The benefits of membership in policy-stimulated clusters in Hungary

ABSTRACT

The purpose of this article is to describe the successes and failures of policy-stimulated business clusters based on regional Hungarian experiences. The after-subsidy survival rate, affected industry domains, and the occurrent advantages of such cooperation for cluster members were examined. Primary data were collected at the member level. The research covers all clusters set up before 2015 with deeds of foundation, in two very different regions. Results show that 24% of the clusters were working between 2015-2016, two years after the period of government subsidy ended. The main benefits to cluster members include better personal connections and increased trust and knowledge transfer, although cost reductions, easier access to finance and product specialization were not reported. Based on this knowledge, cluster policies can be fine-tuned, resulting in more sustainable initiatives, while clusters can be made potential partners in regional planning, research and development policy, as well as in smart specialization.

KEYWORDS:

• Regional policy
• cluster
• cluster advantages
• cluster survival
• cooperation
• industrial policy

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 Usually 2–4 years, depending on the time of application for a grant (Round 2008 or 2011).

2 Labour market pooling, technological spillovers, and specialised intermediate inputs and services.

3 The GDP per capita of Central Hungary as a percentage of national average was 152.5% while that of NH was 65.5% and WT 109.1% (Hungarian Central Statistical Office, 2019).

4 Together with Central Transdanubia.

5 Call ID: ÉMOP-2008-1.2.1; ÉMOP-1.2.1-11; NYDOP-2008-1.1.1/A; NYDOP-1.1.1/A/11.

6 The definitions listed in the official NACE system did not always match the economic activities of innovative companies. New, knowledge-intensive technologies are difficult to distinguish, and some other NACE categories are too broad, with few sub-categories.

7 A total of eighteen papers, because two of them were listed in both the ‘top founders’ and the ‘top disseminators’ rankings. The fact that the same number of articles were analysed and advantages identified is a coincidence.

8 N.b.: these clusters were very few in number: they are estimated at fewer than four in the two regions, representing a proportion of less than 4%.

9 Although some findings indicate that cluster membership can have a negative effect on firm R&D intensity (Lee, 2009).