^{1}

^{*}

^{1}

This paper investigated the key determinants of FDI in the Nigerian telecommunication sector. The study made use of data from 1986 to 2014. Annual data on infrastructure, government expenditure, trade openness and market size, were sourced from the World Development Indicators (WDI) of the World Bank . FDI flow into telecommunication sector, foreign exchange rate, interest rate and inflation, were sourced from Central Bank of Nigeria Statistical Bulletin. Data were analyzed using graphs, t-test and Autoregressive Distributed Lag (ARDL). The results showed that the key determinants of FDI in the sector are market size and trade openness (t = 5.75 to 9.05; p < 0.05) on positive side, as well as Inflation and real interest rate (t = - 0.05 to -4.03; p < 0.05) on negative side. The study therefore concludes that the key determinants of FDI flow into the Nigerian telecommunication sector are market size, trade openness, government expenditure, inflation and interest rate.

Foreign Direct Investment (FDI) has emerged as the most important source of external resource flows to developing countries over the years and has become an integral part in the formation of capital in these countries. Therefore, attempts have been made by Nigerian authorities to try to attract FDI via various reforms. The reforms included the deregulation of the economy, the new industrial policy of 1989, the establishment of the Nigeria Investment Promotion Commission (NIPC) in early 1990s, and the signing of Bilateral Investment Treaties (BITs) in the late 1990s. The benefits of foreign direct investment include promoting economic growth, technology transfer and job-creation in the local economies. Also, FDI can serve to integrate domestic markets into the global economic system far more effectively than could have been achieved only by traditional trade flows.

FDI flow into Nigerian economy has increased over the years from $6 billion in 2009 to $7.03 billion in 2013. The percentage share of telecommunication sector to total FDI increased from 0.9% in 1986 to 2.3% in 1990. Among 1999 to 2006, its share decreased to 1.7% and it increased to 24% between 2007 and 2013 (CBN, 2014). In order to attract more FDI, there is need to improve on the factors that can influence the inflow of FDI into the host country. Many studies have been conducted on the factors responsible for inward FDI into a host country. Pfeffermann and Madarassy [

Among foreign investments, telecommunication is one of most strategic industries of national economic control. This is because telecommunication covers many other industrial sectors including the sectors of manufacture, entertainment and communication. Foreign investment is not merely a provider and improvement of local telecommunication equipments. The banking and finance sector is reaping the benefits of deregulation of telecommunication as this has created more opportunities for investment. Also, foreign investments in the telecommunication subsector have contributed to the creation of job in the economy. As a result of these great benefits derived from this sector, there is need to attract FDI flow to the sector.

Several factors have been identified in literature to determine the inflow of FDI into telecommunication sector. These includes, market size, per capita income, competition, infrastructure, trade openness, political instability, employment and skill level, technology diffusion and knowledge transfer and linkages, exchange rate rules and regulations, resource endowment, among others [

Generally speaking there is a wide range of variables that can influence a foreign investors to invest in a certain location but the truth is not all of them have equal degree of importance to each foreign investor, therefore it is wise to note that some of these determinants may have more weight to one foreign investor and less to the other at a certain period of time. Several studies have articulated empirically the determinants of foreign direct investment in a country, some of which buttressed on FDI at national level and others on FDI in a specific sector.

Pfeffermann and Madarassy [

The study by Elijah [

Aqeel and Nishat [

Anyanwu [

In Nigeria, a large number of empirical works have been carried out on FDI determinants. Using time series econometric technique on annual data of Nigeria, Soumyananda [

Donwa, Mgbame and Ezeani [

Keith [

In the study of FDI flow into Pakistan telecommunication sector, Qaiser [

In an attempt to analyze the relationship between foreign direct investment in telecommunication and its determinant, this study used the OLI-paradigm (ownership, internalization and location) as a guide. However, the key determinant in decision making process to invest abroad is location advantages of the host country which is of paramount concern for this objective. These location advantages (determinants of FDI flow) includes market size, inflation, exchange rate, infrastructure, political stability, portfolio diversification, resource location, differential rate of return, foreign exchange reserves, internalization, openness, government policy, political stability, tax policies, regulatory environment, inflation, industrial organization, the level of external indebtedness, foreign exchange rate, among others.

This study selected some of the determinants noted by Soumyananda [

FDI = β 0 + β 1 marksize + β 2 exchrate t + β 3 inf rate t + β 4 openness + β 5 natresources (3.1)

where FDI represents foreign direct investment, β_{0} is Constant, marksize is market size, exchrate_{t} is the exchange rate, infrate_{t} is inflation rate, openness is the degree of openness and natresources is natural resources.

