Spatial analysis of regional industrial clusters in the German forest sector

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International Forestry Review Vol.11(1), 2009

Spatial analysis of regional industrial clusters in the German forest sector U. KIES, T. MROSEK and A. SCHULTE Wald-Zentrum, Westfälische Wilhelms-Universität, Robert-Koch-Str. 27, 48149 Münster, Germany

Email: uwe.kies@wald-zentrum.de

SUMMARY The economic concept of the forest sector as a cluster of interlinked wood-based industries is contributing to a growing understanding of a large sector in national economies of Europe. Although national level surveys have demonstrated the forest sector’s global impact, neither its role in regional economies nor its distribution in geographic space are well understood. Attempting a regionalized analysis of the forest sector, this paper explores an approach combining regional economics and spatial statistics. Standard concentration indices (Gini coefficient, location quotient) and geostatistical autocorrelation measures for regional clustering (Moran’s I and Getis-Ord G) are combined in an exploratory spatial analysis of detailed county-level employment statistics for Germany. The case study reveals decisive impacts of the forest sector on regional employment especially in rural areas. Regional industrial clusters and pairwise patterns of co-agglomeration of sawmilling, wood-based panels, wood-based construction and furniture industries are identified in geographical space. The pronounced spatial variability within Germany’s forest sector is linked to regional factors influencing geographic location, size and regional association of the industries under study. The research offers a suitable geostatistical approach for regional industrial targeting of the forest sector that can be supportive to informed rational decision-making in forest cluster development and policy.

Keywords: forest sector, wood-based industries, industry agglomeration, exploratory spatial data analysis, Germany

Analyse spatiale de clusters industriels régionaux dans le secteur forestier allemand U. Kies, T. Mrosek et A. Schulte Le concept économique du secteur forestier comme ensemble d’industries mises en rapport par le bois, contribue à une amélioration de la compréhension d’un secteur important dans les économies nationales européennes. Malgré le fait que des études à l’échelle nationale aient démontré l’impact global du secteur forestier; son rôle dans les économies régionales, ainsi que sa distibution dans l’espace géographique ne sont pas bien compris. En essayant d'offrir une analyse régionale du secteur forestier, cet article explore une approche combinant les statistiques spatiales et celles de l’économie des régions. Les indices de concentration standard (coefficient Gini, quotient de location), et les mesures d’auto-corrélation géo-statistiques pour le groupement régional (Moran’s I et Getis-Ord G) sont combinés dans une analyse spatiale d'exploration de l’emploi, détaillé au niveau départemental, en Allemagne. Cette étude-cas révèle que le secteur forestier a un impact décisif sur l’emploi régional, particulièrement dans les zones rurales. Des groupes industriels régionaux et des liens commerciaux répétés entre les industries de coupe, de panneaux à base de bois, de construction basée sur le bois et de fabrication de meubles sont identifiés dans l’espace géographique. La variabilité spatiale prononcée à l’intérieur du secteur forestier de l’Allemagne est lié à des facteurs régionaux, qui influencent la location géographique, la taille et l’association régionale des industries étudiées. Cette recherche offre une approche géostatistique appropriée pour permettre aux industries régionales de prendre meilleur parti du secteur forestier, et de pouvoir soutenir des prises de décision rationnelles informées dans le développement de la politique et des groupements forestiers.

Análisis espacial de clusters industriales regionales del sector forestal alemán U. KIES, T. MROSEK y A. SCHULTE El concepto económico del sector forestal como agrupación localizada de industrias interrelacionadas basadas en la madera contribuye actualmente a una mayor comprensión de un sector importante de las economías nacionales europeas. Aunque las encuestas a nivel nacional han demostrado el impacto global del sector forestal, se sigue sin entender bien ni su papel en las economías regionales ni su distribución geográfica. Este estudio intenta proporcionar un análisis regionalizado del sector forestal e investiga un modelo que combina la economía regional y la estadística espacial. Se integran índices estándar de concentración (coeficientes de Gini y de ubicación) y medidas de autocorrelación geoestadísticas para agrupación regional (I de Moran y Getis-Ord G) en una exploración de análisis espacial de estadísticas detalladas de empleo a nivel provincial en Alemania. El estudio revela el impacto decisivo del sector forestal en el empleo regional, sobre todo en zonas rurales. Se identifican en un contexto geográfico agrupaciones industriales regionales y pautas de conglomeración de aserraderos, la industria de paneles, construcción basada en el uso de la madera, y la industria del mueble. La variabilidad espacial pronunciada dentro del sector forestal alemán se relaciona con factores regionales que afectan la ubicación geográfica, el tamaño y la asociación regional de las industrias estudiadas. El análisis ofrece un modelo geoestadístico idóneo para identificar las industrias regionales del sector forestal que

Spatial analysis of the German forest sector

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podrá servir de apoyo para un proceso racional y bien informado de toma de decisiones con el fin de establecer una política para el desarrollo de agrupaciones regionales en el sector forestal.

INTRODUCTION Wood-based industries form a complete industrial sector in national economies: the forest sector. The sector incorporates a wide range of economic activities in the processing and manufacturing of semi-finished and finished wood, pulp and paper products. Although these manifold industries vary considerably in size, structure and their position in the production chain, their associated activities all depend on a common natural resource: wood. The concept of the forest sector as a `cluster´ of interlinked forest and wood-based industries has been investigated across various contexts and scales, providing evidence of the sector’s socioeconomic impact on national economies in the European Union (Porter 1998, Commission of the European Union 1999, Hazley 2000, Hanzl and Urban 2000, Lebedys 2004). Recent research in the context of Germany reveals the forest sector’s significant and underestimated economic impact on national turnover and employment. With 150 billon € gross turnover and close to 900.000 employees in 2004 (approximately 3.5% of the national economy), the forest sector ranks high among comparable sectors (e.g. agriculture, food industry, textiles, machinery, construction) (Schulte 2002, 2003, Dieter and Thoroe 2003, Schulte and Mrosek 2006, Kies et al. 2008, Klein et al. in press).

