector and inward investors. Cantwell (1991), for example, shows 2
that there are significant benefits to both domestic and foreign firms from agglomeration (see also Shaver, 1998). Location advantages at the local or regional level could be self perpetuating where further growth of an industry sector makes the location even more attractive (Head, Ries and Swenson, 1995, see also Krugman, 1991). Under such circumstances random location decisions in the past can result in the development of specialised support infrastructure and a concentration in a given industry (Wheeler and Mody, 1992). While the importance of agglomeration for attracting FDI has been explored, following the work of Coughlin et al. (1991), the importance of spatial organisation of activity, or clustering for attracting FDI is largely unexplored. This is particularly important in the Italian context, where Local Industrial Systems (henceforth LISs) have a long history. Equally, this provides a link to the relatively recent concept of so called technology (or knowledge) sourcing FDI that is developing within the international business literature. This is discussed in more detail in the following section. In this paper, LIS are defined as local concentrations of firms specialised in one or a few related sectors. This paper examines the importance of local characteristics and agglomeration economies for attracting inward FDI in Italy, within the context of LIS and other forms of spatial organisation. The rest of the paper is set out as follows: section 2 examines the nature of location advantages for MNEs and recent contributions on technology sourcing, and section 3 provides theoretical background and some empirical evidence of LISs’ competitive advantages. Section 4 describes a model of location choice for MNE to test our hypotheses, while data and econometric model are described in section 5. Section 6 provides some thoughts on policy implications and presents some concluding remarks.
2.
The importance of location theory explaining FDI The literature on FDI illustrates that the importance of location advantages has
increased, with the emphasis changing from natural and cost-related inputs endowments to knowledge-based competencies. In particular, as Cantwell and Santangelo (1999) note, the technological strengths of host countries is a relevant feature to discriminate between the location options for the multinational firm. In addition, the localised nature of learning processes has changed the geographical scale of location patterns from the national to the regional or even local level. For instance,
3
Dicken (1998) and Cantwell and Iammarino (2000) show that foreign R&D activities in the UK are strongly concentrated in the South-East of England. In a similar vein, Driffield and Munday (2001) illustrate the importance of agglomeration economies and spillovers on total factor productivity growth of UK regions, and demonstrate that a critical level of regional concentration of economic activities, in effect the existence of significant agglomeration economies, is a necessary condition for spillovers to occur. It is clear, however, that the ability of a locality to attract FDI merely represents the potential for development, and that technology, or knowledge sourcing is by no means automatic, but depends on the actions of the firms concerned (Driffield and Love, 2003). In addition, given the nature of public good embodied in local knowledge, and that the latter is not concentrated in specific firms but embedded in the local industrial system, MNEs may realise knowledge sourcing as long as their foreign affiliates undertake cooperative relationships with local firms rather than engage in a predatory behaviour (Bellandi, 2001). The above discussion illustrates why clusters of activity are likely to be inherently attractive to firms seeking to tap into a pool of specialised knowledge and competence. Cluster firms are characterised by a high degree of specialisation and complementarity, that generates dynamic processes of knowledge creation -learning and innovation -and knowledge transfer- diffusion and synergies. In clusters there are collective learning processes that generate innovation and thereby competitiveness also in non high-tech intensive sectors. In fact, clusters can be extremely competitive in what the literature defines as traditional sectors; for instance, Sassuolo (Italy) ceramic tile industrial district accounts for one third of the sector world export (De Propris et al (2003). An innovative and competitive cluster can produce positive externalities to its entire region: as the cluster grows the extent of vertical and horizontal product differentiation increases. As a result, the cluster becomes a centre of accumulated competencies across a range of related industries, and across various stages of production (the so called production filière). These localised centres of accumulated knowledge can be very attractive to outside firms.
4
3.
The competitive advantages of Local Industrial Systems The theoretical analysis of the LISs as sources of regional competitive
advantage draws on the concept of territorial competitiveness and the tangible and intangible factors that drive it. The conceptual starting point of this stream of literature is the flexible specialisation approach, pioneered by Piore and Sabel (1984), which considers small firms, and especially firms within LISs, as an alternative model of industrial development to large vertically integrated firms. This approach emphasises the specific characteristics of the organisation of production in the LISs that enable them to cope with uncertainty and to be flexible. The first model of firm agglomeration was developed by Becattini (1979) who witnessed this phenomenon in Italy and introduced the concept of industrial district to describe a specific model of social and industrial development, rather than merely a sector-specific geographical agglomeration of firms. As global competitiveness is more and more associated to regional competitive advantages, LISs in Italy, as anywhere else, have become crucial. Trade and factor mobility have diminished the importance of traditional national measures of comparative advantage, while non-tradable knowledge, technology or skills have become increasingly important as measures of competitiveness (Porter, 1998). The literature on the knowledge-based economy has emphasised the importance of local competencies for knowledge creation and learning processes (Prahalad and Hamel, 1990).1 This literature distinguishes two types of knowledge: codified and uncodified or tacit knowledge (Nonaka, 1991). The former is based on standardised scientific protocols and then easily tradable on international markets, while the latter is essentially embedded within single enterprises or specific geographical areas, and cannot be transferred through standard agreements such as licensing. It follows that tacit knowledge is limited spatially, and can only be transferred between organisations with high levels of communication and mutual understanding. This argument strengthens the link between knowledge creation and the geographical, social and institutional frameworks supporting firms at the local level. Becattini (1979) defines the industrial district as “a territorial entity characterised by the active presence of a group of persons and a population of firms in a given historical and geographical dimension.” This perspective on local development clearly highlights the strong 1
See Dosi and Malerba (1996) for further discussion on competencies.
