Abstract
An on-going debate in the innovation policy arena revolves around the effects of public research funding. While government intervention is usually not questioned, appraising the role of direct research funds (government grants for research projects) versus tax incentives (tax exemption/deduction of research expenses) remains a core issue. In this chapter we make methodological contributions to ex-ante evaluation of these alternative government research funding instruments. Building on the SKIN model, we develop an agent-based simulation of a localized life sciences innovation system (Vienna, Austria). Companies, universities, public research and other relevant research organizations are modelled as heterogeneous agents that make investment decisions about conducting research, exchange assets with other agents and produce knowledge output. Simulation runs refer to a 30 year period, distinguishing three funding scenarios: Direct funding (no tax incentives), tax incentives (no direct funding) and the co-occurrence of both (direct funding and tax incentives). First simulation results for the Vienna life sciences innovation system suggest that the overall volume of required public funds could be lower for tax incentives than for direct funding. However, we find also indications that direct funding—in contrast to tax incentives—could have a decreasing effect on public investment per patent in the long run.
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Notes
- 1.
Reiss et al. (2005, pp. 74–75) used historical data (1994–2002) on policy activities and national performance in biotechnology for the validation of the historical analysis and benchmarked data regarding biotech policies in the year 2004.
- 2.
Life Science Austria Vienna Region (LISA VR) cluster (Life Sciences in the Vienna Region 2007).
- 3.
Self-reported research fields of life sciences organizations in the Vienna Region; Source: Data adapted from Austrian Life Sciences Directory (2009).
- 4.
A case-based model refers to an empirically circumscribed phenomenon, integrates rich empirical data and disposes of a high level of target and model details (Boero and Squazzoni 2005, pp. 8–10).
- 5.
This is an improvement of the attribute research direction in the SKIN model which is not empirically based and randomly determined (Gilbert et al. 2007, pp. 102–103).
- 6.
The concept of incremental and radical research behaviour was already introduced in the SKIN model, but implemented without empirical calibration and based on random choice (Gilbert et al. 2007, pp. 102–103).
- 7.
An invention is a new idea before its commercialisation (Fischer 2003, p. 344).
- 8.
The research concept is inspired by the innovation hypothesis used by Pyka et al. (2002, pp. 174–178).
- 9.
Knowledge spills over from the generating agent to other agents and there is a smaller compensation than the value of the knowledge, or even none (Fischer 2003, p. 345).
- 10.
R&D is short for Research and Development.
- 11.
This is done due to simplification reasons, although being aware of the fact that an IPO does not in every case lead to an increase in share capital, i.e. an increase in the financial stock respectively. It might also be the case that a previously private stake is offered at the stock exchange without an increase in share capital.
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Acknowledgements
This chapter reports results of research carried out in the framework of the Innovation Economics Vienna – Knowledge and Talent Development Program. The first author gratefully acknowledges the scholarship provided by this program. Furthermore, the first author would like to thank her doctoral supervisors Manfred M. Fischer, Andreas Pyka and Michael Barber for their valuable contributions in excellent discussions. Part of the research was also funded by the Austrian Science Fund through the project “Innovation networks for regional development” (FWF-DACH I886).
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Appendix
The presented model is programmed with the software tool NetLogo, version 5.0 (Wilensky 1999). The specific program code for the NetLogo model on which this paper is based is available from the author on request. The simulation runs described in Sect. 5.7 are based on the parameter settings given in Tables 5.5, 5.6, 5.7 and 5.8.
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Korber, M., Paier, M. (2014). Simulating the Effects of Public Funding on Research in Life Sciences: Direct Research Funds Versus Tax Incentives. In: Gilbert, N., Ahrweiler, P., Pyka, A. (eds) Simulating Knowledge Dynamics in Innovation Networks. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43508-3_5
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