Estimating the GDP effect of Open Source Software and its complementarities with R&D and patents: evidence and policy implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it