Scandinavian Journal of Forest Research, 2008; 23: 445 457
ORIGINAL ARTICLE
A statistics-based method for cluster analysis of the forest sector at the national and subnational level in Germany
UWE KIES, THORSTEN MROSEK & ANDREAS SCHULTE Wald-Zentrum, Westfa ¨ lische Wilhelms-Universita ¨ t, Mu ¨ nster, Germany
Abstract The cluster concept in economics contributes to new research on the forest sector’s role in national and regional economies, yet incompatible cluster definitions and a variety of methodologies impede an objective comparison of findings. However, governmental statistical reporting systems are not well suited for a direct assessment of the forest sector due to classificatory and methodological shortcomings. This research presents a more standardized approach in the form of a statistics-based method for forest sector benchmarking and monitoring. The specification of the method included an extended cluster definition based on the Classification of Economic Activities in the European Union (NACE) and the validation of two suitable national statistical reporting systems. Testing the method in a case study for Germany demonstrates its capacity to provide consistent socioeconomic information on forest and wood-based industries in a sectoral, spatial and temporal dimension. In 2004, the German forest cluster accounted for 100,000 companies, 150 billion Euro gross turnover and over 900,000 employees (approximately 3.5% of the national economy) and ranked among the strongest manufacturing sectors. Individual wood-based industries indicated regional concentrations in federal states of Germany. Over the past decade, the forest sector was marked by considerable losses in turnover and employment, which increasingly deviated from the overall economic development of Germany. The research contributes to a more standardized, empirical understanding of the forest sector’s role in national and regional economies, supporting rational decision making in cluster policy and management.
Keywords: Classification of Economic Activities in the European Union (NACE), cluster analysis, employment, forest-based industries, forest sector, Germany, wood manufacturing.
Introduction Forest and wood-based industries constitute a complete industrial sector in national economies, which by definition comprises all economic activities with a close linkage of its associated activities to a common resource: wood. The view of one large sector unified by a common resource was put forward by the European Union (EU) to promote a common strategy and structured approach for the sustainable development of one of its largest industrial sectors (EUROFOR, 1997; Commission of the European Communities, 1999; United Nations Economic Commission for Europe & Food and Agriculture Organization, 2005). The forest sector incorporates raw timber-producing forestry enterprises, industries in the processing and manufacturing of semi-finished wood, pulp and paper products, and further
downstream manufacturing industries, which provide numerous finished wood-based products to the end consumer. Industries that are linked to each other by a close relationship to a certain use of resources or form of production, spatial concentration or high connectivity in terms of business activities are understood as ‘‘clusters’’. In line with related approaches (e.g. industrial districts, stakeholder networks, centres of innovation, regional development), the cluster concept is widely adopted in economics (Porter, 1998; Organization for Economic Co-operation and Development, 1999; Stimson et al., 2006). To provide socioeconomic information on forestbased industries, numerous studies have investigated this complex sector on different levels and in various geographical settings. The existing research provides evidence on the suitability of the cluster concept for
Correspondence: U. Kies, Wald-Zentrum, Westfa¨lische Wilhelms-Universita¨t, Robert-Koch-Str. 26, DE-48149 Mu¨nster, Germany. E-mail: uwe.kies@wald-zentrum.de
(Received 27 August 2007; accepted 15 July 2008) ISSN 0282-7581 print/ISSN 1651-1891 online # 2008 Taylor & Francis DOI: 10.1080/02827580802348043
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assessing the forest sector’s complex structure of industry branches. Across various contexts and scales, the research outcomes commonly recognize the important role of the forest sector for employment and value added in both the national and regional economies, which is frequently higher than previously anticipated. Recent examples can be found in the Northern American context (Abt et al., 2002; Helvoigt et al., 2003; Laaksonen-Craig et al., 2003; Sowlati & Vahid, 2006) and in the EU or its individual member countries (Hazley, 2000; Hanzl & Urban, 2000; Blomba¨ck et al., 2003; Bundesamt fu¨r Umwelt, Wald und Landschaft, 2004; United Nations Economic Commission for Europe & Food and Agriculture Organization, 2005). The concept is also gaining in importance in a global forest sector perspective (Lebedys, 2004). In Germany, recent cluster analysis approaches offered a new view of the forest sector and revealed its major economic impact on the national and subnational/state level (Schulte, 2002, 2003; Dieter & Thoroe, 2003; Mrosek et al., 2005; Seegmu¨ller, 2005; Kramer & Mo¨ller, 2006; Lutze et al., 2006; Schulte & Mrosek, 2006; Jaensch & Harsche, 2007; Ru¨ther et al., 2007; Seintsch, 2007). However, the research concepts and methodologies apply to diverse understandings of the forest sector. These studies reveal considerable dissimilarities, such as competing forest sector definitions (i.e. a forestry and primary wood processing focus versus the whole sector, including downstream manufacturing), a variety of information sources with different years of reference (e.g. governmental statistics, private industrial databases and individual company surveys) and the use of different analysis parameters and methods (i.e. numbers of employees versus employee full-time equivalents; full versus partial share of an industry branch as contribution to the value added of the forest sector). Consequently, both methodological approaches and key findings show limited comparability and are not readily transferable to other contexts, and even studies situated within a common spatial setting are not easily compared with one another. Hence, results from the majority of national and subnational studies do not comply with those of international and national studies, respectively. In summary, the available baseline information on the socioeconomic role of the forest sector is inconsistent and insufficient. Not surprisingly, national statistical reporting systems have not yet incorporated these case study findings. From a regional economics perspective, the structural complexity of the forest sector makes statistical assessment of the complete sector challenging. Regular reporting and monitoring of the main socioeconomic parameters of industry development
