Received: 30 August 2020 | Accepted: 10 February 2021
DOI: 10.1111/1467-8268.12511
OR IG INAL ART I C L E
Trade openness, FDI, and income inequality: Evidence
from sub‐Saharan Africa
Chenghong Xu1 | Mingming Han1 | Toyo Amegnonna Marcel Dossou1 |
Festus Victor Bekun2
1School of Economics, Southwestern
University of Finance and Economics, Abstract
Sichuan, China The motivation for this study stems from the United Nations Sustainable
2Istanbul Gelisim University, Istanbul, Development Goals (UN‐SDGs) and their impact by 2030. The UN
Turkey
highlights 17 SDGs that address pertinent local and global issues, one of
Correspondence which—SDG‐10—has been devoted to reducing inequality. This study
Toyo Amegnonna Marcel Dossou, School investigates the nexus between trade openness, foreign direct investment
of Economics, Southwestern University of
Finance and Economics, No. 555 Liutai (FDI), and income inequality in sub‐Saharan Africa using panel data from
Ave, Wenjiang District, Chengdu, 2000 to 2015 and the generalized method of moment (GMM) technique
Sichuan 611130, China. approach. The findings show that FDI and income have a negative,
Email: dossoumarcel863@yahoo.fr
statistically significant relationship with income inequality, signifying that
as FDI and income per capita increase, the level of income inequality de-
creases. However, trade openness, education, political stability, corruption,
and rule of law have a positive, statistically significant relationship with
inequality. This study, therefore, offers some recommendations that will
help policymakers. First, develop good policies to attract more foreign
investors, which will contribute to creating employment opportunities in
the region. Second, create more infrastructures to provide good quality
education. Third, implement a good policy to motivate local production
which will contribute to creating jobs. Fourth, build a strong
institution(s) to fight against corruption.
1 | INTRODUCTION
The positioning of this paper builds on four points in the literature, notably: (i) challenge of inequality to the
achievement of Sustainable Development Goals (SDGs) in sub‐Saharan Africa (SSA); (ii) growing trends of foreign
direct investment (FDI) and its effects on economic growth in SSA; (iii) concerns about trade in SSA and its effects on
economic growth; and (iv) gaps in the inequality literature.
First, despite the resurgence of growth in Africa over the last two decades, inequality still remains high in the
continent (Anyanwu, 2016; Bigsten, 2016; Fosu, 2010, 2015; Kunawotor et al., 2020; Odusola, 2017). For example,
according to the United Nations Development Programme (2017), among the 19 most unequal countries in
the world, 10 are in Africa. Furthermore, a recent report by the United Nations Department of Economic and
Social Affairs (2019) argued that the SSA continent lags just behind Latin America and the Caribbean in global
inequality distribution. As documented by Bigsten (2016) and Odedokun and Round (2004), several factors de-
termine the persistence of income inequality in Africa. Among these factors, we have: (i) the highly dualistic
Afr Dev Rev. 2021;33:193–203. wileyonlinelibrary.com/journal/afdr © 2021 African Development Bank (AFD). | 193
194 | XU ET AL.
economic structure; (ii) the high concentration of physical capital, human capital, and land; (iii) structural change;
(iv) distortion of the labor market; and (v) social stratification. Moreover, Anyanwu (2016) empirically shows that
globalization (foreign direct investment and trade openness) also determines income inequality in Africa,
especially in West Africa. Recently, several studies have investigated the relationship between foreign direct
investment and income inequality (Chen, 2016; Loureiro, 2019; Mahutga & Bandelj, 2008; Wu & Hsu, 2012;) and
the nexus between trade liberalization and income inequality (Bayar & Sezgin, 2017; Brueckner &
Vespignani, 2017; Bukhari & Munir, 2016; Getachew, 2015; Jaumotte et al., 2013; Rivas, 2007; Topuz & Dağdemir,
2020; Weyl, 2016; Zakaria & Fida, 2016). To the best of our knowledge, two pieces of evidence have been supported
by these studies. First, some studies concluded that foreign direct investment and trade liberalization contribute to
exacerbating income inequality. Second, other studies support the view that foreign direct investment and trade
liberalization contribute to reducing income inequality.
