Udanga Agom Jarigbe, Jide Oladipo, Wasiu Akintunde Yusuf
Department of Economics, Nile University of Nigeria, Abuja, Nigeria.
DOI: 10.4236/jss.2024.127032   PDF    HTML   XML   247 Downloads   1,572 Views   Citations

Abstract

This study explores the impact of globalization on economic growth in Nigeria from 1981 to 2021. The study employs various econometric techniques to analyze the data, including unit root tests, cointegration bound tests, and error correction modeling. The unit root tests reveal that certain key variables, such as the degree of openness (DOP), exchange rate (EXCR), interest rate (INTR), and foreign direct investment (LNFDI), exhibit stationarity at the first difference level (I(1)), while inflation rate (INF) and real GDP growth rate (RGDPGR) remain stationary at the level (I(0)). This mixed stationarity necessitates the use of the autoregressive distributed lag (ARDL) method for analysis. Cointegration bound tests support the existence of a long-run relationship among the variables. The F-statistic exceeds the upper and lower bounds, leading to the rejection of the null hypothesis of no level relationship. Further analysis using the ARDL model reveals both short-run and long-run relationships. In the short run, certain variables like exchange rates and interest rates significantly impact GDP growth, with varying degrees of influence. However, foreign direct investment (FDI) at the current time has a negative effect on GDP growth in the short run. In the long run, DOP, EXCR, and INF positively contribute to GDP growth, while interest rates and FDI negatively impact it. The error correction term is negative and significant, indicating that any deviations from the long-run equilibrium are corrected over time, with a speed of adjustment of approximately 58%. The results suggest that globalization, as represented by DOP and EXCR, has a positive influence on Nigeria’s economic growth, while the impact of interest rates and FDI is more complex. Policymakers should consider these findings when formulating economic policies to promote sustainable growth in Nigeria. These findings provide valuable insights into the dynamics of globalization and economic growth in Nigeria and contribute to the ongoing discussion on the role of globalization in developing economies.

Keywords

ARDL, Globalization, Economic Growth, Cointegration, Error Correction Model

Share and Cite:

1. Introduction

Globalization, characterized by increased cross-border flows of goods, services, capital, and information, has been a defining feature of the contemporary world economy. Over the past few decades, it has transformed the economic landscape of both developed and developing nations, presenting opportunities and challenges for governments, businesses, and societies alike. Nigeria, as one of the largest economies in Africa, has not been immune to the effects of this global phenomenon. Nigeria’s journey through the late 20th century into the 21st century has been marked by significant economic and political changes. During this period, the nation faced various economic challenges, including fluctuations in global oil prices, political instability, and the need to diversify its economy away from heavy reliance on oil exports. Amidst these challenges, globalization emerged as a pivotal force shaping Nigeria’s economic destiny. Globalization encompasses a multitude of aspects, such as trade liberalization, financial integration, technological advancements, and increased foreign direct investment. These factors have the potential to both positively and negatively impact economic growth in a nation. In the case of Nigeria, the relationship between globalization and economic growth has been a subject of considerable debate and investigation.

Understanding the effect of globalization on Nigeria’s economic growth is not only academically significant but also of immense practical importance. Policymakers, economists, and stakeholders need comprehensive insights into how globalization has influenced Nigeria’s economic performance over the past four decades. This understanding is essential for formulating effective policies that can harness the benefits of globalization while mitigating its potential drawbacks.

This study seeks to contribute to the existing body of knowledge by rigorously examining the impact of globalization on Nigeria’s economic growth from 1981 to 2021. Through empirical analysis and econometric modeling, it aims to shed light on the dynamics of this relationship, taking into account variables such as trade, exchange rates, interest rates, foreign direct investment, and inflation. By doing so, the study aspires to provide evidence-based recommendations and insights that can inform policy decisions and strategies for achieving sustainable and inclusive economic growth in Nigeria in an increasingly globalized world.

Over the past two decades, world output has been expanding and many countries are benefiting from increased cross-border trade and investments however, several recent studies have been conducted on globalization and economic growth in Nigeria (Feridun, Olusi, & Folorunso, 2006; Akor, Yongu, & Akorga, 2012; Okpokpo, Ifelunini, & Osuyali, 2014) with mixed findings. This study sought to investigate the long-run and causal relationship between globalization and economic growth in Nigeria, given the peculiarities of the Nigerian economy at the moment. Also, this study is significant to policy makers and stakeholders in strengthening and expanding existing policies on Globalisation to enable Nigerians maximize the opportunities available in Globalisation for economic growth. In conclusion, findings from this study will create a new insight on procedures and processes necessary for change.