(e.g. number of companies, employment and value added) is just one example. Focused on commodities and trade of forest products, the forest sector’s statistical information systems provide only limited information on agents and branches of the sector (Wardle et al., 2003). National statistical reporting systems in the EU member states, which are based on the official systematic Classification of Economic Activities in the European Union (NACE) (Statistical Office of the European Communities, 2002) and corresponding national versions of the classification encounter a number of problems in assessing the socioeconomic impact of the forest sector in national economies. In short, the classificatory and methodological shortcomings of these systems include: . The European forest sector is dominated by small and medium-sized enterprises, yet a significant share of these industries is not surveyed by many statistical systems owing to methodological constraints (e.g. companies with fewer than 20 employees are not included). . The European forest industries are assessed by two statistical schemes with separate, often unrelated reporting systems: forestry enterprises are considered under agriculture/primary production (NACE section A), whereas woodbased industries are considered under manufacturing (NACE section D). Agricultural information systems often do not distinguish between forestry and agriculture, making explicit forestry enterprise information scarce and imprecise. . A number of wood-based industries cannot be assessed through the regular statistical systems for two reasons: in many cases the NACE nomenclature of statistical classes does not allow for differentiation among wood-based and non-wood-based industry segments, and a number of wood-based branches that are placed on a low hierarchy level in the NACE classification (e.g. NACE class and subclass 5 6 digit level) are not specified in regular statistical systems. . Total figures for the whole economic entity of wood-based industries in the EU are not available from existing government statistics because the related branches are allocated to separate NACE subsections (DD wood manufacturing, DE paper manufacturing, DN other manufacturing and other subsections). Owing to these restrictions of the reporting systems, the official statistics on the forest sector in European countries are often characterized by
Cluster analysis of the forest sector incompleteness and a high degree of uncertainty. The systems show limited potential for mapping the complex field of industry branches and do not allow for an integrated view on forest and wood-based industries from the perspective of regional economics. Consequently, industry and policy decision makers have only limited access to a suitable information base for strengthening their efforts to mobilize political support and public attention for the forest sector. Considering these shortcomings of the official statistical reporting systems and the limitations of existing cluster case studies, there is a need for research on more standardized, reproducible cluster analysis methods for the forest sector. A more empirical and comparative understanding requires methods that produce comparable, transparent findings, offer a temporal perspective on forest sector development and allow for results to be scaled up and down to other spatial levels. The objective of this research was to develop a cluster analysis approach for standardized forest sector monitoring in Germany. The approach should produce reliable results solely on the basis of official governmental statistics in a transparent, efficient and reproducible manner, which allow for a benchmarking of the forest cluster’s key variables in a sectoral, spatial and temporal context. Specifically, by identifying suitable statistical information sources in line with a solid sector definition, the cluster analysis should account for consistent absolute figures and percentage shares on the sector’s socioeconomic position in the national economy (federal level). Furthermore, the approach should offer regionalized information on the distribution of the sector and individual wood-based industries at the subnational level (scaling down to the state level) and allow for an assessment of specific development trends in the sector.
Materials and methods Theoretical structure of the method and validation criteria In the first step a theoretical structure of the method was formulated that arranges crucial components of a cluster analysis process to ensure consistency of information sources and results. The set-up requires a consistent cluster definition, spatial classification and temporal reference specifying the context, level and stratification of the analysis (Figure 1). On the input side, the multiple statistical information sources available for the cluster analysis differed considerably in format and data quality. To ensure that these specific sources were methodologically
Cluster definition
Spatial classes
Time reference
Forest sector Production chains Branches Branch segments
International National Sub -national Regional
Single years Time span
Analysis-Output
Data-Input Statistics A Statistics B Statistics C
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Cluster analysis of the forest sector
Global indicators Regionalized patterns Development trends
Validation
Parameters
Calculations
Completeness Comparability Scalability Reproducibility
Companies Employees Turnover / Value-added
Aggregates Distributions Indices
Figure 1. Structure of the cluster analysis method for the forest sector.
