can behavioural economics be used to encourage consumers ... trials...power 80% power to detect...

Can behavioural economics be used to encourage

consumers to switch to green energy tariffs?

Moira Nicolson, Gesche Huebner and David Shipworth

Thursday 10 September 2015

A randomised control trial on

a representative sample of British energy bill payers

Climate change - meeting carbon emission

targets means energy use needs to change in two ways

1 – Reduce consumption

2 – Adjust timing of our consumption

(reduce consumption at the right time)

Time of the day

Th

e a

mo

unt o

f e

lectr

icity w

e c

on

su

me

(in

kilo

wa

tts)

0:00 12:00 24:0006:00 18:000.1

0.7

Source: The graph is a hand-drawn replica of a graph showing electricity load profile data (for profile class 1 – “domestic

unrestricted customers”). Number of power stations are hypothetical.

Energy supply needs to be reliable and

affordable – not just clean

100% renewable energy? August 2015: Jacobsen et al published roadmap for all 50 United States to

be 100% renewable by 2050 (Energy Environ, Sci, 2015, 8)

Photo: A wind farm in Tehachapi, California. (REUTERS/Mario Anzuoni )

Smart meter roll-out across world

• Major investment – NPV of £6 billion1

• Real time consumption measured at half

hourly intervals

• Key part of business case is that people

will reduce consumption at particular

times of day

1 Department of Energy and Climate Change, Smart Meter Impact Assessment Final January 2014, p. 59.

Time period Rate you pay

Day 7am-4pm 14p/unit

Peak 4pm-8pm 30p/unit

Night 8pm-7am 10p/unit

Weekend All day 10p/unit

Standing charge 25p per day

Time-of-use electricity tariffs

On this tariff, you’ll have three different electricity

rates: day, peak and night.

12Department of Energy and Climate Change, Smart Meter Impact Assessment Final January 2014, p. 59.

To work, consumers must switch

UK Government needs 20% uptake

by 2030, to realise business case

for smart meters2

But people don’t switch• 50% haven’t switched supplier in 20

years (Defeuilly, 2009)

• Annual tariff switching rates are tiny,

despite large savings (Competition & Markets Authority, 2015)

Traditional methods of boosting switching don’t

seem to be working

Regulation Making switching easier

• Tariff Comparison Rate

• Minimum of 4 tariffs

• Suppliers must write to tell customers if

they aren’t on the cheapest tariff

Mandates?

Opt-outs?

Can we boost uptake to

time of use tariffs without

making them mandatory or opt-

out?

“Stop harming yourself. Stop

smoking.”

“Not exercising regularly can

make you gain weight”

“Start living. Stop

smoking.”

“Exercising regularly can

help you lose weight”

“3 people die every day because

there are not enough organ

donors”

“Early detection of

HIV can prevent

AIDS”

“Late detection of HIV leads to

AIDS”

“You could save or

transform up to 9 lives

as an organ donor ”

GainLoss

Message framing

• Most message-framing studies are tested on health outcomes

(Updegraff et al, 2012); message-framing on environmental

outcomes mostly lab-based on students (following Kahneman

and Tversky, 1981)

• Could it influence people’s choice of energy tariff in real world

amongst population of interest?

• Message framing has many advantages: cheap, respects

freedom of choice, and could be easily implemented by

marketing staff at utility companies if we knew that a particular

frame would be more successful

Tariff marketing gain-frames the financial

benefits of switching –

should we loss-frame them?

…and tell people about this?

The study

Challenge

• Can’t measure switching rates – evidence needed now

so electricity network operators and policymakers can

make decisions

• Stated preference measure using population-based

survey experiment (Mutz, 2011)

• Gerber and Green’s (2002) four criteria measure of

‘fieldiness’: authenticity of treatments, participants,

setting, outcome

Design

Power 80% power to detect effects of >10% in two-tailed test of

significance with 95% confidence

Participants and

sample selection

2020 British adult energy bill payers, members of online

market research panel

Recruitment Recruited by market research company to be nationally

representative (5 demographic criteria: gender, age, region,

social grade, working status)

Instrument Email invitation with link to online survey (20 item

questionnaire)

Randomisation Random assignment to loss/gain-framed digital advert with

and without environmental and energy security information

Outcome measure Intention to switch to tariff on 7-point Likert scale

Additional data

collection

Loss-aversion, manipulation checks, other demographic and

household characteristics

Flow of study participants

Switch to save money

Switch to avoid missing out on

savings

Switch to save money, help the environment and

reduce risk of blackouts

Switch to avoid missing out on

savings, harming the environment

and increasing risk of blackouts

Gain Loss

Money

Money &

environmental

and energy

security

message

Message-frame intervention• Participants exposed to same commercially viable time of use tariff

• Between-subjects random assignment to message-frame (1:1 allocation procedure)

• Randomisation carried out on rolling basis by survey software use Excel’s random

number generator

Scenario used to measure consumer interest in demand-side response

in Spence et al (2015), Nature Climate Change, 5, 550-554.

