Eur J Forest Res DOI 10.1007/s10342-009-0258-6
REVIEW
Regional employment trends of wood-based industries in Germany’s forest cluster: a comparative shift-share analysis of post-reunification development Dajana Klein Æ Uwe Kies Æ Andreas Schulte
Received: 5 May 2008 / Revised: 29 October 2008 / Accepted: 22 December 2008 Springer-Verlag 2009
Abstract After reunification in 1990, Germany’s forest cluster developed anew and employment in the woodbased industries differentiated very quickly. With more than 900,000 employees, it is now considered one of the most important industrial sectors in the country. This paper analysed general trends in the development of employment of wood-based industries in the German forest cluster between 1999 and 2006. Shift-share analysis was considered to be the most appropriate way to determine regional differences in the subsection DD/20 ‘Manufacture of wood and wood products’ of the code ‘‘Classification of Economic Activities in the European Community, Revision 1.1’’ (NACE): the sawmill industry, the wood-based panel industry, the wood construction industry, the wood-based packaging industry, and the miscellaneous wood products industry. This method decomposed the change of employment into three different components that are due to that change: national trends, (industrial) sectoral trends, and regional conditions. Employment in the selected woodbased industries showed a significantly larger decrease than overall trends in both the producing industries and the whole economy of Germany: a continual loss of employees could be observed over the time period, affecting almost all of the selected wood-based industries. However, federal states in western and eastern Germany experienced divergent trends between 1999 and 2006, as different absolute and relative regional share components indicated in the shift-share analysis. This method allows of identifying
Communicated by M. Moog. D. Klein (&) U. Kies A. Schulte Wald-Zentrum, Westfa¨lische Wilhelms-Universita¨t Mu¨nster, Robert-Koch-Str. 27, 48149 Munster, Germany e-mail: dajana.klein@wald-zentrum.de
regional disparities and characterising regions with positive (mainly eastern federal states) and negative (mainly western federal states) rates of employment growth. The research suggests that positive employment trends in eastern Germany’s wood-based industries can mainly be attributed to regional factors such as comparatively higher subsidies for new investments, lower labour costs, lower land values or infrastructural peculiarities. Keywords Shift-share analysis Forest cluster Wood-based industries Labour market Employment Germany Economic transformation process
Introduction The economic development in the Federal Republic of Germany remains influenced by the economic transformation process in the eastern federal states following the reunification of East Germany and West Germany1 in 1990.2 In terms of key labour market indicators, the eastern states continue to lag far behind western Germany. In 2006, the unemployment rate amounted to 17.3% in eastern Germany: far higher than in the western federal states with 9.1% (Bundesagentur fu¨r Arbeit 2007). In addition to Germany’s reunification, changing market conditions in a globalised economy have led to a strong restructuring of the forest cluster, including redevelopment of the wood-based industries and rapid differentiation 1
For a general profile of the eastern and western federal states of Germany, see Table 1. 2 For an overview of the process of reunification and convergence of eastern and western Germany, see Lange and Pugh (1998). For a description of regional differences, see Blien et al. (2006) and Suedekum et al. (2006).
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Forestry
Wood industry
Forest Cluster
Primary wood processing
Forestry and logging (02)
Wood-based panels (20.2)
Wood construction (20.3) Secondary wood-based Wood-based packaging (20.4) manufacturing Misc. wood products (20.5) Furniture (36.1) Wood crafts in construction
Agriculture (A)
Sawmilling (20.1) Manufacture of wood and wood products (DD)
Furniture (DN 36 )
Carpentry (45.22.3) Joinery installation (45.42)
Construction (F)
Parquet laying (45.43.1) Timber trade Paper industry
Timber trade (51* )
Trade (G)
Pulp, paper,paperboard (21.1) Paper articles (21.2) Publishing (22.1)
Classification of Economic Activities in the European Union (NACE Rev.1.1)
Fig. 1 Forest cluster definition on the basis of the statistical classification of economic activities in the European Community, Revision 1.1 (Kies et al. 2008)
Manufacture of pulp, paper (DE)
Printing (22.2) * includes: 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
between eastern and western Germany. In this context it should be noted that the German Federal government has been subsidizing the economic development of eastern Germany since reunification by an annual support of approximately 80 billion euros, which sets regions of eastern Germany apart from any other regions of the European Union (Blien and Wolf 2002). In some years, regional investment subsidies in eastern Germany were ten times higher than in western Germany (Bundesministerium der Finanzen 1999). Wood-based industries in eastern Germany also profited from subsidies encouraging investment. Blien and Wolf (2002) analysed structural changes and general activity changes in the overall employment of eastern Germany, and defined the role of the regional industry structure, the qualification structure of the local workforce, genuine regional factors, the size distribution of establishments and the regional concentration of industries as key factors influencing employment trends. Industries that are linked through close relationship to a particular use of resources or form of production, spatial concentration or high connectivity in terms of business activities are described as ‘clusters’. In line with related approaches (e.g. industrial districts, stakeholder networks, centres of innovation, regional development), the cluster concept has been widely adopted in economics (Stimson et al. 2006; Organisation for Economic Co-operation and Development 1999; Porter 1998). Forest and wood-based industries (which by definition all comprise economic activities with a close linkage to the common resource wood) constitute a complete industrial sector in the economic system. The ‘forest cluster’ incorporates raw timber producing forestry enterprises, industries
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in the processing and manufacturing of semi-finished wood, pulp and paper products and downstream wood-based manufacturing industries, as per the official systematic Classification of Economic Activities in the European Community, Revision 1.1 (NACE) and the German Classification of Economic Activities, Revision WZ 2003 (Statistisches Bundesamt Deutschland 2003; Statistical Office of the European Communities 2002; Commission of the European Union 1999). However, allocating these industries to separate sections of the NACE results in a segregated statistical assessment of wood-based activities and a misconception (or distortion) of the sector’s actual size in national economies (Fig. 1) (Kies et al. 2008). In Germany, recent cluster analysis approaches have offered a new view of the forest and wood-based industries, revealing the significant (and underestimated) economic impact of the forest sector on both national and subnational/state levels (Kies et al. 2008; Jaensch and Harsche 2007; Kramer and Mo¨ller 2006; Schulte and Mrosek 2006; Seegmu¨ller 2005; Mrosek et al. 2005; Schulte 2002, 2003a). In terms of employment the forest cluster is considered to be one of the most important industrial sectors in Germany ranking high among comparable national sectors. With more than 900,000 employees in 2004 and a percentage share of 11.2% of the manufacturing industries in Germany, the forest cluster was more important than the manufacture of food products (10.0%) or the manufacture of textiles (2.3%) and slightly less important than the manufacture of transport equipment (12.8%) or the manufacture of machinery (14.8%). The forest cluster also revealed strong industrial agglomerations (regional clusters) of individual wood-based industries (Kies et al. 2008; Mrosek et al. 2005).
