Experimental Design Details
The experiment will be implemented in the online shop of a German appliance retailer.
Potential customers who visit the website of the appliance retailer are randomly assigned to one of 15 groups with equal probability. We use a 3x5 design in which customers get randomized into 3 different informational groups (groups 1 to 3) and 5 different price discount groups (groups A to E). Visitors receive either 1) no information 2) coarse information or 3) specific information on the financial benefits of energy efficiency. In addition, every visitor is randomly assigned to a price discount on A) incandescent bulbs B) halogen bulbs C) CFL bulbs D) LED bulbs or E) does not receive a price discount.
Visitors in group 1 receive no additional information on the financial benefits of energy efficiency, other than the information already provided by the online retailer (e.g. wattage, energy efficiency label, etc.).
For subjects in group 2, a banner is shown at the top of the browser and contains information on the annual electricity savings of three lighting technologies (halogen, CFL, LED) in comparison to a traditional 40W incandescent light bulb. We visualize these savings by using a bar chart.
In particular, subjects in group 2 are only informed about the savings of these light bulbs in percentage (e.g. a 4 LED saves 90% compared to the 40W incandescent). We do not inform subjects explicitly about how these relative savings in electricity costs translate into monetary savings. This information screen serves as our coarse-information nudge as it provides subjects with potentially useful information but leaves room for interpretation (e.g. it may be unclear to the consumer whether 90% savings translate into 2 or 200 Euros per year).
Subjects in group 3 receive almost the same banner as subject in group 2 but are also informed about the annual savings of the different lighting technologies in Euro. That is, besides receiving information on relative savings (in percent) we also tell them the absolute savings (in Euro). We explicitly tell the subject which electricity price and utilization of the light bulb we have assumed for calculating the monetary savings.
The informational intervention in group 3 serves as our nudge with more specific information as it informs the consumer about both relative and absolute savings of different lighting technologies.
Each of the three groups is divided into 5 subgroups (A-E) in which we offer subjects a 20% price discount on one of the four lighting technologies or no discount. Thus, we have 15 in groups in total: 1.A, 1.B, 1.C, 2.A, …, 3.E. The price discount is shown directly next to the informational intervention so that every subject wo has seen the information should also have seen the individual discount. The randomly assigned price discounts facilitate the estimation of price elasticities which are needed for our welfare analysis.
After subjects made their purchase, they are invited to participate in a survey. Participation is incentivized through a lottery. The survey includes questions on the level of energy literacy, financial literacy, environmental attitudes, patience, education, age, gender and the subject’s zip-code.
We use this survey to deal with the challenge of bias heterogeneity. The recent literature has used within-subject designs to elicit the bias (under-/ overvaluation of energy efficiency) for each subject individually. Since this is generally not possible in a natural field experiment with a between-subject design, we collect information on variables that have predictive power about the individual-specific treatment effect but are not affected by the treatment. This should allow us to identify the “bias type” of the consumer (e.g. high or low bias). We then interact our treatments with these variables and estimate heterogeneous treatment effects (of both the informational and the price interventions). To identify the bias type, we intent to use questions on the level of financial literacy, income, patience and some additional questions on energy literacy that are unlikely to be influenced by our intervention.
As an alternative approach to using answers from the survey, we match the customers zip-code with available data on income and other relevant variables that potentially identify the bias-type of the consumer (e.g. green party vote share).
Our sufficient statistic approach allows us to estimate the effect of our intervention on consumer surplus under a set of assumptions. For a more complete welfare analysis, we also take into account how our intervention could have affected environmental externalities (CO2 emissions) if subjects bought more or less efficient lighting technologies. We plan to take estimates on the social value of a ton of CO2 from the established literature.