Abstract
We examine the effects of agglomeration, differentiating between urbanization and localization economies, on four distinct types of innovation in manufacturing and services. Furthermore, estimating multilevel panel regression models, we investigate the sorting of highly innovative firms into dense urban and/or specialized regions by considering both observable and unobservable firm characteristics. The results indicate that spatial sorting is important. A large portion of the regional differences in firm innovation rates is due to firm characteristics. Estimates that ignore unobserved heterogeneity at the firm level still point to a positive and significant impact of localization economies on different types of innovation. However, once we include firm fixed effects and distinguish between manufacturing and services, only some weak indication for positive effects of localization on radical innovations of manufacturing firms remains. In addition, the probability to adopt an existing product by an manufacturing firm seems to be positively influenced by urbanization economies. For the service sector, in contrast, we find adverse effects of localization on different kinds of innovation and no important effect of urbanization.
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Notes
In our empirical analysis, innovation output is measured by the firm’s probability to report an innovation.
We refrain from considering more inputs to keep the exposition simple.
Alternatively, Baptista and Swann (1998) distinguish between demand and supply side effects. With respect to the demand side they argue, e.g., that customers are a good source of ideas for innovation and, therefore, locating in large markets with many customers likely enhances innovation output of firms. On the supply side, labor market pooling and access to various intermediate inputs are mentioned as strategic advantages of dense metropolitan areas.
Helsley and Strange (2002), for instance, note that in their model (see discussion below) the effects of input sharing and matching on innovation might base on urbanization as well as localization, depending on how one interprets the characteristic space and on the assumptions on how the innovation process exactly works.
There are, of course, large service firms such as Amazon and Facebook that operate at global scale and don’t rely on resources that are locally available. However, the majority of service firms, in particular consumer services, still supply non-tradables and have a strong local focus. Investigating corresponding differences within services or manufacturing is beyond the scope of this analysis. However, a more detailed analysis of branches differentiating e.g., between tradable and non-tradable industries is an interesting issue for future research and might provide more comprehensive evidence on the role of the spatial extent of market areas in this context. We are grateful to a referee for pointing this out and suggesting additional arguments for heterogeneous effects.
Disparities in R&D activity and innovation rates between East and West Germany serve as an illustrative example in this context. As for instance discussed by Niebuhr (2017), the East-West gap in R&D activity is (partially) explained by the fact that the headquarters and R&D units of West German firms remained in West Germany after reunification and only parts of the production moved to East Germany. In the data we use, the IAB Establishment Panel, different sites of one firm are considered independent establishments.
In particular, many firm level and multilevel studies investigate for selected industries specific mechanisms that might be behind the link between agglomeration and innovation (e.g., Fornahl et al. 2011 focusing on R&D subsidies, collaboration networks and biotech firms and Ben Abdesslem and Chiappini 2019 analyzing the impact of a cluster policy on productivity, employment, and total fixed assets in the French optic/photonic industry). While these analyses provide very detailed and robust evidence on specific industries and selected channels through which agglomeration might impact firm innovation, often their results cannot be generalized and it is difficult to compare their findings with studies that apply a more aggregate perspective on agglomeration economies.
Different units of one firm that are located in different municipalities are considered as independent establishments. Unfortunately, it is not possible to identify whether establishments belong to the same firm. In order to improve the readability, we use the terms firms and plants in the following as synonyms for the term establishment.
See Ellguth et al. (2014) for a detailed description of the IAB Establishment Panel.
Until 2007, the question refers to the previous 2 years.
If the answer to one of the questions Q1–Q3 is missing and the other questions have been answered in the negative, the observation is excluded from the analysis of ‘any innovation’. Furthermore, we do not consider an observation in the analysis of ‘improvements’ and ‘imitations’, respectively, if a firm reports the introduction of an entirely new product or service (Q1), but no imitation (Q2, 1.4% of all observations, see Table 8 in the appendix), respectively, no improvement (Q3, 3.9%). Otherwise the dependent variables would take the value 0, even though the firm is highly innovative. ‘Improvement’ is set to missing, in addition, if a firm reports an imitation, but no improvement (4.5%).
A detailed discussion of the pros and cons of different innovation measures is provided by a recent survey in Carlino and Kerr (2015) who focus on regional studies.
Including firm fixed effects implies that we also control for time invariant characteristics of the regions since the establishments in our data set do not relocate between regional labor markets.
The distinction between the three types of regions refers to a classification of the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) based on population density, the size of the largest city within a region, and the population share living in cities. Summary statistics of regional employment figures by region type are given in Table 9 in the appendix.
The MIP is also the German contribution to the CIS.
Alternative specifications including spatial lags of employment density and industry share do not point to significant spatial spillover effects across the borders of the considered labor market regions (Table 12 in the appendix). Therefore, we focus on the impact of employment density and the industry share within the region in which an establishment is located.
Recall that the analyzed panel is highly unbalanced. The median number of observations per establishment is 2. Hence, at different points in time we observe different establishments in one particular region.
The results are robust with regard to the inclusion of the regional share of employment engaged in R&D (see Table 16 in the appendix). The results in Table 5 indicate that firm innovation is not affected by employment density. However, if we estimate Eq. 5 separately by the type of region, i.e., differentiate between rural, intermediate, and urban regions, we find some indication that an increase in employment density in rural and intermediate regions does foster the generation of certain types of innovation, particularly imitations (corresponding results are available from the authors upon request). For firms in urban regions, in contrast, we observe (partly significant) adverse effects suggesting that marginal costs of increasing density in urban areas outweigh marginal gains. However, to analyze corresponding heterogeneity in more detail is beyond the scope of this paper, but it is an interesting task for future research.
Corresponding sector-specific results of regressions including region and industry fixed effects and excluding establishment fixed effects by sector are summarized in Table 17 in the appendix.
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Acknowledgements
We gratefully thank the guest editors Lara Agostini, Barbara Bigliardi, Federico Caviggioli, and Francesco Galati, two anonymous referees, Johannes Bröcker, Timo Mitze as well as participants at the 3rd Geography of Innovation Conference, Toulouse 2016 and the 56th ERSA Congress, Vienna 2016 for their helpful remarks and suggestions which substantially improved the paper. The usual disclaimer applies.
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Accepted for publication in the special section “A social perspective of knowledge-based innovation: mobility and aggregation”: October 07, 2019.
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Niebuhr, A., Peters, J.C. & Schmidke, A. Spatial sorting of innovative firms and heterogeneous effects of agglomeration on innovation in Germany. J Technol Transf 45, 1343–1375 (2020). https://doi.org/10.1007/s10961-019-09755-8
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DOI: https://doi.org/10.1007/s10961-019-09755-8