Be nice to thy neighbors: Spatial impact of foreign direct investment on poverty in Africa

Arogundade, Sodiq; Biyase, Mduduzi; Bila, Santos Article Be nice to thy neighbors: Spatial impact of foreign direct investment on poverty in Africa Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Arogundade, Sodiq; Biyase, Mduduzi; Bila, Santos (2022) : Be nice to thy neighbors: Spatial impact of foreign direct investment on poverty in Africa, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 10, Iss. 6, pp. 1-20, https://doi.org/10.3390/economies10060128 This Version is available at: https://hdl.handle.net/10419/328428 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Arogundade, Sodiq, Mduduzi Biyase, and Santos Bila. 2022. Be Nice to Thy Neighbors: Spatial Impact of Foreign Direct Investment on Poverty in Africa. Economies 10: 128. https://doi.org/ 10.3390/economies10060128 Academic Editor: Sanzidur Rahman Received: 10 April 2022 Accepted: 13 May 2022 Published: 1 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Be Nice to Thy Neighbors: Spatial Impact of Foreign Direct Investment on Poverty in Africa Sodiq Arogundade *, Mduduzi Biyase and Santos Bila College of Business and Economics, Auckland Park Kingsway Campus, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 9020, South Africa; [email protected] (M.B.); [email protected] (S.B.) *Correspondence: [email protected] Abstract: This study examines the spatial impact of FDI on the poverty of 44 African countries. In achieving this, the study uses the Driscoll–Kraay fixed effect instrumental variable regression, the instrumental variable generalized method of moments estimator (IV-GMM), and the spatial Durbin model. The empirical investigation of this study yielded four significant findings: (1) neighboring countries’ FDIs have a positive and significant impact on the incidence and intensity of the host country’s poverty, (2) improved institutional quality in neighboring countries has a significant impact on the FDI–poverty reduction nexus of the host country, (3) the empirical results lend support for a significant spatial spillover of poverty in the region, (4) the marginal effect results indicate that countries within the region are no longer in isolation or independent, i.e., the level of poverty in a particular country is influenced by its determinants in the neighboring country. This result is robust to the alternative proximity matrix, which is the inverse distance. Since there is spatial interdependence among African countries, we recommend that African governments, through the African Union (AU), should not only champion the institutional reform in the region, but also establish a binding mechanism to ensure reform implementation. Keywords: FDI; Driscoll–Kraay fixed effect instrumental variable regression; IV-GMM; spatial Durbin model; poverty; institutional quality; Africa 1. Introduction The United Nations’ Sustainable Development Goals (SDGs) outline seventeen goals that developing nations must meet by 2030. The achievement of these goals will aid in reducing income inequality, poverty alleviation, and the advancement of human development. However, progress made towards these goals is uneven, with some countries meeting the majority of them while others failing to fulfil any of them. Similarly, most African countries are off-track in meeting these targets and require significant foreign capital to achieve these goals (Sustainable Development Goals (SDG) 2019). In achieving these SDG goals, the importance of foreign direct investment (FDI) cannot be underestimated. This is because of its potential in transferring knowledge and technology, enhancing competition, boosting entrepreneurship and productivity, and increasing government revenue through taxes paid by foreign investors (United Nations 2003). Many developing economies, particularly in Africa, have adopted FDI promotion policies as a result of the importance of FDI as a major source of external finance. In 2017, at least 126 investment policy actions and reforms were undertaken by about 65 economies around the world. These reforms include simplifying administrative investment procedures, liberalization of domestic markets, and establishing new special economic zones (SEZs) (for a complete description of these measures, see the 2018 World Investment Report). This has resulted in a massive increase in FDI flow to Africa, which has increased from $59.99 billion in 1990 to $942.05 billion in 2019. (UNCTAD Statistics 2020) Nonetheless, despite a significant rise in FDI inflows, poverty in the region continues to worsen. Figure 1 Economies 2022,10, 128. https://doi.org/10.3390/economies10060128 https://www.mdpi.com/journal/economies Economies 2022,10, 128 2 of 20 shows that the number of people living in extreme poverty increased from 278 million in 1990 to 437 million in 2018 (World Bank 2019). According to the World Bank, extreme poverty will become a largely African problem in the following decade, with the region accounting for the lion’s share of the world’s impoverished by 2030. While extreme poverty is prevalent in the region, nearly half of Africa’s poor people live in