The effect of globalisation on Sub-Saharan Africa's development thrives

Fosah, Mah-Soh Glennice; Mama, Ndam; Dinga, Gildas Dohba; Nchofoung, Tii Njivukuh Article The effect of globalisation on Sub-Saharan Africa's development thrives Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Fosah, Mah-Soh Glennice; Mama, Ndam; Dinga, Gildas Dohba; Nchofoung, Tii Njivukuh (2023) : The effect of globalisation on Sub-Saharan Africa's development thrives, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 7, pp. 1-8, https://doi.org/10.1016/j.resglo.2023.100149 This Version is available at: https://hdl.handle.net/10419/331076 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). The effect of globalisation on Sub-Saharan Africa’s development thrives Mah-Soh Glennice Fosah a , Ndam Mama b , Gildas Dohba Dinga a , * , Tii N. Nchofoung c a Faculty of Economics and Management Sciences, University of Bamenda, Cameroon b Higher Institute of Commerce and Management (HICM), Department of Management, The University of Bamenda, Cameroon c Faculty of Economics and Management Sciences, University of Dschang, Cameroon ARTICLE INFO JEL: C33 F63 O11 Keywords: Globalisation Development DCCE Causality ABSTRACT In this study, the effect of globalisation (both its de jure and de facto components) on economic development in sub-Saharan Africa (SSA) is examined. The work utilises an ex-post facto research strategy and analyses panel data of 35 SSA nations from 1995 to 2018 using the Dynamic Common Correlated Effects (DCCE) estimation methodologies and the System Generalised Method of Moments (SGMM) techniques. These techniques are chosen based on their ability to account for cross-sectional dependence, cross correlation, and endogeneity. The KOF globalisation index, which takes into account the economic, social, and political aspects of globalisation as a predictor, as well as the human development index as indicators of economic development, are used in the study. The results show a strong and positive correlation between globalisation and economic development in SSA, particularly with the integration of global flows and activities (de jure and de facto globalisation index). The findings are robust to different specifications and estimation techniques. The study, therefore, concludes that globalisation could be an engine of SSA’s economic development if governments reviewed their liberalization policies to take into account globalization issues and challenges. 1. Introduction In recent decades, the world economy has increasingly become integrated and interdependent, indicating that globalization is one of the most important forces shaping the global economy (Hasan, 2019; Hassan, Xia, Huang, Khan, & Iqbal, 2019; Sweidan & Elbargathi, 2022; Acheampong et al., 2021: Ngouhouo, Nchofoung, & Njamen Kengdo, 2021; Dinga, Fonchamnyo, Ongo, & Bekun, 2023a; Dinga, Thierry, Fonchamnyo, Emmanuel, & Nginyu, 2023b; Fotio, Nchofoung, & Asongu, 2022). Although globalization is not a new phenomenon, the intensity of the process in contemporary times has increased profoundly through trade and services, movement of capital, growth of the world population, international migration, transportation and communication around the world thus stimulating decreasing transportation costs and the diffusion of Information and Communication Technologies (ICTs), gross trade, foreign direct investment, and a significant rise in capital flows and technology transfers (Awad, 2023; Lee & Vivarelli, 2006). It is thought that this cross-border movement of commodities, services, capital, enterprises, and people facilitates the spread of technology, knowledge, culture, and information which are driving factors of economic development (Goldberg & Pavcnik, 2007; Sweidan & Elbargathi, 2022). Traditional views of development have been that it is an economic phenomenon in which rapid increases in overall Gross Domestic Product and per capita Gross National Income create the necessary conditions for a wider distribution of the economic and social benefits of growth, particularly among the poor, by creating income-generating activities in an economy (Todaro & Smith, 2006). However, the concept of development cannot be limited to economic growth, since development will incorporate changes is the general well-being and quality of life of citizens of an economy (Siqueira, Mtewa, & Fabriz, 2022; Canton, 2021). This implies that the concept of development has evolved from the traditional view of changes of the income level to the enhancement of the incorporation of factors such as better welfare for citizens, enhance human capital and affordable health condition. However, development remains one of the complicated concept to quantify and it enhancements within a globalised world has remain primordial for global stakes holders and policy makers (Canton, 2021). With the advent of globalisation and the expansion in global financial linkages, an economy that is more open to international markets is likely to grow at a faster rate than one that is closed (Prasad et al., 2003; Acheampong et al., 2021). Though there are scholars that argue that globalisation is detrimental to * Corresponding author at: Faculty of Economics and management Sciences, University of Bamenda, Cameroon. E-mail address: [email protected] (G. Dohba Dinga). Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2023.100149 Received 29 April 2023; Received in revised form 14 July 2023; Accepted 19 August 2023 Research in Globalization 7 (2023) 100149 2 sustainable economic development due to its detrimental effect on environmental sustainability (Chishti, 2023). In this respect, it hs been extensively debated that the impact of globalisation on actual economic activity vary from one region to another depending on their trade structure and the development of their financial system (Nchofoung, 2022). Investigating how globalisation has affected economic activity in Sub-Saharan Africa is crucial given assertions that it is connected to global growth and wellbeing. According to Gozgo (2016), For developing nations to catch up to industrialised nations in terms of productivity and GDP levels, they must acquire and apply production technology, for which capital inflows are one of the most crucial sources of such technical transfer. Sub-Saharan Africa’s past growth had been boosted by sustained high prices of natural resources such as oil, gas and minerals, as well as increases in a number of agricultural commodity prices such that from 2001 to 2005, even non-oil producers, including agricultural countries, registered annual GDP growth rates above 5 per cent (United Nations. (2008), 2008). There has also been unprecedented progress in the development of education as well as improving the health outcomes of its population and a brief look over the last decades shows that Sub Saharan African countries have experienced transitions towards more open economies as well as becoming more inclusive of social and political differences within and between countries (Chisadza & Bittencourt, 2018). According to Egbetunde and Akinlo (2015), in the last two decades, external sources of finance for investment and growth in Sub-Saharan Africa have changed dramatically; primarily there has been a tremendous increase in total flows, especially since 2000. The private sector is responsible for a sizable portion of the growth. Private capital comes from both FDI (foreign direct investment) and transfers, where remittances have surpassed official transfers (grants), which have been declining over the past ten years. Nonetheless, with such increasing measure of integration and global exposure, GDP growth rates have been on the decline from about 6% in 2005 to 2.3% in 2019 (World Bank, 2020). Also, the World Bank publication on Millennium Development Goals (MDGs) revealed that poverty reduction policies has led to great improvement and reduction in most economies of the world with the exception of Africa wherein, an approximate 45 percent of economies in SSA were off-track from reaching the MDGs extreme poverty target (Asongu, Efobi, & Beecroft, 2015). This makes it a call for concern and fuels the premise that the fortune and misfortune of Sub Saharan African economic development may be tied to the phenomenal of globalization. While some people believe that globalisation has been a powerful force for reducing poverty and has improved life expectancy and other important aspects of development, others believe that it has had negative effects that have only served to increase income and trade disparities and worsened poverty, unemployment, and environmental destruction, which has a negative impact on developing countries’ ability to close the development gap (Goldin, 2018). This paper therefore assesses the effect of globalisation on the economic development of SSA countries. This is to see whether these countries have been able to successfully adapt and participate in the globalisation process to substantially improve their income levels and social standards. Subsequently, the question to be addressed stand as: is globalisation a blessing or curse to sub-Saharan Africa’s development thrives? The contribution of this study is in many fold. Firstly, it deviates from the common trend of measuring development in existing literature on the subject (Beri, Mhonyera, & Nubong, 2022; Kılıçarslan & Dumrul, 2018; Chang & Lee, 2010; Leit˜ ao, 2012; Ali, 2013; Villaverde & Maza, 2011), which entails using the gross domestic product per capita, and considered a more inclusive measure, that accounts for health, living standard and Knowledge (human capital development). Secondly, the dimension of globalization that is, the de facto and the de jure dimensions are equally analysed for the case of SSA. The employment of robust empirical approach equally entails more robust outcome good for inferencing and the designing of appropriate policies for the SSA sub region. The rest of the paper is laid out as follows. Section 2 surveys significant literature on the relationship between globalization and development. Section 3 deals with data presentation, model formulation, and the highlight of estimation techniques. Section 4 presents and discusses the findings. The last section concludes the article and make policy recommendations. 