Cordelia Onyinyechi Omodero
© 2023 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
This study examines the nexus between political unrest, insurgent activities, and their detrimental impact on Nigeria's economic prospects and the viability of human livelihoods. These factors have been identified as formidable impediments to foreign direct investment (FDI), posing substantial risks to international business engagements within the nation. Utilizing a dataset spanning from 1990 to 2022, the investigation employs a Vector Auto-regression (VAR) model to dissect the influence of insecurity and political volatility on FDI inflows in Nigeria. The findings elucidate a pronounced and adverse response of FDI to the prevailing terrorism, insurgency, and political instability. Moreover, the analysis reveals inflation as a concurrent challenge that jeopardizes international investment. In response to these findings, the study underscores the imperative of democratic consolidation and the implementation of robust strategies to counteract insurgency and terrorism. Additionally, it advocates for monetary policies aimed at inflation control through judicious regulation of the money supply, alongside stabilization of prices for goods and services critical to the industrial sector and household consumption.
inflation, insurgency, insecurity, FDI, political unrest, JEL Classification: E31, H56, P33, P48
The global economic landscape, increasingly characterized by its interconnectedness, has engendered a multitude of commercial opportunities across various regions. In this context, entrepreneurs are naturally inclined towards locales where financial assets are deemed secure and the potential for profit maximization is high. However, it is recognized that environments plagued by political instability and security challenges are less attractive for investment, given the global investor community's predominant risk aversion. Foreign Direct Investment (FDI) is acknowledged as a critical catalyst for economic growth within emerging markets, with its capacity to influence the economic vitality of nations being well-documented. As posited by Wafure and Nurudeen [1], FDI extends beyond capital provision; it is a conduit for enhancing managerial competencies, fostering innovation, and generating employment opportunities, all of which contribute to the broadening and enhancement of an economic system. Nonetheless, the benefits accrued from FDI are not uniformly distributed among emerging nations. The study at hand seeks to address the disparities in FDI benefits, particularly in the context of unanticipated disasters. It has been observed that investors do not generally expect a sustained increase in calamities following an initial event [2]. However, the occurrence of a terrorist attack significantly amplifies investor apprehension and reluctance towards future investments. The psychological impact of terrorism, coupled with its economic repercussions, is distinct from the immediate and long-term effects associated with technological and ecological disasters, given the intentional and violent nature of such acts [2].
In simpler terms, terrorism undermines the perceived safety and confidence of investors in affected nations, thus impeding FDI inflows [3]. Concurrently, the financial burden of anti-terrorism measures places a strain on the economy, diminishing its potential for revenue generation [3]. The presence of political strife and the threat of insurgency further dissuade both international and domestic investors from engaging in commercial ventures within tumultuous regions, leading to a decline in annual FDI and economic growth. In Nigeria, the observed decrease in FDI and economic development is primarily attributed to an escalation in security concerns and political unrest, which has eroded investor confidence in the country. This is in line with the assertion by Oladeji and Folorunso [4] that national security is not only a prerequisite but an essential condition for the advancement and prosperity of any nation, with the potential for progress being severely compromised in the absence of stability.
The socio-political landscape of Nigeria's middle belt has been marred by escalating violence, ostensibly driven by disputes over cattle grazing territory. Attacks, presumed to be conducted by Fulani herdsmen, have resulted in significant devastation within these communities. Despite the severity of these incidents, the international discourse on terrorism frequently overlooks the activities of the Fulani insurgents, with greater attention focused on the more widely recognized extremist group, Boko Haram [5]. It has been reported that, as of early 2016, the death toll attributed to the Fulani was higher than that of Boko Haram, with the former associated with 488 fatalities compared to Boko Haram's 330 [5]. In January 2018, a series of incursions in Benue state culminated in the tragic loss of 73 lives, the destruction of communities, and extensive property damage. These events precipitated the displacement of an estimated 40,000 individuals, as documented by Nigeria's regional emergency management agency [5].
Political instability broadly exerts an adverse effect on the economic environment, with discernible implications for foreign direct investment (FDI) and the pace of economic progress [6]. A pervasive lack of political security in numerous emerging economies is frequently linked to suboptimal governance. Such instability can truncate policymakers' temporal horizons, leading to the adoption of myopic fiscal policies [6]. Furthermore, it can precipitate erratic policy shifts, engendering greater economic volatility and undermining overall economic stability [7]. The prevalence of political turbulence, despite its recognized detrimental impact on economic growth, remains a persistent feature across nations through historical epochs.
