What happens when intellectual property law collides with the social sciences? They meld together for some fascinating experiments. In a lecture given at Osgoode Hall Law School as part of the IP Osgoode Speaks Series, Prof. Chris Buccafusco described three such experiments performed by him and his team.[1] Specifically, their research seeks to develop an understanding of the nuances that drive creative behaviour in intellectual property markets.
Intellectual property law is meant to provide incentives for creators to both engage in useful innovation and make their creative works available to others. In order to do this well, there ought to be an understanding of the kinds of incentives that encourage creativity. “IP law has to have a theory of why people create, how they interact with the things that they create, and how the markets for creativity and innovation are likely to work,” Prof. Buccafusco tells us.
When dealing with this theory of IP law, most of the legal literature and scholarship derives its assumptions on the basis of Rational Choice Theory (RCT). RCT postulates that people weigh the costs and benefits of each decision, and they’ll make decisions that maximize their self-interest by giving them the greatest satisfaction. Prof. Buccafusco then poses the critical question: “are any of these assumptions that underlie the nature of intellectual property law in fact correct?”
His following three experiments seek answers to this question, and they look to do so by finding out which biases cause creators to deviate from the cold, calculated behaviour of RCT.
The Endowment Effect
The first bias Prof. Buccafusco’s examines is the endowment effect – that is, the hypothesis that people place more value in a good when their property right to that good has been established.
He beings by describing an experiment by economist Richard Thaler in which coffee mugs were randomly distributed to half of an undergraduate law and economics class at Cornell University. The students assigned a mug each quoted the lowest price that they’d be willing to accept to sell the mug, while the students not assigned a mug gave the highest price that they’d be willing to pay to buy the mug. Under RCT, one would expect that these prices should be about equal. But they weren’t. By simply being endowed with the mug, the sellers’ prices were much higher than the buyers’ prices on average. As a consequence, the number of completed sale transactions was less than what RCT would predict.
So how might this bias play out in an intellectual property market? This is where Prof. Buccafusco’s experiment comes in. They set up a contest in which ten student painters submitted their paintings to compete for a $100 prize. These “Painters” were told that they would be matched with a second group of “Buyers” who would make the painters a cash offer to purchase the right to the Painter’s prize if their painting won the contest. The Painters were asked to write down their lowest price they’d sell this right for and the Buyers the highest price they’d pay. If the prices met, then a transaction would occur; the Buyer’s cash for the Painter’s chances of winning.
In addition to these two groups, an “Owner” group was each randomly assigned ownership of one of the paintings and stood to win the $100 prize if their painting won. Similar to the Painters, they were asked to write down their lowest price for which they’d sell their chances of winning the prize to one of the Buyers. There being a 1 in 10 chance of winning $100, the rational prices of all three groups (ignoring the perceived quality of each painting) shouldn’t deviate much from $10.
The actual median prices were as follows:
Buyers: $17.88
Owners: $40.67
Painters: $74.53
What do these staggering differences tell us? Not only did the endowment effect create a gap in the valuations of the Owners and the Buyers, but the act of “creating” caused an even bigger gap between the Painters and the Owners. “Initial endowment may be really sticky” explains Prof. Buccafusco. His research tells us that the intellectual property market may not be as efficient as RCT might predict: fewer transactions occurred as the result of this bias than may be ideal.
Content Attribution
Prof. Buccafusco tells us the second experiment’s goal of determining “the extent to which people are willing to trade off real dollars for an opportunity to have their name attached to a work that they’ve created.” How much do creators value receiving credit for their work?
In this experiment, photographers are told that a graphic designer is looking for a photograph to use as the background of an image that she is submitting to a contest for a $1000 prize. They’re told that she is going to see the photographer’s photo plus four others and will buy the rights to use one of them. The photographer would get cash in this transaction, but no prize money from the contest that the designer is entering. However, they’re also told that their photo will appear on a major website if the designer wins the contest.
The photographers are randomly split into two groups. Group 1 is given no default attribution – analogous to the system in the United States. That is, they’re told that they will not be given credit for their photograph’s appearance on the website. Group 2 is given default attribution – analogous to the system in Canada. Here, they’re told that they will be given credit for their photograph’s appearance on the website. Both groups are first asked how much money they’re willing to accept to sell the designer their rights to use their photos in this contest: the first group without credit, and the second group with credit.
