Discussion Paper No. 494
February 13, 2024
Patents, Freedom to Operate, and Follow-on Innovation: Evidence from Post-Grant Opposition
Author:
Abstract:
We study the blocking effect of patents on follow-on innovation by others. We posit that follow-on innovation requires freedom to operate (FTO), which firms typically obtain through a license from the patentee holding the original innovation. Where licensing fails, follow-on innovation is blocked unless firms gain FTO through patent invalidation. Using large-scale data from post-grant oppositions at the European Patent Office, we find that patent invalidation increases follow-on innovation, measured in citations, by 16% on average. This effect exhibits a U-shape in the value of the original innovation. For patents on low-value original innovations, invalidation predominantly increases low-value followon innovation outside the patentee’s product market. Here, transaction costs likely exceed the joint surplus of licensing, causing licensing failure. In contrast, for patents on high-value original innovations, invalidation mainly increases high-value follow-on innovation in the patentee’s product market. We attribute this latter result to rent dissipation, which renders patentees unwilling to license out valuable technologies to (potential) competitors.
Keywords:
follow-on innovation; freedom to operate; licensing; patents; opposition;
JEL-Classification:
O31; O32; O33; O34;
Download:
Discussion Paper No. 492
Interpersonal Preferences and Team Performance: The Role of Liking in Complex Problem Solving
Author:
Abstract:
Organizations increasingly rely on teams to solve complex problems. The ability of teams to work well together is critical to their success. I experimentally test whether team performance is affected by whether team members like each other. I find that teams in which partners like each other do not outperform teams in which partners dislike each other. However, teams in which one partner likes the other more than the other perform best. The performance differences result directly from changes in collaborative behavior when learning the team partner's interpersonal preferences, not indirectly from interacting with different individuals. Participants do not anticipate this pattern and expect to be most successful in a team where partners like each other. This provides insights into how teams should be optimally composed, when self-selection may be detrimental to performance, and what information about others' interpersonal preferences should be revealed.
Keywords:
interpersonal preferences; teamwork; liking; complex problem solving; non-routine tasks;
JEL-Classification:
C92; D23; D83; D91;
Download:
Discussion Paper No. 487
December 20, 2023
More than Joints: Multi-Substance Use, Choice Limitations, and Policy Implications
Author:
Abstract:
As illicit substances move into the legal product space, substitution patterns with legal products become more salient. In particular, marijuana legalization may have implications for the use of other legal “sin” goods. We estimate a structural model of multi-product use of illegal and legal substances considering joint use, limited access to illicit products, and persistence in use. We focus on a young person’s choice to consume marijuana, alcohol or cigarettes (and possible combinations), and we find that sin goods are complements. Furthermore, our findings emphasize the necessity of accounting for joint consumption and access to obtain correct price sensitivity estimates. Post-legalization, youth marijuana use would increase from 25% to 37%. However, counterfactual results show that a combination of (reasonable) tax increases on all goods along with enforcement against illegal use can potentially revert use to pre-legalization levels. The earlier the tax increases are implemented the more effective they are at curbing future use. Our results inform the policy debate regarding the impact of marijuana legalization on the long-term use of sin goods.
Keywords:
complementarity, marijuana legalization, limited choice sets, data restrictions, discrete choice models; marijuana legalization; limited choice sets; data restrictions; discrete choice models;
JEL-Classification:
C11; D12; L15; K42; H02; L66; C35;
Download:
Discussion Paper No. 447
November 9, 2023
Reputational Concerns and Advice-Seeking at Work
Author:
Abstract:
We examine the impact of reputational concerns on seeking advice. While seeking can improve performance, it may affect how others perceive the seeker's competence. In an online experiment with white-collar professionals (N=2,521), we test how individuals navigate this tradeoff and if others' beliefs about competence change it. We manipulate visibility of the decision to seek and stereotypes about competence. Results show a sizable and inefficient decline in advice-seeking when visible to a manager. Higher-order beliefs about competence cannot mediate this inefficiency. We find no evidence that managers interpret advice-seeking negatively, documenting a misconception that may hinder knowledge flows in organizations.
Keywords:
advice-seeking; reputational concerns; stereotypes; higher-order beliefs; knowledge flows; experiment;
JEL-Classification:
D16; D21; D83; D91; M51;
Download:
Discussion Paper No. 440
October 30, 2023
Logic Mill - A Knowledge Navigation System
Author:
Abstract:
Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a generalpurpose tool for future research applications in the social sciences and other domains.
