1. Competition and Innovation: An Inverted U Relationship
Philippe Aghion (Harvard & UCL)
Nick Bloom (CEP, LSE)
Richard Blundell (IFS & UCL)
Rachel Griffith (IFS & UCL)
Peter Howitt (Brown)
“Competition and Innovation Workshop”, 23rd September 2003

2. Motivation and summary of the paper
Model
Empirical results
Policy implications

3. Competition policy is evolving rapidly
Policy concern in UK and Europe that productivity levels and growth rates are low, and that this is due to the lack of product market competition
UK and EU strengthening competition regimes
“The Government has placed competition policy at the heart of its strategy to close the productivity gap.”
UK Pre-Budget Report November 2001
US already has powerful competition policy which even permits imprisoning company directors
What is the theoretical and empirical evidence basis for these policies?

4. Evidence from conventional wisdom, theory and empirics on the impact of competition appears contradictory
Conventional wisdom provides mixed views
“Competition effect”:
“... from Adam Smith to Richard Caves: the belief that competition is good, rests on the idea that competition exerts downward pressure on costs, reduces slack and provides incentives for efficient organisation of production...” (Nickell, 1996 JPE)
“Schumpeterian effect”:
“....anti-trust discourages innovation ” (Bill Gates and lawyers, frequently)
Economic theory often supports the Schumpeterian effect of a negative competition effect on innovation
Empirical work, however, typically supports finds a positive effect – i.e. Geroski (1995 OUP), Nickell (1996 JPE) and Blundell, Griffith and Van Reenen (1999 RES), Mohen and ten Raa (2002, WP)

5. We model both the competition and Schumpeterian effects and estimate the shape of the competition innovation relationship
In our model:
At low levels of competition the “competition effect” dominates leading to a positive relationship
At high levels of competition the “Schumpeterian effect” dominates leading to a negative effect
Overall this leads to an inverted U-shape relationship
We estimate this model on a panel of 460 firms over 20 years and find a robust inverted U-shape between competition and innovation (patenting)
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Innovation
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low
high
Competition

6. Motivation and summary of the paper
Model
Empirical results
Policy implications

7. We develop a stylised model of competition, innovation and growth across multiple industries
The economy contains many industries, with (for simplicity) two firms, which are either:
“neck-and-neck” as firms have the same technology
“leader-follower” as firms have different technologies
Under low competition “neck-and-neck” firms earn moderate profits, yielding little gain from innovation, so
“neck-and-neck” firms undertake little innovation
leading to an equilibrium with mainly “neck-and-neck” industries
so increasing competition raises innovation as “neck-and-neck” firms increase innovation to “escape competition”
Under high competition “neck-and-neck” profits are low, so the rewards to innovating to become a leader are high, so:
“neck-and-neck” firms undertake a lot of innovation
leading to an equilibrium with mainly “leader-follower” industries
so further increases in competition lower the profits for followers to innovate and become “neck-and-neck”, reducing innovation through a “Schumpeterian effect”
This generates an inverted-U as competition first increases then reduces innovation

8. The model provides three empirical predictions
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This higher the share of “neck-and-neck” industries the more positive the effect of competition
The share of “neck-and-neck” industries will decline as competition increases
Share of
neck-&-neck industries
low
low
high
Competition
high
Innovation
Innovation and competition will have an inverted U-shape relationship
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low
high
Competition

9. Motivation and summary of the paper
Model
Empirical results
Policy implications

10. We use UK firm level accounting data
Basic data is UK firm level accounting data including all stock market listed firms
Measure innovation by matching patent data from US Patent Office
Sample of 461 firms randomly selected firms matched to patents data
Matched subsidiary names by hand – painful but much higher accuracy
Weighted patents by citations received to measure innovation “quality”
Also confirm results using an R&D measure of innovation
Final Data set has
330 firms, 4500 observations over
236 patenting firms, 60,000 patents, 200,000 citations
Bloom and Van Reenen (2001, EJ) use this data set and demonstrated patents play a powerful role determining market value and productivity.

