ECONOMIC COMPLEXITY · industry and related raw materials and waste watchesa ndj ewelery...
Transcript of ECONOMIC COMPLEXITY · industry and related raw materials and waste watchesa ndj ewelery...
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ECONOMIC COMPLEXITY
Collaborators: G. Chiarotti1,2, G. Cimini1,2, M. Cristelli1,2, R. Di Clemente1,2, A. Gabrielli1,2,3,E. Pugliese1,2, F. Saracco1,2, T. Squartini1,2A. Tacchella1,2, A. Zaccaria1,2
[1] Institute for Complex Sistems, CNR, Rome, Italy; [2] ”Sapienza” University of Rome, Italy
[3] London Institute for Mathematical Sciences, UK Web Page: http://pil.phys.uniroma1
CRISISLABANALYTICS FORCRISIS PREDICTIONAND MANAGEMENT
JPD/JICA Task Force Columbia, NY, Feb. 19-20 2015
Luciano Pietronero1,2,3
Measuring the Intangible Growth Potential of Countries
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ECONOMICS:
From ”the dismal science” (Thomas Carlyle) to…..
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ECONOMICS:
From ”the dismal science” (Thomas Carlyle) to…..
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Amman conference, June 2014 Stiglitz’s Task Force on Industrialization: Yau Ansu: ACET Report (221pages) Comparison of economic data between 12 african countries and other countries (mostly asiatic) which went through industrialization In the recent past.
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• Aggregated data for the two groups of countries • Interesting information but sometimes conflicting • Difficult to get a unified
comprehensive picture
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More and more data but difficult to draw a clear conclusion ??? And still data are aggregated, no specific information on individual countries
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The Economic Complexity answer: New synthetic concepts Individual country trajectories in the new space Clear interpretation - Complete information - Visual impact
Trajectories refer to the evolution 1995 - 2010
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Countries Products COMTRADE database: Which country exports which product Bipartite Network: New algorithm to extract information for • Fitness of Countries • Complexity of Products NB: this is not an analysis of the export volumes. The information is derived from the nature of products
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THE THEORY OF HIDDEN CAPABILITIES A COUNTRY IS ABLE TO PRODUCE A PRODUCT WHEN IT OWNS ALL
THE CAPABILITIES NEEDED FOR IT (Hausmann& Hidalgo 2009) Products discount all the information on capabilities as stock prices should
discount all the information on companies (except finance fluctuations)
HOW TO MEASURE CAPABILITIES FROM THE AVAILABLE DATA?
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SPECIALIZATION VS. DIVERSIFICATION
Evidence for leading role of diversification with respect to competitive advantage (specialization)
• Globalization • Ecosystems
• Evolvability • Adaptation
From Qualitative to Quantitative
DATA DRIVEN APPROACH:
• Math. Problem: minimal elements to have a triangilar matrix Complex Hierarchical structure, nestdness etc. • For sectors and companies the situation evolves towards specialization
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Monetary measures Metrics for intangibles
NEW INFORMATION M. Cristelli, A. Tacchella, L. Pietronero, The Heterogenous Dynamics of Economic Complexity (in preparation) M. Cristelli, A. Tacchella, L. Pietronero, Economic Complexity: Measuring the Intangibles (ebook)
(GDP, GDPpc, etc)
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We measure the Fitness of countries (DNA/intangibles) and the Complexity of products with an iterative Google-like algorithm for the bipartite country-product network
Fitness Complexity
Fc: diversification weighted by complexity Qp: Extremal non-linear complexity of products a single low fitness producer implies low complexity
A. Tacchella et al., A New Metrics for Countries’ Fitness and Products’ Complexity, Scientific Reports 2, 723 (2012)
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Fc: diversification weighted by complexity
Qp: Extremal non-linear complexity of products a single low fitness producer implies low complexity
Fc
Platinum Nails Wheat Chips Optic Fibers 0.0032 0.0099 0.12 1.81 4.39
+ + + + = Fc
6.3331
A. Tacchella et al., A New Metrics for Countries’ Fitness and Products’ Complexity, Scientific Reports 2, 723 (2012)
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The Economic Dynamical Ecosystem: Data driven approach from micro to macro • Countries: diversified in products Countries and Products: Google like approach – Big Data Countries: Fitness index Products: Complexity index Dynamics: Monetary vs Intangible metrics – Hidden potential • Subsystems: Regions, Districts, Cities (London, Shanghai) • Industrial sectors: Various levels of grouping Evolution of their Complexity Policy making: virtual experiments, what if? Criteria for optimization • Companies: specialized in products But diversified in terms of Technologies in their control (ie patents)
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How the model works: 1. Probability of having a product with combinatorial complexity
C (number of capabilities) is
Meaning of π: how effective is a country in making more products by combining capabilities
2. The diversification d of a country which has K capabilities (K represents the complexity of that country) is
1° Prediction: let’s test, as proxy for K , log(Fitness) and the Economic Complexity Index (ECI, C. Hidalgo et al.
