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<p>This paper introduces a network-based framework to model Foreign Direct Investment (FDI) dynamics using Exponential Random Graph Models (ERGMs). Traditional approaches fail to account for structural interdependencies like reciprocity and transitivity, which are critical in global investment networks. Using bilateral FDI data from 2001–2012 sourced from UNCTAD, the study applies count-based ERGMs to capture these patterns while addressing data sparsity. Results demonstrate that countries embedded in reciprocal and transitive investment ties are more likely to receive and initiate further FDI. This framework provides new empirical insights and offers a policy-relevant approach to understanding global capital flows.</p>

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Network-driven foreign direct investment dynamics: a new approach using exponential random graph models

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Kushal Bhatt

摘要

This paper introduces a network-based framework to model Foreign Direct Investment (FDI) dynamics using Exponential Random Graph Models (ERGMs). Traditional approaches fail to account for structural interdependencies like reciprocity and transitivity, which are critical in global investment networks. Using bilateral FDI data from 2001–2012 sourced from UNCTAD, the study applies count-based ERGMs to capture these patterns while addressing data sparsity. Results demonstrate that countries embedded in reciprocal and transitive investment ties are more likely to receive and initiate further FDI. This framework provides new empirical insights and offers a policy-relevant approach to understanding global capital flows.