Diffusion and Social Returns
Outline
• Modelling the rate of adoption of an
innovation
• Statistical evidence on rates of adoption
• Spillovers and social returns to
innovation
• Empirical studies of social returns
• Spatial dimensions of spillovers
What is diffusion?
Diffusion involves:
• Transfer of information to customers about
innovations
• Decisions by buyers to adopt the innovative
product or process
• Eventual saturation of the market by the new
generation of products or processes
• Then there is full realization of Schumpeter’s
process of creative destruction
Modelling the rate of adoption
of an innovation
• Biological approach – an epidemic model –
random encounters cause transfer of
information (analogy with catching disease)
• Once information has been transmitted there
is a fixed probability of the potential new
customer deciding to buy the innovation
• A few early adopters, then an epidemic
• Lastly the few laggards adopt, so reach
saturation in the market
• Rate of adoption follows a bell-shape so
cumulative proportion is an S-shape
The cumulative path of adoption
for an epidemic model
D
1
Saturation
Cumulative
number of
adopters (as
proportion)
½
0
Point of inflexion
Time
Economic model of diffusion
• Introduce prices, costs of adoption, tastes
• Only adopt when net gain is positive, taking
account of all these factors
• Formally differentiate customers by variety of
taste and cost characteristics (indexed by Z)
• Distribution of Z is bell-shaped (e.g. Normal)
• As product price falls (or costs of adoption
fall) then over time those with less
favourable Z values will find it worthwhile to
adopt
• Again cumulative rate of adoption S-shaped
How characteristics (index Z)
determine the rate of adoption
Frequency
f(Zi)
Do not adopt
Adopt
Z*
Zi
Index of
adoption
Network and lock-in effects
• Network effects arise whenever there are
gains to using the same products or
technologies that others are using
• Lock-in occurs when once consumer adopts
one system it costs a lot to change to
another - examples QWERTY and videos
• Can be due to need for interoperability
and/or to users’ learning investments
• Market failure occurs if the first system to
gain critical mass becomes the standard,
even if does not have the best overall
product characteristics
Statistical evidence on rates of
adoption
• Process of reaching market saturation is
slow and often incomplete
• This is demonstrated for older manufacturing
technologies in Figure 7.3 (not shown here
due to limitation of copyright license!)
• Slow pattern of adoption is also seen in
information and communications technology
• Examples are: (see Tables 7.1 and 7.2)
use of computers in process technology
use of Internet across countries
Spillovers and social returns
to innovation
Three beneficiaries from positive externalities
of innovation:
• Final consumers - Consumer welfare rises
as product price falls or product quality rises
• Competing firms in industry Knowledge spillovers inform their production
Licensing technology can improve their
profitability
• Firms in other industries - Reduced input
costs and/or better input variety
Methods of assessing spillovers
Input–Output Method:
• Models the whole economy
• Traces flows of intermediate products
between firms in same and different sectors
to see who buys what
• Innovations that are produced in one sector
flow out to all their buyers in other sectors
Econometric studies
• These studies use firm or industry data
• Assess how a rise in innovation in one
firm/sector affects performance in another
Empirical studies of social returns
– input-output evidence
• Input-output studies are able to identify
significant differences between sectors in
their roles as innovation producers and as
innovation users
• For the US see study by Scherer
• For the UK see study by Greenhalgh and
Gregory
• Both innovation producers and users
contribute to the diffusion of returns to final
consumers
Empirical studies of social
returns – micro evidence
• Econometric micro-analysis indicates that
knowledge spillovers are substantial
• This implies that policies to encourage
innovation and R&D are justified.
• Knowledge spillovers are influenced by level
of absorptive capacity of receiving firms
• Conducting R&D can increase a firm’s
absorptive capacity
• Policy to encourage R&D thus improves “the
two faces of R&D”
Spatial dimensions of spillovers
If spatial spillovers are instantaneous and complete:
• Countries lagging behind technology frontiers
would catch up quickly showing high growth
• Less incentive exists for subsidising domestic R&D
as the benefits diffuse to other countries
• Evidence that the spatial proximity is still important
in knowledge spillovers
• Spillovers from R&D diminish with distance across
major countries
• Studies of patent citations show:
more likely to be citing domestic than foreign patent
more likely to be citing patent from same region
Questions for discussion
1. Why is diffusion generally a slow process?
2. Are there cases when the epidemic model is
better than the economic model?
3. What factors speed up or slow down the
adoption of new technology by industry?
4. Should policy be concerned about ‘lock-in’ or
‘network effects’?
5. What lessons can be learnt from input-output
analysis of R&D and innovation?
More difficult questions
6. Choose an innovation you are familiar with and
outline the potential customers and firms
affected by it. How would you attempt to
quantify these effects?
7. Define a) knowledge spillovers and b) business
stealing. How could one test the relative
importance of each?
8. What are hedonic price indices? Are they
important?
9. What lessons should policymakers learn from
the economics of diffusion?
References
• Gold, B., W. S. Peirce and G. Rosegger (1970), 'Diffusion of
major technological innovations in U.S. iron and steel
manufacturing', Journal of Industrial Economics, July.
• Scherer, F M (1984), Innovation and Growth:
Schumpeterian Perspectives, Cambridge Mass., MIT
Press.
• Greenhalgh, C and M Gregory (2000) ‘Labour productivity
and product quality: their growth and inter-industry
transmission’, Ch.3 of R. Barrell, G. Mason and M.
O’Mahoney (eds.), Productivity, Innovation and Economic
Performance, Cambridge U.P.
• Bloom, N., M. Shankerman and J. Van Reenen (2007),
'Identifying technology spillovers and product market rivalry',
NBER Working Paper 13060.