Spillover effects of spatial growth poles

Spillover effects of spatial growth poles - a reconciliation of conflicting policy targets? Kubis A.1, Titze M., Ragnitz J.

DRAFT VERSION – PLEASE DO NOT QUOTE! March 2007

Abstract. Regional economic policy faces the challenge of two competing policy goals - reducing regional economic disparities vs. promoting economic growth. The allocation of public funds has to weigh these goals particularly under the restriction of scarce financial resources. If, however, some region turns out to be a regional growth pole with positive spillovers to its disadvantaged periphery, regional policies could be designed to reconcile the conflicting targets. In this case, peripheral regions could indirectly participate in the economic development of their growing cores. We start our investigation by defining and identifying such growth poles among German regions on the NUTS 3 administrative level based on spatial and sectoral effects. Using cluster analysis, we determine significant characteristics for the general identification of growth poles. Patterns in the sectoral change are identified by means of the change in the dependent employment relationships. Finally, we analyze whether and to what extent these growth poles exert spatial spillover effects on neighbouring regions and thus mitigate contradictory interests in regional public policy. For this purpose, we apply a Spatial-Cross-Regressive-Model (SCR-Model) including the change in the secondary sector which allows to consider functional economic relations on the administrative level chosen (NUTS 3).

Keywords: Size and Spatial Distributions of Regional Economic Activity; Cross-Sectional Models; Spatial Models; Treatment Effect Models; Regional, Urban, and Rural Analyses JEL-classification: R12, C21, O18

1

Halle Institute for Economic Research, P.O. Box 11 03 61, 06017 Halle (Saale), Germany, Phone: +49 (0)345 7753 851, Fax: +49 (0)345 7753 820, Email: [email protected].

2 Spillover effects of spatial growth poles - a reconciliation of conflicting policy targets? Kubis A., Titze M., Ragnitz J.

1. Introduction Article 72 (2) Grundgesetz (basic constitutional law) obliges the Federal Government to “create equal conditions of life within the Federal territory” (aim of distribution). Restriction of scarce financial resources, reduced government aid (e.g. European Regional Development Funds (ERDF)) as well as the ongoing discussion about the financial compensation between the Federal States are the motivation for the necessity to adjust the German regional policy fundamentally. Most often, a conflict is assumed between the aim of distribution and the aim of growth (Art. 104a Grundgesetz), as the funds used for economically weak regions are not available for strongly growing regions – even more, they have to be financed by tax revenues from growth poles. So far, a high importance has been attributed to the distribution target, but it has been discussed since to concentrate government aid on so-called growth poles (see for this particularly the results of the discussion round Gesprächskreis Ost, Dohnanyi/Most 2004). With this, the aim of growth would be attributed a higher weight at the expense of the distribution target. This conflict eases (at least partly) when spillover effects of neighbouring regions apply. The following example illustrates this idea (see figure 1.1).

Fig. 1.1: Regional growth Total sum of government aid

growth pole (W)

periphery (P)

Government aid

Government aid Structural subsidisation effect Weak additional regional growth

Strong additional regional growth Spillover effect

Additional regional growth impulse Total regional growth

Total regional growth

Gesamtwachstum Total growth

Source: own presentation.

The kind of policy which is applied today is (mainly) based on the principle of equal treatment, i.e. the government aid is distributed evenly to possible growth poles and periphery regions. Still, government aid can also be distributed selectively (regional, sectoral). We assume that the subsidisation effect depends on the economic structure. Advantages of agglomeration can increase this sector specific

3 growth as well. In a possible scenario, the total sum of government aid is focussed in the strongly growing branches of economy in the growth pole. In the growth pole, the highly growing branches of economy have a larger share than in the periphery region. Therefore the structural subsidisation effect in region W is larger than in region P. There is an additional growth impulse on the respective region because of spillover effects from neighbouring regions. The total growth of both regions results from the growth of the structural subsidisation effect of the own region and the interdependence between the regions. An exclusive government aid to the growth sector in W does not lead to direct growth in P. However, a possible spatial effect lets region P participate in the strengthened growth of W. W has an increased growth because of its sectoral and regional specification. The gain in growth in W might even overcompensate the loss in P, i.e. the total growth in both regions exceeds in the case of equally distributed unspecific government aid. Here, the aim of growth is taken into account without totally giving up the distribution target, as P also receives an additional impulse by the spillover effect from W. This described form of government aid is e.g. applied by the Government of Brandenburg at the grant of investment contribution within the federal project (Gemeinschaftsaufgabe der Verbesserung der regionalen Wirtschaftsstruktur, GA) for the improvement of regional economical structure. Therein, companies in highly growing industry sectors in regions with a large share of highly growing industries, receive a so-called government aid of potentials (addition to the basic government aid) (cf. MW 2006). The paper is organized as follows. To a better understanding the arising questions, it is necessary to determine growth poles at first (see Box 1).

Box 1: Assumptions and procedural method Assumption 1: The trade-off between the distribution target and the growth target can be reconciled if Government aid is focused on high-growing industry sectors in growth poles. Neighbouring regions can enjoy the spillover effects from these high-growing regions. Assumption 2 (derived from assumption 1): The economic growth in small-area regions is influenced by the branches of economy and the spillover effects from neighbouring regions. → focus of the paper Procedural method: 1. 2.

3. 4.

Definition of growth poles – analysis of the change in the gross value added per inhabitant in the 439 NUTS 3-regions in Germany, 1999 to 2004 Identification of the structural change in these regions a. Determination of the relative share of dependent employment relationships in the 60 industry branches (NACE-classification) → Classification of regions similar in the structural change, cluster analysis → Are there clusters represented by a high level of high-growing regions? b. Classification of regions growing at high level (5%, 10%, 25% and 50% quantiles) → Which industry branches represent these “growing-classes”? → Which patterns can be identified in the several “growing-classes”? Proof of the influence of these patterns to the regional growth, spatial-cross-regressive model (SCR Model) → Which influence do these patterns have on the growth of small area regions? Forecast to research activities to furnish proof of assumption 1 → Which task do we have to deal with?