2. Empirical Literature

Most of the empirical studies that examine the effects of globalization on economic growth was done after 2006. The main reason is that most of the studies used the globalization index which was prepared by Dreher (2006) and some used financial integration, liberalizing, trade and financial receptivity variants, to represent globalization. When surveying the literature that analyses the globalization’s effects on economic growth, studies that are done after 2006 are considered.

Altman and Bastain (2022) attempted to assess the state of Globalization in 2022. They saw that companies and states alike are contemplating various adjustments in their globalization strategies. The study asserted that increase in the countries’ Globalisation even in the face of wrenching changes. The study’s argument was centred on established precedence that economic efficiency alone is not the only factor driving the patterns of international trade. The study further asserted that Globalization has always been an uneven process with significant differences between countries. This argument shows that the precursory concept, ideas and discussion on globalization were predetermined to propagate an unfair capitalist economy as a global system. Guzel, Arslan, & Acaravci (2021) attempted to assess the role of Globalisation and democracy on life expectancy in 16 low-income countries over the period 1970-2017. Using the continuous-updated fully modified (CUP-FM) and bias-adjusted ordinary least squares (BA-OLS) they found that economic, social, and political globalization affect life expectancy positively. The results show that a higher level of per capita income is positively associated with higher levels of life expectancy. They concluded that achieving a healthier society requires economic, social, and political integration between governments and societies.

Huh and Park (2021) saw that the debate over globalization has intensified over the past decade; and that globalization comes with grave risks has garnered political support for regionalism as a strategy to build economic and financial resilience. The study is the first attempt to develop a new composite index of globalization building on the separate contributions of intraregional and extra-regional integration. The study also uses the new index to evaluate empirically the possible effects of globalization on economic growth and income inequality. The index comprises 25 indicators that represent the key socioeconomic components of global integration. The principal component analysis was used to weight each component and construct an aggregate measure. The results show that although globalization promotes economic growth, it may worsen income inequality. High-income countries benefit most in that the positive effect of globalization on economic growth is strongest among them than on other income groups, and they experience a less-pronounced widening of income inequality. Between the two drivers of global economic integration, intraregional integration is far more important than extra-regional integration. The analysis also shows that extra regional integration turns out to be mainly responsible for the rise in income inequality that has accompanied globalization. This shows clearly that social and economic globalization has both negative and positive impacts on the social and economic growth of the country since most of the productive ventures in an economy are carried out by this group of businesses.

Naz and Ahmad (2018) in their study sought to gain insight into the driving factors of globalization. They ascertained that there are several economic, political, social and technological factors that have contributed to Globalization in recent decades. They saw that the literature is yet to come up with a comprehensive analytical framework for globalization. The study attempted to develop a formal framework, which highlights the sources of Globalization; then carried out an empirical test for driving factors of Globalization in developed and developing countries. The results of dynamic ordinary least square showed human capital, capital, labour, transportation, communication and financial index as the important drivers of Globalization in both developed and developing countries. However, capital, labour and financial index appear to determine the process of Globalization in developing countries. Using a Generalized Method of Moments (GMM); the study further saw that the efficiency index appears to be significant in both developing and developed countries while human capital, transportation, communication and financial index were significant in developed economies. This implies that the influence of capital, transportation, communication and financial indices were higher in developed countries which gives them higher participation and gain in globalization.

