Systemic interventions in regional innovation systems: entrepreneurship, knowledge accumulation and regional innovation

ABSTRACT

The paper employs a regional innovation system concept and divides system failures into three categories: institutional infrastructure, organizational landscape and structural connectedness. To analyze the economic effects of systemic interventions, it employs the VISIBLE model, which allows ex-ante policy experiments to be conducted in a virtual simulation environment. First, the findings show that regional learning and knowledge exchange are accompanied by pronounced non-linearities and combined learning strategies generate highest regional returns. Second, systemic interventions, originally designed to stimulate qualitatively different types of entrepreneurial entries, show – against the backdrop of different regional learning regimes – rather ambiguous effects for both the target firms and the incumbent firms. Finally, it can be seen that interventions designed to affect individual linking behaviour of entrepreneurial firms are effective and robust even for different learning regimes.

KEYWORDS:

• system failure
• regional innovation policy
• agent-based model

JEL:

• B52
• O31
• O38
• R11

ORCID

Andreas Pyka http://orcid.org/0000-0001-6207-6690

Muhamed Kudic http://orcid.org/0000-0003-3677-8300

Matthias Müller http://orcid.org/0000-0001-8213-0447

Notes

1. There is no general rule that allows for specifying imperfections within these categories in the sense that particular rules, or certain types of organization and cooperation, are missing. Instead, systemic characteristics are conceived to be imperfect based on systematic comparisons with benchmark systems.

2. Appendix D in the supplemental data online provides examples for potential system failures, corresponding policy interventions and related simulation scenarios.

3. Appendix C in the supplemental data online provides an overview of data sources and parameter settings.

4. For a discussion of the scientific value of ABM, the kene approach, calibration, validation and the usability of ABM for the ex-ante analysis of policy interventions, see the editorial.

5. The simulation is implemented in NetLogo (https://ccl.northwestern.edu/netlogo/).

6. For a detailed explanation, see Ahrweiler et al. (2011).

7. For the simulation results presented in the fourth section, it is assumed that: $\{C_{\mathrm{n}}\in \mathbb{N}|0\le C_{\mathrm{n}}\le 50\}$; $\{A_{\mathrm{n}}\in \mathbb{N}|0\le A_{\mathrm{n}}\le 20\}$; and $\{E_{\mathrm{n}}\in \mathbb{N}|0\le E_{\mathrm{n}}\le 20\}$.

8. See Appendix A in the supplemental data online for a flowchart of the model procedures.

9. Numerically, for organizational learning we use 7, for intra-regional learning we use 5 and for interregional learning we use 3 to express the increasing difficulties of learning, first outside the firm and second outside the region.

10. See Appendix B in the supplemental data online.

11. We assume one new entrant to the region at each simulation tick, while each of these newcomers automatically establishes a link to two randomly chosen incumbent firms.

12. The standard error for all presented results is lower than 10.

 

Additional information

Funding

This work was supported by Dieter Schwarz Foundation [VISIBLE 2.0 Project].