consistent with the set-up of the analysis, four criteria of source validation (defining the information standards for a holistic spatial and temporal cluster analysis) were established: . Completeness: the cluster analysis should be the best possible comprehensive mapping of the multiple wood-based branches in the forest sector. The statistical branch definitions of the selected classes must be evaluated for content and data availability in the reporting systems, and the influence of the midget census unit (minimum threshold value below which data are not recorded) and the statistical confidentiality, which affect the completeness of information, must be assessed. . Comparability: to produce solid, comparable results, both the cluster and statistical branch definitions should be as consistent and stable as possible. The information sources must also allow for direct assessment of the results in the socioeconomic context, meaning that suitable total figures for the overall economy and/or comparable economic branch units are necessary. . Scalability: the information sources should enable adaptation of the cluster analysis to different spatial levels, in particular scaling down from the national to subnational or regional level. In Germany these hierarchical levels comprise federal [Bundesrepublik], state [Bundesland], district [Regierungsbezirk] and county [Landkreis]. . Reproducibility: for a cluster analysis approach to be reproducible in other contexts it must be based on stable and readily available sources. Furthermore, sources must contain appropriate information for time series analysis. Information sources that met this set of criteria were validated for the analysis, which focused on the
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key socioeconomic parameters: number of companies, number of employees and turnover. These parameters were analysed through standard calculation (i.e. aggregates, distributions and indices) to produce three types of results related to global, regionalized or temporal dimensions. Specification of the method for the German context In the second step, the general theoretical form of the method was specified for a forest cluster analysis in the context of the Federal Republic of Germany. Three regular national statistical reporting systems maintained by the Federal Statistical Office of Germany [Statistisches Bundesamt Deutschland], the State Statistical Offices [Statistische Landesa¨mter] and the Federal Employment Agency [Bundesagentur fu¨r Arbeit] were investigated to identify suitable information sources. The statistical reporting systems were assessed for both their methodological foundation and content of relevant information (census unit, classification, parameters and periodicity), according to the validation criteria: . The statistics of the producing industries [Statistik des Produzierenden Gewerbes] surveys plants with more than 20 employees with social insurance registration on a monthly basis, reporting on the number of plants, employees and turnover in the NACE sections C D on the four-digit level. . The value added tax statistics [Umsatzsteuerstatistik] is based on the tax census and surveys all tax-obliged companies with annual sales above 17,500 Euro on a yearly basis, reporting on the number of companies and turnover of selected branches in the NACE sections A O on the six-digit level (turnover is defined here as all tax-relevant deliveries and other services of a registered self-employed entrepreneur). . The statistics of employees with social insurance registration [Statistik der sozialversicherungspflichtig Bescha¨ftigten] (abbreviation: employment statistics) surveys all employees with social insurance registration, reporting monthly on the number of employees and apprentices of all branches in the NACE sections A O on the six-digit level. A cluster definition was developed adapting the EU concept of forest-based and related industries (Commission of the European Communities, 1999) and the Classification of Economic Activities in the European Union, NACE Rev. 1.1 (Statistical Office of the European Communities, 2002), and the corresponding national version, the German Classi-
fication of Economic Activities WZ 2003 (Statistisches Bundesamt Deutschland, 2003). The definition is a construct of industry branches specified in the NACE classification, which were selected according to the following criteria: . Content: the economic activity or production defined in a particular NACE class shows a close relationship to the primary resource (wood). . Data availability: the particular NACE class is specified in the official statistical information systems and regular reporting on the class exists. Testing of the cluster analysis method The third and final step involved application of the specified form of the cluster analysis method in a case study for the Federal Republic of Germany to test the suitability of the approach for a socioeconomic analysis of the forest sector. Yearly statistical reports on industrial development in Germany and its states were compiled from the federal and state statistical offices. This information was complemented with specific additional queries of the statistical reporting systems to build a complete data set for all considered wood-based branches comprising the socioeconomic parameters number of companies, number of employees and yearly turnover. On the basis of the single branch data sets, structural indicators were calculated for both the total forest cluster and subgroup aggregates and compared against reference information on the overall national economy and the manufacturing industries. At the subnational level, all 13 territorial states in Germany were analysed to identify regional differences in industry structure (excluding the German city states Hamburg, Bremen and Berlin, for the year 2004). Time series were analysed at the national level for the decade 1994 2006 to investigate trends in German forest sector development.
Results Suitable statistical information sources for forest cluster analysis in Germany Analysis of the three German statistical reporting systems revealed differences in census method, coverage of industry branches and reporting periodicity, all of which have implications for their suitability for analysis. On the basis of the validation criteria, two systems were identified as suitable information sources for forest cluster analysis and the third was rejected.