Eur J Forest Res
To date, these studies have focused on macroeconomic assessments of the forest cluster within a particular geographical context (e.g. federal state). However, neither employment trends in the forest sector in general nor regional employment differences within a larger geographical context have ever been investigated. Empirical research has not yet answered the questions whether employment in Germany’s wood-based industries shows regional disparities as well as in which way and to what extent eastern and western federal states have been affected by the economic transformation process following Germany’s reunification. We propose the hypothesis that over the observed period employment in wood-based branches has developed differentially in federal states and especially in eastern compared to western Germany. Furthermore, we assume that wood-based industries are shifting from western to eastern Germany. The overall objective of this study is to analyse regional employment trends of five wood-based industries in the German forest sector on the federal state level. Choosing these five primary processing and manufacturing industries that are collected under the NACE subsection DD ‘wood industry’, as defined by the Statistical Office of the European Communities (2002) and the Statistisches Bundesamt Deutschland (2003), offers the opportunity for consistent comparisons within the NACE system. The study’s empirical focus is a comparative analysis of the development of employment in Germany’s federal states. Furthermore, the study focuses in particular on the development of employment separated into eastern and western federal states. The specific objectives of that research are to: (a) quantify total employment changes in different wood-based industries; (b) analyse differential effects of these changes in each region and (c) decompose the employment changes in the federal states into national growth effects and regional competitive effects.
Methods Shift-share analysis Shift-share analysis is a commonly used technique in regional science. Knudsen’s (2000) literature survey reports, it is a well-established analytical tool among planners, geographers and regional scientists and has been applied in various fields of regional economy. However, employment growth and decline remain the primary focus of research. Going beyond a mere comparison of absolute and relative employment changes, shift-share analysis offers a quantitative compilation of diverging employment trends among geographical units. Its simple, intuitive logic and uncomplicated data requirements make it a well-suited
analysis tool for exploratory targeting of regional industrial dynamics (Blien and Wolf 2002; Esteban 2000; Dinc et al. 1998; Tassinopoulos 1996). Shift-share analysis has developed over time, and criticism in the scientific discourse applies to the method’s applicability, accuracy and limited explanatory functions for factors affecting growth dynamics. The method has been modified by numerous extensions and adaptations of varying complexity, yet the standard model first introduced by Dunn (1960) remains a valid analytical tool that can adequately present general differential effects (Dinc et al. 1998). Examples of previous shift-share analysis approaches in the forest sector can be found in Bilek and Ellefson (1984) and Størdal et al. (2004). The conventional shift-share model by Dunn (1960), which identifies general differential employment changes among different regional units and industries, has been applied in this analysis, as per Stimson et al. (2006); Dinc et al. (1998) and Ashby (1970). The model divides regional employment changes into component parts that are used to explain regional disparities and are traced back to specific regional structures. Specifically, the model decomposes economic change (in terms of employment growth or decline) observed over a particular time interval into three components: national share (NS), industrial mix (IM) and regional share (RS) (Formula 1 and 2). tþ1 tþ1 Entþ1 t En t Ein DEir Eir 1 þ Eir t Et Et Ein n n tþ1 tþ1 Ein t Eir þ Eir t ð1Þ Eirt Ein or DEir NS þ IM þ RS
NS IM RS E i r n t t ?1
ð2Þ
National share Industrial mix Regional share Employment Industry Region Reference area Employment at time t Employment at time t ? 1
Regional employment is investigated in relation to a superior reference area (in most cases, a nation). (E) represents the employment level of an industry (i) in a region (r) or in the reference area (n). Employment at the beginning of the determined time period is represented by t and at the end of the period by t ? 1. The NS component measures the employment change that could have been expected if the region had grown at
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the same rate as the corresponding reference area. Since regions are part of the reference area, and therefore influenced by these overall conditions and trends, it is assumed that any positive (or negative) change in employment at the reference area level will involve a corollary rising (or declining) change in regional employment. The IM component represents the share of regional economic change attributable to a region’s specialisation in industries with rapid (or slow) growth at the national level (reference area). Regions with a large share of employees in an industry growing rapidly (or slowly) on the national level will therefore also have high (or low) IM components. As this component determines employment changes within a specific industry in relation to overall employment trends in the national economy, it also indicates overall employment trends in each industry. However, since this analysis concentrates on specific regional trends, the IM component results are not presented in detail. The RS component measures regional employment change in an industry conditioned by regional factors. It is defined as the difference between an industry’s regional growth (or decline) and the industry’s reference area growth (or decline). The effect of specific regional factors on employment growth (or decline) in a regional industry is determined by subtracting overall national growth impact from absolute regional change. Of course, some regions and industries generally grow faster than others, independent of periods of nationwide prosperity. On the regional level, this competitive advantage can be attributed to several factors, including (but not limited to) natural resources, entrepreneurial capacity and regional policy effects. Although shift-share analysis provides no explanation of such (dis)advantages, the RS component can be used to indicate, which regional industries perform well in terms of employment change. Research design Germany, a country with an area of more than 350,000 km2 and a population of 82.3 million people (2006), is divided into 16 federal states (13 territorial states and 3 city states), that range considerably in size, population and employment (Fig. 2; Table 1). For the purpose of this shift-share analysis, federal and city states are designated as regions and the Federal Republic of Germany as the reference area. The federal states are further examined in two separate subgroups: the old West German States (western Germany) and the newly-formed German States (eastern Germany). Although the city states and the federal state of Saarland are included in all calculations, these results are not presented here owing to the small geographical size and minor economic impact of these regional units.
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This analysis uses data from the official labour market statistics maintained by the German Federal Employment Agency (Bundesagentur fu¨r Arbeit), which surveys all employees with social insurance registration (except civil servants and the self-employed). These data are available on a monthly basis for all industries under NACE (Bundesagentur fu¨r Arbeit 2007; Statistical Office of the European Communities 2002). We selected a data set for the period from 1999 to 2006 because pre-1999 employment data refer to an earlier version of the NACE-based German Classification of Economic Activities (WZ 1973), and hence time series results for the forest sector are not directly comparable. Over this eight year period, all but one of the investigated industries showed continuous trends in employment (Table 2), allowing for a methodologically consistent shiftshare approach with a start year of 1999 and an end year of 2006. We analysed all industry branches of the NACE subsection DD/20 ‘Manufacture of wood and wood products’: the sawmill industry (NACE 20.1); the wood-based panel industry (NACE 20.2); the wood construction industry (NACE 20.3); the wood-based packaging industry (NACE 20.4); and the miscellaneous wood products industry (NACE 20.5). As primary and secondary wood processing industries with a completely wood-based production, all these industries are directly linked to the resource wood. The producing industries are a commonly used statistical reference unit in Germany, representing the aggregate of NACE sections C (Mining and quarrying), D (Manufacturing), E (Electricity, gas and water supply) and F (Construction) (Statistical Office of the European Communities 2002). The wood industry aggregate (NACE DD/20) is categorised under the Manufacturing section (NACE D), making its development in relation to the overall trend of the producing industries a legitimate comparison. Therefore we excluded other industries such as pulp and paper industry, which can be analysed in further research. Shift-share calculations produce results in absolute values, which impedes a direct comparison of geographical units of different size. To facilitate a comparison between federal states as well as between eastern and western Germany, which vary considerably in geographical and economic size (Table 1), the RS component’s absolute values were also transformed to relative values. Regional employment shift in absolute figures was compared to absolute employment at the beginning of the period (i.e., employment in 1999) to derive a percentage deviation of the RS (Tables 4, 5). A peculiarity of the shift-share results has to be noted in Table 5: considering Formula 1 applied to a space subdivision of only two regions (e.g. eastern and western Germany), the absolute RS of a total shift are equal
Eur J Forest Res Fig. 2 Map of the federal states of Germany 2008
by definition. The small differences in absolute values between eastern and western Germany are explained by the fact that the city states and Saarland are excluded from the analysis. These absolute values serve only for a comparison among the industries of the amount of the regional losses and gains in employees.