Secondly, these growth poles are examined considering similarities and differences to non-growth poles. The question in mind is whether agglomeration effects or the structural change plays a role for

4 growth poles. The observation, that companies of similar or also different branches are often concentrated at certain locations should be analysed as well (cf. Marshall A. 1952 pp.267-277; Porter M.E. 1990 and Krugman P.R. 1991). The structural change is displayed in the changes of new and old branch focusses. Our paper searches for branch focusses in regions with a similar cluster structure. Thirdly, these characteristics should be examined this respect to their influence on the total growth of NUTS-3 regions.2 At the same time, a proof of spatial effects becomes necessary for the dissolution of the trade-off. Finally we conclude with a short summary and outlook to new fields of research resulting from our work. This paper shows a strong influence of structural change with a high share of economical branches of the secondary sector on the growth of small-area regions. In a second step, determinants of growth within a regional production function are analysed, where the function is expanded by a sectoral component and spatial effects are considered.

2. Determination of growth poles In fig. 2.1, the growth of the 439 German NUTS-3 regions, measured by means of the gross value added (GVA), becomes evident.

Fig. 2.1: Growth of the gross value added in 1000 Euro per inhabitant (NUTS-3, 1999-2004) Schweinfurt

30

Dresden Eisenach

Sömmerda

40

Pfaffenhofen an d. Ilm

50

Teltow-Fläming Merseburg-Querfurt

60

Dindolfing Landau

70

Gross value added per inhabitant 2004

München LK

80

20

10 10

15

20

25

30

35

40

45

50

55

60

65

Gross value added per inhabitant 1999 Source: National Accounting, own presentation.

2

NUTS, Nomenclature des Unités Territoriales Statistiques, Germany : districts, distict-free cities and federal states Berlin and Hamburg.

5 The x-axis shows the gross value added per inhabitant of the year 1999, while the y-axis contains the corresponding values of the year 2004. The horizontal and vertical lines describe the average values of the years 1999 and 2004. The point of origin as well as the intersection of average gross value added describe the fixed points of the diagonal (solid black line) and result in the average growth within Germany. All regions above the diagonal show growth above average in the comparison of 1999 and 2004. Which regions can be identified as growth poles in a simple way? The larger above average the growth of a region is, the higher above the diagonal the region can be found in fig. 2.1. A counter clockwise rotation of the diagonal limits the number of groth poles. The regions with the highest growth rates are situated above the dashed line. There are shown different “growth classes” (table 2.1)

Tab. 2.1: Classification of growth Regions with growth above average

aelow average

Growth between quantiles >5% 5 % - 10 % 10 % - 25 % 25 % - 50 % growth positive negative

Classification growth pole strong growth growth weak growth substandard growth negative growth

Source: Own calculation.

The regional distribution of growth within Germany in the examined time period can be gathered from fig. 2.2. Growth, measured by the change in gross value added per inhabitant from 1999 to 2004, concentrates on selected East German and Bavarian NUTS-3 regions. In the examined areas, we also find regions with a substandard or a shrinking growth. The top 5 % of the high-growing regions are marked as growth poles. We speak of a strong growth when considering the upper 5 to 10 % of the high-growing regions. The following class characterizes the upper 10 to 25 % of the high-growing areas as growing NUTS-3 regions. Regions with an aboveaverage growth, which do not belong to the mentioned groups are displayed as regions with a weak growth. Besides, two further groups are displayed – regions with a negative growth and other regions with substandard growth. The aim is now to search for determinants of growth of the regions. The size of agglomeration, the structural change or the spatial-functional connections could play a role in this context. An influence of strong government aid can be assumed for East Germany (cf. Ragnitz et al. 2006). The supposed determinants are analysed descriptively in the following chapters and, based on this analysis, inserted to a neoclassical growth function.

6 Fig. 2.2: Growth poles (1999-2004) GVA per inhabitant 04/99

GVA 04/99

Inhabitants 04/99

growth poles strong growth

weak growth substandard growth

growth

negative growth

Source: own presentation.

3. Agglomeration effects Fig. 3.1 shows the German metropolitan areas as well as the 22 strongest growth poles. The current German/European definition of metropolitan areas is they are “engines of economical, social and cultural development...” (cf. MKRO 1995, p. 87). For the political definition of metropolitan areas, assets (potential of inhabitants, economic power) as well as functional criteria play a crucial role (cf. Heimpold 2006, p. 61). Metropolitan areas represent highly agglomerative, strongly connected areas. In this case we should see a high correlation between agglomeration and growth. There are only few intersections or overlaps between the politically defined metropolitan regions and the groth poles identified above. A further simple measure, to describe the relation between growth and size of agglomeration, is the number of inhabitants per hectare. The correlation between the examined growth and the size of agglomeration of the region in 2004 does not speak for a linear relation (R2 = 0.013). This asset value is therefore not part of the growth function.

7 Fig. 3.1: Growth poles and metropolitan areas

Metropolitan area

Growth pole

Source: MKRO, own presentation.

4. Structural change For a more detailed analysis of the influence of structural change on growth, NUTS-3 regions are examined considering dependent employment relationships (B) in the 60 NACE-branches of economy (cf. Federal Statistical Office Germany (2002)). The aim is to determine sectoral growth engines of the economic development in the region. Here, the share of dependent employment relationships is understood as a proxy for total output and the resulting level of welfare.3 According to equation 4.1, the change is analysed as a percentage of the share of dependent employment relationships of an economical branch i in region j on the total dependent employment relationships of region j.

3

For NUTS-3 regions, the Official Statistics does not supply any informations about Gross value added in the 60 NACE branches of economy.

8

(4.1)

B(i , j ),04−98

     B i , j ,2004   B i , j ,1998  ( )  −  60 ( )  =  60  B   B   ∑ ( i , j ),2004   ∑ (i , j ),1998   i =1   i =1 

The analysis of structural change is carried out in two parts. In the first part, regions are clustered due to their structural change, whereby such that areas with equal economical structure are attributed to similar groups (see Box 2 for a decription of the clustering procedure). It is of special interest whether growth poles accumulate in certain clusters and which patterns can be shown by this procedure. In this part, increases and decreases of the number of dependent employment relationships in the different branches of economy are considered. In the second part, all regions with a certain growth structure are examined to find out whether particularities concerning the change of dependent employment relationships in economical branches show up.