Second, many studies have concluded that foreign direct investment has a positive and statistically significant
effect on economic growth in developing countries (Abdalla & Samira SidAhmed, 2018; De Gregorio &
Lee, 2002; Khaliq & Noy, 2007; Malikane & Chitambara, 2017). For example, according to the United Nations
Conference on Trade and Development (UNCTAD, 2019), in Africa, FDI flows increased by 11% to $46 billion in
2018. This increase is due to Africa's natural resources and recent discovery of oil in many countries in the
region (Aust et al., 2019). Several ways in which foreign direct investment promotes economic growth have been
identified, notably: (1) FDI provides capital; (2) FDI removes the balance‐of‐payments constraint; (3) FDI brings
technology, management and marketing skills; (4) FDI generates a competitive environment in the host country;
(5) FDI provides increased employment; (6) FDI results in higher wages; and (7) FDI promotes exports of the
host developing country.
Third, a vast literature has investigated the role of trade on economic growth and the conclusion of most studies is
that trade promotes economic growth (Awokuse, 2008; Kim, 2011; Muhammad et al., 2015; Sakyi et al., 2014, 2015;
Ula, 2015; Zahonogo, 2017). For instance, in his study, Zahonogo (2017) shows that trade openness has a positive
relationship with economic growth in Africa. Moreover, he has pointed out several channels from openness at the
country level. Notable channels include: specialization, investment in innovation, productivity improvement, or en-
hanced resource allocation.
The positive effect of foreign direct investment and trade openness on economic growth is expected to translate into
economic development (Malikane & Chitambara, 2017; Zahonogo, 2017). Thus, recent studies in the field of trade
openness and foreign direct investment explore the spill‐over of trade openness and foreign direct investment on
human capital (Intisar et al., 2020; Kheng et al., 2017; Ma et al., 2019; Su & Liu, 2016; Zhuang, 2016) and poverty (Le &
Jan, 2014; Magombeyi & Odhiambo, 2018). Unfortunately, the attendant literature has not investigated the relationship
between trade openness, FDI, and income inequality in Africa, especially in SSA.
Four, to the best of our knowledge, the extant literature on inequality in Africa has fundamentally focused on
15 main strands, notably: the determinant of income inequality (Anyanwu, 2016; Bigsten, 2016); the relationship
between economic structure, growth, and evolution of inequality and poverty (Fosu (2018); the relationship between
inequality, information and communication technology (ICT), and financial access (Tchamyou et al., 2019a); the effect
of ICT, education and lifelong learning on income inequality and economic growth (Tchamyou et al., 2019b); the nexus
between inequality, poverty and growth (Fosu, 2015); the relationship between FDI and inequality (Kaulihowa &
Adjasi, 2018); the relationship between inequality, ICT and inclusive education (Asongu et al., 2019); the linkage
between inequality, gender inclusion and ICT (Asongu & Odhiambo, 2019c); the nexus between inequality, governance
and gender economic inclusion (Odhiambo & Asongu, 2020); the relationship between education, lifelong learning,
inequality and financial access (Tchamyou, 2018); the relationship between finance, inequality and inclusive education
(Asongu et al., 2020); the effect of finance and CO2 emissions thresholds on inequality (Asongu & Vo, 2020); the
relationship between structural transformation and income inequality (Ibrahim et al., 2020) and the impact of mobile
phone penetration on African inequality (Asongu, 2015). Thus, the relationship between trade openness, FDI and
inequality is missing in contemporary African inequality literature.