Results National employment trends in the selected wood-based industries Considerable variation exists in the absolute numbers of employees within the selected wood-based industries of the German forest sector (Table 2). In 2006, the wood
construction industry (more than 59,000 employees) was the strongest industry in this regard, followed by the sawmill industry (more than 29,000 employees). The investigated wood-based industries were characterised by strong negative employment trends over the observed period of time, with the exception of the wood-based packaging industry remaining stable (increasing by only ?0.5%). The miscellaneous wood products industry experienced the strongest negative trend (-35%) in employment. Overall, a continuous negative employment trend of -26% in the German wood industry (NACE 20) can be noticed between 1999 and 2006: a loss of around 46,300 jobs. The wood industry’s negative employment trend was far stronger than the trends in both the producing industries (-16%) and the German economy as a whole (-4%) over the same period.
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Eur J Forest Res Table 1 Land area, forest cover, urban area, population, employment and unemployment in the federal states of Germany (Bundesagentur fu¨r ¨ mter des Bundes und der La¨nder 2007) Arbeit 2007; Statistische A Land area
Forest
Urban
Population
Employment
Unemployment
2006
cover
area
2006
2006
1999–2006
2006
(%)
(%)
(mio.)
(mio.)
(%)
(mio.)
2
(tsd. km )
(%)
(%)
(%)a
Baden-Wu¨rttemberg
35.8
10.0
38.1
13.6
10.7
13.0
3.7
0.7
0.3
Bavaria
70.6
19.8
34.9
10.8
12.5
15.2
4.3
1.4
0.4
6.8
Bremen
0.4
0.1
1.9
56.5
0.7
0.8
0.3
-3.0
0.0
14.9
5.8
Hamburg
0.8
0.2
Hesse
21.1
5.9
Lower Saxony
47.6
North R.-Westphalia
34.1
Rhineland-Palatinate
6.3
58.6
1.8
2.1
0.8
1.0
1.0
11.0
40
15.1
6.1
7.4
2.1
-1.0
0.3
9.2
13.3
21.2
13.1
8.0
9.7
2.3
-3.0
0.4
10.5
9.5
24.9
21.6
18.0
21.9
5.6
-4.0
1.0
11.4
19.9
5.6
41.5
13.8
4.1
4.9
1.2
-1.0
0.3
8.0
2.6
0.7
33.4
20.1
1.0
1.3
0.3
-3.0
0.1
9.9
15.8
4.4
10
11.9
2.8
3.4
0.8
-3.0
0.1
10.0
248.5
69.6
30.6
14.1
65.7
79.8
21.3
21.4
3.0
9.1
0.9
0.2
18.0
69.4
3.4
4.1
1.0
-9.0
0.3
17.5
Brandenburg
29.5
8.3
35.1
8.6
2.5
3.1
0.7
-15.0
0.2
17.0
Meckl.-W. Pomerania Saxony
23.2 18.4
6.5 5.2
21.4 26.8
7.2 11.7
1.7 4.2
2.1 5.2
0.5 1.3
-17.0 -14.0
0.2 0.4
19.0 17.0
Saarland Schleswig-Holstein Western Germany total Berlin
Saxony-Anhalt
20.4
5.7
23.9
10.3
2.4
3.0
0.7
-16.0
0.2
18.3
Thuringia
16.2
4.5
31.9
9.0
2.3
2.8
0.7
-15.0
0.2
15.6
Eastern Germany total
108.6
30.4
28.0
9.7
16.6
20.2
5.0
214.1
1.5
17.3
Germany total
357.1
100.0
29.8
12.8
82.3
100.0
26.4
24.1
4.5
10.8
a
Unemployed proportional to all civilian labour force
Table 2 National employment trends in selected wood-based industries in Germany, 1999–2006 (absolute figures in thousand) (Bundesagentur fu¨r Arbeit 2007) NACE Wood-based industries and aggregates 1999
2001
2003
2004
2005
2006
Change 1999–2006 Change (%)
20.1
Sawmilling
36.7
33.7
30.4
30.3
29.5
29.2
-7.5
-20.4
20.2
Wood-based panels
21.3
20.4
18.0
17.8
17.0
16.0
-5.3
-24.9
20.3 20.4
Wood construction Wood-based packaging
82.9 10.4
77.5 10.1
67.0 10.0
64.2 10.3
60.3 10.3
59.2 10.4
-23.7 0.1
-28.6 0.5
20.5
Miscellaneous wood products
20
Wood industry total
28.4
26.9
22.7
21.5
19.9
18.6
-9.9
-34.7
179.7
168.6
148.0
144.1
136.9
133.4
-46.3
-25.8
9,737.5
9,054.4
8,787.6
8,553.8
C–F
Producing industries
10,036.3
8,480.8 -1,555.6
-15.5
A–O
Economy total
27,482.6 27,817.1 26,954.7 26,524.0 26,178.3 26,354.3 -1,128.2
-4.1
Regional employment trends in the federal states In terms of regional employment distribution in Germany’s federal states, the results show pronounced disparities among wood-based industries. Also, absolute concentrations of employment can be identified (Table 3). •
In 2006, the sawmill industry was concentrated in two federal states in southern Germany: around 15,000
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•
•
(more than 50%) of total employees in this industry were located in Baden-Wuerttemberg and Bavaria. The wood-based panel industry could be localised in the state of North Rhine-Westphalia, which represents around 5,800 (more than 37%) of total employees in this German industry. The highest absolute employment figures for the wood construction industry occurred in Bavaria (around
Eur J Forest Res Table 3 Regional employment in selected wood-based industries in federal states of Germany: absolute values (thousand) for 2006 and relative change (%) over 1999–2006 (Bundesagentur fu¨r Arbeit 2007) excluding Saarland and City States (Berlin, Bremen, Hamburg) Sawmilling (NACE 20.1)
Wood-based panels (NACE 20.2)
Wood construction (NACE 20.3)
Wood-based packaging (NACE 20.4)
Miscellaneous wood products (NACE 20.5)
Wood industry total (NACE 20)
Absolute Relative Absolute Relative Absolute Relative Absolute Relative Absolute Relative Absolute Relative BadenWuerttemberg
8.0
-13.2
1.8
-30.2
10.8
-27.2
1.4
5.0
3.9
-18.1
26.0
-20.9
Bavaria
6.9
-19.9
1.3
-63.8
15.8
-22.2
1.4
-11.8
3.2
-43.5
28.6
-27.9
Hesse
1.6
-34.0
0.4
-55.4
5.4
-37.4
1.2
17.8
0.6
-61.3
9.2
-36.1
Lower Saxony
2.2
-37.4
1.5
-30.6
2.8
-42.4
0.8
39.1
1.5
-22.4
8.7
-32.7
North RhineWestphalia
3.7
-31.1
5.8
-26.5
10.7
-26.8
3.0
-9.1
4.7
-40.7
27.8
-28.6
RhinelandPalatinate
1.5
-25.0
0.9
-24.2
5.2
-26.0
0.4
1.4
0.7
-34.5
8.7
-25.4
Schleswig-Holstein
0.4
-42.4
0.1
-6.4
1.0
-26.2
0.2
-7.3
0.3
-15.4
2.0
-26.1
Western Germany total
24.3
224.0
11.8
236.0
51.6
228.0
8.4
20.3
14.7
235.9
110.9
227.5
0.9 0.7
-14.9 16.9
1.1 0.6
18.2 731.0
1.3 0.8
-23.1 -23.1
0.2 0.2
-21.7 60.7
0.5 0.4
-8.7 -43.5
3.9 2.7
-10.6 7.6 -12.1
Brandenburg Meckl.-W. Pomerania Saxonia
1.1
0.3
0.9
41.0
1.8
-20.7
0.4
-20.6
2.0
-21.6
6.2
Saxony-Anhalt
0.2
-26.8
0.5
487.4
1.0
-29.2
0.2
5.4
0.2
-48.3
2.1
-9.2
Thuringia
1.7
46.8
0.6
11.5
1.5
-52.3
0.4
42.5
0.6
-47.1
4.6
-23.1
Eastern Germany total
4.6
10.0
3.7
64.0
6.3
233.0
1.4
5.1
3.6
230.3
19.5
212.3
28.9
220.0
15.5
-25.0
57.9
228.0
9.8
0.4
18.3
234.9
130.4
225.6
Germany total
•
16,000), followed by Baden-Wuerttemberg and North Rhine-Westphalia (around 11,000 each). The wood-based packaging industry (around 3,000; 30% of total employees) and the miscellaneous wood products industry (around 4,700; more than 25% of total employees) were primarily located in North Rhine-Westphalia.