2. Literature review Theoretical growth studies provide a mixed picture of the relationship between globalization and development. According to neoliberal school, global commerce, cross-border investment, and technological innovation, improves production efficiency and provides unprecedented affluence despite the replacement of old employment and a drop in pay for unskilled employees. Neoliberals, regard the world system as interdependent and indicate that there is essential harmony of interest between various social groups (Jackson & Sorensen, 2010). This can be translated to suggest that liberals believe in a positive-sum game between global actors where everyone gains (O’Briens and Williams, 2004); and as the dominant driving force in the world economy, reshaping societies and policies as it changes lives, globalisation improves human development. This view has been validated empirically by several authors while employing different proxies for development (Beri et al., 2022; Kılıçarslan & Dumrul, 2018; Leit˜ ao, 2012; Ali, 2013). To the contrary, dependency theorists contend that current national and international economic and political structures are to be blamed for unfair situations in which resources flow from a periphery of underdeveloped and poorer states to a core of wealthy states, enriching the latter at the expense of the former (Tausch, 2010). Moreover, the efficiency hypothesis, postulated on the premise that the instrumental capacity of the nation-state is decisively undermined by the globalisation of core economic activities (Blackmon, 2006), contends that globalisation is the cause of welfare state decline. This is because the globalisation of production and investment necessitates welfare state cutback, since states lose autonomy over social welfare policies in the face of the overwhelming global economy. Osaro (2022) goes farther by considering globalization as a new order of marginalization and recolonisation in a “neo-neo-colonial fashion”, mostly of the African continent. Such postulations have been recently supported by different authors (Dollar & Kraay, 2004; and Dreher, Gaston, & Martens, 2008). Numerous studies have empirically investigated the link between globalisation and economic growth, which is typically regarded as a leading indication of development in both developed and developing countries, in light of the competing theoretical viewpoints. Studies have revealed certain benefits of globalisation for growth (Beri et al., 2022; Kılıçarslan & Dumrul, 2018; Chang & Lee, 2010; Leit˜ ao, 2012; Ali, 2013; Villaverde & Maza, 2011; Güzel, Arslan, & Acaravci, 2021), while others argued that globalization has a harmful effect on growth (Dollar & Kraay, 2004; and Dreher et al., 2008) as well as no significant effect (Gurgul & Lach, 2014). Kılıçarslan and Dumrul (2018) used the Fully Modified Ordinary Least Squares method and the KOF globalisation index to investigate the effects of globalisation on economic growth in Turkey from 1980 to 2015. They concluded that Turkey’s economic growth is accelerated by globalisation. Similarly, Chang and Lee (2010) examined the connection between globalisation and economic growth for 23 Organisation for Economic Cooperation and Development (OECD) nations from 1970 to 2006 using Pedroni’s Panel Estimation Technique. Their findings indicate a weak relationship in the short term, but a significant long-term relationship whereby globalisation leads to economic growth. Furthermore, Leit˜ ao (2012) in attempts to establish the relationship between globalisation and development, analysed the interrelations between economic growth, trade, and globalisation in the USA for the period of 1995 to 2008 using the fixed effects and GMM statistical tools for data analysis, and also confirms that globalisation promotes economic growth. Hasan (2019) found that globalisation has a favourable impact on economic growth in a panel of South Asian M.-S. Glennice Fosah et al. Research in Globalization 7 (2023) 100149 3 nations, although the effect is more obvious over the long term. Ali (2013) in exploring the influence of economic globalization on growth, with a sample of 41 African countries, find a positive correlation between economic globalization and economic growth. Similarly, Villaverde and Maza (2011) used the Generalised Method of Moments (GMM) estimator to examine whether globalisation impacts economic growth over a sample of 101 countries from 1970 to 2005. They observed that globalisation has been one of the main drivers of economic growth, which has helped to promote convergence in per capita income. Equally, Awad (2023) using economic growth as a measure of development showed that ICT penetration and economic globalisation enhances growth in SSA. The continuous-updated fully modified and bias-adjusted ordinary least squares analysis was performed by Güzel et al. (2021) to investigate the effects of globalisation and democracy on life expectancy in 16 low-income countries from 1970 to 2017. They discovered that achieving a healthier society requires economic, social, and political integration between governments and societies. They did this by using globalisation as an indicator of the partnership between countries and democracy as an indicator of accountability and cooperation between governments and societies. Kilic (2015) examined the effects of globalisation on economic growth for 74 developing nations between 1981 and 2011 using panel data analysis. The results reveal that economic growth is positively affected by economic globalisation and the causality tests indicated a uni-directional relationship between economic globalisation and economic growth. Ojeyinka