Collaboration for the Bioeconomy -- Evidence from Innovation Output in Sweden, 1970-2021

Collaboration is expected to play a central role in the transition to a bioeconomy - a central pillar of a green economy. Such collaboration is supposed to connect traditional biomass processing firms with diverse actors in fields where biomass ought to substitute existing or create novel products and processes. This study analyzes the network of technology collaborations among innovating firms in Sweden between 1970 and 2021. The results reveal generally positive associations between direct and indirect ties, with meaningful increases in innovation output for each additional direct collaboration partner. Relationships between brokerage positions and innovation output were statistically insignificant, and cognitive proximity - while following theoretical expectations - materially insignificant. These associations are mostly equal between actors heavily invested in the bioeconomy and those focusing on other innovation areas, indicating that these actors operate under largely similar mechanisms linking collaboration and subsequent innovation output. These results suggest that stimulating collaboration broadly - rather than attempting to optimize collaboration compositions - could result in higher number of significant Swedish innovations, for bioeconomy and other sectors alike.

💡 Research Summary

This paper investigates how technology collaboration networks have shaped innovation output among Swedish firms over a five‑decade period (1970‑2021), with a particular focus on the forest‑based bioeconomy. Unlike most prior work that relies on patents or R&D expenditures as proxies, the authors employ a literature‑based innovation output (LBIO) database (SWINNO) that records 4,972 commercially launched products and processes identified from trade‑journal articles. By extracting all firms and institutions mentioned in the development of each innovation, they construct yearly, firm‑level collaboration networks based on joint technology development.

The network variables include (i) the number of direct partners (degree), (ii) the number of indirect partners (two‑step neighbors), (iii) a structural‑hole (brokerage) measure, and (iv) cognitive proximity, operationalised through patent‑class similarity as a proxy for knowledge base overlap. Innovation output is the annual count of new products/processes per firm. The authors estimate Poisson panel regressions and supplement the analysis with instrumental‑variable techniques to mitigate endogeneity between network positions and innovation performance.

Key findings:

1. Direct collaborations matter most – each additional partner raises a firm’s expected yearly innovation count by roughly 4‑5 % (statistically significant).
2. Indirect ties also have a positive effect, albeit smaller than direct ties, confirming the “knowledge spill‑over” hypothesis.
3. Structural‑hole (brokerage) positions do not exhibit a significant relationship with innovation output, contradicting the classic Burt (1992) argument that spanning disconnected sub‑groups boosts creativity.
4. Cognitive proximity shows no meaningful linear or inverted‑U effect; firms neither benefit from moderate similarity nor suffer from excessive similarity in this context.

When the sample is split between bioeconomy‑intensive firms (≈22 % of innovations) and other firms, the coefficients on all network variables are virtually identical. Bioeconomy firms produce fewer innovations overall, but the mechanisms linking collaboration to innovation are not distinct from those of non‑bioeconomy firms.

Policy implications are clear: stimulating the sheer volume of collaborations—through broader platforms, joint research facilities, and measures that lower entry barriers for small and medium enterprises—should be more effective than attempts to fine‑tune partnership composition or to engineer optimal cognitive distances. The study also underscores the value of using direct commercialisation data rather than patents, especially in sectors like forestry where patenting rates are low.

Limitations include the focus on formal joint‑development ties (informal knowledge exchange is omitted), reliance on patent‑class similarity for cognitive proximity (which may not capture non‑patented knowledge), and the single‑country, single‑industry context that may limit external validity. Future research could integrate social network data, geographic proximity, and policy shock analyses (e.g., EU bioeconomy strategies) to deepen understanding of how collaboration shapes bio‑innovation.