The motivation behind this study can be explained as follows. Recently, in their empirical work, Asongu and
Odhiambo (2019a, 2019b, 2019c) and Zahonogo (2017) have shown that both foreign direct investment and trade
liberalization can positively affect economic growth in Africa. It is important to highlight that the level of export
diversification and foreign direct investment is increasing, according to Asongu and Odhiambo (2019a). Therefore, the
current study is trying to investigate the impact of foreign direct investment and trade on income inequality. As we
mentioned above, income inequality in Africa still remains high. However, as documented by Asongu and Odhiambo
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XU ET AL. | 195
(2019a, 2019b, 2019c), to achieve successfully the SDGs in developing countries, especially in Africa, inequality must be
targeted and reduced (Asongu & Odhiambo, 2019c). Additionally, our study adds to the inequality–FDI and economic
growth debate—Acemoglu and Robinson (2010) discussed the pertinent role of institutional quality in sustainable
economic development and also in closing the inequality gap. To this end, the current study incorporated institutional
covariates such as education, control of corruption, rule of law and political stability into the current theme. This
inclusion will help circumvent the shortcoming of model misspecification, that is, omitted variable bias. This study
draws strength from the Simeon Kuznets hypothesis that highlights the trade‐off between inequality and income level
for theoretical strength. Also, our study draws strength from the Greenwood–Jovanovic hypothesis. Both hypotheses
explain the connection between the study variables. Furthermore, the study by Kunawotor et al. (2020) lends support to
modernization theory and dependency theory, which are also in alignment with our current study.
The major contributions of this study are twofold. First, this study provides empirical evidence into the relationship
between trade openness, FDI and inequality in SSA, which has received little attention in the literature. This study
examines these relationships by extending the generalized method of moments (GMM) technique to inequality
function to include trade openness and FDI. Furthermore, to circumvent for omitted and regional specific variables, the
present study accounts for institutional variables (such as education, rule of law, control of corruption, and political
stability). It is noteworthy that credible and strong institutions will reduce the inequality gap in the region and foster
sustainable economic growth (Acemoglu & Robinson, 2010). Second, this article contributes to the literature by not
only classifying the sample countries into developing countries such as SSA, but also by focusing on major determi-
nants of inequality. Given that, our study aims to fill these important research gaps in the empirical literature and to
offer constructive policy recommendations for the SSA economies.
The remainder of this article is organized as follows. Section 2 provides a brief literature review on inequality in
Africa, and the data and methodology are presented in Section 3. Section 4 presents and discuss the findings and
Section 5 concludes with implications.
2 | LITERATURE REVIEW
In light of the fourth point discussed in the Introduction, inequality literature in Africa has fundamentally focused on
15 main strands. For example, Bigsten (2016) examines the determinants of inequality in Africa. His empirical evidence
shows that the highly dualistic economic structure, the high concentration of physical capital, human capital, and land,
the structural change, the distortion of the labor market and the social stratification explain the persistence of high
inequality in Africa. Likewise, focusing on West Africa, Anyanwu (2016) identified the main drivers of inequality for a
panel of 17 West African countries. Among them, we have foreign direct investment and trade liberalization. Similarly,
in his study, Fosu (2018) has pointed out poor governance as a determinant of high inequality in Africa. In the same
vein, Tchamyou et al. (2019b) use a sample of 48 African countries to investigate the relationship between inequality,
ICT and financial access over the period 1996–2004. Their results show that as the level of digital finance increases, the
level of inequality decreases. Likewise, Tchamyou et al. (2019a) investigate the moderating impact of ICTon the nexus
between education and inequality and economic growth in 48 African countries during the period 2004–2014. Their
following findings show that digital education could contribute to reducing inequality in Africa. By conducting a study
on the relationship between structural transformation and income inequality in Africa, Ibrahim et al. (2020) found that
structural transformation contributes to exacerbating income inequality in the selected countries. Also, the relationship
between growth, inequality and poverty has been investigated by Fosu (2015). His results showed that income growth
might be considered as the main determinant of poverty reduction in SSA. The nexus between foreign direct invest-
ment and income inequality has been examined by Kaulihowa and Adjasi (2018). Focusing on 16 African countries
over the period 1980–2013, the authors concluded that foreign direct investment contributes to reducing inequality in
the selected countries. Asongu et al. (2019) also examine the relationship between inequality, gender inclusion and ICT
for a panel set covering 42 SSA countries over the period 2004–2014. They use three inequality indicators, namely the
Gini coefficient, Atkinson index and Palma ratio. The empirical results of GMM for dynamic panel data analysis show
ICT as an important factor that could promote gender inclusion to contribute to reducing inequality. Similarly, Asongu
and Odhiambo (2019a, 2019b, 2019c) using panel data regression of a sample of 42 SSA countries from 2004 to 2014
confirm that ICT could mitigate income inequality through promoting female employment. Odhiambo and Asongu
(2020) with a sample of 42 SSA countries over the period 2004–2014, show that income inequality could be mitigated
through good governance on gender economic inclusion. Likewise, the relationship between education, lifelong
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196 | XU ET AL.