just five countries: Nigeria (79 million), the Democratic Republic of Congo (60 million), Tanzania (28 million), Ethiopia (26 million), and Madagascar (20 million). These statistics become even more worrisome when compared to the level of extreme poverty in other regions (Schoch and Lakner 2020). Economies 2022, 10, x 2 of 21 This has resulted in a massive increase in FDI flow to Africa, which has increased from $59.99 billion in 1990 to $942.05 billion in 2019. (UNCTAD Statistics 2020) Nonetheless, despite a significant rise in FDI inflows, poverty in the region continues to worsen. Figure 1 shows that the number of people living in extreme poverty increased from 278 million in 1990 to 437 million in 2018 (World Bank 2019). According to the World Bank, extreme poverty will become a largely African problem in the following decade, with the region accounting for the lion’s share of the world’s impoverished by 2030. While extreme poverty is prevalent in the region, nearly half of Africa’s poor people live in just five countries: Nigeria (79 million), the Democratic Republic of Congo (60 million), Tanzania (28 million), Ethiopia (26 million), and Madagascar (20 million). These statistics become even more worrisome when compared to the level of extreme poverty in other regions (Schoch and Lakner 2020). Figure 1. Poverty in Sub-Saharan Africa. Source: World Bank (2019). However, the theoretical underpinnings of the FDI–poverty nexus are far from being conclusive. For example, advocates of FDI (Mankiw et al. 1992; Hansen and Rand 2006), Soumare (2015); Bharadwaj (2014); Fowowe and Shuaibu (2014) take the view that FDI is welfare-enhancing/reduces poverty. The most obvious link through which FDI affects poverty is through job creation in the host countries (Gohou and Soumare 2012). Beyond that, FDI also delivers a much-warranted transfer of valuable technology and know-how (Javorcik 2015). This positive view of FDI is not universally shared among important scholars in this field (Arabyat 2017; Rye 2016; Gohou and Soumare 2012). One of the most forceful non-proponents of this view is Stiglitz (2002), who argues that FDI is likely to be affected by market imperfections and unequal bargaining power that may elevate inequality and impede welfare enhancement strategies. An emerging strand of literature argues that the extent to which FDI is welfare-en- hancing depends very much on initial conditions or certain circumstances in the host country’s ‘quality institutions and a functioning financial system’ (Arogundade et al. (2021), Yeboua (2020), Jude and Levieuge (2017), and Agbloyor et al. (2016), Lehnert et al. (2013) and Cleeve (2012), Fowowe and Shuaibu (2014). What this means is that host countries with relatively good institutions and developed financial systems are likely to experience more welfare-enhancing effect of FDI compared to nations with poor institutions and less-developed financial systems. Reaching a similar conclusion, the theoretical framework of Chenery and Stout (1966) also provides the theoretical foundation for the 0 200 400 600 800 1000 1200 1400 1600 1800 2000 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 People in Extreme Poverty 'Million Africa South Asia ROW Latin America & Carribean Europe and Central Asia East Asia and Pacific Figure 1. Poverty in Sub-Saharan Africa. Source: World World Bank (2019). However, the theoretical underpinnings of the FDI–poverty nexus are far from being conclusive. For example, advocates of FDI (Mankiw et al. 1992;Hansen and Rand 2006), Soumare (2015); Bharadwaj (2014); Fowowe and Shuaibu (2014) take the view that FDI is welfare-enhancing/reduces poverty. The most obvious link through which FDI affects poverty is through job creation in the host countries (Gohou and Soumare 2012). Beyond that, FDI also delivers a much-warranted transfer of valuable technology and know-how (Javorcik 2015). This positive view of FDI is not universally shared among important scholars in this field (Arabyat 2017;Rye 2016;Gohou and Soumare 2012). One of the most forceful non-proponents of this view is Stiglitz (2002), who argues that FDI is likely to be affected by market imperfections and unequal bargaining power that may elevate inequality and impede welfare enhancement strategies. An emerging strand of literature argues that the extent to which FDI is welfareenhancing depends very much on initial conditions or certain circumstances in the host country’s ‘quality institutions and a functioning financial system’ (Arogundade et al. (2021), Yeboua (2020), Jude and Levieuge (2017), and Agbloyor et al. (2016), Lehnert et al. (2013) and Cleeve (2012), Fowowe and Shuaibu (2014). What this means is that host countries with relatively good institutions and developed financial systems are likely to experience more welfare-enhancing effect of FDI compared to nations with poor institutions and less-developed financial systems. Reaching a similar conclusion, the theoretical framework of Chenery and Stout (1966) also provides the theoretical foundation for the importance of external capital flows for low-income countries. In an economy characterized by savings or foreign exchange