Box 2: Cluster analysis The cluster analysis requires non-correlated variables. The given NUTS-3 regions are assigned to disjoint groups (subsets) so that the clusters are as similar as possible concerning their structure and well-differentiated from the regions in other clusters. A great variety of potential cluster approaches are applied within the analyses of hierarchical-agglomerative procedures. As a measure for distance, the squared euclidian distance is applied. By means of the single linkage procedure the regions are examined for outlies considering the given structure. For the further course of the analysis, the WARD-procedure is applied,as it leads to “robust” classes of approximately the same size. The outlies are assigned to the clusters with FisherDiscriminant-Criteria before the interpretion of the clusters. The squared residuals in the WARD-linkage as well as the dendrogram refer to an optimal number of 4 clusters.

The distribution of growth regions within the clusters can be gathered from the following table 4.1.

Tab. 4.1: Growth poles in the economical structure: absolute (relative) Cluster 1 2 3 4 Germany

Average growth 0.09 0.09 0.15 0.14 0.10

N 76 258 54 51 439

50% quantile 31 (0.41) 109 (0.42) 42 (0.78) 37 (0.73)

25% quantile 17 (0.22) 40 (0.16) 27 (0.50) 26 (0.51)

10% quantile 6 (0.08) 13 (0.05) 15 (0.28) 10 (0.20)

5% quantile 2 (0.03) 5 (0.02) 9 (0.17) 6 (0.12)


In this table, the assignment of growth clusters becomes obvious. In general, the regions in cluster 3 and 4 have the highest average total growth in the examined time period 1998 to 2004. For the argumentation of appropriate growth clusters, we refer to the 25%-quantil. This quantile covers 25% of the regions with the strongest growth. The distribution on the 4 clusters shows that approximately 50% of the regions contained in cluster 3 and 4 are growth poles. Table 4.2 shows the biggest changes in the share of dependent employment relationships of the growth clusters 3 and 4.

9 Tab. 4.2: Average (proportional) change in dependent employment relationships by branches a) Cluster 3 4 Germany

Average growth 0.15 0.14 0.10

Branches of economy (highest increase) 85, 74, 28, 55, 63 80, 85, 74, 34, 63 74, 85, 80, 63, 72

Branches of economy (highest decrease) 45, 75, 01, 91, 36 45, 75, 35, 90, 01 45, 75, 36, 26, 17

a) NACE-Classification Source: Own calculation.

This table shows a remarkable correspondence in the branches of economy with the highest increase or decrease. There are NACE branches (bold numbers in table 4.2), which are different from the growing branches in Germany. Specifically, the all-German increases concern the economical branches of Other business activities (74), Health and social work (85), Education (80), Supporting and auxiliary transport and activities of travel agencies (63) as well as Computer and related activities (72). The strong structural change in Germany shows in the strong decreases of dependent employment relationships in the economical branches of Construction (45), Public administration and defence (75), Manufacture of furniture, manufacturing n.e.c. (36), Manufacture of other non-metallic mineral products (26) as well as Manufacture of textiles (17). In general, the growth clusters reflect these allGerman processes or even press ahead with the structural change. Of special interest are these economical branches, which represent the differences. Growth cluster 3 is determined by a strong increase in importance concerning the dependent employment relationships in the branches Hotels and restaurants (55) and above all in Manufacture of fabricated metal products, except machinery and equipment (28). This last branch represents a branch of the secondary sector. The agricultural sector (01) as well as Activities of membership organizations n.e.c. (91) have decreased above-average in their importance in these areas. Growth cluster 4 reflects the all-German development as well as an above-average increase in importance of the economical branch of Manufacture of motor vehicles, trailers and semi-trailers (34). The Manufacture of other transport equipment (35) decreased overaverage. Branch 34 is characterized by a high degree of linkages with a variety of suppliers. The regions of cluster 4 show an extraordinary decrease in the agricultural sector (01), which means a strong structural importance. Besides, there are clearly less dependent employment relationships in Sewage and refuse disposal, sanitation and similar activities (90). The branches 91 and 90 as a part of public services can be connected with the trend towards privatization in the public sector. In the growth clusters mainly regions from East Germany are represented (cf. table 4.3).

Tab. 4.3: Regional characteristics of growth clusters Cluster 3 4 Germany Source: Own calculation.


Share of district-free cities 14.8 % 35.4 % 26.4 %

Share of regions from East Germany 85.2 % 84.3 % 25.5 %

10 The share of district-free cities is below the all-German average in cluster 3 and above-average in cluster 4. The following table 4.4 shows a larger share of regions with a decrease of inhabitants in comparison to Germany for both growth clusters.

Tab. 4.4: Population in the growth clusters Cluster 3 4 Germany


Regions with a decrease of inhabitants 79.6 % 86.3 % 42.1 %

Average change of inhabitants - 2 989 - 5 838 1 076


The (few) regions with an increase of inhabitants do not compensate the loss of inhabitants in other regions, as the average number of inhabitants of one representative in these two clusters is decreasing. The next table makes clear that approximately ¾ of all German regions are characterized by a decrease of the rate of dependent employment relationships. The share in the growth clusters is even higher. The rate of dependent employment relationships in a region in Germany has an approximate decrease of 1.2 % points. The decrease is even on average higher for a representative of both growth clusters.

Tab. 4.5: Dependent employment relationship in the growth clusters Cluster 3 4 Germany


Regions with a decrease of the rate of dependent employment relationship 94.4 % 82.4 % 75.2 %

Average decrease of the rate of dependent employment relationship 2.8 % - point 2.4 % - point 1.2 % - point


Alltogether it can be concluded that the growth clusters are very strongly dominated by East German regions. The number of inhabitants decreases. The migration effect from East to West Germany is reflected in this process (cf. Kubis 2005). The decrease of the rate of dependent employment relationships at the same time means a loss of jobs. This is a hint on the kind of policy in the East Federal States, which mainly aims at modernization of the stock of capital by granting investment subsidy (tax benefit) and investment grant (government aid) – Investment subsidy act and federal project for the improvement of the regional economical structure.