The Nigerian economy is contending with a range of impediments to Foreign Direct Investment (FDI) inflows, among which infrastructural decay, insecurity, currency fluctuation, and general business operational challenges are particularly salient [8]. Political risk emerges as a pivotal factor that must be mitigated to attract and sustain appropriate FDI within African nations. In an effort to minimize the potential disruptions caused by political instability, several post-colonial African states have engaged in international investment treaties that obligate them to protect foreign investors' assets and ensure compensation at market value in instances of expropriation [9]. While warfare and terrorism are less frequent manifestations of political risk, their impacts can be catastrophic, potentially leading to the total loss of an investor's assets [9].
Empirical evidence indicating the negative correlation between terrorism, insecurity, and FDI has been well-established in both historical and more recent studies within the Nigerian context [10-13]. Moreover, the detrimental impact of terrorism on FDI in Pakistan has been corroborated by Alam et al. [14], Alarm and Mingque [15]. Extending the scope of investigation, Yoon et al. [2], Bandyopadhyay et al. [16], and Nusrat [17] have also affirmed the threat posed by terrorism to FDI in diverse economic settings. Concurrently, literature examining the nexus between political unrest and FDI has consistently indicated that FDI fares poorly in nations afflicted by political instability [3, 18]. Considering the separate analyses of terrorism/insecurity and political violence in previous research, this study posits a gap in the collective understanding of their cumulative impact on FDI, particularly within a developing economy like Nigeria. Therefore, it is imperative to evaluate the extent to which both terrorism and political unrest concurrently influence FDI, thus contributing to a more nuanced comprehension of the investment climate in emerging markets.
2.1 Conceptual clarification
Insecurity may be defined as a dearth of safety, risk, dangers, apprehension or an inadequate level of defense [5]. Béland [19] characterizes insecurity as a condition of worry or nervousness caused by a real or perceived absence of safety. It symbolizes the absence of or insufficient liberty from risk. Institutional instability and inconsistent fiscal regulations are among the most major barriers to FDI [20].
The act of terrorism is defined as an unlawful act with specific features. It is essentially a deed with an intended purpose that speaks to the attempts and goals of the terrorist activity's perpetrator. As a result, following a terrorist attack, terrorists accept ownership of the terrorist act [13]. Another crucial feature of extremism is panic and the instilling of dread. It is a desired outcome that is necessary to attain a goal. Terrorist activity has both immediate and long-term consequences. Human Beings displacement, distress, and economic harm are among the most obvious expenses [2]. The threat of terrorism also has substantial secondary expenses that affect numerous facets of economic activity, among which are general commerce [21], FDI [2, 3, 17], gross domestic product (GDP) per individual, the rate of joblessness [22], economic expansion velocity, insurance premiums and government spending [23]. Unlike civil conflicts, terrorist activity is frequently targeted at certain sectors of the economy, such as the tourism industry, exporting goods, or FDI [24]. A number of these industries are also more costly and resource demanding than others as well, therefore terrorist attacks cannot impact all production elements uniformly.
Foreign investors take into account the nation's governance issues when deciding on an investment choice [25]. Political instability is associated with seizing or destruction of possessions, manufacturing interruption, menaces to staff members, limitations on operations that interfere with the capacities of financiers to undertake certain acts, riots, and modifications in regulations or macroeconomic policies [26]. Investors will opt not to put their money into jeopardy in an unpredictable atmosphere. Political risk assesses the potential hazards of carrying out business in a nation that is that is marked by political upheaval and unrest caused by an uprising, electoral violence, and resource-driven agitations [18].
2.2 Review of past studies
2.2.1 Terrorism and insecurity effects on FDI and other commercial activities
In an inquiry on the causal connection between instability and the growth of society in Nigeria, [10] discovered that an increasing degree of feeling unsafe in the entire nation between 2007 and 2012 slowed the pace of economic growth, causing an overall decrease in FDI inflows and a downturn in commercial operations. Adesegun and Olumide [11] conducted a quantitative investigation of the influence of insurgency on FDI inflows in Nigeria from 2003 to 2012. The study found a negative association between FDI and insecurity. Bandyopadhyay et al. [16] demonstrated how the occurrence of terrorist attacks in various countries might alter trade patterns using a conventional rivalry-based trade scenario. Countries with a higher counterterrorism occurrence would send abroad items that were more resilient to terrorist-related disturbances while buying more violence-impacted commodities. In addition, as a result of terms-of-trade external factors, extremism could benefit some countries. Furthermore, the contributors provided a couple of qualitative variables that indicated when a country's commerce turnover might increase (or decrease) in reaction to increased terrorist activity. Considering the disparities in effects between states, these commercial and wellbeing findings emphasized the possible problems associated with global terrorism prevention strategy cooperation.
