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 formation Final choice

Co_0No 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_3Frame 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 experimentChoice experiment

Between-subject design, 11 different conditions3. Questionnaire (10 minutes)

Sampling

Country selection: 10 countriesRespondent selection: 1000 respondents per countryTarget population = internet population Product category selection: 4Criteria: 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