Re-examining foreign direct investment and growth nexus in Nigeria
Enisan A. Akinlo
Department of Economics
Obafemi Awolowo University,
Ile-Ife, Nigeria.
Tel: +234(0)803 370 0756
E-mail: aakinlo@oauife.edu.ng
1
Abstract
The paper investigates the FDI-growth nexus in Nigeria during the period 1970-2009
using a multivariate VAR model. The results of the estimation show that real gross
domestic product, oil FDI and non oil FDI are cointegrated and that there is only
unidirectional causality running from real GDP to non oil FDI in the short run. Non oil
FDI has larger significant positive impact on economic growth than oil FDI. Thus,
government should develop a set of policies that are not only focused on inwards FDI
promotion in the non oil sector but also on implementation of policies to integrate the oil
sector into the national economy to enhance oil FDI spillover.
JEL classification: E21; C32
Keywords: Foreign direct investment, Economic growth, Cointegrating VARs, Impulse
response functions
2
Re-examining foreign direct investment and growth nexus in Nigeria
1. Introduction
The neoclassical and endogenous growth models provide the foundation for the
theoretical relationship between foreign direct investment (FDI) and economic growth. In
the neoclassical models of growth, it is emphasized that FDI increases the volume of
investment and its efficiency resulting in long term growth. Models based on the
endogenous growth theories also established a connection between increased FDI and
economic growth through technology transfer, diffusion and spillover effects. The
theoretical postulate that FDI and economic growth are positive related is substantiated
by legion of empirical studies (see De Mello, Jr.; Huang 2004; Ram and Zhang 2002;
Zhang 2001; Alguacil et al. 2011). However, few studies including Chakraborty and Basu
(2002), for India; De Mello Jr. (1997) for Brazil; Mah (2010) for China and Oyinlola
(1995) for Nigeria, have found no support for FDI-Led growth (FLG); in fact the first
three empirical studies found causality running from GDP to FDI while the last empirical
work establish negative effect of FDI on growth.1
Aside from the fact that the debate on the nature of the relationship between FDI
and economic growth is unsettled, most existing studies especially Nigeria have not
examined the differential effects of oil and non oil FDI on economic growth in Nigeria2.
This is very important as studies have argued that positive spillover effects might be
1
The empirical finding that the line of causality runs from GDP growth to FDI is consistent with the
market-seeking FDI hypothesis of Dunning (1988) and Mortimore (2003) which states that FDI is attracted
to growing internal markets for services such as telecommunications, gas and electricity, retail commerce,
and financial services.
2
Indeed, most empirical studies in the developed and industrialized countries have concentrated majorly
on effect manufacturing FDI since it constitutes the major inwards inflow in these economies.
3
higher in manufacturing FDI than extractive FDI3. Recent study by Ekperiware (2011)
that attempted to verify the above conjecture has a lot methodological defects. The paper
employed simple log-linear models estimated by ordinary least squares (OLS) without
any regard for the nature of the time series properties of the variables involved. However,
as has been proven, most economic time series are non stationary in levels form (See
Granger and Newbold, 1974). Thus, failure to account for such properties could result in
misleading relationships among variables.
This paper, therefore, examines the FDI-growth nexus in Nigeria confronting the
main criticisms of some existing studies by (1) studying the problem of unit roots,
cointegration and Granger causality, (2) separating the effects of oil and non FDI, and (3)
increasing the time span data.
The remainder of the paper is organized as follows. In section 2, a capsule summary
of the empirical literature is provided. Section 3 discusses the methodology and the data.
Section 4 contains the analysis of the results. Section 5 provides the conclusion.
2. Review of related studies
The theoretical and empirical literature on the growth effects of FDI on host countries
is voluminous4. However, findings from these studies have been mixed. Some studies
found that FDI could stimulate economic growth through spillover effects such as new
technologies, capital accumulation, increased export, and human capital development
(Balasubramanyam, et al., 1996, 1999; Borensztein et al., 1998; Alguacil et al., 2002;
3
Akinlo, (2004) identified some of the factors that might make the indirect benefits of the FDI to be less in
extractive especially oil industries. These include (1) the enclave nature of the oil sector, (2) presence of
large economies of scale in the extractive industries, and (3) low forward and backward linkages in the
subsector (for detailed discussions see Akinlo, 2004)
4
The literature is replete with several empirical studies on the FDI-growth nexus. Hence, this section only
provides a brief summary of the related studies. For detailed survey of theoretical and empirical literature,
see de Mello, (1997, 1999); Buckley, et al. (2002), among several others.