Cluster analysis of the forest sector As all three statistical systems use complete inventory counts (rather than statistical sampling surveys) for their census methods, the statistical data are generally precise and of high quality. However, to protect the confidentiality of individual enterprises, individual data records are omitted in cases where a statistical class is comprised of only a small number of companies or employees (generally fewer than three) or one company covers more than 80% of the total turnover (dominance rule) (State Statistical Office of North Rhine-Westphalia, personal communication, April 18, 2007). The likelihood of omitted, ‘‘shadowed’’ information through statistical confidentiality increases with higher threshold values and stronger stratification of the analysis (e.g. differentiation of statistical classes in lower NACE code levels or smaller geographical units). As a result, differences associated with both the midget census unit (minimum census value) and statistical confidentiality strongly impact on the completeness of information in a statistical system: . The statistics on the producing industries only survey the industry segment of larger enterprises, which excludes small-scale companies. Owing to the high threshold value (enterprises with more than 20 employees, which considerably reduces the total number of surveyed enterprises), statistical confidentiality has a comparatively strong impact here, especially in a stratified analysis. . The value added tax statistics assess all taxobliged companies above a low threshold value (annual sales of 17,500 Euro), resulting in a medium level of statistical confidentiality. . The employment statistics (an assessment of all formally registered employees in the economy) offer the most comprehensive information on employment, as a very low threshold value (fewer than three employees) is applied for statistical confidentiality. Considerable differences were also observed in the completeness of the branch classification standard: . The statistics on the producing industries cover an incomplete spectrum of economic activities and display branches on the NACE four-digit group level, impeding further branch specification. Hence, several wood-based branches that are not part of manufacturing (e.g. forest enterprises, wood crafts and timber traders) are not covered by this system. . The value added tax statistics cover the whole range of economic activities but make use of a modified NACE standard that omits a number
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of wood-based branches on the six-digit subclass level. . The employment statistics assess the whole NACE classification on all levels and thus contain information on all specified woodbased branches. Notable aspects of the comparability criterion include the following: . Owing to a limited branch classification standard, the statistics on the producing industries do not include total figures for the national economy, making it impossible to compare directly branch information within the national context in a methodologically consistent manner (total figures are only available for the manufacturing industries). . Both the value added tax and employment statistics offer total figures for the national economy and the manufacturing industries, allowing for a direct comparison of the cluster analysis results. The scalability of the statistical information in the sources investigated differs according to the impact that the statistical confidentiality has on the system: . For the statistics on the producing industries, branch information can only be meaningfully scaled down to the state [Bundesland] level because more than 50% of the information on many wood-based branches is shadowed on lower spatial levels (State Statistical Office of North Rhine-Westphalia, personal communication, April 18, 2007). . An in-depth assessment of the impact of statistical confidentiality in the value added tax statistics (Table I) showed that this system provides meaningful information for forest cluster analysis down to the district [Regierungsbezirk] level (i.e. statistical shadowing amounts for less than 50% in all branches). Several branches may even be mapped to a major extent on the county [Kreis] level. . Statistical confidentiality is insignificant in the employment statistics (only classes of fewer than three employees are omitted). Therefore, this system provides consistent statistical information down to the county [Kreis] level. Notable aspects of the reproducibility of the statistical information include: . Both the statistics on producing industries and the employment statistics are published
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Table I. Statistical confidentiality of the value added tax statistics on different spatial levels: regional level turnover of selected wood-based branches as a percentage share of state-level turnover, 2004. Spatial level (state 100) NACE
Industry branch
20.1 20.2 20.3 20.4 36.1 21.1 21.2
Sawmill industry Wood-based panel industry Wood construction industry Wood-based packaging industry Furniture industry Pulp and paper manufacturing Pulp and paper processing
District
County
Municipality
100 75 100 100 100 93 75
77 45 84 38 67 27 56
21 0 19 4 29 3 8
Note: source: State Statistical Office of North Rhine-Westphalia (2007).
monthly in preliminary form and finalized figures are available by the first half of the following year. The value added tax statistics are only available after a time lag of 2 years. . Both the statistics on the producing industries and the value added tax statistics offer consistent time series information for the period 1994 2004. However, pre-1999 employment statistics are classified according to the older NACE standards WZ 1973 and WZ 1993 and are therefore not completely comparable with data from 1999 onwards. Because of these limitations, the statistics on the producing industries were rejected as a suitable information source. However, both the value added tax and employment statistics emerged as reporting systems capable of providing comprehensive (high content and quality) information on companies, turnover and employment, and were therefore selected as a suitable combination of information sources for the cluster analysis of the German forest sector. Defining the forest sector for a statistics-based cluster analysis The definition of the forest and wood-based industry cluster (hereafter referred to as ‘‘forest cluster’’) combines individual industry branches representing NACE classes, which form several branch group aggregates in a hierarchical structure (Table II). This definition expanded the original EU concept of the forest sector to include several other wood-based branches from lower NACE levels (the woodcraft branches in construction industries and the timber trade industry). The furniture industry, which is a separate branch under the NACE, is also included in the wood manufacturing group. Because of content and data availability concerns, a number of NACE classes were excluded from the selection of wood-based branches:
. Industry branches with only a minor share of wood-based production (e.g. NACE 35.1: Building and repairing of ships and boats) owing to their weak link to the forest sector. . Industry branches that do not allow for differentiation between wood-based and non-woodbased industry segments (e.g. NACE 36.3: Manufacture of musical instruments and 36.5: Manufacture of games and toys). Although part of these industries manufacture distinct woodbased products, their share of the wood-based economic activity cannot be determined from the statistics. . Low-level wood-based branches in the NACE classification hierarchy that are unspecified in the statistical reporting systems (e.g. NACE 51.13.1: Agents involved in the sale of wood in the rough, products of primary processing of wood, and wooden construction elements). Three aggregated totals were defined as global classes: . Total forest cluster (comprises all forest and wood-based branches). . The cluster excluding printing and publishing (specified because linkage of these industries to the primary resource wood remains questionable). . Cluster branches belonging to the manufacturing industries (NACE section D), which form an aggregate of downstream industries with secondary wood-based production. Testing of the cluster analysis method for the forest sector in Germany This section demonstrates several cluster analysis procedures for structural indicators and highlights exemplary results from the German forest sector case study.
