The Impact of Globalization on Economic Growth in Sub-Saharan Africa: Evidence from the Threshold Effect Regression

Abstract

This study employs the panel quantile regression (QR) technique to evaluate whether globalization threshold conditions are essential for achieving effective economic growth, utilizing data from 47 Sub-Saharan African (SSA) countries for the period from 2000 to 2021. The bootstrap simultaneous conditional QR analysis was conducted using the fixed-effects panel QR approach. The study findings revealed that the globalization thresholds at which the total effect of globalization as a percentage of global integration changes from negative to positive are 3.82% and 4.36%, respectively. Furthermore, the critical mass of FDI and trade thresholds at which the total effects of FDI and trade, as a percentage of knowledge spillovers, change from negative to positive is 4.66% and 2.19%, respectively. Conversely, these results revealed an asymmetric relationship between globalization and growth among SSA countries. Therefore, these triggers and globalization thresholds serve as essential conditions and catalysts that will foster economic development in SSA economies. The results also indicate significant effects of globalization thresholds on economic growth among the SSA countries. Regarding policy relevance, these findings are also crucial for policymakers when they are developing strategies that will promote equal opportunity and balance development in the region through knowledge spillovers and improvements in global integration.

1. Introduction

Globalization is widely recognized as a driving force of international integration through urbanization and economic growth in many developing countries. Yet, Sub-Saharan African (hereafter SSA) countries are often left behind in the overpowering impact of globalization. Despite the theoretical debate on the globalization–growth nexus, the effect of globalization–growth is still very much inconclusive (Bataka, 2019; Dreher, 2006; Grossman & Helpman, 2015; Gygli et al., 2019; Khurshid et al., 2024; Rocha et al., 2023). Recently, South African President Cyril Ramaphosa’s remarks on the perspective that “Africans are not beggars” at the New Global Financing Pact summit held in France in 2023 have reopened the debate on the globalization–growth nexus.1 The context in which the globalization index is defined in the current research stems from the perspective of the KOF globalization index (economic, social, and political dimensions), as envisaged by (Gygli et al., 2019). In this study, we explore the question of whether globalization thresholds in the context of knowledge spillovers (innovation), foreign direct investment (FDI), and trade openness conditions are effective for creating economic growth and development in these SSA countries. Globalization is widely seen as an inevitable aspect of economic, political, and social development. It has facilitated the creation of global value chains, which in turn demand economic upgrading and structural transformation. This presents a significant challenge for the SSA economies; for example, leaders from these economies, at the aforementioned forum led with remarks on their resentments regarding the vaccines’ availability during the spread of COVID-19. At the same time, the developed countries had bought all the vaccines in the world, and they are hoarding them at a time in which SSA countries are dearly in need. Conceptually, globalization must enhance the competitiveness of SSA countries in technology, innovation, and value chains. Essentially, international trade and foreign direct investment (FDI) increase the scale of production and inherently drive resource consumption growth. Globalization, characterized by international dependencies and interactions among countries, could have a positive impact in some countries and a negative impact in others (Bataka, 2019; Gygli et al., 2019). To put the above intuition into perspective, the impact of globalization on economic growth in developing economies is still open to further academic debate. In this study, we investigate whether the impact of globalization on economic growth in SSA countries could withstand empirical scrutiny.

The contribution of this study to the contemporary literature is two-fold. First, we differ from the mainstream literature on the globalization–growth nexus and assess the effect of globalization on knowledge spillovers and economic growth in SSA countries using the three dimensions mentioned above (innovation, foreign direct investment, and trade openness) (Asongu & Odhiambo, 2022; Gygli et al., 2019). Accordingly, Grossman and Helpman (2015) unveiled the significant effect of knowledge spillovers in the nexus between global integration and growth, aligning with the literature (Helpman, 2004; Keller, 2010). However, considering that the magnitude of the effect of these knowledge spillovers depends upon a country’s economic endowment, the question of which factor contributed the most is still open to further debate. It is glaring that the debate on the impact of global integration on growth is still far from settled. Hence, we ask how lower or higher SSA countries’ integration influences the economic activities of the region, and whether the globalization threshold can be considered as a renewed-hope policy target for policymakers and the governments of these nations. The motivation and standing of these knowledge spillovers in the nexus between globalization and growth are based on the fact that, when governments are aware of globalization thresholds that can either positively or negatively drive the economy of these nations, these governments may be likely to tailor policies to reach the desired globalization thresholds needed to achieve the anticipated economic growth agenda. In this paper, we have found overwhelming evidence supporting the presence of a significant globalization threshold in 47 SSA countries over the period of 2000–2021.

Second, departing from the mainstream literature, the closest studies positioned to the current study are studies conducted on globalization proxies for environmental sustainability (Asongu & Nnanna, 2021; S. Asongu & Odhiambo, 2021), income inequality (Elvis & Ewane, 2023), and the shadow economy (Ajide & Dada, 2024). Accordingly, Asongu and Nnanna (2021) explored the globalization and governance thresholds for a green economy. However, their analysis was based on the globalization proxies (i.e., financial openness and trade openness). Elvis and Ewane (2023) unveiled the nexus between globalization and income inequality in 19 SSA countries over the period of 2000–2015 using two-stage least squares (2SLS) regression. Ajide and Dada (2024) uncovered the globalization threshold of 48.837% for the shadow economy of 24 African countries using panel threshold analysis over the period of 1995–2017. The current study advanced from the extant literature through an attempt to investigate whether the globalization threshold conditions are necessary for effective economic growth in 47 SSA countries using panel data from 2000–2021. This study is the most recent and is intended to investigate whether the previous studies could withstand empirical scrutiny using a more robust bootstrap simultaneous conditional QR.

