Challenges in the Era of Globalization - iaabd

Proceedings of the 12th Annual Conference © 2011 IAABD
Methodology
The econometric approach in this paper is the modified version of the Granger (1969) and Sims (1972)
framework. In 1969, Granger developed a causality concept using time series to show that a cause must
precede an effect in the sense that a cause can produce a superior forecast of the effect. In some sense,
Granger causality is a measure of whether one variable takes a value before another variable which helps
predict the latter. Granger causality test is usually undertaken within a vector autoregressive (VAR)
framework using stationary series. Therefore, the first step in a Granger causality application is to
determine the stationarity properties of the sampled series.
Data
The data series are per capita total health expenditures and per capita GDP for all ECOWAS countries
except Liberia from 1998 through 2006. The real per capita health expenditures in a panel data setting are
obtained from the World Development Indicators produced by the World Bank. On the other hand, the
per capita real GDP in purchasing power parity (PPP) dollars are obtained from Penn World Tables 6.2
(Heston, et al. (2009). Liberia was excluded because of a lack of data. The countries covered are Benin,
Burkina Faso, Cape Verde, Cote D’Ivoire, The Gambia, Ghana, Guinea, Guinea Bissau, Mali, Niger,
Nigeria, Senegal, Sierra Leone, and Togo. According to Gbetnkom (2006) the ECOWAS countries are
quite integrated and this makes them to be relatively homogeneous.
Unit Root Tests:
As noted above, a panel causality test is usually preceded by a test of unit roots in time series studies.
However, testing for unit roots in panel data is a more recent undertaking and this test has more power
than the usual tests associated with time series data (Taguchi et al., 2009). The tests employed in this
paper are Levin, Lin and Chu (2002), Im, Pesaran and Shin (2003) and Maddala and Wu (1999) Fisherbased
tests. The Levin, Lin and Chu (LLC) test is discussed in the EViews 6 User’s Guide and it employs
the following equation:
(4)
Where, i=1,2,…., N cross-section or series that are observed for periods t=1,2, ..,T and Xit are the
exogenous variables in the panel which include fixed or individual effects. Under the null hypothesis, α=0
with an alternative hypothesis assuming α