Cluster analysis of the forest sector
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Table II. German forest cluster, 2004 [Statistical Classification of Economic Activities in the European Community (NACE), value added tax statistics and employment statistics]. NACE Rev. 1.1
Wood-based industries and aggregates
02.01 02.02
Forestry Forestry companies Forestry consultants
45.22.3 45.42 45.43.1
Wood industry (a) Primary wood processing Sawmilling Wood-based panels (b) Secondary wood manufacturing Wood construction Wood-based packaging Other wood products Furniture (c) Wood crafts in construction Carpentry Joinery Parquet
21.1 21.2
Pulp and paper industry Pulp, paper and paperboard manufacturing Paper articles
22.1 22.2
Publishing and printing industry Publishing Printing
51.53.2 51.53.3 52.44.6
Timber trade industry Sawn timber wholesale Processed timber wholesale Wood retail sale
20.1 20.2 20.3 20.4 20.5 36.1
Companies (thousand)
Cluster total Cluster, excluding printing and publishing Cluster, in manufacturing (NACE D)
Turnover (billion t)
Employment (thousand)
3.8 1.3 2.6
1.0 0.4 0.6
18.8 10.1 8.7
67.1 4.0 3.7 0.3 28.0 11.2 0.8 4.1 12.0 35.2 11.1 22.7 1.4
51.5 9.6 5.2 4.4 30.8 9.2 1.2 2.9 17.4 11.1 4.5 6.2 0.4
407.9 48.1 30.3 17.8 243.1 64.2 10.3 21.5 147.1 116.7 57.1 56.3 3.3
2.8 0.7 2.1
33.2 15.6 17.7
140.9 62.1 78.8
25.0 9.5 15.5
59.6 36.9 22.7
329.7 143.6 186.1
3.7 1.4 1.6 0.7
8.3 3.9 4.1 0.3
12.1 4.7 5.6 1.8
102.6 77.5 59.8
153.7 94.1 133.2
909.5 579.8 761.8
Note: sources: Statistical Office of the European Communities (2002), Statistisches Bundesamt (2003, 2007), Bundesagentur fu¨r Arbeit (2007).
Key outcomes included consistent figures on companies, turnover and employment according to the NACE-based definition of forest cluster (Table II). Forest cluster aggregates account for the socioeconomic weight of the forest sector and its segments in absolute figures and represent new key data unavailable from the official statistics. The total figures were compared with reference information to assess the role of the forest sector in the national economy. The forest cluster accounts for approximately 3.5% of companies, gross turnover and employment in the German economy. The forest cluster share in manufacturing represents 21.6% of the companies but only 11.2% of the employees and 8.6% of turnover in total manufacturing. These unbalanced distributions (showing higher ranks for the number of companies than both employees and turnover) characterize the comparatively small-sized company structure predominant in the German forest sector. Relative to other manufacturing sectors in Germany (Table III), the
forest cluster holds a comparatively strong position, slightly less important in terms of turnover than the chemical industry (NACE DG: 9.6%), but nearly as important in terms of number of employees as the manufacturing of transport equipment (e.g. automobile industry) (NACE DM: 12.8%). Testing of the spatial stratification of the forest cluster analysis method Distributions of these parameters across spatial classes were analysed to generate regionalized key figures, which allow for a first investigation of the federal states’ positions in the national forest sector and the regional economy (Tables IV and V). Three states (Bavaria, Baden-Wu¨rttemberg and North Rhine-Westphalia) accounted for nearly 60% of employment (536,000 employees) and gross turnover (95 billion t) in the German forest cluster. All other territorial states contributed less than 10% in both categories. North Rhine-Westphalia was the
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Table III. Percentage share of the forest cluster and selected sectors in the German manufacturing industries, 2004. NACE Rev. 1.1
Industrial sector
DA DB DC DF DG DH DI DJ DK DL DM
Food products Textiles Leather Coke, petroleum and nuclear fuel Chemicals and fibres Rubber and plastic Glass and ceramic Metal Machinery Electrical and optical equipment Transport equipment Forest cluster in manufacturing (NACE D)
largest contributor (40 billion t or 26%) to absolute turnover at the national level. Forest sector shares in the regional economy were distributed in the range 2.0 4.9% for turnover and 1.7 3.8% for employment, indicating regionally different positions of the total sector within federal states. The strongest impact revealed the sector in Rhineland-Palatinate and Thuringia, whereas the Saarland and Mecklenburg-Western Pomerania showed the weakest positions below the national average of 3.5%. Positive deviations from the regional share of federal states in the national economy (expressed as percent points) indicate spatial industry concentrations of the forest sector in respective federal states that do not follow the overall spatial trend. In Germany, the distribution of turnover and employment in the total forest cluster shows no decisive
Companies
Turnover
Employment
15.6 4.1 0.8 0.1 2.0 3.1 5.0 19.2 8.9 12.8 2.2 21.6
11.1 2.0 0.3 7.4 9.6 4.1 2.4 11.6 10.3 13.0 19.6 8.6
10.0 2.3 0.4 0.4 6.7 5.7 3.2 15.6 14.8 15.3 12.8 11.2
deviations (more than 5%) from the national economy, suggesting no pronounced spatial concentration of the total forest cluster among the federal states in Germany. However, a comparative analysis of the single wood-based industries indicates that industry branches have stronger impacts at the regional level: . The sawmill industry impacted both turnover and employment in the State of Baden-Wu¨rttemberg. . The sawmill industry strongly impacted turnover in Thuringia (although its regional impact on employment remains insignificant). The wood-based panel industry exerted similar impacts on both regional turnover and employment in Brandenburg.