5
interplay of social and economic factors as basic conditions for the successful development of industrial districts. The role of local technological externalities is important in this context, and draws on the innovative milieu approach, developed by Aydalot (1986) and Perrin (1988). Here the organisation of production at the local level is considered a complex and self-contained micro-system, promoting strong interaction and cumulative processes. The geographical boundaries of the innovative milieu are defined both by spatial proximity effects, and by economic and cultural homogeneities within the milieu. In particular, innovation processes and factors of success are specific to each context and depend on (a) strong specialisation of the local industry in a filière or technology, (b) dense interactions and synergies among local firms, (c) collective learning processes, and (d) a strong sense of belonging to the local community. The result is that the milieu stimulates “collective learning” (Camagni, 1991 and 1995; Lawson and Lorenz, 1999). Also Storper (1995) stresses the intangible factors of regions’ competitiveness and defines the region, and a LIS, as a “nexus of untraded interdependencies”. His approach outlines how strong competitive regions develop successful models of production that can not be easily imitated since they are embedded in the underlying system of shared conventions and norms. In the literature, definitions of agglomeration have widened to include networks (Hakansson, 1987), commodity chains (Dicken, 1998), production systems (Scott and Storper, 1992), and business systems (Whitley, 1992). Recent contributions qualify LISs as systems of economic and social relations, emphasising the role of the relational capital (Camagni, 1999) and social capital (Putnam, 1993; World Bank, 2001) for local development. Other contributions have looked at the development of a specialised service sector as a result of co-operation among local firms (Brusco, 1989), or at the role for local development of business associations (Best, 1990; Humphrey and Schmitz, 1996; Maskell et al., 1998) or at the targeting of LISs in wider industrial policy framework (Bennet and McCoshan, 1993). 4.
The determinants of the spatial distribution of FDI Much of the recent work in this area is based on Coughlin et al. (1991), who
develop a model that of MNE location choice based on profit maximisation. Coughlin et al. (1991) identify the main factors determining the spatial distribution of inward FDI in the US. They demonstrate that FDI is attracted to regions with high levels of 6
final demand for the output, but also to regions with high densities of manufacturing activity and extensive transportation infrastructure, whereas higher wages and taxes deter FDI location. The analysis of FDI location was recently extended, see for example Basile’s (2003) analysis of Italy; Crozet et al (2003) of France; Devereux, Griffith and Simpson (2003) of the UK, and Togo and Arikawa (2002) of Malaysia. Coughlin and Segev (2000) extend the analysis by including educational attainment as a possible determinant of FDI attraction. In addition, they show that whilst the condition of the existing manufacturing base and taxation levels affect location at the state level, urban regions are more conducive to FDI than rural ones. In his analysis on the location determinants of FDI across Italian provinces, Basile (2003) considers the indicators of agglomeration such as public research institutions and the relative concentration of specialised providers of firm services as potential sources of positive externalities to attract FDI. The analysis presented here will extend the literature discussed above in two ways. Firstly, it focuses on the potential heterogeneity of location determinants across industries linking the location of the firm to the analysis of FDI, based on the standard explanations of FDI from the international business literature, viz. (a) resource seeking, (b) market seeking, (c) efficiency seeking, and (d) strategic asset seeking (Dunning, 1998). Secondly, it will link the analysis to more complex considerations of the local organisation of industrial activity, by distinguishing different kinds of agglomeration economies at the local level. For example, while efficiency seeking may be associated with the availability of cheap labour, or with high productivity levels and agglomeration economies, strategic asset seeking behaviour is linked to the sourcing of industry-specific knowledge that is embedded in specific LISs. In fact, LISs are expected to hold specific localised competitive advantages (knowledge creation and other locally embedded intangible assets) absent in other locations with the same industry, and for this they can attract higher levels of inward investment.
5.
Econometric analysis The basic theoretical model assumes that a firm in a given industry will
choose to locate in a particular region if and only if that choice will provide the highest return to its investment: 7
(1) where i denotes the firm and j indicates the locality providing the highest profit among a set of k regions denoted by the suffix m. Following the study of Carlton (1983) on the location choice of domestic firms, Coughlin et al. (1991) assume that the profit earned by the firm i in location j is linked to a set of observable characteristics of location j. (2) where c is the constant term, Xj is a vector of observable characteristics of location j expressed in log term, β is a vector of unknown coefficients to be estimated and
are the random terms. Given the individual nature of the data and assuming
that the random terms are independent log-Weibull distributed, Coughlin et al. (1991) were able to use McFadden (1974) conditional logit model that allows to express the location choice in terms of the probability to locate in a given area, conditional to the relative characteristics of other location, and to estimate the model using maximum likelihood. Coughlin and Segev (2000) outline several alternative econometric specifications to model location choice. McFadden’s (1974) conditional logit model provides a consistent framework for the econometric analysis only when individual data are available. The presence of significant data constraints and the availability of aggregated data has meant considering alternative solutions, such as the adoption of model for count data (Coughlin and Segev, 2000). In order to bring together all these models within a common framework of specification and estimation, we rely on the contribution of Nelder and Wedderburn (1972) who group all these models under a single class of models, defined as the generalised linear models (GLM), on the basis of three common features: 1. The response variable Y has a distribution from the exponential family. This family of statistical distributions allows heteroscedasticity related to the mean (expected) value of the distribution. In particular,
where
is the
dispersion parameter. 2. A link function connects the average value of the response variable to a linear predictor: link function
(3) 8
linear predictor
(4)
where x represents the vector of explanatory variables and µ =E(Y/X) is the mean (expected) value of the response variable. If we also assume that the response variable is measured in terms of number of foreign owned firms entering a given location j and the link function has a logarithmic form, then equations 3 and 4 can be combined in the following form: n
µj =e
∑ βi xij i =1
(5)
where the average value of FDI entering a location is assumed to be an exponential function of the following linear combination of explanatory factors. The estimation of equation (5) within the framework of generalised linear model (GLM) requires the specification of both the link function and the theoretical distribution of the response variable. Given the nature of count data used in this paper , it is reasonable to assume that the response variable Y, number of occurrences of an event, has a Poisson distribution given the independent variables X1, X2, X3, ……., XM.
