Related variety, unrelated variety and regional growth: Development patterns and role of absorptive capacity. Sandra Kublina*

Transcription

1 Related variety, unrelated variety and regional growth: Development patterns and role of absorptive capacity Sandra Kublina* This version: May 10, 2015 * Friedrich Schiller University Jena, Germany Abstract This paper investigates long term patterns of diversification in terms of related and unrelated variety in West German regions and tests their effect on regional growth. Effect on growth is estimated following conventional as well as extended approach to test if diverse variety structures perform differently under different levels of regional absorptive capacity. Among several structural measures related variety is the only measure that reveals pronounced changing trend in terms of constant increase in value and convergence to the mean level variety pointing that regions tend to diversify in related fields. Results are in contrast to the conventional hypothesis that unrelated variety is not likely to generate employment growth. Analysis reveals that West German regions benefit from both types of varieties, giving new insights that positive effect of unrelated variety on growth is even more pronounced under higher levels of regional absorptive capacity. Keywords: Related variety, unrelated variety, knowledge spillovers, regional absorptive capacity, regional growth JEL classification: R11, R12, D62 Address for Correspondence: Friedrich Schiller University Jena School of Economics and Business Administration Carl-Zeiss-Str. 3 D Germany sandra.kublina@uni-jena.de 1

2 1. Introduction The industrial dynamics and economic development literature provide strong theoretical and empirical supports for both, diversified as well as specialized, industry structure to have positive effects on growth (Gleaser 1992, Henriksen 1995, Combes 2000). Empirical results, however, are far from conclusive in this regard (De Groot et al. 2009). Despite contradictory evidence, stylized facts seem to favor diversification rather than specialization for economic development (Duranton and Puga 2000, Rodrik 2008). A concept by Frenken et al. (2007) proposes to disentangle diversification into the related and unrelated by claiming that it is not diversification as such, but diversification of related industries that enhances knowledge spillovers and has positive effect on employment growth thus highlighting spillovers among sectors that are cognitively proximate. While the impact of related and unrelated variety on regional growth has been widely empirically examined in recent years, some open questions about the concept and its empirical application can still be identified. This study investigates long term development patterns of regional industry diversity, in particular related and unrelated variety, for West German regions over a period of thirty years, from 1980 up to The paper deals with following main research questions: How stable are related and unrelated variety compared to other general diversity patterns over the long time period? To what extent is there a relationship between related and unrelated variety. Are these concepts mutually exclusive? In how far can both types of variety co-exist? Do any of these variety structures have an effect on regional growth in West Germany? Does this effect vary once seminal application proposed by Frenken et al. (2007) is supplemented by accounting for regional absorptive capacity that might moderate effects from knowledge spillovers? There are many studies dealing with regional industry structure and industry relatedness in particular thus pointing to the question of the novelty of this study. While many analytical and empirical studies reveal high persistence in regional industry trends over time, most of them look at single indicator only and to the best knowledge of the author, there is no long term analysis of related and unrelated variety trends. The first question of this study, after providing short overview of several structural measures, analyzes long term trends of related variety (hereinafter-rv) and unrelated variety (hereinafter-uv) as compared to other industry measures and discusses possible reasons for increasing RV over time. The motivation for the second research question comes from the fact that majority of empirical studies at best exploit RV and UV among other measures to explain regional growth. Only few studies note their relation in terms of correlation. Are these sectoral structures related and can be co-existing? Is their interrelation persistent over time? Availability of long term data allows such investigation. While first two research questions are of analytical nature, third question is an empirical application of the RV and UV concept to West German regions. There have been many applications of the variety concept to European economies 1, however to the best knowledge of the author there is only one study by Brachert et al. (2011) that 1 Frenken et al. (2007) for Netherlands; Boschma and Iammarino (2009) and Mameli at al. (2012) for Italy; Bishop and Gripaios (2010) for the UK; Hartog et al. (2012) for Finland; Boschma et al. (2011) for Spain. 2