Existing forest sector studies in European countries (including Germany) still lack a true regional perspective on the geographic distribution of these industries. To date, comparative distributions of wood-based industries across administrative boundaries (e.g. European countries) and cartographic mapping of individual enterprises have relied upon unspecified databases (e.g. Hazley 2000) or sampling surveys (Mantau et al. 2002). Apart from these approaches, forest sector research has focused on macroeconomic assessments in specific geographic contexts. Hanzl and Urban (2000) reported forest sector employment shares of up to 15% of total manufacturing in eastern European countries in 1998. Hazley (2000) estimated an average forest sector share of 9% of total manufacturing in the European Union in 1994, yet pointed out national differences between 8-24%.While most of these studies argue that forest sector industries have a strong impact on regional and especially rural economies, significant empirical evidence supporting this hypothesis is still lacking. Furthermore, a true understanding of the regional association of different industries in the woodbased production chain has been limited by the scarcity of regional information.

Nevertheless, a strong body of knowledge on the study of industry concentration does exist in the economic geography literature, which focuses on clusters, networks and regional centres of innovation. Both the location and stationary nature of economic factors, particularly the role of human capital and knowledge in innovation, are recognised as key

components of regional economic growth in a globalized market economy (Porter 1998, OECD 1999). Therefore methodologies for analysing regional economic strengths and industrial targeting of growth hotspots, along with their application in regional development, have become focal topics in the scientific discourse (Clark et al. 2000, Bröcker et al. 2003, Bathelt and Glückler 2003, Feser et al. 2005, Stimson et al. 2006).

There exists a considerable variety of common locationbased measures in the literature, including the location quotient, the agglomeration coefficient, the Gini or the locational Gini coefficient and the more complex EllisonGlaeser index (Krugman 1991, Ellison and Glaeser 1997, Holmes and Stevens 2004, Stimson et al. 2006). These coefficients and indices are used to measure industry concentration, agglomeration or clustering. However, these terms are often used interchangeably in a somewhat diffuse manner and the measures do not truly consider the spatial dimension of industry concentration. New theoretical impulses come from both the regional and geoinformation sciences. The study of industrial location and clustering as a spatial phenomenon needs to address the geostatistical characteristics and limitations of commonly used types of data (e.g. spatial autocorrelation). From this perspective, measures such as Moran’s I and Getis-Ord G statistics are adequate statistical approaches for the study of clustering in geographical space (Anselin 1988, 1995, Getis and Ord 1992, 1996, Ord and Getis 1995, Arbia 2001, Le Gallo and Ertur 2003, Feser et al. 2005, Lafourcade and Mion 2007).

While forest sector research has incorporated basic regional analysis for some time (e.g. Flick et al. 1980), research into regional industrial concentration of woodbased industries is still relatively new in forest sciences (e.g.

Braden et al. 1998, Hazley 2000, Abt et al. 2002, Stordal et al. 2004, Aguilar and Vlosky 2007, Rojas 2007, Aguilar 2008, Herruzo et al. 2008). In the field of economics single wood-based industries have been investigated as a part of a wider spectrum of sectors (e.g. Lindqvist et al. 2003, Porter 2003), and some studies refer to Germany (e.g. Krätke and Scheuplein 2001, Brenner 2004, Litzenberger 2007). It is known from prior sampling-based questionnaire studies (Schulte 2002, Mantau et al. 2003) and regional analysis (Litzenberger 2007) that several wood-based industries are more concentrated in particular regions of Germany (e.g. sawmilling in the southern states, wood-based panels and furniture in the state of North Rhine-Westphalia). However, none of these studies applied a consistent spatial analysis methodology to a larger spectrum of wood-based industries or the total forest sector that is suited to precisely localize and delineate concentrations. Overall, the scientific evidence for regional wood-based industries remains scarce.

This research is based on the hypotheses that wood-based enterprises form industrial clusters in particular regions of

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Germany and that industries interlinked in supply chains are co-located. The overall objective of this research is to analyse the spatial distribution of the German forest sector using exploratory geostatistical methods for regionalized cluster mapping. The specific objectives of the case study are to investigate regional employment patterns of the total forest sector and individual wood-based industries with regard to spatial concentration, agglomeration and co-agglomeration trends on the global (federal) and local (county) level. The research presents a suitable process for regional industrial targeting and benchmarking of the forest sector.

Figure 1 Administrative units and population density in Germany, 2006

METHOD Study area and data material The research is based on a common statistical concept of space, in which a global geographical space is a construct of smaller local spatial units. These units generally correspond to administrative boundaries (e.g. states, districts, counties) and the associated spatial classification schemes of available statistical data. Germany, with an area of 357 000 km² and a population of 82.4 million in 2005 (Statistische Ämter des Bundes und der Länder 2007), is sub-divided into 439 counties and urban districts [Landkreise und Stadtkreise], representing the administrative bodies within the 16 federal states [Bundesländer] (Figure 1). This research studies the number of employees in forest sector industries across German counties in 2006, a commonly accepted, valuable measure of industry size. The data source is the official statistics of employees with social insurance registration [Statistik der sozialversicherungspflichtig Beschäftigten] (employment statistics), a national labour market information system maintained by the Federal Employment Agency (Bundesagentur für Arbeit 2007), which assesses the number of employees on the plant level. Access to information on other, equally important parameters of economic activity (e.g. absolute turnover, production or output) is limited at the county level in Germany due to statistical confidentiality concerns, thus the parameter selection is also a trade-off against spatial precision.

