Peer Effects in the Diffusion of Solar Photovoltaic Panels

Marketing Science Science-to-Practice Initiative

Bryan Bollinger and Kenneth Gillingham

Motivation: Social Contagion

• A causal peer effect is necessary if marketers wish to

utilize the social spillover effect, in which marketing

activity directed at one person then has a spillover effect

to their peers.

Application - The Diffusion of Solar Photovoltaic Panels (PV)

• Policies to promote adoption of solar photovoltaic (PV)

panels have been gaining momentum throughout the world.

– In the US: 30% solar energy federal investment tax credit.

– In California: $3.3 billion 10 year California Solar Initiative (CSI).

• The nature of the peer effects will determine how to

optimally allocate marketing effort by firms and

policymakers.

Accelerating Empirical Adoption Rate for Solar PV in California

Clustering of Installations

2001-2003 2001-2006

The Method

• Bollinger and Gillingham (2012) estimate causal peer effects

by determining the effects of a difference in the local installed

base of solar panels on the difference in the adoption rate for

different zip codes.

• By using differences, the authors control for both static and

time-varying unobserved factors in the zip code which lead to

different adoption rates (see paper for details).

Findings #1

• Causal peer effects exist in the diffusion of solar panels.

• For the average zip code, an additional installation

increases the probability of an adoption in the zip code

by 0.78 percentage points.

• There is significant heterogeneity in the peer effects

across zip codes which can be partly explained by

demographics.

Findings #2

• The peer effect is slightly

increasing over time.

• This indicates that firms

and policy markers may

be effectively leveraging

them in their marketing

efforts.

Findings #3

• Peer effects in the diffusion of solar panels work even

more efficiently at a smaller geographic scale (the street-

level).

• Using a Bass model to explain diffusion separately for each

zip code does a good job at estimating the peer effect (the

coefficient of imitation) but significantly over-estimates the

market size due to the significant acceleration of adoption.

Key implications #1

• Once we’ve obtained a better / more precise estimate what

can we do with it?

– Identify “low hanging fruit” (regions where peer effects will work

hard for you to maximize the impact of initial marketing efforts)

– Allocate resources more efficiently

– Design product attributes that are conducive to stimulating peer

effects (for solar, installers will post signs to increase the visibility

of completed installations)

Key implications #2

• For what kinds of products should we expect to

see peer effects?– Experience goods which have low

trialability (see Rogers Five

Factors), increasing the value of

Word-of-Mouth

– Visible products that communicate

the consumer’s values (in this case,

environmental stewardship)

Summary

• Causality of peer effects is important in allocating marketing

resources.

• Estimating causality is a challenge.

• Causal peer effects can be identified with rich enough data

on the timing of adoption (Ideally, experiments could be

used to identify and measure causal peer effects.)

• Peer effects can be effectively utilized in marketing efforts.