How crude oil prices shape the global division of labor

Francesco Picciolo*, Andreas Papandreou, Klaus Hubacek, Franco Ruzzenenti

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Scopus)

Abstract

Our work sheds new light on the role of oil prices in shaping the world economy by investigating flows of goods and services through global value chains between 1960 and 2011, by means of Markov Chain and network analysis. We show that over that time period the international division of labor and trade patterns are tightly linked to the price of oil. We observe a remarkably high negative correlation (−0.85) between the oil price and the share of cyclical value, i.e. the share of value embodied in raw materials and intermediate products that are conserved across direct and indirect relationships. We demonstrate that this correlation does not depend on the balance of payments nor on the nominal value of trade or trade agreements; it is instead linked to the way Global Value Chains (GVCs) shape global trade. The cycling indexes show two majors structural breaks in terms of distance and length of GVCs, hinting at two phases of the recent globalization dynamics, sustained by two major transport modes. Our study suggests that transport played an important structural role in shaping GVCs, unveiling the deep, long-term impact of energy costs on the structure and connectivity of the global economy. In more theoretical term, our results indicate that the production structure could be approached as an energy system, forged by the efficiency in the transport sector. Understanding the role of oil in a globalized economy is of paramount importance for decoupling of economic growth from energy growth and transitioning toward a de-carbonized economy.

Original languageEnglish
Pages (from-to)753-761
Number of pages9
JournalApplied Energy
Volume189
DOIs
Publication statusPublished - 1-Jan-2017
Externally publishedYes

Keywords

  • Global supply chains
  • Globalization
  • Markov chain analysis
  • Network analysis
  • Oil price

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