To fill the gap in the contemporary literature, this paper explored the globalization threshold effect on economic growth while controlling for the effect of knowledge spillovers using the quadratic modeling method employed by Brambor et al. (2006) and Koenker (2005) and the conditional quantile regression employed by Koenker and Bassett (1978) (henceforth QR). Koenker (2004) has championed the general technique of estimating QR from the perspective of panel models with individual-specific fixed effects. Recently, there have been new developments in contemporary studies on this conditional QR-centric literature (Chernozhukov et al., 2019; Galvao et al., 2020; Galvao et al., 2013; Galvao et al., 2024; Galvao & Kato, 2017; Galvao & Montes-Rojas, 2015; Gu & Volgushev, 2019; Isayev et al., 2024; Ohene Kwatia et al., 2024; Parker, 2019; Singh et al., 2024). The advantage of conditional QR is that it allows us to capture heterogeneous effects of the covariates on the dependent variable while exploring various conditional heterogeneities under weak distributional assumptions (Galvao et al., 2024). Interaction quadratic modeling is utilized in this study to examine the threshold effect of the variable of interest on the dependent variable (Abdulqadir, 2024; S. Asongu & Odhiambo, 2021; S. Asongu et al., 2023).

Stylized Facts

We consider the underlying debate on the globalization–growth nexus in SSA countries, as observed from the data obtained on the KOF globalization index and GDP per capita (a measure of economic growth) through KOF Swiss and World Development Indicators (WDI), respectively. Figure 1 and Figure 2 present the mappings across quintiles corresponding to the variables under consideration. Figure 1 discloses the distribution of GDP per capita across SSA countries. The quintile distribution of GDP per capita ranges from $4000 to above, from $2000 to $3999, and from $1999 to below as the highest, median, and lowest quintiles, respectively. Figure 2 similarly discloses the distribution of the KOF globalization index across SSA countries. The quintile distribution of the KOF globalization index ranges from 60 to above, from 50 to 59, and from 49 to below as the highest, median, and lowest quintiles, respectively. Conversely, a glossary review of Figure 1 along the quintile distributions of GDP per capita revealed the highest quantile countries to be Seychelles, Gabon, Mauritius, Botswana, South Africa, and Equatorial Guinea, and that only Seychelles, Mauritius, and South Africa have finally made it to the highest quintile distribution of the globalization index, respectively.

Equally, the quintile distributions from Figure 2 revealed countries belonging to the highest quintile distribution of the globalization index, which are Ghana, Senegal, Seychelles, South Africa, and Mauritius, as, against all odds, Ghana and Senegal made it to the highest quintile distribution of GDP per capita, respectively. However, these same characteristics exhibit a similar trend in all remaining clusters.

Next, the remainder of this paper is organized as follows: Section 2 presents the stylized facts, theoretical underpinnings, and a brief literature review. Section 3 discloses the data and models of the investigation strategy. Section 4 reports the empirical results. Section 5 presents the conclusion of this paper.

2. Literature Review

2.1. Theoretical Underpinnings and Hypothesis Development

The theoretical support of our empirical investigation can be traced to the work by Grossman and Helpman (2015), which found that international knowledge spillovers enhanced economic growth through research facilitation (Grossman & Helpman, 1991a, 1991b; Helpman, 2004; Khurshid et al., 2024). Hence, it is an important insight to link enhanced globalization with improvement in the knowledge spillover effect in these two sectors (i.e., FDI and trade). The attendant effects are likely to enhance economic growth in SSA countries (Gossel, 2024; Pejović, 2023). However, the motivation of this paper is aligned with the question of whether the impact of global integration on growth may not necessarily provide policymakers with direction for policy tools. This requires the prescription of a policy threshold on the nexus between global integration and economic growth. To put the above intuition into perspective, we propose the first hypothesis of this study:

Hypothesis 1.

The greater the globalization threshold a country attains, the greater the possibility of the spillover effect on the economy.

The second strand of the theoretical debate regards the spillover effect of FDI on the nexus between globalization and growth.

Consequently, we propose the second hypothesis of the study:

Hypothesis 2.

The greater the globalization a country attains, the greater the possibility of the spillover effect on FDI.

The third strand of the theoretical debate regards the spillover effect of trade on the nexus between globalization and growth.

Hence, we propose the third hypothesis of this study:

Hypothesis 3.

The greater the global integration, the greater the possibility of the spillover effect for trade openness on the economy.

Next is a snippet of the extant literature to support the underlying intuitions positioned in this study and the use of the needed policy thresholds as a tool in the nexus between globalization and economic growth in SSA countries over the period of 2000–2021.

2.2. Snippet of the Previous Studies

There are several empirical debates on the nexus between globalization and economic growth (Awosusi et al., 2023; Bilal et al., 2022; Fatima et al., 2023; Jahanger et al., 2022; Patel & Mehta, 2023; Rehman et al., 2023; Sultana et al., 2023; Wang et al., 2023; Asongu & Nnanna, 2021; Bataka, 2019; Dreher, 2006; Grossman & Helpman, 2014, 2015). Although the role of global integration has been discussed in the contemporary literature, while the forefront of the debate concerns the threshold at which the positive or negative impact of globalization on economic growth in SSA countries is relatively scant. Grossman and Helpman (2015) unveiled the impact of globalization on economic growth from the perspective that global integration enhances the flow of knowledge across borders through people and culture. Further, global integration improves knowledge acquisition through the exchange of goods and prices as well as innovation to market domination through perfection and competition. Asongu and Odhiambo (2023) unveiled the positive impact of globalization from the perspectives of gender employment in the service sectors. Asongu and Nnanna (2021) discovered how globalization mediates the effect of governance on environmental pollution in SSA countries. In another study, Asongu (2013) revealed that globalization had a positive impact on human development in Africa. Contrarily, Elvis and Ewane (2023) discovered the negative effect of globalization on income inequality in 19 SSA countries over the period of 2000–2015. However, their findings revealed that, given the information and communication technology (ICT) penetration, FDI, and trade openness, income distribution has been dampened by inequality, while remittance improves the income redistribution in the sample countries.