gaps, the model claims that external finance can play a crucial role in boosting domestic resources. Economies 2022,10, 128 3 of 20 In an IMF paper entitled, “Why Does FDI Go Where it Goes? New Evidence From the Transition Economies”, Campos and Yuko (2003) identify key factors that are crucial in attracting FDI. They write “countries with a large market, low-cost labor, abundant natural resources, and close proximity to the major Western markets would attract large amounts of FDI inflows. FDI would thus go to countries with favourable initial conditions.” Derived from these theoretical augments, the broad hypotheses of our study are that (1) neighboring countries’ FDIs have a significant positive impact on the incidence and intensity of a host country’s poverty; (2) improved institutional quality in neighboring countries has a significant impact on the FDI–poverty reduction nexus of a host country. The non-uniformity of the theoretical arguments highlights the need for more empirical research to further verify if FDI is welfare-enhancing or not. In fact, a brief review of empirical studies on the FDI–poverty nexus proved to be rather ambiguous. While some studies suggest that FDI has a positive impact on poverty reduction (Sukhadolets et al. 2021;Sikandar et al. 2021;Magombeyi and Odhiambo 2018;Gani´c 2019), others suggest the opposite: a negative impact (Ali et al. 2009). For example, Sukhadolets et al. (2021) studied the relationship between GDP, FDI, investment in construction, and poverty. The authors compared a number of countries such as the Russian Federation, including a sample of developed and developing nations. Using non-linear autoregressive distributed lag, the study found that investment in construction stimulates the economies of countries in the long term and maintains or reduces the poverty level by increasing the assets of the population. Using pool mean group estimation techniques, Sikandar et al. (2021) investigated the impacts of six types of foreign capital inflows on the parameters of poverty reduction and agriculture development in the short-term and long-term perspectives across fourteen developing economies of Latin America, Asia, and Eastern Europe. The study found evidence to suggest that poverty reduction could be positively affected by an increase in the values of agricultural exports, foreign direct investment, foreign development assistance, and remittances received from migrant workers. In sharp contrast, using the autoregressive distributed lag (ARDL) model, Ali et al. (2009) found that FDI increased poverty in Pakistan both in the long-run and short-run. The reason for the conflicting results on the impact of FDI on poverty is that majority of these studies neglect the importance of space in their model. Ignoring the importance of spatial interdependence in regional empirical studies may result in either inefficient or biased estimates (Anselin 2009). While the application of spatial econometrics to FDI literature is still at the embryonic stage, some empirical studies have taken into account the role of third-country effects: Gutiérrez-Portilla et al. (2019) for Spain; Do et al. (2021) for Vietnam; Madariaga and Poncet (2007) for China; Uttama (2015) for Southeast Asia. These studies conclude that FDI in neighboring countries significantly influences the host country’s economy. To the best of this authors’ knowledge, this study is not aware of any literature that has specifically examined the spatial impact of FDI on poverty in Africa. The closest attempt is that of Chih et al. (2021). However, this study failed to account for the role of neighboring countries’ institutional quality on the nexus between FDI and the economy. The study also assumes that what is good for economic growth is also good for the poor. Since economic growth does not imply a reduction in poverty, it is essential we examine: (1) whether neighboring countries’ FDI matters on poverty reduction of the host country, (2) examine the spatial impact of institutional quality on the FDI–poverty nexus in Africa, and (3) determine whether there is a spatial spillover of poverty in Africa. Doing this study for Africa is vital for the following reasons: (1) the region is challenged with poor welfare, and often termed the world capital of poverty. (2) The positive spillover of FDI in the region may be hampered by a poor institutional environment. As a result, attracting multinational corporations to invest in these conditions may not produce the desired benefits, as investment flourishes in a competitive atmosphere. (3) Since countries in this region belong to a regional organization designed to encourage mutual economic Economies 2022,10, 128 4 of 20 development among member countries, the level of economic activity in one member state may influence economic activity of another country. The empirical findings from the spatial Durbin model indicate a significant spatial spillover of FDI on the incidence and intensity of poverty in Africa. The results further provide support for the significant role of institutional quality on the nexus between FDI and poverty reduction. Furthermore, the marginal effect results suggest that countries in Africa are not in isolation, i.e., the level of poverty in a particular country is influenced by its determinants in the neighboring country. This calls for a coordinated policy toward eradicating poverty and establishing institutional reform. The rest of this paper is structured as follows: Section 2provides stylized facts on FDI and the spatial pattern of poverty in Africa. Section 3houses the methodology and estimation techniques. The presentation and discussion of the empirical results is discussed in Section 4, while Section 5concludes and provides critical policy implications. 