and Osinub (2022) using a system GMM and Nat Saving as measure of sustainable development in Africa, concluded that political globalisation has a positive effect on sustainable development while social and economic globalisation exert a negative effect. While investigating the effect of economic globalization on economic growth within the Organisation of Islamic Countries (OIC), Samimi and Jenatabadi (2014), utilised the GMM estimator and a dynamic panel data approach. Their findings reveal that the economic dimension of globalisation enhances economic growth for the OIC economies considered. In addition, the results demonstrate that economic globalisation has a more improved effect on economic growth within economies with educated workers and better financial system. Also, Anyanwu (2014) examined factors that determine economic growth in Africa with the use of an Africa-only sample between 1996 and 2010. The outcome indicates that domestic investment, net official development and aid inflows, enhance growth in Africa. Sweidan and Elbargathi (2020) empirically noted that globalisation boost economic development in Saudi Arabia. Similarly, Hassan et al. (2019) ascertain a positive effect of globalisation on the pace of growth of the Pakistan economy. Although a great number of studies have pointed to the positive effects of globalization, in some studies, this positive effect did not extend to all of its components. Dreher (2006) found in his estimations that overall, globalization actually shows a positive effect on the economic growth of their samples, but also concludes that not all of its subdimensions seem to have the same positive or significant impact on growth. Tumwebaze and Ijjo (2015), equally investigated how the Common Market for Eastern and Southern Africa (COMESA) integration has influenced growth while employing the instrumental variables GMM regression and find no significant impact on growth within the region. They argued that the strongest drivers of growth in the COMESA region during that time were increases in capital stock, population, global GDP, and trade openness. Similarly, Dreher et al. (2008) show that globalisation often worsens inequality in OECD countries but has little to no impact on inequality in developing nations. This follows Azzimonti, De Francisco, and Quadrini (2014) who claim that globalization over the past 30 years has progressed with escalating financial liberalization and growing inequality both for developing and developed nations. Gurgul and Lach (2014) in their study also conclude that political globalization did not have a statistically significant effect on growth. Consequently, numerous studies on the relationship between development measured principally by growth and globalization, many studies such as Sachs and Warner (1995), and Edwards (1998) establish that more open economies are associated with higher levels of growth while others such as Dollar and Kraay (2004), and Dreher et al. (2008) found that globalization has a negative impact on growth and development. As observed from the empirical review, most studies have focused on examining the relationship between globalization and economic growth (Awad, 2023; Beri et al., 2022; Kılıçarslan & Dumrul, 2018; Chang & Lee, 2010; Leit˜ ao, 2012; Ali, 2013; Villaverde & Maza, 2011) with little attention to economic development, most especially in Sub Saharan Africa. Whereas, growth as a measure of development is limited to income with no consideration of health and human capital enhancement (education) as defined by the Sustainable development goals as key components of development. Secondly, the empirical approach adopted in most studies is found wanting. Most of the empirical studies employ the conventional estimation techniques like the GMM, Ordinary least square, pooled mean group and mean group, which have all been criticised in the literature recently due to their inability to account for cross-sectional dependence. Unlike the others, this study therefore brings marginal value to existing knowledge on globalisation and development by using the Human Development index as a measure of development which lays greater emphasis on human development, taking into account the quality of life, the level of human capital formation and not just the production capacity of a country. Also, the study makes use of the KOF Globalisation Index because of its level enhance components and its measurement of all kinds of foreign capital and related restrictions, including the social and political dimension of globalization more extensively than other indices. Moreover, this study uses the DCCE technique which accounts for cross sectional dependence within panels (Ditzen, 2016) as opposed to previous studies which are mostly based on traditional estimation methods. The study equally makes a distinction between the dejure and defacto measures of globalisation which have been given little or no consideration by previous studies to the best of our knowledge. 