Table IV. Turnover in the forest cluster and deviation of selected wood-based industries from the national economy in federal states of Germany, 2004.
Federal state Baden-Wu¨rttemberg Bavaria Brandenburg Hesse Lower Saxony Mecklenburg-Western P. North Rhine-Westphalia Rhineland-Palatinate Saarland Saxony Saxony-Anhalt Schleswig-Holstein Thuringia City states, and confidential data
Federal state Forest cluster Percentage point deviation from national economya (Germany 100) share in share in Turnover national regional Forest Wood-based Wood (billion t) economy (%) economy (%) cluster Sawmilling panels construction Furniture Paper 28.7 25.6 2.0 9.5 13.0 0.8 40.3 7.6 0.9 2.7 1.4 3.8 2.0 15.2
Note: aconfidential data for several states.
16.4 16.5 1.2 8.6 9.0 0.7 28.3 3.6 1.0 2.0 1.0 2.5 1.0 8.2
4.0 3.6 3.9 2.6 3.3 2.7 3.3 4.9 2.0 3.1 3.1 3.5 4.7 4.3
2.3 0.2 0.1 2.4 0.5 0.2 2.1 1.4 0.4 0.3 0.1 0.0 0.3 1.7
15.4 3.7 0.5 4.4 1.2
2.2 8.0 11.7 6.2 4.8
15.5 1.0 0.9 0.0 0.6 0.9 7.0 2.0
2.1 4.0
2.3 6.1
6.1 2.6 0.3 2.2 3.8 1.2 4.4 3.8 1.0 0.2 0.4 0.6 1.1 6.9
2.4 1.7 0.1 6.3 0.1 0.0 11.9 1.9 0.6 0.6 0.0 1.1 0.4 6.5
6.2 2.2 0.6 1.6 0.2 0.7 1.5 3.9 0.9 0.7 0.1 0.5 0.0 2.5
Cluster analysis of the forest sector
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Table V. Employment in the forest cluster and deviation of selected wood-based industries from the national economy in federal states of Germany, 2004.
Federal state Baden-Wu¨rttemberg Bavaria Brandenburg Hesse Lower Saxony Mecklenburg-Western P. North Rhine-Westphalia Rhineland-Palatinate Saarland Saxony Saxony-Anhalt Schleswig-Holstein Thuringia City states, and confidential data
Federal state Forest cluster Percentage point deviation from national economy (Germany 100) share in share in Employees national regional Forest Wood-based Wood (thousand) economy (%) economy (%) cluster Sawmilling panels construction Furniture Paper 160.3 176.7 18.2 58.7 82.4 11.6 198.7 44.2 5.8 37.7 17.6 28.3 25.0 44.2
14.1 16.2 2.7 8.0 1.9 8.8 21.2 4.4 1.3 5.2 2.8 2.9 2.7 7.7
4.3 4.1 2.5 2.8 3.5 2.3 3.5 3.8 1.7 2.8 2.4 3.6 3.4 2.2
. The inverse situation occurred in North RhineWestphalia, where the regional impact of the wood-based panel industry was strong in terms of employment but insignificant in terms of turnover. A similar impact was identified for Bavaria’s wood construction industry. . North Rhine-Westphalia’s furniture industry had a strong regionalized impact on turnover with a slightly weaker impact on employment. . In Baden-Wu¨rttemberg, the paper industry had a regional impact on both turnover and employment, although the deviation was less pronounced than in other wood-based branches. Testing of the temporal stratification of the forest cluster analysis method Time series analysis accounts for structural developments of the forest cluster and its contribution to the German national economy. Development trends were assessed over a decade-long period (1994 2006) as absolute and relative changes in the parameters. The time series of overall turnover in the German forest cluster (Figure 2) indicated steady growth (close to 20%) in the earlier period from 1994 2000. However, at this point there was a reversal of trend, followed by a considerable loss ( 14%) of turnover between 2000 and 2004. The wood industry, which is the core segment of the forest cluster, showed stagnating growth in the earlier period followed by a stronger negative trend ( 18%) over the 2000 2004 period. However, this trend of weak and even negative development in turnover was not mirrored
3.5 3.3 0.7 1.5 0.6 0.2 0.6 0.5 0.7 1.0 0.9 0.2 0.0 2.9
14.7 7.7 0.4 1.7 0.1 1.1 8.5 0.7 1.2 2.2 2.2 1.9 1.9 6.6
3.7 5.9 3.3 5.8 1.3 0.4 17.1 2.0 1.2 0.4 0.0 2.2 0.2 7.5
3.9 10.4 0.7 0.9 0.7 4.1 3.2 4.3 0.2 2.3 1.2 1.3 0.0 6.1
2.5 3.3 0.9 3.9 0.8 1.5 8.4 0.8 0.8 1.5 0.7 0.9 0.6 5.8
5.8 0.2 0.9 2.5 1.5 3.0 4.0 2.1 1.1 0.8 1.6 0.3 0.6 6.1