2. African Poverty in Space This section provides key stylized facts that motivate this study. Figure 2presents the contour map of headcount poverty across the 47 selected African countries. The map displays spatial clustering of poverty in 1996 and 2019. As shown in the map, there is evidence of higher clustering of poverty in 2019 compared to 1996. Economies 2022, 10, x 4 of 21 result, attracting multinational corporations to invest in these conditions may not produce the desired benefits, as investment flourishes in a competitive atmosphere. (3) Since countries in this region belong to a regional organization designed to encourage mutual economic development among member countries, the level of economic activity in one member state may influence economic activity of another country. The empirical findings from the spatial Durbin model indicate a significant spatial spillover of FDI on the incidence and intensity of poverty in Africa. The results further provide support for the significant role of institutional quality on the nexus between FDI and poverty reduction. Furthermore, the marginal effect results suggest that countries in Africa are not in isolation, i.e., the level of poverty in a particular country is influenced by its determinants in the neighboring country. This calls for a coordinated policy toward eradicating poverty and establishing institutional reform. The rest of this paper is structured as follows: Section 2 provides stylized facts on FDI and the spatial pattern of poverty in Africa. Section 3 houses the methodology and estimation techniques. The presentation and discussion of the empirical results is discussed in Section 4, while Section 5 concludes and provides critical policy implications. 2. African Poverty in Space This section provides key stylized facts that motivate this study. Figure 2 presents the contour map of headcount poverty across the 47 selected African countries. The map displays spatial clustering of poverty in 1996 and 2019. As shown in the map, there is evidence of higher clustering of poverty in 2019 compared to 1996. Figure 2. Spatial pattern of poverty in Africa. Source: Authors’ calculation using data from the World Bank database. The plots also show that countries such as the Democratic Republic of Congo, Central Africa Republic, Congo Republic, Angola, Zambia, Malawi, Mozambique, Rwanda, Burundi, and Madagascar are epicenters of poverty incidence in Africa in 2019, while countries such as Botswana, Gabon, Ghana, Mauritania, Algeria, Morocco, Tunisia, and Egypt have low poverty rates. In determining whether the incidence of poverty in one country influences the poverty incidence of other proximate countries, this study conducted local Figure 2. Spatial pattern of poverty in Africa. Source: Authors’ calculation using data from the World Bank database. The plots also show that countries such as the Democratic Republic of Congo, Central Africa Republic, Congo Republic, Angola, Zambia, Malawi, Mozambique, Rwanda, Burundi, and Madagascar are epicenters of poverty incidence in Africa in 2019, while countries such as Botswana, Gabon, Ghana, Mauritania, Algeria, Morocco, Tunisia, and Egypt have low poverty rates. In determining whether the incidence of poverty in one country influences the poverty incidence of other proximate countries, this study conducted local and global spatial autocorrelation tests. The former test, which is based on a specific Moran’s I statistic, identifies local “hot spots,” or in other words, the countries where strong spatial correlations exist. The latter test is based on the Moran’s (1950) I spatial Economies 2022,10, 128 5 of 20 autocorrelation statistic; this test determines whether poverty incidence globally observed depends on geographical distribution. The null hypotheses of these tests suggest that poverty incidence in different countries is considered to be spatially independent. The p-value of the global autocorrelation test is significant, indicating the existence of spatial dependence (see Appendix ATable A1). Similarly, the local indicators of spatial association (LISA) test identifies countries with strong spatial correlations in poverty incidence (see Appendix ATable A2 for more). In addition to this, Figure 3presents the univariate Global Moran’s I statistic calculated from headcount poverty over the period of 1996 to 2019 for each country. Economies 2022, 10, x 5 of 21 and global spatial autocorrelation tests. The former test, which is based on a specific Moran’s I statistic, identifies local “hot spots,” or in other words, the countries where strong spatial correlations exist. The latter test is based on the Moran’s (1950) I spatial autocorrelation statistic; this test determines whether poverty incidence globally observed depends on geographical distribution. The null hypotheses of these tests suggest that poverty incidence in different countries is considered to be spatially independent. The p-value of the global autocorrelation test is significant, indicating the existence of spatial dependence (see Appendix A Table A1). Similarly, the local indicators of spatial association (LISA) test identifies countries with strong spatial correlations in poverty incidence (see Appendix A Table A2 for more). In addition to this, Figure 3 presents the univariate Global Moran’s I statistic calculated from headcount poverty over the period of 1996 to 2019 for each country. Figure 3. Global Moran’s I spatial autocorrelation statistic. Source: Author’s computation using data from the World Bank database. Quadrant A in Figure 3 are countries with negative spatial clustering of low poverty incidence, while B indicates countries with positive spatial clustering of high poverty incidence, C indicates positive spatial cluster of countries with low poverty incidence, and D is a negative spatial cluster of high poverty incidence. The Moran’s I correlation test, which indicates the degree of spatial autocorrelation, suggests a positive spatial clustering of poverty incidence Error! Reference source not found. . Thus, we can conclude that there is spatial dependence of poverty incidence across African countries from 1996 to 2019. This evidence provides an impetus for the inclusion of space in this study. Figure 4 shows a scatter plot of FDI and the incidence of poverty in Africa. The plot reveals that countries with relatively high FDI inflows are characterized with high incidence of poverty. However, countries with low FDI inflows are associated with low poverty rate. This indicates that the level of a countries’ foreign investment is positively correlated with the poverty rate. This is also consistent with the argument of Rye (2016) and Arabyat (2017), who argue that foreign investment increases poverty due to its crowd-out effect on domestic capital. This conjecture is perhaps meaningless and lacks objectivity if not subjected to empirical verification. A B C D Figure 3. Global Moran’s I spatial autocorrelation statistic. Source: Author’s computation using data from the World Bank database. Quadrant A in Figure 3are countries with negative spatial clustering of low poverty incidence, while B indicates countries with positive spatial clustering of high poverty incidence, C indicates positive spatial cluster of countries with low poverty incidence, and D is a negative spatial cluster of high poverty incidence. The Moran’s I correlation test, which indicates the degree of spatial autocorrelation, suggests a positive spatial clustering of poverty incidence. 1 Thus, we can conclude that there is spatial dependence of poverty incidence across African countries from 1996 to 2019. This evidence provides an impetus for the inclusion of space in this study. Figure 4shows a scatter plot of FDI and the incidence of poverty in Africa. The plot reveals that countries with relatively high FDI inflows are characterized with high incidence of poverty. However, countries with low FDI inflows are associated with low poverty rate. This indicates that the level of a countries’ foreign investment is positively correlated with the poverty rate. This is also consistent with the argument of Rye (2016) and Arabyat (2017), who argue that foreign investment increases poverty due to its crowd-out effect on domestic capital. This conjecture is perhaps meaningless and lacks objectivity if not subjected to empirical verification. The scatter plot of the institution quality 2 and poverty rate is presented in Figure 5. The figure reveals that countries with relatively high poverty incidence are characterized by poor institutional frameworks. However, countries with a robust institutional framework have relatively low poverty rate. Countries with sound institutions, such as efficient and good governance, low corruption, rule of law, and property rights, tend to improve the process of technology spillovers to local enterprises. On the other hand, countries with weak institutions may prevent indigenous enterprises from benefiting from multinational corporation (MNC) knowledge and technology spillovers (Agbloyor et al. 2016;Brahim and Rachdi 2014). Hence, the impact of FDI on poverty reduction is expected to vary between countries and regions with varying level of institutional quality. Economies 2022,10, 128 6 of 20 Economies 2022, 10, x 6 of 21 Figure 4. Scatter plot of FDI and poverty in Africa. Source: Authors’ computation from World Bank PovcalNet and UNCTAD database. The scatter plot of the institution quality 1 and poverty rate is presented in Figure 5. The figure reveals that countries with relatively high poverty incidence are characterized by poor institutional frameworks. However, countries with a robust institutional framework have