Adopting a definition formulated for the European Union (Commission of the European Communities 1999), the forest sector comprises all industries that are interlinked by the common resource or commodity wood. The forest sector definition was elaborated in previous research as a component of a standardized forest sector monitoring concept for Germany (Kies et al. 2008). It is related to the official Classification of Economic Activities in the European Union, Revision 1.1 (NACE) (Statistical Office of the European Union 2002) and comprises 17 wood-based industries that can be monitored consistently on the basis of official statistical information systems (Table 1). Three forest sector aggregates are specified that determine percentage shares of the sector in commonly used statistical reference units, namely the total economy, the producing industries and the manufacturing industries. The producing industries

comprise the NACE sections C (mining and quarrying), D (manufacturing), E (electricity, gas and water supply) and F (construction).

Basic measures of economic concentration Since the local units vary in size (in terms of area, population density and economic activity) both relative and weighted measures must be adopted for a comparative analysis. In the context of this research, concentration represents a local deviation of an industry from its overall global trend, as measured by standard economic coefficients. Measures of concentration are investigated at both global and local levels.

Global level measures allow for an overall comparison of the degree of concentration across different industry branches.

Local level measures detect locally developed concentrations of industries, even if globally observed values are low.

Standard measures of global and local industry concentration are applied in the analysis of the total forest sector and each single wood-based industry. The Gini coefficient (GC) (Schätzl 2000, Südekum 2004) measures overall concentration across local units relative to the global activity of the industry (Formula 1). It produces values in the range between 0, where all local units have an equivalent share of an industry, and 1,where an industry is entirely concentrated within one local unit. The location quotient (LQ) (Bathelt and Glückler 2003, Stimson et al.

2006) compares an industry’s share in a local unit’s activity against its share of overall activity (e.g. total employment) in the global unit (Formula 2). LQ values in the range of 0-1

Spatial analysis of the German forest sector

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TABLE 1 Global structure, concentration and agglomeration of employment in the German forest sector, 2006

NACE Industries and Plants 2006 Employees 2006 Employees per Sector share Rev. 1.1 Aggregates [ths.] [ths.] plant [%] 02 Forestry 4.4 17.4 4.0 2.0 20.1 Sawmilling 3.7 29.2 7.9 3.4 20.2 Wood-based panels 0.3 16.0 53.3 1.9 20.3 Wood-based construction 11.2 59.2 5.3 6.9 20.4 Wood-based packaging 0.8 10.4 13.0 1.2 20.5 Misc. wood products 4.1 18.6 4.5 2.2 36.1 Furniture 11.8 135.1 11.4 15.8 45.22.3 Carpentry 11.8 4.5 6.2    53.3         45.42 Joinery installation 23.4 54.4 2.3 6.4 45.43.1 Parquet laying 2.0 3.5 1.8 0.4       21.1 Pulp, paper, paperboard 0.6 59.3 98.8 6.9       21.2 Paper articles 2.1 75.8 36.1 8.9    22.1 Publishing 9.7 137.6 14.2 16.1   22.2 Printing 15.3 174.6 11.4 20.4    3.6 11.2 3.1 1.3 5x Timber trade (1)

Gini (GC) 0.61 0.74 0.93 0.61 0.79 0.77 0.64 0.51 0.47 0.77 0.83 0.72 0.76 0.61 0.71

Moran’s I Z (5) 4.98 5.12 3.21 8.72 4.83 3.98 9.44 15.66 5.72 3.80 3.82 5.02 1.33 5.20 2.35

        -  Forest sector total 101.1 855.6 8.2 100.0 0.47 7.88    (2)    Forest sector, in C-F 96.7 827.0 8.5 96.7 0.48 7.94       (3) 59.5 715.8 12.0 83.7 0.52 7.87 Forest sector, in D          A-O Total economy 3,099.5 26,354.3 8.5 3.2 0.45 4.19    (4)    630.3 8,480.8 13.5 9.8 0.42 10.78 C-F Producing industries      (4) D Manufacturing 274.1 6,595.0 24.1 13.0 0.46 12.78             Sources: Commission of the European Communities 1999, Statistical Office of the European Communities 2002, Bundesagentur für Arbeit       2007, Kies et al. 2008.   (1)    Notes: includes classes: 51.53.2 Wholesale of wood in the rough, 51.53.3 Wholesale of products of primary processing of wood, 52.44.6       Retail sale of wood. (2) excludes classes: 02, 5x. (3) excludes classes: 02, 45x, 5x. (4) sector share is based on the related forest sector    aggregate in C-F. (5) standard deviation in permutation test (significance level p): <1.65 (random/not significant), 1.65 - 1.96 (0.10), 1.96       2.58 (0.05), > 2.58 (0.01)                indicate that no concentration is observed in these local units.

Spatial statistics and economic agglomeration       LQ values >1 signify local shares greater than the global       share and are interpreted as a local concentration. Higher LQ Concentration is defined here as an industry’s local deviation      values indicate higher concentrations.

