The market for online influence

Recent developments in social media have morphed the age-old practice of paying influential individuals for product endorsements into a multibillion-dollar industry, extending well beyond celebrity sponsorships. We develop a parsimonious model in which influencers trade off the increased revenue they obtain from paid endorsements with the negative impact that these have on their followers' engagement and, therefore, on the price influencers receive from marketers. The model provides testable predictions that match suggestive evidence, reveals a novel type of inefficiency that emerges in this market, and clarifies the role of search technology and advice transparency in shaping market activity. In particular, we show that recent policies that make paid endorsements more transparent can backfire, whereas an increase in the effectiveness of the search technology that matches followers to influencers has both direct and strategic positive welfare effects. Our model informs influencers on how to optimally select their mix of products endorsement and organic content. It also shows that managers of platforms hosting influencers may gain from investing in algorithms that screen good influencers and match them to core platform users. This strategy creates competitive pressure amongst influencers to provide better advice, thereby increasing the value of the platform's advertising service. In fact, regulators may also find it beneficial to encourage the implementation of these strategies, because the increase in overall welfare is large, and may not be fully appropriated by platforms

Digital Privacy

We study the incentives of a digital business to collect and protect users’ data. The users' data the business collects improve the service it provides to consumers, but they may also be accessed, at a cost, by strategic third parties in a way that harms users, imposing endogenous users' privacy costs. We characterize how the revenue model of the business shapes its optimal data strategy: collection and protection of users' data. A business with a more 'data-driven' revenue model will collect more users' data and provide more data protection than a similar business that is more 'usage-driven'. Consequently, if users have small direct benefit from data collection, then more usage-driven businesses generate larger consumer surplus than their more data-driven counterparts (the reverse holds if users have large direct benefit from data collection). Relative to the socially desired data strategy, the business may over- or under-collect users' data and may over- or under-protect it. Restoring efficiency requires a two-pronged regulatory policy, covering both data collection and data protection; one such policy combines a minimal data protection requirement with a tax proportional to the amount of collected data. We finally show that existing regulation in the US, which focuses only on data protection, may even harm consumer surplus and overall welfare

Pricing network effects: competition

We study the practice of in influencer marketing in oligopoly markets and its effect on market effciency. In our model, each consumer is influenced by choices of a subset of other consumers. Firms gather information on consumers influence and price discriminate using this information. In equilibrium, firms charge premia/subsidize below/above-average-influential consumers; the premia/discounts depend on the strength of network effects and on how much information firms have on consumers influence. Influencer marketing leads to inefficient consumer-product matches. Firms investments in information are strategic complements, leading to a race for information acquisition that erodes welfare and firms profits but increases consumer surplus

Vacuum extraction and neonatal jaundice

Peer Reviewe