Alam et al. [14] evaluated the implications of insurgency on FDI inflows into Pakistan from 2000 to 2015. The investigations revealed a negative correlation between influxes of FDI and terrorist attacks, implying the act of terrorism had a detrimental effect and constituted a source of FDI variance in Pakistan. In another study, Alarm and Mingque [15] investigated the consequences of terrorist activity on FDI inflows as well as foreign visitor arrivals in Pakistan from 1995 to 2016. The interconnection of terrorist attacks, foreign investment, and ecotourism was validated using the ARDL constraint assessment. Furthermore, the expected findings unambiguously demonstrated that violent extremism has a detrimental instantaneous and the future negative effect on FDI and the tourism industry. The researchers also agreed that terrorist acts had a far larger adverse effect on FDI than the tourism industry. Ultimately, the estimations indicated a one-way causation between violence and FDI, as well as terrorists and tourists. Yoon et al. [2] used panel data from 202 economies to investigate whether terrorist incidences affected foreign direct inflows. Within the constraints of the model's number of samples, the study found evidence that terrorist occurrences and FDI inflows were adversely associated. Furthermore, the model findings revealed that terrorist occurrences, particularly those targeting companies, reduce FDI inflows, and this conclusion was statistically important at the 99th percentile.
Nusrat [17] used a survey instrument to collect primary data from Afghan businesspeople who have been directly and indirectly impacted by the impacts of insecurity and instability. To analyse and summarise the research findings, a combination of qualitative and quantitative methodologies were used, which revealed that unstable and insecure conditions had a detrimental influence on the inflow of foreign direct investments (FDI) and commercial activity in Afghanistan. Danjuma [12] investigated the impact of terrorist activities, violence related to politics, bribery, and conflicts over religion on foreign investment into Nigeria's the financial sector, construction, manufacturing, petroleum and gas, and telecoms industries, amongst many. Terrorist activity had a negative influence on FDI inflows to the telecoms industry, but malfeasance had beneficial effects on the petroleum and natural gas industry, according to the results of the study. Olasehinde [13] statistically studied the influence of security and defense concerns on FDI in Nigeria by employing every quarter past data from 1994q1 to 2019q4, with ARDL and VAR parametric approaches used as computational resources. FDI responded unfavorably to the uncontrollable existence of safety fears in the near as well as long term, according to the findings from the two empirical methods. Furthermore, industrial unrest and unpredictability were found to be a drag on FDI inflows into Nigeria. The research also reveals that financial liberalization, represented by freedom of trade, and prosperity in the economy, measured by GDP for each individual improvement, both contributed significantly to the influx of FDI into Nigeria.
2.2.2 Effect of political instability on economic growth and general business climate
In the studies on the menace of political instability, Barro [27] found from his investigations that instability in government as well as expansion are inversely associated. Additionally, Fosu [28] investigated the political turmoil and turbulence of governance, rulers, and societies within a single country, along with expansion in Sub-Saharan African nations, discovering that political upheaval had a negative influence on financial development. Bezi et al. [3] conducted quantitative studies on the consequences of extremism on FDI in specific EU and EEA nations. The technique used a system-GMM classifier for flexible panel information models on samples of up to 29 nations and 13-year intervals from 2000 to 2013. The major findings suggested that terrorist episodes, as well as macroeconomic and political determinants, were discovered to decrease FDI in the analyzed EU and EEA nations. Terrorist activity and governance instability were shown to be the most impactful factors on the flow of foreign direct investment into the EU and EEA nations studied. Terrorist actions undermined shareholders' trust and safety in nations susceptible to terrorist operations, limiting the entry of foreign direct investment.
Vangjeli and Mancka [29] examined the many elements that influenced Albania's economic development from 1996 to 2014. According to the research's findings, a stable political environment had a statistically significant influence on the growth of the economy. The association between prosperity and stability in government was favourable. As a result, the investigation indicated that as long as a nation is characterized by a stable political climate, such will have a significant impact on its economic performance. Sundas et al. [21] used yearly secondary time series data to statistically illuminate the factors of FDI in Pakistan from 1970 to 2013. In the course of the research, factors in the mixture of terrorist activities, security in politics, transparency in trade, and GDP were analyzed using the Ordinary Least Square (OLS) technique. As predicted, the estimated outcomes indicated that the gross domestic product, freedom of trade, and governance stability have an encouraging and considerable contribution to foreign investment in Pakistan, but terrorist activity has detrimental effects. Due to political equilibrium and a consistent rise in the gross domestic product amount, the negative consequence of terrorist attacks has been shown to be quantitatively negligible.