In the following discriminant analysis we show the multivariate discriminatory power of the 4 clusters (cf. Backhaus K. 2003, pp.187). Table 4.6 presents the industry branches having the greatest multivariate discriminatory power. As it is shown in table 4.6 the discriminatory power is dominated by industry branches with declining economical impact (from 1998 to 2004 in the industry branches Construction (45) and Public administration and defence (75)). Furthermore we also have several

11 high-growing industry branches (e. g. Manufacture of motor vehicles, trailers and semi-trailers (34)), which contribute to the discriminatory power.

Tab. 4.6: Multivariate discriminatory power a) No. Branches of economy (NACE) 1 45 2 75 3 02 4 35 5 52 6 90 7 80 8 70 9 51 10 64 11 34 12 91 13 29 14 01 15 61 16 85 17 40

multivariate discriminatory power 5.95 % 4.48 % 3.61 % 3.50 % 3.49 % 3.28 % 2.95 % 2.84 % 2.71 % 2.63 % 2.61 % 2.51 % 2.35 % 2.34 % 2.34 % 2.27 % 2.18 %

cum. multivariate discriminatory power 5.95 % 10.43 % 14.04 % 17.54 % 21.03 % 24.31 % 27.27 % 30.11 % 32.81 % 35.45 % 38.05 % 40.57 % 42.92 % 45.26 % 47.60 % 49.86 % 52.04 %

a) Bold face: NACE branches with a high loss respectively a high gain in importance. See also table 4.2. Source: Own calculation.

In the second part of the analysis of structural change we examine the influence on the regionally distinguishable growth structure. Here, we examine the change of dependent employment relationships in the different branches of economy w.r.t. growth pole classes (regional differentiation). The central results are summarized in table 4.7.

Tab. 4.7: Change of dependent employment relationships according to branches due to economical structure a) Growth classes 5% 10 % 25 % 50 % Germany

Average growth 0.28 0.24 0.19 0.15 0.10

Branches of economy (highest increase) 85, 74, 34, 80, 28 85, 74, 34, 28, 80 74, 85, 80, 63, 34 85, 74, 80, 63, 34 74, 85, 80, 63, 72

Branches of economy (highest decrease) 45, 75, 01, 29, 36 45, 75, 01, 36, 26 45, 75, 01, 36, 65 45, 75, 26, 36, 01 45, 75, 36, 26, 17

a) NACE-Classification Source: Own calculation.

Particularly for the narrowly defined term of strong growing regions (5% and 10% quantile), the clear increase in dependent employment relationships in Manufacture of motor vehicles, trailers and semitrailers (34) (34) as well as Manufacture of fabricated metal products, except machinery and equipment (28) become obvious. There are NACE branches (bold numbers in table 4.7), which are different from the growing branches in Germany. The regional specifics displayed in table 4.8 show an increasing share of East German cities with a sharpened classification of growth classes. Nevertheless this share is lower than the share of East German cities in the examined growth clusters. The share of district-free cities is lower than the Federal average.

12 Tab. 4.8: Regional characteristics of growth clusters Growth classes 5% 10 % 25 % 50 % Germany

Average growth 0.28 0.24 0.19 0.15 0.10

Share of district-free cities 18.2 % 13.6 % 28.2 % 21.9 % 26.4 %

Share of regions from East Germany 72.7 % 61.4 % 49.1 % 38.8 % 25.5 %


In general, there can be observed an above-average decrease of population in all growth classes. The results we have shown in the first part (see table 4.4 and 4.5) can be detected in the second part too (cf. Tab. A.1 and A.2 in the Appendix). Summarizing it can be said that there exists a certain pattern for growth regions. For sectoral change, selected economical focusses seem to play a role. There is a relative increase in the number of dependent employment relationships in certain branches of the secondary sector. This minimum seems to be necessary for the strong growth in the service sector. Based on these facts we model the increase of dependent employment relationships of the secondary sector within the neo-classical growth function. The determinants of growth are analysed in the fifth section considering their spatial effects. The distinction of regional spillover effects between corresponding regions plays an elementary role.

5. Regional production function At first, we formulate a regional production function for determinating of the growth of a region on a regional level (NUTS 3). In contrast to Eckey et al. (2007) we use a regional sector specific component.

(5.1)

Y = F ( K , L, X q )

The welfare level Y is approximated by the gross value added, weighted by inhabitants of the region.4 As an exogenous variable for the capital stock K we utilize the following Proxy. We assume that the capital stock varies regionally and sectorally. The stocks of capital on NUTS-1 level presented by the German Federal Statistical Office are distributed on the according sectors and are presented as an aggregated total amount on NUTS-3 level. As a proxy for the level of labour supply L of a region, we implement the dependent employment relationships of a region. The dependent employment relationships are, like the capital stock, weighted with the inhabitants of the concerned region as well.

4

We decided to measure welfare level – general accepted – as Gross value added per inhabitant. We did not use producitivity (Gross value added per dependent employment relationship). The correlation between the change in inhabitants and the change in the dependent employment relationships is very strong (R2 = 0.751). Furthermore, in our model we do not consider a commuter-effect separately because of its small-sized correlation between the change in inhabitants and in commuters (R2 = 0.092). Nevertheless we absorb this effect in our model due the spatial-compent.

13 In the foregoing chapter, we use further regional components Xq in the regional production function. The growth of a region depends on the individual branches of economy, which, as basic sectors of the regional economy, press ahead the growth. In order to consider this regional fact, we would like to describe the modification of the industrial sector of a region L2 in the production function. For the description of the human capital H of a region, we use the dependent employment relationships register, from which each person working in a scientifical-technical profession can be determined (ISCO-88 COM group 2 or 3). By these means, we can determine the intensity of human capital as a share of the inhabitants of the examined regions and use it in the model as an exogenous variable. A dummy for East German Nuts-3 regions, having the value one if the concerned region is situated in East Germany, zero otherwise, proved to be insignificant for the explanation of growth differences in the period of examination. The formulation of the production function with regional components is made in analogy to the CobbDouglas-production function (cf. Mankiw et al. 1992).

Q

(5.2)

Y = c ⋅ K α1 ⋅ Lα 2 ⋅ ∏ xq q ⋅ ε α

q =3

Regional differences are modelled w.r.t. the exogenous variables capital stock K, the level of dependent employment relationships L and further regional components xq. The growth rate of the welfare level Y can be approximately described as follows.