Another strand of the literature has focused on the nexus between globalization and energy. Khurshid et al. (2024) discovered the effect of knowledge spillover, energy efficiency, and globalization on Pakistan’s economic growth using time-series data from 1980–2021. However, their findings revealed that energy efficiency and knowledge spillovers negatively affect the environment, while globalization positively affects environmental sustainability. Another strand of the literature has focused on the nexus between globalization and energy. Khurshid et al. (2024) discovered the effect of knowledge spillovers, energy efficiency, and globalization on Pakistan’s economic growth using time-series data from 1980–2021. However, their findings revealed that energy efficiency and knowledge spillovers negatively affect the environment, while globalization positively affects environmental sustainability. Onifade et al. (2023) uncovered the impact of globalization and energy on the environment in oil-producing African countries using panel data from 1990–2015. Nevertheless, their findings revealed that renewable energy enhances decarbonization while globalization promotes environmental pollution. Fotio et al. (2023) unveiled the effect of globalization from the perspective of achieving Sustainable Development Goal 7 (SDG 7) using panel data from 42 African countries over the period of 2000–2015. However, their findings revealed that globalization undermines environmental degradation on one hand while promoting energy efficiency through increased renewable energy deployment on the other hand. Tariq et al. (2023) discovered the positive effect of globalization and electricity consumption in China’s Belt and Road Initiative (BRI) countries.

Building from the perspective of the recent developments in the literature, several studies have shown the link between globalization and the energy or environment to be substantial. Accordingly, Gygli et al. (2019) discovered that the effect of globalization on the economy, observed using the KOF Globalization index, revealed different responses between the de facto and de jure globalization indicators. Ahmad and Civelli (2016) discovered the effect of globalization on inflation dynamics using quarterly data from a panel of 16 OECD countries over the period of 1985–2006. However, their investigation analysis utilized the threshold regression analysis within the theoretical framework of the new Keynesian Phillips curve. To sum up, we observe that there is no consensus due to several arguments concerning either the positive or negative impact of knowledge spillovers in the nexus between globalization and growth. Hence, these responses may not be likely to provide policymakers with policy tools unless they prescribe infliction points to the exact magnitude. This has opened a gap in the literature that calls on the use of the new KOF globalization index to examine other important issues like the critical pathways of globalization and their effects on economic growth within the losers and winners. Perhaps this, among many factors, has motivated the current study to revisit the debate by exploring the effect of the globalization threshold on economic growth in SSA countries.

3. Methodology

3.1. Data

The data supporting our empirical investigation are sourced from the World Development Indicators (WDI) and KOF Swiss Economic Institute and are from the period from 2000 to 2021. The variables used to measure economic growth (dependent variable) are GDP per capita, patent applications, foreign direct investment (inflows % of GDP), and trade openness (exports + imports % of GDP). Globalization is measured using the globalization index and urbanization is measured by urban population (% of the total population). Consistent with the previous studies, the present study applied a panel data model of 47 SSA countries (Dreher, 2006; Gygli et al., 2019)2. The Data definitions and sources are presented in Appendix A

Table A1. Definitions and sources of data.

Variable	Definitions	Source
GDPC	GDP per capita (constant 2015 US$)	WDI
Globalization	KOF Globalization index (Aggregation of the three dimensions of globalization “Economic, Social and Political dimensions”)	KOF Swiss Economic Institute
FDI	Foreign direct investment inflow (% of GDP)	WDI
Trade Openness	Exports plus Imports (% of GDP)	WDI
Innovations	Patent applications, residents	WDI
Urbanization	Urban Population (% of total population)	WDI

GDP: gross domestic product. WDI: world development indicators.

.

3.2. Methods

Consistent with the existing literature, the present study applied a panel data model of 47 SSA countries. The model specification is supported by QR along with the recent development in panel QR analysis (De Castro et al., 2019; Chernozhukov et al., 2019; Machado & Silva, 2019; Zhang et al., 2019). Furthermore, this study adopted an augmented model in Abdulqadir (2024), which was amplified with the globalization index and knowledge spillovers variable for the sampled countries.

3.2.1. Baseline Model

The model considered in this study is the ordinary least squares (OLS) regression with a one-way fixed effects model to account for the unobserved time-invariant heterogeneity in the model specification, which is carried out as follows:

$$\begin{array}{l}{y}_{gdp{c}_{i,t}}={\alpha }_0+{\beta }_{Glo}Globalisatio{n}_{i,t}+{\beta }_{FDI}FD{I}_{i,t}+{\beta }_{Trade}Trad{e}_{i,t}+{\beta }_{Innovation}Innovatio{n}_{i,t}\\ +{\beta }_{Urban}Urbanizatio{n}_{i,t}\end{array}$$

(1)

where $y_{gdp{c}_{i,t}}$ denotes economic growth, the dependent variable of country i in year t, $\alpha$ is the intercept, ${\beta }_{Glo}, {\beta }_{FDI}, {\beta }_{Trade}, {\beta }_{Innovation}, {\beta }_{Urban}$ are independent variables, and $\epsilon$ is the error term.

