Professor Younge is an applied economist at the Swiss Federal Institute of Technology.
He uses big data, econometrics, and machine learning to examine the strategic importance of patent portfolios, financial disclosures, knowledge spillovers, and artificial intelligence. His work has been published by RAND, IEEE, the Review of Economics and Statistics, the Journal of Economics and Management Strategy, the Journal of Economic Behavior and Organization, the Strategic Management Journal, and the National Bureau of Economic Research.
He is a past winner of the Best Conference Paper of the Strategic Management Society and the BPS Outstanding Dissertation Award of the Academy of Management.
Professor Younge has co-founded five firms over the course of his career and worked as a Chief Technology Officer, Director of Development, and President. He also is a Director at Quant AI - an advisory firm focused on executive education and strategic consulting in the field of Artificial Intelligence. A recipient of numerous teaching awards, Professor Younge leads training programs and consulting engagements in Europe and the United States.
Review of Economics and Statistics
Forthcoming ( available online under Just Accepted )
Abstract: The United States patent system is unique in that it requires an applicant to cite documents they know to be relevant to the examination of their patent application. Lampe (2012) presents evidence that applicants strategically withhold 21-33% of relevant citations from patent examiners, suggesting that more than one in ten patents are fraudulently obtained. We challenge this view. We examine the institutional details of how courts identify strategic withholding and find that Lampe’s empirical design is inconsistent with both legal standards and standard operating procedures. We then compile a more up-to-date and detailed set of data to reassess the empirical basis for Lampe’s main claim. We find no evidence that applicants withhold citations from examiners.
Joint work with Jeffrey Kuhn and Alan Marco
Review of Economics and Statistics
Vol. 102, No. 3: pp. 569–582 ( 2020 )
Abstract: We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how individuals persist at exploration in an uncertain environment when confronted by prolonged periods of negative feedback. To examine this question, we design a two-dimensional maze game and run a series of randomized experiments with human subjects in the game. Our results suggest that individuals explore more when they are reminded of the incremental cost of their actions, a result that extends prior research on loss aversion and prospect theory to environments characterized by model uncertainty. In addition, we run simulations based on a model of reinforcement learning, that extend beyond two-period models of decision-making to account for repeated behavior in longer-running, dynamic contexts.
Joint work with Yaroslav Rosokha
The RAND Journal of Economics
Vol. 51, No. 1: pp. 109–132 ( 2020 )
Abstract: Many studies rely on patent citations to measure intellectual heritage and impact. In this article, we show that the nature of patent citations has changed dramatically in recent years. Today, a small minority of patent applications are generating a large majority of patent citations, and the mean technological similarity between citing and cited patents has fallen considerably. We replicate several well-known studies in industrial organization and innovation economics and demonstrate how generalized assumptions about the nature of patent citations have misled the field.
Joint work with Jeffrey Kuhn and Alan Marco
IEEE - 18th International Conference on Machine Learning and Applications
DOI 10.1109/ICMLA.2019.00120 ( 2019 )
Abstract: Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.
Joint work with Omid Shahmirzadi and Adam Lugowski
Journal of Economic Behavior & Organization
Vol. 150: pp. 162-181 ( 2018 )
Abstract: While executives play an important role in leading firm innovation, they may economize on efforts to innovate when protected from takeover threat. Middle managers may curtail the rate and scope of innovation when executives are expected to reduce their innovation involvement. We test our prediction by exploiting a natural experiment in Delaware where court rulings increased takeover protection for Delaware firms. Difference-in-differences estimates show that increased takeover protection reduced the rate of innovation by firms, and that it also reduced the scope of innovation across several key dimensions (technological, temporal, organizational, and international). Consistent with our argument, we find that the negative effect of takeover protection on innovation was weaker for larger firms, where innovation decision making authority is more likely to be delegated to middle managers and executive involvement is lower. Finally, we examine the substitutive relationship between competitive pressures from the takeover market and the product market, and find that the negative effect of takeover protection on innovation was stronger for firms facing low competitive pressure from the product market.
Joint work with Tony Tong
Journal of Economics & Management Strategy
Vol. 25: pp. 652-677 ( 2016 )
Abstract: We estimate the firm‐level returns to retaining employees using difference‐in‐differences analysis and a natural experiment where the enforcement of employee noncompete agreements was inadvertently reversed in Michigan. We find that noncompete enforcement boosted the short‐term value of publicly traded companies by approximately 9%. The effect is increasing in local competition and growth opportunities, and offset by patenting.