Matyushok, Vera, Andrey and Javier (2021) thought it necessary to identify and analyse the most important new trend in the world economy and investigate its effect on society using technological transformation as one item in their study. The study sought to understand the global economy in technological transformation using system analysis tools and econometric methods for the inquiry. The study identified six trends of growing uncertainty in the financial market: capital outflows from emerging markets, new market formation, end of hydrocarbon era of dominance, neo-industrial technological growth and industrial revolution. Regrettably, the finding saw these trends in emerging and developed economies and not in developing economies such as Nigeria. It then concluded that technology has invaded every aspect of human life and needs to be consistent with development for a country’s economic growth. Oruma and Amah (2021) sought to examine the impact of globalization on technology in Nigeria. Though their methods were not clearly defined, they could show the different perspectives of globalization and types of technology available within the Nigerian context and concluded that globalization (economic, social and political) leads to economic growth in Nigeria. In contrast, Williams, Andem, Ogosi, & Weniebi (2022) studied Globalization and indigenous entrepreneurship development in developing economies using manufacturing and trade as cases in Nigeria. They surveyed entrepreneurs’ assessments of the impact of the Globalization phenomenon on manufacturing and commerce (trade) in developing economies. In the same vein, Samimi and Jenatabadi (2014) investigated the effect of economic Globalization on economic growth in OIC countries. The study examined the effect of complementary policies on the growth effect of Globalization. It also investigated whether the growth effect of Globalization depends on the income level of countries. Using the generalized method of moments (GMM) estimator within the framework of a dynamic panel data approach, they provide evidence which suggests that economic Globalization has a statistically significant impact on economic growth in OIC countries. Their results showed that this positive effect is increased in the countries with better-educated workers and well-developed financial systems. The finding showed that the effect of economic Globalization also depends on the country’s level of income, where high and middle-income countries benefit from Globalization, whereas low-income countries do not gain from it. It then suggested that this can change with complementary reforms.

Goryakin, Lobstein, James, & Suhrcke (2015) in their study suggested that anecdotal and descriptive evidence has led to the claim that Globalization plays a major role in affecting social indicators such as life expectancy in developing countries. They undertook extensive econometric analyses of several datasets, using a series of new proxies for different dimensions of social Globalization potential in affecting health outcomes and using data in 49 developing countries between 1991 and 2009. After controlling for relevant individual and country-level factors, they found that social Globalization is substantially and significantly associated with an increase in the individual propensity for positive or negative health outcomes. The study also found that political and social Globalization dominates globalization’s influence on the economic dimension. It then suggested that consideration needs to be given to the forms of governance required to shape a more health-oriented Globalization process.

3. Materials and Methods

Globalization and other related characteristics are used to model growth. As a result, globalization will play a crucial role in economic development. The platforms of economic globalization, according to researchers, are trade openness and the market (Ajudua & Okonkwo, 2014). In addition, the exchange rate has an influence on the economy. According to Obadan (2001), devaluation or depreciation of the naira (i.e., the naira increasing) will boost export, resulting in an injection of cash into the economy and a positive impact on economic growth. The movement of commerce in and out of a country is captured by trade openness/degree of openness. Increased trade openness enhances economic growth, whether it is favorable or bad.

As a consequence, the following multiple regression model is adopted from Adesoye et al. (2015) in order to estimate the influence of economic globalization on Nigerian economic development. The model is as follows:

${\text{RGDP}}_{gr}=f\left(\text{DOP},\text{FDI},\text{EXR},\text{INF},\text{INTR}\right)$ (1)

Expressing Equation (1) in log-linear econometric form becomes:

${\text{RGDP}}_{grt}={\beta }_0+{\beta }_1{\text{DOP}}_t+{\beta }_2{\text{LNFDI}}_t+{\beta }_3{\text{EXR}}_t+{\beta }_4{\text{INF}}_t+{\beta }_4{\text{INTR}}_t+{\epsilon }_t$ (2)

Secondary data was sourced from Central Bank of Nigeria (CBN) Statistical Bulletin (various issues); the World Bank Economic indicators, (various issues); andthe National Bureau of Statistics (NBS). Data was subjected to various statistical analysis ranging from descriptive analysis, unit root test, cointegration test, and the Autoregressive Distributed Lag (ARDL) method. In the traditional ARDL cointegration approach, the dependent variable is expected to respond the same to both increases and decreases of each independent variable. The linear UECM specification without asymmetric adjustment in the short and long run is written as follows. Generally, the unrestricted linear error correction model is presented as follows