PNAS, 2009)
S. Inoua, On the Complexity Approach to Economic Development, 2013 http://vixra.org/pdf/1301.0182v1.pdf
p(C) ⇠ ⇡C
d =KX
C=1
p(C)
✓K
C
◆⇠ (1 + ⇡)K
NB: no loss of generality assuming minimum number of capabilities =1
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log(DIVERSIFICATION) vs log(FITNESS)
Log(Fitness) is good proxy for the complexity K of countries R2≈0.92-0.94 in the period 1995-2010
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Hausmann & Hidalgo have tried to use exactly the Google algorithm but their ECI is not a good proxy for complexity K, R2 ≈ 0.52-0.65 in the period 1995-2010
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MICRO ORIGIN OF POVERTY TRAP? No longer exponential relationship btw diversification and complexity (i.e. Log(Fitness))
Poverty trap
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1995
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ECONOMIC DYNAMICS IS HETEROGENEOUS
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South Korea Evolution
••
••
••••••••••••••••••••••••
• ••••••
•••
1963
2000
-8 -6 -4 -2 01.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0-8 -6 -4 -2 0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Log10FF
Log 10HGD
P percapitaL
Some examples of different regimes…
South Korea
• Starting from low values to arrive to high values of GDP per capita; • First period of increasing fitness, at GDP almost constant; • Subsequently rapid growth in GDP per capita w/ slow increasing Fitness; => Exit from the poverty trap
1963 - 2000
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Method of Analogs: forecasting the future by the knowledge of the past
Empirical Evo.Distribution
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In the laminar regime (green area) the evolution of countries tends to be highly predictable
The Selective Predictability Scheme (SPS)
SPS = forecasting the future by the knowledge of the past (green area)
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1
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6
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9
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NEW: SCIENTIFIC COMPETITIVENESS OF COUNTRIES Do countries specialize or diversify their research Activity?
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Is it economically worth spending in research?
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Extensive adiacency matrix
Intensive adiacency matrix
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Extensive Intensive
Ranking of scientific domains
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COUNTRY SPECTROSCOPY
Product Complexity
• Products appear clustered in Quality Space
• The revanche of specialization – Industrial sectors and individual companies tend to be reasonably specialized
Oil, Potatoes Smartphone Textiles
Exp
orte
d vo
lum
e
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COUNTRY SPECTROSCOPY
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COUNTRY SPECTROSCOPY
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COUNTRY SPECTROSCOPY
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METALLURGIC INDUSTRY AND RELATED RAW
MATERIALS AND WASTE
WATCHES AND JEWELERY
PREFABRICATED BUILDINGS, CONTAINERS, TANKS
WIRES
MECHANICAL INDUSTRY
TEXTILE
PAINTS, GLUES,
PIGMENTS
SPECIALIZED INDUSTRIAL MACHINERY
LAB EQUIPMENT
AGRIFOOD
SWEDEN: PORTION OF THE PRODUCT SPACE
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Example: SK 81 detailed products
Automatic data processing machines
Sound recordings
Office machines
Thermionic, valves, transistors
Typewriters Optical Instruments
Radio broadcast receivers
Photographic cameras
Other musical instruments
Parasols, walking sticks Television receivers
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Diffusion of South Korea 1963-2000 1963
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1963 1966
Example: Diffusion of SK 1963-2000
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1963 1966
Example: Diffusion of SK 1963-2000
1971
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1963 1966
Example: Diffusion of SK 1963-2000
1971 1977
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1963 1966
Example: Diffusion of SK 1963-2000
1971 1977
1993
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1963 1966
Example: Diffusion of SK 1963-2000
1971 1977
1993 2000
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METALLURGIC INDUSTRY AND RELATED RAW
MATERIALS AND WASTE
WATCHES AND JEWELERY
PREFABRICATED BUILDINGS, CONTAINERS, TANKS
WIRES
MECHANICAL INDUSTRY
TEXTILE
PAINTS, GLUES,
PIGMENTS
SPECIALIZED INDUSTRIAL MACHINERY
LAB EQUIPMENT
AGRIFOOD
SWEDEN: PORTION OF THE PRODUCT SPACE
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NEW: Forecasting of the new products (sectors) which have a high probability to appear in the near future
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These concepts are general exist in ecology too
Economics
Ecology
Countries Products
Plants Pollinators
J. Bascompte et al. PNAS (2003)
M. Munoz et al, preprint 2014
NEW:
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Economics
Ecology
Great divergence sudden increase in income PRODUCTS’ DIVERSITY
Cambrian Explosion “sudden” increase in BIODIVERSITY
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Future developments of Economic Complexity • COUNTRIES have a long time horizon and they have to diversify
for their long range stability. Meaning and implications of product complexity Q yet to be fully explored
• COMPANIES have a short time horizon (3 months) and have to specialize and compete on few products. A company which diversifies its products looses 14% of the stock value (BCG report).
Also for companies diversification helps long range stability • New database on trading between companies – Supply Chain: - Bloomberg: 38,000 quoted companies including volumes - Standard&Poor: 4 millions companies without volumes Fantastic information on the infrastructure and dynamics of economics. New ideas and algorithms are needed. - Possibly Alibaba data on trade between chinese companies
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Policy making and consulting
• Institute for New Economic Thinking (2013) • The Boston Consulting Group (New York) Report on Sweden (2013) • Royal Dutch Shell (NL), Report on South Africa (2014) • Institute for Public Policy Research (UK), Report for UK
government on UK industrial competitiveness (2014) • Azimut private bank, Asset allocation Fund (2015) • Alibaba Complexity Research Center (Hangzhou, China)