Reproduction Package for Collaboration for the Bioeconomy

This repository contains the reproduction package for: Kreutzer, P.J. (2026). "The Bioeconomy — A New Life Cycle Phase For Swedish Forestry: Evidence from 50 Years of Significant Innovation Output." This study is included as a chapter in the PhD dissertation, Trees Like Cabbages – Innovation Towards a Forest-Based Bioeconomy, Lund University, 2026. Contents The package includes all code and data needed to reproduce the figures, tables, and inline statistics reported in the paper.The analysis pipeline is implemented in Python and the manuscript is authored in Quarto. The repository contains: - The manuscript source files (paper.qmd, _code-setup.qmd, _appendix-data.qmd, _appendix_robustness.qmd) - The Python analysis package (swinno_bioeconomy_network/), which constructs collaboration networks, estimates panel regressions, and generates all figures - Innovation and collaboration data derived from the SWINNO database (Swedish innovations, 1970--2021), including aggregated collaboration records, firm entry/exit years, and industry classification codes - External reference data for sectoral patent propensity - Pre-built regression models for robustness checks that require non-default pipeline parameters - A permanent copy of the interactive companion website referenced in the paper How to Reproduce The package uses https://pixi.sh to manage all dependencies, including Python, Quarto, and a TeX Live distribution.To reproduce: pixi install -e repropixi run -e repro reproduce This executes seven tasks in sequence: processing innovation experience data, creating collaboration edge lists, building network graphs, constructing the panel dataset, running Poisson panel regressions, generating all publication figures, and rendering the final PDF via Quarto and LuaLaTeX. The package has been tested on macOS (ARM).Dependencies are solved for macOS, Linux, and Windows.On Windows, I recommend running inside WSL (Windows Subsystem for Linux) for full compatibility. Data Sources Innovation and collaboration data are drawn from the SWINNO database, a literature-based innovation output database covering significant innovations commercialized in Sweden. Collaboration networks are constructed from SWINNO's producer and collaborator records, aggregated to the firm level. Firm entry and exit years are derived from the same source. Industry classification codes are matched from Statistics Sweden (SCB) registry data. Sectoral patent propensity data are from Johansson and Taalbi.