3 explores impact of RV and UV on regional growth in Germany, nevertheless in a modified manner (based on the categorization of occupations) of that used by Frenken et al. (2007) and therefore results are not directly comparable. Novelty of the empirical analysis is an attempt to incorporate the concept of absorptive capacity (Cohen and Levinthal 1990) by claiming that depending on the R&D intensity of regions their ability to benefit from related and unrelated industry mix can differ thus resulting in distinctive effects on regional growth. The underlying idea is that not all regions might be equally benefitting from diverse industry composition the higher the regional knowledge base in terms of R&D, the higher is the expected ability to absorb knowledge from other related and unrelated sectors. The paper is organized as follows. Section 2 provides an overview of the main theoretical concepts related to industrial composition and its linkage to regional growth as well as summarizes hypothesis for the empirical part. Particular attention is devoted to the emergence and justification of the concept of RV and UV. Data and spatial framework is outlined in the Section 3. Section 4 provides short overview on the long term development patterns of industry structure in West Germany. Section 5 deals with the first two research questions regarding development patterns of RV and UV and their relatedness over time. Section 6 is devoted to empirical analysis starting with the replication of the study by Frenken et al. (2007) with a consequent modification to account for regional absorptive capacity. Section 7 summarizes some critiques of the RV and UV concept and discusses limitations of the study. Section 8 concludes providing some policy implications and indicating some future research directions on this topic. 2. Theoretical background and hypothesis: Concepts of industry structure and its link to regional growth The link between industry composition and economic development is gaining increasing interest from scientific society and attention from policy makers. One of the main questions in regional growth and spatial externalities literature is whether firms learn from other local firms in same industry or from firms in other industries (Boschma et al. 2011). Namely, is it a rather specialized or diversified industrial composition that could foster knowledge spillovers and enhance growth? Historically, most pronounced debates on industry composition and its effect on growth can be traced back to works by Marshall (1920) and Jacobs (1969) that have evolved into two dominant theoretical concepts, feasibility of which is still debated and empirically tested. Benefitting from firms in same or similar sectors is known as localization externalities and theoretical conceptualization dates back to ideas by Marshall on industrial districts (Boschma 2010). Potential benefits in such industrial structure are expected to come from specialized labor, specialized suppliers and large markets. Therefore positive externalities in such industrial setting are arising from intra-sectoral knowledge spillovers. Due to the conceptualization by Marshall (1920) and later formalization by Arrow (1962) and Romer (1986) these types of positive externalities which are associated with a specialized sectoral composition are commonly noted as Marshall-Arrow-Romer or MAR externalities. Theoretical conceptualization for benefitting from firms in other sectors dates back to work of Jacobs (1969). Such type of externalities are associated with diversified 3

4 industry structure what provides opportunities to interact and recombine practices and thus could foster generation of new ideas and innovations (Boschma et al. 2011). Positive externalities in such industrial setting are assumed to be arising from intersectoral knowledge spillovers. These types of positive externalities that are mainly associated with a diversified sectoral composition are commonly noted as Jacobs externalities. Empirical assessment of MAR externalities and Jacobs externalities activated in 1990s by emergence of several seminal studies. Glaeser et al. (1992) by analyzing employment growth in the US cities between 1956 and 1987 found that industries grow faster in the diversified and highly competitive industrial environment thus giving support to Jacobs externalities. Specialized industrial setting on the other hand dampened employment growth thus neglecting the role of MAR externalities. In contrast, Henderson et al. (1995) by analyzing growth in the US metropolitan areas between 1970 and 1987 found that specialized industrial environment is conducive to growth thus supporting MAR type externalities. Exceptions were new high-tec industries where Jacobs externalities were dominating. Combes (2000) in his analysis for France for time period 1984 to 1993 found distinct effects for manufacturing and service sectors diverse industry composition is beneficial for service sectors whereas in manufacturing diversity reduces growth. Feldman and Audretsch (1999) are among first who empirically link industrial structure with innovative outcome. Using data on the US product innovations in 1982 they found negative effect of specialization on innovative output however diversity of industries that share common scientific base has a positive effect. Altogether findings of these empirical applications and results of many of the subsequent replications (Mameli et al. 2008, Blien and Suedekum 2005, for an overview see De Groot et al. 2009) point to the inconclusive results regarding industrial composition and growth. Part of such inconclusive results can be attributed to the diverse methodological approaches and various levels of spatial and sector aggregations that have been applied in these studies (Beaudry and Schiffauerova 2009). Another part to the fact that taxonomy of sectoral structure into diversified or specialized might be an oversimplification of a phenomenon, especially one of diversified structure (i.e. Jacobs externalities) that could include many distinct effects within it. One of the first and prominent studies that theoretically and empirically pointed to the oversimplification of the notion of Jacobs externalities is a study by Frenken et al. (2007). Frenken et al. (2007) in their study emphasized the need to disentangle diversification based on the degree of relatedness, namely, to split it into the related and unrelated. They claimed that knowledge spillovers within a region primarily take place among related sectors whereas unrelated sectoral composition likewise in portfolio theory helps to protect region from external and sector-specific shocks. According to Frenken et al. (2007) different variety 2 types can have distinct economic effects on regional performance and should therefore be empirically separated from each other. They argued that UV due to portfolio effect should reduce regional unemployment 2 Terms diversity and variety are used interchangeably in this study, though variety is more attributed to the concept that distinguishes between related and unrelated variety. Frenken et al. (2004) point to their preference for term variety since diversity according to their view is more attributable to the biology. Similarly sectors and industry is used interchangeably in this study. 4

