Innovation, Economic Growth and development

Merit course – 2006

� Stylized facts of economic growth (Kuznets)� Two visions of economic growth� Evolutionary growth theory

Simon Kuznets: Modern Economic Growth

� High rate of growth of GDP per capita– Relative to previous periods– Relative to non-developed countries

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Simon Kuznets: Modern Economic Growth

� High rate of productivity growth (not just growth of GDP)

Simon Kuznets: Modern Economic Growth

� High rate of structural change– agriculture -> industry -> services– Small enterprises -> large enterprises (managerial

firm)

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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

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

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

Technology in evolutionary theory

� Strong uncertainty (vs. risk)� Who copes with uncertainty: homo

economicus or evolution?� The metaphor of the Blind Watchmaker in

economics

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”

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

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

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

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?

Technology and economic growth: evolution and history

Two models:– Conlisk model: evolution and growth– Silverberg/Verspagen: structural transformations

and growth

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 =

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 +=

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:

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

Conclusion

� Randomness plays an essential role� The growth rate is a “random walk”: random shocks

have a permanent effect on the growth path

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

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

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

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

Model results – a typical run

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