learning, inequality and financial access has been investigated by Tchamyou (2018). Focusing on 48 African countries
from 1996 to 2014, her corresponding finding shows that, first, the interaction between school enrolment and all
financial channels has a negative effect on inequality; second, the connection between lifelong learning and financial
deposits has a negative effect on income inequality; finally, the relationship between educational levels and financial
access channels has no effect on inequality. Asongu et al. (2020) also show that inequality could be reduced through the
impact of financial access on education inclusion in the region. Asongu and Vo (2020) examined the relationship
between financial development, inequality and CO2 emissions in 39 SSA countries over the period 2004–2014. Using an
interactive quantile, their corresponding findings show that financial development could unconditionally mitigate
inequality. However, the opposite result has been found when they consider the interaction between financial de-
velopment and CO2 emissions. The impact of mobile phone penetration on African inequality has been investigated by
Asongu (2015). Focusing on 52 African countries and using ordinary least squares (OLS) and two‐stage least squares
empirical strategies (2SLS), the result shows that mobile phone penetration can be used to reduce inequality.
An empirical investigation into the interaction between trade openness, FDI, and inequality has so far been largely
ignored in the region. Moreover, literature on empirical investigations into the relationship between trade openness,
FDI, and inequality using GMM techniques is virtually non‐existent. This study addresses the relationship between
trade openness, FDI, and inequality in SSA using GMM techniques. The findings will contribute to the existing
literature in this regard and will be significant to African economies and policymakers for the achievement of SDGs.
3 | DATA AND METHODOLOGY
3.1 | Data and variable description
The sample employed in the study consists of 38 SSA countries from 2000 to 2015 (Table 1). The considered sample of
countries and periodicity of this investigation are constrained by issue of availability data at the time of the research.
Several sources have been used to collect the data, notably: (i) United Nations of Development Programme for income
inequality variable; (ii) United Nations Conference on Trade and Development for foreign direct investment variable;
(iii) World Development Indicators of the World Bank for education and gross domestic product variable; and (iv)
World Governance Indicators of the World Bank for political stability, rule of law, and control of corruption.
In line with the standard inequality literature, we use the Gini coefficient to measure the inequality variable (Fang
et al., 2020; Folarin & Adeniyi, 2019; Fosu, 2015; Kaulihowa & Adjasi, 2018; Nguyen et al., 2020; Tchamyou
et al., 2019a, 2019b). As argued by Fang et al. (2020), the Gini coefficient is the best indicator—it is calculated based on
the disposable income of the selected countries. Following the study by Alvarado et al. (2017b), FDI inflows are used in
this study. In accordance with recent literature, trade openness has been used to show the relationship between Africa
and the rest of the world in terms of trade. Trade openness is the sum of import and export over gross domestic product
(Brueckner & Vespignani, 2017; Bukhari & Munir, 2016).
Based on the main cause of inequality in SSA, we consider four control variables—education, political stability, rule
of law, and control of corruption. As documented by Folarin and Adeniyi (2019), the current level of human devel-
opment in Africa is low, which results in a kind of deprivation. However, to achieve goal 10 of the SDGs, human
development must be improved. Moreover, Zhuang (2016) shows education (human capital) as a critical determinant
of FDI. The motivation for choosing these control variables is consistent with studies such as Tchamyou et al. (2019)
and Tchamyou (2018). In addition, we use GDP per capita as a proxy for economic growth, which plays an important
role in the trade openness–FDI–income inequality nexus.