• $\alpha$ represents the intercept where the base level of growth is considered; all the independent variables are set to zero;
• ${\beta }_{Glo}$ denotes the coefficient associated with globalization, signifying the effect of changes on economic growth;
• ${\beta }_{FDI}$ denotes the coefficient linked to knowledge spillover in FDI changes that affect economic growth;
• ${\beta }_{Trade}$ denotes the coefficient linked to knowledge spillover from trade changes that affect economic growth;
• ${\beta }_{Innovation}$ denotes the coefficient associated with knowledge spillover. Patent innovation by resident changes affects economic growth;
• ${\beta }_{Urban}$ represents the coefficient connected to infrastructural development through urbanization dynamics that affect economic growth.

However, Equation (1) will allow us to investigate the nexus between globalization and economic growth in SSA countries while controlling for the effect of the knowledge spillovers. To address the primary objective of this paper, we employed the simultaneous conditional quantile QR regression model with individual-specific fixed effects using a transformed Equation (1), which is represented by the following equation:

$$Q{y}_{gdp{c}_{i,t}}\left|x_{i,t}(\tau )\right.={\alpha }_{\tau }+x_{i,t}^{\prime }{\beta }_{\tau }+{\in }_{i,t,\tau }$$

(2)

The above QR with the fixed effects model is explained by ${\alpha }_{\tau }$ and ${\beta }_{\tau }$, which are the intercept and coefficient of the quantile-specific parameters. $\tau$ denotes the conditional quantile of interest and ${\in }_{i,t,\tau }$ represents the error term.

Let us consider the reparametrized Equation (2), using which we obtain the one-way fixed effects in Equation (3):

$$\begin{array}{l}{y}_{gdp{c}_{i,t}}={\alpha }_0+{\beta }_{1}Globalisatio{n}_{i,t}+{\beta }_{2}FD{I}_{i,t}+{\beta }_{3}Trad{e}_{i,t}+{\beta }_{4}Innovatio{n}_{i,t}\\ +{\beta }_{5}Urbanizatio{n}_{i,t}+{\in }_{i,t,\tau }\end{array}$$

(3)

where $\left(\tau \left|·\right.\right)$ is the indicator variable and $x_{i,t}$ denotes the vector of the independent variables corresponding to the various coefficients ${\beta }_1, {\beta }_2, {\beta }_3, {\beta }_4,$ and ${\beta }_5$, respectively. To account for the presence of the threshold effect in the nexus between globalization and growth in SSA countries, this paper also utilized the quadratic model regression by Brambor et al. (2006) to explore the turning point of our threshold variable of interest (see Abdulqadir, 2024). The approach in Equation (3) treats each country in the SSA countries as a parameter to be estimated (Galvao et al., 2024). The objective of the fixed-effects QR estimate is to solve the minimization problem in Equation (4):

$$\left(\widehat{\alpha },\widehat{\beta }\right)=\underset{\alpha ,\beta }{{\displaystyle \arg \min }}{\displaystyle \frac{1}{nT}}{\displaystyle \sum _{n=1}^n{\displaystyle \sum _{t=1}^T\rho (y_{i,t}-{\alpha }_{i,t}-x_{i,t}^T)}}$$

(4)

where ${\rho }_{\tau }(\mu )=\mu \left\{\tau -1\left[\mu \le 0\right]\right\}$ is the check function, while $\beta \in {\mathbb{R}}^p$ is the vector of fixed-effect QR slope coefficients and $\alpha =\left({\alpha }_{1,}\dots \dots {\alpha }_n\right)$ is the $n\times 1$ vector of individual-specific effects, which depend on the $n \mathrm{and} T$ (Koenker & Bassett, 1978). Consistent with the procedure of Brambor et al. (2006), we consider including the squared term by separately reparameterizing Equation (3), through which we obtain a two-way fixed-effects model, as shown in Equation (5):

$$\begin{array}{l}{y}_{gdp{c}_{i,t}}={\alpha }_0+{\beta }_{1}Globalisatio{n}_{i,t}+{\beta }_{2}FD{I}_{i,t}+{\beta }_{3}Trad{e}_{i,t}+{\beta }_{4}Innovatio{n}_{i,t}\\ +{\beta }_{5}Urbanizatio{n}_{i,t}+{\in }_{i,t,\tau }\end{array}$$

(5)

Similarly, we apply the same analytical procedure involved in Equation (5), to Equations (6) and (7), respectively.

$$\begin{array}{l}{y}_{gdp{c}_{i,t}}={\alpha }_0+{\beta }_{1}Globalisatio{n}_{i,t}+{\beta }_{2}FD{I}_{i,t}+{\beta }_{3}FD{I}_{i,t}^2+{\beta }_{4}Trad{e}_{i,t}+{\beta }_{5}Innovatio{n}_{i,t}\\ +{\beta }_{6}Urbanizatio{n}_{i,t}+{\in }_{i,t,\tau }\end{array}$$

(6)

$$\begin{array}{l}{y}_{gdp{c}_{i,t}}={\alpha }_0+{\beta }_{1}Globalisatio{n}_{i,t}+{\beta }_{2}FD{I}_{i,t}+{\beta }_{3}Trad{e}_{i,t}+{\beta }_{4}Trad{e}_{i,t}^2+{\beta }_{5}Innovatio{n}_{i,t}\\ +{\beta }_{6}Urbanizatio{n}_{i,t}+{\in }_{i,t,\tau }\end{array}$$