5 growth whereas RV via knowledge spillovers and generation of innovations should foster employment growth. Empirical application of the RV and UV concept to the Dutch regions for the time period 1996 to 2002 confirmed these theoretical expectations. Conceptualization of diversity by Frenken et al. (2007) and evidence for the Dutch regions gave rise to enormous empirical investigations afterwards (Boschma and Iammarino 2009, Boschma et al. 2011, Bishop and Gripaios 2010, Hartog et al and others). However, again, with no fully conclusive findings. Reasons for such results were partly attributed to the empirical ability to capture RV and UV in a way it was done by Frenken et al. (2007) who followed standard industry classification. Critiques of the empirical application of the concept (see more detailed overview in Section 7) caused new methodological approaches to emerge that could possibly better capture the level of sector relatedness within a region (e.g. study of Neffke et al. 2011). Conceptual summary of the diverse theoretical concepts and their relation to growth is provided in Table 1 (own illustration, adapted from Szakalne Kano, 2014). Important to note that this summary is used as a rough conceptual guideline for this study because many linkages are still subject of an ongoing research and debate. Table 1: Conceptual theoretical framework for industry structure and its relation to growth Type of externalities (Conceptualized by Marshall 1920, Jacobs 1969) Agglomeration externalities (Conceptualized by Hoover, 1937) Sources and ways of knowledge transfer Possible growth drivers Specialized industry structure MAR externalities Localization externalities Intra-industry learning Diversified industry structure Jacobs externalities Urbanization externalities Inter-industry learning RV UV Incremental innovation Incremental innovation Radical innovation Source: Own illustration, adapted from Szakalne Kano (2014) 5

6 Hypothesis Conceptualization of RV and UV and its underlying mechanisms have led Frenken et al. (2007) to formulate few dominant hypotheses regarding the effects of RV and UV on regional growth what has been tested in many subsequent studies and will serve also for the empirical part of this study. A typical expectation regarding diversified but related industry composition is that such structure is conducive for innovation emergence thus creating positive effects on employment growth (Frenken et al. 2007) what leads to following hypothesis associated with RV: H1: Related variety is positively related to regional employment growth. In contrast, UV is less likely to generate spillover effects and therefore is not expected to be directly linked to employment effects. However typical assumption behind unrelated industry structure is that it provides portfolio type effect hence protecting regional economy from external sector-specific shocks. Therefore unrelated industry structure is expected to reduce regional unemployment growth (Frenken et al. 2007) what leads to the following hypothesis associated with UV: H2: Unrelated variety is negatively related to regional unemployment growth. Apart from conventional expectations formulated in the H1 and H2, this study incorporates the concept of absorptive capacity (Cohen and Levinthal, 1990) into the mechanism of localized knowledge spillovers by arguing that the existing knowledge stock of a region can reinforce abilities of its economic agents to absorb knowledge flows from related as well as unrelated sectors. Absorptive Capacity (hereinafter-ac) concerns two aspects that are at the core of RV and UV studies knowledge spillovers and ability to use external knowledge. According to Cohen and Levinthal (1990) AC is the ability to recognize the value of new information, assimilate it, and apply it to commercial ends. Cohen and Levinthal (1990) argue that prior knowledge gives the ability to acquire new information and since most innovations result from borrowing from others, firms that have their R&D are better able to use external information. In the AC concept Cohen and Levinthal (1990) emphasize importance of related knowledge to assimilate and use new knowledge ( learning performance is greatest when the object of learning is related to what is already known ) what could point to the higher importance of existing knowledge to benefit from related sectors. At the same time, they also emphasize the importance of diversity ( diverse knowledge structures in the same mind provoke the sort of learning and problem solving that yields innovation ; interactions among diverse structures should lead to more novel linkages and associations ) what, on the other hand, could point to the importance of existing knowledge to benefit from unrelated sectors. These notions complicate formulation of clear expectation either higher levels of AC could reinforce knowledge flows among related or unrelated sectors. It might be that mechanism beyond different types of varieties acts differently but currently we miss theoretical argumentations to formulate such expectations. Therefore an integration of the AC concept into RV and UV concept leads to the following additional hypothesis for this study: H3: Higher absorptive capacity of a region facilitates effects of knowledge spillovers among related and unrelated sectors on regional employment growth. In this study AC is applied in a regional context. Notion of AC originated from the firm level however has been applied at different levels and although scarcely but literature 6

