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Foreign Direct Investment and
Some Selected Macroeconomic
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improve income distribution or poverty reduction especially for the developing countries of the world like Nigeria. It is on this background that this paper is set to answer two questions:
Does a causal relationship exists between FDI, economic growth, inflation and interest rate in Nigerian? Does FDI have a significant effect in both short and long run in Nigeria? An overview of empirical literature reviews on the selected variables was presented in Section Two. Section Three contains methodology while data presentation and discussion of result are presented in Section Four. Section Five contains conclusion and policy recommendations.
Empirical Literature Review
The study by Gandu and Yusha'u (2019) investigated the impact of foreign direct investment on economic growth in Nigeria using quarterly data from 2009Q1 to 2016Q3. With the help of ARDL and VECM models, results indicate a long-run relationship between FDI, economic growth, exchange rate, interest rate and inflation. It appears that FDI inflow has significant negative impact on economic growth in both short run and the long. Gatawa (2019) empirically examined the impact of money supply, inflation, and interest rate on Economic Growth in Nigeria using annual time series data from 1973-2013. VAR Model and Granger Causality test within error correction framework were used. The findings reveal a positive impact of broad money supply while inflation and interest rate exhibit a negative impact on economic growth most especially in the long run. The short run results revealed that with the exception of inflation, broad money supply and interest rate were negatively related to economic growth.
Furthermore, Ershad and Haque (2018) carried out a study on foreign direct investment, trade, and economic growth from 1973 to 2014 using VECM. The study revealed that there is a relationship between foreign direct investments, trade, and growth rate of per capita GDP for Bangladesh. Findings further suggested a long-term relationship between trade, foreign investment and economic growth.
In a different study, Patricia and Izuchukwu (2018) investigated the relationship between money supply and economic growth in Nigeria. Findings revealed that there is a positive and significant relationship between money supply and economic growth in Nigeria. This implies that M2 has dominant influence on output and prices.
Similarly, Bhunia (2018) investigated the relationships between inflation, interest rate and economic growth of India from 1992 to 2015. Their empirical results confirmed that a long run causality exist from economic growth to inflation and interest rates. Hussain and Haque ( 2018) studied the relationship between FDI inflows, trade openness and economic growth for Bangladesh over the period 1973–2014 using the multivariate co integration approach. They found that both FDI inflows and trade openness have significant effects on economic growth. Because FDI and trade are two important components of economic growth in Bangladesh, it is important to frame policies that promote growth and reduce the barriers for capital flows.
Moreover, Iya and Aminu (2018) investigated the influence of foreign direct investment and domestic investment on economic growth in Nigeria over the period 1992–2013. The study found a positive and significant relationship between economic growth and domestic investment. Temiz and Gökmen (2018) investigated the relationship between FDI inflows and economic growth in Turkey. By applying quarterly time series from quarter 1 1992 to quarter 3 2007 and OLS regression model used to estimate the relationship between the
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variables, the authors conclude that there is no meaningful relationship between FDI and economic growth in Turkey in the short and long terms.
Omri and Kahouli (2017) studied FDI-economic growth nexus for 124 cross-country data over the period 1971–2010. His empirical results indicated that overall FDI affects positively economic growth and vice versa. In addition, some determinants of FDI, such as labor force, trade openness, and economic freedom stimulated economic growth. Omri and Kahouli (2017) found also that there is bi-directional causality between domestic capital and economic growth in all the 13 MENA countries that were studied, including Saudi Arabia.
In contrast, Bayar and Yilmaz (2017) found that DI has a significant positive effect on economic growth in both the short and long run in Nigeria .
Methodology and Data
The data for this study was gathered from World Bank‘s world development indicator (WDI, 2017).
Econometrics Model
The econometric model of FDI is specified as follows:
0 1 2inf 3int ...(3.1) fdi gdp
Equation (3.2), which include the error term gives us
0 1 2inf 3int ...(3.2) fdi gdp
introducing the foreign direct investment variable into FDI equation provides the basis for estimating ARDL, with aim to establish the links among the variables as shown in Equation (3.3).
0 1 1 2 1 3ln 1 4ln 1 1
ln ln ...(3.3)
p p
t t t t t i i t i j t j
p p
p t p k t k t
i i
fdi fdi gdp f t fdi gdp
f t
where, FDI is refers to as a foreign direct investment which is the dependent variable, while GDP, INFL and INT are the independent variables which are denoted as an economic growth, inflation and interest rate respectively.
In order to determine error correction model so as to isolate the dynamic nature of the variables, Schwarz Bayesian criterion (SBC) is selected. Equations 3.4 and 3.8 are the respective short run and long run models.