(7)

where $Q_{gdp{c}_{i,t}}\left(\tau \left|x_{i,t}\right.\right)$ represents the $\tau$ quantile of our variable of interest under the given conditions of the independent variable $x_{i,t}$, which includes the core independent variable and control variable; $\varphi \left(\tau \right)$ denotes the estimated coefficient of simultaneous panel QR, and the specific estimated value is attained by analyzing the following objective function:

$$\underset{\theta ,\varphi }{{\displaystyle \min }}{\displaystyle \sum _{k=1}^q{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{i=1}^n{\beta }_k{\mu }_{{\tau }_k}(y_{i,t}-{\theta }_i-\varphi (\tau )x_{i,t})}}}$$

(8)

where ${\beta }_k$ denotes the weight corresponding to each quantile. Next, in the quantile selection procedure in this paper, we consider the contemporary practice of the extant literature and choose five important representation quantiles, such as 25% and 40% (lower quartile), 50% (median), and 75% and 90% (upper quartile), respectively.

3.2.2. Robustness Checks

How robust are the estimated results? To check how robust our estimates from the QR approach are, we adopt and draw the asymptotic critical diagram advanced by Hansen (1999) for threshold regression (TR)3. The method takes the following specification as follows:

$$y_{i,t}={\alpha }_0+{\beta }_{1}Gl{o}_{i,t}(Gl{o}_{i,t}\le \gamma )+{\beta }_{2}Gl{o}_{i,t}(Gl{o}_{i,t}>\gamma )+\mu$$

(9)

where $(Gl{o}_{i,t}\le \gamma )$ and $(Gl{o}_{i,t}>\gamma )$ are the threshold variables corresponding to the threshold parameter $\gamma$. Next, the section discloses the results of the models presented above.

4. Results

4.1. Preliminary Data Analysis

The preliminary data analysis presents the descriptive statistics for the data and the correlation matrix in Appendix A

Table A2. Descriptive Statistics.

Variable	Obs	Mean	Std.Dev.	Min	Max
GDPC	1034	5.791	1.309	0	6.919
Globalization	1034	3.804	0.204	3.173	4.276
FDI	1034	5.872	1.019	0	6.916
Trade openness	1034	4.897	2.269	0	6.775
Innovations	1034	0.807	1.505	0	4.595
Urbanization	1034	3.597	0.452	2.11	4.504

and

Table A3. Pairwise correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
(1) GDPC	1.000
(2) Globalization	0.181 *	1.000
(3) FDI	0.205 *	0.263 *	1.000
(4) Trade openness	0.233 *	0.164 *	0.007	1.000
(5) Innovations	0.102 *	0.330 *	0.122 *	−0.036	1.000
(6) Urbanization	−0.103 *	0.360 *	0.132 *	0.065 *	−0.026	1.000

* shows significance at the 0.05 level.

. However, due to the sensitivity of outliers to the key results, this study conducted robustness checks on the variance inflation factor (VIF) detection, which revealed no multicollinearity problem.

4.2. Baseline Results

The results from Equation (3), shown in

Table 1. The one-way fixed-effects conditional QR.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Fixed Effects	Q25	Q40	Q50	Q75	Q90
$Gl{o}_{it}$	0.903 **	0.548	0.115	0.459 *	0.610 ***	0.289 ***
	(0.431)	(0.523)	(0.327)	(0.259)	(0.0851)	(0.0875)
$\begin{array}{c}FD{I}_{it}\end{array}$	0.153 ***	0.335 ***	0.134 **	0.0920 ***	0.0733 ***	−0.0158 *
	(0.0340)	(0.0735)	(0.0553)	(0.0353)	(0.0284)	(0.00940)
$\begin{array}{c}Trad{e}_{it}\end{array}$	0.306 ***	0.0763 *	0.0631 ***	0.0534 ***	0.0319 ***	0.00407
	(0.0243)	(0.0422)	(0.0175)	(0.0155)	(0.0104)	(0.00583)
$\begin{array}{c}Pat{ent}_{it}\end{array}$	0.0641 **	0.0360	0.0448 **	0.00933	−0.0385 ***	−0.0119
	(0.0294)	(0.0278)	(0.0206)	(0.0212)	(0.0118)	(0.00900)
${Urbanisation}_{it}$	−1.349 ***	−0.962 ***	−0.319 ***	−0.323 ***	−0.101 ***	−0.0867 ***
	(0.431)	(0.126)	(0.0809)	(0.0758)	(0.0314)	(0.0240)
Constant	4.758 ***	4.344 ***	5.532 ***	4.783 ***	4.064 ***	6.086 ***
	(1.235)	(1.658)	(1.085)	(0.793)	(0.389)	(0.331)
R-squared	0.178
Pseudo R2		0.0536	0.0346	0.0307	0.0289	0.0141
Observations	1034	1034	1034	1034	1034	1034

Note(s): Standard errors are given in parentheses; *, **, *** indicate the 10, 5, and 1% statistical significance levels. Source(s): The author.

, revealed the one-way fixed-effects conditional QR results following established findings via a premised testable hypothesis. First, the first hypothesis is valid because globalization conditionally promotes economic growth, as the significant QR coefficients increase from the middle quintile to the higher quintile distribution of economic growth models in Table 1.