for k = 0,1,2……
(6)
The link function usually assumes the canonical form
. So the
logarithm of the mean of the response variable is a linear function of independent variables. In particular, in the Poisson distribution model the variance of response variable is assumed to be equal to the mean. If this hypothesis is too restricted, underdispersion or, more often, overdispersion occurs. In case of overdispersion, quasi-likelihood estimation can be used. However it is more suitable to adopt an alternative specification for the distribution of the response variable. The Negative Binomial distribution provides a more general framework to model the response variable. In particular, the variance is allowed to follows a quadratic function of the mean, Var(y) =
, where
is the dispersion
parameter. The presence of overdispersion in the Poisson model can be detected on the basis of simple statistic ratios (Scale Deviance or Pearson chi squared deviance
9
divided by degree of freedom), while the choice between Poisson and Negative Binomial distributions can be performed by testing the null hypothesis H0: :
=0 by a
likelihood ratio (LR) test. Tests for the statistical significance of the parameters in the model can be performed via the Wald statistic, the likelihood ratio (LR) or score statistic. The Wald statistic is an efficient statistics for testing the significance of effects while the likelihood ratio (LR) test requires provides the most asymptotically efficient test known following Gourieroux et al. (1984a,b). The goodness of fit of the model can be assessed on the basis of the scaled deviance or the Person’s chi-square statistic. For a given value of the dispersion parameter φ , the scaled deviance is defined as twice the difference between the maximum achievable log likelihood and the log likelihood at the maximum likelihood estimates of the regression parameters. If l(y, µ ) is the log-likelihood function expressed as a function of the predicted mean values µ and the vector y of response values, then the scaled deviance is defined by D * ( y; µ ) = −2(l ( µ ; y ) − l ( µ max ; y ))
where l ( µ ; y ) is the log-likelihood under the model and
l ( µ max ; y ) is the log-
likelihood under the maximum achievable (saturated) model. For generalised linear models, the scaled deviance, can be expressed as D * ( y; µ ) =
1
ϕ
D ( y; µ )
where D ( y; µ ) is the residual deviance for the model and is the sum of individual deviance contributions. The scaled version of both deviance or the Person’s chisquare statistics, under certain regularity conditions, has a limiting chi-square distribution, with degrees of freedom equal to the number of observations minus the number of parameters estimated. The two basic types of residuals used for diagnostic checking are the so-called Pearson residuals and deviance residuals.
Inter-Industry heterogeneity
10
An obvious, though often ignored problem with this type of work is the issue of heterogeneity across industries. In order to address this we employ the classification scheme developed by Pavitt (1984). Starting from a sample of UK innovating firms, Pavitt defined an empirical classification of industries according to their technological trajectories. In particular, Pavitt’s taxonomy classifies industries as characterised either by (i) ‘science based‘ firms; (ii) ‘production intensive‘ firms, or (iii)‘supplier dominated‘ firms. The second group is further subdivided into the categories of ‘scale intensive‘ production or ‘specialised suppliers‘. Each typology of industries is characterised by different profiles in terms of level of R&D expenditure, knowledge creation and learning behaviour, as well as firm size and main activity. In particular, science based industries are characterised by high levels of R&D expenditures. In addition, their knowledge creation and learning processes combine internal and external sources (from other firms and/or universities and research centres). In scale intensive industries, R&D expenditures may also be important, but they are mostly realised internally and focused on process innovation and efficiency seeking. Specialised suppliers industries are characterised by comparatively smalland medium-sized firms, focussed on product innovation. Finally, supplier dominated firms are considered with small innovative capabilities and little R&D oriented. Although Pavitt’s taxonomy was defined in a rather eclectic manner, it has been extremely influential, providing a sound basis for the comparison of firm behaviour across industries. In addition to addressing heterogeneity, such a classification provides a link to the motives for FDI discussed above, following Dunning (1998), where location choice within industries will vary by type of industry. For example, resource seeking FDI is associated with the local endowment of assets and resources, including: infrastructure, skilled labour and business services supply. Market Seeking FDI includes variables connected to the exploitation of the potential of local input or output markets, identify by the local level of income per capita and by rates of overall and young unemployment, respectively. Efficiency seeking FDI include all factors that affect the costs and efficiency of local production such as investment incentives, manufacturing labour productivity and presence of static or dynamic agglomeration economies. Finally, Strategic asset seeking FDI focuses on knowledge-related assets, and in particular on public institutions as well as local industrial systems potentially promoting learning processes. Clearly the nature of the industry will determine the 11
type of FDI that is attracted, which will in turn determine the location. As such, imposing uniform coefficients across these types of industries is likely to be invalid. Further details of the classification scheme are presented in Appendix B, while the breakdown of inward FDI across these sectors is presented in table 1. This classification identifies four broad typologies of manufacturing sector: science-based industries, scale-intensive industries, specialised-suppliers industries and supplier dominated industries.
Table 1 Distribution of firms in Italy under foreign control by Pavitt sector -1996-1999 No. foreign firms
Share in % of the total
No. foreign firms located in metropolitan areas
Share of foreign firms in metropolitan areas over the total of foreign firms
Science-based industries
426
13.8
261
61.3
Scale-intensive industries
1324
42.8
662
50.0
Specialised suppliers industries
754
24.4
396
52.5
Supplierdominated industries
589
19.0
208
35.3
Total
3093
100.0
1527
49.4
6
Data The territorial unit of analysis used in this paper is the Italian province.
