Title:      Modeling Foreign Direct Investment as a Longitudinal
Social Network

Authors:    Nathan Jensen, Andrew Martin, Anton Westveld

Entrydate:  2007-07-13 12:52:11

Keywords:   foreign direct investment, social network data,
longitudinal data, hierarchical modeling, mixture modeling, Bayesian
inference.

Abstract:   An extensive literature in international and comparative
political economy has focused on the how the mobility of capital
affects the ability of governments to tax and regulate firms.  The
conventional wisdom holds that governments are in competition with
each other to attract foreign direct investment (FDI).  Nation-states
observe the fiscal and regulatory decisions of competitor governments,
and are forced to either respond with policy changes or risk losing
foreign direct investment, along with the politically salient jobs
that come with these investments.  The political economy of FDI
suggests a network of investments with complicated dependencies.

We propose an empirical strategy for modeling investment patterns in
24 advanced industrialized countries from 1985-2000.  Using bilateral
FDI data we estimate how increases in flows of FDI affect the flows of
FDI in other countries.  Our statistical model is based on the
methodology developed by Westveld & Hoff (2007).  The model allows
the temporal examination of each notion's activity level in
investing, attractiveness to investors, and reciprocity between pairs
of nations.  We extend the model by treating the reported inflow and
outflow data as independent replicates of the true value and allowing
for a mixture model for the fixed effects portion of the network
model.  Using a fully Bayesian approach, we also impute missing data
within the MCMC algorithm used to fit the model.

http://polmeth.wustl.edu/retrieve.php?id=702