The relative employment changes allow for comparison of the extent of change between the federal states (Table 3) and reveal notable regional differences, including: • •
•
•
The sawmill industry lost 24% of the jobs in all western states, while it increased in eastern Germany by 10%. The strongest positive employment changes in the sawmill industry occurred in Thuringia (?47%) and Mecklenburg-Western Pomerania (?17%), both located in eastern Germany. Employment in the wood-based panel industry decreased by 36% in western Germany, in strong contrast to federal states in eastern Germany, which gained 64% of new jobs over the period. Growth rates in the wood-based panel industry were strongest in Mecklenburg-Western Pomerania (?731%) and Saxony-Anhalt (?487%), which can be
•
•
attributed to the construction of new processing plants in these regions. Mecklenburg-Western Pomerania is the only federal state, which exhibited a positive employment growth in the wood-based industries at all (?8%). Compared to western Germany, the eastern states showed greater relative employment growth in the wood-based packaging industry (?5%), the sawmill industry (?10%) and the wood-based panel industry (?64%). All the other industries exhibited similar trends for eastern and western Germany.
Shift-share analysis of the federal states Since overall economic development and industry structures vary by region, total shifts do not allow for an assessment of specific regional trends. Shift-share analysis results identify employment shares attributable to overall economic conditions (NS), overall development of an industry (IM) or specific development of a region (RS). The NS results indicate the potential employment change within each industry if regional development had grown at the same rate as the overall negative national
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Eur J Forest Res
construction industry (Fig. 5). This federal state would have lost more than 800 jobs had it matched the overall rate of employment change in Germany.
trend of -4% (Table 2). NS values vary within each federal state and industry (Figs. 3, 4, 5, 6, 7), depending on the absolute employment figure of each state and industry at the beginning of the time period. The most remarkable results are: •
•
•
The IM component reflects employment changes at the regional level as a function of national industry trends and regional industry size. Thus it is suitable for comparison of industries within one region—yet the purpose of this study is to compare regional differences. However, in the results presented here (Fig. 3, 4, 5, 6, 7), the IM component, specifically its percentage of the total shift compared to the NS and RS, allows identifying the particular overall industry trends and the size of the industry in the particular region. The RS component highlights those federal states that gained (or lost) employees due to specific regional conditions. A positive RS suggests that a federal state offered locational advantages. In addition to the absolute figures,
NS components are smaller for all industries and all federal states than the IM and RS components. Thus only a minor portion of these negative employment changes were due to overall development of employment in the German economy. Although some federal states showed slight positive developments in some industries (Table 3), these positive trends were attenuated by downward pressure from Germany’s overall negative employment trend. The highest value of the NS component of all industries under study appeared in the Bavarian wood
Fig. 3 Employment shift in the sawmill industry (NACE 20.1) in federal states of Germany, total shift 1999–2006 per state in ascending order
Western Germany Bavaria N. R.-Westphalia Lower Saxony B.-Wuerttemberg Hesse Rhinel.-Palatinate Schleswig-Holstein Eastern Germany Brandenburg Saxony-Anhalt Saxonia M.-W. Pommerania Thuringia -2000
Fig. 4 Employment shift in the wood-based panel industry (NACE 20.2) in federal states of Germany, total shift 1999–2006 per state in ascending order
National Share Industrial Mix Regional Share -1500
-1000
-500
0
500
1000
Western Germany Bavaria N. R.-Westphalia B.-Wuerttemberg Lower Saxony Hesse Rhinel.-Palatinate Schleswig-Holstein
Eastern Germany Thuringia Brandenburg Saxonia Saxony-Anhalt
National Share Industrial Mix Regional Share
M.-W. Pommerania -2500
123
-2000
-1500
-1000
-500
0
500
1000
Eur J Forest Res Fig. 5 Employment shift in the wood construction industry (NACE 20.3) in federal states of Germany, total shift 1999–2006 per state in ascending order
Western Germany Bavaria B.-Wuerttemberg N. R.-Westphalia Hesse Lower Saxony Rhinel.-Palatinate Schleswig-Holstein Eastern Germany Thuringia Saxonia Saxony-Anhalt Brandenburg M.-W. Pommerania -6000
Fig. 6 Employment shift in the wood-based packaging industry (NACE 20.4) in federal states of Germany, total shift 1999–2006 per state in ascending order
National Share Industrial Mix Regional Share -5000
-4000
-3000
-2000
-1000
0
1000
2000
-200
-100
0
100
200
300
Western Germany N. R.-Westphalia Bavaria Schleswig-Holstein Rhinel.-Palatinate B.-Wuerttemberg Hesse Lower Saxony Eastern Germany Saxonia Brandenburg Saxony-Anhalt M.-W. Pommerania Thuringia -500
Fig. 7 Employment shift in the miscellaneous wood products industry (NACE 20.5) in federal states of Germany, total shift 1999–2006 per state in ascending order
National Share Industrial Mix Regional Share -400
-300
Western Germany N. R.-Westphalia Bavaria Hesse B.-Wuerttemberg Lower Saxony Rhinel.-Palatinate Schleswig-Holstein Eastern Germany Saxonia Thuringia M.-W. Pommerania Saxony-Anhalt Brandenburg -3500
National Share Industrial Mix Regional Share -3000
-2500
-2000
-1500
-1000
-500
0
500
1000
123
Eur J Forest Res
which describe the amount of change in a particular state and industry, relative RS values indicate the potential for regional employment shifts to influence the total shift (Table 4). The highest absolute values for RS components in the sawmill industry occurred in Thuringia (?756) and BadenWuerttemberg (?665), suggesting these federal states had locational advantages for sawmill industry development on an approximately comparable level (Fig. 3). However, the relative RS values reveal a divergent effect on the federal states. In Baden-Wuerttemberg, the influence of RS on the total change was a rate of only 7% (Table 4), insufficient to effect any positive change (Table 3). In Thuringia, however, the RS component influenced the total change at a rate of ?67% (Table 4), resulting in general growth for that industry (Table 3). The wood-based panel industry’s highest RS values in both absolute and relative terms occurred in MecklenburgWestern Pomerania (537 employees or 756%) and SaxonyAnhalt (446 employees or 512%) (Fig. 4; Table 4), indicating that RS impacted the total shift more than either NS or IM in these cases. Positive development in these two federal states was almost entirely attributable to the RS component. Furthermore, all federal states in eastern Germany in the wood-based panel industry also revealed positive RS components. Even though the hub of this industry (i.e., majority of employees) was located in western states, western Germany experienced a loss in almost all federal states, most notably Bavaria. In contrast, eastern German states experienced a growth in the number of employees due to the RS component.