Provinces represent a further administrative disaggregation from the 20 Italian standard planning regions, and thus provide a more suitable level of analysis of industrial location. There are currently 103 such provinces in Italy. The reference period of the analysis is 1996-1999. Data on the number of enterprises under foreign control at the provincial level were provided by CNEL-ICE- Politecnico di Milano. These data are stratified by province and industry, providing unique and hitherto unexplored data on firm location. Data used to generate the independent variables at the provincial level were provided by ISTAT (Italian Office of National Statistics), and mostly harmonised within the national account conceptual framework, with the relevant exception of the provincial index of infrastructure endowment, elaborated by the Italian industrial
12
confederation. In addition, the data used to identify LISs refer to the employment in manufacturing plants, stratified by industry (three digit level of NACE) and province, and are provided by ISTAT, Census of Industry and Service statistics, reference year 1996. Industry level data, including FDI and the input data to identify LISs, were aggregated according to Pavitt’s (1984) taxonomy, described in the previous section. In addition, an attempt to classify independent variables on the basis of their relative importance in terms of different typologies of location choice behaviour (resource, market, efficiency and strategic assets seeking) was also performed and included in table 2. A regional dummy variable, MILAN was introduced to capture specific local phenomenon. FDI may be attracted to Milano by the high concentration of firm Head Offices in Milan, as well as the main office of the National Stock Exchange Market. The reference period of the analysis is 1996-1999. The list of explanatory variables, their sources and definitions are provided in table 2. In particular, the definitions of agglomeration-related variables are discussed at length in the next section.
13
Table 2 Data Summary Code
Definition
FDI
No. firms under foreign control, including greenfield 30.03 investment and M&A (1996-1999) Breakdown of FDI data according to Pavitt’’s industry classification No. firms under foreign control in science-based 4,14 16,05 0-154 industries No. firms under foreign control in scale-intensive 12,85 41,67 0-411 industries No..firms under foreign control in specialised-suppliers 7,32 24,92 0-244 industries No.firms under foreign control in supplier-dominated 5,72 13,60 0-131 industries Location variables related to resource seeking FDI Infrastructure endowment, measured by a composite 100 37.66 15.40-195.40 index of economic infrastructure endowment at the provincial level, Italy=100 (1997) Average level of educational attainment, measured as % 17.83 2.46 12.20-26.30 of provincial population aged 6 and over with an high school diploma (1991) High level of educational attainment, measured as the 3.39 0.87 1.54-6.96 percent of graduate by province (1991) Risk of the social environment, measured as the no. 6.52 6.83 1.16-50.60 extortions per 100.000 units of the resident population (1996) Degree of economic conflict in the local labour market, 193.46 190.68 8.16-1268 measured as the no. labour disputes per 100.000 units of the resident labour-force (1996) Supply of real business services, measured by the 9.25 2.07 5.67-17.37 employment in non-financial business services (K section of NACE) as % of the local labour force (1996) Location variables related to market seeking FDI Potential market share, measure as the ratio of total 218.03 104.56 89.09-593.87 personal income relative to manufacturing employee in the province (Wheat, 1986 and Duffy, 1994). Not gravity adjusted (1996) Unemployment rate (1996) 11.21 7.29 2.52-31.83 Unemployment rate for young people, aged 14-24 , 1996 17.90 6.87 5.44-41.11 Location variables related to efficiency seeking FDI Manufacturing labour productivity, measured as value 41552 6390 25784-71575 added (in euro) per labour unit in manufacturing production (1996) Manufacturing base, measured by the employment in 22.90 10.44 7.14-46.41 manufacturing (D section of NACE) as % of total employment (1996) Dummy variable that detects provinces with potential national or EU investment incentives (all Southern provinces, the central provinces of Lazio and provinces that received financial benefits from the “Cassa del Mezzogiorno” ) Location variables related strategic asset seeking FDI Dummy variable for provinces that include large metropolitan areas, defined by ISTAT (11 areas) Dummy variable that detect LIS by industry and province. In particular, LIS identifies combination of province and sector with a LQ above 1 Classification variables related to different types of province with the same industry Dummy variable for two types of LIS by industry and province: (a) strongly specialised LIS are identified by a LQ above 1,5; (b) weakly-specialised LIS are identified by a LQ in the region 1-1,5
FDI_RD FDI_SE FDI_SS FDI_TS
INFRA
MEDU
HEDU SOCENV
LABENV
SERV
DEM
UNEMP YUNMP PROD
AGGL
SOUTH
METROPOL LIS
LIS
ID-like provinces LAND
Mean
Standard Deviation 94.86
Min-Max range 0-940
Dummy variable for two additional types of province in supplier-dominated or specialised-suppliers industries, characterised by a significant presence of industrial districts (ID). Location variables related to the “dartboard” effect Geographical extension of the province, measured in hectares
Sources CNEL-ICEPolitecnico di Milano
CNEL-ICEPolitecnico di Milano CNEL-ICEPolitecnico di Milano CNEL-ICEPolitecnico di Milano CNEL-ICEPolitecnico di Milano Italian Industrial Union
ISTAT
ISTAT ISTAT
ISTAT
ISTAT
ISTAT
ISTAT ISTAT ISTAT
ISTAT
Administrative data
ISTAT ISTAT
Authors’ elaboration with ISTAT census data Authors’ elaboration
ISTAT
14
Identifying different sources of agglomeration economies Despite the importance of agglomeration economies in attracting FDI there is only limited study in the literature of the different types of agglomerative forces. The “new economic geography” approach (Krugman 1991, Ottaviano 2003) focuses on agglomeration effects driven by pecuniary or technological externalities and cumulative processes. In contrast industrial and urban economics focuses on the specific externalities generated by strongly specialised local industrial systems (LIS) and metropolitan areas, respectively. A relatively restricted literature has developed along these lines following Brusco (1990) and Glaeser at al (1992). In the spirit of Coughlin et al. (1991), agglomeration economies are measured in terms of local density of manufacturing activities, given by the share of manufacturing employment over total employment at the provincial level. variable captures
This
both statistic agglomeration economies and potential dynamic
effects, since it provides also a measure of the level of industrial development of the local manufacturing industry. In addition, in contrast to some empirical studies that consider the number of manufacturing firms as a measure of agglomeration economies of the local industry, it removes the problem of spurious correlation with the geographical size of the province. LISs are identified at the provincial and sector level on the basis of location quotients (LQ), calculated with respect to census of industry and service data for the year 1996. LQ is defined as follows: empij LQij =
emp j empi emptot
Where Emp represents employment in local production units and i and j denote respectively sector and province. Values of LQit ranging from 1 to 1.5 identify weakly specialised LIS, while values above 1.5 denote strongly specialised LIS. This approach generates a set of industry-specific dummy variables that vary across industries, classified on the basis of Pavitt (1984) taxonomy. A specific typology of LIS, the industrial district (ID), is also identified for the supplier-dominated and
15
specialised-suppliers industries only, on the basis of the ID-like provinces taxonomy proposed by Becattini and Menghinello (1998). This defines a set of indicators that measure the concentration of IDs at the provincial level and across all industries. Following Coughlin and Segev (2000), urban areas are identified at the provincial level on the basis of the inclusion of metropolitan areas, officially defined by ISTAT.
