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Wenn die AfD hier gewinnt, wären die Folgen überall in Deutschland deutlich zu spürenReint GroppDer Spiegel, 8. Januar 2026
This study estimates the firm-level employment effects of investment grants in Germany. In addition to the average treatment effect on the treated, we examine discrimination in the funding rules as a potential source of effect heterogeneity. We combine a staggered difference-in-differences approach with a matching procedure at the cohort level. The findings reveal a positive effect of investment grants on employment development. The subsample analyses yield strong evidence for heterogeneous effects based on firm characteristics and the economic environment. They highlight the responsibility of the local funding authorities to clarify ex ante which goals of a funding programme are most important in their regions.
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.
The German government provides discretionary investment grants to structurally weak regions in order to reduce regional inequality. We use a regression discontinuity design that exploits an exogenous discrete jump in the probability of regional actors to receive investment grants to identify the causal effects of the policy. We find positive effects of the programme on district-level gross value-added and productivity growth, but no effects on employment and gross wage growth.
Interactive regional learning involving various actors is considered a precondition for successful innovations and, hence, for regional development. Diasporas as non-native ethnic groups are regarded as beneficial since they enrich the creative class by broadening the cultural base and introducing new routines. Using data on research and development (R&D) collaboration projects, the analysis provides tentative evidence that the size of diasporas positively affects the region’s share of outward R&D linkages enabling the exchange of knowledge. The empirical analysis further confirms that these interactions mainly occur between regions hosting the same diasporas, pointing to a positive effect of ethnic proximity rather than ethnic diversity.
This paper assesses firm-level effects of the single largest investment subsidy programme in Germany. The analysis considers grants allocated to firms in East German regions over the period 2007 to 2013 under the regional policy scheme Joint Task ‘Improving Regional Economic Structures’ (GRW). We apply a coarsened exact matching (CEM) in combination with a fixed effects difference-in-differences (FEDiD) estimator to identify the effects of programme participation on the treated firms. For the assessment, we use administrative data from the Federal Statistical Office and the Offices of the Länder to demonstrate that this administrative database offers a huge potential for evidence-based policy advice. The results suggest that investment subsidies have a positive impact on different dimensions of firm development, but do not affect overall firm competitiveness. We find positive short- and medium-run effects on firm employment. The effects on firm turnover remain significant and positive only in the medium-run. Gross fixed capital formation responses positively to GRW funding only during the mean implementation period of the projects but becomes insignificant afterwards. Finally, the effect of GRW-funding on labour productivity remains insignificant throughout the whole period of analysis.
R&D collaborations and the role of proximity. Regional Studies. This paper explores the impact of proximity measures on knowledge exchange measured by granted research and development (R&D) collaboration projects in German NUTS-3 regions. The results are obtained from a spatial interaction model including eigenvector spatial filters. Not only geographical but also other forms of proximity (technological, organizational and institutional) have a significant influence on the emergence of collaborations. Furthermore, the results suggest interdependences between proximity measures. Nevertheless, the analysis does not show that other forms of proximity may compensate for missing geographical proximity. The results indicate that (subsidized) collaborative innovation activities tend to cluster.
Although the phase of euphoria seems to be over, policy makers and regional agencies have maintained their interest in cluster policy. Modern cluster theory provides reasons for positive external effects that may accrue from interaction in a group of proximate enterprises operating in common and related fields. Although there has been some progress in locating clusters, in most cases only limited knowledge on the geographical extent of regional clusters has been established. In the present article, we present a hybrid approach to cluster identification. Dominant buyer–supplier relationships are derived by qualitative input–output analysis from national input–output tables, and potential regional clusters are identified by spatial scanning. This procedure is employed to identify clusters of German research and development-intensive industries. A sensitivity analysis reveals good robustness properties of the hybrid approach with respect to variations in the quantitative cluster composition.
We consider the simultaneity bias when examining the effect of individual risk attitudes on entrepreneurship. We demonstrate that entry into self-employment is related to changes in risk attitudes. We further show that these changes are correlated with the probability to remain in entrepreneurship.
Our paper pursues two aims: first, it presents an approach based on input–output innovation flow matrices to study intersectoral innovation flows within industrial clusters. Second, we apply this approach to the identification of structural weaknesses in East Germany relative to the western part of the country. The case of East Germany forms an interesting subject because while its convergence process after unification began promisingly in the first half of the 1990s, convergence has since slowed down. The existing gap can now be traced mainly to structural weaknesses in the East German economy, such as the absence of strong industrial cluster structures. With this in mind, we investigate whether East Germany does in fact reveal the abovementioned structural weaknesses. Does East Germany possess fewer industrial clusters? Are they less connected? Does East Germany lack specific clusters that are also important for the non-clustered part of the economy?
This paper contributes to the empirical literature on systematic methodologies for the identification of industrial clusters. It combines a measure of spatial concentration, qualitative input–output analysis, and a knowledge interaction matrix to identify the production and knowledge generation activities of industrial clusters in the Federal State of Saxony in Germany. It describes the spatial allocation of the industrial clusters, identifies potentials for value chain industry clusters, and relates the production activities to the activities of knowledge generation in Saxony. It finds only a small overlap in the production activities of industrial clusters and general knowledge generation activities in the region, mainly driven by the high-tech industrial cluster in the semiconductor industry. Furthermore, the approach makes clear that a sole focus on production activities for industrial cluster analysis limits the identification of innovative actors.
