Economists have long been interested in trying to figure out people's tolerance for risk. Such information is useful in predicting, for examples, which crops farmers will plant, which stocks investors will buy, how much insurance is bought, how much of a premium one is willing to pay for organic food, and how fast people drive. Of course, we don't expect all people to have the same risk preferences, so for decades economists have sought to identify tools and methods that will allow them to discover different people's levels of risk aversion.
One of the most popular techniques is the so-called Holt and Laury (H&L) multiple price list (MPL) based on this paper in the American Economic Review. As of this writing, the paper has been cited 3,900 times according to googlescholar, making it one of the most cited economic papers published in the last 15 years. The approach requires people to make a choice between a relatively safe lottery (e.g., 10% chance of $2 and a 90% chance of $1.60) and a relatively risky lottery (e.g., 10% chance of $3.85 and a 90% chance of $0.10). Then, the subject repeats the choice except the probability of the higher payoffs increases. This process is repeated again and again about 10 times until one gets to the very easy choice between 100% chance of $2 and 100% chance of $3.85 (If you don't know which of those you prefer, give me a call. We need to talk). One very crude measure of risk aversion is simply the number of times a person chooses the relatively safe lottery over the relatively risky lottery.
The H&L method is relatively easy to use, which goes a long way toward explaining it's popularity.
With all that as a backdrop, I'll point you to a new paper I published with Andreas Drichoutis in the Journal of Risk and Uncertainty. We point out an important problem with using the H&L method as a measure of risk aversion and propose a new, yet equally easy to use, MPL that helps solve the problem. If you're not an academic economist, the rest of this may get a bit wonky, but here goes:
Here is one of the main critiques of H&L, which relates to whether people weight probabilities non-linearly (the parameter γ is a measure of the extent to which probabilities are "distorted").
The other problem we point out with the H&L approach is that it provides very little information about the shape of U(x) as only four dollar amounts are used in the design (and only two differences are uniquely identified). Instead, 10 different probabilities are used, which provides much more information about the shape of γ. What can one do about this if they truly want to know about the shape of U(x)? We suggest a new kind of payoff-varying MPL.
I'm under no allusion that our new MPL will become nearly as popular as the original H&L task. But, if we even get one-tenth their number of citations, I'll be thrilled.