7 talks about region s AC (see Miguélez and Moreno 2013, Mukherji and Silberman 2013). 3. Data description The spatial framework of the analysis is 71 planning region of West Germany. Planning regions are functional spatial units that consist of at least one core city and the surrounding area and are comparable to the labor market areas in the United States. Choice of planning regions over districts is justified by the fact that various effects (e.g., knowledge spillovers what are of the particular importance in this study) might be relevant for larger units of observation than districts and therefore could decrease potential presence of spatial autocorrelation in the empirical estimations. Furthermore, labor market regions are considered to be the most appropriate spatial unit of analysis for agglomeration research (Frenken et al. 2004). The analysis is restricted to West Germany because many empirical studies indicate that the East German economy in the 1990s was a special case with very specific conditions that cannot be directly compared to those of West Germany (see Fritsch 2004, Brixy and Grotz 2004). Besides, long term data (before 1990s) is not available for East Germany. Data for RV and UV development trend analysis that are stated in the first two research questions covers 1980 to 2010, however empirical application for the third research question covers years from 1999 to Data on industry composition is obtained from the German Social Insurance Statistics. This dataset contains every establishment in Germany that employs at least one person obliged to make social insurance contributions (Spengler 2008). Each establishment can be assigned to a 3 digit level industry class over the whole time period of the analysis. All industry related measures account for changes in the industry classification over time (for details see Eberle et al. 2011). Public, agriculture and mining industries are excluded from the analysis. Data on qualification of workforce and R&D employment are also obtained from the Social Insurance Statistics; other information is from the Statistical Offices and other sources. 4. Development patterns of overall industry structure in West Germany This section provides short overview of the long term development trends of industry structure in West German regions to give an impression of the levels of structural change before turning to the in-depth investigation of the RV and UV over time. Literature provides wide variety of indices to capture industry structure. One should be careful in choosing the measures as they might have different meanings and empirical results can be highly dependent on which measure is employed (for the analysis of such diverse effects for specialization measures see Palan 2010). In a broad sense we can talk about absolute and relative measures for both diversification and specialization. They differ with regard to the benchmark that is used. For absolute measures reference level is an equal distribution of employment (or any other measure applied, e.g., exports) across all industries whereas for relative measures average economic structure of the reference level, e.g., country under study. Hence relative measures reveal of how dissimilar the sectoral composition of a region is compared to the reference level. Another important aspect is the choice of industry aggregation what can deliver diverse empirical evidence (see Mameli et al for analysis). In this 7

8 study overall industry structure is measured based on two digit industry classification to allow for comparison with RV and UV what measure entropy within and across two digit industries..... Figure 1: Development of diversity (upper graphs) and specialization (lower graphs) over time To give an overview of West German regions one absolute and one relative measure for diversification and specialization is chosen 3 (See Figure 1). Explicit formulas of all indices are summarized in the Appendix Table 1A. Absolute diversity is measured by normalized Theil (1972) index where 0 would represent the presence of only one industry in the region and 1 would represent situation where all industries employ equal number of employees. Relative diversity is measured by inverse of a Hirshman-Herfindahl index (Herfindahl 1950, Hirschman 1964) as applied by Duranton and Puga (2000) and Noseleit (2013). The index increases the more the sectoral diversity in a region mirrors sectoral diversity of the 3 Choiche of indices is made without detailed analysis of which index could capture the diversified or specialized structure possibly best what is not the focus of this stufy (for such and similar discussions see Palan 2010 or Mameli and Faggian 2008). 8

9 national economy. Absolute specialization is measured by Hirshman-Herfindahl index (Herfindahl 1950, Hirschman 1964) which is defined as the sum of the squares of sector employment shares in total regional employment. The lowest degree of specialization is reached if each industry has similar employment share, the highest if the region is specialized in one industry only. By its construction it gives more weight to larger sectors. According to Palan (2010) Hirshman-Herfindahl index is the only specialisation index that fulfills all the properties set for a favorable index. Relative specialization is measured by Balassa (1965) index. The value for sectors is obtained by dividing each sectoral share in regional employment by the corresponding total sectoral share of the country (basically measured as a location quotient). The index for region is retrieved by taking median from region s sectoral values (following Benedictis et al. 2009). Trends in all figures reveal minor changes over long time period. While absolute indices seem to be largely unchanged, relative indices show slight however constant increase in both diversification as well as specialization. Indeed, both industry structures can co-exist a region with a dominant industry and a variety of other industries can be both diversified and specialized (Duranton and Puga 2000). Such highly persistent trends in industry structure are consistent with other studies for developed economies (e.g., Goya 2014). Nevertheless it might be that general diversification and specialisation measures are not capable to capture structural change that takes place at more discrete levels and ways. 5. Related and unrelated variety development and relation over time Construction of variety indices Distinction between RV and UV is made following the methodology used by Frenken et al. (2007) who apply decomposable nature of an entropy measure to distinguish between related and unrelated variety. Unrelated variety is measuring entropy across two digit industries and thus is based on the assumption that industries at this level of aggregation are unrelated to each other because they are not cognitively proximate. A common belief is that effective learning is not expected to be present among unrelated sectors 4. UV is calculated as: G UV = P g log 2 ( 1 P g ) g=1 where P g is the share of employment in two digit sector S g (where g=1,,g) over the total employment in a region. The UV measures the degree to which employment shares are evenly distributed across unrelated (in this case two digit) sectors. The values of UV can vary from 0 (when all employment is concentrated in only one two digit sector) up to log 2 (G) when all sectors employ an equal number of employees. In this study there are total of 41 two digit private sectors what means that theoretical upper bound of the index is There are, however, other views and recent evidence that unrelated type of diversity can be the source for the emergence of breakthrough or radical innovations, see Castaldi et al. (2015). 9