3.2 | Model specification
To investigate the relationship between trade openness, FDI, and inequality, we followed the study by Khan and
Nawaz (2019). Our model, however, is different from this study: first, their study focused on the nexus between
trade openness, FDI, and inequality in the Commonwealth of Independent States; in our case we focused on SSA.
Second, their study considers school enrolment and inflation as a control variable; in our study we consider the
major determinants of inequality in the region as control variables, that is, corruption, political stability, educa-
tion, and rule of law. Moreover, we followed a theory developed by Kuznets (1955), who has shown an inverted
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XU ET AL. | 197
TABLE 1 List of sub‐Saharan African countries used for the study
1. Angola 2. Benin 3. Botswana 4. Burkina Faso 5. Burundi 6. Cabo‐Verde 7. Cameroon 8. Central African
Republic
9. Chad 10. Comoros 11. Congo 12. D.R. of the 13. Ethiopia 14. Gabon 15. Ghana 16. Guinea
Congo
17. Guinea 18. Côte d'Ivoire 19. Kenya 20. Lesotho 21. Liberia 22. Madagascar 23. Malawi 24. Mali
Bissau
25. Mauritania 26. Mozambique 27. Namibia 28. Niger 29. Nigeria 30. Rwanda 31. Senegal 32. Seychelles
33. Sierra 34. South Africa 35. Tanzania 36. Togo 37. Uganda 38. Zambia
Leone
U‐shaped relationship between economic development and income inequality. Thus, we arrived at a trade
openness–FDI–inequality equation as follows:
inequalityi,t = σ0 + σ1inequalityi,t−1 + σ1TOPi,t + σ2FDIi,t + σ3 incomei,t + σ4 income 2i,t + σ5Xi,t + +ηi + ξt + εi,t (1)
where inequalityi,t depicts an indicator of income inequality (i.e. Gini coefficient) of period i in period t, σ0 is a constant,
TOPi,t represents trade openness, FDI denotes foreign direct investment inflows, incomei,t represents the GDP per
capita, Xi,t is vector of control variables (education, rule of law, control of corruption, and political stability), ξt is the
time‐specific constant, ηi is the country‐specific effect, and εi,t the error term.
3.3 | Methodology
Based on the nature of our data and the form of our equation, in this study we employed GMM techniques. It is based
on Roodman (2009b, 2009a), which is an extension of Arellano and Bover (1995). We choose GMM for three main
reasons, first, GMM is suited to assess whether the previous level of inequality is related to the present level of
inequality, which explains the persistence of inequality in SSA region. Second, to solve the endogeneity problem, the
GMM approach is well suited. Third, the number of observations is higher than the number of periods of each country;
in this study, there are 16 periods (i.e. 2000–2015) for 38 countries. Following the study by Asongu and Odhiambo
(2020), we choose the two‐step procedure of the GMM technique with robust standard errors to ensure and control
heteroskedasticity issues. It is important to highlight that the system GMM technique approach has been used in
several inequality studies (Tchamyou et al., 2019a; Tchamyou, 2018).
Table 2 provides summary statistics and Table 3 presents the correlation matrix analysis. As shown in Table 2, the
mean value of the Gini coefficient is 46.28. This indicates that inequality (Gini coefficient) is relatively higher in the
selected African countries between 2000 and 2015. This result is consistent with recent empirical studies (Kunawotor
et al., 2020; Shimeles & Nabassaga, 2018), which reveal that 6 out of the 10 most unequal countries were in SSA, with
South Africa being the highest, having a Gini coefficient of about 0.7 according to the World Bank. The standard
deviation of Gini coefficient is 7%, which is similar to Osabuohien and Bowale (2017) who studied the role of
Institutions in the finance–inequality nexus in SSA. The high standard deviation suggests that the level of income
inequality in Africa is dispersed. This is further supported by the wide difference between the minimum and the
maximum value of the Gini coefficient.
From the correlation analysis presented in Table 3, the results show that there is a positive correlation between
foreign direct investment and economic growth across the region. This corroborates the findings of Alvarado et al.