The above results are apparent in Table 1: (1) the globalization index has a significant positive effect on economic growth. It is imperative to articulate that the result from the one fixed effect discloses a significantly higher magnitude compared to the significant quintile coefficients along the distribution of the economic growth. (2) The FDI and trade positively affect the nexus between globalization and economic growth. (3) The innovations variable (patent applications) positively affects the nexus between globalization and economic growth, which varies in magnitude and significance. The fixed effect specification is evidence of the perspective that multicollinearity affects the signs and significance. (4) Lastly, the control variables are statistically significant and are consistent with the prior expectations.

Similarly,

Table 2. The two-way fixed effects along with the globalization squared term.

	(1)	(2)	(3)	(4)	(5)
Variables	Q25	Q40	Q50	Q75	Q90
$Gl{o}_{it}$	77.56 ***	20.82	4.719	4.221 **	0.400
	(23.93)	(13.26)	(6.648)	(2.002)	(2.596)
$Gl{o}_{it}\times Gl{o}_{it}$	−10.16 ***	−2.791	−0.565	−0.484 *	−0.0137
	(3.112)	(1.750)	(0.908)	(0.266)	(0.333)
$\begin{array}{c}FD{I}_{it}\end{array}$	0.276 ***	0.134 **	0.103 ***	0.0545 *	−0.0157
	(0.0843)	(0.0570)	(0.0385)	(0.0307)	(0.00967)
$\begin{array}{c}Trad{e}_{it}\end{array}$	0.0768 **	0.0620 ***	0.0549 ***	0.0324 ***	0.00403
	(0.0349)	(0.0181)	(0.0175)	(0.00946)	(0.00547)
$\begin{array}{c}Pat{ent}_{it}\end{array}$	0.0566 **	0.0362 **	0.00395	−0.0324 ***	−0.0119
	(0.0244)	(0.0180)	(0.0218)	(0.0103)	(0.0106)
${Urbanisation}_{it}$	−0.723 ***	−0.355 ***	−0.295 ***	−0.0942 ***	−0.0873 ***
	(0.0958)	(0.0959)	(0.0884)	(0.0307)	(0.0248)
Constant	−141.8 ***	−32.64	−3.397	−2.569	5.862
	(45.94)	(25.06)	(12.26)	(3.676)	(5.057)
Net effect	39.42	na	na	1.385	na
Threshold	3.817	na	na	4.361	na
Pseudo R2	0.0868	0.0405	0.0310	0.0307	0.0141
Observations	1034	1034	1034	1034	1034

Note(s): Standard errors are given in parentheses; *, **, *** indicate the 10, 5, and 1% statistical significance levels. The lower quantiles (e.g., Q.25) signify nations with the least economic growth. na: not applicable because at least one estimated coefficient is needed to compute the thresholds, and the net effect is not significant. The mean value of the Globalization index is 3.804. Source(s): The author.

discloses the two-way fixed-effects along with the squared term that describes the net effect as well as the globalization threshold. Hence, the net effects of globalization on growth are [2 × (5.791 × (−10.16)) + 77.56] = 39.42 and [2 × (5.791 × (4.221)) + (−0.484)] = 1.385 in Table 2, columns 1 and 4, respectively. It is worth noting that −10.16 is the conditional effect of globalization on growth, 5.791 is the mean value of the globalization index, 77.56 is the marginal impact of globalization on growth, and two terms are the quadratic derivation. The globalization thresholds are [77.56/(2 × (−10.16))] = −3.817 for the low quintile 25% and [4.221/(2 × (−0.484))] = −4.361 for the high quintile 75% distribution of economic growth, respectively. Conversely, the globalization thresholds at which the total effect of globalization as a percentage of global integration changes from negative to positive are 3.82%, and 4.36%, respectively. The overall net effects are zero, i.e., [2 × (3.817 × (−10.16)) + 77.56] = 0 and [2 × (4.361 × (−0.484)) + 4.221] = 0, respectively.

Second, the second and third hypotheses are also valid because the knowledge spillovers have conditionally stimulated the nexus between globalization and economic growth, as given by their significant estimated coefficients. Notwithstanding, a similar analytical procedure is applied to the knowledge spillover effects of FDI and trade empirical results from Equations (4) and (5), in

Table 3. The two-way fixed effects, along with the FDI squared term.

	(1)	(2)	(3)	(4)	(5)
Variables	Q25	Q40	Q50	Q75	Q90
$Gl{o}_{it}$	0.539	0.254	0.330	0.625 ***	0.326 ***
	(0.527)	(0.351)	(0.314)	(0.0625)	(0.0967)
$\begin{array}{c}FD{I}_{it}\end{array}$	−0.572	−0.577	−0.477	−0.511 ***	−0.174
	(0.446)	(0.358)	(0.298)	(0.174)	(0.108)
$FD{I}_{it}$ × $FD{I}_{it}$	0.0846 **	0.0717 **	0.0575 **	0.0548 ***	0.0168
	(0.0412)	(0.0341)	(0.0292)	(0.0152)	(0.0115)
$\begin{array}{c}Trad{e}_{it}\end{array}$	0.0812 **	0.0492 ***	0.0499 ***	0.0329 ***	0.00641
	(0.0400)	(0.0145)	(0.0180)	(0.00824)	(0.00628)
$\begin{array}{c}Pat{ent}_{it}\end{array}$	0.0402	0.0355 *	0.0238	−0.0313 ***	−0.0142
	(0.0321)	(0.0213)	(0.0200)	(0.00836)	(0.00878)
${Urbanisation}_{it}$	−0.929 ***	−0.408 ***	−0.302 ***	−0.125 ***	−0.0879 ***
	(0.138)	(0.0859)	(0.0800)	(0.0378)	(0.0274)
Constant	6.588 ***	7.009 ***	6.504 ***	5.576 ***	6.257 ***
	(1.888)	(1.453)	(1.415)	(0.545)	(0.327)
Net effect	na	na	na	0.133	na
Threshold	na	na	na	4.662	na
Pseudo R2	0.0592	0.0410	0.0344	0.0369	0.0173
Observations	1034	1034	1034	1034	1034

Note(s): Standard errors are given in parentheses; *, **, *** indicate the 10, 5, and 1% statistical significance levels. The lower quantiles (e.g., Q.25) signify nations with the least economic growth. na: not applicable because at least one estimated coefficient is needed to compute the thresholds, and the net effect is not significant. The mean value of FDI is 5.872. Source(s): The author.

and

Table 4. The two-way fixed effects, along with the trade openness squared term.