After stating this price they’re each then told that they have the opportunity to switch the status of their attribution. Group 1 is asked how much money they’d be willing to accept to sell their photo if the designer chose to give them credit. One would expect that this second price would be lower than the first price if content creators value having their names attached to their work. Meanwhile, Group 2 is asked how much money they’d now be willing to accept to sell their photo if the designer decided to remove the photographer’s credit from the website. Because, again, of the value creators place in their names, one would expect that this second price would be higher than the first price.
In essence, Group 1 was now buying their right to attribution and Group 2 selling their right to attribution.
By looking at the average difference between the non-credited and credited prices for each group, it can be determined if the attribution rights were valued more in the no-default or default system. The results were as follows:
Group 1 (No-Default Attribution) Difference: $3.61
Group 2 (Default Attribution) Difference: $14.77
The results are significant. The data shows us that the act of being endowed with attribution caused Group 2 to value being credited for their work over four times as much as Group 1. Because of the endowment effect, people seem less willing to sell their credit than they do to buy it.
What does this tell us in practical terms? Again, “the default creates a kind of stickiness,” says Buccafusco. Being given attribution rights by default might make the access to the works of creators especially sticky, he explains, and this makes the markets for them deeply inefficient. The American system appears to make more sense here: if we want more transactions for creative works to occur thereby increasing the number of works in use, then we may not want to be starting creators off with the right to credited use by default. For efficiency’s sake, perhaps we should allow them to bargain for the right at a cheaper price through contracts.
Borrow or Innovate?
Third and finally, Prof. Buccafusco’s seeks to determine how people make decisions to create in markets characterized by sequential innovation. Innovation is built on innovation. Buccafusco illustrates this: we first had Dracula the book, and then we had Dracula the movie. Then came the Twilight novels, the Twilight movies, the Twilight Fan Fiction, and 50 Shades of Grey. Innovation almost never happens in isolation.
There are two choices for a creator in a market with existing works: borrow or innovate. To borrow is to pay for a license, and to innovate is to work around the existing IP. Both of these options have costs. Borrowing has licensing fees, and innovation has risks of uncertainty. As existing rights fill up the innovation space and there’s less room to innovate, we’d expect this innovation would decrease as it becomes harder to do.
The experiment focused on this borrow/innovate decision. In it, subjects are given Scrabble letters with which to earn points by forming words. They are given two options: they can borrow from a pre-existing solution that is first shown to them, or they can innovate and create a solution from scratch. Innovating gives them bonus points, while borrowing does not.
The subjects are split into three groups based on the quality of the pre-existing solution shown to them without being told this quality: 60% of the best solution, 80% of the best solution, and 100% of the best solution. If the subjects were to act rationally, one would hypothesize that the subjects presented with more complete solutions would be more likely to borrow from the solutions given to them (and less likely to innovate).
The results are unexpected:
60% Group: 46.8% innovators
80% Group: 40.9% innovators
100% Group: 85.7% innovators
We saw a drop in the innovators between 60% and 80%, but why did we see an increase in the number of innovators between 80% and 100%? Prof. Buccafusco thinks that the subjects were influenced by an “innovation heuristic.” Subjects didn’t look at the value of the solutions in terms of the points they were worth; they simply considered how easy it would be to come up with some new words that weren’t in the pre-existing solution. Since the 100% solution had quite difficult words, the subjects had little trouble coming up with alternative words of their own without regard for the fact that their low values make them terrible Scrabble words. And they lost a lot of points because of this.
So what does this mean for innovation? “We tend to think about innovation as always good,” Prof. Buccafusco continues. The bias revealed in this experiment shows that creators may be predisposed to spend energy coming up with solutions that already exist for license. “Innovation is often terrible,” Buccafusco concludes. It often leads to “producing stuff that we already have, but expending substantial amounts of resources to do so.”
Prof. Buccafucso’s research shows us that creators’ decision-making in IP markets appears to be significantly altered by these three biases. Perhaps the resulting market “stickiness” is even causing the decisions of these creators and their welfare to diverge. If we want our intellectual property law to be effective when it provides incentives for creators to engage in useful innovation and make their creative works available to others, then it’s clear that we need to take into consideration biases like these and others.
Mike Noel is an IPilogue Editor and a JD candidate at Osgoode Hall Law School.
[1] “Valuing Intellectual Property: An Experiment” is available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1568962