Anyanwu [

FDI i j t = β 0 + β 1 ( UrbanPop ) i j t + β 2 ( GDPPC ) i j t + β 3 ( Openness ) i j t + β 4 ( Financialdev ) i j t + β 5 L ( Inflation ) i j t + β 6 ( ExchangeRate ) i j t + β 7 ( Infrastructure ) i j t + β 8 ( Govcons exp ) i j t + β 9 ( Re m i t tan c e s ) i j t + β 10 ( PoliticalRights ) i j t + β 11 ( F D I − 1 ) i j t + α β 12 ( Oilexporters ) i j t + ψ ( Re gions ) i j t + ε i j t (3.2)

This study will select the factors incorporated from the above studies as the possible factors that determine FDI inflow into the Nigerian telecommunication sector. Some of these factors such as natural resources, political rights, regions, and financial development would be ignored in this study due to unavailability of data.

The model for this study is specified using ARDL model because it allows for the model to take a sufficient number of lags to capture the data generating process in a general-to-specific modeling framework [

FDIT t = f ( INFR t , OPN t , MSZ t , FEX t , GEXP t , INTR t , INFL t ) (3.3)

The model expresses foreign direct investment in telecommunication(FDIT) as a function of infrastructure (INFR), trade openness (OPN), market size (MSZ), foreign exchange rate (FEX), government expenditure (GEXP), interest rate (INTR) and inflation (INFL).

From Equation (3.3), the hypothesized relationship between foreign direct investment in Nigerian telecommunication sector and its determinant is expressed in a linear form thus;

FDIT = α 0 + α 1 INFR t + α 2 OPN t + α 3 MSZ t + α 4 FEX t + α 5 GEXP t + α 6 INTR t + α 7 INFL t + μ t (3.4)

where α_{0} … α_{7} are parameter estimates and µ is the error term.

To illustrate the ARDL modelling approach, Equation (3.4) will further be expressed as;

Δ ln FDIT = α 0 + ∑ i = 1 q β j Δ INFR t − i + ∑ i = 1 q γ l Δ OPN t − l + ∑ i = 1 q φ k Δ ln MSZ t − i + ∑ i = 1 q λ m Δ FEX t − i + ∑ i = 1 q η r Δ GEXP t − i + ∑ i = 1 q π s Δ INTR t − i + ∑ i = 1 q ϕ n Δ INFL t − i + ∑ i = 1 q ξ u Δ ln FDIT t − i + δ 1 INFR t − 1 + δ 2 OPN t − 1 + δ 3 ln MSK t − 1 + δ 4 FEX t − 1 + δ 5 GEXP t − 1 + δ 6 INTR t − 1 + δ 7 INFL t − 1 + δ 8 ln FDIT t − 1 + ε t (3.5)

The terms with the summation signs in Equation (3.5) represent the Error Correction Model (ECM) dynamics. The coefficients δ i are the long run multipliers corresponding to long run relationship and ln is the natural log operator. α 0 and ε t represent the constant and the white noise respectively while β j , γ l , ϕ k , λ m , η r , π s , φ n , ξ u represents the short run effects. Δ is the first difference operator while q is the lag length for the ECM. Equation (3.5) is therefore estimated to obtain the relationship between FDIT and the explanatory variables.

This study made use of secondary data. The data for the variables are annual in nature from 1986-2014. Data for infrastructure, trade openness, market size and government expenditure, would be sourced from the World Development Indicators (WDI) of the World Bank (2015); FDI flow into telecommunication sector, foreign exchange rate, interest rate and inflation, would be sourced from Central Bank of Nigeria Statistical Bulletin (2015).

The Autoregressive Distributed Lag (ARDL) model will be used to achieve the objective of this study. The ARDL approach to cointegration which was introduced originally by Pesaran and Shin [

To test whether the lagged levels of the variables in the equation are statistically significant or not, the calculated F-statistics is compared with the critical value tabulated by Pesaran et al. [

Once the long run relationship or co-integration has been established, the second stage of testing involves the estimation of the long run coefficients which represents the optimum order of the variables. This can be done using selection criteria like Akaike’s Information Criteria (AIC), Schwartz-Bayesian Criteria (SBC), Hannan Quinn Information Criteria (HQIC) and R-bar Square Criteria. However, the most consistent lag length chosen by the criterion will be used. Then the associated error correction is derived in order to calculate the adjustment coefficients of the error correction term. Therefore, the short run effects are captured by the coefficients of the first differenced variables in the ECM model.