(5.3)

Yɺ Y − Y Yˆ = = 04 99 ≈ ln (Y04 / Y99 ) Y Y99

Therefore we can describe the log of the regional production function as follows.

(5.4) ln ( Y04 / Y99 ) = α 0 ⋅ c + α1 ⋅ ln ( K 03 / K 99 ) + α 2 ⋅ ln ( L04 / L99 ) + α 3 ⋅ ln ( L 204 / L 299 ) + α 2 ⋅ ln ( H 04 / H 99 ) + ε

We denote the growth rate of the welfare of a region by Y. K stands for be the modification of the capital stock, L for the modification of the total labour supply, an L2 for the modification in the secondary sector. The modification of the size of the human resources is denoted by H. The estimated function corresponds to a decomposition of growth, while the different growth variables can be examined with regard to their significant share of explanation. The variables total in dependent employment relationships and in dependent employment relationships of the secondary sector, however, show multicollinearity. The multicollinearity problem might be solved in a simple way by the following auxiliary calculation.

14 (5.5)

ln ( L 204 / L 299 ) = γ 1 ⋅ ln ( L04 / L99 ) + u L 2

In a „direct“ regression“ of the concerned parameters for L2 and L, the whole information which cannot be explained by L moves to the residual. uL 2 forms a structural change of the own region – towards a higher share of dependent employment relationships of the secondary sector. This residual is estimated instead of the original variable in the model. It could be interpreted as a pure industrialization effect.

(5.6) ln ( Y04 / Y99 ) = α 0 ⋅ c + α1 ⋅ ln ( K 03 / K99 ) + α 2 ⋅ ln ( L04 / L99 ) + α 3 ⋅ ( u L 2,04 / uL 2,99 ) + α 2 ⋅ ln ( H 04 / H 99 ) + ε

As another important aspect, the examination and consideration of spatial effects has to be pointed out. We use the weighting matrix W to describe spatial correlations and spatial filtering. The weighting matrix W that has been used models the distance in minutes between all 439 NUTS-3 regions.5 The explanatory variables, weighted with W, determine as “average” level of the exogenous variables of the corresponding regions the own level of welfare. We assume that nearby regions have, due to the modelling, a higher weight and therefore a greater influence on the own level of welfare. This assumption of regional linkages of economy is taken into account by integrating the relation modelled in W into the estimation of a spatial cross regressive model (SCR model) as follows (cf. Eckey et al. 2005, p. 6).

(5.7)

Yˆ = α 0 c + α1 Kˆ + α 2 Lˆ + α 3uˆ L 2 + α 4 Hˆ + β1WKˆ + β 2WLˆ + β3Wuˆ L 2 + β 4WHˆ + ε

The model from equation 5.6 has been estimated accordingly and has been examined for spatial effects. Here as well, the proxy for the human capital turned out not to be significant. However we leave this variable in the model because of theoretical considerations. The LM-lag-test confirms a highly significant spatial context, so that its modelling is not only possible but necessary (cf. Anselin 2001, p. 324). Insignificant spatial effects of the examined exogenous variables have been removed by a backward procedure. The results of the regression are presented in table 5.2.

5

Own calculation.

15 Tab.5.2: Regression growth determinants Endogenous Variable: ∆ gross value added per inhabitant 2004-1999 Exogenous Variables c constant

Kˆ Lˆ uˆL 2 Hˆ WuˆL 2 WHˆ

Coefficient 0.036

t-value 3.246 ***

capital stock

0.637

10.654 ***

labour labour supply

0.170

2.244 **

0.043

2.557 **

0.009

0.169

0.197

1.760 *

human capital spatial labour supply effect spatial human capital effect Signif. codes: Adjusted R-squared:

0.378 0.010 ***, 0.050 **, 0.100 * 0.295 F-statistic p-value:

2.044 ** 0.0000

Source: own calculation with R.

The table shows that the change in capital stock is responsible to a high degree for the growth of a region. The change in labour supply has significantly positive effects on the growth rate of the level of welfare as well. Besides, the change in the degree of industrialization has also significantly positive consequences. With the assumption of constant productivity, a growth in the secondary share of dependent employment relationships results in a higher total growth. The spatial effects turn out to be significant for the exogenous examined variables degree of industrialization as well as human capital and positive in their effective direction. This means, that the growth of the significant variables in the corresponding regions (mainly nearby regions) influences the own growth positively. We have shown an above-average growth in regions with a high growth in the degree of industrialization, that means with a probable growth sector. At the same time we could prove a positive spatial effect for exactly this sector. By this means it is possible to compensate, to a certain extent, the loss of direct government aid of region P by the increased growth, in combination with the connected increase in the effect of spillover effect of region W to P in the case of a sectoral and regional focussed government aid.

5. Summary and Outlook From the point of view of government aid that is both regionaly as well as sectoraly focussed within Germany, the question arised whether the contradiction between the legally fixed aim of equalisation and the German aim of growth might be partly reduced by modified government aid. In this paper we could show that the growth of regions is intensely determined by a sectoral change. This change is based on the increased importance of the service sector. Besides, we could also show within the analysis of economical structure the dependence of the tertiary sector from an (increasing) secondary sector. This important result leads us to an adequate modelling within the regional production function. The significant proof of a positive effect of the increased importance of the

16 secondary sector as well as its positive spillover effect in neighbouring periphery regions leads to the conclusion that a growth pole requires an economic structure with corresponding spillover effects. From the initial point of rare financial resources and reduced government aid has raised the question whether it is necessary to change the regional policy in Germany fundamentally. Do we have to focus government aid to high-growing industry branches in growth poles? This kind of policy would lead to a welfare-loss in the periphery (slow-growing regions). However, under the conditions of spillover effects from the growth pole, the periphery will not loose as much as in the case without these effects. We showed that there are patterns in the structural change of high-growing regions and identified some branches in the service sector. We also indicated that high-growing regions are distinguished by a high share in the secondary sector. Using a Spatial-Cross-Regressive-Model (SCR Model) we determined that the secondary sector has a great positive influence on the regional economic growth and, in addition to that, this sector initiates high spillover effects to neighbouring regions. Therewith peripheral regions can benefit from government aid which is focussed on high-growing regions. Questions for ongoing research in this context are: •

What is the reason that several industry branches have a strong importance to the regional growth? Which common attributes do high-growing industry branches in the secondary sector have? Does the level of networking play an important role? How can we measure the level of networking between several firms?