4
Chakraborty and Basu, 2002;
Liu et al., 2002; Akinlo, 2004; Ekperiware 2011;
Omonkhanlen, 2011) However, studies by Bende-Nabende et al., ( 2002, 2003) found
that FDI had negative effect on growth in some countries. Likewise, Durham (2004)
failed to find positive relationship between FDI and growth, while Cobham (2001)
postulates the crowding out of domestic firm and possible contraction in total industry
size and /or employment. Adams (2009) found that FDI crowded out domestic
investment with adverse effect on economic growth in Sub-Saharan Africa.
The general consensus in both theoretical and empirical literature is that the
productivity of the FDI is contingent on initial conditions of the host country including
the absorptive capacity of host country and degree of complimentarity between domestic
investment and FDI5.
3. Econometric methodology
In this section, a multivariate cointegration is conducted using VAR model
developed by Johansen, (1988) and Johansen and Juselius (1990, 1992). This approach is
adopted as against other possible approaches for several reasons. One, it does not require
a priori assumption of exogeneity of variables. Two, vector auto regressive model allows
each variable in the system to interact with itself and with each other without having to
impose a theoretical structure on the estimates. More importantly, it provides a
convenient method of analyzing the impact of a given variable on itself and all other
variables by using variance decomposition (VDCs) and impulse response functions.
5
Details of other factors could be found in the works of Blomstrom et al., 1994; Akinlo, 2004; Durham,
2004; Alguacil, et al., 2011 among others)
5
The goal is to ascertain whether economic growth (gdp), oil FDI (oil), and non oil
FDI (nol) are cointegrated or xt = (gdp, oil, nol)'. Therefore, VAR model as in Johansen
and Juselius(1992) is given as6:
∆xt = ∑𝑝𝑖=1 𝝅𝒊 ∆xt-i + 𝝅xt-1+ Ψzt + εt
………………………………………(1)
where 𝝅’s (i = 1,……, ρ) are (3x3) matrices for the variables ∆xt-1, ∆xt is a (3x1) column
vector of the first differences of xt; 𝝅 is a (3x3) matrix for the variables xt-1 which is a
(5x1) column vector of lagged dependent variables, zt is a (3xs) matrix containing s
deterministic variables for each dependent variable; εt is a (3x1) column vector of
disturbance terms normally distributed with zero means and constant variances. In
general, the time series characteristics of the variables determine whether vector-error
corrections model (VECM) or an unrestricted vector autoregression model (VAR) will be
specified. For non-stationary variables that are cointegrated, the correct specification is
VECM. However, for non-stationary variables that are not cointegrated, the correct
specification is an unrestricted VAR model.
Annual data spanning the sample 1970 to 2009 sourced from the Central Bank of
Nigeria (CBN) Statistical Bulletin 2009 edition were used. The variables are real GDP at
time t (gdpt), oil FDI at time t (oilt), and non oil FDI at time t (nolt). All variables were
transformed to natural logarithm in order to minimize the scale effect.
4. Empirical results
The results of the Augmented Dickey Fuller (ADF) (Dickey and Fuller, 1981 and
KPSS (Kwiatkowski-Phillips-Schmidt-Shin (1992) tests7 with a constant and a
6
The VAR methodology has been widely explained and employed in the literature to warrant detailed
discussion here. We only provide a highlight of the technique in this paper. For detailed discussions of the
paper , see Juselius (2006), Harris (1995 and Renote , Jr. (2001) among several others.
6
deterministic trend are shown in table 1. The results in table 1 show that all the variables
are integrated of order one, I(1).
Table 1
Nigeria: Unit root tests for stationarity with constant and linear trend, 1970-2009
Series
ADF
Level
ln gdp(constant)
(constant & linear)
ln oil(constant)
(constant & linear)
ln nol(constant)
(constant & linear)
-0.401
-1.733
-0.189
-2.103
0.083
-3.438
KPSS
Ist difference
-2.812
-2.712
-4.135
-4.108
-4.515
-4.557
level
1.413
0.202
1.368
0.146
1.356
0.171
Ist difference
0.096
0.094
0.067
0.062
0.133
0.075
Notes: Critical values for ADF are: -3.62, -2.94, and -2.61 (constant only); -4.23, -3.54, and -3.20 (constant and linear)
at 1%, 5% and 10% level of significance respectively. However, the critical values for KPSS test are: 0.739, 0.463 and
0.347 (constant only), 0.216, 0.146 and 0.119 (constant and linear) at 1%, 5% and 10% level of significance,
respectively.
