In recent literature, much attention has been devoted to the impact of Foreign
Direct Investment (FDI) on economic growth in host countries, especially in
developing countries. This debate assumes special importance in view of the recent changes in
the composition and direction of FDI and the liberalization policies towards FDI in
those countries. Theoretically, the foundation for empirical studies on FDI and growth
derives from either neoclassical models of growth or endogenous growth models. FDI
in neoclassical growth models promotes economic growth by increasing the volume
of investment and/or its efficiency. The new endogenous growth models assume that
FDI raises economic growth through technology transfer, diffusion and spillover effects.
The impact of FDI on growth is expected to be twofold. First, through
capital accumulation in the host economy, FDI is expected to be growth enhancing
by encouraging the incorporation of new inputs and technologies in the production
process. Second, through knowledge transfers, FDI is expected to augment the existing stock
of knowledge in the recipient economy through labor training, skill acquisition and
through the introduction of alternative management practices and organizational
arrangements (Balasubramanyam et al., 1996; and De Mello, 1999).
Unfortunately, there is conflicting evidence in the empirical literature regarding
the impact of FDI on economic growth. While some studies observe a positive influence
of FDI on economic growth, others detect an insignificant or negative relationship.
This controversy has arisen partially due to data insufficiency in both time and
cross-section studies. One possible solution for these kinds of problems regarding the analysis of
FDI and growth is the use of panel data models (Bende-Nabende and Ford, 1998; De
Mello, 1999; Soto, 2000; Nair-Reichert and Weinhold, 2001; Buckley et al., 2002; Bende-Nabende et al., 2003; Li and Liu, 2005; and Yang, 2007) to correct for continuously
evolving country-specific differences in technology, production, and socioeconomic factors,
thus eliminating many of the difficulties encountered in cross-country estimations. This
allows the researchers to control country-specific effects and include dynamic, lagged
dependent variables which can help to control omitted variables and endogeneity bias, respectively. |