Abstract
The aim of the study is to explore the effect of innovation on growth of firms placed at different percentiles of the growth distribution in manufacturing and services sectors in Turkey. We show that innovation crucially adds to the likelihood of being a high growth firm. Innovation efforts are found to have more pronounced effects on growth performance of firms at the upper end of the distribution of growth. Results indicate that compared with services, manufacturing firms are influenced more by R&D whereas innovation outputs are more beneficial for services firms.
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Notes
Although various types innovation activities take place in services industries, their economic impact is heavily neglected in terms of firm level studies (Cainelli et al. 2006).
Pellegrino and Piva (2020) show that younger firms perform better in translating R&D efforts into innovation in some entrepreneurial services sectors, whereas older firm perform better in routinized manufacturing sectors with low technology in terms of successful innovations.
There are some high-technology manufacturing sectors where technological opportunities take place more frequently and radically whereas for some low-technology manufacturing and services sectors they are scarce and specific leading greater impact on firm performance (Pavitt 1984; Dosi and Nelson 2013; Ortega-Argilés et al. 2015).
In general, HGFs are defined to be a share of fastest growing firms over a certain time period.
See e.g. Lenger and Taymaz (2006).
These datasets are available under a confidential agreement and all the elaborations can only be conducted at the Microdata Research Centre of TURKSTAT in respect of the laws on the confidentiality of statistics and personal data protection.
After applying a cleaning routine inspired by Hall and Mairesse (1995) we limit our sample to firms with a decline or growth in number of employees and sales smaller than 250 percent per year.
An SME is defined as firms with number of employees 10 to 249 where firms with number of employees 10 to 49 and 50 to 249 are defined as small and medium, respectively (OECD 2005).
These differences are statistically significant by Kolmogorov–Smirnov test results, which reveal the rejection of the equality of distributions between HGFs and Non-HGFs.
We calculate capital stock series by applying perpetual inventory methodology.
The correlation matrix of variables is presented in Table A.1 in Appendix.
Time dummies are defined for each wave of CIS.
We identify exogenous independent variables from the first-stage equation, which could be eliminated from the set of independent variables in the second-stage estimation. Following Helpman et al. (2007) we include \({Support}_{it}\) in the selection equation as the exclusion restriction.
A larger share of manufacturing industry’s value added is produced by high technology sectors like pharmaceuticals and motor vehicles industry compared to that share of services industry (Dinçer and Tekinkoru 2018).
Since first stage and the coefficients on the control variables in the second stage estimations give similar results to the previous findings above, for the sake of brevity, we do not report them.
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Acknowledgement
We thank TURKSTAT and particularly TURKSTAT staff Şehmus Şenol Bozdağ, Ümit Öğüt, Erdal Yıldırım, Sabit Cengiz Ceylan, Ferhat Irmak, and Esra Sazak for providing access to firmlevel data.
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Dalgıç, B., Fazlıoğlu, B. Innovation and firm growth: Turkish manufacturing and services SMEs. Eurasian Bus Rev 11, 395–419 (2021). https://doi.org/10.1007/s40821-020-00176-4
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DOI: https://doi.org/10.1007/s40821-020-00176-4