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Peer Effects in the Diffusion of Solar Photovoltaic Panels. Bryan Bollinger and Kenneth Gillingham. Marketing Science Science-to-Practice I nitiative. Motivation: Social Contagion. - PowerPoint PPT Presentation
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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.