Ndubuisi and Uma [18] investigated the influence of political decisions on regulating foreign direct investment in Nigeria from 1980 to 2014. The findings demonstrated an unfavourable and substantial link between FDI and the regulatory parameters of the rate of inflation, foreign exchange rate, governmental budgetary deficit/surplus, and risk of political instability. Kamara and Jian [30] examined the success of FDI in Sierra Leone from 1980 to 2015. The long term results indicated that the amount of natural resources and trade accessibility had beneficial and mathematically significant correlations with FDI, but price hikes, size of the market, and unstable politics exhibited adverse connections. The near-term outcome revealed that the richness of natural resources had the greatest effect on FDI, next to openness to commerce, rising prices, and political turbulence.
Nazeer and Masih [6] investigated the influence of unrest in politics on foreign direct investments and economic expansion in Malaysia from 1984 to 2013. The research results indicated that there is a long-run link between political volatility, foreign investment, and revenue growth in Malaysia, with financial progress becoming the primary facilitator of FDI and electoral instability. Okeke and Kalu [31] investigated how governance stability affects foreign direct investment in Nigeria. According to the report, political equilibrium has a major influence on foreign direct investment in Nigeria. Fagbemi and Fajingbesi [32] investigated the relationship between socioeconomic hardship and unstable politics in the setting of Sub-Sahara Africa. Furthermore, bigger population nations possessed a stronger proclivity for political turmoil than lesser populous nations. Foreign direct investment (FDI), on the other hand, seemed to have had no actual influence, good or bad, on the stability of politics.
The study investigates the effects of insecurity, terrorism and political instability on foreign direct investment inflows in Nigeria. The investigation covers a period from 1990 to 2022 within which the country has passed through series of attacks from terrorist groups which include: Niger Delta Militants, Boko Haram, Fulani Herdsmen and other innumerable bandits and kidnappers. These nefarious activities which also include political turbulence have affected the inflows of foreign investments which would have engendered economic expansion and job creation in Nigeria.
3.1 Variable description
The variables for this study include foreign direct investment (FDI), terrorism and insecurity (TRS), political instability (PNS) and rising inflation rate (INF). The data for FDI and INF are obtained from the World Bank while the data for TRS and PNS are collected from the Central Bank of Nigeria Statistical Bulletin. However, for the purpose of this study, the natural log of all datasets were used to achieve uniformity of series. Due to the unit root result which showed that all data were stationary in first difference or order one, the Vector Auto-regression becomes the most appropriate tool for this study.
3.2 Vector Auto-regression model specification
$\begin{gathered} LnFDIt=\propto+\sum_i^k=1 \beta iLnFDIt-i+\sum_j^k= 1 \emptyset j L n T R S t-j+\sum_l^k=1 \emptyset j L n P N S t-l+\sum_m^k= 1 \varphi LnINFt -m+\mu 1 t\end{gathered}$ (1)
$\begin{gathered} LnTRSt =\propto+\sum_i^k=1 \beta iLnFDIt -i+\sum_j^k= 1 \emptyset j L n T R S t-j+\sum_l^k=1 \emptyset j L n P N S t-l+\sum_m^k= 1 \varphi LnINFt -m+\mu 2 t\end{gathered}$ (2)
$\begin{aligned} LnPNSt =\propto+\sum_i^k=1\beta iLnFDIt -i+\sum_j^k= 1 \emptyset j LnTRSt-j+\sum_l^k=1 \emptyset j LnPNSt-l+\sum_m^k= 1 \varphi LnINFt -m+\mu 3 t \end{aligned}$ (3)
$\begin{gathered} LnINFt=\propto+\sum_i^k=1 \beta i L n F D I t-i+\sum_j^k= 1 \emptyset j L n T R S t-j+\sum_l^k=1 \emptyset j L n P N S t-l+\sum_m^k=1 \varphi LnINFt-m+\mu 4 t\end{gathered}$ (4)
where:
Ln = Natural log; t = Time; k = maximum lag; β = coefficients; Ø = difference in parameters;
FDI = Foreign Direct Investment; TRS = Terrorism and insurgency (cost of fighting them applied);
PNS = Political instability (cost of internal security applied); INF = Inflation
$\varphi$ = Co-integrating coefficients
µ = The µ’s are the stochastic error terms often referred to as impulses, or innovations or shocks in the language of VAR.
The application of VAR model was based on the Johansen co-integration test where all the null hypotheses were accepted because the results showed that there was no co-integration among the variables.
This section contains a full examination of the data used in this investigation. The descriptive statistics are utilized in the research to confirm the normalcy and applicability of the datasets used in this study. There is also a unit root test result in Table 2 to check the sequence of variable data integration, which leads to the co-integration test in Table 3 before the Vector Auto-regression Estimates in Tables 4 and 5.