Having done this, there is also the need to perform a series of diagnostic tests on the stochastic properties established in the model. This is because the existence of a long run relationship does not necessarily imply that the estimated coefficients are stable [

All data series used in the study covers the period from 1986 to 2014.

Time series data are characterized to be either stationary or non stationary. However, regressing a stationary time series variable on a non stationary time series variable or a non stationary variable on a non stationary variable will result to a spurious regression. Unit root tests such as the Augmented Dickey Fuller (ADF) test and Philips-Perron (PP) test were carried out to ascertain if the variables are stationary or not. In conducting the unit root test, the variables can be I(0), I(1) or I(2). However, auto regressive distributed lag (ARDL) technique does not accommodate I(2) variables. This is because the bound test is based on the assumption that the variables are either I(0) or I(1).

It is observed in

LOGFDIT | LOGMSZ | RINTR | INFL | |
---|---|---|---|---|

Mean | 21.13594 | 6.516062 | −0.628649 | 20.82815 |

Median | 20.52518 | 6.343662 | 2.767927 | 12.21701 |

Maximum | 25.17303 | 7.001282 | 25.28227 | 72.83550 |

Minimum | 18.14097 | 6.203019 | −43.57266 | 5.382224 |

Std. Dev | 1.888953 | 0.273519 | 18.20473 | 19.34185 |

Skewness | 0.382659 | 0.614375 | −0.819032 | 1.427361 |

Kurtosis | 2.330839 | 1.658278 | 3.169784 | 3.579496 |

Jarque-Bera | 1.248799 | 3.999634 | 3.277100 | 10.25302 |

Probability | 0.535583 | 0.135360 | 0.194262 | 0.005937 |

Sum | 612.9422 | 188.9658 | −18.23083 | 604.0164 |

Sum Sq. Dev | 99.90798 | 2.094748 | 9279.537 | 10475.00 |

Observations | 29 | 29 | 29 | 29 |

INFRA | FEX | GEXP | OPN | |
---|---|---|---|---|

Mean | 0.482854 | 109.8392 | 8.780584 | 22.31429 |

Median | 0.373598 | 89.65000 | 8.200050 | 21.49798 |

Maximum | 1.177806 | 272.3700 | 17.94384 | 36.48173 |

Minimum | 0.102674 | 49.73417 | 4.833249 | 10.40073 |

Std. Dev | 0.278451 | 60.43724 | 3.135207 | 7.574603 |

Skewness | 1.063896 | 1.692839 | 1.057334 | 0.461189 |

Kurtosis | 3.083853 | 4.716055 | 3.722030 | 2.280494 |

Jarque-Bera | 5.479223 | 17.40925 | 6.033386 | 1.653568 |

Probability | 0.064595 | 0.000166 | 0.048963 | 0.437454 |

Sum | 14.00276 | 3185.337 | 254.6369 | 647.1144 |

Sum Sq. Dev | 2.170982 | 102274.5 | 275.2266 | 1606.489 |

Observation | 29 | 29 | 29 | 29 |

Source: Author’s Computation from EViews 9 (2017).

The aim of this study is to investigate the determinants of FDI in the Nigerian telecommunication sector over the study period, the ARDL approach was employed in achieving this objective. There is need to determine the optimal lag structure in the ARDL models, followed by the bounds test to show if the variables are cointegrated.

The lag order selection is determined using the Akaike Criterion (AIC), Schwartz Bayesian Criterion (BIC) and Hannan-Quinn Criterion (HQC). As

Augmented Dickey-Fuller (ADF) Test | Philips-Perron (PP) Test | |||||
---|---|---|---|---|---|---|

Variable | Level | 1^{st} Difference | Status | Level | 1^{st} Difference | Status |

logfdit | −1.019524 (0.7301) | −2.506344 (0.3224) | −1.433259 (0.5517) | −12.35701*** (0.0000) | I(1) | |

logmsz | 0.594886 (0.9870) | −5.235247*** (0.0013) | I(1) | 0.538953 (0.9851) | −5.217091*** (0.0013) | I(1) |

fex | −3.733436*** (0.0090) | I(0) | −3.833681*** (0.0071) | I(0) | ||

dinfra | −3.383578** (0.0207) | I(0) | −3.383578** (0.0207) | I(0) | ||

infl | −2.510853 (0.1237) | −3.435291* (0.0712) | I(1) | −2.598355 (0.1052) | −5.767097 (0.0004) | I(1) |

rintr | −5.308931*** (0.0002) | I(0) | −5.308896*** (0.0002) | I(0) | ||

opn | −3.243412** (0.0279) | I(0) | −3.154872** (0.0338) | I(0) | ||

gexp | −3.373271** (0.0208) | I(0) | −3.426223** (0.0185) | I(0) |

Source: Author’s Computation from EViews 9 (2017). Note: *, ** and *** denotes significance at 10%, 5% and 1% levels respectively.