	(1)	(2)	(3)	(4)	(5)
Variables	Q25	Q40	Q50	Q75	Q90
$Gl{o}_{it}$	0.403	−0.0217	0.380	0.627 ***	0.309 ***
	(0.507)	(0.450)	(0.288)	(0.0740)	(0.0888)
$\begin{array}{c}FD{I}_{it}\end{array}$	0.334 ***	0.140 **	0.116 ***	0.0553 *	−0.0119
	(0.0723)	(0.0554)	(0.0319)	(0.0301)	(0.0119)
$\begin{array}{c}Trad{e}_{it}\end{array}$	−0.0666	−0.0814	−0.129 **	−0.0676	0.0253
	(0.111)	(0.0849)	(0.0596)	(0.0492)	(0.0285)
$\begin{array}{c}Trad{e}_{it}\end{array}$ × $\begin{array}{c}Trad{e}_{it}\end{array}$	0.0239	0.0196 *	0.0294 ***	0.0151 **	−0.00322
	(0.0155)	(0.0119)	(0.00851)	(0.00718)	(0.00393)
$\begin{array}{c}Pat{ent}_{it}\end{array}$	0.0438 *	0.0261	−0.00853	−0.0334 ***	−0.0177 *
	(0.0264)	(0.0183)	(0.0199)	(0.0113)	(0.00903)
$\begin{array}{c}{Urbanisation}_{it}\end{array}$	−0.892 ***	−0.359 ***	−0.334 ***	−0.130 ***	−0.0960 ***
	(0.152)	(0.118)	(0.0749)	(0.0320)	(0.0234)
Constant	4.700 ***	6.302 ***	5.050 ***	4.253 ***	6.014 ***
	(1.591)	(1.490)	(0.935)	(0.326)	(0.338)
Net effect	na	na	0.159	na	na
Threshold	na	na	2.194	na	na
Pseudo R2	0.0555	0.0375	0.0373	0.0315	0.0148
Observations	1034	1034	1034	1034	1034

Note(s): Standard errors are given in parentheses; *, **, *** indicate the 10, 5, and 1% statistical significance levels. The lower quantiles (e.g., Q.25) signify nations with the least economic growth. na: not applicable because at least one estimated coefficient is needed to compute the thresholds, and the net effect is not significant. The mean value of trade openness is 4.897. Source(s): The author.

, respectively. Hence, the net effect and threshold of FDI on economic growth are [2 × (5.872 × 0.0548) + (−0.511)] = 0.133 and [−0.511/(2 × 0.0548)] = −4.662, as shown in Table 3, column 4. Similarly, the net effect and threshold of trade on economic growth are [2 × (4.897 × 0.0294) + (−0.129)] = 0.159 and [−0.129/(2 × 0.0294)] = −2.194 for the mid quintile 50% distribution of economic growth in Table 4, column 3, respectively. Conversely, the FDI and trade thresholds at which the total effects of FDI and trade as a percentage of knowledge spillovers change from negative to positive are 4.66% and 2.19%, respectively.

4.3. Supplementary Robustness Analysis

Table 5. Robustness analysis of the nexus between globalization and economic growth.

	Full Sample		Highest Quintile		Median Quintile		Lower Quintile
Dependent Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
GDPC	First Regime	Second Regime	First Regime	Second Regime	First Regime	Second Regime	First Regime	Second Regime
Threshold	3.2717	3.9883	4.2707	3.9612	3.9683	3.8759	3.2717	3.4204
F-statistics (Prob)	0.0333	0.1667	0.0067	0.2467	0.6933	0.8433	0.0133	0.9070
95% confidence interval 1%	96.0361	106.5524	70.7158	317.7137	72.9712	32.2538	55.3728	81.7284
95% confidence interval 5%	61.6668	64.8621	40.6924	196.3496	44.3947	25.4639	42.0971	59.0936
95% confidence interval 10%	48.7549	47.0529	25.9035	98.5263	36.6459	22.6006	35.6088	50.3844
$\begin{array}{c}FD{I}_{it}\end{array}$	0.108 ***	0.102 ***	0.0892	0.146	0.0165	0.00822	0.153 ***	0.213 ***
	(0.0337)	(0.0331)	(0.0923)	(0.0895)	(0.0359)	(0.0358)	(0.0447)	(0.0445)
$\begin{array}{c}Trad{e}_{it}\end{array}$	0.260 ***	0.278 ***	0.140 *	0.0895	−0.0591 **	−0.0483	0.385 ***	0.402 ***
	(0.0244)	(0.0242)	(0.0737)	(0.0717)	(0.0295)	(0.0294)	(0.0314)	(0.0315)
$\begin{array}{c}Pat{ent}_{it}\end{array}$	0.0707 **	0.0506 *	0.0121	0.0469	0.00396	0.00745	0.0545	0.0271
	(0.0286)	(0.0284)	(0.0519)	(0.0505)	(0.0230)	(0.0229)	(0.0515)	(0.0521)
${Urbanisation}_{it}$	−1.627 ***	−2.181 ***	7.831 ***	7.330 ***	0.660 *	0.495	−4.782 ***	−3.719 ***
	(0.421)	(0.425)	(1.214)	(1.165)	(0.397)	(0.393)	(0.639)	(0.678)
Test for the number of $\gamma$ p-values
0._ No threshold	0.0463	−0.0179	1.388	−0.770	1.114 ***	0.499	0.641	3.408 ***
	(0.434)	(0.427)	(1.065)	(1.201)	(0.393)	(0.483)	(0.615)	(0.763)
1._Atleast one threshold $\gamma =1$	0.814 *	0.770 *	0.698	−0.585	1.043 ***	0.556	1.390 **	3.139 ***
	(0.419)	(0.413)	(1.055)	(1.172)	(0.393)	(0.470)	(0.600)	(0.721)
2._Atmost two thresholds $\gamma =2$		0.969 **		−1.250		0.501		2.909 ***
		(0.413)		(1.160)		(0.468)		(0.716)
Constant	6.613 ***	8.620 ***	−32.26 ***	−22.24 ***	−0.445	2.392	14.77 ***	4.137 *
	(1.226)	(1.256)	(3.883)	(4.752)	(1.267)	(1.748)	(1.723)	(2.223)
Observations	1034	1034	110	110	352	352	572	572
R-squared	0.223	0.248	0.699	0.730	0.094	0.106	0.371	0.362
Number of country	47	47	5	5	16	16	26	26

