When interaction matters: the contingent effects of spatial knowledge spillovers and internal R&I on firm productivity
This work studies the linkages between spatially bound knowledge spillovers, internal research, and innovation (R&I) activities and firm productivity. Spillovers are modeled to emanate from intra- and extra-sectoral R&I activities in the firms’ regional business environments. We specifically test for non-linearities in the complex relationship between these internal and external knowledge sources and quantify their joint marginal effect on firm productivity. Our empirical results for a large panel of German manufacturing firms (1) underline the overall importance of knowledge spillovers in driving productivity and (2) point at distinct interactions between the included knowledge sources: First, we find that intra-sectoral knowledge spillovers only have a statistically significant effect on firm productivity when extra-sectoral spillovers are sufficiently large. Secondly, the link between knowledge spillovers and productivity varies with the level of the firms’ internal R&I activities.
KeywordsFirm productivity Research Innovation Spatial knowledge spillovers Interaction terms
JEL ClassificationC23 O10 O30 R11
- Audretsch, D., & Feldman, M. (2004). Knowledge spillovers and the geography of innovation. In J. Henderson & J. Thisse (Eds.), Handbook of regional and urban economics (pp. 2713–2739). Amsterdam: Elsevier.Google Scholar
- Beugelsdijk, S. (2007). The regional environment and a firm’s innovative performance: A plea for a multilevel interactionist approach. Economic Geography, 83, 181–199. https://doi.org/10.1111/j.1944-8287.2007.tb00342.x.CrossRefGoogle Scholar
- Cheng, W., Morrow, J., & Tacharoen, K. (2012). Productivity as if space mattered: An application to factor markets across China. CEP discussion papers CEPDP1181. London School of Economics and Political Science, London.Google Scholar
- d’Aspremont, C., & Jacquemin, A. (1988). Cooperative and noncooperative R&D in duopoly with spillovers. American Economic Review, 78, 1133–1137.Google Scholar
- Eberhardt, M., & Helmers, C. (2016). Untested assumptions and data slicing: A critical review of firm-level production function estimators. https://sites.google.com/site/medevecon/publications-and-working-papers. Accessed September 04, 2017.
- European Commission. (2017). Smart specialisation: Strengthening innovation in Europe’s regions. http://ec.europa.eu/regional_policy/en/information/publications/factsheets/2017/smart-specialisation-strengthening-innovation-in-europe-s-regions. Accessed September 04, 2017.
- Eurostat. (2018). High-tech classification of manufacturing industries. https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech_classification_of_manufacturing_industries. Accessed October 15, 2018.
- Gordon, R. (1995). Is there a tradeoff between unemployment and productivity growth? NBER working paper no. 5081. National Bureau of Economic Research, Cambridge, MA.Google Scholar
- Griliches, Z. (1995). R&D and productivity: Econometric results and measurement issues. In P. Stoneman (Ed.), Handbook of the economics of innovation and technological change (pp. 52–89). Hoboken: Blackwell Publishers.Google Scholar
- Hospers, G., Sautet, F., & Desrochers, P. (2008). Silicon somewhere: Is there a need for cluster policy? In C. Karlsson (Ed.), Handbook of research on innovation and clusters: Cases and policies (pp. 430–446). Cheltenham: Edward Elgar.Google Scholar
- Jacobs, J. (1969). The economy of cities. New York: Random House.Google Scholar
- Junankar, P. N. (2013). Is there a trade-off between employment and productivity? IZA discussion paper no. 7717. Institute for the Study of Labor, Bonn.Google Scholar
- Kalemli-Özcan, S., Sorensen, B., Villegas-Sanchez, C., Volosovych, V., & Yesiltas, S. (2015). How to construct nationally representative firm level data from the ORBIS global database. NBER working paper no. 21558. National Bureau of Economic Research, Cambridge, MA.Google Scholar
- Maraut, S., Dernis, H., Webb, C., Spiezia, V., & Guellec D. (2008). The OECD REGPAT database: A presentation. OECD STI working paper 2008/2. OECD, Paris.Google Scholar
- Marshall, A. (1890). Principles of economics. London: Macmillan.Google Scholar
- Mikkonen, K. (2002). The competitive advantage of regions and small economic areas: The case of Finland. Fennia, 180, 191–198.Google Scholar
- Ramadani, V., Abazi-Alili, H., Dana, L., Rexhepi, G., & Ibraimi, S. (2017). The impact of knowledge spillovers and innovation on firm-performance: Findings from the Balkans countries. International Entrepreneurship and Management Journal, 13, 299–325. https://doi.org/10.1007/s11365-016-0393-8.CrossRefGoogle Scholar
- Roshchina, E. (2016). The impact of labor market conditions on job creation: Evidence from firm level data. In Paper presented at the American economic association annual meeting, Chicago, 6–8 January 2016. www.aeaweb.org/-conference/2017/preliminary/paper/YNrnR64N+&cd=2&hl=de&ct=clnk&gl=de. Accessed September 23, 2017.
- Schmoch, U., LaVille, F., Patel, P., & Frietsch, R. (2003). Linking technology areas to industrial sectors: Final report to the EU commission. https://cordis.europa.eu/pub/indicators/docs/ind_report_isi_ost_spru.pdf. Accessed September 23, 2017.
- Segerstrom, P. (1998). Endogenous growth without scale effects. American Economic Review, 88, 1290–1310.Google Scholar
- Tokunaga, S., Kageyama, M., Akune, Y., & Nakamura, R. (2014). Empirical analysis of agglomeration economies in the Japanese assembly-type manufacturing industry for 1985–2000: Using agglomeration and coagglomeration indices. Review of Urban & Regional Development Studies, 26, 57–79. https://doi.org/10.1111/rurd.12019.CrossRefGoogle Scholar
- van Looy, B., Vereyen, C., & Schmoch, U. (2015). Patent statistics: Concordance IPC V8-NACE REV.2. https://circabc.europa.eu/webdav/CircaBC/ESTAT/infoonstatisticsofsti/Library/methodology/patent_statistics/IPC_NACE2_Version2%200_20150630.pdf. Accessed September 23, 2017.
- Zhu, H., Dai, Z., & Jiang, Z. (2017). Industrial agglomeration externalities, city size, and regional economic development: Empirical research based on dynamic panel data of 283 cities and GMM method. Chinese Geographical Science, 27, 456–470. https://doi.org/10.1007/s11769-017-0877-7.CrossRefGoogle Scholar