by either the manufacturing industries or national economy, both of which showed almost constant growth with an increase of approximately 30% over the long term. Consequently, the turnover trend for the forest sector increasingly deviated from the overall economic development of Germany. The time series of employment in the forest cluster (Figure 3) revealed a different pattern. Since the pre1999 data were only available from an earlier revision of the NACE classification standard, these figures for both the forest cluster and wood industry are not directly comparable to those for later years. Nonetheless, strong negative trends were identified in the forest cluster and the wood industry in particular. Losses between 1999 and 2006 account for more than 160,000 employees in the wood industry ( 30%) and 250,000 employees in the forest cluster ( 23%) as a whole. These developments far exceed the trends in the national economy ( 4%) and the manufacturing industries ( 9%). As a result, the forest cluster’s share of the national economy was constantly reduced over the long-term period: employment by 0.5% and turnover by 0.7%. Discussion In view of the manifold incompatible definitions and survey designs used in forest sector analysis, the research requires more standardized approaches applicable to a range of spatial scales. The selection of a solid cluster definition and a spatial and temporal reference system in combination with suitable information sources are essential components of a cluster analysis method, which must be
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validated for context and meet criteria for completeness, comparability, scalability and reproducibility of the information to achieve consistency and quality results at different levels of differentiation. Various forest sector approaches in Germany are documented in the literature, to which the cluster analysis method presented in this paper can be related: . Schulte (2002, 2003) first applied the European forest sector concept in the German context in a large-scale forest cluster analysis for the State of North Rhine-Westphalia. Combining and crosschecking official governmental statistics with comprehensive enterprise surveys, the forest sector’s size (35 billion Euro turnover and 20,000 employees in 2001) and major impact on the regional economy were demonstrated (accounting for 4.5% of total employment, the forest cluster ranked among the strongest sectors), which represented crucial findings for decision makers in industry and policy and induced a number of similar investigations in other states and on the federal level. . Dieter and Thoroe (2003) adopted a strictly NACE-based forest sector classification (wood crafts and timber trade were also included, but the furniture industry was omitted) and used a combination of several governmental statistical systems to describe the German sector’s economic size with 102 billion Euro turnover and 915,400 employees in 1997 (2% of total employment).
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. Mrosek et al. (2005) presented findings from a national case study that combined multiple data sources to assess the forest sector’s size and structural complexity. This study demonstrated the special role of small enterprises (in particular wood craft industries) and revealed the sector’s underestimated impact in comparison to other strongly developed German industries (3.4% of total employment in 2004, stronger employment than machinery, electrical or automobile manufacturing). . Several state-level studies were the first to use combinations of the value added tax statistics and the employment statistics for the purpose of forest sector analysis in a particular state (Seegmu¨ller, 2005; Lutze et al., 2006; Jaensch & Harsche, 2007; Ru¨ther et al., 2007). . Seintsch (2007) presented a national approach considering a classification similar to the present study’s cluster definition (omitting a minor class ‘‘wood retail sale’’) as well as the value added tax statistics and employment statistics. Additional information to depict the forestry branches (sampling-based extrapolated data from the Integrated Environmental Accounting for Forests [Waldgesamtrechnung] in Germany) and an extended definition of the employment parameter were used (including registered employees, marginally employed persons and enterprise owners), which resulted in a somewhat higher total employment figure for the German forest cluster (1.2 million in 2005).
