One of the main rationales for the existence of the patent system is to encourage knowledge diffusion through the full disclosure of the technical knowledge embodied in a patented invention. Yet, economists and legal scholars cast doubts on the validity of the disclosure theory. The empirical evidence on the actual benefits of the disclosure function remains limited. The present paper aims to expand our understanding of how information spreads via patent disclosure and exploits recent improvements in machine translation (MT) to identify the effect of broader access to patented knowledge. More specifically, the paper uses a unique natural experiment. In September 2013, Google launched a major upgrade to its Google Patents service and added patent applications from the China National Intellectual Property Agency (CNIPA) to its searchable patent database. Using a difference-in-differences approach, we show that the translation of the Chinese patents into English resulted in an increase in citations received from patents filed by US inventors compared to a suitable control group comprising patents that Google translated only in 2016. Our results suggest that improved access to patented knowledge fosters knowledge diffusion.
@article{buttner2022,title={Patents and Knowledge Diffusion: The Impact of Machine Translation},shorttitle={Patents and Knowledge Diffusion},author={Büttner, Benjamin and Firat, Murat and Raiteri, Emilio},year={2022},month=dec,journal={Research Policy},volume={51},number={10},pages={104584},issn={0048-7333},doi={10.1016/j.respol.2022.104584},urldate={2025-05-07},langid={english},dimensions={false},}
Deriving Experience Curves: A Structured and Critical Approach Applied to PV Sector
Prapti Maharjan, Mara Hauck, Arjan Kirkels, Benjamin Büttner, and Heleen de Coninck
Technological Forecasting and Social Change, Dec 2024
Experience curves are widely used for cost estimates in energy-economy models and are proposed as a forecasting tool for projecting the future environmental impact of emerging technologies. However, further application is limited by data availability and methodological challenges related to modelling the dynamic relationship between cost, different kinds of learning, and scale effects. This paper systematically compares existing experience curves using empirical data from the PV sector. We compare the cost forecast of the assessed experience curves, derive the learning rates over different periods, and draw parallels to the environmental experience curve. Our results show that the single-factor experience curve (SEFC) is the most stable model, showing consistent performance across different technological eras, train-test splits and validation methods. Two-factor and multi-factor experience curves exhibit higher sensitivity, with their performance metrics varying significantly based on the data subsets used. Diagnostic tests are important to examine the robustness of the results. For the environmental experience curve, data quality and model explanatory power are lower, yet there is potential for its applicability in projecting environmental impact and energy use. Policymakers and modellers should consider the specific technological era when using learning rates for decision-making. Our findings indicate that learning-by-doing provides a steady learning rate across all experience curves. In the early stages of technological maturity, cost reductions in the PV industry are driven by learning-by-innovation, which is later dominated by economies of scale.
@article{maharjan2024,title={Deriving Experience Curves: A Structured and Critical Approach Applied to {{PV}} Sector},shorttitle={Deriving Experience Curves},author={Maharjan, Prapti and Hauck, Mara and Kirkels, Arjan and Büttner, Benjamin and {de Coninck}, Heleen},year={2024},journal={Technological Forecasting and Social Change},volume={209},pages={123795},publisher={Elsevier},doi={10.1016/j.techfore.2024.123795},urldate={2025-05-01},langid={english},dimensions={false},}
Conference papers
Patent Disclosure and Migration: Unraveling the Role of Examiners in Signaling Talent and Knowledge Transfer