Introduction:

Risk preferences are integral to many fields of economic study. They are the central focus of literature on decision making under uncertainty, also playing a key role in financial and insurance economics. Risk preferences are also major drivers in consumption models, investment and asset pricing in macroeconomics. As risk preferences play such a key role in many economic fields, it follows that the ability to accurately estimate these preferences is of major importance.

Thinking about agent’s risk aversion or lack thereof started in 1738 with Daniel Bernoulli’s paper. This predates economics and psychology as distinct subsets of inquiry. Bernoulli points out that the expected value of payoffs is not a sufficient description of agent’s behaviour as fails to consider the risks associated with these payoffs. His experiment consisted of a repeated coin flip, with no payoff occurring until heads appears. The payoff is doubled after every tails. Expected payoff is an infinite sum of ones so you would expect agents would always pay for the chance to receive infinite reward. However, this is not the case and this experiment is now known as the ‘St Petersburg paradox’. Bernoulli concluded that the utility function is nonlinear and concave, meaning higher payoffs are underweighted regardless of risk. This could be considered the first economic experiment.

The main article I will be looking at in this essay is ‘Assessment and Estimation of Risk Preferences’ by Holt and Laura (2014). This article looks at literature pertaining to experimental methods available to measure risk attitudes.

Assessment approaches:

Binary choice:

Nearly 200 years after Bernoulli’s solution to the ‘St Petersburg paradox’, little progress had been made in the field of decision making under uncertainty. This remained the case until Markowitz (1952) proposed an experiment, constructed as follows: Markowitz asked colleagues if they would certainly lose a penny or gamble, with the gamble being a 1/10 chance to lose 10 cents. Almost all colleagues exhibited risk aversion (preferring the sure loss of a penny). Markowitz scaled this up to see if people’s risk preferences changed and indeed they did, with people preferring a 1/10 chance of losing $10,000 compared to a sure loss of $1000. The inverse however was true when it came to gains. In the case of small gains people tended to prefer the gamble of 1/10 chance of gaining $10 compared to a sure dollar. This pattern did not continue in large gambles where people almost always preferred a sure $1million compared with 1/10 chance of gaining $10million.

Continuing from Markowitz’s seminal paper, Kahneman and Tversky (1979) conducted a similar experiment. Given a choice between a sure $3 and a 0.8 chance of $4, 80% of subjects prefer the safe choice. This indicates a degree of risk aversion as the safe payoff is below the expected payoff of 0.8(0.4) = $3.20. When the gains were flipped into losses only 8% of people preferred the 0.8 chance of losing $4. This intuits that losses are avoided more than gains are sought. To further test this loss aversion, they scaled down the probabilities in the gain cases. The sure $3 became a 0.25 chance of $3 and the 0.8 chance of $4 became 0.2 chance of $4. An expected utility maximiser that chose the sure $3 in the first task was expected to choose 0.25 chance of $3 in the second as probabilities and therefore expected utilities had been quartered. Howeve