The actual LQ range from the global mean, as measured by economic coefficients       depends on the granularity of the spatial classification. relating to the single local unit. However, such local units

may be more or less evenly distributed as isolated spots  ∑  −          ∑∑  − −            −         −         −      alternatively, several  across   =        the global space (dispersion); ∑     ∑  = ∑        =   =     =      − neighbouring  −   high concentration values may     −   −   −   =    −  local units with       −  −   −   −   −−   

   ∑ 

∑ ∑ ∑ 

        =        =  

=

  ∑    ∑        ∑   =  = ∑       =     ∑       ∑      ∑ ∑   

                                                                                     

     

be grouped together within one or more regions (clustering).

To distinguish between these spatial distribution patterns, measurement must take the  neighbourhood of local  spatial   units into consideration.

Agglomeration (in contrast to concentration) is therefore defined as a regional deviation of an industry from its overall global trend, as described by geostatistical measures accounting for both the impacts of and distances of a spatial unit to its neighbouring units in a geographical space (spatial clustering) (Arbia 2001, Lafourcade and Mion 2004, Guillain et al. 2006). The geostatistical concept of spatial autocorrelation describes the interdependence of spatial events. In contrast to most

                       

     

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    42 U. Kies et al.                 the sample (clusters of high and low values of the variable sampling-based statistical testing, in which stochastic     under study), the latter implies a 4-way split of the sample: independence of variables is generally a fundamental     clusters of high and low values of the variable, and also requirement, spatial statistics explicitly utilize the     atypical locations (observations with low values surrounded autocorrelation effects inherent in spatial data to measure     by observations with high values and observations with high geographic patterns. A positive spatial autocorrelation     values surrounded by observations with low values). indicates a tendency to similarity in neighbouring     observations (clustering), whereas a negative spatial     Specific research definitions and settings autocorrelation suggests predominant dissimilarity among     neighbours (dispersion). Where spatial autocorrelation     does not exist, the spatial pattern is then considered one of This research uses the following specific definitions and settings: Agglomeration includes significant impacts of random chance (spatial randomness) (Anselin 1988, 1995;     neighbouring units on either absolute employment (number Cressie 1993).         of employees per local unit) or relative employment The Moran’s I and the Getis-Ord G statistics are among     (measured through the LQ). Both were considered equally the most frequently used spatial autocorrelation measures     important because both indicate considerable positive (Anselin 1988, 1995; Getis and Ord 1992, 1996; Ord and     deviations from the overall mean. Absolute employment Getis 1995).

Comparable, though not identical, they detect     clusters are considered because they represent high shares interboundary clustering of local concentrations (i.e.     of the global employment within a particular industry, agglomeration) by comparing the impact of an individual     even though their contribution to the local unit’s economy local unit and its neighbouring units on the global trend.     might not be significant (which is often the case in densely A distance weights matrix, which records distances     populated areas). Relative employment clusters might be between a given local unit and the other units, defines the     neighbourhood size according to a critical distance threshold of minor importance to an industry’s total abundance, but     they do have a comparatively higher impact on the regional value (the local unit’s geographical centre is the reference     economy. For regional targeting of the forest sector, which point). These autocorrelation coefficients exist in two forms:     intends to draw an overall picture of the industries’ spatial a global coefficient for analysing the overall agglomeration     distribution, both types of impacts (internal and regional) are trend in the whole study area, and a local coefficient for     therefore of interest. detecting agglomeration in local units, even where the     Uniform analysis settings are used to compute spatial overall trend is low (Formulae 3-6).

statistics in common geographical information software packages: ESRI’s ArcGIS Spatial Statistics Toolbox        (Mitchell 2005) and the GeoDa programme (Anselin et al.     2004). Based on polygonal centroids the neighbourhood weights matrix is defined through inverse Euclidean distances                                     and row standardized spatial weights. A neighbourhood  =  =   =             =             threshold distance of 50 km is used, which proofs to be most                suitable for the spatial classification of German counties as   −           − [ − −−[M−  [    −   − a result of trainings on the data set. Test runs with lower  [  MZ L − G    LM −  (15 - 35 km) are because they reduce = , =      =     thresholds  ineffective,         L G =    =     −       −       −    −      

[ − [

Q the number of neighbours too drastically in some regions.     M   M   Higher thresholds (75 - 150 km) result in either too large and in the sense of a regionalized  −   − few  agglomeration regions  −          −    −       −     −too    −         industrial targeting (agglomerations =     =          spanning across several         =    =       −     −    −   −          federal states in the case of strongly concentrated industries),                                or no agglomerations are detected at all (in the case of  weakly concentrated industries).       ≠    Based on these settings the spatial autocorrelation of     absolute employment and the corresponding LQ pattern of   each industry under study are assessed. Only the Global   Moran’s I is considered further in the analysis, because both      global coefficients generate very similar results in test runs on   the data sets (i.e. rankings of global agglomeration trends in    different industries). Both local coefficients however are used  in parallel, because they differ conceptually in their behaviour.

Specifically, the Getis-Ord G compares the sum of a local Local Getis-Ord Gi*, which includes the impact of the local unit’s neighbourhood (including the local unit itself) as a  unit’s activity itself in the weighing, effectively identifies proportion of the globally expected sum, while the Moran’s  agglomerated regions of joint local units, which are referred I measures spatial co-variance among local units normalized  to as ‘regional clusters’ in the following. Local Moran’s I by the total variance. Getis-Ord Gi* and Local Moran’s I  allows to categorize atypical locations and thus identifies also differ on one point: while the former implies a 2-way split of

∑ = ∑



                  

∑∑ ∑∑ ∑



       

   

  

   



∑ ∑ ∑ ∑∑ ∑ ∑ ∑ ∑ ∑∑ ∑ ∑¦

∑

∑∑ ∑∑

∑   ∑  = ∑ =  =  ∑ ∑ ∑ 

∑¦

∑

∑∑ ∑∑

∑ ∑ ∑ ∑

∑ ∑∑ ∑ ∑∑∑∑ ∑ ∑ ∑

    

Spatial analysis of the German forest sector

isolated individual local units with high impacts (high-low clusters), which are referred to as ‘local hotspots’.

