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Journal of Statistical Physics

, Volume 151, Issue 3–4, pp 523–548 | Cite as

The Local Structure of Globalization

The Network Dynamics of Foreign Direct Investments in the International Electricity Industry
  • Johan KoskinenEmail author
  • Alessandro Lomi
Article

Abstract

We study the evolution of the network of foreign direct investment (FDI) in the international electricity industry during the period 1994–2003. We assume that the ties in the network of investment relations between countries are created and deleted in continuous time, according to a conditional Gibbs distribution. This assumption allows us to take simultaneously into account the aggregate predictions of the well-established gravity model of international trade as well as local dependencies between network ties connecting the countries in our sample. According to the modified version of the gravity model that we specify, the probability of observing an investment tie between two countries depends on the mass of the economies involved, their physical distance, and the tendency of the network to self-organize into local configurations of network ties. While the limiting distribution of the data generating process is an exponential random graph model, we do not assume the system to be in equilibrium. We find evidence of the effects of the standard gravity model of international trade on evolution of the global FDI network. However, we also provide evidence of significant dyadic and extra-dyadic dependencies between investment ties that are typically ignored in available research. We show that local dependencies between national electricity industries are sufficient for explaining global properties of the network of foreign direct investments. We also show, however, that network dependencies vary significantly over time giving rise to a time-heterogeneous localized process of network evolution.

Keywords

Dynamic stochastic models for networks Electricity industry Foreign direct investments Globalization Gravity model Longitudinal exponential random graph models Ensemble 

Notes

Acknowledgement

We are grateful to Clare Hall College, University of Cambridge (U.K.) for hospitality and support during the work leading to this paper.

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Authors and Affiliations

  1. 1.Social Statistics Discipline AreaUniversity of ManchesterManchesterUK
  2. 2.Faculty of EconomicsUniversity of LuganoLuganoSwitzerland

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