3. Data and model specification 3.1. Data This study uses the most recent data from three principal sources, which are the World Development Indicator (2020) of the World Bank group, the KOF Globalisation index database (2018) and the United Nation Development Programme (2020) data base. The panel used in this study consist of 35 1 SSA countries ranging from 1995 to 2018. The dependent variable is the human development index (HDEV) used as a proxy for development. The human development index accounts for the key dimensions of development by taking into consideration, long and healthy life, being knowledgeable and a decent standard of living. The human development index is scaled from 0 to 1, with values closer to one indicating high level of economic development. Aggregate globalisation index (GLOBGX) captures globalisation in the economic, social and political dimensions, meanwhile the de-facto globalisation index (GLOBDFX) captures actual international flows and activities, the dejure globalisation index (GLOBDJX) measures policies and conditions which in principle ease and promote activities and flows. The index scaled up the component of social, economic and political globalisation from 1 to 100, with 1 representing least globalised and 100 representing most globalised. The control variables are trade openness (export plus import, constant 2010 US dollars), external debt (XDBT) and domestic 1 Benin; Botswana; Burkina Faso; Burundi; Cameroon; Central African Republic; Chad; Comoros; Congo. Dem. Rep; Congo. Rep; Cote D’Ivoire; Eswatini; Gabon; Gambia; Ghana; Guinea; Guinea-Biseau; Kenya; Madagascar; Malawi; Mali; Mauritius; Mozambique; Namibia; Niger; Nigeria; Rwanda; Senegal; Sierra Leone; South Africa; Sudan; Tanzania; Togo; Uganda; Zimbabwe. M.-S. Glennice Fosah et al. Research in Globalization 7 (2023) 100149 4 investment (gross domestic fixed capital formation, constant 2010 US dollars). The descriptive statistics of these variables presented in Table 1 provides the average values of all the variables used in the analysis. It reveals that, the mean of human development index is 0.452 with a standard deviation of 0.099 indicating a small deviation of the observations from the mean. The mean of the aggregate globalisation index is 45.224 with small deviations from the mean of 9.385. That of the defacto globalisation index has a mean of 43.174 and a standard deviation of 10.524 while the de-jure globalisation index has an average value of 47.3 and relatively small deviations from its mean 9.557. 3.2. The model Inspired from the theoretical views of the neoliberals that globalisation benefit all actors, and in line with models proposed within extant empirical setting by different authors (O’Briens and Williams, 2004; Beri et al., 2022; Kılıçarslan & Dumrul, 2018), we adopt the following model presented in it functional form, where development (LHDEV) is in function of globalisation (LGLOBG). LHDEV =f(LGLOBG)(1) Form the functional model, we proceed to employ the dynamic common correlation procedure developed by Chudik and Pesaran (2015) and estimate two (model with aggregated globalisation and a model for disaggregated globalisation) baseline models. Based on the descriptive statistics and the quest to avoid a spurious regression, we make use of a linear-log model specification. The log linear model is adopted based on its ability to reduces outliers and equally ensure outcomes are elasticities. Further, based on the graph plots, the pattern of the graph is not linear, and therefore the use of log-linear model is appropriate to conduct the relationship of the study. The model employed is defined as LHDEVit =λ0+λ1LHDEVit−1+λ2LGLOBGXit +λ4LXit +γift+ ε it (1) Where LHDEV is the log of human development index, LGLOBGX, LGLOBDFX and LGLOBDJX are the respective log of globalization, defacto and de-jure globalization index. LX is the log of a vector of other control variables [trade openness (LOPEN), external debt (LXDBT) and domestic investment (LDINV)]. f t denotes an unobserved common factor while heterogeneous factor loadings, γ i and ξ it are the idiosyncratic error terms. Model 1 and 2 entails the study will first examine the effect of general index of globalisation on development and subsequently disaggregate globalisation into the dejura and defacto dimensions in order to gain more specific insight in their dynamics. 3.3. Estimation procedure This study used six econometric procedures: (a) cross sectional dependence (CD) test, (b) slope homogeneity test, (c) unit root analysis, (d) co-integration analyses, (e) estimation of development model, and (f) Granger causality. The primary phase of the inspection of panel data is the evaluation of CD. The validation of CD will indicate that second generation tests are more efficient and the use of estimation technique that accounts for CD more efficient. For that purpose, the Pesaran (2004) and 2015CD test were used in this study. The Pesaran and Yamagatta (2008) test of slope heterogeneity is equally used to check for slope heterogeneity since the presence of cross sectional dependence entails that most countries may be having economic development similarities (Ditzen, 2020; Chishti & Dogan, 2022). The essence of the slope heterogeneity test is to ensure amidst cross-sectional dependence, differences still exist among different economies and examine changes in one economy cannot be generalised in all economies, hence justifying a panel framework. The Pesaran CADF (cross-sectional augmented Dickey Fuller) and CIPS (Cross-section version of Im-Pesaran-Shin) unit roots tests are used to detect the order of integration of variables. The