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In their report to the US Securities and Exchange Commission from July 2019, Alphabet (GOOGL) reported that advertising revenues—generated by the company’s data-driven ad targeting services—reached $32.6 billion in the latest quarter, making up 83.7 percent of total revenue.
The IMF (2017a) has also explored how the combination of big data with artificial intelligence, distributed computing, and cryptography could change the provision of financial services, with a special emphasis on cross-border payments.
As we will discuss in the following section, a failure to recognize the right to privacy can be conceived as an economic externality.
Winegar and Sunstein (2019) present survey evidence that individuals demand significantly more money in exchange for granting access to their health data than to their demographic data.
In many cases, data is generated as a byproduct of other economic activities. Even then, its storage, management, and ensuring its security all represent costs to the collector.
Data may also be collected for use as a consumption good itself, as in the case where users use smart devices to monitor their biometric activity—the number of steps they have taken in the day or the length of their sleep cycles—often as a hobby and sometimes with medical ends in mind. We focus here on data collected for commercial purposes.
Note that under the European Union’s General Data Protection Regulation, the act of data collection is also considered “processing.”
This function is not restricted to the private sector. Public institutions also analyze data to better understand the impact of their policies.
Ezrachi and Stucke (2016) argue that data-based “behavioral discrimination” has the potential to complement perfect price discrimination strategies by using personalized emotional cues to influence consumer preferences.
Adverse selection refers to the fact that the borrower’s willingness to accept a very high interest rate signals to the lender that they are unlikely to repay the loan. The higher the interest rate offered by the lender, the riskier the pool of borrowers willing to accept it.
Some scholars argue that this is not a paradox at all, but rather reflects the difficulty of evaluating these types of trade-offs, which are inherently intertemporal and opaque. For instance, a user must decide if they are willing to sacrifice the tangible benefit of using a webpage today versus the potential cost of having their privacy compromised in unspecified ways at an unspecified time in the future.
Pagano and Jappelli (1993) present a model where data sharing about customers can arise endogenously among banks. They argue that the incentives to participate in a bureau are greatest when the bank is faced by many customers on which it has little prior information. However, information sharing may not be in the interest of an incumbent that enjoys some degree of market power, as doing so would subject them to increased competition and face the loss of monopoly profits.
The Stanford dog breed classification project ran a public competition where teams were provided with a data set of tagged photos containing dogs of different breeds. Competitors then wrote machine learning algorithms that could predict the breed from a new image containing a dog.
Some data involves multiple data subjects. For instance, one person may divulge that they are friends with another or that they both attended a political gathering. If the first decides to share this data, it may have implications for the second.
Bank for International Settlements (2019) discusses the use of data held by nonbank Big Tech firms to provide financial services such as credit scoring and loan monitoring.
Financial Stability Board (2017) flags several channels for data-driven financial technology to impact stability, including some of those emphasized here.
Another important objective of portability is to reduce opacity by revealing to users the data that is held about them.
Although very early to judge, elements to explore in such frameworks could include modalities for allowing efficient management of data across national boundaries while addressing concerns, potentially utilizing the services of trust-generating entities—which may include new private-public partnerships or even certain financial centers—that store and validate data subject to standards that provide assurance to national authorities.