•

Which is the optimum level of government aid in a region? How could it be determined?

•

Under which circumstances can we notice a total welfare effect to peripheral regions due to the focussing of government aid on growth-poles in contrast to the equal treatment of all regions?

References ANSELIN L. (2001) Spatial Econometrics, in: BALTAGI B.H. (ed.) A Companion to theoretical Econometrics, Malden, pp. 301-300. BACKHAUS K., ERICHSON B., PLINKE W., WEIBER R. (2003) Multivariate Analysemethoden, 10th edition, Berlin. (in German) DOHNANYI K.VON, MOST E. (2004) Kurskorrektur des Aufbau Ost. Bericht des Gesprächskreises Ost der Bundesregierung, Hamburg/Berlin. (in German) ECKEY H.-F., KOSFELD R., TÜRCK M. (2005) Regionale Produktionsfunktionen mit Spillover-Effekten für Deutschland – Empirischer Befund und wirtschaftspolitische Implikationen, in: Schmollers Jahrbuch 125, pp. 239-267. (in German) ECKEY H.-F., KOSFELD R., TÜRCK M. (2007) Regionale Entwicklung mit und ohne räumliche Spillover-Effekte, in: Jahrbuch für Regionalwissenschaft 27, pp. 23-42. (in German)

17 HEIMPOLD, G. (2006) Neue Orientierungen für die deutsche Raumentwicklungspolitik, in: Wirtschaft im Wandel 2, pp. 60-65. KRUGMAN P. (1991) Increasing Returns and Economic Geography, in: Journal of Political Economy 99/ 3, pp. 483-499. KUBIS, A. (2005) Sectoral Movement as an Incentive for Interregional Migration, in: Discussion Papers in Economics 42, Martin Luther University of Halle (Saale). MANKIW N.G., ROMER D.N., WEIL D.N. (1992) A contribution to the empirics of economic growth, in: The Quarterly Journal of Economics 107, pp. 407-437. MARSHALL A. (1952) Principles of Economics, 8th Edition, New York. MKRO (Ministerkonferenz für Raumordnung) (1995) Raumordnungspolitischer Handlungsrahmen, Beschluß der MKRO vom 8.3.1995, in: Bundesministerium für Raumordnung, Bauwesen und Städtebau (ed.): Raumordnung in Deutschland, 2th edition, Bonn, pp. 75-96. (in German) MW BRANDENBURG, MINISTERIUMS FÜR WIRTSCHAFT DES LANDES BRANDENBURG (2006) Richtlinie des Ministeriums für Wirtschaft zur Förderung der gewerblichen Wirtschaft im Rahmen der Gemeinschaftsaufgabe "Verbesserung der regionalen Wirtschaftsstruktur " - GA - (GA-G), Bekanntmachung des Ministeriums für Wirtschaft des Landes Brandenburg vom 7. Dezember 2006, in: http://www.ilb.de/rd/data/ga_gewerbe_2007_richtlinie.pdf. (Download 28.02.2007, in German) PORTER M.E. (1990) The Competitive Advantage of Nations, London and Basingstoke. RAGNITZ J., KREIS C., KUBIS, A. (2006) Die formale und effektive Inzidenz von Bundesmitteln. Gutachten für das Bundesamt für Bauwesen und Raumordnung (BBR). (in German) RAGNITZ J., LEHMANN H. (2005) Wirkungsanalyse der Wirtschaftsförderung in Ostdeutschland, in: Engel D. (ed.): Mittelstandsfinanzierung, Basel II und die Wirkung öffentlicher sowie privater Kapitalhilfen, Schriftenreihe „Veröffentlichungen des Round Table Mittelstand“, Band 5. (in German) FEDERAL STATISTICAL OFFICE GERMANY (2002) Klassifikation der Wirtschaftszweige, Ausgabe 2003 (WZ 2003), Wiesbaden. (in German)

18 Appendix

Tab. A.1: Population in the growth classes Growth classes 5% 10 % 25 % 50 % Germany

Average growth 0.28 0.24 0.19 0.15 0.10

Regions with a decrease of inhabitants 68.2% 59.1% 57.3% 50.7% 42.1%

Average change of inhabitants - 1 003 - 309 - 1 311 - 155 1 076


Tab. A.2: Dependent employment relationship in the growth clusters Growth classes

Average growth

5% 10 % 25 % 50 % Germany

0.28 0.24 0.19 0.15 0.10

Regions with a decrease of the rate of dependent employment relationship 68.2 % 70.5 % 69.1 % 71.7 % 75.2 %

Average decrease of the rate of dependent employment relationship 1.2 %-point 1.2 %-point 1.3 %-point 1.3 %-point 1.2 %-point


Tab. A.3: Regional cluster allocation and growth classes by Quantiles

1001 1002 1003 1004 1051 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 2000 3101 3102 3103 3151 3152 3153 3154 3155 3156 3157 3158

REGION

Flensburg Kiel Lübeck Neumünster Dithmarschen Herzogt.Lauenburg Nordfriesland Ostholstein Pinneberg Plön Rendsb.-Eckernförde Schleswig-Flensburg Segeberg Steinburg Stormarn Hamburg Braunschweig Salzgitter Wolfsburg, Gifhorn, Landkreis Göttingen, Landkreis Goslar, Landkreis Helmstedt, Landkreis Northeim, Landkreis Osterode am Harz Peine Wolfenbüttel

1 2 1 1 2 2 2 2 2 2 2 2 1 2 2 1 3 2 1 1 2 4 2 2 4 1 2

Quantiles in % AGS

50

25

10

5

0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3241 3251 3252 3254 3255 3256 3257 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3401 3402 3403 3404 3405 3451 3452 3453 3454

REGION

Region Hannover Diepholz Hameln-Pyrmont Hildesheim Holzminden Nienburg (Weser) Schaumburg Celle Cuxhaven Harburg Lüchow-Dannenberg Lüneburg Osterholz Rotenburg (Wümme) Soltau-Fallingbostel Stade Uelzen Verden Delmenhorst Emden Oldenburg Osnabrück Wilhelmshaven Ammerland Aurich Cloppenburg Emsland

