European Commission – DG SANCO Marcel Canoy (Ecorys) Jorna Leenheer (CentERdata, TiU) Brussels, 30 September 2013 Driving customers toward greener choices through the inclusion of products' environmental information online

The role of behavioural economics in EU policy making Importance cannot be underestimated Economic models based on traditional behaviouristic assumptions still dominate almost all strands of policy making at EU and national level Strong and persistent bias for model outcomes, GDP growth numbers and other so-called hard evidence Policies where consumer choice plays explicit role are ideal ‘playgrounds’ for showing that the world may look different

The role of behavioural economics in EU policy making But not for window dressing, behavioural equivalent of greenwashing Straightforward consumer surveys have little to do with drawing genuine lessons from the literature Whilst restricted to limited area of behavioural economics so far, welcome that DG Sanco insists on using full range of tools –surveys –(field, quasi-, natural, lab) experiments Makes policies evidence based whilst contributing to broader applications of BE

1) Two-step decision process How can online information provision be improved to promote energy efficient choices to the level observed in offline settings? 2) Online retailing: Information overload, fast decision-making, limited screen space Full label Rating-only label

Labels tested in the experiments Consideration set formationFinal choice Co_0 No information (control) Ch_0 Non-prominent class-only information (control) Co_1 Class-only Ch_1 Class-only Co_2 Meaning Ch_2 Meaning Co_3 Frame of reference (FoR) Ch_3 Frame of reference (FoR) Co_4 Meaning + FoR Ch_4 Meaning + FoR Ch_5 Full label

Empirical study 1. Screening questions (1 minute) 2. Experiment (14 minutes) – Simulated shopping trip, 4 product purchases – Two sub-experiments: Consideration experiment Choice experiment Between-subject design, 11 different conditions 3. Questionnaire (10 minutes) Sampling Country selection: 10 countries Respondent selection: 1000 respondents per country Target population = internet population Product category selection: 4 Criteria: high penetration, variation between products –Washing machine –Refrigerator with freezing compartment –Flat screen television –Light bulb Number of mock-up websites: 4*11*10*2=880!

Example of mock-up website (1) International brands Realistic sets of: - Price levels - Energy-efficiency levels Translation Currency adaptation (Poland, Romania, Sweden)

Example of mock-up website (2) Indicate which washing machine you would prefer if you were looking today for a washing machine for your household. (select one) Indicate which light bulbs you would seriously consider and about which you would like to receive more information if you were looking today for light bulb for your hall. (select max 6)

Data-analysis: effect of energy labels What works best overall? Under which circumstances? countries, product categories What works best, why and for whom? consumer segmentation (socio-demographics + attitudinal/behavioural background information) Large number of different dependent variables and explanatory models will be tested Subquestions