Scaling of global input–output networks

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input–output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input–output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input–output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input–output networks to support economic policymaking.

[1]  Zaiwen Wen,et al.  Robust linear optimization under matrix completion , 2014 .

[2]  Lada A. Adamic,et al.  Power-Law Distribution of the World Wide Web , 2000, Science.

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Andreas Klaus,et al.  Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches , 2011, PloS one.

[5]  Matthias Schroder,et al.  Input–Output Analysis , 2011 .

[6]  Manfred Lenzen,et al.  AGGREGATION VERSUS DISAGGREGATION IN INPUT–OUTPUT ANALYSIS OF THE ENVIRONMENT , 2011 .

[7]  Ming Xu,et al.  Interconnectedness and Resilience of the U.S. Economy , 2011, Adv. Complex Syst..

[8]  Bo Zhang,et al.  Multi-regional input-output analysis for China’s regional CH4 emissions , 2014, Frontiers of Earth Science.

[9]  Manfred Lenzen,et al.  BUILDING EORA: A GLOBAL MULTI-REGION INPUT–OUTPUT DATABASE AT HIGH COUNTRY AND SECTOR RESOLUTION , 2013 .

[10]  Ming Xu,et al.  Structure of the Global Virtual Carbon Network: Revealing Important Sectors and Communities for Emission Reduction , 2015 .

[11]  R. Gibrat,et al.  Les inégalités économiques : applications, aux inégalitês des richesses, a la concentration des entreprises, aux populations des villes, aux statistiques des familles, etc. : d'une loi nouvelle la loi de l'effet proportionnel , 1931 .

[12]  H. Stanley,et al.  Power laws and universality , 1995, Nature.

[13]  V. Plerou,et al.  Institutional Investors and Stock Market Volatility , 2005 .

[14]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[15]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[16]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[17]  P. Ivanov,et al.  Common scaling patterns in intertrade times of U. S. stocks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  E. Dietzenbacher,et al.  An Illustrated User Guide to the World Input–Output Database: The Case of Global Automotive Production , 2015 .

[19]  Erik Dietzenbacher,et al.  THE MEASUREMENT OF INTERINDUSTRY LINKAGES - KEY SECTORS IN THE NETHERLANDS , 1992 .

[20]  Florian Blöchl,et al.  Vertex centralities in input-output networks reveal the structure of modern economies. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Hollis B. Chenery,et al.  International Comparisons of the Structure of Production , 1958 .

[22]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[23]  César A. Hidalgo,et al.  The Product Space Conditions the Development of Nations , 2007, Science.

[24]  Alessandro Chessa,et al.  World Input-Output Network , 2014, PloS one.

[25]  Bart Los,et al.  THE CONSTRUCTION OF WORLD INPUT–OUTPUT TABLES IN THE WIOD PROJECT , 2013 .

[26]  Ashok Parikh,et al.  Forecasts of Input-Output Matrices Using the R.A.S. Method , 1979 .

[27]  Brian D. Fath,et al.  Network structure of inter-industry flows , 2012, ArXiv.

[28]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[29]  Manfred Lenzen,et al.  A STRUCTURAL DECOMPOSITION APPROACH TO COMPARING MRIO DATABASES , 2014 .

[30]  Stanley,et al.  Barkhausen noise: Elementary signals, power laws, and scaling relations. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[31]  Ian G. Stewart,et al.  Studies in Inter-Sectoral Relations. , 1956 .

[32]  W. Leontief.  Quantitative Input and Output Relations in the Economic Systems of the United States , 1936 .