shown in

The bounds test results are presented in

The result of the long run and short run relationship among the variables is presented in

Lag | LogL | LR | FPE | AIC | SC | HQ |
---|---|---|---|---|---|---|

0 | −478.857 | NA | 11363110 | 38.94856 | 39.3386 | 39.05674 |

1 | −375.179 | 132.7077 | 622491.7 | 35.77433 | 39.28469 | 36.74795 |

2 | −217.829 | 100.7039* | 2948.781* | 28.30634 | 34.93702 | 30.14541 |

3 | 5061.736 | 0 | NA | −388.9389* | −379.1879* | −386.2344* |

Source: Author’s Computation from EViews 9 (2017). Note: * denotes lag order selection by the criterion. LR: sequential modified LR test statistic (each test at 5% level); FPE: Final Prediction Error; AIC: Akaike Information Criterion; SC: Schwarz Information Criterion; HQ: Hannan-Quinn Information Criterion.

Model | F-statiatic | No. of Regressors (K) | |
---|---|---|---|

f (logmkz, dinfra, fex, gexp, opn, infl, rintr) | 16.57586 | 7 | |

Critical value bounds | |||

Significance | I(0) Bound | I(1) Bound | |

10% | 2.03 | 3.13 | |

5% | 2.32 | 3.5 | |

1% | 2.96 | 4.26 | |

Source: Author’s Computation from EViews 9 (2017).

[

Similarly, in the long run, trade openness has a positive and significant effect on FDI in telecommunication with coefficient value of 0.12 at 1% significant level. This implies that a unit increase in trade openness would increase FDI flow into telecommunication by 12%. This also applies in the short run where the current year value of trade openness had positive but insignificant impact on FDI in telecommunication while its last one year value is negatively related to FDI. From this result a deduction can be made that trade openness is positively related to the inflow of FDI in telecommunication sector. This is supported by Anyanwu [

Likewise, the coefficient of infrastructure is positive (0.10) but insignificant in the long run. This implies that one percent increase in infrastructure does not have significant impact on FDI in telecommunication. Also, in the short run the current year value of infrastructure had positive (0.61) and insignificant impact on FDI flow in telecommunication. Meanwhile its last one year value had a

Long Run Coefficients | |||
---|---|---|---|

Variable | Coefficient | t-Statistic | Prob. |

LOGMSZ | 5.08705 | 9.054031*** | 0.0008 |

DINFR | 0.099051 | 0.114501 | 0.9144 |

FEX | −0.000204 | −0.13285 | 0.9007 |

GEXP | −0.074958 | −1.43759 | 0.2239 |

OPN | 0.117488 | 5.745742*** | 0.0045 |

INFL | −0.051712 | −3.61493** | 0.0225 |

RINTR | −0.062692 | −4.02929** | 0.0157 |

C | −12.547069 | −3.11259** | 0.0358 |

Short Run Coefficient | |||
---|---|---|---|

Variable | Coefficient | t-Statistic | Prob. |

D(LOGRFDI(−1)) | −0.358012 | −4.29152** | 0.0127 |

D(LOGRFDI(−2)) | −0.923824 | −12.3211*** | 0.0002 |

D(LOGMSZ) | 3.916468 | 4.336566** | 0.0123 |

D(LOGMSZ(−1)) | 2.560737 | 1.902323 | 0.1299 |

D(DINFR) | 0.614838 | 1.41994 | 0.2286 |

D(DINFR(−1)) | 2.017467 | 3.736869** | 0.0202 |

D(FEX) | −0.000162 | −0.13317 | 0.9005 |

D(GEXP) | 0.054205 | 1.84342 | 0.139 |

D(GEXP(−1)) | 0.135455 | 3.610545** | 0.0225 |

D(OPN) | 0.013376 | 1.06752 | 0.3459 |

D(OPN(−1)) | −0.022726 | −2.21049* | 0.0916 |

D(INFL) | −0.033872 | −4.19254** | 0.0138 |

D(INFL(−1)) | 0.010541 | 2.782017** | 0.0497 |

D(RINTR) | −0.024514 | −5.53679*** | 0.0052 |

ECT(−1) | −0.796752 | −6.91415*** | 0.0023 |

R^{2} = 0.998706 Adjusted R^{2} = 0.991913 F-statistic = 147.0178 Prob (F-statistic) = 0.000100 |