Testing for cointegration among these three variables with the procedure advocated
in Johansen (1990, 1992) we found at most a single cointegrated vector (see table 2)8,9.
The cointegrating equation shown in panel B of Table 2 indicates that economic growth,
oil FDI, and non oil FDI are positively related. All the coefficients are significant as
shown by the t-ratios indicated in parentheses. The results show that a 1% increase in oil
Table 2
Nigeria: Johansen co-integration tests where r is the number of cointegrating vectors
Panel A: Estimates of λ-max and trace tests
Null
Alternative r
λ-max
Critical value (95%)
Trace
Critical value (95%)
0
1
21.34
21.13
35.85
29.79
≤1
2
14.49
14.26
14.51
15.49
≤2
3
0.02
3.84
0.02
3.84
Panel (B): Estimate of co-integrating vector
gdp
-1.0000
oil
0.271(2.65)***
nol
0.523(4.90)***
Notes: t-ratios are in parentheses. *** Significant at 1% level
The limitation of the ADF statistic in deciding whether φ =1 or φ = 0.98, in a model like: Xt = μ + φXt-1 +
εt is remedied by the application of the KPSS statistic simultaneously.
8
The trace test suggests one cointegrating vector while the λ-maximum test suggests two cointegrating
vectors. However, as pointed out by Cheung and Lai (1993), the trace test shows more robustness to both
skewness and excess kurtosis in the residuals than does the λ-maximum test.
9
The existence of cointegrating relationship among real GDP, oil FDI and non oil FDI suggests that there
must be causality in at least one direction, but it does not indicate the direction of temporal causality
between the variables.
7
7
FDI will lead to 0.271% increase in economic growth while a 1% increase in non oil FDI
will increase economic growth by 0.523%.
The Granger representation theorem states that a system of cointegrated variables has
an error correction and that combines the short run dynamics of the variables with their
long run properties as implied by the cointegrating relationships. Hence, the Vector Error
Correction Model (VECM) has been formed and estimated10. The results are presented in
Table 3. The error correction term is significant for growth and non oil FDI equations and
Table 3
VEC model estimates
Equation
ξt-1
α
∆gdpt-1
∆gdpt-2
∆oilt-1
∆oilt-2
∆nolt-1
∆nolt-2
R-2
F-statistics
Log Likelihood
AIC
∆gdpt
-0.1936
(-2.281)
0.2154
(3.540)
0.1939
(1.084)
-0.0170
(-0.089)
-0.0437
(-1.219)
0.00003
(0.00009)
-0.0670
(-1.479)
-0.0417
(-1.031)
0.0101
1.0524
13.953
-0.322
∆oilt
-0.0784
(-0.154)
0.3146
(0.864)
0.9964
(0.930)
-0.4162
(-0.367)
-0.5914
(-2.759)
-0.2417
(-1.292)
0.1209
0.446)
-0.1057
(-0.437)
0.1389
1.8361
-52.239
3.256
∆nolt
1.0341
(3.451)
-0.2713
(-1.262)
1.1219
(1.775)
1.0996
(1.643)
0.1146
(0.905)
-0.0751
(-0.680)
-0.1344
(-0.839)
0.0244
(0.171)
0.3843
4.2101
-32.729
2.202
Notes: ξt-1 is the lagged error correction term and ∆ is the first difference operator. All the variables are
as earlier defined.
except for non oil FDI, they are of correct sign. This suggests that there is channel of
causality among the variables.
10
The Akaike information Criterion (AIC) and Schwatz Criterion (SC) both yielded optimal lag length of
two.
8
The summary of the direction of causality is as shown in table 4. The results in table 4
show that both oil and non oil FDI do not cause GDP in the short run, but they do in the
long run. In the oil FDI equation, both GDP and non oil FDI are statistically insignificant,
implying GDP and non oil do not cause oil FDI in the short and long run, while there is
neutrality between oil FDI and non oil FDI; there is unidirectional causality running from
GDP to non oil FDI11.