The spatial autocorrelation coefficients are tested for statistical inference based on the Null hypothesis that no spatial autocorrelation exists (meaning that the observed spatial pattern would be one of random chance). Using randomized permutation tests, the observed patterns are compared against random patterns of neighbourhood relationships.

Standardized Z values (standard deviation from the mean) of Moran’s I and Getis-Ord G* indicate the significance of the observed autocorrelation pattern. Spatial agglomeration (clustering of local units) is defined as follows: where either absolute employment (i.e. absolute number of employees) or relative employment (LQ) of an industry in a local unit (county) shows a statistically significant autocorrelation Z score (Z>‫׀‬1.65‫ ׀‬standard deviations at (p<0.10) confidence level), the local unit are classified as an agglomeration unit. To ensure transparency of the results, three classes of increasing Z scores and corresponding confidence levels (1.65 - 1.96 (0.10), 1.96 - 2.58 (0.05), > 2.58 (0.01)) are distinguished in the mapping. It is notable that agglomeration patterns of different industries are not directly comparable since the Z score significance levels represent peaks in the distribution rather than absolute figures of the degree of agglomeration.

Consequently, the autocorrelation pattern must be regarded as contextual per industry.

The concept of co-agglomeration describes the pairwise occurrence of different industries in the same location.

Global co-agglomeration can be measured using for example the Ellison-Glaeser co-agglomeration index (Ellison and Glaeser 1997, Ellison et al. 2007). However, this index is merely a global concentration measure, which produces a ranking of co-locating industry pairs. It is a-spatial in nature and can not be applied in a local analysis; which is why it was not explored in this research. Here, co-agglomerations are investigated as geographical linkages among industries (assessing functional linkages would require inter-industry or inter-plant level production data), which are identified using a simple logical procedure: where local units show statistically significant autocorrelation Z scores within two industries (p<0.10), they are classified as co-agglomeration regions. Primary wood processing and secondary wood manufacturing are interlinked by supply-chain relationships based on processed timber or semi-finished wood products.

Two pairs of closely connected industries are elected examples for this analysis: NACE 20.1 sawmilling and NACE 20.3 wood-based construction industries (because sawn timber is a major supply product for wood-based manufacturing of builders’ carpentry and joinery) and NACE 20.2 wood-based panels and NACE 36.1 furniture industries (because wood-based panels are crucial semifinished supplies for furniture production).

Cartographic representation The results are finalized in map form according to principles of cartography (MacEachren 1995, Slocum et al. 2003). The map design comprises four thematic layers using the national

43

and federal state borders as the geographical reference frame. The absolute number of employees per county is mapped in proportional circular symbols to allow for a direct visual assessment of the absolute employment pattern.

The additional thematic layers are mapped as parallel line hachures (Local Moran’s I) and choropleth-fill grey colour shades (Getis-Ord Gi*). The forest sector’s regional impact (Figure 2) is depicted as classified percentages per local unit in relation to population density. The individual industries’ spatial distributions (Figures 3-6) are mapped as overlays of absolute employment, Local Moran’s I and Getis-Ord Gi*, which are classified according to increasing significance levels. The co-agglomeration maps (Figures 7, 8) visualize pairs of industries as two types of proportional symbols and agglomeration regions as choropleth shades.

RESULTS Global structural statistics of the forest sector The German forest sector contains a diverse structure of employment in the related industries (Table 1). The forestry enterprises, representing the initial link in the wood manufacturing chain, account only for a minor share (17 400 or 2%). The group of manufacturing industries based on solid wood (in contrast to cellulose-based paper) comprises the wood processing industries (NACE 20.x: 133 400 or 16%), the furniture industry (NACE 36.1: 135 100 or 16%) and the wood crafts in construction (NACE 45.x: 111 200 or 13%) and accounts for 379 700 employees or 44% of the total sector. The cellulose-based paper industries (NACE 21.x: 135 100 or 16%) and publishing and printing industries (NACE 22.x: 312 200 or 37%) unite 447 300 employees or 52% of the sector. The forest sector is generally dominated by small-to-medium-sized enterprises (SME’s): although some branches reveal typical large scale enterprise structures (e.g.

NACE 20.2 wood-based panels or NACE 21.1 Pulp, paper, paperboard), the majority of industries is characterized by less than 15 employees per plant. The forest sector as a whole comprises 855 600 employees and has a considerable impact on overall employment in Germany, accounting for 24.1% of total manufacturing, 9.8% of total producing industries and 3.2% of total employment in 2006.

Global concentration and agglomeration trends in the forest sector Global spatial concentration in the total forest sector (GC=0.47) shows no decisive deviation from either the producing industries or overall economy (Table 1). However, individual wood-based industries show higher values, enabling a ranking of the concentration among the branches.

In decreasing order, the strongest globally concentrated branches are wood-based panel production, pulp and paper production and wood-based packaging. The carpentry and joinery industries are the least concentrated. Moran’s I Z values of absolute employment facilitate a comparison of

44

U. Kies et al.

global agglomeration trends in the industries under study.

