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What’s behind the disparities in firm innovation rates across regions? Evidence on composition and context effects

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  • Amber Naz
  • Annekatrin Niebuhr
  • Jan Peters

Abstract

Empirical evidence on innovation activity points to both significant disparities in innovation output across regions and important differences in firm innovation rates. These differences suggest that firm characteristics as well as regional factors might impact on innovation. Evidence on the relative importance of the two groups of factors is still scarce and ambiguous. We analyze the impact of firm characteristics and the regional context on differences in firm innovation rates in Germany for the period 1998–2009. By combining firm-level data with information on the regional environment, we can distinguish between composition effects caused by the selection of highly innovative firms in specific regions and the impact of regional factors. Our results indicate that the propensity to innovate of firms located in agglomerations significantly exceeds the innovation output of plants in rural regions. To analyze the role of the regional context for the firm’s probability to innovate, we use a multilevel approach. Besides controlling for important firm-level factors such as R&D employment, size and age of the firm, we also account for different regional factors. The regression results point to a positive association between regional R&D activity and the firm’s innovation output. Moreover, the effect of the regional context seems to differ with the size, age and R&D intensity of the firms. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Amber Naz & Annekatrin Niebuhr & Jan Peters, 2015. "What’s behind the disparities in firm innovation rates across regions? Evidence on composition and context effects," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(1), pages 131-156, October.
    • Handle: RePEc:spr:anresc:v:55:y:2015:i:1:p:131-156
    DOI: 10.1007/s00168-015-0694-9
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    Cited by:

    1. Rodríguez-Pose, Andrés & Zhang, Min, 2020. "The cost of weak institutions for innovation in China," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    2. Lara Agostini & Federico Caviggioli & Francesco Galati & Barbara Bigliardi, 2020. "A social perspective of knowledge-based innovation: mobility and agglomeration. Introduction to the special section," The Journal of Technology Transfer, Springer, vol. 45(5), pages 1309-1323, October.
    3. Lenihan, Helena & McGuirk, Helen & Murphy, Kevin R., 2019. "Driving innovation: Public policy and human capital," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    4. Judyta Lubacha, 2024. "The Role of Regional Intangible Assets for the Innovation Activity of Enterprises: The case of Polish Regions," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 3635-3659, March.
    5. Peters, Cornelius, 2015. "Do age complementarities affect labour productivity? Evidence from German firm level data," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112941, Verein für Socialpolitik / German Economic Association.
    6. Annekatrin Niebuhr & Jan Cornelius Peters & Alex Schmidke, 2020. "Spatial sorting of innovative firms and heterogeneous effects of agglomeration on innovation in Germany," The Journal of Technology Transfer, Springer, vol. 45(5), pages 1343-1375, October.
    7. Lorena M. D'Agostino & Rosina Moreno, 2019. "Green regions and local firms' innovation," Papers in Regional Science, Wiley Blackwell, vol. 98(4), pages 1585-1608, August.
    8. Fornaro, Paolo & Maliranta, Mika & Rouvinen, Petri, 2019. "Immigrant Innovators and Firm Performance," ETLA Working Papers 63, The Research Institute of the Finnish Economy.
    9. Lina Mao & Chongluan Lu & Guangfan Sun & Chunyan Zhang & Changwei Guo, 2024. "Regional culture and corporate finance: a literature review," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    10. Fernando Bruna & Juan Fernández‐Sastre, 2021. "Regional characteristics and the decision to innovate in a developing country: A multilevel analysis of Ecuadorian firms," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1337-1354, December.
    11. Tojeiro-Rivero, Damián & Moreno, Rosina, 2019. "Technological cooperation, R&D outsourcing, and innovation performance at the firm level: The role of the regional context," Research Policy, Elsevier, vol. 48(7), pages 1798-1808.

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    More about this item

    Keywords

    C25; D22; O31; R15; R16;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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