Pesaran (2007) co-integration test is equally used to examine the panel for cointegration and existence of a long run relation. The different pretest considered accounts for CD in line with current approaches in econometrics (Chishti & Patel, 2023; Chishti, Lorente, & Bulut, 2023; Chishti & Dogan, 2022). After the various pre-tests, we made use of the DCCE technique of Chudik and Pesaran (2015) to estimates our different models. This technique is chosen primarily because it accounts for cross sectional dependence within panels (Ditzen, 2016; Dinga et al., 2023a; Dinga et al., 2023b). The technique equally corrects for small sample bias within panels using different techniques within which the Jackknife correction technique used in this study. It equally supports instrumental variable estimation and allows for mean group and pooled mean group estimation (Ditzen, 2016; Dinga et al., 2023a; Dinga et al., 2023b). To Table 1 Descriptive Statistics and pairwise correlation. Variable Obs Mean Std. Dev. Min Max HDEV 840 0.452 0.099 0.228 0.728 GLOBGX 840 45.224 9.385 22.532 72.354 GLOBDFX 840 43.174 10.524 18.689 72.051 GLOBDJX 840 47.312 9.557 24.107 81.288 OPEN 840 1.707e +10 3.885e +10 1.403e +08 2.593e +11 XDBT 816 63.137 59.835 3.899 504.477 DINV 840 5.966e +09 1.400e +10 −37371138 8.667e +10 Variables (1) (2) (3) (4) (5) (6) (7) (1) HDEV 1.000 (2) GLOBGX 0.713 1.000 (3) GLOBDFX 0.674 0.940 1.000 (4) GLOBDJX 0.664 0.924 0.739 1.000 (5) OPEN 0.413 0.474 0.443 0.442 1.000 (6) XDBT −0.333 −0.370 −0.248 −0.455 −0.180 1.000 (7) DINV 0.374 0.431 0.386 0.419 0.960 −0.190 1.000 LHEDEVit =δ0+δ1LHEDEVit−1+δ2LGLOBDFXit +δ3LGLOBDJXit +δ4LXit +γift+ ε it (2) M.-S. Glennice Fosah et al. Research in Globalization 7 (2023) 100149 5 understand the nature of the link between development and the various variables under consideration, the Dumitrescu and Hurlin (2012) Granger non-causality test was used. 4. Main outcomes Table 1 presents the descriptive statistics and the pairwise correlation among the variables of this study. A mean value of 0.452, 45.224, 43.174 and 47.174 with respective standard deviations of 0.099, 9.385, 10.524 and 9.557 are reported for human development, globalization, and de-facto and de-jure globalization index respectively. This shows the dispersions of the variables of interest away from their respective mean. Table 1 equally reports the pairwise correlation coefficients. The pairwise correlation coefficients indicates high and positive correlation between development and the different measures of globalization that is 0.713, 0.674 and 0.664 respectively for the aggregate globalization index, de-facto and de-jure index. The correlation matric indicates strong correlation among the variables of interest and such gives a preliminary view of globalisation as a defining factor for the path of human development in Africa. From the correlation outcome, aggregate globalisation and it subsequent disaggregated dimensions of dejura and defacto all have a positive and strong relationship with development, with respective coefficient standing as 71.3, 67.4 and 66.4 percent respectively. Further, the fitted line graph of globalization and development equally shows a positive relationship between the two variables as shown in Fig. 1. Indicating a direct an a priori direct relationship between globalisation and human development within the selected economies of SSA. Table 2 presents the cross sectional dependence (CD) test and slope homogeneity test of our two models. The outcome of the Pesaran (2004) and Pesaran (2015) CD test indicates that the null hypothesis of no cross sectional dependence is rejected at one percent significant level for both test in both models. This indicates the presence of cross sectional dependence within the panel and shows countries degree of interdependence. From the Pesaran and Yamagatta (2008) slope heterogeneity test shown in Table 2 the null hypothesis of slope coefficients being homogeneous is rejected for both models hence confirming slope heterogeneity. With the validation of cross sectional dependence, second generational test that accounts for CD are more efficient. From Table 3, the second generation unit root test CADF and CIPS outcomes are presented. The results indicates that HDEV, LGLOBX, LGLOBDFX and GLOBDJX are integrated at level [I(0)] meanwhile, LOPEN, LXDBT and LDINV are I(1) variables. The outcome of the Westerlund (2007) second generation co -integration test presented in Table 4 indicates strong co-integration between development and globalization since the null hypothesis of no cointegration is strongly rejected for all the four test statistics. Fig. 1. Fitted line graph of development and globalization. Table 2 Cross sectional dependence test and slope heterogeneity. Cross sectional dependence test CD-test 2015 CD-test 2004 Coefficient p-value Coefficient p-value Model 1 113.330*** 0.000 104.63*** 0.000 Model 2 113.334*** 0.000 105.00*** 0.000 Slope homogeneity test Model 1 Model 1 Coefficient p-value Coefficient p-value Delta 6.618*** 0.00 6.289*** 0.00 Delta-adjusted 7.935*** 0.00 7.787*** 0.00 *** denotes 1% significant level. Table 3 Panel Unit Root test. CADF CIPS Decision Variables Coefficient Coefficient HDEV −2.665** −2.632** I(0) LGLOBGX −3.049*** −2.842*** I(0) LGLOBDFX −2.638** −2.693** I(0) LGLOBDJX −2.918*** −2.889*** I(0) LOPEN −2.173 −2.069 D(LOPEN) −3.522*** −4.353*** I(1) LXDBT −2.482 −2.61* D(LXDBT) −3.566*** −4.508*** I(1) LDINV −2.246 −2.090 D(LDINV) −3.772*** −4.569*** I(1) ***,** denotes significant levels at 1% and 5% respectively. M.