All of these scores are highly significant (Z>2.58 at p=0.01), indicating that all industries show pronounced agglomeration.

The strongest global agglomeration trends are found in the carpentry, furniture and wood construction industries. Since low Moran’s I Z scores indicate a low spatial autocorrelation, this agglomeration trend is not as significant, due either to a more complex spatial distribution (the case of the sawmilling and the paper article manufacturing industries) or a considerable number of counties with zero employment in the particular industry (the case of the highly concentrated wood-based panels, pulp and paper production or publishing industries).

Figure 2 Regional distribution of employment in the German forest sector, 2006

Local concentration of the total forest sector The distribution of the total forest sector across Germany’s counties reveals a heterogeneous spatial pattern (Figure 2).

Absolute employment in the sector is strongly concentrated in several regions within the states of North Rhine-Westphalia and Baden-Württemberg. Weaker concentrations are found in counties within the states of Lower Saxony, Hesse and Bavaria. In the remaining states, forest sector employment is only weakly concentrated. Across the states, this pattern is similar to the overall distribution of the German population (compare to Figure 1). However, a closer look reveals that concentrations of absolute employment in the forest sector and high relative shares of overall employment occur mainly in predominantly rural regions with low population density (less than 250 inhabitants per km²). In 85 counties (19% of 439 in total) the forest sector accounts for more than 5% of total employment and in 30 counties for more than 7.5%.

In nine counties the forest sector employs even more than 10% up to 18% (in increasing order: Gütersloh, MainTauber-Kreis, Düren, Amberg-Sulzbach, Saale-Orla-Kreis, Osterode, Bernkastel-Wittlich, Herford, Coburg).

Local agglomeration patterns of wood-based industries Each of the four wood-based industries under study reveals a distinct agglomeration pattern (regional clusters).

The sawmill industry (Figure 3) shows a number of concentrations dispersed across Germany (GC=0.74), mainly located in border regions of the federal states. Ten significant agglomerations (Gi* Z>2.58) are identified. The two largest agglomerations stretch across 5-10 neighbouring counties in eastern Baden-Württemberg (Franken region) and northern Bavaria (Oberfranken region), comprising 3 300 (12%) and 1 500 (5%) of total employment in sawmilling, respectively. The other agglomerations are smaller and more local (Moran’s I Z >1.65). It is notable that only local hotspots occur in the eastern German states, where the sawmill industry structure is dominated by a number of large plants settled in strategic locations (e.g. in close proximity to the Polish border or the Baltic Sea port of Wismar in Mecklenburg-Western Pomerania).

Regional agglomerations of the The wood-based panel industry (Figure 4) shows the highest concentration

(GC=0.93) of all industries under study. Employment is mainly located in one large agglomeration in the east of North Rhine-Westphalia (Eastern Westphalia region), where more than 5 000 (30%) of the total employees in the industry are concentrated in only four counties. Two smaller agglomerations also occur in eastern German states: one in northern Brandenburg (Prignitz and Ostprignitz-Ruppin counties) and another in the Wismar county in MecklenburgWestern Pomerania. The high Gi* significance scores of these agglomerations based on the LQ indicate a comparatively high impact on regional employment. Only smaller local hotspots or a lack of concentrations are identified in the remaining states.

The wood-based construction industry (Figure 5) is characterized by a considerably different spatial pattern.

Although it is more evenly distributed across Germany (GC=0.61), a clearly detectable agglomeration trend is evident (Global Moran’s I Z=8.72). Remarkably, both the industry’s major share of absolute employment and the agglomerations are confined to western and southern German states. Four larger agglomeration regions are identified in four separate states: North Rhine-Westphalia (Eastern Westphalia region), Rhineland-Palatinate (Eifel-Trier region), BadenWürttemberg (Schwarzwald [Black Forest] region) and Bavaria (Oberpfalz, Niederbayern region). This industry is the determining factor in a high share of regional forest sector employment in sparsely populated regions of RhinelandPalatinate and Bavaria (compare to Figure 1 and 2).

Although the furniture industry (Figure 6) is distributed across nearly all states, one large and three smaller regional

Spatial analysis of the German forest sector

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Figure 3 Regional agglomerations of the German sawmill industry (NACE 20.1), 2006

Figure 5 Regional agglomerations of the German woodbased construction industry (NACE 20.3), 2006

Figure 4 Regional agglomerations of the German woodbased panel industry (NACE 20.2), 2006

Figure 6 Regional agglomerations of the German furniture industry (NACE 36.1), 2006

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agglomerations are clearly delineated. These clusters show also pronounced specialisation trends in different types of production (regional segmentation). In the Eastern Westphalia region, the major furniture agglomeration comprises 15 neighbouring counties, uniting 30 500 (23%) of industry employees. The small county of Herford (population 254 500 in 2005) is the focal point, accounting for 7 600 employees (6%) alone. Enterprises in this agglomeration are highly specialised in the production of kitchen and other furniture (NACE 36.13 and 36.14) and account for 52% and 27% of all employees in these branch segments, respectively. The two smaller adjacent agglomerations in Bavaria (Oberfranken and Oberpfalz regions) are mainly comprised of chair and seat furniture industries (NACE 36.11) and represent 25% of all employees in this segment. The county of Coburg (population 91 300 in 2005) is the focal point and accounts for 2 700 (9%). The smallest agglomeration, localised in Baden-Württemberg (Franken region), does not specialise in any certain branch segment.

