Events

Prof. Jinhwan Kim
Associate Professor of Accounting
Graduate School of Business
Stanford University

We examine how patent disclosures influence patent examiners’ technology classification decisions and how firms strategically tailor these disclosures to affect classification outcomes. Using a novel machine learning approach, we measure “greenwashing” in patent disclosures by comparing the likelihood of receiving a green classification based on the text of the claims section (a succinct, standardized articulation of the invention’s key features and legal scope) versus the detailed description section (a full technical explanation of the invention). Analyzing U.S. patent applications, we find that examiners place greater weight on the claims section, especially when they are busier, creating opportunities for firms to strategically craft claims to increase the chances of receiving a green classification, even when the invention is less fundamentally green. Firms are more likely to engage in greenwashing following negative environmental incidents, when incentives to appear environmentally friendly intensify. These effects are stronger when firms expect examiners to be busier, employ more sophisticated patent lawyers, and face greater public attention to environmental incidents. Patents filed by these opportunistic firms are less likely to be accepted by examiners and cited by subsequent innovations. Overall, our findings highlight an unintended consequence of emphasizing claims over detailed descriptions to reduce examiners’ information processing costs: this emphasis can facilitate greenwashing in patent disclosures.