Bavaria’s RS value in the wood construction industry represented around 1,300 employees: the highest value for that industry (Fig. 5). However, Bavaria’s relative value reveals it had no significant effect on the overall change in Bavaria’s wood construction industry or influence over the general negative development of this industry in Bavaria (Table 3). Altogether, the relative RS components were less divergent in the wood construction industry than in the other wood-based industries. Federal states with positive values for the RS component in the wood-based packaging industry—Baden-Wuerttemberg, Hesse, Mecklenburg-Western Pomerania, Lower Saxony, Rhineland-Palatinate, Saxony-Anhalt and Thuringia—have all gained employees due to locational advantages. In Mecklenburg-Western Pomerania, the RS component increased employment by ?60% (Table 4), which was more than the NS and IM component. The RS component of the miscellaneous wood products industry was highest in the federal state of Baden-Wuerttemberg (Fig. 7), indicating a locational advantage in this industry. However, only ?17% of the employment shift in this industry in Baden-Wuerttemberg was due to RS component (Table 4). Locational advantages were also unable to produce a net gain of employees in this industry for the federal state of Brandenburg despite a relative RS component of ?26% (Table 4). Over the period from 1999 to 2006, western Germany and eastern Germany experienced divergent employment trends in the industries under study. This is evidenced by both different absolute RS components and the relative RS components results (Table 5).
Table 4 Regional share of the employment shift in selected wood-based industries in federal states of Germany, 1999–2006 [relative shift (%) in relation to 1999] excluding Saarland and City States (Berlin, Bremen, Hamburg) Sawmilling (NACE 20.1)
Wood-based panels (NACE 20.2)
Wood construction (NACE 20.3)
Wood-based packaging (NACE 20.4)
Miscellaneous wood products (NACE 20.5)
Baden-Wuerttemberg
7.2
-5.3
1.4
4.5
16.6
Bavaria
0.5
-38.8
6.4
-12.3
-8.8
Hesse
-13.6
-30.5
-8.8
17.2
-26.6
Lower Saxony North Rhine-Westphalia
-17.0 -10.7
-5.7 -1.5
-13.8 1.8
38.6 -9.6
12.2 -6.1
Rhineland-Palatinate
-4.6
0.7
2.6
0.9
0.2
Schleswig-Holstein
-22.0
18.5
2.4
-7.9
19.3
23.2
210.6
0.8
20.8
21.2
5.5
43.1
5.5
-22.2
26.0
37.3
755.9
5.5
60.2
-8.8
Western Germany total Brandenburg Meckl.-W. Pomerania Saxonia
20.7
66.0
7.9
-21.2
13.0
Saxony-Anhalt
-6.4
512.3
-0.7
4.9
-13.7
Thuringia
67.2
36.5
-23.7
42.0
-12.4
Eastern Germany total
30.0
89.1
24.3
4.6
4.4
123
Eur J Forest Res Table 5 Regional share of the employment shift in selected wood-based industries in western Germany and eastern Germany, 1999–2006 [absolute and relative shifts (%) in relation to 1999] excluding Saarland and City States (Berlin, Bremen, Hamburg) Sawmilling (NACE 20.1)
Wood-based panels (NACE 20.2)
Wood construction (NACE 20.3)
Wood-based packaging (NACE 20.4)
Miscellaneous wood products (NACE 20.5)
Absolute
Relative
Absolute
Relative
Absolute
Absolute
Relative
Absolute
Relative
Western Germany total
-1,025
-3.2
-1,939
-10.6
561
0.8
-71
-0.8
-276
-1.2
Eastern Germany total
1,247
30.0
2,000
89.1
-407
-4.3
61
4.6
226
4.4
All but one of the assessed industries showed positive absolute RS values for eastern Germany, and contrasting negative absolute RS components for western Germany. Only the wood construction industry presented contrary results: i.e., negative values for eastern Germany and positive values for western Germany (Table 5). For an analytical comparison, relative RS figures must also be considered. RS component had only limited influence over general development in the wood construction industry, the wood-based packaging industry and the miscellaneous wood products industry in both eastern and western Germany. However, higher values are found in eastern Germany’s sawmill industry (?30%) and woodbased panel industry (?89%). In other words, for the industries under study, RS component impacted the general development of employment more positively in eastern Germany.
Discussion The purpose of this time series research was to investigate employment change in selected wood-based industries of the German forest cluster between 1999 and 2006. The results reveal a disproportional decrease in wood-based employment, compared to both the producing industries and the German economy as a whole over the observed time period. The immense continual loss of employees over the period affected all investigated industries except the wood-based packaging industry, which contradicts the overall trend of job loss in Germany’s wood manufacturing. This competitive advantage can be attributed to increasing German exports of goods particularly machinery, which is mainly supplied in traditional wooden packaging. The research figures out that development of employment varied in the industries under study among the German federal states, and sizeable differences indicated the existence of regional disparities. In some industries (sawmill industry, wood-based panel industry, wood-based packaging industry), results provide a clear picture of opposing development patterns in eastern versus western
Relative
Germany, illustrated by certain federal states notably in eastern Germany achieving positive employment growth. In addition to overall employment changes, a shift-share analysis was applied to differentiate specific regional tendencies from general economic and industrial developments. By detecting divergent trends among the regions under study, the method turned out to be a valuable analytical tool for exposing and targeting regional trends of wood-based industries in the forest cluster that are developing independent from and opposed to overall economic trends. The study identified positive wood-based employment trends particularly in eastern German states. A category of federal states was identified that is characterised by RS components exceeding the relative employment change, suggesting that positive development in these federal states was primarily a function of regional conditions. The federal states of Mecklenburg-Western Pomerania and Thuringia belong to this category in the context of their sawmill industry. In the case of the wood-based packaging industry, the states of Brandenburg, Mecklenburg-Western Pomerania, Saxonia, Saxony-Anhalt and Thuringia exhibited such positive regionally-induced development. It is notable that all of these federal states are located in eastern Germany and accounted for the resultant employment growth effects in these industries. Blien et al. (2006) identified comparative regional labour markets in Germany and showed that eastern Germany is mainly characterised by areas with high unemployment rates and even the poorest labour market conditions. In contrast, federal states in southern Germany (such as Bavaria and Baden-Wuerttemberg) are characterised by good to the best labour market situations. Absolute employment trends (Table 1) also reveal that the overall negative employment trend in the national economy was weaker in western Germany than eastern Germany. In fact, the identified federal states in eastern Germany showing positive regional trends in wood-based employment are characterised by predominantly negative structural factors, such as poor labour market conditions, high migration rates, unqualified and absent human resources or weak geographical proximity (Amend and Bogai 2005).
