ECONOMIC COMPLEXITY Measuring the Intangible Growth Potential of - - PowerPoint PPT Presentation

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ECONOMIC COMPLEXITY Measuring the Intangible Growth Potential of - - PowerPoint PPT Presentation

Aug 29, 2023 167 likes •722 views

JPD/JICA Task Force Columbia, NY, Feb. 19-20 2015 ECONOMIC COMPLEXITY Measuring the Intangible Growth Potential of Countries Luciano Pietronero 1,2,3 Collaborators: G. Chiarotti 1,2 , G. Cimini 1,2, M. Cristelli 1,2 , R. Di Clemente 1,2 , A.

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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

CRISISLAB ANALYTICS FOR CRISIS PREDICTION AND 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

  • ther 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

  • n 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

  • f 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) ∼ πC

d =

K

X

C=1

p(C) ✓K C ◆ ∼ (1 + π)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

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

  • 8
  • 6
  • 4
  • 2

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Log10FF Log10HGDPper capitaL

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

Exported volume

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COUNTRY SPECTROSCOPY

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COUNTRY SPECTROSCOPY

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COUNTRY SPECTROSCOPY

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MET ALLURGIC INDUSTRY AND RELA TED RA W MA TERIALS AND W ASTE

W A TCHES AND JEWELERY PREF ABRICA TED BUILDINGS, CONT AINERS, T ANKS 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 instru 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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MET ALLURGIC INDUSTRY AND RELA TED RA W MA TERIALS AND W ASTE

W A TCHES AND JEWELERY PREF ABRICA TED BUILDINGS, CONT AINERS, T ANKS WIRES

MECHANICAL INDUSTRY TEXTILE PAINTS, GLUES, PIGMENTS

SPECIALIZED INDUSTRIAL MACHINERY LAB EQUIPMENT

AGRIFOOD

SWEDEN: PORTION OF THE PRODUCT SPACE

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  • 5

10 15 20

China

  • 5

10 15 20

Brazil

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)