Network structure of inter-industry flows

McNerney, James; Fath, Brian D.; Silverberg, Gerald

doi:10.1016/j.physa.2013.07.063

Cited by 107 publications

(76 citation statements)

References 39 publications

(42 reference statements)

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“…In fact, J. McNerney et al (1995) analyzed the Korean IO table classified into 39 industries and reported that the completeness(density) was 0.888, which is much higher than the results of this study [4]. …”

Section: Resultscontrasting

confidence: 73%

“…In network theory, centralities such as degree centrality, closeness centrality, and betweenness centrality have been used. J. McNerney et al have defined nodes with large in-flow (that is, out-flow) as important nodes [4]. D. Aobdia et al used eigenvector centrality to find central industries in inter-industry networks [2], and reported that the accounting performance of the central industries is more closely related to macroeconomic indicators than noncentral industries is and better explains the performance of the surrounding industries.…”

Section: Introductionmentioning

confidence: 99%

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A Study on Strategic Industry Selection by Analysis of Inter-industry Network: The Case of Korea

Yoo¹

2017

IJITMSB

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In order to maximize the efficiency of the economic activation policy through fostering specific industries, it is important to prioritize the industry and it is necessary to understand the inter-industry network. We obtained an inter-industry network from the recent inputoutput table in Korea and analyzed the structure of the same. In addition, we analyzed various index values provided from the inter-industry analysis and the network theory for each industry. From the results of the network analysis, it is expected that the chemical and metal industries, transportation machinery industry

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Section: Resultscontrasting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

A Study on Strategic Industry Selection by Analysis of Inter-industry Network: The Case of Korea

Yoo¹

2017

IJITMSB

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“…Also, McNerney and Fath (2013) have previously applied information theory and other network metrics to I-O matrices of multiple countries. However, McNerney and Fath (2013) were limited to either one or two years of I-O tables for any given country.…”

Section: Economic Structure Perspectivementioning

confidence: 99%

“…That is to say I performed no computational analysis, such as clustering (McNerney et al 2013), to group the economic sectors. To explore the implications of the size of the network (e.g., the number of sectors) on the calculations in this paper, I also reduce the I-O tables to 12, 7, and 2 sector versions and report on the trends (see ''Information Theory Trends as a function of Network Size'' section for results and Tables S1 and S2 for sector descriptions).…”

Section: I-o Data and Harmonizationmentioning

confidence: 99%

Information Theory to Assess Relations Between Energy and Structure of the U.S. Economy Over Time

King

2016

Biophys Econ Resour Qual

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This paper describes the changing structure of the United States' (U.S.) domestic economy by applying information theory-based metrics to the U.S. input-output (I-O) tables from 1947 to 2012. Here the I-O tables are an economic network where the sectors are the nodes. The value of these metrics is that they describe the balance or trade-off between efficiency and redundancy of network flows as well as equality and hierarchy of flows through nodes in a network. I relate these metrics to the U.S. gross power consumption and annual intermediate spending by the food and energy sectors, the latter being a proxy for the inverse of the net power ratio (or net energy) of the economy, to test hypotheses of energy-economy structural linkages. The results of this paper show that increasing gross power consumption, as well as a decreasing share of intermediate expenditures of the food and energy sectors, correlates with increased distribution of money among economic sectors, and vice versa. The information theory metrics indicate two time periods at which major structural shifts occurred. The first was between 1967 and 1972, and the second was around the turn of the twenty-first century when food and energy expenditures no longer continued to decrease after 2002. In response to the latter, it is clear that the U.S. economy did trade off structural reserves (e.g., decreasing metrics of conditional entropy, redundancy, and equality) for structural efficiency (e.g., increasing metrics of efficiency, mutual constraint, and hierarchy) after food and energy expenditures increased post-2002. I also test the structural trends with increasingly simpler (e.g., fewer sectors) representations of the I-O tables, and the results are more consistent for I-O representations that account for inputs and outputs (e.g., value added and gross domestic product) rather than only the intermediate transactions among sectors. The findings of this paper have important implications for economic modeling in at least two ways. First, the paper helps explain how fundamental shifts in resources costs relate to economic structure and economic growth. Second, the paper shows that the number of sectors used to represent economic transactions influences the systemic metrics themselves.

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“…Such a research avenue has been boosted by a rising number of papers exploring the structure and consequences of economics networks, including trade (Fagiolo et al, 2009), production (Hausmann andHidalgo, 2011), input-output interdependencies (McNerney et al, 2012) and finance (May et al, 2008, Haldane and May, 2011, NP, 2013. Furthermore, in the last years, the topics of international migration and human temporary mobility have received an increased attention in the field of network science, for their straightforward connections with crucial large-scale phenomena like global-epidemic spreading (Balcan et al, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Human-Mobility Networks, Country Income, and Labor Productivity

Fagiolo

Santoni²

2014

SSRN Journal

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This paper asks whether the level of integration of world countries in the international network of temporary human mobility can explain differences in their per-capita income and labor productivity. We disentangle the role played by global country centrality in the network from traditional openness measures, which only account for local, nearestneighbor linkages through which ideas and knowledge can flow. Using 1995-2010 data, we show that global country centrality in the temporary human-mobility network enhances both per-capita income and labor productivity. Our results hold cross-sectionally, as well as in a dynamic-panel estimation, and take into account potential endogeneity issues. Our findings imply that how close a country is to the theoretical technological frontier, depends not only on how much she is open to temporary human mobility, but mostly on whether she is embedded in a web of relationships connecting her with other influential partners in the network. Our exercises also suggest that most of the gain in income and productivity can be attained if country centrality in the network comes mostly from influential partners that lie not too far away from, but neither too close to them in the network.

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Network structure of inter-industry flows

Cited by 107 publications

References 39 publications

A Study on Strategic Industry Selection by Analysis of Inter-industry Network: The Case of Korea

A Study on Strategic Industry Selection by Analysis of Inter-industry Network: The Case of Korea

Information Theory to Assess Relations Between Energy and Structure of the U.S. Economy Over Time

Human-Mobility Networks, Country Income, and Labor Productivity

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