10 Related variety is measured as the weighted sum of entropy at three digit level within each two digit sector. An assumption is that industries within this level of aggregation are related based on cognitive proximity and thus can effectively learn from each other (Frenken et al. 2007). RV is calculated as: RV = G g=1 P g H g where H g = I i=1; S i ϵs g p i log P 2 ( 1 g p i /P g ) where P g is the share of employment in two digit sector S g over the total employment in a region; p i is the share of employment in three digit sector S i (where i=1,,i) belonging to the same two digit sector S g. The RV indicates the degree to which employment at two digit level is evenly spread across its three digit subsectors. The values of RV can differ from 0 when within each two digit sector employment is concentrated under only one of its three digit subsectors up to log2(i)-log2(g) when all subsectors employ an equal number of employees (based on entropy decomposition theorem by Theil 1972, as applied by Castaldi et al. 2015). The higher the RV value is, the more evenly employment is spread across the subsectors indicating to the higher number of technologically related industries in a region. According to the underlying concept of the RV such industry structure is conducive to inter-sectoral knowledge spillovers. In this study there are total of 174 three digit subsectors (I) under 41 two digit sectors (G) what means that theoretical upper bound of the index is For a long term trend analysis (Section 5) RV is measured as 3 digit level entropy within 2 digit industry class. In the empirical analysis (Section 6) RV is measured as 5 digit level entropy within 2 digit class to follow the approach by Frenken et al. (2007) 5. Calculations of RV based on 5 digit level entropy within 2 digit class is limited to the years and therefore not suitable for long term analysis. Also no time consistent industry classification for longer time series is available at such disaggregated level (Eberle et al. 2011). Nevertheless correlation between both RV indices is 0.92 and also empirical results tested on alternative RV measure do not lead to any significant differences Development trends of variety indices over time This section deals with the first stated research question which is of analytical nature and aims to investigate how stable are RV and UV compared to other diversity patterns over the long time period. Long term patterns of industry structure shown in Section 4, in line with other studies on developed economies, reveal highly persistent overall diversification and specialization trends. Evidence of the RV and UV trends in the literature is almost non-existing and therefore expectations are unclear. Boschma and Frenken (2011) basing on findings by Neffke and Svensson Henning (2008) and Hidalgo et al. (2007) conclude that number of related sectors in regions is likely to be persistent over time because it is more likely for regions to diversify in activities that are 5 Approaches to RV calculation vary between 3 to 5 digits within 2 or even 1 digit classes. The study by Frenken et al. (2007) applies 5 within 2 whereas Boschma and Iammarino (2009), Saviotti and Frenken (2008), Mameli et al. (2012) apply 3 digit entropy within 2 digit sectors. 10

11 related to existing fields. Also Boschma et al. (2013) notes that existing evidence shows that new industries do not start from nowhere, rather they evolve from existing industries and competences that are available in regions. Long term development patterns of RV and UV is shown in Figure 2. Average values of UV over years of observation lie between Taking into account the theoretical upped bound of UV being 5.36, this indicates to rather diverse, however stable unrelated industry composition of West German regions. Such high stability might be explained by difficulties to attract new industries if they are not technologically proximate to current regional activities (as shown by Neffke et al. 2011). While UV remains largely unchanged over time, RV reveals persistent increase for West German regions (See Figure 2). Average values of RV over years of observation have increased from 1.34 up to An increase in RV means that the employment distribution among three digit sectors within each two digit class is becoming more evenly spread. It could have emerged from either entry of related industries, distribution change across existing industries or exit of some related industry. Which is the case (and in general what is the level of dynamics in terms of entry, exit and distribution change across sectors behind the overall trend) asks for a further detailed examination. Such analysis is not performed in the study due to unavailability of disaggregated industry data at hand jahr jahr Related variety, median Related variety, mean Unrelated variety, median Unrelated variety, mean Figure 2: Development of related and unrelated variety over time With regard to the RV development over time, there is almost no evidence for long term RV trends in literature to make reasonable comparison between West Germany and other economies. Slight exception is the study by Hartog et al. (2012) which for the time period between 1993 and 2006 reveals similar trend of persistent increase in RV for Finnish regions. Reasons for such trend, however, are unclear and have not been analyzed. Nevertheless the particular time period in Finland is described as the one during which the Finnish economy experienced a shift to a high tech economy and the relevance of inter industry knowledge spillovers for high tech sectors is emphasized (Hartog et al. 2012). Constant increase in RV might also be viewed in context of those few studies that show evidence that regions tend to expand and diversify in activities that are related of those of existing ones. By exploring long term development patterns of 11