-S. Glennice Fosah et al. Research in Globalization 7 (2023) 100149 6 The estimated results of our models are presented in Table 5. From the outcome of the baseline model presented in column 1, consistent with Fig. 1, a positive relationship is established between globalization and development. The outcome shows that a unit increase in globalization increases human development in the selected economies of SSA by 0.0578%. This is in conformity with the neoliberal theory which posits that trade, cross-border investment and technological innovation improve production efficiency and generate extraordinary prosperity. The results also corroborate with those of Awad (2023), Ali (2013) and Villaverde and Maza (2011) who concluded that globalization has been one of the main drivers of economic growth, thus fostering convergence in per capita income. However, the findings run counter to dependence theorists’ claims that global flows are to be blamed for unfair conditions in which resources are transferred from a perimeter of underdeveloped and poorer states to a core of wealthier ones, enriching the latter at the expense of the former. Dollar and Kraay (2004) are of the view that globalization has a negative impact on growth and development. Column 2 presents the baseline model when globalization is partitioned into the de-jure and de-facto dimensions. The results show that comparatively, both the de-facto and de-jure indicators show a positive relationship with development, but only the de-facto indicators which captures actual international flows and activities plays a significant role in fostering development within SSA countries. This is consistent with the findings of Kilic (2015) and Dreher (2006), who came to the same conclusion that building networks of connections among actors separated by intraor intercontinental distances and mediated by a variety of flows—including those of people, information and ideas, capital, and goods—has positive, significant growth outcomes. Column 3–8 indicate different specification of the two baseline models with the inclusion of other control variables. Globalization variable and the de-factor globalization variables consistently show a positive and significant results from the different specifications meanwhile the de-jure variable remains insignificant. In all the models (1–8), the F-statistic and CD test are all significant indicating goodness of fit of our results. In order to evaluate our outcome presented in Table 5 for robustness, we employ the system GMM estimation technique. The results of the System GMM estimate presented in Table 6, shows that the globalization variable remains positive and statistically significant, confirming the results previously established in Table 5. Equally, the coefficient of defacto globalization variable equally remains positive and statistically significant, confirming the results establish in Table 5. Meanwhile, the de-jure globalization variable remains positive and insignificant. The diagnostic test of the GMM result indicates that all the models are well specified. The Sargan-Hansen test does not reject the validity of instruments in all models, and the absence of second order serial correlation is also not rejected. Due to globalisation, businesses are able to produce their goods at a cheaper cost. Additionally, it heightens international competition, which lowers costs and gives customers a wider range of options. People in both developing and developed nations can live better on less money because consumers have a choice of goods at lower prices. In the SubSaharan African case, the sub-region is among the poorest in the world and both the health and educational development are lagging behind other developing regions of Asia, Europe and America. However, the sub-region is rich in natural capital and globalisation through trade Table 4 Westerlund cointegration test. LGLOBGX LOPEN LXDBT LDINV coefficient p-value coefficient p-value coefficient p-value coefficient p-value HDEV Gt −4.582*** 0.00 0.594 0.724 −0.795 0.21 −0.253 0.40 Ga −10.282*** 0.00 −10.088*** 0.00 −13.275** 0.00 −11.288*** 0.00 Pt −1.869** 0.03 2.011 0.978 −0.770 0.22 3.228 0.99 Pa −6.201*** 0.00 −3.996*** 0.00 −9.135*** 0.00 −4.215*** 0.00 **,*, respective significant level at 1% and 5%. Table 5 Estimation Outcome. DCCE-estimate Dependent variable: HDEV (1) (2) (3) (4) (5) (6) (7) (8) L.HDEV 0.9578** (0.00) 1.0278*** (0.00) 0.9728** (0.00) 0.9789*** (0.00) 0.1979*** (0.00) 0.0512 (0.24) 0.5849 (0.17) 0.0466 (0.245) LGLOBGX 0.0578*** (0.00) 0.0472*** (0.00) 0.0426** (0.01) 0.044*** (0.00) LGLOBDFX 0.0149** (0.03) 0.0129* (0.06) 0.0156** (0.04) 0.0170** (0.03) LGLOBDJX 0.0157 (0.29) 0.0056 (0.39) 0.0039 (0.80) 0.0094 (0.53) LOPEN 0.0178** (0.04) 0.0149** (0.04) 0.5910 (0.32) 0.0202** (0.02) 0.0168** (0.03) LXDBT −0.0031* (0.09) −0.0037 (0.13) −0.0019 (0.31) −0.0021 (0.29) LDINV −0.0244 (0.60) 0.0081* (0.06) constant −0.1995*** (0.00) −0.1235* (0.03) −0.1893*** (0.00) −0.1492** (0.01) −0.1578** (0.00) −0.0880 (0.10) −0.0942 (0.11) −0.1130** (0.04) F-stat/ 13880.7 (0.00) 6285.8 (0.00) 8270.9 (0.00) 6093.4 (0.00) 27.51 (0.00) 4.34 (0.00) 3.37 (0.00) 3.40 (0.00) CD-stat 8.57 (0.00) 1.80 (0.07) 8.16 (0.00) 5.54 (0.00) 4.06 (0.00) 0.97 (0.03) 1.76 (0.08) 3.71 (0.00) No obs 782 782 782 782 782 782 782 782 ***,**,* are the respective significant level at 1%, 5% and 10%. F-stat, is the Fisher statistics, CD-stat is the cross sectional dependence statistics, () are p-values and (). M.