11. Measuring Product Market Competition
Traditional measures based on market share
But problem defining the product & location market.
For the UK international markets important – i.e. Glaxo has 7% global market but 70% share of UK market as defined by sales of UK listed firms
So we use the Lerner Index and assume MC AC
Mean of (1-L) is 0.915, min is 0.794, max is 0.976
Following Griffith (2001) we also cross check this measure using changes in competition following the 1992 Single Market Program, major privatisations and Monopolies and Merger Commission remedies, and found a significant positive correlation

12. Flexible estimation of the competition innovation relationship (1) - using Kernels
Want to estimate a robust innovation estimator imposing as little structure as possible
A flexible Kernel estimator (a local ‘moving average’) is a natural place to start
Kernel estimation shows an underlying inverted U-shape relationship (see below)
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Patents
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low
high
Competition

13. Flexible estimation of the competition innovation
Relationship (2) - using splines and quadratics
Also want to condition on other variables so move to semi-parametric model
Estimate the key moment condition E[P|C] = exp(g(C))
P is the patent count, C the competition measure, and g(.) a flexible function
We use an exponential `Poisson style’ model because patent counts are skewed with many zeros
g(.) is non-parametrically approximated using a quadratic spline-function (see Ai and Chen, 2002 Econometrica)
the variance-covariance matrix is non-parametrically approximated using a White’s (1982, Econometrica) asymptotic estimator
To allow for industry and time variables effects these are parametrically included in addition to yield a final estimating equation E[Pit|Cit] = exp(g(Cit) + Xit’b)
The spline estimation shows a strong inverted U-relationship
This is closely approximated by a more parsimonious quadratic approximation

14. Exponential quadratic with year and industry dummies
Each point is an industry year observation, indicating most industries are clustered around the peak innovation level
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Patents
Outer feint lines provide the point-wise 95% confidence interval
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low
high
Competition

15. Dealing with endogeneity of competition
PMC may be endogenous as higher patenting firms may gain higher rents
Firstly, we include time and industry dummies to remove much of the spurious correlation between competition and innovation
changes in competition identify changes in patenting
Secondly, we instrument changes in competition using the large number of competition changes that have occurred in the UK since 1970:
Differential changes in competition across industries following the 1992 EU single market program
Changes in competition following major privatisations
Change in completion following structural and behavioural remedies imposed on industries after a Monopolies and Mergers Commission

16. Exponential quadratic with year and industry dummies after controlling for endogeneity
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Patents
low
low
high
Competition

17. We also estimation our predictions on the average technological distance from the industry frontier
Define frontier as highest TFP firm
For each firm measure distance from frontier as:
Use industry averages
As predicted, average distance increases as competition rises – indicating industries are becoming less “neck-and-neck” and more “leader-follower” type
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Average distance to frontier
Kernel estimator of the average distance competition relationship
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low
high
Competition

18. “Neck-and-Neck” industries also show a steeper inverted U-shape as predicted
More “neck and neck” firms with more positive competition effect
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Average distance to frontier
Less “neck and neck” firms with less positive competition effect
low
low
high
Competition

19. Motivation and summary of the paper
Model
Empirical results
Policy implications

20. Competition policy implications
In industries with low or moderate levels of competition increasing competition is unambiguously good
Positive impact on innovation as predicted by inverse-U shaped curve
Additional benefits from price reductions through reducing the size of the monopolistic deadweight loss triangle
Low or moderate competition could indicated by
High average industry price-cost markups
Industries with firms with similar product or process technologies
U-shape suggests greatest gain from focusing on industries with lowest levels of competition
At higher levels of competition policy is less obvious – results suggests reducing competition, but
Positive competition effects on price
Distributional effects due to reallocation of welfare from firms to consumers