When would online platforms pay data dividends?

Online platforms, including social media and search platforms, have routinely used their users’ data for targeted ads, to improve their services, and to sell to thirdparty buyers. But an increasing awareness of the importance of users’ data privacy has led to new laws regulating platform data-sharing. Further, there have been political discussions on introducing data dividends, that pay users for their data. Three interesting questions are then: When would these online platforms be incentivized to pay data dividends? How does their decision depend on whether users value their privacy more than the platform’s free services? And should platforms invest in protecting users’ data? This paper considers various factors affecting the users’ and platform’s decisions through utility functions. We construct a principal-agent model using a Stackelberg game to calculate their optimal decisions and qualitatively discuss the implications. Our results could inform a policymaker trying to understand the consequences of mandating data dividends.