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The shift-share method allows for identifying regions (federal states) that reveal locational advantages. The RS component suggests only a presence or absence of locational advantages, but cannot identify them. Advantages (or disadvantages) of regions for example can be natural endowments, the geographical position, advantageous transport routes, effects of national and regional policy, entrepreneurial abilities, labour market conditions or other comparative advantages (Dinc et al. 1998). In the following we discuss several potential factors impacting on the development of employment, which are considered to be plausible arguments for our results, yet we highlight the speculative nature of these explanations with regard to a lacking evidence for direct causal relationships. When considering the factor of natural endowments, it could be assumed that higher forest cover shares or lower urban area shares in a region entail a stronger presence of wood-based industries owing to the larger contingent of wood resources. Higher investment shares in new plants in a region should induce an increase of employees. Yet for example in Mecklenburg-Western Pomerania, where wood-based employment increased stronger than in any other federal state of Germany (Table 3), forest cover is lower than in many other federal states (Table 1). Although forest cover and urban area values vary across the federal states, the average is approximately equal for eastern and western Germany. Therefore it is unlikely that differences in employment trends between eastern and western Germany (Table 3) can be attributed to this factor. Our results suggest that the observed regional advantages (at least in part) are an outcome of German postreunification policy on subsidising federal states in eastern Germany. Since the 1990s, all German federal states have had access to financial assistance from the federal government, which targeted a stimulation of economic activity and employment. Numerous business enterprises made use of these subsidies to invest in eastern Germany and/or transfer their production from western Germany to the federal states of eastern Germany (Eickelpasch and Pfeiffer 2006; Blien et al. 2001). It is known that a number of largescale wood-based enterprises were subsidised to start new businesses in several eastern federal states (e.g. Ministerium fu¨r Wirtschaft, Arbeit und Tourismus MecklenburgVorpommern 2006; Holz-Zentralblatt 2004), yet the amount of these subsidies is confidential and cannot be determined. A statistical correlation analysis between employment growth and the amount of subsidies cannot be tested due to unavailable data. Detailed information about subsidies is subject to privacy laws by the federal government. However, even if relevant data are not available, the following can be at least determined. Subsidies for industry investments in general or with a particular regional focus can be
123
Table 6 Subsidies (million euros per year) of the German Federal Republic between 1997 and 2006 (Bundesministerium der Finanzen 2007, 2006, 2003, 2001, 1999) Year
Subsidies for industry totalb
Regional financial grants for investmenta Western Germany
Eastern Germany
1997
136.7
928.7
7,176
1998
93.3
913.6
6,937
1999
99.9
898.7
6,544
2000
95.6
800.1
5,934
2001
110.7
744.3
5,742
2002
96.1
587.7
4,779
2003
90.2
541.1
4,210
2004
65.1
529.1
3,661
2005
500.2
3,263
2006
509.5
3,149
a
Gemeinschaftsaufgabe Wirtschaftsstruktur
zur
Verbesserung
der
regionalen
b
Finanzhilfen des Bundes fu¨r die Gewerbliche Wirtschaft (ohne Verkehr)
identified for the German Federal Republic between 1997 and 2006 (Table 6). Among a total of subsidies of 113,000 million euros in 1997 for a variety of subsidised targets, 62% account for subsidies for industry investments. These subsidies were reduced from 71,000 million euros in 1997 to 31,000 million euros in 2006. Subsidies for the ‘‘improvement of regional economic structure’’ (Gemeinschaftsaufgabe zur Verbesserung der regionalen Wirtschaftsstruktur) demonstrate decisive distinctions between eastern and western Germany. These non-repayable subsidies were granted to enterprises that invest in producing goods predominantly for supra-regional sale. Although the amount’s share to wood-based branches cannot be determined, the total amount reveals that eastern Germany gained much more subsidies than western Germany (in 1999 it was about ninefold). Suedekum et al. (2006) have found that a higher share of medium-sized firms induces stronger employment growth in the region. In western Germany many small-scale sawmilling enterprises (annual cut less than 50 T fm/a), which are important contributors to regional employment in rural areas, have terminated their business activities during the last decade. Considerable losses in Germany’s small-scale sawmills of up to 28% have been documented between 1995 and 2001. This development is mainly determined through a strong competition from new large-scale sawmills (annual cut more than 500 T fm/a) with modernised and less labour-intense technology (So¨rgel and Mantau 2006; Tesch et al. 2004; Schulte 2003b). Our study confirms that notably eastern Germany experienced a positive employment growth in sawmill and
Eur J Forest Res
wood-based panel industry attributed to the development of new enterprises. Subsidised new investments in eastern Germany’s wood industry have most often been realised by larger businesses that founded new large-scale, technologically highly developed processing plants. These results are consistent with Kra¨tke and Scheuplein’s (2001) case study of wood-based branches in the federal state of Brandenburg, which found a dual structure of wood-based industries in eastern Germany characterised by a few large enterprises (often owned by West German proprietors) versus many traditional small-scale enterprises. These authors emphasise the problems of low own capital, low capacity for innovation and high adjustment pressure on small-scale enterprises. Although this ongoing structural and technological change in the wood processing and manufacturing industries may be attributed to an overall trend in an increasingly globalised market economy (Sowlati and Vahid 2006; Lebedys 2004), it remains very questionable, whether such investments in high-tech, less labour-intense large plants are rightfully co-financed by federal subsidies that are mainly targeted at the reduction of unemployment. Regional labour costs, defined as the total expenditure borne by employers in order to employ workers, can be an important factor for regional disparities in employment growth. An overly high regional wage level significantly reduces employment growth especially in manufacturing industries, because enterprises keeping pace with the technological progress (automatisation) replace employees by machines if wages and labour costs increase (Suedekum et al. 2006; Suedekum and Blien 2004; Blien et al. 2003). Labour costs in both the wood-based industries and the producing industries were considerably higher in western Germany than in eastern Germany (Table 7). In 2004, wood industry employers (NACE 20) had to pay averagely 28,100 euros per employee per year in eastern Germany, which was about 72% of labour costs in western Germany (averagely 39,300 euros per year). These figures suggest that imbalanced labour costs in eastern and western Germany may be strongly influenced by regional employment differences. Land values are a further important factor affecting investment decisions in target regions. Average land values Table 7 Labour costs (euros per year) of the wood industry (NACE 20) and the producing industries (NACE C–F) in eastern and western Germany in 2004 (Statistisches Bundesamt Deutschland 2007) Wood industry (NACE 20)
Producing industries (NACE C–F)
Eastern Germany
28,120
34,108
Western Germany
39,296
51,954
Germany total
37,325
49,770
amounted 105 euros/m2 in western Germany and 29 euros/ m2 in eastern Germany in 2006 (Statistisches Bundesamt Deutschland 2008). This large difference should have been a further motivation for entrepreneurs to invest in eastern Germany. Blien et al. (2001) demonstrated for Germany that a region’s geographical location can strongly influence its development of employment. Regions in close proximity of the Baltic Sea for example profited from better infrastructure and market opportunities, which also relates to the wood industry. A special locational advantage represents the seaport in Wismar (Mecklenburg-Western Pomerania), which is a specialised import and export hub including many goods of wood. North–south traffic between Central Europe and Scandinavia, the Baltic States and Russia are bundled and distributed here. Furthermore, (re-)buildings of the regional highway network (Autobahn) in eastern Germany during the 1990s were an important step to connect far-off regions to markets in western Germany (Ministerium fu¨r Wirtschaft, Arbeit und Tourismus Mecklenburg-Vorpommern 2005, 2006).