7.
Results The results from the estimation of equation (5) are presented in Table 3. The
presence of a high degree of correlation among explanatory variables is one of the major problems in regional models, especially when social and economic covariates are combined. In addition, location choice models require controlling for the “dartboard effects”2.. In order to identify a set of independent covariates, a process of a priori variable selection was performed. The essential problem was one of correlation between two sets of variables, the education and unemployment variables. Specifically HEDU was found to be highly correlated with SERV due to the fact that business services require higher standard of education than manufacturing. Furthermore, AGGL is strongly and negatively correlated with UNEMP, DEM and, indirectly, with LABENV; a reason for this could be that the intensity of manufacturing activity is also a good index of local development. As a result, only a limited set of not strongly correlated variables enter the model: SERV, AGGLOM, INFRA and PROD. In particular, the variable SERV captures the concentration of business services, as well as the local standard of high education. The AGGL variable measures the intensity of agglomeration economies derived from the concentration of manufacturing activities at the local level; it also captures negative conditions demand for final products and labour, as well as a low conflict rate in the local labour market. The territorial extension of each province (measured in hectares) was used in the model to control for potential “dartboard effects”. The hypothesis of the variable having a Poisson distribution was rejected in favour of a Negative Binomial by standard LR test on the dispersion parameter. . 2
The "dartboard" effect in location model occurs as a result of variations in size of region. While it is possible to address this by using normalised or relative measures, a strong correlation between these variables and the geographical size of the region may occur. It is important therefore to control for these effect by including a log variable that captures the size of each region.
16
Table 3. Negative Binomial Regression Results by Pavitt Industries (1996-1999) Specialisedsuppliers industries
Supplierdominated sectors industries
-17.187 (203.42)***
-16.726 (155.49) ***
-18.125 (109.48)***
0.103 (32.71)***
0.065 (35.40)***
0.066 (26.06)***
0.065 (26.30)***
0.145 (8.22)***
0.217 (4.05)**
0.109 (2.40)
0.169 (5.94)**
0.109 (2.25)
INFRA
0.016 (25.93)***
0.011 (2.53)
0.018 (16.57)***
0.019 (14.16)***
0.011 (4.01)**
PROD
0.000 (16.31)***
0.000 (5.68)**
0.000 (3.67)**
0.000 (0.01)
0.000 (6.95)***
MILANO
1.408 (7.89)***
0.597 (0.44)
1.607 (4.96)**
1.442 (5.18)**
1.63 (6.17)**
-0.232 (0.71)
-0.470 (0.56)
-0.284 (0.49)
0.004 (0.00)
-0.459 (0.92)
1.450 (15.35)***
0.340 (2.51)
0.561 (5.43)**
0.259 (0.83)
Sciencebased industries
Parameter
All industries
Intercept
-17.015 (483.52)***
-22. 179 (140.96)***
AGGL
0.068 (71.67)***
SERV
METROPOL Industry-specific LIS
Scaleintensive industries
Main Typologies of LIS De-specialised provinces Weakly specialised provinces Strongly specialised provinces
-1.842 (11.56)***
-0.758 (6.81)***
-0.540 (1.04)
-0.314 (1.44)
0
0
Typologies of LIS and ID De-specialised and non ID intensive provinces Weakly specialised and non ID intensive provinces Strongly specialised and non ID intensive provinces Low-intensive ID provinces High-intensive ID provinces
Dispersion
-0.908 (4.15)** -0.440 (1.66) -0.225 (0.29) -0.576 (2.74) 0
0.181
0.540
0.377
0.258
0.24
Log Likelihood
10762.792
898.283
3562.815
1808.383
953.390
Scaled Deviance
68.189
67.400
74.109
85.204
87.480
Scaled Pearson Chi-Square
71.095
67.789
70.640
73.616
77.591
59
58
58
58
58
DF
17
Sample size
66
66
66
66
66
The results of the first set of regressions show that agglomeration economies, calculated in terms of local concentration of manufacturing activities, have a positive impact in attracting FDI across all Pavitt industries and this is particularly true for science-based industries. The local offer of business services, which is strongly correlated with high levels of education, positively affect FDI location in sciencebased industries, and in specialised suppliers industries, but is below the national average and not statistically significant for the other two types of industry. This result seems to be consistent with the argument that these two industries rely on a network of specialised business service firms and require a highly skilled local labour market. Infrastructure endowment is relatively more important for the localisation of foreign firms in the specialised-suppliers industries and scale-intensive industries, while it is below the national average for supplier-dominated and science-based industries; in particular, it does not appear to be statistically significant. The relevance of the infrastructure endowment for specialised-suppliers and scale-intensive industries is consistent with the complex nature of these productions that require the minimisation of transportation costs and the adoption of optimal logistic solutions. Productivity is positive and statistically significant across all sectors, except for specialised-suppliers industries. The dummy variable MILANO is positive and statistically significant for all sectors, except for science-based industries. The METROPOL variable is mainly negative but not statistically significant across all sectors, thus showing that urban economies are not relevant for FDI location, besides the effects accounted for infrastructure endowment and the local offer of business services.. Industry-specific LIS effects are strongly positive and statistically significant for science-based and specialised-suppliers industries. This illustrates that the LIS effect is distinct from the agglomeration and urban economies effects. One possible explanation for this is that LIS effects are associated with technology sourcing FDI. In science-based industries, foreign firms would benefit from the externalities generated in specialised LISs through their engagement in formal and informal linkages with local high-tech firms or institutions. On the other hand, in specialisedsuppliers industries, foreign firms are very likely to benefit from location spillovers through learning-by-interacting processes, mainly realised via user-producer linkages with other local firms along the local production filière. In contrast, the insignificance 18