Source: Author’s Computation from EViews 9 (2017). Note: *,** and *** denotes significance at 10%, 5% and 1% levels respectively.

positive and significant impact on FDI in telecommunication. Overall, it can be deduced that an increase in infrastructure does not necessarily imply an increase in FDI flow into the Nigerian telecommunication sector. This contradicts the view of Keith [

Furthermore, the coefficient value of government expenditure (−0.07) in the long run showed that it had negative and insignificant effect on FDI in telecommunication. This implies that government expenditure is unimportant in attracting FDI in the long run. However, in the short run the current year value of government expenditure had a positive but insignificant impact on FDI in telecommunication. Meanwhile its last one year value had a positive and significant impact with coefficient value of 0.14, implying that an increase in government expenditure will increase FDI flow into the sector by 14%. Therefore it can be concluded that government expenditure is vital in attracting FDI into telecommunication sector. The finding is supported by Anyanwu [

In addition, the coefficient values and p-values of foreign exchange rate indicates that it had negative and insignificant impact on FDI flow into telecommunication both in the long and short run. This implies that foreign exchange rate is not an important determinant of FDI flow into the Nigerian telecommunication sector. A possible reason for this result is that the exchange rate effects of third countries come through correlations that affect location choice of risk averse firms, which invest in countries whose exchange rates are negatively correlated to other exchange rates as a way of diversifying FDI [

Consequently,

The Error Correction Term (ECT) for this cointegrating relationship was negative as expected (−0.80) and significant which showed that about 80% of short run deviations would be corrected for annually. Also from the ARDL regression result, the various tests (R^{2}, Adjusted R^{2}, F-statistic, and p-value) of significance on the model showed good result. The R^{2 }of 0.998 indicated high explanatory power of the independent variables. The adjusted R^{2} value of the model also supported this fact. F-statistic which measures the overall significance of the model suggests that all estimated regression model is statistically significant. This is indicated by the F-statistic (147.0178) and p-value (0.000100).

Consequently, the result of the normality test indicated that the JB statistic and the probability value are insignificant which implies that the model is well specified. The CUSUM and CUSUMsq are stability tests were conducted to test for recursive residuals in mean and variance of the series. The result in

Test Statistic | F-statistic | P-value |
---|---|---|

Breusch-Godfrey Serial Correlation LM Test | 3.590 | 0.366 |

Breusch-Godfrey Heteroskedasticity Test | 2.512 | 0.239 |

Jarque-Bera (JB) Normality Test | 0.322 | 0.851 |

Source: Author’s Computation from EViews 9 (2017).

evidence of recursive residuals in both mean and variance.

Based on the findings of this study, the following recommendations are thereby suggested in order for Nigeria to attract more foreign direct investment in the Telecommunications sector and harness its benefits better.

The Nigerian government needs to make more effort in the expansion of market by instituting agency (ies) or regulatory bodies to bring about transparency in the market, hence encourage the flow of FDI into the telecommunication sector.

Government should remove structural barriers by offering incentives such as tax holidays, import duties exemptions and subsidies to foreign firms. This will lead the sector to higher level of openness and internationalization and consecutively attract more FDI.

Low inflation is considered to be a sign of internal economic stability in a host country whereas higher interest rate would discourage the flow of FDI in the telecom sector. Therefore, government should improve on the close monitoring of these macroeconomic stability indicators by balancing the budget and restricting the money supply so as to put them under control.

More government expenditure should be channeled towards creation of investment friendly and enabling environment for foreign investors. This includes addressing the issue of insecurity in the country, giving of incentives and reducing the bureaucracy associated with starting a business.

Having investigated the determinants of FDI in the Nigerian telecommunication sector, this study revealed a number of factors that can attract or discourage the flow of FDI into the sector. These factors include, market size, trade openness, inflation, interest rate and government expenditure. Ultimately, the study found that market size, trade openness and government expenditure had a positive and significant effect on FDI flow into the sector while inflation and interest rate had negative and significant relationship with FDI flow in the sector. However, infrastructure had a positive but insignificant effect on FDI flow while foreign exchange rate had a negative and insignificant effect on FDI, making these factors less important in attracting FDI into the sector. The study therefore concludes that the key determinants of FDI flow into the sector are market size, trade openness, government expenditure, inflation and interest rate.

Arawomo, O. and Apanisile, J.F. (2018) Determinants of Foreign Direct Investment in the Nigerian Telecommunication Sector. Modern Economy, 9, 907-923. https://doi.org/10.4236/me.2018.95058