Note(s): Standard errors are given in parentheses; *, **, *** indicate the 10, 5, and 1% statistical significance levels. Similarly, the F-statistic and p-value are calculated through 400 grid searches to examine the threshold-effect test using the 300 bootstrapping replications procedure. Source(s): The author.

reports the robustness analysis using the threshold regression in Equation (9). The supplementary analysis revealed various thresholds corresponding to the full sample, along with the various quintile distributions of the economic growth variable. Columns 1 and 2 report a significant single threshold effect of the globalization–growth threshold that corroborates the result using the baseline model.

Columns 5 and 6 report a significant single threshold effect of the globalization–growth threshold corresponding to the median quintile, which corroborates the earlier result obtained using the baseline model. Finally, columns 7 and 8 report significant single and double threshold effects of the globalization–growth threshold corresponding to the lower quintile, which validates the previous result obtained using the baseline model. The diagrams in Figure 3, Figure 4, Figure 5 and Figure 6 report an asymptotic critical diagram of various thresholds from the general sample and the quantile clusters that robustly corroborate the threshold effect of globalization.

In summary, the above findings can be established from the corresponding slate tables. (i) Globalization thresholds are a necessary condition for the improvement of economic growth and the development of SSA countries. The policy relevance of these significant thresholds can be drawn from the perspective of the improved knowledge spillovers obtained through the inflow of FDI and trade openness. (ii) The control variables in the tables show some level of significance, thus validating the theory and the literature (Ajide & Dada, 2024; Aluko et al., 2023; Tariq et al., 2023; Asongu & Odhiambo, 2023).

5. Conclusions and Policy Implications

5.1. Conclusions

This study contributes to the field of knowledge by establishing the critical mass of the globalization threshold prerequisite for sustainable economic growth in SSA countries over the period of 2000–2021. This study is motivated by the fact that the findings were achieved by adopting a bootstrap simultaneous panel quantile model for the baseline investigation strategy and augmenting the empirical strategy for robustness analysis using the dynamic panel threshold model as supplementary analysis.

Following the conclusion of this study, the following findings are established: the critical mass of globalization thresholds at which the total effect of globalization as a percentage of global integration changes from negative to positive is 3.82% and 4.36%, respectively. The findings also revealed that there is a critical mass of FDI and trade thresholds at which the total effects of FDI and trade as a percentage of knowledge spillovers change from negative to positive, and these are 4.66%, and 2.19%, respectively. These FDI and trade thresholds are effective in the nexus between globalization and economic growth in SSA countries over the period of 2000–2021. To put this conclusion of the findings into more perspective, the findings corroborate the globalization theory and the literature (Ajide & Dada, 2024; Aluko et al., 2023; Tariq et al., 2023; Asongu & Odhiambo, 2023).

5.2. The Policy Implications of the Study

The policy implication of the findings of this study is straightforward: the novelty of these findings stems from the contribution of this study to the extant literature on actionable policy to enhance the region’s economic growth through these thresholds. Secondly, another reason that these findings are relevant to policy and thus equally important to policymakers when they are formulating economic policies is that that the effect of globalization is contingent on the market dynamics and regional developmental needs. Some of the critical imperatives of global integration are discussed in the document from the New Global Financing Pact summit held in Paris (FOCUS2030, 2023). Furthermore, the triggers and globalization thresholds provide a necessary condition and catalysts that will drive economic growth in SSA economies.

5.3. Future Research Direction and the Caveat

This study is limited to the investigation of globalization thresholds for economic growth in 47 SSA countries from 2000–2021. Future research directions can be oriented toward addressing the other critical financial integration paths for the region. Further studies may look at appropriate investigation techniques for country-specific analysis to explore whether these established globalization thresholds for economic growth in SSA countries can withstand empirical scrutiny. This recommendation for a country-specific analysis builds on the caveat that time series are considered in the threshold regression approach. Hence, researchers can explore a systematic literature review approach, considering the globalization and growth nexuses.