of LIS effects in supplier-dominated sectors may be explained by the fact that LISs in these industries present entry barriers, such that entry by FDI is viewed as very risky and possibly unprofitable.. The second set of results refers to the link between FDI and types of LISs. The analysis of the impact of different types of LIS in attracting FDI within the same industry provides some interesting insights. The analysis is carried out by considering one type of LIS as a “benchmark” that is set equal to zero: in our case this is represented by “strongly specialised provinces”. In both science-based and specialised-suppliers industries, weakly specialised provinces and, in particular, despecialised provinces have a relatively negative impacts on FDI attraction at the local level: this strengthens previous evidence suggesting the key role of specialised LIS as local catalysts of FDI. In the last set of regressions, we extend the analysis to include industrial district as a specific type of LIS. We find that in specialised-suppliers industries highly intensive ID provinces overperform types of LIS in terms of FDI attraction . 6.
Policy implications and concluding remarks The potential for agglomeration economies appears to be important for MNEs
comparing location, and this is particularly important for science-based industries. In contrast, urban economies are not important for the attraction of FDI, unless they are specific to a particular urban centre, i.e. Milan. Focussing on the LIS effect, FDI in science-based and specialised industries is attracted to strongly specialised provinces, while industrial districts attract specialised-suppliers industries. Finally, FDI is not attracted by industrial districts in supplier-dominated industries or in scale-intensive industries. Such, industries are often very competitive in the world markets, demonstrating high degrees learning and innovation, so in Dunnings (1979) terminology, potential inward investors possess no ownership advantages here in order to encourage FDI.. These findings have important policy implications for the attraction and retention of FDI. On the one hand, one of the main competitive advantages of LISs is the intangibility of the learning processes and innovation, with this varying across industries according to their reliance on tacit and/or codified knowledge. Our results seem to suggest that FDI is attracted to LISs in industries where knowledge is mostly codified and, therefore, more easily transferable from the host to the foreign firms. 19
This happens through foreign firms’ engagement in R&D activities with local firms and institutions (science-based industries) and where inter-firm relations are structured in user-producer linkages. An important mechanism here is vertical cooperation creating a channel for knowledge transfer, particularly in specialisedsuppliers industries. It appears that those industries where tacit knowledge constitutes an important component of their embedded knowledge and learning are less attractive for FDI, in that the tacitness of knowledge and the embedded nature of firms’ interaction act as barriers to entry. While the contribution of FDI to local development per se is beyond the scope of this paper, the results here provide an important distinction when seeking to evaluate the potential impacts of FDI. It is important to determine whether potential inward investors will seek to become embeded locally or adopt predatory behaviours. A MNE is locally embedded when it promotes cooperation and development of trust with local firms, as well as it is committed to a locality with long-term investments in both physical and human capital. This decision is determined by the analysis of the firm concerning the potential gains from long-term cooperation compared with the short term gain from predatory behaviour (Bellandi, 2001). The strengthening of the degree of local or regional embeddedness of the MNE may produce strong benefits for the local industry and community, although the long-terms effects on local governance should be carefully evaluated. An important question for future research therefore concerns the distinction between the short and long run in this respect. Drawing on the results of our paper, it can be argued that it questionable whether FDI in science-based and specialisedsuppliers industries are likely to be associated with long-term commitments, while the lack of foreign firms’ interest in supplier-dominated sectors can also be due to the fact that the appropriation of benefits would entail monetary and non-monetary investments. To conclude, we would suggest that policy-makers haveto be aware of the need to strike a balance between attracting FDI, without falling into the trap of adopting short-term measures by targeting and opening up sectors palatable to foreign investors, and ensuring that foreign firms commit themselves to localities since only embedded FDI benefits both foreign and host firms.
