Challenges in the Era of Globalization - iaabd

The Model, Data and Variable Definitions
Challenges in the Era of Globalization
Edited by Emmanuel Obuah
For this study, we utilize a multiple regression analysis to determine the factors that affect the inflow of
FDI to sub-Saharan Africa. The model is of the form:
FDIit = A + αXit + ßHUMANit + εit
Where FDIit is the dependent variable measuring the inflow of FDI into country i in time t. Xit represent
factors that explain the inflow of FDI to SSA, such as the size of the domestic market and the growth of
the economy. HUMANit is the target explanatory variable, which is proxied by different measures of
human capital (education levels and labour skills). εit is an error term and A is a constant term, which may
capture the effects of other non-specified factors.
All SSA countries for which data was available form the sample for our analysis and snapshot the periods
1985, 1995, 2005. We are using these three periods, separated by 10 years interval, to tease out the
changing requirements of FDI/MNE via the significance of their determinants.
The choice of selecting only SSA countries is because several authors, including Asiedu (2002) and
Barta, Kaufmann and Stone (2003), believe that the factors that determine the inflow of FDI to SSA are
different from those that determine FDI elsewhere. In addition, the structure and characteristics of SSA
countries are different from other developing countries. This choice will also ensure that the results are
exclusively SSA.
This study draws its data from two primary sources: UNCTAD’s FDI database and World Bank’s
World/African Development Indicators. Data was also drawn from several other sources such as Freedom
House; World Resources Institute’s Earth Trends; International Telecommunications Union (ITU), etc.
As mentioned earlier, we use two sets of explanatory factors in this study to explain the inflow of FDI to
SSA. There is only a small number of empirical research on the determinants of FDI to SSA, and most of
these concentrate on the traditional factors that would normally stimulate market-seeking and natural
resource-seeking FDI. This is often at the expense of other factors that might provide further explanations
for these types, as well as for other types of FDI.
For this study, we define the dependent variable as the ratio of FDI inflow to GDP (FDI/GDP) of the host
SSA country. The data source for this variable is the UNCTAD FDI database.
The independent variables we have used in this study are as follows:
The attractiveness of the host SSA country’s market we proxy by its GDP per capita and GDP growth
rate, which measure market size and growth respectively. We expect these variables to be positively
significant determinants of FDI size, particularly for the market-seeking type of FDI.
Recently, many SSA countries have attempted to open up their economies to both trade and investment.
Many have recorded some success as a result in attracting more FDI. We therefore include in our study a
proxy for openness, defined as exports plus imports divided by GDP, i.e. (X+M)/GDP. We expect that the
higher is the ratio, the more open is the country and the higher will be the inflow of FDI.
We also include a proxy for political stability as a measure of country risk. We combine two indicators;
political freedom and civil liberty to measure political stability. We expect political stability to have a
positive impact on FDI inflows. This data for this variable was drawn from freedom House.
We select three proxy variables for human capital in this study. We expect these variables to capture
aspects of the efficiency-seeking types of FDI. Following Root and Ahmed (1979), we use secondary
school enrolment ratio (SSER) as a measure, since it reflects the flow of investment in human capital in
SSA and it is customary to use it in the empirical literature on growth (see Barro 1991). This flow
measure, however, does not take the accumulated stock of human capital in the economy into
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