Joint work with Matt Marx
Strategic Management Journal
Vol. 36: pp. 686-708 ( 2015 )
Abstract: This study draws on strategic factor market theory and argues that acquirers' decisions regarding whether to bid for a firm reflect their expectations about employee departure from the firm post‐acquisition, suggesting a negative relationship between the anticipated employee departure from a firm and the likelihood of the firm becoming an acquisition target. Using a natural experiment and a difference‐in‐differences approach, we find causal evidence that constraints on employee mobility raise the likelihood of a firm becoming an acquisition target. The causal effect is stronger when a firm employs more knowledge workers in its workforce and when it faces greater in‐state competition; by contrast, the effect is weaker when a firm is protected by a stronger intellectual property regime that mitigates the consequences of employee mobility.
Joint work with Tony Tong and Lee Fleming
National Bureau of Economic Research
Adam Jaffe and Ben Jones, editors -- The Changing Frontier: Rethinking Science and Innovation Policy
Chapter 7: pp. 199 - 232 ( 2015 )
Abstract: We document three facts related to innovation and entrepreneurship in renewable energy. Using data from the US Patent and Trademark Office, we first show that patenting in renewable energy remains highly concentrated in a few large energy firms. In 2009, the top 20% firms accounted for over 40% of renewable energy patents in our data. Second, we compare patenting by venture capital-backed startups and incumbent firms. Using a variety of measures, we find that VC-backed startups are engaged in more novel and more highly cited innovations, compared to incumbent firms. Incumbent firms also have a higher share of patents that are completely un-cited or self-cited, suggesting that incumbents are more likely to engage in incremental innovation compared to VC-backed startups. Third, we document a rising share of patenting by startups that coincided with the surge in venture capital finance for renewable energy technologies in the early 2000s. We also point to structural factors about renewable energy that have led the availability of venture capital finance for renewable energy to fall dramatically in recent years, with potential implications for the rate and trajectory of innovation in this sector.
Joint work with Ramana Nanda and Lee Fleming
Wiley Blackwell Outstanding Dissertation Award ( 2013 )
Abstract: In this dissertation, I argue that employee mobility is a key consideration of the firm. Firms often rely on human assets to generate and maintain knowledge. When key individuals depart the firm, they take knowledge with them, potentially undermining the firm or helping competitors. Specifically, I theorize as to how the potential for employee departure affects firm value, and empirically examine my hypotheses in strategy contexts such as M&As, R&D, and equity investment.
National Renewable Energy Laboratory
NREL/TP-6A20-50624 ( 2011 )
Abstract: Low-carbon energy innovation is essential to combat climate change, promote economic competitiveness, and achieve energy security. Using U.S. patent data and additional patent-relevant data collected from the Internet, we map the landscape of low-carbon energy innovation in the United States since 1975. We isolate 10,603 renewable and 10,442 traditional energy patents and develop a database thatcharacterizes proxy measures for technical and commercial impact, as measured by patent citations and Web presence, respectively. Regression models and multivariate simulations are used to compare the social, institutional, and geographic drivers of breakthrough clean energy innovation. Results indicate statistically significant effects of social, institutional, and geographic variables ontechnical and commercial impacts of patents and unique innovation trends between different energy technologies. We observe important differences between patent citations and Web presence of licensed and unlicensed patents, indicating the potential utility of using screened Web hits as a measure of commercial importance. We offer hypotheses for these revealed differences and suggest a researchagenda with which to test these hypotheses. These preliminary findings indicate that leveraging empirical insights to better target research expenditures would augment the speed and scale of innovation and deployment of clean energy technologies.
Joint work with Thomas Perry, Mackay Miller, Lee Fleming, and James Newcomb
An award winning teacher in 2011, 2014, 2015, 2016, 2017, 2020, and 2021.
I'm married, with one wonderful wife, two spunky children, and three crazy dogs. I'm also an avid climber, with ascents on 5.13 rock, WI5 ice, D8 dry tooling, A4 big walls, and adventures in Yosemite, Nepal, Bolivia, Argentina, and Chile. I still love to get out, and up, whenever I can.
I don't have a Facebook page and I don't tweet. My LinkedIn profile has zero connections and you won't find much about me online. Frankly, I'm skeptical that 'social' media is making our lives better. If you would like to connect, just email me and let's have coffee or zoom. Or stop by my office.