Conclusion In this study, divergent employment trends in wood-based industries of the forest cluster have been studied for the first time for Germany as a whole as well as its individual federal states. The research depicts a massive ongoing structural change in wood-based industries contrasting in eastern and western German states and diverging from overall economic trends. Federal subsidies for industrial investments, labour costs, land values and locational advantages linked to infrastructural peculiarities are suggested to be decisive factors of concern that are likely to have caused the regionally contrasting employment trends. Owing to data confidentiality, the impact of subsidies in the wood industry lacks empirical evidence and can only be inferred from the total amount of subsidies. Nevertheless, considering the disproportionally stronger employment losses in the wood industry, it can be concluded that during the last decade federal subsidies could not stimulate overall employment in these industries. If at all, they have led to a relocation of wood-based employment from western to eastern federal states resulting in a comparatively weak growth in eastern German states, which could not compensate for the rapid decline in western Germany. Especially in view of these results from our research, which must be seen in the context of a harsh ongoing structural change and market competition in the wood processing industries, the justification for financial support of largescale plants through federal tax-based subsidies loses its legitimacy from a regional economic perspective.
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As an alternative, the authors advocate the governmentsupported introduction of a regional forest cluster management, which attempts a holistic approach for the whole wood-based value added chain and fosters horizontal and vertical cooperation of small and medium-sized enterprises in a regional context. Experiences from other countries in Europe, particularly Austria and Switzerland, 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 (Denner 2007; Mrosek and Kies 2006; Sautter 2004; Raines 2002). The study presents a first-hand empirical basis for further research on factors influencing employment growth and decline in the forest cluster. An analysis of these employment trends in lower level geographical units such as functional planning regions (Bundesraumordnungsregionen) would add to a more detailed understanding of the dynamics within the federal states. Also the larger context of employment shifts among countries in the European Union is of strong interest to forest sector research. Furthermore other industries such as pulp and paper or furniture are candidates for future investigations. Concerning different industry branches, a potential analysis should also consider models of co-agglomeration. Acknowledgments The authors would like to thank staff members of the Federal Employment Agency (Bundesagentur fu¨r Arbeit) for helpful support in providing access to their statistical reporting system. We also thank Ms. Denise Allen, University of British Columbia, Sustainable Forest Management Research Group and Jo¨rg Gauerke 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 Wirtschaft, Arbeit und Tourismus MecklenburgVorpommern 2005, 2006).
References Amend E, Bogai D (2005) Regionale Arbeitsma¨rkte in Ostdeutschland. Was hat das eine, was das andere Land nicht hat? IAB Kurzbericht 25/2005: p 6 Ashby LD (1970) Changes in regional industrial structure: a comment. Urban Stud 7(3):298–304 Bilek E, Ellefson P (1984) Employment in Minnesota’s wood-based industry: a shift-share analysis. Department of forest resources. Staff Paper Series 46. College of Natural Resources and Minnesota Agricultural Experiment Station, University of Minnesota, St. Paul, Minnesota, p 51. Accessed May 2007 http://fr.cfans.umn.edu/publications/staffpapers/Staffpaper46.pdf Blien U, Wolf K (2002) Regional development of employment in eastern Germany: an analysis with an econometric analogue to shift-share techniques. Pap Reg Sci 81(3):391–414 Blien U, Blume L, Eickelpasch A, Geppert K, Maierhofer E, Vollkommer D, Wolf K (2001) Einflussfaktoren der Regionalentwicklung, Studie von IAB/DIW im Auftrag der KfW, p 75 Blien U, Haas A, Wolf K (2003) Regionale Bescha¨ftigungsentwicklung und regionaler Lohn in Ostdeutschland. Mitt Arbeitsmarkt Berufsforsch 4:476–492
123
Blien U, Hirschenauer F, Hong Van P thi (2006) Model-based classification of regional labour markets. For purposes of labour market policy. IAB discussion paper. Institut fu¨r Arbeitsmarkt und Berufsforschung der Bundesagentur fu¨r Arbeit 29, p 51 Bundesagentur fu¨r Arbeit (2007) (Federal Employment Agency) Statistics of employees with social insurance registration (Statistik der sozialversicherungspflichtig Bescha¨ftigten), Nu¨rnberg Bundesministerium der Finanzen (1999) Bericht der Bundesregierung u¨ber die Entwicklung der Finanzhilfen des Bundes und der Steuervergu¨nstigungen gema¨ß § 12 des Gesetzes zur Fo¨rderung der Stabilita¨t und des Wachstums der Wirtschaft (StWG) vom 8. Juni 1967 fu¨r die Jahre 1997 bis 2000, Bonn, p 244 Bundesministerium der Finanzen (2001) Bericht der Bundesregierung u¨ber die Entwicklung der Finanzhilfen des Bundes und der Steuervergu¨nstigungen gema¨ß § 12 des Gesetzes zur Fo¨rderung der Stabilita¨t und des Wachstums der Wirtschaft (StWG) vom 8. Juni 1967 fu¨r die Jahre 1999 bis 2002 (18. Subventionsbericht), Berlin, p 147 Bundesministerium der Finanzen (2003) Bericht der Bundesregierung u¨ber die Entwicklung der Finanzhilfen des Bundes und der Steuervergu¨nstigungen gema¨ß § 12 des Gesetzes zur Fo¨rderung der Stabilita¨t und des Wachstums der Wirtschaft (StWG) vom 8. Juni 1967 fu¨r die Jahre 2001 bis 2004 (19. Subventionsbericht), Berlin, p 155 Bundesministerium der Finanzen (2006) Bericht der Bundesregierung u¨ber die Entwicklung der Finanzhilfen des Bundes und der Steuervergu¨nstigungen fu¨r die Jahre 2003 bis 2006 (20. Subventionsbericht), Berlin, p 256 Bundesministerium der Finanzen (2007) Bericht der Bundesregierung u¨ber die Entwicklung der Finanzhilfen des Bundes und der Steuervergu¨nstigungen fu¨r die Jahre 2005 bis 2008 (21. Subventionsbericht), Berlin, p 308 Commission of the European Union (1999) Communication on the state of competitivness of the EU forest-based and related industries. Communication of the Council, the European Parliament, the Economic and Social Committee and the Committee of the Regions. COM (1999) 457, Luxembourg, p 19 Denner C (2007) Steuerungsinstrumente von high-tech Clustern. Eine Analyse auf Grundlage der Komplexita¨tstheorie. VDM, Saarbru¨cken, p 112 Dinc