$\begin{array}{l}\Delta \left({\text{RGDP}}_{grt}\right)={\beta }_0+{\beta }_1\left({\text{DOP}}_{t-1}\right)+{\beta }_2\left({\text{INTR}}_{t-1}\right)+{\beta }_3\left({\text{INF}}_{t-1}\right)\\ +{\beta }_4\left({\text{LNFDI}}_{t-1}\right)+{\beta }_5\left({\text{MEXCR}}_{t-1}\right)\\ +{\displaystyle {\sum }_{i-1}^p{\vartheta }_6\Delta \left({\text{RGDP}}_{grt-1}\right)}+{\displaystyle {\sum }_{i-1}^q{\vartheta }_7\Delta \left({\text{DOP}}_{t-1}\right)}\\ +{\displaystyle {\sum }_{i-1}^r{\vartheta }_8\Delta \left({\text{INF}}_{t-1}\right)}+{\displaystyle {\sum }_{i-1}^t{\vartheta }_9\Delta }\left({\text{LNFDI}}_{t-1}\right)\\ +{\displaystyle {\sum }_{i-1}^u{\vartheta }_{10}\Delta }\log {\left({\text{EXCR}}_{t-1}\right)}_t+\delta ec{m}_{t-1}+{\epsilon }_{t-1}\end{array}$ (3)

The null hypothesis must be tested to see if there is a long-term link between the variables by cointegration. For the combined significance of the lagged levels of the variables in the hypothesis, the Wald test (F-statistics) is used:

H₀: ${\vartheta }_1$ = ${\vartheta }_2$ = ${\vartheta }_3$ = ${\vartheta }_4$ = ${\vartheta }_5$ = 0 Absence of co-integration

H₁: ${\vartheta }_1$ ≠ ${\vartheta }_2$ ≠ ${\vartheta }_3$ ≠ ${\vartheta }_4$ ≠ ${\vartheta }_5$ ≠ 0. Presence of co-integration

If the F statistic is greater than the upper bound critical value at the standard significance level, the null hypothesis is rejected. H₀ cannot be rejected if the F statistic is less than the lower bound critical value. If the F statistic falls between the two critical values, no conclusion can be drawn about H₀. The following tests must be run on the ARDL model to confirm its stability and dependability after variable cointegration has been identified: The Lagrange multiplier (LM) test, the Wald test, the Ramsey’s RESET test using the square of the fitted values, the CUSUM (Cumulative Sum of Recursive Residuals) and CUSUMSQ (Cumulative Sum of Square of Recursive Residuals) tests, and the CUSUMSQ (Cumulative Sum of Square of Recursive Residuals) test are some examples of tests that allow for some significant examinations like serial Short-run and long-run estimations can be used following the validation of the ARDL model’s dependability and stability.

4. Results and Discussion

4.1. Summary Statistics

The result of descriptive statistics in Table 1 reveals that all the variables except foreign direct investment have means greater than or less than the median except for the mean and the median of foreign direct investment that are the same. The mean for the degree of openness (DOP) is slightly less than the median, the mean of exchange rate EXCR) is less than the medium and the mean of GDP growth rate is equally less than the median. However, the mean of inflation rate (INF) and interest rates (INTR) is greater than the media in all the cases shown on Table 1, the standard deviation are less than the mean except for GDP growth rate. This shows that the dispersion is very for GDP growth rate. The degree of openness and interest rate exhibit negative skewness as well as GDP growth rate whereas exchange rate, inflation rate and foreign direct investment exhibit positive skewness. For Kurtosis, the degree of openness, interest rates, foreign direct investment, have values below the 3.0 and they are considered to be platykurtic whereas exchange rate, inflation rate and real GDP growth rate are leptokurtic. The probability of Jarque-Bera, indicates that inflation rate, have a value less than 0.05 and is considered not normally distributed, all other variables are normally distributed with the probability of Jarque-Bera greater than 0.05.

Table 1. Descriptive statistics.

	DOP	EXCR_	INF	INTR	LNFDI	RGDPGR
Mean	32.01192	110.1263	16.87920	22.63950	0.180000	3.446872
Median	33.87182	115.2550	11.27016	22.46500	0.180000	3.921549
Maximum	53.27796	399.9600	57.16525	36.09000	0.270000	15.32913
Minimum	9.135846	0.670000	0.150000	11.50000	0.120000	−10.92407
Std. Dev.	12.39591	108.3330	15.45256	5.823195	0.034418	4.810640
Skewness	−0.318364	0.913075	1.573549	−0.133722	0.382136	−0.418694
Kurtosis	2.201633	3.060755	4.400429	2.770394	2.974082	4.249658
Jarque-Bera	1.738022	5.564187	19.77570	0.207075	0.974641	3.771443
Probability	0.419366	0.061909	0.000051	0.901642	0.614270	0.151720
Sum	1280.477	4405.050	675.1679	905.5800	7.200000	137.8749
Sum Sq. Dev.	5992.687	457705.2	9312.485	1322.475	0.046200	902.5480

Source: Author’s computation 2023 using E-views 10.