The results in Table 1 serve to establish the adequacy of the datasets used in this investigation. The mean values for FDI, TRS, PNS, and INF are 7.63, 4.23, 4.33, and 2.65, respectively. FDI, TRS, PNS, and INF have standard deviations of 0.98, 1.71, 1.82, and 0.63, respectively. It appears that the datasets have a reduced dispersion and cluster around the mean values. Most significantly, the Kurtosis is between 2 and 3, indicating a normal distribution of datasets. Again, the p-values of the Jarque-Bera for all variables are larger than the 0.05 level of significance, demonstrating that all datasets are normally distributed and adequate for the investigation.
Table 2 shows the results of the Unit root test, which aids in determining the best instrument to use for data analysis. Since the series are integrated of order 1, that is, stationary in first difference, a co-integration test is required to demonstrate a long-run connection. Considering that all of the variables in this study are integrated of I(1), we opt for the co-integration test designed by Johansen (as shown in Table 3). At the 5% level of statistical significance, the decision criteria will be to reject the null hypothesis of no co-integrating equation. As a result, if the value of the Trace and Max statistics is more than the 5% critical threshold, the null hypothesis is rejected. We will not be able to reject the null hypothesis if otherwise.
Table 1. Descriptive statistics
|
|
FDI |
TRS |
PNS |
INF |
|
Mean |
7.63 |
4.23 |
4.33 |
2.65 |
|
Median |
7.74 |
4.28 |
4.77 |
2.55 |
|
Maximum |
9.09 |
6.46 |
6.59 |
4.29 |
|
Minimum |
5.70 |
1.41 |
1.42 |
1.68 |
|
Std. Dev. |
0.98 |
1.71 |
1.82 |
0.63 |
|
Skewness |
-0.29 |
-0.40 |
-0.47 |
1.12 |
|
Kurtosis |
2.05 |
1.91 |
1.76 |
3.77 |
|
Jarque-Bera |
1.73 |
2.52 |
3.32 |
7.68 |
|
Probability |
0.42 |
0.28 |
0.19 |
0.12 |
|
Sum |
251 |
139 |
143 |
87.5 |
|
Sum Sq. Dev. |
30.9 |
93.4 |
105 |
12.8 |
|
Observations |
33 |
33 |
33 |
33 |
Table 2. Unit root test
|
Variable |
ADF T- Statistic |
Critical Value @ 5% |
P-Value |
PP T-Statistic |
Critical Value @ 5% |
P-Value |
Order of Co-Integration |
|
LNFDI |
-6.88 |
-3.56 |
0.00 |
-6.87 |
-3.56 |
0.00 |
I(1) |
|
LNTRS |
-7.34 |
-3.56 |
0.00 |
-7.81 |
-3.56 |
0.00 |
I(1) |
|
LNPNS |
-3.64 |
-3.58 |
0.04 |
-7.91 |
-3.56 |
0.00 |
I(1) |
|
LNINF |
-4.69 |
-3.57 |
0.00 |
-4.56 |
-3.56 |
0.00 |
I(1) |
Table 3. Johansen co-integration test
|
Series: LNFDI LNTRS LNPNS LNINF |
|
|
||
|
Hypothesized |
|
Trace |
0.05 |
|
|
No. of CE(s) |
Eigenvalue |
Statistic |
Critical Value |
P-value |
|
None |
0.59 |
43.5 |
47.8 |
0.12 |
|
At most 1 |
0.29 |
15.6 |
29.8 |
0.74 |
|
At most 2 |
0.09 |
4.87 |
15.5 |
0.82 |
|
At most 3 |
0.06 |
2.03 |
3.84 |
0.15 |
Table 4. Vector auto-regression estimates without p-values
|
Sample (adjusted): 1992 2022 |
|
|
||
|
Included observations: 31 after adjustments |
|
|||
|
|
LNFDI |
LNTRS |
LNPNS |
LNINF |
|
LNFDI(-1) |
0.42 |
-0.33 |
-0.21 |
0.09 |
|
|
(0.19) |
(0.14) |
(0.11) |
(0.17) |
|
|
[ 2.18] |
[-2.44] |
[-1.95] |
[ 0.50] |
|
LNFDI(-2) |
0.16 |
0.15 |
0.16 |
-0.12 |
|
|