12 Swedish regions over 1969 to 2002 Neffke et al. (2011) show that regions are more likely to expand in industries that are related to their existing industry portfolio. Furthermore, entering industries even being related are still more distant than the current industry proximity among existing portfolio industries thus increasing related type variety. Boschma et al. (2013) investigate similar question for Spain regions over 1988 to 2007 and conclude that new industries that emerged in Spain regions tend to use similar capabilities as existing industries. Important to note that these studies apply different relatedness indicators to measure regional related diversification. Neffke et al. (2011) measure relatedness based on co-occurrence of products from diverse industries within portfolios of manufacturing plants whereas Boschma et al. (2013) use product proximity indicator developed by Hidalgo et al. (2010) to determine to what extent two products share similar capabilities. Analysis of the probability density function over time (Figure 3) reveals another interesting trend for RV. Besides the shift of the RV distribution what reflects increase in absolute values, there is also a pronounced change in distribution shape. Distribution of the RV changes from being close to normal at the beginning of the period of analysis to becoming steeper over time. Such change indicates that regions converged around mean variety in related activities. Distribution of UV confirms long term persistence in absolute values. There is also change in shape of distribution however less pronounced compared to RV. Kernel density estimate Kernel density estimate Density Density Related variety Unrelated variety Figure 3: Probability density for related and unrelated variety Persistence of related and unrelated variety over time Figure 4 illustrates range of variation and level of persistence of variety indices for time periods of ten and thirty years. In these graphs, the 45 degree line represents those regions that have identical variety level in different time periods. Figures reveal high levels of persistence over a decade (correlation for RV is 0.93, for UV 0.90) and over a period as long as thirty years (accordingly 0.71 and 0.70). Changes over time confirm previously described increase in overall RV for planning regions. Among the top 5 regions with most unrelated industrial structure over all years of observations is Munich which is known for its vibrant entrepreneurial spirit. Other regions are two neighboring regions Unterer Neckar and Mittlerer Oberrhein; also Aachen and Göttingen are among top unrelated regions. Nearly in all years of analysis the lowest UV is observed for Ingolstadt which is a host of the automobile producer 12

13 giant Audi group (other regions with the lowest UV over all years are Main-Rhön, Braunschweig, Landshut and Siegen). Among the top 5 regions with the most related industrial structure over all years of observation are Düsseldorf; Bochum/Hagen; Duisburg/Essen; Hamburg and Siegen. Interesting to note that 4 out of the bottom 5 regions with the lowest average RV are the same as bottom UV positions these are Ingolstadt, Braunschweig, Main-Rhön, Landshut and Rheinpfalz. This might indicate to the higher correlation between RV and UV at lower levels, nevertheless asks for a further analysis of the relation between both concepts what is performed in the Section 5.2. Figure 4: Relationship between related and unrelated variety in period t and t-10 (upper graphs) and t and t-30 (lower graphs) Altogether analysis of the RV and UV development trends have revealed rather stable pattern for UV and persistent increase of RV. Change is observed not only in the magnitude but also in the shape of RV distribution. With regard to persistence, bottom and top positions of RV and UV seems largely unchanged over time and there is a strong relation among bottom positions for both variety structures. All in all analysis of RV and UV development trends raise at least three important further questions what has been the cause of the persistent increase in RV over time? What effect it has had 13

14 on regional growth? Finally, diverse development trajectories of RV and UV together with a strong relation at the bottom positions of both variety types is rising a question to what extent different variety types are related and co-existing. This is analyzed in the next Section Relation and co-existence of related and unrelated variety This section deals with the second stated research question which is also of analytical nature and aims to investigate to what extent is there a relationship between RV and UV. This research question is rather explorative and it is somewhat difficult to formulate any expectations for the results. Empirical applications of the RV and UV nearly always emphasize decomposable nature of these entropy measures thus pointing to their statistical independence (Frenken et al. 2007, Castaldi et al. 2015). Relation between both types of varieties is provided rarely. Empirical evidence reveals rather positive relation between both measures 6. To analyze the relation between RV and UV several analytical tools are applied. Analysis starts with the comparative histogram at the beginning and end of the study (Figure 5). RV is ordered in a descending sequence and corresponding UV values are designated. Figures show rather random relation between two concepts high RV might co-exist with high as well as low UV... Figure 5: Comparative histogram of related and unrelated variety in 1980 (on the left) and 2010 (on the right). As another analytical tool a scatterplot diagram is provided (Figure 6). Locally weighted scatterplot smoothing regression curve (lowess curve, see Cleveland and Devlin, 1988) points to pronounced positive relation at the lower levels of UV and RV, however at the higher levels relation becomes random. Indeed, out of the 5 least unrelated regions in terms of industry structure, 4 belong also to the bottom 5 least related regions 7 whereas 6 E.g., Frenken et al. (2007) for the Netherlands reports correlation of ; Boschma et al. (2011) for Spain 0.16; Hartog et al. (2012) for Finland 0.66; Castaldi et al. (2015) for the US patent counts 0.57; Quatraro (2010) for Italy 0.2; Quatraro (2011) for France In Figure 6 these regions are excluded as outliers that could have caused pronounced positive relationship at lower levels of both types of varieties. 14