M, Haynes KE, Quiangsheng L (1998) A comparative evaluation of shift-share models and their extensions. Australas J Reg Stud 4(2):275–302 Dunn ES (1960) A statistical analytical technique for regional analysis. Pap Reg Sci Assoc 6:97–112 Eickelpasch A F, Pfeiffer I (2006) Standortverlagerungen in der ostdeutschen Industrie. Wochenbericht DIW Berlin 73/14: 181– 185 Esteban J (2000) Regional convergence in Europe and the industry mix: a shift-share analysis. Reg Sci Urban Econ 30:353–363 Holz-Zentralblatt (2004) Konsequenzen aus verfehlter Sa¨gewerksfo¨rderung ziehen. VDS: Subventionen fu¨r neue Kapazita¨ten volkswirtschaftlich nicht sinnvoll, Holz-Zentralblatt 130/50 Jaensch K, Harsche J (2007) Der Cluster Forst und Holz in Hessen. Bestandsanalyse und Entwicklungschancen. HA Hessen Agentur GmbH 712, p 109 Kies U, Mrosek T, Schulte A (2008) A statistics-based method for cluster analysis of the forest sector at the national and subnational level in Germany. Scand J For Res 23:445–457 Knudsen DC (2000) Shift-share analysis: further examination of models for the description of economic change. Socioecon Plann Sci 34:177–198 Kramer M, Mo¨ller L (2006) Struktur- und Marktanalyse des Clusters Forst und Holz im Freistaat Sachsen und in ausgewa¨hlten Regionen des niederschlesischen und nordbo¨hmischen
Eur J Forest Res Grenzraums unter den Bedingungen der EU-Osterweiterung (Cluster-Studie). Internationales Hochschulinstitut Zittau, p 200 Kra¨tke S, Scheuplein C (2001) Produktionscluster in Ostdeutschland. Methoden der Identifizierung und Analyse. VSA-Verlag, Hamburg, p 221 Lange T, Pugh G (1998) The economics of German unification: an introduction. Edward Elgar, Cheltenham, p 240 Lebedys A (2004) Forest finance. Trends and current status of the contribution of the forestry sector to national economies (Working paper: FSFM/ACC/07). Rome: Food and Agriculture Organization of the United Nations, Forest Products and Economics Division Ministerium fu¨r Wirtschaft, Arbeit und Tourismus MecklenburgVorpommern (2005) Mittelstandsbericht Mecklenburg-Vorpommern 2002–2006, p 64 Ministerium fu¨r Wirtschaft, Arbeit und Tourismus MecklenburgVorpommern (2006) Die internationale Ausrichtung der Wirtschaftspolitik Mecklenburg-Vorpommerns 2005, Außenwirtschaftsbericht Mecklenburg-Vorpommern, p 25 Mrosek T, Kies U (2006) Clustermanagement in der Forst- und Holzwirtschaft. Handlungsempfehlungen als Ergebnis der ,,Clusterstudie Forst und Holz Deutschland’’. Holz-Zentralblatt 29: 841–843 Mrosek T, Kies U, Schulte A (2005) Clusterstudie Forst und Holz Deutschland 2005. Forst- und Holzwirtschaft hat sehr große volkswirtschaftliche und arbeitsmarktpolitische Bedeutung. Holz-Zentralblatt 84: 1113–1117 Organisation for Economic Co-operation and Development (1999) Boosting Innovation: the cluster approach. OECD Proceedings, p 428 Porter M (1998) On competition. Harvard Business School Press, Boston, p 197 Raines P (ed) (2002) Cluster development and policy. Ashgate Publishing Limited, Burlington Sautter B (2004) Regionale Cluster—Konzept, Analyse und Strategie zur Wirtschaftsfo¨rderung. Standort—Zeitschrift fu¨r Angewandte Geographie 28/2: 66–72 Schulte A (ed) (2002) Clusterstudie Forst und Holz NordrheinWestfalen. Gesamtbericht. Schriftenreihe der Landesforstverwaltung NRW 17. MUNLV Ministerium fu¨r Umwelt, Naturschutz, Landwirtschaft und Verbraucherschutz, Du¨sseldorf, p 138. Accessed Nov 2007 http://www.forst.nrw.de/nutzung/ cluster/cluster.htm Schulte A (2003a) Nordrhein-Westfalen zieht Bilanz fu¨r Forst und Holz. Cluster-Studie weist unerwartete volkswirtschaftliche Gro¨ße der Forst- und Holzwirtschaft aus. Holz-Zentralblatt 74 Schulte A (2003b) (ed) Wald in Nordrhein-Westfalen. Aschendorff, Munster, p 1082 Schulte A, Mrosek T (2006) Analysis and assessment of the forestry and wood-processing industry cluster in the State of North-Rhine Westphalia, Germany. Forstarchiv 4:136–141
Seegmu¨ller S (2005) Die Forst-, Holz- und Papierwirtschaft in Rheinland-Pfalz. Clusterstudie. FAWF Forschungsanstalt fu¨r Waldo¨kologie und Forstwirtschaft Rheinland-Pfalz, Trippstadt, p 63 So¨rgel C, Mantau U (2006) Strukturentwicklung der Sa¨geindustrie in vier Jahren. Abschlussbericht einer Studie u¨ber Vera¨nderungen in der deutschen Sa¨gewerkssparte auf der Basis zweier Vollerhebungen. Holz-Zentralblatt 21: 651–653 Sowlati T, Vahid S (2006) Malmquist productivity index of the manufacturing sector in Canada from 1994 to 2002, with a focus on the wood manufacturing sector. Scand J For Res 21:424–433 ¨ mter des Bundes und der La¨nder (2007) GENESIS Statistische A Online. Regionaldatenbank Deutschland. Accessed Feb 2007 https://www.regionalstatistik.de/ Statistical Office of the European Communities (2002) Statistical classification of economic activities in the European Community, Rev. 1.1 (NACE). Metadata. RAMON Classification Server. EUROSTAT, Luxembourg. Accessed March 2008 http:// ec.europa.eu/eurostat/ramon/index.cfm?TargetUrl=DSP_PUB_ WELC Statistisches Bundesamt Deutschland (2003) Klassifikation der Wirtschaftszweige, Ausgabe 2003 (WZ 2003). Mit Erla¨uterungen. Metzler Poerschel Verlag, Wiesbaden, p 860 Statistisches Bundesamt Deutschland (2007) Arbeitskostenerhebungen 2004. Arbeitskosten im Produzierenden Gewerbe und im Dienstleistungsbereich. Ergebnisse fu¨r Deutschland, Wiesbaden, p 209 Statistisches Bundesamt Deutschland (2008) Kaufwerte fu¨r Bauland und landwirtschaftliche Grundstu¨cke. Ausgewa¨hlte Baulandarten und La¨nder 2006. Accesed August 2008 http://www.destatis.de/ jetspeed/portal/cms/Sites/destatis/Internet/DE/Content/Statistiken/ Preise/Baupreise/Tabellen/Content75/KaufwerteBaulandLaender, templateId=renderPrint.psml Stimson RJ, Stough RR, Roberts BH (2006) Regional economic development. Analysis and planning strategy, 2nd edn. Springer, Heidelberg, p 452 Størdal S, Lein K, Orbeck M, Hagen S (2004) Regional differences in harvesting levels and wood-based employment in Norway. Small-scale forest economics. Manage Policy 3(1):35–47 Suedekum J, Blien U (2004) Wages and employment growth: disaggregated evidence for West Germany. Hamburg Institute of International Economics, Discussion paper 275, p 31 Suedekum J, Blien U, Ludsteck J (2006) What has caused regional employment growth differences in Eastern Germany? Jahrbuch fu¨r Regionalwissenschaft 26: 51–73 Tassinopoulos A (1996) Eine regionale Bescha¨ftigungsprognose. Ergebnisse fu¨r Arbeitsmarktregionen auf dem Gebiet der alten Bundesla¨nder. Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Sonderdruck 29: 361–377 Tesch D, Mrosek T, Schulte A (2004) Strukturen und Ma¨rkte der Sa¨geindustrie in NRW. Holz-Zentralblatt 90
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