4.2. Correlation Matrix

The correlation matrix in Table 2 shows that there is a positive relationship of moderate status between GDP growth rate and degree of openness, whereas it’s very low among GDP growth rate, exchange rate, interest rates. The correlation between GDP growth rate and inflation rate is negative though very small. The correlation between degree of openness and all other variables on the matrix are positive and very small, (less, than 50%) though the correlation between degree of openness exchange rate inflation rates and interest rates are positive, it shows a negative correlation between the degree of openness and foreign direct investment. Exchange rate was negatively correlated with inflation rate, whereas the correlation between exchange rate interest rate and FDI are positive and strong. correlation between inflation rate and interest rates was positive but very small. Foreign direct investment and inflation rate exhibits negative correlation while interest rates and foreign direct investment exhibit very small positive correlation.

Table 2. Pearson coefficient of correlation.

	RGDPGR	DOP	EXCR_	INF	INTR	LNFDI
RGDPGR	1.000000
DOP	0.492633	1.000000
EXCR_	0.104129	0.073438	1.000000
INF	−0.128276	0.046326	−0.300538	1.000000
INTR	0.214245	0.346714	0.521597	0.168972	1.000000
LNFDI	0.122291	−0.074249	0.710830	−0.417021	0.174873	1.000000

Source: Author’s computation 2023 using E-views 10.

4.3. Unit Root Tests

Table 3 shows the results of Unit Root Tests test that determines the level of stationarity of individual time series variable. The degree of openness (DOP), exchange rate (EXCR), interest rate (INTR) and foreign direct investment (LNFDI) were stationary at first difference whereas inflation rate (INF), and real GDP growth rate were stationary at level. This mixed level of stationarity shows that no other analytical method could be adopted for this study except ARDL, autoregressive distributed lag method. Given this, it is therefore necessary to proceed by conducting cointegration band test for long-run relationship.

Table 3. Result of unit root testsfor stationarity.

Variable	ADF t-statistic	Critical Value	Order of Integration
D(DOP)	−4.814421**	−3.562882	I(1)
D(EXCR)	−3.514899**	−3.514899	I(1)
INF	−4.107796	−2.936942	I(0)
D(INTR)	−7.017115	−2.941145	I(1)
D(LNFDI)	−5.603755	−2.938987	I(1)
RGDPGR	−4.015057	−2.938987	I(0)

Source: Author’s computation 2023 using E-views 10; **significant at 5 percent.

4.4. Bound Test for Cointegration

Table 4 reveals the result of cointegration bound test. In this result, the rule of thumb is that the F statistics should be greater than the upper and lower bounds. The F-statistic value of 7.131675 is seen to be greater from the table than the upper bound of 4.16 and the lower bound of 3.06. Given this, the null hypothesis of no level relationship is hereby rejected, and it’s affirmed that there is a long run relationship among the variables.

Table 4. Cointegration bound test.

Test Statistics	Value	Significance	I(0)	I(1)
			Asymptotic: n = 1000
F-statistic	7.131675	10%	2.08	3
k	5	5%	2.39	3.38
		2.5%	2.7	3.73
		1%	3.06	4.15
			Finite Sample: n = 40
Actual Sample Size: 36
		10%	2.306	3.353
		5%	2.734	3.92
		1%	3.657	5.256
			Finite Sample: n = 35
		10%	2.331	3.417
		5%	2.804	4.013
		1%	3.9	5.415

Source: Author’s computation 2023 using E-views 10.

Figure 1. Lag selection criteria.

In Figure 1, Akaike Information Criteria (AIC) was automatically selected and Lag 4 happened to be the best for the top 20 models as shown in the results on the graph.

4.5. ARDL Estimation Result

Table 5. ARDL long run result.