15 no such regularity is observed at the top levels. Correlation between both varieties over all years 1980 up to 2010 is 0.4. However, once the sample is split at the median of UV, correlation between lower values of UV and RV is 0.5, however for higher values drops to Such disparity at different levels inquires closer inspection based on which Figure 7 is created. It compares overall correlation between UV and RV, as well as upper and lower distribution of UV split by median value. Figure 7 reveals growing disparities between correlations of UV and RV at different levels of UV. Increasing correlation for lower UV values might mean that over time lower UV values are becoming more associated with lower RV values. Once UV increases and exceeds median level, variation in RV also increases and higher UV values can be associated with low as well as high RV with no definite relation trend. One explanation could be that regions with low UV have rather limited number of related industries where to expand whereas regions with high UV have large variation of potential industries. Since regions are more likely to expand and diversify in related fields to the existing industry structure (Neffke et al. 2011), this shows potential for further related diversification for many West German regions, especially those having high UV. Lowess smoother 1 Related variety Median Unrelated variety Figure 6: Relation of related and unrelated variety (years 1980, 1990, 2000 and 2010 are included) Figure 7: Correlation between related and unrelated variety Altogether it seems that likewise as evidence reveals co-existence of diversified and specialized sectoral structure (Henderson 1997 shows that the US cities tend to be not 15

16 only more diversified but also more specialized in certain industries) also RV and UV can co-exist. Basing on evidence from this study as well as other studies, it can be concluded that the relation and co-existence of both concepts can be country and time specific, besides findings for West German regions show that it tends to differ at different levels of UV The spatial distribution of related and unrelated variety Figure 8: Related variety in 1980 (on the left) and 2010 (on the right) Figure 9: Unrelated variety in 1980 (on the left) and 2010 (on the right) 16

17 Spatial distribution of the variety measures for the beginning and end of the analysis is displayed in Figures 8 and 9. Apart from constant increase in value and change in distribution, RV also reveals change in spatial distribution. At the beginning of the analysis high values of RV are rather evenly distributed across the space whereas at the end of analysis tend to localize in middle and North West regions creating kind of cluster in the middle regions. Other pronounced trend is decrease in relative RV positions for southern regions. Causes for changes in spatial distribution of RV is an issue for further research. What regards to the spatial distribution of UV than high values seem to be rather evenly distributed across space. Only slight change is observed for the North regions which decreased in relative positions in UV. 6. Empirical analysis This section deals with the final stated research question what is an empirical application of the RV and UV concept to analyze if any of diversity structures has an effect on regional growth in West Germany and does it vary depending on the regional AC Variables and model setting Dependent variable The dependent variable for the study is employment growth (or unemployment growth in case of portfolio hypothesis testing) in regions what is measured as a change in employment over the five-year time periods. Five year time period has been chosen following study by Frenken et al. (2007) and other studies that use relatively long time lags to analyze the impact of industry structure on growth 8. It should be noted that there is no theoretical argumentation provided behind the choice for such particular time lags in the studies. Explanatory variables The choice of the explanatory variables for the base model is reasoned to make it possibly close to the study by Frenken et al. (2007) to ensure comparability of the results. Other models extend the base model by accounting for regional characteristics that literature has argued to be important in regional growth models and that might help to reveal new insights for the variety concept, especially that of regional AC. All explanatory variables are measured at the beginning of each time period. Following explanatory variables are applied in this study: RV and UV. The key explanatory variables of this study are measures of related and unrelated variety among regional industries. Entropy across 2 digit industries is used to measure UV and entropy within 2 digit industries to capture RV. For details of calculations see Chapter 5. Knowledge base and regional absorptive capacity. To measure the regional knowledge base two variables have been considered. First, the qualification level of the workforce measured by the share of employees with a tertiary degree. 8 E.g. Boschma et al. (2011) apply 4 year time periods; Bishop et al. (2010) analyze growth over 7 years; Boschma et al. (2009) growth over 8 years. 17

18 Second is R&D intensity measured by the share of R&D employees (engineers and natural scientists) in private-sector employment. Since correlation of both variables for the years of analysis is 0.86 only share of private-sector R&D is applied as it is more relevant to the concept of AC what is applied to test if diverse variety types perform differently under different levels of regional AC. Population density. Population density is used as a catch-all variable of various regional characteristics (e.g., housing and land prices, infrastructure availability etc.) and is normally applied to control for the effect of urbanization economies. Year dummies. Year dummies are included to control for time-specific effects. Apart from the previously mentioned explanatory variables that serve for the base model, the study considers following other regional characteristics that have been admitted by other studies to be of importance in relation between regional industry structure and growth: Industry structure. Studies that explore regional industry structure and its linkage to growth have shown that the potential effects from spillovers might differ across sectors (Combes 2000, Mameli et al. 2008, Bishop and Gripaios 2010). Bishop and Gripaios (2010) by referring to the study of Deidda et al. (2006) mention different levels of tradability of sectors across regions as one of the potential reasons for such diverse effects. Services as non-tradables are more constrained by their location choice since they should be close to their customers whereas manufacturing is less restricted by a need to be located close to costumers and can take advantages of location externalities. To account for this control for broad industry structure (share of employment in manufacturing) is included. Spatial dependence. It is likely that not only regional characteristics, but also spatial proximity to other regions and their markets can effect growth of a particular region. The importance of such type of controls can arise from strong economic linkages across regions as well as the fact that regional boundaries are normally defined based on administrative rather than geographical areas that are economically linked (Bishop and Gripaios 2010). To account for such spatial dependencies, Harris-type market potential function is applied. The market potential variable is calculated as the distance weighted sum of the total population in all other regions (Redding and Sturm 2008, Suedekum 2008). The idea for such control originates from Harris (1954) market potential function according to which demand for goods from a particular location is a sum of purchasing power from other locations weighted by transportation costs (Hanson 2004). Moderating effect of region s absorptive capacity. Not all regions might be equally exploiting benefits of knowledge flows from related and unrelated fields. The hypothesis regarding AC is that AC can strengthen the effect of variety on growth and therefore can be considered as a moderator between structural composition and growth. This assumption is tested by employing interaction of AC (measured by R&D intensity) with both types of varieties. Positive sign of interaction would indicate to stronger effect of variety under higher levels of AC. 18