Figure 7 Co-agglomerations of the German sawmill and wood-based construction industries (NACE 20.1 and 20.3), 2006

Co-agglomeration of wood-based industries In the research local co-agglomeration patterns of two pairs of wood-based industries are analysed. The pair of sawmilling and wood-based construction reveals five centres of co-agglomeration (Figure 7). One of the larger sawmilling agglomerations (eastern Baden-Württemberg) and three larger wood-based construction agglomerations are part of these co-locating centres (Eastern Westphalia region, Eifel-Trier region, Niederbayern region) (compare to Figure 3 and 5). It is notable that all these co-agglomerations are situated in rural areas with less than 250 inhabitants per km² (compare to Figure 1).

The wood-based panel and furniture industries share a single co-agglomeration centre in the Eastern Westphalia region (Figure 8). Although other regional hotspots exist in both industries, none of them are co-located. It is notable, that the largest agglomerations of both industries uniting major shares of employees (30% respectively 23%) are located in this particular region.

DISCUSSION Socioeconomic impact of the forest sector National-level forest sector research has provided considerable socioeconomic evidence that contributes to a growing understanding of a large sector in national economies of Europe (Commission of the European Union 1999, Hazley 2000, Lebedys 2004, Kies et al. 2008). Because empirical evidence on the forest sector’s regional structure remains scarce, this study adds a spatial perspective to the research presenting a geostatistical mapping of regional wood-based clusters. Based on our exploratory spatial analysis results we argue that the forest sector can have decisively stronger regional impacts exceeding the reported national averages. In Germany, the national average for the

Figure 8 Co-agglomerations of the German wood-based panel and furniture industries (NACE 20.2 and 36.1), 2006

Spatial analysis of the German forest sector

forest sector’s share in total employment figured 3.2% in 2006, yet a number of counties were identified where the forest sector contributed more than 7.5% and in a few cases even more than 10% up to 18%. These counties are dispersed across the whole of Germany, but they seem to occur more frequently in the peripheral border regions of the federal states that are often sparsely populated. The results prove that the forest sector can account for decisive impacts on regional employment especially in rural areas, which is still likely to be underestimated due to lacking regional scientific evidence.

Industry-specific agglomeration trends Spatial clustering of industries in general (Porter 1998, Clark et al. 2000, Bröcker et al. 2003) and of wood-based industries in particular (Braden et al. 1998, Aguilar and Vlosky 2006, Aguilar 2008) has been related to the influence of regional factors. Reducing transportation costs was traditionally seen to play the key role in the establishment of natural resource-based industries in proximity of their resources, yet today it is acknowledged that the location of industries is determined by a complex set of regional factors such as natural endowments, availability of and costs for skilled labour, connection to large markets in populated areas, favourable regional policy and/or structural changes and concentration trends induced by technological progress.

Although this research did not study the influence of regional factors, their discussion can offer plausible explanations for the observed spatial patterns.

The overall spatial distribution of employment in Germany’s wood-based industries reflects variations in both population density and labour market conditions across the federal states. In all four wood-based industries under study occurs a gradient of absolute employment from the western and southern federal states to the six eastern German states, which more than 15 years after the German reunification still display the lowest shares in the national economy (e.g. low industrialisation, high unemployment rates). In 2006, the unemployment rate was 17.3% in eastern Germany: far higher than in the western federal states with 9.1% (Bundesagentur für Arbeit 2007).

Although this overall gradient is recognizable in all four investigated industries, each of them reveals a number of particular significant peaks in the distribution representing characteristic agglomeration regions and local hotspots. Two different agglomeration patterns are distinguishable among the primary and secondary wood-based industries. Primary wood processing industries, which do not occur in all regions owing to the rather small size of the industries (close to 4 000 sawmilling and wood-based panels’ enterprises in 2006), reveal agglomerations and local hotspots that are dispersed more or less across the whole of Germany.

In contrast, the secondary wood manufacturing industries under study, which are larger in size (23 100 wood-based construction and furniture enterprises in 2006) and occur in more than 90% of all counties, are characterized by agglomeration centres that are solely confined to regions

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within the western and southern states. These diverging patterns can be attributed to contrasting developments of the wood-based industries of western versus eastern Germany, which emerged anew after reunification in 1990.

Over the last decade, the eastern German states have seen considerable investments in new primary wood processing plants that were encouraged by regional policy in the form of federal post-reunification reconstruction subsidies, whereas the secondary wood-based manufacturing remained still underdeveloped and insignificant in the national context (Klein et al. in press). In most cases these identified local hotspots in the eastern states represent individual plants with large processing capacities instead of strong agglomerations uniting numerous enterprises located in several neighbouring counties.

Furthermore the spatial extent of these agglomeration patterns varies considerably. The sawmilling and woodbased construction industries are characterised by a number of smaller, more disjunctive agglomerations, indicating a weaker trend towards industrial concentration in these predominantly small-sized industries. By contrast, there are only a few (albeit larger) wood-based panel and furniture production agglomerations, suggesting a stronger concentration trend and larger impact on both regional employment and acquisition of resources for production (e.g. raw timber and semi-finished wood products).

Recent research also points out the importance of centrifugal (dispersive) forces such as undesired competition for resources in particular in the primary wood processing industries (Aguilar 2008). This factor may hold true as one driving force behind the identified agglomeration pattern of the German sawmill industry, which clearly reveals regionally separated centres.

Co-agglomeration of interdependent industries Spatial proximity or co-location of interdependent woodbased industries may offer a competitive advantage by reducing both transportation and logistical costs and facilitating knowledge exchange. Spatial co-agglomeration patterns can therefore be interpreted as an indication of potentially competitive centres in regional supply chains.