1 inf 1 ...(3.4)
p p p p
t i i t i j t j i p t p i k t k t t
fdi fdi gdp int ecm
0 p 1 p p inf p int ...(3.8)
t i i t i j t j i p t p i k t k t
fdi
fdi
gdp
Stability Test
One of the key assumption of the econometric model is the Parameters are constant throughout the sample period. In this study, the recursive residuals test that is CUSUM is adopted to check the validity of this assumption. The test according to Brown et al. (1975)
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cited in Lutkepohl et al. (2005) is based on the cumulative sum of recursive residuals. The test plots the cumulative sum together with the 95% critical line. It finds parameter instability if the cumulative sum goes outside the area between the two critical lines, up to a particular period t.
1
...(3.9)
T r r k
CUSUM W
S
Where W is the recursive model, s is the standard error of the regression fitted to all T sample point, k is the number of coefficient to be estimated. The significance of any departure from the zero line is assessed by reference to a pair of 95% significance lines. The distance which increases with Movement outside the critical lines is suggesting coefficient instability. On the other hand the CUSUM square test is based on the statistic
2 1
2 1
...(3.10)
T r r k
T r r k
W CUSUM SQ
W
If the CUSUM – SQ cross the pair of 95% critical lines it indicates the structural instability.
Presentation of Results and analysis
Summary statistics
The summary statistics of the key variables included in the study is depicted on Table 4.1. It was found that the average FDI is 2.559, GDP with an average of 3.050, Inflation Rate averagely 18.637% and average of interest rate is 5.950% within the period of the study.
Table 1
Summary statistics
variables Mean Median Maximum Minimum Standard Deviation
FDI 2.559 2.147 10.832 -1.150 2.167
GDP 3.050 2.070 7.160 1.520 1.790
INF 18.637 12.876 72.835 3.457 16.053
INT 5.950 6.285 12.500 0.650 2.973
SOURCE: Researchers Computation using e-views 10 software
Unit Root Test
The result shows that we cannot reject the null hypothesis of unit roots for all the variables in level form except for FDI and inflation that is stationary at level I (0) and therefore, the remaining variable integrated at I (1). However, due to the mixture of the variables we can now apply ARDL model.
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Table 2: Unit root test result
ADF PP
Level variables Constant
without trend
Constant with trend
Constant without trend
Constant with trend
FDI -3.663*** -3.614* -3.630*** -3.522*
GDP 2.747 -1.818 2.117 -1.795
INF -2.946** -3.599** -2.816 -2.907
INT -2.680 -3.476 -2.585 -3.438
First Difference
FDI -8.417*** -8.411*** -13.714*** -18.037***
GDP -4.165*** -5.298*** -4.165*** -5.298***
INF -5.678*** -5.590*** -11.280*** -10.993***
INT -5.970*** -5.957*** -11.391*** -12.872***
ADF and PP test for stationary include both constant and trend terms. Asterisk represents significance level for the rejection of hypothesis which are *, ** and *** indicating 10%, 5% and 1% respectively.
Bound Test
The results of the Bound tests are presented in Table 3. It specifies the relationship between FDI (dependent variable) and other explanatory variables. The existence of long run co-integration relationship for the variables occurs when the computed F test statistic is greater than the upper and lower bounds. However, the F statistic for testing the joint null hypothesis that there exists no long run relationship is 11.96. Since the F statistic exceeds the upper bound I (1) of the critical value band, the null hypothesis of no long run relationship between the variables is rejected.
Table 3: ARDL Bounds Test Results
Critical Values
Variables F-stat Lag
selection
Sig.
Level
I(0) I(1)
, , &
FDI GDP INF INT 11.96*** 6-6 10% 2.72 3.77 5% 3.23 4.35 2.5% 3.69 4.89 1% 4.29 5.61
*** Asterisk denotes 1% significant level. Critical values are obtained from Narayan (2005).
Short Run Result
Table 4.4 shows the result of short run coefficient. The results show that GDP has a positive and statistically significant effect on FDI in the short- run. Thus, a unit increases in GDP leads to 0.025% increase in FDI in Nigeria. Furthermore, when inflation rate increase by unit in the short run it leads to 0.055% rise in FDI in Nigeria and also, statistically significant at 5%. Likewise, the fourth lag of interest rate exhibits negative and significant effect on FDI. Moreover, a unit increases in interest rate it will lead to 0.27 declines in FDI in Nigeria. A significant Count Eq (-1) coefficient means that all things being equal, whenever the actual value of FDI falls below the value consistent with its long-term equilibrium relationship, changes in the independent variables help bring it up to the long-term equilibrium value.
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Table 4: Short Run Coefficients. Dependent Variable: FDI ARDL(6, 6, 5, 5)
Variable Coefficient Standard Error t-Statistic P-value
ΔGDP -0.035 0.026 -0.112 0.913
Δ(GDP(-4)) 0.025*** 0.050 4.211 0.005
Δ (INF) 0.055** 0.021 2.579 0.041
Δ(INF(-1)) -0.105** 0.040 -2.597 0.040
Δ(INT) 0.394* 0.176 2.234 0.066
Δ(INT(-4)) -0.271* 0.131 -2.062 0.084
CointEg(-1) -0.318*** 0.435 -5.417 0.001
Note: ***, **, * represents1, 5 and 10 percent level of significance, respectively.