Measured loss-aversion-50/50

gamble questionsGamble Yes I would

take this

gamble

No, I would

not take this

gamble

#1 If the coin turns up heads then you lose £2; if the coin turns up

tails then you win £6 o o











Source: Loss-aversion questions the same as used in Gachter et al (2010),

adapted from Kahneman and Tversky (1979) to make suitable for online survey.

Analysis• Pre-analysis plan

• Treatment effects estimated using OLS linear regression with

and without select baseline covariates (Ordered Logit

robustness check)

• Heterogenous treatment effects

- Pre-payment customers

- Loss-averse (continuous, categorical)

- Infrequent switchers

• Highly specified model to investigate what variables are

statistically significantly correlated with intention to switch to a

time of use tariff

• Multiple comparisons correction applied throughout (Benjamini

and Hochberg [1995]) method)

Results

Randomisation and manipulation checks

• Randomisation check on baseline characteristics revealed covariate balance across groups was excellent (no more differences than expected by chance)

• Manipulation checks: participants perceived framing manipulation as intended and no differences in evaluation of messages across groups (e.g. ease of understanding, knowledge test)

• Attrition – 14 dropped out after randomisation but prior to responding to outcome measure (0.7% attrition rate)

• Average time taken to complete survey – 30 minutes (20 items)

1: High variation in willingness to switch to a

time of use tariff (outcome measure)

0

.05

.1.1

5.2

.25

1 2 3 4 5 6 7

Pro

port

ion o

f consum

ers

Stated willingness to switch to tariff on Likert scale

2: British energy bill payers are loss-averse

Percent accepted

Implied

acceptable

loss

Unweighted

proportion

(standard

deviation)

Weighted

proportion

(standard

error)

Accept all lotteries 0.05 (0.22) 0.05 (0.01) £7

Accept lotteries #1-#5, reject lottery #6 0.02 (0.16) 0.02 (0004) £6

Accept lotteries #1-#4, reject lotteries #5-#6 0.03 (0.17) 0.03 (0.005) £5



Accept lottery #1, reject lotteries #2-#6 0.24 (0.43) 0.23 (0.01) £2

Reject all lotteries 0.28 (0.45) 0.28 (0.01) £

12

34

56

7

Mean s

tate

d w

illin

gn

ess t

o s

witch

Increasing consumer loss-aversion

3: Loss aversion was statistically significantly negatively

correlated with willingness to switch at 99.9% level

95% of British

population

30% of British

population

Notes: Statistical significance testing performed using OLS linear regression, with survey weights and correction for multiple comparisons (Benjamini and

Hochberg [1995], with and without region fixed effects. Ordered Logit robustness checks. Covariates included: a continuous measure of loss-aversion (0-6

ascending); gender; education; tenure; age; employment status; household occupancy patterns; interruptible electric goods ownership; children at home;

switched tariff this year, last year, ever; existing time of use tariff customer. Alpha = p

4 – Message framing results

4: No statistically significant difference in willingness to switch

across experimental groups

Experimental group

Me

an

will

ing

ne

ss t

o s

witch

to

ta

riff o

n L

ike

rt s

ca

le

Error bars represent standard error around mean intention to switch by experimental group. p-values ranged

between 0.448-0.840 across OLS and Ordered Logit robustness checks.

2.88 2.71 2.89 2.900.00

1.00

2.00

3.00

4.00

5.00

6.00

Money (gain) Money (loss) Money &environment/security

(gain)

Money &environment/security

(loss)

7

1

2

3

4

5

6

5 – Causal chain of process: no

sub-group effects

• Loss-framing had no effect on loss-averse

(continuous/categorical)

• Environmental framing had no effect on

hybrid vehicle owners (not in PAP)

Notes: Statistical significance testing performed using OLS linear regression, with survey weights and correction for multiple

comparisons (Benjamini and Hochberg [1995], with and without region fixed effects. Ordered Logit robustness checks. The treatment

dummy was created by pooling participants assigned to groups 1 and 3 (loss-framed messages) into one large treatment group and

pooling participants assigned to leaflets 0 and 2 (gain-framed messages) into one large control group. Alpha = p

Conclusions• British energy bill payers are loss-averse and this is negatively correlated with

willingness to switch to time of use tariff tested – priming effects? Measurement error?

• No influence of message framing (80% power, balance checks fine)

• Framing was too weak? But manipulation checks fine and if too different it isn’t framing

• Loss-framing, with or without environmental message, either:

o Won’t work to boost uptake rates to time of use tariffs – no treatment effect OR;

o It won’t work very well – small treatment effect in order of

Thank you –

any questions/feedback?

Moira Nicolson

[email protected]

With thanks to Engineering and Physical Sciences Research Council (EPSRC) for

doctoral training grant and to Smart Energy GB, for supporting the research.

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