The investigated two cases are convincing examples of pronounced regional industrial clusters in the forest sector linking pairs of closely related industries. Although the primary wood processing industries are smaller in size, higher employment in secondary wood manufacturing is partly dependent on these industries. These results are a first empirical evidence for plausible geographical linkages between wood-based industry agglomerations in Germany and reinforce existing descriptions of regional wood clusters in the literature generated using different methodologies, such as Hazley’s (1999) enterprise mapping and Litzenberger’s (2007) complex economic clustering index (however neither of these investigations addressed aspects of spatial autocorrelation).

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Relevance and implications for forest cluster research and policy Exploratory spatial analysis techniques are gaining more and more attention in economic cluster research, but they are still new to forest sector analysis. The presented approach combines a statistics-based definition of the forest sector, detailed local labour market data, standard spatial economics procedures and cartographic visualization techniques into a method for regional industrial targeting of the total forest sector and individual wood-based clusters.

The method’s advantage lies in its increased spatial resolution and its capacity to not only measure clustering and co-agglomeration of industries with global indices, but to detect explicit regional patterns diverging from nationallevel trends and to precisely locate and delineate (co-) agglomerations in geographical space (Feser et al. 2005, Guillain and Le Gallo 2007).

First, regional cluster analysis allows for a benchmarking of the forest sector in the national and regional context.

Key figures on the sector’s socioeconomic size and impact (e.g. contribution to total employment, ranking among other strong sectors, identification of outstanding regions, internal structure of a region’s forest sector) are generally not available to industry managers, associations or governmental bodies, owing to the distorted representation of the forest sector in official statistics (i.e. segregated allocation of wood-based industries to separate NACE sections) and a misconception of the whole sector in the industries themselves (i.e. uninformed image, poor sectoral organization and joint representation in politics, the media and the public compared to other leading, dominant national sectors). In this sense, forest cluster analysis provides crucial baseline information for the understanding and formation of a commonly underestimated, yet still rather fragmented sector (Hazley 2000, Commission of the European Union – DG Enterprise 2002, Kies et al. 2008).

Second, the spatial analysis allows localizing industrial clusters and potentially competitive centres in regional woodbased supply chains. The geostatistical targeting of woodbased clusters in the form of detailed visual maps presents again mostly new, previously inaccessible information for decision makers in industry, government and research.

The identification of nationally and regionally outstanding employment hotspots of interrelated industries encircles areas of interest for targeted business support activities and cluster research (e.g. the formation of enterprise networks, joint cluster development initiatives, research in regional industry structure and its competitive factors).

Economic research acknowledges the fact that most industrial clusters emerged without a steering policy and were triggered through complex sets of specific regional competitive factors (Porter 1998, Bröcker et al. 2003, Brenner 2004), which remain yet to be investigated in the case of this resource-based sector and its structurally diverse industries. The authors remain yet careful about recommending the initiation of targeted cluster planning or management activities: cluster management has become an

overly popular, inflationary concept in regional development and policy that has often been applied deliberately on a weak scientific basis and resulted in precipitated attempts to form and support `wishful thinking´-cluster initiatives (Enright 2003, Kiese and Schätzl 2008).

But nevertheless, examples of serious, successful attempts to form regional wood-based cluster initiatives of small and medium-sized enterprises cooperating along regional value added chains exist in several European countries, in particular in Austria, Switzerland and Sweden.

The majority of these pilot projects in the forest sector were initiated with considerable governmental funding during the starting phase, but only few managed to become selfsupporting on the basis of their own network partners’ fees and fund raising during the consolidation phase. First experiences have shown that regional wood-based cluster initiatives can stimulate innovative business potentials and stabilise or even reverse negative employment trends in the wood industry, yet their capacity to ensure long term growth is still questionable (Raines 2002, Mrosek and Kies 2006).

Future research on regional wood-based clusters should focus on the following aspects. Further testing of the depicted approach in other geographical settings (e.g. other regions in the EU, regions in transition and developing countries) could evaluate the influence of other data sources as well as other industrial and spatial classification schemes on the applicability of the method and offer insight into woodbased clusters situated in different socioeconomic contexts.

Further refinement of the spatial analysis tools is needed; in particular more advanced measures for co-agglomeration of interrelated industries (the simple logic used in this research could be expanded into bivariate autocorrelation measures).

Content wise the decisive underlying factors influencing the formation of regional wood-based clusters are of strong interest to research: besides commonly considered regional factors in cluster formation, the regional availability of forests and timber resources plays a key role in the development of this resource-based forest sector, yet their relationship with wood-based industries has not yet been researched from a truly spatial perspective.

In conclusion, spatial analysis methods offer valuable new tools for forest science to explore regional impacts of the forest sector and target regional wood-based industry clusters in geographical and socioeconomic contexts. The research contributes to a more standardised empirical understanding of the forest sector’s role in national and regional economies and can be a valuable support for informed rational decision making in forest cluster development and policy.

ACKNOWLEDGMENTS The authors would like to thank three anonymous referees for their valuable comments and suggestions. We also thank the staff members of the Federal Employment Agency [Bundesagentur für Arbeit] for their helpful support in providing background information about and access to the statistical information systems. We also like

Spatial analysis of the German forest sector

to thank Ms. Denise Allen, University of British Columbia, Sustainable Forest Management Research Group for editing the manuscript. The research was financially supported by the State government of North Rhine-Westphalia [Landesregierung Nordrhein-Westfalen].

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