CLUSTER

AGSa)

CLUSTER

Quantiles in %

2 2 2 2 2 2 2 2 2 1 3 2 2 2 2 2 1 2 1 4 2 1 2 2 2 2 1

50

25

10

5

0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19

3455 3456 3457 3458 3459 3460 3461 3462 4011 4012 5111 5112 5113 5114 5116 5117 5119 5120 5122 5124 5154 5158 5162 5166 5170 5313 5314 5315 5316 5354 5358 5362 5366 5370 5374 5378 5382 5512 5513 5515 5554 5558 5562 5566 5570 5711 5754 5758 5762 5766 5770 5774 5911 5913 5914 5916 5954 5958 5962 5966 5970

REGION

Friesland Grafschaft Bentheim Leer Oldenburg LK Osnabrück Vechta Wesermarsch Wittmund Bremen Bremerhaven Düsseldorf Duisburg Essen Krefeld Mönchengladbach Mülheim an der Ruhr Oberhausen Remscheid Solingen Wuppertal Kleve Mettmann Rhein-Kreis Neuss Viersen Wesel Aachen Bonn Köln Leverkusen Aachen Düren Rhein-Erftkreis Euskirchen Heinsberg Oberbergischer Kreis Rh.-Bergischer Kreis Rhein-Sieg-Kreis Bottrop Gelsenkirchen Münster Borken Coesfeld Recklinghausen Steinfurt Warendorf Bielefeld Gütersloh Herford Höxter Lippe Minden-Lübbecke Paderborn Bochum Dortmund Hagen Herne Ennepe-Ruhr-Kreis Hochsauerlandkreis Märkischer Kreis Olpe Siegen-Wittgenstein

1 2 1 1 2 2 1 1 1 2 2 1 2 1 2 2 1 2 2 2 2 2 1 2 2 1 1 1 2 1 2 2 2 2 2 2 1 2 4 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2

Quantiles in % AGS

50

25

10

5

0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0

0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5915 5974 5978 6411 6412 6413 6414 6431 6432 6433 6434 6435 6436 6437 6438 6439 6440 6531 6532 6533 6534 6535 6611 6631 6632 6633 6634 6635 6636 7111 7131 7132 7133 7134 7135 7137 7138 7140 7141 7143 7211 7231 7232 7233 7235 7311 7312 7313 7314 7315 7316 7317 7318 7319 7320 7331 7332 7333 7334 7335 7336

REGION

Hamm Soest Unna Darmstadt Frankfurt am Main Offenbach Wiesbaden Bergstraße Darmstadt-Dieburg Groß-Gerau Hochtaunuskreis Main-Kinzig-Kreis Main-Taunus-Kreis Odenwaldkreis Offenbach Rheing.-Taunus-Kreis Wetteraukreis Gießen Lahn-Dill-Kreis Limburg-Weilburg Marburg-Biedenkopf Vogelsbergkreis Kassel Fulda Hersfeld-Rotenburg Kassel Schwalm-Eder-Kreis Waldeck-Frankenberg Werra-Meißner-Kreis Koblenz Ahrweiler Altenkirchen Bad Kreuznach Birkenfeld Cochem-Zell Mayen-Koblenz Neuwied Rhein-Hunsrück-Kreis Rhein-Lahn-Kreis Westerwaldkreis Trier Bernkastel-Wittlich Bitburg-Prüm Daun Trier-Saarburg Frankenthal Kaiserslautern Landau i.d.Pfalz Ludwigshafen Mainz Neustadt a.d.Weinstr. Pirmasens Speyer Worms Zweibrücken Alzey-Worms Bad Dürkheim Donnersbergkreis Germersheim Kaiserslautern Kusel

CLUSTER

AGS

CLUSTER

Quantiles in %

2 2 2 1 1 3 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 2 2 4 2 2 2 1 1 1 1 2 1 2 1 1 2 2 4 1 1

50

25

10

5

0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

20

7337 7338 7339 7340 8111 8115 8116 8117 8118 8119 8121 8125 8126 8127 8128 8135 8136 8211 8212 8215 8216 8221 8222 8225 8226 8231 8235 8236 8237 8311 8315 8316 8317 8325 8326 8327 8335 8336 8337 8415 8416 8417 8421 8425 8426 8435 8436 8437 9161 9162 9163 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180

REGION

Südliche Weinstraße Ludwigshafen Mainz-Bingen Südwestpfalz Stuttgart Böblingen Esslingen Göppingen Ludwigsburg Rems-Murr-Kreis Heilbronn Heilbronn Hohenlohekreis Schwäbisch Hall Main-Tauber-Kreis Heidenheim Ostalbkreis Baden-Baden Karlsruhe Karlsruhe Rastatt Heidelberg Mannheim Neckar-Odenw.-Kr. Rhein-Neckar-Kreis Pforzheim Calw Enzkreis Freudenstadt Freiburg i.Breisgau Br.-Hochschwarzw. Emmendingen Ortenaukreis Rottweil Schwarzw.Baar-Kr. Tuttlingen Konstanz Lörrach Waldshut Reutlingen Tübingen Zollernalbkreis Ulm Alb-Donau-Kreis Biberach Bodenseekreis Ravensburg Sigmaringen Ingolstadt München Rosenheim Altötting Berchtesg. Land Bad Tölz-Wolfratshs. Dachau Ebersberg Eichstätt Erding Freising Fürstenfeldbruck Garmisch-Partenk.