(0.19) |
(0.14) |
(0.11) |
(0.17) |
|
|
[ 0.81] |
[ 1.07] |
[ 1.49] |
[-0.71] |
|
LNTRS(-1) |
-0.05 |
0.38 |
0.01 |
-0.31 |
|
|
(0.37) |
(0.26) |
(0.20) |
(0.32) |
|
|
[-0.13] |
[ 1.48] |
[ 0.02] |
[-0.96] |
|
LNTRS(-2) |
-0.52 |
0.11 |
0.08 |
0.11 |
|
|
(0.36) |
(0.25) |
(0.19) |
(0.32) |
|
|
[-1.44] |
[ 0.41] |
[ 0.40] |
[ 0.35] |
|
LNPNS(-1) |
0.57 |
0.32 |
0.64 |
-0.20 |
|
|
(0.46) |
(0.32) |
(0.25) |
(0.41) |
|
|
[ 1.25] |
[ 0.99] |
[ 2.54] |
[-0.49] |
|
LNPNS(-2) |
0.03 |
0.17 |
0.24 |
0.30 |
|
|
(0.49) |
(0.35) |
(0.26) |
(0.43) |
|
|
[ 0.07] |
[ 0.49] |
[ 0.89] |
[ 0.69] |
|
LNINF(-1) |
0.05 |
-0.05 |
-0.09 |
0.77 |
|
|
(0.23) |
(0.16) |
(0.12) |
(0.20) |
|
|
[ 0.23] |
[-0.31] |
[-0.75] |
[ 3.81] |
|
LNINF(-2) |
-0.43 |
0.04 |
-0.01 |
-0.32 |
|
|
(0.21) |
(0.15) |
(0.11) |
(0.18) |
|
|
[-2.05] |
[ 0.26] |
[-0.09] |
[-1.69] |
|
C |
3.97 |
1.66 |
1.03 |
2.19 |
|
|
(1.42) |
(1.00) |
(0.78) |
(1.22) |
|
|
[ 2.80] |
[ 1.66] |
[ 1.33] |
[ 1.75] |
|
R2 |
0.79 |
0.96 |
0.98 |
0.64 |
|
Adj. R2 |
0.73 |
0.95 |
0.97 |
0.51 |
|
S.E. equation |
0.51 |
0.36 |
0.28 |
0.45 |
|
F-statistic |
10.9 |
71.8 |
139 |
4.88 |
|
Akaike AIC |
1.71 |
1.02 |
0.51 |
1.48 |
|
Mean |
7.71 |
4.41 |
4.52 |
2.68 |
|
S.D. |
0.97 |
1.59 |
1.71 |
0.64 |
Table 3 shows the results of the Johansen co-integration test, which was used to determine whether or not a long run connection existed. The number of co-integrating equations (CEs) hypothesized is four. The value 'None' indicates that there is no co-integrating equation in this model; 'At most 1' indicates that there is at least one co-integrating equation in this model; 'At most 2' indicates that there are at least two co-integrating equations in this model; and 'At most 3' simply indicates that there are at least three co-integrating equations in this model. Our decision criterion indicates that we shall reject the null hypothesis if the Trace statistic is larger than the crucial value at the 5% level of significance. As a result, with respect to the 'None' in Table 3, the Critical value at 5% level of significance (47.8) is greater than the Trace statistic (43.5). In this scenario, we fail to reject the null hypothesis, which claims that the model lacks a co-integrating equation. The same judgment is made for the remaining three hypotheses (At most 1, At most 2, and At most 3) since the Trace statistic is less than the Critical value at the 5% level of significance in each case. As a result, we conclude that the null hypothesis is true in all cases under this model.
Table 4 displays the coefficients, standard errors, and t-statistics for all variables at lags 1 and 2, however the p-value is unknown. Nevertheless, the p-values are included in Table 5 to help readers appreciate the significance of the factors under consideration. It is worth noting that the standard error in each scenario is lower than one in all circumstances. This indicates that the forecast for this study is correct and error-free.
In Table 5, the analysis provides a more specific information concerning the variables and the four models applied in this study.