20
Appendix A Pavitt’s classification of manufacturing industries (1992) Pavitt’s ’sectors Science-based industries
NACE three digits codes 244, 300, 321, 322, 323, 331, 332, 333, 334, 335, 353
Scale-intensive Industries
211, 212, 221, 222, 223, 231, 232, 233, 241, 242, 243, 245, 246, 247, 251, 252, 261, 264, 265, 266, 267, 268, 271, 272, 273, 274, 275, 283, 284, 285, 296, 297, 341, 342, 343, 354 291, 292, 293, 294, 295, 311, 312, 313, 314, 316, 351, 352, 355
Specialised Suppliers Industries Supplierdominated Industries
151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 171, 172, 173, 174, 175, 176, 177, 181, 182, 183, 191, 192, 193, 201, 202, 203, 204, 205, 262, 263, 281, 282, 286, 287, 315, 361, 362, 363, 364, 365, 366, 371, 372
21
References Aydalot, P. (1986) Milieux innovateurs en Europe, Paris, Groupe de Recherche Européen sur les Milieux Innovateurs. Basile, R. Castellani, D. and Zanfei, A. (2003) Location choices of multinational firms in Europe: the role of national boundaries and EU policy, Working Paper Series on Economics, Mathematics and Statistics, N.78, University of Urbino, Italy. Basile, R. (2002) The Locational Determinants of Foreign-Owned Manufacturing Plants in Italy: Preliminary Results, Rivista di Politica economica, July-August. Becattini G. (1979) Dal “Settore” Industriale Al “Distretto” Industriale. Alcune Considerazioni Sull’unità Di Indagine Dell’economia Industriale, Rivista Di Economia E Politica Industriale, (1). Bellandi M. (2001) Local development and embedded large firms, Entrepreneurship & Regional development, (13): 189-210. Bennett R, McCoshan A and Coulson. A (1993) Enterprise and human resource development - local capacity building, Local government studies, 19 (4), p.599. Best M. (1990) The new competition, Cambridge, Policy Press. Brusco S. (1990) The idea of the industrial district. Its genesis, in Pyke F., Becattini G. and Sengenberger W. (Eds.) Industrial districts and inter-firm co-operation in Italy, International Institute for Labour Studies, Geneva, pp.10-19. Camagni, R.P. , Ed. , (1991) Innovation networks - spatial perspectives, Belhaven Press, London and New York, 247p. Camagni, R.P. (1995) ‘The concept of innovative milieu and its relevance for public policies in European lagging regions’, Papers in regional science,74 (4), pp. 317-40 Camagni, R.P. (1999) “The city as a milieu: applying the GREMI approach to urban evolution”, Revue d'économie régionale et urbaine, 3: 591-606 Cantwell, J. A. (1991) The international agglomeration of R&D, In M.C. Casson, (Ed.), Global research strategy and international competitiveness, Oxford, Blackwell. Cantwell, John .A. (1991) ‘The international agglomeration of R&D.’ In M.C.Casson, editor, Global research strategy and international competitiveness. Oxford: Blackwell. Cantwell, J.A. and Janne, O. (1999) ‘Technological globalisation and innovative centres: The role of corporate technological leadership and location hierarchy’. Research Policy, Vol. 28, pp119-144.
22
Cantwell, J and S. Iammarino (2000) ‘Multinational corporations and the location of technological innovation in the UK regions’. Regional Studies, 34(4), pp. 317-332. Cantwell and Santangelo, GD (1999) ‘The frontier of international technology networks: sourcing abroad the most highly tacit capabilities’, Information economics and policy, 11 (1): 101-123 Carlton, D.W. (1983) ‘The location and employment choices of new firms: an econometric model with discrete and continuous endogenous variables’, Review of Economics and Statistics, 65(3): 440-449 Chung, W. and Alcàcer, J (2002) Knowledge Seeking and Location Choice of Foreign Direct Investment in the United States, Management Science, 48 (12): 1534-54 Coughlin, C et al. (1991) State Characteristics and the Location of Foreign Direct Investment within the United States, The Review of Economics and Statistics, 73(4): 675-683. Coughlin, C.C. and Segev, E. (2000) Location determinants of new foreign-owned manufacturing plants, Journal of Regional Science, 40 (2): 323-51 Crozet, M. Mayer, T. and Mucchielli, J. (2003) 'How do Firms Agglomerate? A Study of FDI in France', CEPR Discussion Paper no. 3873. London, Centre for Economic Policy Research De Propris L and Driffield N (2003) FDI, Clusters and Knowledge Sourcing, Birmingham Business School Working Paper 2003-14. De Propris L, Driffield N. and Menghinello S (2003) Localised Competitive Advantages and Local Industrial Systems in Italy, Birmingham Business School Working Paper 2003-29. Devereux, Griffith and Simpson (2004) "The Geographic Distribution of Production Activity in the UK"(2003) Regional Science and Urban Economics, forthcoming
Dicken, P. (1998) Global Shifts: Transforming the World Economy, London, Paul Chapman Publishing. Dosi, G. and Malerba, F., (1996) Organisation And Strategies In The Evolution Of The Enterprise, London, Macmillan. Driffield, N.L. and Love, J.H. ‘FDI, (2003) Technology Sourcing and Reverse Spillovers.’ The Manchester School, vol. 71 (6) pp. 659-672. Driffield, N.L. and Love, J.H. (2003a) Does The Motivation for Foreign Direct Investment Affect Productivity Spillovers to The Domestic Sector? Available at: http://research.abs.aston.ac.uk/working_papers/0201.pdf
23