Conditional Error Correction Regression
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	1.267085	0.311457	4.068244	0.0016
RGDP(−1)*	−0.119873	0.035145	−3.410800	0.0052
DOP(−1)*	0.004382	0.000812	5.396634	0.0002
EXCR(−1)*	0.000917	0.000229	4.001063	0.0018
INF(−1)*	0.003656	0.001691	2.162342	0.0515
INTR(−1)*	−0.014233	0.004035	−3.527279	0.0042
LNFDI(−1)	0.215144	0.338909	0.634814	0.5375
D(RGDP(−1))	−0.351441	0.219418	−1.601695	0.1352
D(RGDP(−2))	0.219864	0.159293	1.380249	0.1927
D(RGDP(−3))	0.181096	0.132177	1.370101	0.1957
D(DOP)	−0.000839	0.000813	−1.031998	0.3224
D(DOP(−1))*	−0.004143	0.001263	−3.281638	0.0066
D(DOP(−2))*	−0.003717	0.000923	−4.027598	0.0017
D(DOP(−3))	−0.000792	0.000760	−1.042018	0.3179
D(EXCR)	−4.80E−06	0.000368	−0.013053	0.9898
D(EXCR(−1))*	−0.001017	0.000429	−2.371238	0.0353
D(EXCR(−2))	0.001008	0.000604	1.668487	0.1211
D(INF)*	0.001393	0.000640	2.176531	0.0502
D(INF(−1))	−0.001738	0.000955	−1.820029	0.0938
D(INF(−2))	0.000654	0.000472	1.385890	0.1910
D(INTR)*	0.003471	0.001535	2.260571	0.0432
D(INTR(−1))*	0.014719	0.003879	3.794772	0.0026
D(INTR(−2))*	0.005418	0.002240	2.418750	0.0324
D(LNFDI)	−0.340550	0.444995	−0.765290	0.4589
D(LNFDI(−1))	−0.303843	0.299574	−1.014248	0.3305
Case 2: Restricted Constant and No Trend
DOP	0.036558	0.010032	3.644039	0.0034
EXCR	0.007652	0.001492	5.128524	0.0002
INF	0.030500	0.013304	2.292566	0.0407
INTR	−0.118734	0.044151	−2.689292	0.0197
LNFDI	1.794770	2.550928	0.703575	0.4951
C	10.57024	0.820856	12.87710	0.0000
EC = RGDP − (0.0366*DOP + 0.0077*EXCR + 0.0305*INF −0.1187*INTR + 1.7948*LNFDI + 10.5702 )

Source: Author’s computation 2023 using E-views 10; *significant at 1 percent.

Table 5 shows the result of conditional error correction regression, and from the table, RGDP lagged by 1 has the tendency to contribute negatively to the dependent variable, same goes for INTR lagged by 1, D(EXCR) lagged by 1, and 2; D(DOP) lagged by 1, and lagged by 2, the values of their coefficients were significant at 5% though the magnitude of their contribution were very small. The degree of openness DOP lagged by one contribute positively at a magnitude of 0.004, INF lagged by 1 has a positive coefficient of (0.004) as well as all the lagged version of INTR and they were all significance at 5%. However, all the five variables in the long run restricted form and no trend showed significant contribution to GDP growth rate. DOP, EXCR, INF, and LNFDI are positively contributing to GDP growth rate in the long run whereas interest rates is negative. However, LNFDI that exhibits the highest positive contributionin the restricted constant form and no trend is not significant at 5%.

Table 6. Summary of long run result.

Variable	Coefficient	Std. Error	t-Statistic
R-squared	0.999439	Mean dependent var	10.47633
Adjusted R-squared	0.998317	S.D. dependent var	0.516865
S.E. of regression	0.021207	Akaike info criterion	−4.643654
Sum squared resid	0.005397	Schwarz criterion	−3.555196
Log likelihood	110.9076	Hannan-Quinn criter.	−4.259922
F-statistic	890.5407	Durbin-Watson stat	2.808334
Prob(F-statistic)	0.000000

Source: Author’s computation 2023 using E-views 10.

The R-squared value which is the coefficient of multiple determination has a value of 0.999, this shows that the Independent variables account for about 99% changes in the GDP growth rate. This is an indication that the model is fit, the Durbin-Watson statistics is less than 3 and greater than one which is a good indication that this model is not suffering from serial correlation. Lastly, the F-Statistics of 890.54 is equally significant (Table 6).

ARDL Short Run

Short run results indicate that D(GDP) lagged 1, D(DOP) lagged to I, D(DOP) lagged 2, D(DOP) lagged 3 and the D(EXCR) lagged by 1 have negative coefficients, and were significant at 5%. Conversely, the EXCR lagged by 2, D(INF), D(INF) lagged by 2, D(INTR), D(INTR) lagged by 1, D(INTR) lagged by 2, are all positive and significant at 5%. The error correction term is expected to be negative and significance. Going by this rule of thumb, error correction term (ECM) is negative as expected and it is significant at 5%. This gives hope that if there’s a shock to the system, the speed of adjustment is about 12% as seen in the coefficients of the error correction term which is −0.119873 (Table 7).