(2017a) and Iamsiraroj and Ali (2015), who have studied the effects of FDI on economic growth. Noticeable results
from the correlation matrix analysis have shown that FDI could contribute to reduce income inequality in the region.
However, trade openness, education, political stability, and control of corruption could be sources of rising inequality
in the region.
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198 | XU ET AL.
4 | EMPIRICAL RESULTS AND DISCUSSION
Table 4 presents findings corresponding to the relationship between trade openness, FDI, and inequality. The per-
sistence of inequality measured by the lagged value of the Gini coefficient has a positive and statistically significant
effect, implying a high level of inequality in SSA. This is consistent with studies such as Adeleye et al. (2017), who have
investigated the moderating role of institutions on the finance–inequality nexus in SSA. Similarly, our finding is in line
with Asongu and Odhiambo (2019b) who examine the influence of information and communication technology on
income inequality. It is important to note that these studies have used the system GMM (sys‐GMM) as their estimation
techniques. The finding shows that income has a negative, and statistically significant effect, meaning that an increase
in income per capita could contribute to reduce income inequality. This corroborates the findings of Fosu (2015) who
emphasized the role of increasing income for reducing inequality. However, the results show that the square of GDP
has no effect on income inequality, showing that the Kuznets hypothesis is not valid in our study. This finding is
consistent with the finding of Canh et al. (2020) who examined the determinants of income inequality through Kuznets
theory. It is important to note that our findings contradict Le et al. (2020) who examined the influence of export
diversification on income inequality in 90 countries over the period 2002–2014 and revealed the presence of Kuznets
theory.
Similarly, the estimated coefficient of FDI is negative and statistically significant at 10%. This means that
increasing the level of FDI could contribute to reducing inequality in the region. This is in line with previous
TABLE 2 Descriptive statistics
Variable Obs. Mean SD Min. Max.
Gini index 624 46.28 7.07767 34.40 70.27
FDI 622 5.02 9.25 −5.20 103.33
Trade openness 601 4.34 0.941 3.06 9.42
Income 624 7.70 1.13 3.04 10.09
Education 400 0.90 0.124 0.562 1.07795
Rule of law 624 −0.655 0.623 −2.01 1.078
Control of corruption 624 −0.57 0.617 −1.552 1.22
Political stability 624 −0.483 0.890 −2.47 1.2821
Note: Trade openness and Income are in natural logarithm. Max.: maximum; Min.: minimum; Obs.: observation.
TABLE 3 Correlation matrix analysis
Gini FDI TOP Income E RL CC PS
Gini 1
FDI −0.0200* 1
TOP 0.513*** −0.0725 1
Income 0.0640 0.0755*** 0.264*** 1
E 0.175*** 0.0605 0.185*** 0.432*** 1
RL 0.226*** 0.00437 0.241*** 0.578*** 0.549*** 1
CC 0.284*** 0.0420 0.236*** 0.484*** 0.594*** 0.840*** 1
PS 0.174*** 0.0616 0.0840 0.529*** 0.407*** 0.779*** 0.714*** 1
Note: Trade openness and Income are in natural logarithm. CC: control of corruption; E: education; FDI: foreign direct investment; GDP: gross domestic
product; PS: political stability; TOP: trade openness; RL: rule of law.
*p< .1.
