Foreign direct investment and its determinants: A regional panel causality analysis
Introduction
Foreign direct investment (FDI) and its relationship with economic growth have led to numerous empirical studies (see DeMello, 1997, DeMello, 1999). Traditionally, in the literature, one or possibly two-way causality involving FDI and one factor such as GDP, local investment and infrastructure or quality of labor have been examined. Our intention is to widen the discussion by providing a systematic approach for considering all possible flows of causality involving FDI and the other variables of influence. We outline and implement a sequential econometric methodology that firstly determines whether the relevant variables of potential interest are stationary or integrated, that secondly identifies the exogeneity and endogeneity of these variables, that thirdly determines whether relationship among variables are long or short run in nature, and that finally establishes the relevant causality relationships. We do this in the context of regional panel data from China over the period, 1995–2010. China has the highest FDI among developing countries, but within China a great deal of diversity exists across regions. While not limited to the coastal provinces of China, traditionally most of the FDI inflows have been in the coastal region. Furthermore, with a limited length of reliable annual data in China, a regional panel approach provides statistical efficiency benefits.
While originally the focus in the literature had been on the one-way causal link from FDI to growth, following criticisms such as Kholdy (1995) and others, newer studies emerged allowing for the possibility of two-way causality among FDI and growth. That is, FDI can Granger cause GDP growth and GDP growth could also affect the inflow of FDI. Nevertheless, it soon becomes apparent that possible linkages inevitably involve other determinants. For example, the contribution of FDI to growth is influenced by such factors as domestic investment, technology and skills of the labor force (e.g., Apergis et al., 2006, Gholami et al., 2006, Kartircioglu and Naraliyeva, 2006, Sun et al., 2002).
Indeed, how these other determinants influence FDI and the growth in GDP undoubtedly can shape the prescriptive policies that lead to targeted growth and investment. In searching for the relevant interaction and causality among FDI, growth in GDP and other determining factors, this study proposes a comprehensive search strategy for uncovering the most suitable dynamic specification. While section 2 of this paper will provide a number of plausible explanations of the relationship between growth in FDI and possible determinants, ultimately we have to go to the data to statistically identify the appropriate endogenous variables, control variables and specification that best describes these relationships. Section 3 documents the definitions of the data variables and the data sources. Section 4 provides a preliminary exploration of what variables are exogenous and what are endogenous, the nature of the stationarity of the variables, and whether the variables are cointegrated. Taking into account the findings of Section 4, Section 5 presents the causality exploration strategy and Section 6 presents the empirical results of the Granger causality tests and policy implications.
Section snippets
Potential determinants of FDI
The purpose of this section is to set the stage for exploring the possible factors having a significant influence on the growth in FDI. Traditionally in the literature, theoretical and empirical arguments have been provided to explore one- or possibly two-way causality involving FDI and GDP, FDI and local infrastructure and investment, or FDI and characteristics of labor. In Fig. 1, this causality, represented by the solid bidirectional arrows, appears as the spokes of a wheel with FDI as the
Data description
Understanding the direction of causality between economic growth and FDI provides the basis for developing sound economic policies. Nowhere can this be better appreciated than in China where there has been phenomenal growth (around 9% per annum) since 1979. Across the provinces and autonomous municipalities within China there is great diversity in both growth rates GDP and FDI, which provides us a great deal of variation to estimate the causal relationship between these two key variables of
Preliminary establishment of the nature of the variables
Before we undertake a causality exploration among the previously identified variables of interest, we need to establish
- (i)
whether the variables are stationary, and if not, whether integrated variables of order 1, I(1), are appropriate
- (ii)
which variables are exogenous and which variables are endogenous
- (iii)
whether the variables are cointegrated and if so, the appropriate error correction mechanism to be added.
We will now endeavor to address points (i) to (iii) as part of our preliminary establishment of the
Causality exploration methodology
Without loss of generality, consider one of the endogenous variables , for i = 1, 2, …, 29, t = 1, 2, …, 15. Suppose there are three other such endogenous variables , and , and G exogenous variables, .
Consider the following four dynamic models:
Results of Granger causality tests and policy implications
In order to apply Granger causality test on each dependent variable, say Δln FDI, we have to exhaust all the combination of other endogenous variables with different lags structure as well as exogenous variables, since we have four endogenous variables which may have contemporaneous effects on each other. Since there are several hundreds of such combinations, we further decompose the test procedure into four sequential steps as described in the previous section. In all steps, we start by taking
Conclusions
This paper has extended the discussion of causality involving FDI by providing a systematic approach for examining all possible flows of causality involving FDI, both in the short run and in the long run. This is the first systematic attempt to examine causality in both the short and long run for a comprehensive set of variables affecting and being affected by FDI. For FDI and its relationship with many of the key economic variables that we examine, this is an important issue. We dichotomize
Acknowledgements
We thank Cheng Hsiao and two anonymous reviewers for valuable comments and suggestions. Keqiang Hou thanks the Chinese Social Sciences and Humanities Research Council for funding this research.
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