Keywords:
JEL-Classification:
Download:
Discussion Paper No. 439
Ruled by Robots: Preference for Algorithmic Decision Makers and Perceptions of Their Choices
Author:
Abstract:
As technology-assisted decision-making is becoming more widespread, it is important to understand how the algorithmic nature of the decisionmaker affects how decisions are perceived by the affected people. We use a laboratory experiment to study the preference for human or algorithmic decision makers in re-distributive decisions. In particular, we consider whether algorithmic decision maker will be preferred because of its unbiasedness. Contrary to previous findings, the majority of participants (over 60%) prefer the algorithm as a decision maker over a human—but this is not driven by concerns over biased decisions. Yet, despite this preference, the decisions made by humans are regarded more favorably. Participants judge the decisions to be equally fair, but are nonetheless less satisfied with the AI decisions. Subjective ratings of the decisions are mainly driven by own material interests and fairness ideals. For the latter, players display remarkable flexibility: they tolerate any explainable deviation between the actual decision and their ideals, but react very strongly and negatively to redistribution decisions that do not fit any fairness ideals. Our results suggest that even in the realm of moral decisions algorithmic decision-makers might be preferred, but actual performance of the algorithm plays an important role in how the decisions are rated.
Keywords:
delegation; algorithm aversion; redistribution; fairness;
JEL-Classification:
C91; D31; D81; D9; O33;
Download:
Discussion Paper No. 438
Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making
Author:
Abstract:
Are people algorithm averse, as some previous literature indicates? If so, can the retention of human oversight increase the uptake of algorithmic recommendations, and does keeping a human in the loop improve accuracy? Answers to these questions are of utmost importance given the fast-growing availability of algorithmic recommendations and current intense discussions about regulation of automated decision-making. In an online experiment, we find that 66% of participants prefer algorithmic to equally accurate human recommendations if the decision is delegated fully. This preference for algorithms increases by further 7 percentage points if participants are able to monitor and adjust the recommendations before the decision is made. In line with automation bias, participants adjust the recommendations that stem from an algorithm by less than those from another human. Importantly, participants are less likely to intervene with the least accurate recommendations and adjust them by less, raising concerns about the monitoring ability of a human in a Human-in-the-Loop system. Our results document a trade-off: while allowing people to adjust algorithmic recommendations increases their uptake, the adjustments made by the human monitors reduce the quality of final decisions.
Keywords:
automated decision-making; algorithm aversion; algorithm appreciation; automation bias;
JEL-Classification:
O33; C90; D90;
Download:
Discussion Paper No. 434
October 24, 2023
Self-preferencing, Quality Provision, and Welfare in Mobile Application Markets
Author:
Abstract:
Platforms often display their products ahead of third-party products in search. Is this due to consumers preferring platform-owned products or platforms engaging in self-preferencing by biasing search towards their own products? What are the welfare implications? I develop a structural model of mobile application markets to identify self-preferencing and quantify its welfare effects, taking into account third-party developers' quality adjustment. A new dataset on app downloads, prices, characteristics, and search rankings is used to estimate the model. Estimates indicate self-preferencing. Simulations show higher consumer welfare and third-party profits without self-preferencing.
Keywords:
competition policy; platform design; consumer search; endogenous product choice;
JEL-Classification:
D12; D83; L13; L86;
Download:
Discussion Paper No. 432
October 19, 2023
Monopsony and Automation
Author:
Abstract:
We examine the impact of labor market power on firms' adoption of automation technologies. We develop a model that incorporates labor market power into the task-based theory of automation. We show that, due to higher marginal cost of labor, monopsonistic firms have stronger incentives to automate than wage-taking firms, which could amplify or mitigate the negative employment effects of automation. Using data from US commuting zones, our results show that commuting zones that are more exposed to industrial robots exhibit considerably larger reductions in both employment and wages when their labor markets demonstrate higher levels of concentration.
Keywords:
automation; employment; labor market concentration; industrial robots; wage setting;
JEL-Classification:
J23; J30; J42; L11; O33;
Download:
Discussion Paper No. 419
August 25, 2023
Allegations of Sexual Misconduct, Accused Scientists, and Their Research
Author:
Abstract:
Does the scientific community sanction sexual misconduct? Using a sample of scientists accused of sexual misconduct at US universities, we find that their prior work is cited less after allegations surface. The effect weakens with distance in the coauthorship network, indicating that researchers learn about allegations through their peers. Among the closest peers, male authors react more strongly, suggesting that they feel a greater need to disassociate themselves from the accused. In male-dominated fields, the effects on citations are more muted. Accused scientists are more likely to leave academic research, to move to non-university institutions, and to publish less.
Keywords:
sexual misconduct; scientific community; scientific impact;
JEL-Classification:
J16; M14; I23; K4;