The impact of innovation and innovation subsidies on economic development in German regions

ABSTRACT

Public innovation subsidies in a regional environment are expected to unfold a positive economic impact over time. The focus of this paper is on an assessment of the long-run impact of innovation and innovation subsidies in German regions. This is scrutinized by an estimation approach combining panel model and time-series characteristics and using regional data for the years 1980–2014. The results show that innovation and innovation subsidies in the long run have a positive impact on the economic development of regions in Germany. This supports a long-term strategy for regional and innovation policy.

KEYWORDS:

• public funding
• innovation
• regional development
• Germany

JEL:

• O10
• O18
• O30

ACKNOWLEDGEMENTS

The authors are indebted to the Institute for Employment Research for the provision of social data (Establishment History Panel), which made the empirical study possible. They greatly acknowledge the comments and suggestions of the participants at scientific conferences and workshops, in particular the Heilbronn Symposium 2017, the Jena Lecture in Economic Geography 2018, and the Research Seminar of the Institute for Economic Research and Policy at the University of Bremen (ierp). Furthermore, the paper benefited greatly from the comments of three anonymous referees, who are thanked.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

ORCID

Uwe Cantner http://orcid.org/0000-0002-2067-493X

Jutta Guenther http://orcid.org/0000-0001-5354-3541

Maria Kristalova http://orcid.org/0000-0001-5855-9753

Notes

1. Before German reunification, no comparable data sources for the chosen variables existed in the former German Democratic Republic (GDR). Therefore, it is not possible to extend the analysis to Germany as a whole. Available data, starting 1995, are not sufficient for this study.

2. The Förderkatalog is a unique source freely available to identify the type and volume of direct project funding by the German government (see https://foerderportal.bund.de/foekat/jsp/StartAction.do?actionMode=list). Several studies in regional economics use it (e.g., Broekel, 2015; Broekel et al., 2017; Marek, Titze, Fuhrmeister, & Blum, 2017; Titze, Brachert, & Kubis, 2014; Schneider, Kubis, & Titze, 2019).

3. The German Research Foundation (DFG) funds basic research projects that are not in the database, just like the institutional funding of universities and research institutes.

4. Figures A2–A4 in Appendix A in the supplemental data online allow for a comparison of the level of all three main variables for the beginning (1980) and end of the observation period (2014).

5. Illustrations of all regions are available from the authors on request.

6. Standard panel models, such as fixed-effects, or dynamic panel estimators, such as the Arellano–Bond-generalized method of moments (GMM), often produce inconsistent and potentially misleading results for panels with a large number of observation periods, since the assumptions of homogeneous slope coefficients and error variances are not appropriate (Pesaran et al., 1999; Phillips & Moon, 2000).

7. As a robustness check, we also report specifications estimated with the help of a dynamic fixed-effects (DFE) model. However, as mentioned above, these models assume slope homogeneity across all regions, which makes the results not very reliable in the case of a certain degree of heterogeneity.

8. As a robustness check, we implement a lag specification commonly used in the empirical literature (e.g., Castellacci and Natera, 2013; Wang, 2010) with the same delay for all variables of one year (see Appendix A in the supplemental data online).

9. For the estimation, we use the Stata command XTPMG of Blackburne and Frank (2007).

10. We look at the so-called ‘top performer’ sample within the setting of the ARDL specification (1,1,1,1,1), which we use as a robustness check for our main models that have a more complex lag structure.