**p< .05.
***p< .01.
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XU ET AL. | 199
TABLE 4 Trade openness, FDI and income inequality (two‐step GMM with standard error)
(1) (2) (3) (4) (5)
Inequality (−1) 0.998*** 0.997*** 0.993*** 0.994*** 0.992***
(0.000) (0.000) (0.000) (0.000) (0.000)
FDI −0.00155*** −0.00192*** −0.00207*** −0.00182*** −0.00189***
(0.002) (0.000) (0.001) (0.007) (0.002)
TOP 0.00443*** 0.00343*** 0.00442* 0.00139* 0.00238**
(0.000) (0.000) (0.044) (0.032) (0.044)
Income −0.00307*** −0.00409** −0.00230*** −0.00223** −0.00172***
(0.002) (0.001) (0.004) (0.002) (0.004)
Income2 0.00409 0.0283 0.0654 0.0262 0.0507
(0.324) (0.145) (0.202) (0.108) (0.314)
E 0.0127*
(0.043)
RL 0.0154**
(0.045)
CC 0.00229*
(0.055)
PS 0.00235*
(0.028)
Constant −0.0324*** −0.0120* −0.0365** −0.0448** −0.0518**
(0.007) (0.052) (0.017) (0.035) (0.041)
AR (1) p value 0.001 0.003 0.007 0.001 0.004
AR (2) p value 0.742 0.747 0.749 0.765 0.772
Hansen test: p value 0.772 0.703 0.664 0.692 0.629
Wald test 999.14*** 954.65*** 1665.90*** 1654.26*** 1668.15***
Instrument/groups 21/36 21/33 21/36 21/36 21/36
No. of obs. 563 563 563 563 563
Note: Values in parentheses are the p values.
CC: control of corruption; E: education; FDI: foreign direct investment; obs.: observations; PS: political stability; TOP: trade openness; RL: rule of law.
*p< .1.
**p< .05.
***p< .01.
studies (Jensen & Rosas, 2007; Kaulihowa & Adjasi, 2018; Sharma & Abekah, 2017) that found that FDI con-
tributes to reducing income inequality. Moreover, they point out that most FDI in the region goes to the agri-
culture sector, which is able to employ low‐skilled labor. However, the estimated coefficient of trade openness has
a positive impact on inequality, meaning that as the level of trade openness increases, the level of inequality also
increases. The result confirms that most African countries export raw material and are unable to manufacture
their own products. Moreover, most African countries import more than they export. Exporting from abroad
means creating jobs for other countries. So that is why trade contributes to exacerbating inequality in SSA.
Likewise, education also contributes to exacerbating inequality in the region. Our findings contradict the findings
of Kunawotor et al. (2020) who examined the determinants of income inequality in Africa and found that edu-
cation can help to reduce income inequality. Such contradiction can be attributed to the variables, sample,
estimation techniques, and the period used by different studies.
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200 | XU ET AL.
Moreover, the finding shows that there is a positive relationship between political stability and inequality, meaning
that political stability contributes to increased inequality in the region. The result confirms the position of Nguyen et al.
(2020) who conclude that a better institution may increase income inequality. Furthermore, rule of law and corruption
contribute to increase inequality.
The diagnostic test for the study shows that the Wald test of all models is significant at 1%, indicating that the
findings of our study are valid. And the recommendation issued from this study could be used anywhere. Moreover, by
considering the Arellano and Bond test for autocorrelation for the result presented in Table 4, the result does not suffer
any serial order autocorrelation problem. In addition, the Hansen test of over‐identification restrictions shows that the
instruments used were robust.
5 | CONCLUSION AND RECOMMENDATIONS
This study investigates the nexus between trade openness, foreign direct investment and income inequality in SSA,
using panel data from 2000 to 2015. To achieve these objectives, which are in line with SDGs, we apply the GMM
techniques approach. Our findings show that FDI and income have a negative and statistically significant relationship
with inequality; as FDI and income per capita increase, the level of inequality decreases. However, trade openness,
education, political stability, corruption and rule of law have a positive, statistically significant relationship with
inequality; as trade openness, education, political stability, corruption and rule of law increase, the level of inequality
also increases. This study, therefore, provides some recommendations that will help policymakers. First, provide some
good policies to attract more foreign investors, which constitutes a source to create job opportunities. Second, create
more infrastructures to provide good quality education. Third, implement a good policy to motivate local production
which will contribute to creating jobs. Fourth, build a strong institution(s) to fight against corruption.
CONFLICT OF INTERESTS
The authors declare that there are no conflict of interests.
ORCID
Toyo Amegnonna Marcel Dossou http://orcid.org/0000-0002-8296-9477
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How to cite this article: Xu, C., Han, M., Dossou, T. A. M., & Bekun, F. V. Trade openness, FDI, and income
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