Long Run Coefficients
Table 5 The Table presents the estimated long run coefficients for the Model based on the ARDL (6, 6, 5, 5) specifications selected using Akaike information criterion. At long run all the variables are statistically significant. GDP has negative effects on FDI and statistically significant at 1% which is briefly explained that a unit increases in GDP will lead to a decline of FDI by 0.035% and vice versa, and also, all the remaining variables have a positive effect on Foreign Direct Investment. This indicates that, inflation and interest rate have positive and significant effects on FDI at 1% level. The table also reveals that in the long run a unit increase in the inflation level it will require an increase in FDI by 0.07%, and lastly an increase in interest rate say by unit will lead to an increase in FDI by 0.22%.
Table 5: Long Run Coefficients. Dependent Variable:FDI ARDL(6, 6, 5, 5)
Variable Coefficient Standard Error t-Statistic P-value
GDP -0.035*** 0.018 -5.762 0.001
INF 0.0704*** 0.016 4.350 0.004
INT 0.227** 0.081 2.778 0.032
C 2.168*** 0.572 3.790 0.009
Researchers Computation using e-views 10 software
Table 6: Causality Test Results
Null Hypothesis: F-Statistic Probability. Decision
GDP does not Granger Cause FDI 1.546 0.228 No
FDI does not Granger Cause GDP 0.483 0.621 No
* INF does not Granger Cause FDI 5.492 0.009 Yes
FDI does not Granger Cause INF 1.469 0.245 No
INTR does not Granger Cause FDI 1.465 0.246 No
FDI does not Granger Cause INTR 0.601 0.554 No
INF does not Granger Cause GDP 0.163 0.850 No
GDP does not Granger Cause INF 0.922 0.408 No
INTR does not Granger Cause GDP 0.090 0.913 No
GDP does not Granger Cause INTR 1.055 0.360 No
INTR does not Granger Cause INF 0.056 0.944 No
INF does not Granger Cause INTR 0.891 0.420 No
SOURCE: Researcher’s computation using E-Views soft were 10
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Post-Estimation
Table 7
Diagnostic Test of the ARDL Model
Test Statistics F-statistics Prob.
Autocorrelation 0.381 0.705
Normality 3.206 0.020
Heteroscedasticity 0.240 0.995
The Breusch-Godfrey LM test indicates acceptance of null hypothesis of no serial correlation among the error term. Since the p value (0.705) is insignificant. However, the statistical justification concludes that, there is no serial correlation among the variables.
Normality: hare we are going accept the null hypothesis which says that the error terms are normally distributed because the P-value is less than 0.05 which is (0.020).
Stability Test
-15 -10 -5 0 5 10 15
98 00 02 04 06 08 10 12 14 16
CUSUM 5% Significance
-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
98 00 02 04 06 08 10 12 14 16
CUSUM of Squares 5% Significance
The CUSUM test result indicates the acceptance of null hypothesis of stability in the model.
Therefore, the study concludes that the model is stable and can be used for forecasting within the period of study.
In sum, the Findings of the study explained that there exists a long-run relationship between foreign direct investment, economic growth, inflation and interest rate. At both short run and long run, thus the major findings from this study indicate that GDP has significant negative impact on FDI, while INF and INT exhibit relationship and significant impact on FDI. At long run all the variables are statistically significant. GDP has negative effects on FDI and statistically significant at 1% which is briefly explained that a unit increases in GDP it will lead to decline of FDI by 0.035% and vice versa, and also, all the remaining variables have a positive effect on Foreign Direct Investment. This indicates that, inflation and interest rate have positive and significant effects on FDI at 1% level. The table also reveals that in the long run a unit increase in the inflation level it will require an increase in FDI by 0.07%, and lastly an increase in interest rate say by unit will lead to an increase in FDI by 0.22%.
Furthermore, when inflation rate increase by unit in the short run it leads to 0.055% rise in FDI in Nigeria and also, statistically significant at 5%. Likewise, the forth lag of interest rate exhibits negative and significant effect on FDI. Moreover, a unit increases in interest rate it will lead to 0.27 declines in FDI in Nigeria. A significant Count Eq (-1) coefficient means that all things being equal, whenever the actual value of FDI falls below the value consistent with its long-term equilibrium relationship, changes in the independent variables help bring it up to the long-term equilibrium value.
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Conclusion and Recommendations
The study analyses the causal linkages between, FDI, and economic growth, inflation and interest rate in Nigeria by using the ARDL bounds testing to cointegration approach. This study in interesting for policy makers in Nigeria to undertake the effective policies that can promote and lead FDI inflows to enhance economic growth in the country.
The following are some policy implications of the results. Firstly, the Nigerian government should give more attention to the nature of domestic and foreign investments. It should orient its domestic and foreign investments to more productive projects that promote economic growth. Secondly, Nigerian government should diversify its economic activities not to be more independent from oil revenue as we are nowadays. Therefore, it should create an environment that attracts more foreigners to invest into our both sectors in the economy.
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