Table 4. Nigeria: Block exogeneity wald tests
gdp
oil
gdp
2.304
oil
0.936
nol
6.576*
3.128
nol
2.525
0.577
-
Joint
6.907*
1.573
24.952*
As is the case with any vector autoregressive model, individual coefficients from
error correction model are hard to interpret. Consequently, the dynamic properties of the
model are analyzed by examining impulse response functions and the variance
decompositions12.
Table 5 shows the results using the Cholesky variance decomposition technique. A
good proportion of the variance in GDP shocks is attributable to shocks in non oil FDI
and oil FDI. GDP also accounts for shocks in non oil FDI, but not nearly as much in oil
FDI. The importance of oil FDI shocks to non oil FDI is also apparent in contrast to the
11
This finding is consistent with that of Mah (2010) for China; Chakraborty and Basu (2002), for India and
De Mello Jr. (1997) for Brazil
12
We are aware of the fact that Cholesky decomposition is sensitive to the ordering of the variables and the
possibility of correcting it by using generalized decomposition process (GIRF) proposed by Pesaran and
Shin (1998). However, as recently pointed out by Kim (2009), GIRF may yield quite misleading economic
inferences. This is because the method yields a set of response functions that are based on extreme
identifying assumptions that contradict each other, unless the covariance matrix is diagonal. To check the
consistency of the estimates from Cholesky decompositions, we re-estimated the model reversing the order
of the first and the last variables. The results obtained were not significantly different from the one reported
in the paper.
9
reverse case where shocks to oil FDI explained by innovation in non oil FDI are almost
negligible. These results support the findings from the causality tests.
Table 5
Nigeria: Variance decompositions
Horizon (yrs)
gdp
Shock to real gdp (gdp) explained by innovations in:
1
5
10
100.00
89.66
83.33
Oil FDI
Non FDI
0.00
1.89
2.04
0.00
8.45
14.63
88.96
83.22
81.66
0.00
0.77
0.85
Shock to oil FDI (oil) explained by innovations in:
1
5
10
11.04
16.01
17.49
Shock to non oil FDI (nol) explained by innovations in:
1
5
10
1.57
36.88
41.69
8.17
22.13
24.79
90.26
40.99
33.52
The impulse response functions emanating from standard deviation shocks to oil FDI
and non oil FDI with the response in GDP are traced out in fig 1. Responses from shocks
in both variables provide not entirely similar paths for GDP. In the case of oil FDI, the
path increased and climaxed after 3 years and thereafter assumed constant level. With
respect to non oil FDI, it increased sharply up to 4th year and thereafter increased at a
decreasing rate. The time profiles of these responses illustrate the positive and persistent
impact shocks to FDI have on GDP.
10
Response to Cholesky One S.D. Innovations
Response of LOG(GDP) to LOG(OIL)
.12
.08
.04
.00
1
2
3
4
5
6
7
8
9
10
11
12
Response of LOG(GDP) to LOG(NOL)
.12
.08
.04
.00
1
2
3
4
5
6
7
8
9
10
11
12
Fig. 1. Response of GDP to single standard deviation shock
to oil and non oil FDI
5. Conclusion
This paper analyzed the FDI-Led growth hypothesis for Nigeria using a set of annual
data spanning 1970-2009. Our main findings were as follows. First, the three variables
namely economic growth, oil FDI and non oil FDI tend to move together in the long run.
Second, oil FDI and non oil FDI had a positive and statistically significant impact on
GDP in Nigeria. Third, the results showed unidirectional causality running from GDP to
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non oil FDI in the short run. Four, IRFs and VDC analyses showed that non oil FDI had
larger significant impact on economic growth than oil FDI.
Our findings lead to the following policy implications. Government should ensure
that more foreign investment flows into the economy. This will entail creating
appropriate enabling environment to attract more FDI. There is a need for major
institutional-legal changes such as the relaxation or elimination of restrictions on profit
and capital remittances. In addition, government must open up the formerly classified
“priority sectors” to foreign investors.
Moreover, there is need to rehabilitate the existing infrastructural facilities-many of
which are outdated and moribund. The provision of the infrastructural facilities will help
to reduce the cost of production in the economy thereby making the economy more
competitive in the international arena.
In order to ensure that oil FDI enhances growth and development in Nigeria, there is
the need to mainstream the oil sub sector into the national economy. Government should
make efforts to stimulate local labour and capital and institute appropriate reforms to
enhance efficiency and transparency. These reforms will involve institution of
appropriate pricing policy in the sub sector and elimination of massive corruption in the
sector.
12
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