2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 4 2 2 2 2 4 2 2 1 2 2

Quantiles in % AGS

50

25

10

5

1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 1 1 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

9181 9182 9183 9184 9185 9186 9187 9188 9189 9190 9261 9262 9263 9271 9272 9273 9274 9275 9276 9277 9278 9279 9361 9362 9363 9371 9372 9373 9374 9375 9376 9377 9461 9462 9463 9464 9471 9472 9473 9474 9475 9476 9477 9478 9479 9561 9562 9563 9564 9565 9571 9572 9573 9574 9575 9576 9577 9661 9662 9663 9671

REGION

Landsberg a.Lech Miesbach Mühldorf a.Inn München Neuburg-Schrobenhs. Pfaffenhofen a.d.Ilm Rosenheim Starnberg Traunstein Weilheim-Schongau Landshut Passau Straubing Deggendorf Freyung-Grafenau Kelheim Landshut Passau Regen Rottal-Inn Straubing-Bogen Dingolfing-Landau Amberg Regensburg Weiden i.d.OPf. Amberg-Sulzbach Cham Neumarkt i.d.OPf. Neust. a.d.Waldnaab Regensburg Schwandorf Tirschenreuth Bamberg Bayreuth Coburg Hof Bamberg Bayreuth Coburg Forchheim Hof Kronach Kulmbach Lichtenfels Wunsiedel i.Fichtelg. Ansbach Erlangen Fürth Nürnberg Schwabach Ansbach Erlangen-Höchstadt Fürth Nürnberger Land Nstdt./Aisch-Bad W. Roth Weißenb.-Gunzenhs. Aschaffenburg Schweinfurt Würzburg Aschaffenburg

CLUSTER

AGS

CLUSTER

Quantiles in %

2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 3 2 3 1 2 1 2 3 2 2 3 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2

50

25

10

5

0 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1

0 0 1 1 0 1 1 0 0 1 1 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 0

0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

21

9672 9673 9674 9675 9676 9677 9678 9679 9761 9762 9763 9764 9771 9772 9773 9774 9775 9776 9777 9778 9779 9780 10041 10042 10043 10044 10045 10046 11000 12051 12052 12053 12054 12060 12061 12062 12063 12064 12065 12066 12067 12068 12069 12070 12071 12072 12073 13001 13002 13003 13004 13005 13006 13051 13052 13053 13054 13055 13056 13057 13058

REGION

Bad Kissingen Rhön-Grabfeld Haßberge Kitzingen Miltenberg Main-Spessart Schweinfurt Würzburg Augsburg Kaufbeuren Kempten Memmingen Aichach-Friedberg Augsburg Dillingen a.d.Donau Günzburg Neu-Ulm Lindau Ostallgäu Unterallgäu Donau-Ries Oberallgäu Stadtv. Saarbrücken Merzig-Wadern Neunkirchen Saarlouis Saar-Pfalz-Kreis Sankt Wendel Berlin Brandenb. a.d.Havel Cottbus Frankfurt (Oder) Potsdam Barnim Dahme-Spreewald Elbe-Elster Havelland Märkisch-Oderland Oberhavel Oberspr.-Lausitz Oder-Spree Ostprignitz-Ruppin Potsdam-Mittelmark Prignitz Spree-Neiße Teltow-Fläming Uckermark Greifswald Neubrandenburg Rostock Schwerin Stralsund Wismar Bad Doberan Demmin Güstrow Ludwigslust Mecklenburg-Strelitz Müritz Nordvorpommern Nordwestmecklenb.

2 2 2 2 1 2 2 3 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 3 4 4 4 3 2 2 2 2 4 4 2 3 3 3 1 3 3 3 4 2 1 2 4 3 3 3 3 3 3 3 3

Quantiles in % AGS

50

25

10

5

0 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 0

0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0

0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0

13059 13060 13061 13062 14161 14166 14167 14171 14173 14177 14178 14181 14182 14188 14191 14193 14262 14263 14264 14272 14280 14284 14285 14286 14287 14290 14292 14365 14374 14375 14379 14383 14389 15101 15151 15153 15154 15159 15171 15202 15256 15260 15261 15265 15266 15268 15303 15352 15355 15357 15358 15362 15363 15364 15367 15369 15370 16051 16052 16053 16054

REGION

Ostvorpommern Parchim Rügen Uecker-Randow Chemnitz Plauen Zwickau Annaberg Chemnitzer Land Freiberg Vogtlandkreis Mittl. Erzgebirgskreis Mittweida Stollberg Aue-Schwarzenberg Zwickauer Land Dresden Görlitz Hoyerswerda Bautzen Meißen Niederschles. Oberl.kr. Riesa-Großenhain Löbau-Zittau Sächsische Schweiz Weißeritzkreis Kamenz Leipzig Delitzsch Döbeln Leipziger Land Muldentalkreis Torgau-Oschatz Dessau Anhalt-Zerbst Bernburg Bitterfeld Köthen Wittenberg Halle (Saale) Burgenlandkreis Mansfelder Land Merseburg-Querfurt Saalkreis Sangerhausen Weißenfels Magdeburg Aschersleben-Staßfurt Bördekreis Halberstadt Jerichower Land Ohre-Kreis Stendal Quedlinburg Schönebeck Wernigerode Altmarkkr. Salzwedel Erfurt Gera Jena Suhl

CLUSTER

AGS

CLUSTER

Quantiles in %

3 3 4 4 4 3 1 4 4 3 3 4 2 3 3 2 3 4 4 3 3 2 3 2 2 4 2 4 4 3 1 3 3 4 4 2 4 3 3 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 4

50

25

10

5

1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1

0 0 1 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 1 0 0 0 1 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 0 1 0

0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0

22

16055 16056 16061 16062 16063 16064 16065 16066 16067 16068

REGION

Weimar Eisenach Eichsfeld Nordhausen Wartburgkreis Unstrut-Hainich-Kr. Kyffhäuserkreis Schmalkalden-Mein. Gotha Sömmerda

4 3 3 3 3 2 3 4 2 3

AGS 50

25

10

5

0 1 1 0 1 0 0 1 1 1

0 1 1 0 0 0 0 1 0 1

0 1 1 0 0 0 0 1 0 1

0 1 0 0 0 0 0 0 0 1

a) AGS: Allgemeiner Gemeindeschlüssel, German Regional Code Source: Own calculation.

Quantiles in %

16069 16070 16071 16072 16073 16074 16075 16076 16077

REGION

Hildburghausen Ilm-Kreis Weimarer Land Sonneberg Saalfeld-Rudolstadt Saale-Holzland-Kreis Saale-Orla-Kreis Greiz Altenburger Land

CLUSTER

AGS

CLUSTER

Quantiles in %

3 3 3 1 3 3 3 4 3

50

25

10

5

1 1 1 1 1 1 1 1 1

1 1 1 1 1 0 1 0 1

1 1 1 1 0 0 1 0 0

1 1 0 0 0 0 0 0 0