19 Model specification and methodology To analyze the relationship between RV, UV and regional growth following equation is estimated: Y i,t+5 = β 0 + β 1 RV i,t + β 2 UV i,t + β 3 RD i,t + β 4 RD i,t RV i,t + β 5 RD i,t UV i,t + β X i,t + Ɛ i,t where Y i,t+5 denotes employment change (or unemployment change in case of testing for portfolio effect) in region i over five year period, RV i,t is related variety in region i in time t, UV i,t is unrelated variety in region i in time t, RD i,t is regional R&D intensity what serves also as a proxy for absorptive capacity, RD i,t * RV i,t (RD i,t * UV i,t ) represents moderating effect of regional absorptive capacity on related (unrelated) variety and X i,t is a vector what includes other factors that might influence regional growth and that are controlled for, such as share of manufacturing employment, market potential, population density, Ɛ i,t accounts for the error term. Different methods are used to study the causal relationship between industry structure and regional growth. Frenken et al. (2007) apply OLS and the available data allows them to construct cross sectional observations. Other studies vary from pooled OLS (like Boschma et al. 2011, Mameli et al. 2012) to models that can possibly mitigate spatial autocorrelation and endogeneity (like spatial lag models and GMM). Data for this analysis allows constructing balanced panel. To account for unobserved time invariant regional specific characteristics fixed effects model specification is applied. A typical problem in growth models is that industry related indicators might be endogenous since they might be affected by regional growth. Particularly this might be a problem for RV indicator that reveals constant increase reasons for what are not clear. The first and simplest way to mitigate the problem is the application of time lags between dependent and explanatory variables what this study does. Another common approach would be to search for the external instrument that is correlated with explanatory variables however uncorrelated with the dependent variable. Finding an additional regional characteristic that would not be related to growth is a rather challenging task and is not available for this study. An approach that mitigates the problem of a possible endogeneity is the application of the dynamic generalized method of moments (GMM) estimators (Arellano and Bond 1991) what generates internal instruments based on the lagged differences and lagged levels in explanatory variables. This approach is applied as a robustness check to test for the endogeneity presence in the base model. Dynamic GMM approach has been applied also in other studies that link regional industry composition with growth like Hartog et al. (2012) and Boschma et al. (2013). 19

20 6.2. Effect of related und unrelated variety on regional employment growth This section presents and discusses regression results regarding effects of RV and UV on regional employment growth. Three different models are estimated. To enhance the comparison with the existing studies the base model (Model 1) is constructed possibly close to the study of Frenken et al. (2007). In the next model (Model 2) some further controls are introduced that help to account for spatial dependence and general industry structure (Model 2). Finally, Model 3 tests whether different levels of AC have different effect on the relationship between variety and growth. Empirically it is tested by interaction terms of RV and UV with regional AC what is captured by R&D intensity. Table 2: Effect of related und unrelated variety on regional employment growth Indicator Model I Model II Model III Related variety Unrelated variety R&D intensity Population density (log) Market potential Share of manufacturing employment Related variety*r&d intensity Unrelated variety*r&d intensity Constant 0.16*** 0.30*** 0.27*** (0.03) (0.04) (0.07) 0.41*** 0.42*** 0.20** (0.04) (0.04) (0.09) 2.32** 1.63* *** (0.96) (0.93) (9.80) (0.18) (0.19) (0.19) (0.79) (0.79) 0.96*** 0.97*** (0.16) (0.16) 0.25 (1.81) 6.18** (2.54) -3.20*** *** (1.01) (9.44) (9.43) Year dummies Yes*** Yes*** Yes*** Number of observations R2 within Mean variance inflation factor Notes: Dependent variable: Change in regional employment; Panel fixed effects regression; Standard errors in parentheses; ***, **, *: statistically significant at 1%, 5% and 10%, respectively Results in all model specifications reveal significant and positive impact for both variety types on regional employment growth. The most striking difference compared to results by Frenken et al. (2007) is the positive effect of UV on growth. Nevertheless there are other studies with similar findings. Study by Mameli et al. (2012) for Italian regions find significant positive effect for both variety types on growth. Once 20