Driffield, N.L. and Munday, M.C. (2001) ‘Technical Efficiency in UK Industries: A Stochastic Production Frontier Approach.’ Regional Studies, Vol. 35(5) pp. 391-99. Dunning, J.H. (1998) Location and the multinational enterprise: a neglected factor?”, Journal of International Business Studies, 29 (1) Fosfuri, A. and Motta, M. (1999) Multinationals without advantages, Scandinavian Journal of Economics, vol. 101, 617-30. Friedman et al. (1992) What attracts foreign multinational corporations? Evidence from branch plant location in the United States, Journal of Regional Science,32 (4): 403-18. Friedman, J. et al. (1996) A Note On “State Characteristics and the Location of Foreign Direct Investment within the United States”, The Review of Economics and Statistics, 78 (2): 367-368. Glaeser, E. et al. (1992) Growth in Cities, Journal of Political Economy, 100 (6): 1261152 Görg, H. & E. Ströbl (2001) Multinational companies and productivity spillovers: a meta-analysis, Economic Journal, vol. 111, pp. F723-39. Gourieroux, C., Monfort, A. and Trognon, A., (1984a) Pseudo maximum likelihood methods : Theory. Econometrica, 52 , 681-700. Gourieroux, C., Monfort, A. and Trognon, A., (1984b) Pseudo maximum likelihood methods :applications to Poisson models. Econometrica, 52 , 701-720. Hakansson, H. (1987). Industrial Technological Development: A Network Approach, London, Croom Helm. Head, K., Ries, J. and Swenson D. (1995) Agglomeration benefits and location choice: Evidence from Japanese manufacturing investments in the United States, Journal of International Economics, vol. 38, 223-247. Hines, J.R.jr (1996) Altered States: Taxes and the Location of Foreign Direct Investment in America, The American Economic Review, 86 (5): 1076-1094. Humphrey, J and Schmitz, H (1996) ‘The Triple C Approach to Local Industrial Policy’, World Development 24 (12): 1859-1877. Kogut, B. and Chang, S. J. (1991) Technological capabilities and Japanese foreign direct investment in the United States, Review of Economics and Statistics, vol. 73, 401-13. Krugman, P. (1991) ‘Increasing returns and economic geography’, Journal of political economy, 99(3): 483-499
24
Kuemmerle, W. (1999), ‘Foreign direct investment in industrial research in the pharmaceuticals and electronics industries – results of a survey of multinational firms’, Research Policy, Vol. 28, pp. 179-93. Lawson, C. and Lorenz, E. (1999) Collective learning, tacit knowledge and regional innovative capacity, Regional studies, 33 (4): 305-17 Makino, S. et al. (2002) Asset-Exploitation Versus Asset-Seeking: Implications for Location Choice of Foreign Direct Investment from Newly Industrialised Economies, Journal of International Business Studies, 33 (3): 403-421 Maskell P et al. (1998) Competitiveness, localised learning and regional development: specialisation and prosperity in small open economies, Environment and planning, 16, (4), 503. McFadden, D. (1974) ‘Conditional Logit Analysis of Qualitative Choice Behavior,’ in P.Zarembka, ed. Frontiers in Econometrics, pp105-142, New York: Academic Press.
Nelder, J.A. and Wedderburn R.W.M. (1972) ‘Generalized linear models’, Journal of the Royal Statistical Society, A 135(3): 370-384. Neven, D. and Siotis, G. (1993) Foreign Direct Investment in the European Community: Some Policy Issues. Oxford Review of Economic Policy, Vol. 9 (2), pp 72-93. Nonaka, I. (1991) ‘The knowledge-creating company’, Harvard business review, 69 (6) p.96Pavitt, K. (1984) Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory, Research Policy 13, 343-373. Pearce, R. D. (1999) ‘Decentralised R&D and strategic competitiveness: globalised approaches to generation and use of technology in multinational enterprises (MNEs)’ Research Policy, Vol. 28, pp. 157-78. PERRIN J.-C. (1988) Nouvelles technologies et développement régional: l'analyse des milieux innovateurs. Aix-en-Provence, CER, Research paper no 94-95. Piore, M.J. and Sabel, C.F. (1984) The Second Industrial Divide, Basic Books, New York. Mariotti, S. – Piscitello, L. (1994) ‘Le determinanti dei differenziali di attrazione territoriale degli investimenti diretti esteri’, L’Industria, n. 2. Porter M. (1998) Clusters and The New Economics of Competition, The Harvard Business Review, Nov-Dec. Prahalad, C. and Hamel G. (1990) ‘The Core Competence Of Corporations’, Harvard Business Review, 68 (3).
25
Putnam R. (1993) Making Democracy Work, Princeton, Princeton University Press. Scott, A.J. and Storper, M. (1992) ‘Regional development reconsidered’ in Regional development and contemporary industrial response - extending flexible specialisation, Belhaven Press, London and New York, pp.3-24 Sforzi, F. (1990). ‘The Quantitative Importance Of Marshallian Local industrial system In The Italian Economy’ In Pyke F. - Becattini G. - Sengenberger W. (ed.), Local industrial system and Inter-Firm Co-Operation In Italy, ILO, Ginevra. Shaver, Myles J. 1998. Do foreign owned and US owned establishments exhibit the same location pattern in US manufacturing industries? Journal of International Business Studies. 29 (3): 469-492. Siotis, G. (1999) Foreign direct investment strategies and firms’ capabilities, Journal of Economics and Management Strategy, vol.8, 251-70 Storper, M. (1995) The resurgence of regional economies, ten years later, European Urban and Regional Studies, 2: 191-221. Togo, K. and Arikawa, Y. (2002) Agglomeration Effects versus Policy Effects: The case of the Electronics Industry in Malaysia, Asian Economic Journal, 16 (3): 229245. UNCTAD (2001) World Investment Report 2001: Promoting Linkages. United Nations. UNCTAD (2003) World Investment Report 2003. United Nations. Van Pottelsberghe de la Potterie B. and Lichtenberg, F. (2001) ‘Does Foreign Direct Investment Transfer Technology Across Borders?’, The Review of Economics & Statistics, 83 (3): 490-497. Vernon, R. (1966) ‘International investment and international trade in the product cycle’, Quarterly Journal of Economics, Vol.80, pp.190-207. Wheeler, David. & Ashoka Mody. 1992. International investment location decisions: The case of US firms. Journal of International Economics, 33: 57-76. Whitley, R. Ed (1992) European business systems - firms and markets in their national contexts, SAGE Publications, London, 291p. Woodward, D. (1992) “Locational Determinants of Japanese Manufacturing Startups in the United-States” Southern Economic Journal, Vol. 58 Issue 3, pp. 690709 World Bank (2001) World Development Report: Building institutions for markets, World Bank, Washington DC.
26
Zanfei, A. (2000) Transnational Firms and the Changing Organisation of Innovative Activities.. Cambridge Journal of Economics. Vol. 24 (5). p 515-42
27
