Consumers increasingly rely on intermediaries (“influencers”) to provide information about products, often because product choice is vast. Examples include blogs, Twitter endorsements, and search engine results. Such advice is typically not paid for directly by the consumer, but instead the benefit to the influencer comes from mixing advice and endorsement, often in a way that is unobservable to the follower. Giving enough good advice is necessary to keep followers, but there is a tension between the best advice and most revenue. This paper models such a dynamic relationship between such an influencer and their follower. The relationship between influencer and follower evolves between periods of less and more ads. Influencers who inherently value attention provide better advice for followers. The model can provide insight into stricter enforcement of policies like the FTCs mandate of disclosure on paid Twitter endorsements. If disclosure makes adds less valuable, it may be that superior policies to tweet-by-tweet disclosure might exist. For instance a opt-in policy that effectively deregulates influencers with good reputations. The model can also be interpreted as a search engine that biases organic search results to maximize profits, potentially at the expense of providing advice that leads to competing services. Market power by the influencer may be good or bad for welfare, despite bias, suggesting that biased search results by a dominant engine is not necessarily a justification for antitrust-type action.
The past decade has witnessed a resurgence in innovation awards, in particular of Grand Innovation Prizes (GIPs) which are rewards to innovatorsdeveloping technologies reaching performance goals and requiringbreakthrough solutions. GIPs typically do not preclude the winner also obtaining patent rights. This is in stark contrast with mainstream economics of innovation theories where prizes and patents are substitute ways to generate revenue and encourage innovation. Building on the management of innovation literature which stresses the difficulty to specify ex-ante all the technical features of the winning technologies, we develop a model in which innovative effort is multi-dimensional and only a subset of innovation tasks can be measured and contracted upon. We show that in this environment patent rights and cash rewards are complements, and that GIPs are often preferable to patent races or prizes requiring technologies to be placed in the public domain. Moreover, our model uncovers a tendency for patent races to encourage speed of discovery over quality of innovation, which can be corrected by GIPs. We explore robustness to endogenous entry, costly public funds, and incomplete information by GIP organizers on the surplus created by the technology.
This paper considers the optimal design of patent rights for sequential innovations in an oligopoly. Firms contribute sequentially with unobserved efforts that transform ideas into improvements on a quality ladder. The optimal patent mechanism trades off incentives to encourage innovation efforts at different points in time. The optimal provision of incentives leads to strong asymmetries in the allocation of patent rights to firms and excludes all but one successful innovator in the limit. Treatment of the ex ante identical firms is ex post discriminatory. A simple implementation of the optimal mechanism with a system of patent fees is provided. Under an alternative policy arrangement, the allocation can be interpreted as optimal regulation of competition for the market.
This paper considers a moral hazard problem where the output is a design. The design refers to something whose value is subjective, possibly difficult to describe, and can be returned if the principal views it as unsatisfactory. Correlation of the principal's signal with the his true value plays a role, in contrast to standard principal agent problem; experience goods are different from credence goods. The principal's ability to forecast value corresponds to a notion of taste for the principal that distinguishes taste from judgment. The importance of “taste” impacts the cost of contracting as well as the type and number of agents contracted with. For examples where taste is relevant, uncertainty in the outcome may make the incentive contract less costly. Negative correlation between principal and agent's signals can sometimes be valuable; this can be interpreted as a value in “bad” taste of the principal. The results can be applied to the use of termination in dynamic relational contracts.