Lecture 6 - Economic growth: an evolutionary view

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Lecture 6 - Economic growth: an evolutionary view

  1. 1. Innovation, Economic Growth and development Merit course – 2006 Stylized facts of economic growth (Kuznets) Two visions of economic growth Evolutionary growth theory
  2. 2. Simon Kuznets: Modern Economic Growth High rate of growth of GDP per capita – Relative to previous periods – Relative to non-developed countries 100,000 10,000 1,000 100 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 World US UK
  3. 3. Simon Kuznets: Modern Economic Growth High rate of productivity growth (not just growth of GDP)
  4. 4. Simon Kuznets: Modern Economic Growth High rate of structural change – agriculture -> industry -> services – Small enterprises -> large enterprises (managerial firm) Japan United States United Kingdom 100 100 100 90 90 90 80 80 80 70 70 70 60 60 60 50 50 50 40 40 40 30 30 30 20 20 20 10 10 10 0 0 0 1840 1860 1880 1900 1920 1940 1960 1980 2000 1840 1860 1880 1900 1920 1940 1960 1980 2000 1840 1860 1880 1900 1920 1940 1960 1980 2000
  5. 5. Kuznets – MEG (continued) Change in structure of society (secularization, urbanization) Developed countries reach out to the rest of the world (“globalization”) Inequality between countries
  6. 6. 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
  7. 7. 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
  8. 8. Technology in evolutionary theory Strong uncertainty (vs. risk) Who copes with uncertainty: homo economicus or evolution? The metaphor of the Blind Watchmaker in economics
  9. 9. 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”
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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?
  14. 14. Technology and economic growth: evolution and history Two models: – Conlisk model: evolution and growth – Silverberg/Verspagen: structural transformations and growth
  15. 15. The Conlisk model y( t ) = [ y i ( t )] 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 Int [ L ( t )] ∑ y (t ) Y (t ) = i i =1 New plants m arrive due to exogenous saving: m ( t ) = Int [ sL ( t )]
  16. 16. Growth in the Conlisk model y( t + 1) = Rank[ x ( t ), (1 − δ ) y( t )] x(t) is the vector of m(t) new plants log x i ( t ) = µ ( t ) + σε i ( t ), ε i ( t ) IID( 0,1)
  17. 17. Specification of novelty (evolutionary mechanism) z(t +1) = Rankx(t ),z(t )) ( Knowledge stock (all inventions ever made): k( t ) 1 ∑ log z i ( t ) µ( t ) = Innovations are “mutations” of k( t ) i =1 best-practice knowledge: k(t ) = Int(βL(t ))
  18. 18. Results for evolutionary specification growth converges to a fixed rate g ∂g ∂g ∂g > 0, > 0, < 0. ∂σ ∂m ∂k Greater 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
  19. 19. Conclusion Randomness plays an essential role The growth rate is a “random walk”: random shocks have a permanent effect on the growth path
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. Model results – a typical run
  25. 25. 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

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