The study went further to conducted test to determine the behavior of the residuals. The LM test for serial correlation in Table 8 and the Breusch-Pagan-Godfrey test for heteroscedasticity in Table 9 were carried out. In the two tables, the probability of F-statistics was greater than 0.05 level of significance. Hence, the null hypotheses of “no serial correlation” and “homoskedasticity” were not rejected. These two post estimation tests were also validated by the CUSUM and CUSUM square tests (see Figure 2 and Figure 3).

Table 7. ARDL short run (ECM) regression.

Variable	Coefficient	Std. Error		t-Statistic		Prob.
D(RGDP(−1))*	−0.351441	0.158292		−2.220201		0.0464
D(RGDP(−2))	0.219864	0.114101		1.926924		0.0780
D(RGDP(−3))	0.181096	0.089141		2.031573		0.0649
D(DOP)	−0.000839	0.000434		−1.930991		0.0774
D(DOP(−1))*	−0.004143	0.000833		−4.975096		0.0003
D(DOP(−2))*	−0.003717	0.000576		−6.458134		0.0000
D(DOP(−3))	−0.000792	0.000481		−1.645244		0.1258
D(EXCR)	−4.80E−06	0.000230		−0.020904		0.9837
D(EXCR(−1))*	−0.001017	0.000335		−3.030677		0.0105
D(EXCR(−2))*	0.001008	0.000274		3.676476		0.0032
D(INF)*	0.001393	0.000275		5.064671		0.0003
D(INF(−1))*	−0.001738	0.000326		−5.337485		0.0002
D(INF(−2))*	0.000654	0.000265		2.465574		0.0297
D(INTR)*	0.003471	0.000934		3.715069		0.0030
D(INTR(−1))*	0.014719	0.002033		7.240890		0.0000
D(INTR(−2))*	0.005418	0.001272		4.261016		0.0011
D(LNFDI)	−0.340550	0.239082		−1.424409		0.1798
D(LNFDI(−1))	−0.303843	0.184670		−1.645326		0.1258
CointEq(−1)*	−0.119873	0.013853		−8.653472		0.0000
R-squared	0.891258		Mean dependent var		0.040810
Adjusted R-squared	0.782515		S.D. dependent var		0.037129
S.E. of regression	0.017315		Akaike info criterion		−4.967978
Sum squared resid	0.005397		Schwarz criterion		−4.140750
Log likelihood	110.9076		Hannan-Quinn criter.		−4.676342

Source: Author’s computation 2023 using E-views 10; *significant at 1 percent.

Table 8. Serial correlation test.

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	0.819166	Prob. F(2, 4)	0.5033
Obs*R-squared	10.46053	Prob. Chi-Square (2)	0.0054

Table 9. Heteroskedasticity test.

Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic	1.487638	Prob. F(29, 6)	0.3272
Obs*R-squared	31.60453	Prob. Chi-Square (29)	0.3375
Scaled explained SS	0.598289	Prob. Chi-Square (29)	1.0000

Figure 2. CUSUM test stability.

Figure 3. CUSUM Sq. test stability.

5. Conclusion and Recommendations

5.1. Conclusion

This study provides empirical content effect of Globalisation on economic growth in Nigeria spanning the period of 1981 to 2021 examining the effect of economic growth in Nigeria and the relationship between Globalisation and economic growth in Nigeria. The following conclusions are made: First, the finding of the study concludes that the effect of Globalisation in the economic growth of Nigeria has an insignificant impact. In addition, this study established that there is no significant relationship between Globalisation and economic growth in both short and long run. Therefore, the study concludes that policy maker expand and strengthen existing policies to enable Nigerians maximize the opportunities inherent in Globalisation.

5.2. Recommendations

Economic diversification and industrialization in Nigeria: To survive the current global economic uncertainty brought on by the drop in oil prices, boost development and economic growth, combat poverty, and create jobs for its citizens, the government must diversify and industrialize. Policies to encourage local production and consumption is required to reduce reliance on importation. The influx of multinational corporations (MNCs) and foreign goods and services must have some restrictions and pay attention to reducing illiteracy and focus on knowledge and skills required for the Nigerian economy and generally as a globe.

