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

Philipp Jonas Kreutzer, Josef Taalbi

TL;DR

This study investigates how collaboration networks influence actual innovation output in Sweden's forest-based bioeconomy from 1970–2021 by linking a literature-based innovation dataset (SWINNO) with firm-level collaboration data and estimating Poisson panel models. It finds robust evidence that direct collaboration ties increase subsequent innovation, while indirect ties and brokerage show no clear effects; cognitive proximity follows an inverted-U relationship, but the optimal proximity yields only modest gains. Importantly, bioeconomy firms do not operate under fundamentally different collaboration dynamics than other firms, though they exhibit lower overall innovation counts, suggesting barriers beyond collaboration patterns. The results imply that policy should emphasize broad, barrier-reducing collaboration across the innovation system rather than optimizing the composition of bioeconomy partnerships, to foster more Swedish innovations in bioeconomy and related sectors.

Abstract

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.

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

TL;DR

This study investigates how collaboration networks influence actual innovation output in Sweden's forest-based bioeconomy from 1970–2021 by linking a literature-based innovation dataset (SWINNO) with firm-level collaboration data and estimating Poisson panel models. It finds robust evidence that direct collaboration ties increase subsequent innovation, while indirect ties and brokerage show no clear effects; cognitive proximity follows an inverted-U relationship, but the optimal proximity yields only modest gains. Importantly, bioeconomy firms do not operate under fundamentally different collaboration dynamics than other firms, though they exhibit lower overall innovation counts, suggesting barriers beyond collaboration patterns. The results imply that policy should emphasize broad, barrier-reducing collaboration across the innovation system rather than optimizing the composition of bioeconomy partnerships, to foster more Swedish innovations in bioeconomy and related sectors.

Abstract

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.
Paper Structure (34 sections, 3 equations, 4 figures, 8 tables)

This paper contains 34 sections, 3 equations, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Previous Literature
  3. Collaboration Networks and Innovation
  4. Collaboration Networks in the Bioeconomy
  5. Data and Methods
  6. Collaborations in Swedish Literature-Based Innovation Output Data
  7. Collaboration Data
  8. Bioeconomy Operationalization
  9. Bioeconomy Innovation
  10. Bioeconomy Organizations
  11. Network Construction and Variable Definition
  12. Network Position Variables
  13. Direct and Indirect Ties
  14. Brokerage
  15. Cognitive Proximity
  16. ...and 19 more sections

Figures (4)

  • Figure 1: Patent Propensity of Bioeconomy Innovations by Sector. Calculations based on Johansson et al. ( ref-johansson2022LinkingInnovationsPatents) and Taalbi ( ref-taalbi2025InnovationPatentsInformationtheoretic).
  • Figure 2: Panel Composition and Innovation Output Over Time. Number of active firms (panel a), total annual innovations (panel b), mean innovation rate per firm (panel c), and standard deviation (panel d). Bioeconomy firms (dashed) and total firms (solid).
  • Figure 3: Sweden's Innovation Collaboration Network (1970--2021). Panel a) depicts all collaborations observed in our study period; panel b) depicts component size distributions and panel c) the log-binned degree distribution. Isolated nodes are omitted. Green edges represent bioeconomy collaborations, gray edges other collaborations.
  • Figure 4: Predictions of Subsequent Innovation by Network and Knowledge Predictors. Predicted innovation counts comparing bioeconomy firms (dashed green) and non-bioeconomy firms (solid gray). Panel a) direct ties (nodes distance 1), panel b) indirect ties (nodes distance 2), panel c) two-step betweenness, panel d) cognitive proximity. Dots show sample means, shaded areas show 95% confidence intervals. Based on Poisson model including year FE and standard errors clustered by firm (column 8 in Table \ref{['tbl-panel-main-results']}). Panel a) and b) cover the 99th precentile of sample values, to avoid distortion from outliers; panel c) displays the full empirically observed range; and panel d) the theoretically possible range of the cognitive proximity variable. Other covariates held at representative values.