-S. Glennice Fosah et al. Research in Globalization 7 (2023) 100149 7 and financial openness gives them a comparative advantage in terms of commodity export to different destinations. In fact, rising income in other parts of the world increases demand for these commodities, boosting national economies. It also enables the transfer of technological both for environmental, social and economic development of the subregion. Finally, the results of the Dumitrescu and Hurlin (2012) Granger non-causality test presented in Table 7 indicates that, there exist a bidirectional relationship between development and globalization, development and de-facto globalization dimension, development and de-jure globalization dimension. 5. Conclusion This study had as objective to empirically evaluate the link between globalization and economic development within 35 SSA countries from 1995 to 2018. It uses the Dynamic common correlated effects (DCCE) which takes care of cross sectional dependence among cross sections, to analyse panel data of 35 SSA countries over the period 1995–2018. The KOF index has been used as indicator of globalisation (which encompasses the economic social and political dimensions of globalisation), whereas human development index has been considered as indicator of development. The findings reveal that there is a positive and significant relationship between globalization and development in SSA, more specifically for the de-facto indicators which captures actual international flows and activities. The study therefore concludes that globalisation is a blessing to SSA’s developmental outcome. Thus, African countries should remove the cross-border barriers to facilitate the sub-regional integration, which will enable them to take advantage of the numerous opportunities given by the irreversible globalization wave. In this light, the responsibility of policymakers is to embark in economic reforms that include: increased trade openness, financial market deepening and integration, and the creation of a conducive business environment at the subregional level to attract foreign direct investments, facilitate immigration of skilled labour, and technology transfer from developed nations. This will contribute to Africa’s integration into the global economy. 6. Availability of data and material Data will be made available upon request form the corresponding author. However all the data employed in the study are open access online. 7. Code availability Code will be made available upon request. CRediT authorship contribution statement Mah-Soh Glennice Fosah: Conceptualization, Investigation, Methodology. Ndam Mama: Conceptualization, Investigation, Methodology. Gildas Dohba Dinga: Conceptualization, Investigation, Methodology. Tii N. Nchofoung: Conceptualization, Investigation, Methodology. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Table 6 Robust check on estimates. System GMM Dependent variable: HDEV (9) (10) (11) (12) (13) (14) (15) (16) L.HDEV 0.9177*** (0.00) 0.9414*** (0.00) 0.9289*** (0.00) 0.9235*** (0.00) 0.9246*** (0.00) 0.9394*** (0.00) 0.9379*** (0.00) 0.9397*** (0.00) LGLOBGX 0.030** (0.01) 0.0262** (0.04) 0.0289** (0.02) 0.0284** LGLOBDFX 0.0187** (0.01) 0.0194** (0.01) 0.0208*** (0.00) 0.0199*** (0.00) LGLOBDJX 0.0036 (0.76) 0.0038 (0.76) 0.0037 (0.76) 0.0038 (0.75) LOPEN −0.0008 (0.80) −0.0008 (0.79) −0.0011 (0.72) −0.00012 (0.694) −0.0011 (0.70) LXDBT −0.0018* (0.05) −0.0018* (0.05) −0.0016* (0.06) −0.0016* (0.06) LDINV 0.0002 (0.28) 0.0001 (0.63) constant −0.0714** (0.03) −0.052* (0.08) −0.0620* (0.07) −0.070** (0.04) −0.0688** (0.04) −0.0541* (0.08) −0.0585* (0.05) −0.0564* (0.05) AR(1) 0.002 0.002 0.003 0.003 0.003 0.002 0.003 0.003 AR(2) 0.102 0.101 0.112 0.129 0.139 0.112 0.126 0.117 SH-OIR 2.484 (0.12) 1.350 (0.25) 4.461 (0.11) 3.870 (0.14) 3.898 (0.14) 1.362 (0.24) 0.923 (0.34) 0.888 (0.35) No obs 782 782 782 782 782 782 782 782 ***,**,* are the respective significant level at 1%, 5% and 10%. F-stat, is the Fisher statistics, CD-stat is the cross sectional dependence statistics, R-sq coefficient of determination, [] are p-values and () are the standard errors. Table 7 Granger non-causality test. Dumitrescu and Hurlin (2012) Granger non-causality test W-bar Z-bar p-value Decision GLOBGX gcause HDEV 4.7887 15.6213 0.000 HDEV gcause LGLOBGX 4.1613 13.0343 0.000 Yes LGLOBDFX gcause HDEV 3.1812 8.9933 0.000 Yes HDEV gcause LGLOBDFX 4.9596 16.3260 0.000 Yes GLOBDJX gcauseHDEV 19.5577 22.8211 0.000 Yes HDEV gcause GLOBDJX 1.9334 3.8484 0.000 Yes OPEN gcauseHDEV 4.7260 15.3629 0.000 Yes HDEV gcause OPEN 4.1124 12.8327 0.000 Yes XDBT gcauseHDEV 5.6359 19.1143 0.000 Yes HDEV gcause XDBT 2.5292 6.3049 0.000 Yes DINV gcauseHDEV 5.3129 17.7824 0.000 Yes HDEV gcause DINV 5.3048 17.7492 0.000 Yes M.-S. Glennice Fosah et al. Research in Globalization 7 (2023) 100149 8 Appendix References A. Ali K.S. 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