Fiscal and Monetary policy review: Local regulation is also required to prevent MNCs from abusing their Corporate Social Responsibility obligations to uphold international standards, safeguard human rights, and maintain Nigeria’s sustainable growth. Nigeria should also avoid pre-conditional financial help from international donors to Nigeria. This is because these policies put the nation in a worse position than it was before borrowing. It also hinders environmentally friendly policies and creativity needed for growth.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References

[1]	Adesoye, A., Ajike, E. O., & Maku, O. E. (2015). Economic Globalisation and Economic Growth in Developing Economies—A Case of Nigerian Economy. International Journal of Economics, Commerce and Management, 3, 340-355.
[2]	Ajudua, E. I., & Okonkwo, O. N. (2014). Empirical Review of Globalisation and Nigerian Economic Performance. European Journal of Business and Management, 6, 36-45.
[3]	Akor, A. A., Yongu, V. M., & Akorga, M. T. (2012). Effects of Globalization on the Nigeria Economy. Journal of Management and Corporate Governance, 4, 45-51.
[4]	Altman, S. A., & Bastian, C. R. (2022). Measuring Globalisation: Myths, Trends, and Variation across Countries. In The Routledge Handbook of Diplomacy and Statecraft (pp. 330-341). Routledge.
[5]	Dreher, A. (2006). The Influence of Globalization on Taxes and Social Policy: An Empirical Analysis for OECD Countries. European Journal of Political Economy, 22, 179-201. [Google Scholar] [CrossRef]
[6]	Feridun, M., Olusi, J. O., & Folorunso, B. A. (2006). Analyzing the Impact of Globalization on Economic Development in Developing Economies: An Application of Error Correction Modeling (ECM) to Nigeria. Applied Econometrics and International Development, 6, 174-182.
[7]	Goryakin, Y., Lobstein, T., James, W. P. T., & Suhrcke, M. (2015). The Impact of Economic, Political and Social Globalization on Overweight and Obesity in the 56 Low and Middle Income Countries. Social Science & Medicine, 133, 67-76. [Google Scholar] [CrossRef] [PubMed]
[8]	Guzel, A. E., Arslan, U., & Acaravci, A. (2021). The Impact of Economic, Social, and Political Globalization and Democracy on Life Expectancy in Low-Income Countries: Are Sustainable Development Goals Contradictory? Environment, Development and Sustainability, 23, 13508-13525. [Google Scholar] [CrossRef] [PubMed]
[9]	Huh, H.-S., & Park, C. Y. (2021). A New Index of Globalisation: Measuring Impacts of Integration on Economic Growth and Income Inequality. The World Economy, 44, 409-443. [Google Scholar] [CrossRef]
[10]	Matyushok, V., Krasavina, V., Berezin, A., & García, J. S. (2021). The Global Economy in Technological Transformation Conditions: A Review of Modern Trends. Economic Research-Ekonomska Istraživanja, 34, 1471-1497. [Google Scholar] [CrossRef]
[11]	Naz, A., & Ahmad, E. (2018). Driving Factors of Globalization: An Empirical Analysis of the Developed and Developing Countries. Business & Economic Review, 10, 133-158. [Google Scholar] [CrossRef]
[12]	Obadan, M. I. (2001). African and the Challenge of Globalisation: How Should the Continent Respond. Nigerian Journal of Economic and Financial Review, 6, 35-42.
[13]	Okpokpo, G. U., Ifelunini, I. A., & Osuyali, F. (2014). Is Globalisation a Potent Driver of Economic Growth? Investigating the Nigerian Non-Oil Exports. Asian Economic and Financial Review, 4, 781-792.
[14]	Oruma, O., & Amah, E. (2021). Globalisation and Technology: Implications in Nigeria. African Journal of Business and Economic Development, 2782, 7658.
[15]	Samimi, P., & Jenatabadi, H. S. (2014). Globalization and Economic Growth: Empirical Evidence on the Role of Complementarities. PLOS ONE, 9, e87824. [Google Scholar] [CrossRef] [PubMed]
[16]	Williams, A., Andem, F., Ogosi, F., & Weniebi, Z. (2022). Globalisation and Socio-Economic Growth in Nigeria. Marketing and Business Research Review, 2, 46-61.