DLT and the Hold-up Problem

One of the biggest promises of Distributed Ledger Technology (DLT) is that it empowers each network user to have complete control of the data they provide, by granting and revoking access to other member participants. Moreover, by forcing data to be compatible across users, it promotes competition and provides data owners with increased bargaining power. In this note, we analyse how these advantages of DLT can alleviate one of the biggest issues that arise in contractual relationships, the hold-up problem.

The Hold-up Problem

A contract (between firms, users, employees etc.) cannot provide clauses for every possible contingency, since the future is very complicated to describe. In the terminology of economics, contracts are incomplete. Therefore, if an unforeseen contingency arises in the future, involved parties need to renegotiate their contract. However, their relative bargaining power may have changed over the course of the relationship, for example because one party has already undertaken a significant investment that they cannot recoup if they exit. In such a case, this party may now be forced to make a choice that, at the beginning of the relationship, they would be unwilling to make. When this happens, we say that this party is in a weaker position and is “held-up” by the other, stronger party. This is a problem not because the stronger party may appropriate most of the gains from trade with the weaker party, but because the weaker party may choose not to enter into a contractual relationship in the first place, thus generating underinvestment and an inefficient outcome.

The hold-up problem has been known and studied by economists for several decades. However, it becomes more complicated when the contractual relationship concerns the sharing and use of data, because defining property rights on data is more difficult relative to other factors of production, such as capital or labour. For example, firm A may use the data of firm B and extract some insights, possibly using data from other sources. If firm B withdraws its data from A’s database, is firm A still entitled to use the insights that it has generated? More generally, since data can be employed in many different ways, it is very difficult to write a complete contract on its uses, thus exacerbating the hold-up problem. 

Relation to DLT

To explain the hold-up problem and its relation to DLT, consider the following example. Firm A (e.g. Facebook) creates a network and enters into a contractual relationship with users, who deposit their data in its private database, in exchange for interacting with other users in the network. Each user needs to invest a considerable amount of time and effort in order to provide their data to the network. Moreover, the value of this data outside of the network is essentially zero, as it cannot be used in other networks. More importantly, the contract between Facebook and the user does not describe how the data will be used in all possible future contingencies.

Once an unforeseen contingency arises, that was not explicitly written in the contract, there has to be a renegotiation between the two parties. In this example, new technology has demonstrated that data across users can be combined and the insights can be sold to advertisers. Such a technology was not available at the beginning of the contractual relationship, so it was not written explicitly in the contract how the data could be used in such an event. As a result, Facebook has the option to sell the data to advertisers, without being constrained by the contract. The user can then choose to exit the network and lose all of their investment in inputting and utilising their data, or stay within the network and accept that their data is sold to third parties.

The game between Facebook and the user is depicted in Figure 1 below. At stage 1, the user chooses whether to enter the network or stay out. If the user enters, Facebook decides whether to sell the user’s data to advertisers.  If Facebook sells data, the user decides whether to stay in the network or exit. The payoffs for each combination of moves are given in parentheses, where the first number is the user’s payoff and the second is the payoff of Facebook.

Figure 1: Hold-up Problem without DLT

Source: Aaro Capital Research

We solve this game using the principle of backwards induction. This means that we solve the game backwards, taking the optimal choices as given as we move towards the beginning. At the last stage, the user will optimally choose to stay in the network, because exiting means a lower payoff (-50 vs. 25), as the data and all of their time investment will be lost. At the second stage, Facebook knows that if it does not sell data to advertisers its payoff will be 50, however by selling data this will increase to 75, because the user at the last stage will choose to stay in the network. In other words, Facebook can hold-up the user by changing the way they monetise their data, after the user has entered the network.

Finally, at the first stage, there are two cases. If x is greater than 25, the user does not participate in the network. This is the case where there is underinvestment, because the weaker party foresees that it will be held up and chooses not to invest at all, at the detriment to both parties. If x is lower than 25, the user enters the network and is being held up at stage two.

Consider now, in Figure 2, how the game is modified when the user has complete control of their data and its uses, because the network is implemented using DLT. At stage three, after Facebook chooses to sell data to advertisers, the user has the option to stay in the network, as before, but also to exit the network and redeploy their data to a different network. This can happen because Facebook no longer stores the user’s data in their private database. Instead, the data is stored in a distributed ledger, which is owned by no-one. Networks, such as the one created by Facebook, request access to the data and can read/write only while this access is granted by the user. Because access to the network can be effortlessly granted to other networks, the user’s payoff from leaving the network is y, which is greater than 25. This implies that, at stage three, the user will switch to a different network. Such an outcome would be very bad for Facebook, with a payoff of -50. As a result, at stage two, Facebook chooses not to sell data to advertisers and the hold-up problem disappears.

Figure 2: Hold-up Problem with DLT

Source: Aaro Capital Research

Concluding Remarks

It is worth noting that DLT does not solve the hold-up problem under all circumstances. If there are no competing networks with Facebook, for example, the user will not be able to redeploy their data and therefore can still be held up. However, the barriers to enter the market for a new network are significantly lower when the main asset, user data, is not the private property of the incumbent firm. DLT alleviates the hold-up problem, mainly because ownership of the ledger is shared, so there is no single owner who could abuse their market power at a future date. Moreover, the data created by the users are compatible with multiple networks, so that users can easily switch between them, promoting competition. However, because there will always be unforeseen contingencies that cannot be written in a contract, the hold-up problem may still arise under some circumstances.

 

Disclaimer:

The material provided in this article is being provided for general informational purposes. Aaro Capital Limited does not provide, and does not hold itself out as providing, investment advice and the information provided in this article should not be relied upon or form the basis of any investment decision nor for the potential suitability of any particular investment. The figures shown in this article refer to the past or are provided as examples only. Past performance is not reliable indicator of future results.

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