Table 5. Vector Auto-regression Estimates With p-values
|
Estimation Method: Least Squares |
|
|||
|
Sample: 1992 2022. Included observations: 31 |
|
|
||
|
|
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
C(1) |
0.42 |
0.19 |
2.18 |
0.03** |
|
C(2) |
0.16 |
0.19 |
0.81 |
0.42 |
|
C(3) |
-0.05 |
0.37 |
-0.13 |
0.89 |
|
C(4) |
-0.52 |
0.36 |
-1.44 |
0.15 |
|
C(5) |
0.57 |
0.46 |
1.25 |
0.21 |
|
C(6) |
0.03 |
0.49 |
0.07 |
0.94 |
|
C(7) |
0.05 |
0.23 |
0.23 |
0.82 |
|
C(8) |
-0.43 |
0.21 |
-2.05 |
0.04** |
|
C(9) |
3.97 |
1.42 |
2.80 |
0.01*** |
|
C(10) |
-0.33 |
0.14 |
-2.44 |
0.02** |
|
C(11) |
0.15 |
0.14 |
1.07 |
0.28 |
|
C(12) |
0.38 |
0.26 |
1.48 |
0.14 |
|
C(13) |
0.11 |
0.25 |
0.41 |
0.68 |
|
C(14) |
0.32 |
0.32 |
0.99 |
0.32 |
|
C(15) |
0.17 |
0.35 |
0.49 |
0.62 |
|
C(16) |
-0.05 |
0.16 |
-0.31 |
0.75 |
|
C(17) |
0.04 |
0.15 |
0.26 |
0.79 |
|
C(18) |
1.66 |
1.00 |
1.66 |
0.10 |
|
C(19) |
-0.21 |
0.11 |
-1.95 |
0.05** |
|
C(20) |
0.16 |
0.11 |
1.49 |
0.14 |
|
C(21) |
0.01 |
0.20 |
0.02 |
0.99 |
|
C(22) |
0.08 |
0.19 |
0.40 |
0.68 |
|
C(23) |
0.64 |
0.25 |
2.54 |
0.01*** |
|
C(24) |
0.24 |
0.27 |
0.89 |
0.37 |
|
C(25) |
-0.09 |
0.12 |
-0.75 |
0.45 |
|
C(26) |
-0.01 |
0.11 |
-0.09 |
0.92 |
|
C(27) |
1.03 |
0.78 |
1.33 |
0.18 |
|
C(28) |
0.08 |
0.17 |
0.50 |
0.61 |
|
C(29) |
-0.12 |
0.17 |
-0.71 |
0.48 |
|
C(30) |
-0.31 |
0.33 |
-0.96 |
0.34 |
|
C(31) |
0.11 |
0.32 |
0.35 |
0.73 |
|
C(32) |
-0.20 |
0.41 |
-0.49 |
0.62 |
|
C(33) |
0.30 |
0.43 |
0.69 |
0.48 |
|
C(34) |
0.77 |
0.20 |
3.81 |
0.00*** |
|
C(35) |
-0.31 |
0.19 |
-1.69 |
0.09* |
|
C(36) |
2.19 |
1.26 |
1.75 |
0.08* |
|
Determinant residual covariance |
5.47 |
|
|
|
|
Equation: LNFDI = C(1)*LNFDI(-1) + C(2)*LNFDI(-2) + C(3)*LNTRS( |
||||
|
-1) + C(4)*LNTRS(-2) + C(5)*LNPNS(-1) + C(6)*LNPNS(-2) + |
||||
|
C(7)*LNINF(-1) + C(8)*LNINF(-2) + C(9) |
||||
|
Observations: 31 |
|
|
||
|
R2 |
0.79 |
Mean |
7.71 |
|
|
Adj. R2 |
0.72 |
S.D. |
0.97 |
|
|
S.E. |
0.51 |
Sum squared resid |
5.64 |
|
|
D/W |
2.09 |
|
|
|
|
Equation: LNTRS = C(10)*LNFDI(-1) + C(11)*LNFDI(-2) + C(12) |
||||
|
*LNTRS(-1) + C(13)*LNTRS(-2) + C(14)*LNPNS(-1) + C(15) |
||||
|
*LNPNS(-2) + C(16)*LNINF(-1) + C(17)*LNINF(-2) + C(18) |
||||
|
Observations: 31 |
|
|
||
|
R2 |
0.96 |
Mean |
4.42 |
|
|
Adj. R2 |
0.95 |
S.D. |
1.59 |
|
|
S.E. |
0.36 |
Sum squared resid |
2.82 |
|
|
D/W |
2.20 |
|
|
|
|
Equation: LNPNS = C(19)*LNFDI(-1) + C(20)*LNFDI(-2) + C(21) |
||||
|
*LNTRS(-1) + C(22)*LNTRS(-2) + C(23)*LNPNS(-1) + C(24) |
||||
|
*LNPNS(-2) + C(25)*LNINF(-1) + C(26)*LNINF(-2) + C(27) |
||||
|
Observations: 31 |
|
|
||
|
R2 |
0.98 |
Mean |
4.52 |
|
|
Adj. R2 |
0.97 |
S.D. |
1.71 |
|
|
S.E. |
0.28 |
Sum squared resid |
1.69 |
|
|
D/W |
2.28 |
|
|
|
|
Equation: LNINF = C(28)*LNFDI(-1) + C(29)*LNFDI(-2) + C(30) |
||||
|
*LNTRS(-1) + C(31)*LNTRS(-2) + C(32)*LNPNS(-1) + C(33) |
||||
|
*LNPNS(-2) + C(34)*LNINF(-1) + C(35)*LNINF(-2) + C(36) |
||||
|
Observations: 31 |
|
|
||
|
R2 |
0.64 |
Mean |
2.67 |
|
|
Adj. R2 |
0.51 |
S.D. |
0.64 |
|
|
S.E. |
0.45 |
Sum squared resid |
4.45 |
|
|
D/W |
2.03 |
|
|
|
*, **, & *** imply significant level at 10%, 5% and 1% respectively
Table 5 displays the four equations given in this model. The R-squared value in Eq. (1) is 79%, suggesting that TRS, PNS, and INF account for up to 79% of the fluctuations in FDI, with only 21% attributed to components not included in this model. Furthermore, the regression standard error is smaller than one, indicating that the prognosis is right. The Durbin-Watson of 2.09 further demonstrates that the model has no serial or auto-correlation. When LNFDI is the dependent variable in Eq. (1), the result reveals that LNFDI has a considerable positive influence on itself at lag 1. This result shows that, under normal conditions and with all other factors being equal, FDI is intended to prosper in the Nigerian economy and does not actually affect political stability negatively or positively as confirmed by Fagbemi and Fajingbesi [32]. However, the impact of inflation on FDI is detrimental at lag 2. This indicates that price increases are harmful to FDI and are as antagonistic to FDI as terrorism and political violence. This result is supported by the works of Ndubuisi and Uma [18], Kamara and Jian [30].