Cluster analysis of the forest sector The approach formulated a closed analysis concept for the German context, but suggested no tests for a spatiotemporal benchmarking of the empirical findings. NACE-independent sector studies based on primary data collections effectively demonstrated the forest cluster’s size within the overall economic context (e.g. Schulte, 2002, 2003; Mrosek et al., 2005). Although such approaches produce more detailed, precise information, they are more timeconsuming and expensive and are therefore not a suitable template for standardized monitoring. However, NACE-dependent sector definitions in line with complex combinations of several statistical information sources (e.g. Dieter & Thoroe, 2003; Seintsch, 2007) also impede a consequent spatial scaling down and a continuous follow-up. This research presented an evaluation of three German statistical reporting systems, which revealed distinct capacities for application to forest cluster analysis. The statistics on the producing industries are only marginally suitable to the task owing to incomplete industry coverage and imbalanced representation of companies in a small-scale dominated forest sector. Nonetheless, this system is widely accepted in business circles as a primary source for up-to-date information on industrial development, if only because of the monthly periodicity of reporting. Unfortunately, the use of this source in regular reporting and strategic decision making in wood-based industries is one major reason for the common underestimation of the forest sector as a whole, both within the industries themselves and in wider public opinion (Schulte, 2002, 2003). German statistics on value added tax and employees with social insurance registration represent two statistical information systems, which have emerged as the preferred, easily reproducible data sources in German forest sector analysis. Reporting on the whole spectrum of economic activities, they reflect low or irrelevant impacts of statistical confidentiality and allow information to be effectively scaled down to regional and local levels. Early on, Kra¨tke and Scheuplein (2001) used these two suitable sources in a study of the wood industry in the State of Brandenburg. Recent forest cluster studies in Germany made further progress towards a statisticsbased sector assessment (Seegmu¨ller, 2005; Lutze et al., 2006; Seintsch, 2007) and widened the spectrum of analysis methods (Jaensch & Harsche, 2007; Ru¨ther et al., 2007), but did not offer a unified, standardized framework for the validation of a scalable forest sector approach. In this context, this research developed an approach that is based on regular governmental statis-
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tics in line with a NACE-dependent forest cluster definition, fulfilling and extending the EU forest sector concept (Commission of the European Communities, 1999). Testing the validated cluster analysis method has shown that the approach provides comprehensive and comparable information on key socioeconomic parameters (e.g. number of companies, employment and turnover) and indicators (e.g. share, ranking and trends in the overall economic context) of the forest cluster consistently on different spatial scales and in a temporal dimension. As such, it offers a solid, transparent framework and an efficient technique for sectoral, spatial and temporal benchmarking and monitoring of the forest sector on the national and regional scale. The EU forest sector definition based on the common resource or commodity wood distinguishes it from other sectors, which are mostly formed around a group of similar finished products (e.g. the automobile industry). The forest cluster, comprising various groups of wood-based products and value added chains, is a holistic, challenging concept and the formulation of an accurate definition based on statistical classes captures only part of the true complexity of industries involved. The major advantage of a cluster definition based solely on the NACE is that a consistent referential classification system allows for both clear distinction of statistical classes and direct comparison to the overall economy or other economic units. The cluster definition proposed here offers an extended view of the forest sector, incorporating more wood-based industries than commonly used in forest sector studies (wood crafts, timber trade, etc.). Although the definition does not distinguish between different shares in wood-based production (e.g. completely wood-based primary processing industries versus partial wood-based upstream manufacturing industries), it adequately represents a wider spectrum of wood-based industries belonging to the forest sector. Classes with known weaknesses in the NACE-based cluster definition include forestry enterprises, which exclude state forestry enterprises that are incorporated unspecified under ‘‘Administration’’ (NACE section L), and wood crafts, which are limited to wood crafts in the construction sector and thereby neglect a number of other, mostly smallscale, wood craft industries. The empirical findings of this study describe the structure and trends in Germany’s forest cluster and produced several original (or previously not quantified) indicators of its contribution to both the national and regional economies. Although the potential for assessing patterns of industry concentration at the federal state level was limited by the coarse spatial resolution, regionally distinct impacts
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and compositions of the forest cluster dominated by different wood-based industries were identified. The temporal analysis offers insight into a comprehensive structural change within the forest sector, which has seen considerably stronger losses in turnover and employment compared with the national economy. Comparable forest sector trends showing an increasing deviation from the overall economy have also been described in the international literature (Lebedys, 2004; Sowlati & Vahid, 2006). These figures provide an empirical background for the socioeconomic importance of wood-based industries in Germany currently competing for wood as an increasingly valuable production resource in a growing, globalized timber market. The results correspond to and support individual findings of other German case studies, yet they constitute a consistent information base at both the federal and state level, allowing for comparison and benchmarking within the sector, among the federal states and within the overall economic development of Germany. Theoretically, the method’s components and validation principles are transferable to other socioeconomic settings and geographical contexts and scales. Further research needs to look for suitable approaches to scale the method up to the international level (e.g. a cluster analysis of the forest sector in the EU), which requires a focus on the consistency of information sources from different countries. Conversely, scaling down to the regional and subregional level requires a shift in emphasis to the precision of information for the particular geographical context. Here the research needs to tackle an increase in spatial resolution to localize wood-based industry agglomerations (regional clusters) and investigate explicit regional developments diverging from national-level trends. In conclusion, the cluster concept from economics increasingly builds understanding around the large industrial forest sector unified by the common resource of wood. Approaching a more generalized knowledge on the forest cluster will require stronger transferability in research and reporting methodologies. The crucial aspects of forest cluster analysis are therefore consistency of the cluster definition as well as comparability and scalability of the analytical approaches. The method outlined in this paper represents a tested template offering functions for sectoral, spatial and temporal benchmarking. Contributing to a more standardized, empirical understanding of the German forest sector and its role in the national and regional economy, this research is also a valuable support for informed rational decision making in cluster policy and management on different levels.
Acknowledgements We would like to thank the staff members of the Federal Statistical Office [Statistisches Bundesamt Deutschland], the State Statistical Offices [Statistische Landesa¨mter] and the Federal Employment Agency [Bundesagentur fu¨r Arbeit] for their helpful support in providing background information about and access to their statistical reporting systems. We also thank Ms Denise Allen, University of British Columbia, Sustainable Forest Management Research Group, for editing the manuscript. The research was funded by the Ministry of Environmental Protection and Nature Conservation, Agriculture and Consumer Protection of North Rhine-Westphalia [Ministerium fu¨r Umwelt und Naturschutz, Landwirtschaft und Verbraucherschutz Nordrhein-Westfalen].
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