Metric and Scale Design as Choice Architecture Tools

Abstract

Interest is increasing in using behavioral decision insights to design better product labels. A specific policy target is the fuel economy label, which policy makers can use to encourage reduction in carbon dioxide emissions from transport-related fossil-fuel combustion. In two online experiments, the authors examine whether vehicle preferences can be shifted toward more fuel-efficient vehicles by manipulating the metric (consumption of gas vs. cost of gas) and scale (100 miles vs. 15,000 miles vs. 100,000 miles) on which fuel economy information is expressed. They find that preference for fuel-efficient vehicles is highest when fuel economy is expressed in terms of the cost of gas over 100,000 miles, regardless of whether the vehicle pays for its higher price in gas savings. The authors discuss the underlying psychological mechanisms for this finding, including compatibility, anchoring, and familiarity effects, and conclude that policy makers should initiate programs that communicate fuelefficiency information in terms of costs over an expanded, lifetime scale.

Thinking about Prices versus Thinking about Returns in Financial Markets

“…It is therefore important to highlight the need for evidence‐based policymaking. Our results support the recent appeal of Camilleri and Larrick () that policymakers take into account format design as a choice architecture tool. This is particularly important for automated robo advisors, who “communicate” with their clients only through quantitative and graphical information.…”

Assessing the Energy-Efficiency Gap

“…First, experimental evidence has revealed that consumers systematically misperceive the information contained in fuel economy ratings, due to the inverse relationship between gasoline consumption and miles per gallon ("the MPG illusion") (Larrick and Soll 2008;Allcott 2013). Other research has shown that stated preferences for cars of various efficiencies depend on the metric and scale of information provided on energy labels (Camilleri and Larrick 2014). Translations of fuel economy into multiple perfectly correlated metrics (that is, gallons per mile, estimated annual fuel cost, and greenhouse gas rating) alter stated preferences (Ungemach et al forthcoming).…”

“…The findings suggest that tailoring the scale of energy labels based on the expected lifetime of a given product or providing multiple translations of energy-efficiency metrics could help guide decisions (Camilleri and Larrick 2014;Ungemach et al forthcoming). To some extent, this is already happening: the US Environmental Protection Agency recently redesigned new vehicle labels to include gallons per miles, multiple cost estimates, comparisons with other vehicles in the same class, and environmental ratings (US Environmental Protection Agency 2015).…”

Nudges and signposts: The effect of smart defaults and pictographic risk information on retirement saving investment choices

“…A key element of choice architecture is how information is presented (Goldstein, Hershfield, & Benartzi, 2016). Research on this topic has shown that expressing the same information in different formats can influence people's preferences (Camilleri & Larrick, 2014;Wulff, Canseco, & Hertwig, 2018). Risk information, in particular, can be expressed in different formats, such as frequencies and probabilities.…”