lecture 6 - economic growth: an evolutionary view
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Innovation, Economic Growth and development
Merit course – 2006
� Stylized facts of economic growth (Kuznets)� Two visions of economic growth� Evolutionary growth theory
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Simon Kuznets: Modern Economic Growth
� High rate of growth of GDP per capita– Relative to previous periods– Relative to non-developed countries
100
1,000
10,000
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World US UK
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Simon Kuznets: Modern Economic Growth
� High rate of productivity growth (not just growth of GDP)
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Simon Kuznets: Modern Economic Growth
� High rate of structural change– agriculture -> industry -> services– Small enterprises -> large enterprises (managerial
firm)
United Kingdom
0
10
20
30
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1840 1860 1880 1900 1920 1940 1960 1980 2000
United States
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1840 1860 1880 1900 1920 1940 1960 1980 2000
Japan
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1840 1860 1880 1900 1920 1940 1960 1980 2000
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Kuznets – MEG (continued)
� Change in structure of society (secularization, urbanization)
� Developed countries reach out to the rest of the world (“globalization”)
� Inequality between countries
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Economic growth & economic theory
� Classical economists (18th and 19th century): Smith, Ricardo, Marx
� Schumpeter: the role of innovation� Postwar: neo-classicals, post-Keynesians� Modern: evolutionary and endogenous
growth
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Modern theory
� Evolutionary theory emerges as an attempt to endogenize technology in economic theory
� Endogenous growth theory is the (later) neo-classical attempt to do the same
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Technology in evolutionary theory
� Strong uncertainty (vs. risk)� Who copes with uncertainty: homo
economicus or evolution?� The metaphor of the Blind Watchmaker in
economics
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The Blind Watchmaker (Dawkins)
� Uses (random) trial and error� Does not optimize, but adapt� May realize completely different development
paths, if “the tape were played twice”
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Two world views
� Economic growth as an equilibrium process: smooth growth patterns
� Economic growth as a dis-equilibrium process: Transformation (in the underlying structure) and changes of rhythm
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Deterministic and reversible time
� In a deterministic system, if you know the laws of nature, and the initial state, you can predict the future perfectly– For example, Newtonian mechanics
� Even with “weak randomness”, you may have a system that is essentially deterministic
� In economic growth theory, the steady state plays an important role– Key variables in the economy grow at a fixed and
constant rate
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Historical time
� Mixture of chance and necessity– Random factors can change the course of history
� Transformation of structures and institutions� Irreversible and path dependent� Evolutionary economics portrays economic
growth as a process in historical time, but much of mainstream economic theory is based on a reversible time
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Some questions on economic growth
� GDP pc in Japan in 1900 was 1180, in Argentina in 1900: 2756; in 2000 it was 21069 and 8544 (respectively); how can we explain this?
� How did regional growth patterns in Italy diverge so much?
� Can we explain such questions with a deterministic, a-historical approach?
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Technology and economic growth: evolution and history
Two models:– Conlisk model: evolution and growth– Silverberg/Verspagen: structural transformations
and growth
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The Conlisk model
)]([)( tyty i=
∑=
=)]([
1
)()(tLInt
ii tytY
� is an infinitely long, ordered vector of plant productivities
� Labour L(t) populates plants (1 unit for each plant), grows exogenously at rate n, and is allocated efficiently over plants, hence
� New plants m arrive due to exogenous saving:
)]([)( tsLInttm =
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Growth in the Conlisk model
)]()1(),([)1( tytxRankty δ−=+
x(t) is the vector of m(t) new plants
)1,0()(),()()(log IIDttttx iii εσεµ +=
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Specification of novelty (evolutionary mechanism)
))(),(()1( tztxRanktz =+
∑=
=)(
1
)(log)(1)(
tk
ii tz
tktµ
))(()( tLInttk β=
Knowledge stock (all inventions ever made):
Innovations are “mutations” of best-practice knowledge:
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Results for evolutionary specification
� growth converges to a fixed rate g
.0,0,0 <∂∂>
∂∂>
∂∂
kg
mgg
σGreater variation in productivities in new plants increases the likelihood of large innovations
Larger number of new plants every year increases the number of opportunities to increase best-practice
If k grows larger, the list grows longer and it is harder to improve on it
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Conclusion
� Randomness plays an essential role� The growth rate is a “random walk”: random shocks
have a permanent effect on the growth path
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Evolutionary models and historical transformations
� Can an evolutionary model explain a phenomenon like the emergence of modern economic growth?
� Evolution, self-organization and complexity theory: emergent properties– Micro-level interactions lead to ordered patterns at
the macro level� A model by Silverberg & Verspagen
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Silverberg Verspagen model
� Competing technologies, diffusing according to differential profit rates
� Firms, each innovating� Innovations drawn from Poisson distribution,
R&D determines arrival rate, fixed innovation step
� Profits re-invested, in R&D or capital expansion: trade-off for the firm
� R&D strategies
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R&D strategies and firm interaction
� R&D strategy is fraction R&D/profits� Change strategy as a result of
– Random mutation (fixed probability)– Relatively bad performance (compared to other
firms)� Imitation of R&D strategy of other (successful) firms� Mutation of R&D strategy
� All formulated in terms of probabilities and thus governed by randomness
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Experimental setup
� Start all firms with 0 R&D strategies� Allow firms to “discover” R&D� Observe what happens if and when they
discover R&D
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Model results – a typical run
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Summary of the results
� Sudden change of system characteristics is like the transformation to modern economic growth (Industrial Revolution)
� The exact timing of the “transformation to modern economic growth” is hard to predict
� The probability of the transformation happening (within a fixed time window) depends on model parameters, such as technological opportunities