Estimating the GDP effect of Open Source Software and its complementarities with R&D and patents: evidence and policy implications
Bibliographic record
Abstract
Abstract Open Source Software (OSS) has become an increasingly important knowledge asset in modern economies. However, the economic impact of OSS on countries’ GDP is ambivalent due to its public good character. Using a cross-country panel from 2000 to 2018, including 25 of the largest EU countries plus the USA, Japan, Korea, Canada, China, Norway, and Switzerland, matching OSS commits to GitHub to macroeconomic data provided by the OECD, our results confirm the dual nature of OSS. On the one hand, the open-access character creates great learning potential by providing a commonly accessible productive resource for all countries. On the other hand, it creates outward-directed spillovers associated with own OSS contributions. Accordingly, on average, we find that countries experience an increase in GDP when the world stock of OSS grows. However, smaller countries experience a decline in GDP resulting from their own contributions due to knowledge spillovers. The net effect is nonetheless positive. If no country contributed to OSS development, GDP for the average country would be 2.2% lower in the long run. Moreover, the losses associated with unintended spillovers are lower for countries with a higher R&D and patenting intensity. Based on our findings, we derive implications for policies and regulations concerning OSS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".