The outcome of the second equation, where LNTRS is the dependent variable, shows that FDI reaction to terrorism is strongly negative at the 1% level. This discovery is concerning and supports the results of the previous studies [2, 10-17]. The R-squared is 96%, and the standard error of regression is 0.36 < 1. The result suggests that FDI, INF, and PNS reactions to terrorism and instability is determined at 96%. As a consequence of the standard error of regression, which is 0.36, this prediction is determined to be error free. The Durbin-Watson value of 2.20 supports the absence of auto-correlation.
In the third equation, C(19) demonstrates that the reaction of FDI to political instability (PNS) is strongly negative at the 5% level. The results demonstrate that Nigeria's regular political turmoil has hampered inflows of foreign investment into the nation since no investor wants to jeopardize his or her financial resources. This conclusion is supported by the works of Bezić et al. [3], Nazeer and Masih [6], Barro [27], and Fosu [28]. In this equation, the coefficient of determination is 98%, indicating that political instability has a stronger impact on FDI. This prediction is true and supported by the standard error of regression, which is 0.28, which is significantly less than the value of one. The Durbin-Watson test demonstrates that no autocorrelation exists.
Finally, C(35) in Eq. (4) illustrates that inflation is also harming itself at lag 2. What this means is that there is no economic climate that flourishes with inflation, and it is an inferno on its own. The Durbin-Watson also indicates lack of auto-correlation in our model, nevertheless the standard error of regression and other parameters are within acceptable limits. The Durbin Watson statistic is a type of statistical measure used for determining hysteresis in regression residues. The extent of resemblance between a particular time series and a lagged version of itself across subsequent time periods is represented mathematically as autocorrelation. This test is critical for confirming the lack of similarities between the time series utilised and the delays in them.
The study explores the consequences of terrorism, insecurity, and inconsistency in governance on foreign direct investment inflows into Nigeria. The findings of this analysis reveal that terrorism, insurgency, and political unrest in Nigeria have discouraged international investors from considering Nigeria as a destination to do business. Inflation is also an attack on FDI and the economy as a whole, as the findings illustrate. This has resulted in a decrease in foreign direct investment inflows into the nation during 1990 to the present. The socioeconomic effect is that the economy would be become stagnant, and the advantages associated with FDI will be lacking. As a result, all job possibilities and technical advancements brought forth by FDI will be redirected to a more tranquil economic climate. Nigeria's unemployment rate will continue to rise, as well as the country's poverty level. As a result, the government is expected to act quickly to put an end to the threat of terrorism and insurgency in Nigeria. The investigation emphasizes real democracy and suggests that democratic principles be upheld in order to eliminate political instability in the country.
The government can engage in bilateral commerce with nations that have the economic foundation to help our economy grow through FDI. In order to manage inflation, it is necessary to stabilize the pricing of economic products and activities. Similarly, the monetary authority is required to mitigate the impact of inflation by ensuring that the amount of money in circulation corresponds to economic demands.
This present study is concerned about threats affecting FDI inflows in the country generally. Further research is required to confirm the effect of insecurity on both local and foreign businesses in each state of the federation of Nigeria.
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