relatively low poverty rate. Countries with sound institutions, such as efficient and good governance, low corruption, rule of law, and property rights, tend to improve the process of technology spillovers to local enterprises. On the other hand, countries with weak institutions may prevent indigenous enterprises from benefiting from multinational corporation (MNC) knowledge and technology spillovers (Agbloyor et al. 2016; Brahim and Rachidi 2014). Hence, the impact of FDI on poverty reduction is expected to vary between countries and regions with varying level of institutional quality. Figure 4. Scatter plot of FDI and poverty in Africa. Source: Authors’ computation from World Bank PovcalNet and UNCTAD database. Economies 2022, 10, x 7 of 21 Figure 5. Scatter plot of institutional quality and poverty in Africa. Source: Author’s computation based on World Governance Indicator and World Bank Povcal Database (World Bank 2019). 3. Data and Methodology 3.1. Data This study employs a panel dataset of 44 African countries, with annual data over the period of 1996–2019. The choice of period and countries (see Appendix A Table A1) were dictated by the availability of data. In this study’s analysis, we follow Gnangnon (2020), Agarwal et al. (2017), and Perera and Lee ( 2015 ) by using headcount ratio, which is a measure of the incidence of poverty and poverty gap index, which measures the intensity of poverty. Both headcount and poverty gap indexes are measured using the international poverty line of $1.90 per day. This study follows Arogundade et al. (2022) and Nunnenkamp (2004) by measuring FDI as FDI inward stock as a percentage of GDP. The problem of endogeneity biases linked with the FDI–welfare nexus is also mitigated by using FDI stock (Nunnenkamp 2004). We used the International Monetary Fund’s newly constructed aggregate financial development index to measure financial development. Other metrics such as credit to the private sector, stock market capitalization, and monetary aggregates have flaws as they do not capture the financial system’s multidimensionality, which is why this index was created. See Arogundade et al. (2021) for a similar approach. Institutional quality is measured using the average of the six indicators (voice and accountability, the rule of law, regulatory quality, control of corruption, government effectiveness, and political stability). These indexes range from 0 (weak) to 100 (strong). See Siriopoulos et al. (2021), Peres et al. (2018), Utesch-Xiong and Kambhampati (2021), and Ajide and Raheem (2016) for a similar approach. In measuring infrastructure, we used mobile telephone subscribers (per 100 people). We use the growth rate of GDP per capita as a proxy for economic growth and the total active labor force as a proxy for labor, as Kaulihowa (2017) suggests. Descriptive Statistics of the Variables Figure 5. Scatter plot of institutional quality and poverty in Africa. Source: Author’s computation based on World Governance Indicator and World Bank Povcal Database (World Bank 2019). 3. Data and Methodology 3.1. Data This study employs a panel dataset of 44 African countries, with annual data over the period of 1996–2019. The choice of period and countries (see Appendix ATable A1) were dictated by the availability of data. In this study’s analysis, we follow Gnangnon (2020), Agarwal et al. (2017), and Perera and Lee (2015) by using headcount ratio, which is a measure of the incidence of poverty and poverty gap index, which measures the intensity of poverty. Both headcount and poverty gap indexes are measured using the international poverty line of $1.90 per day. This study follows Arogundade et al. (2022) and Nunnenkamp Economies 2022,10, 128 7 of 20 (2004) by measuring FDI as FDI inward stock as a percentage of GDP. The problem of endogeneity biases linked with the FDI–welfare nexus is also mitigated by using FDI stock (Nunnenkamp 2004). We used the International Monetary Fund’s newly constructed aggregate financial development index to measure financial development. Other metrics such as credit to the private sector, stock market capitalization, and monetary aggregates have flaws as they do not capture the financial system’s multidimensionality, which is why this index was created. See Arogundade et al. (2021) for a similar approach. Institutional quality is measured using the average of the six indicators (voice and accountability, the rule of law, regulatory quality, control of corruption, government effectiveness, and political stability). These indexes range from 0 (weak) to 100 (strong). See Siriopoulos et al. (2021), Peres et al. (2018), Utesch-Xiong and U. S. Kambhampati (2021), and Ajide and Raheem (2016) for a similar approach. In measuring infrastructure, we used mobile telephone subscribers (per 100 people). We use the growth rate of GDP per capita as a proxy for economic growth and the total active labor force as a proxy for labor, as Kaulihowa (2017) suggests. Descriptive Statistics of the Variables Table 1shows the descriptive statistics of the variables used in this study. From 1996 to 2019, and among the 44 countries, the average values for poverty headcount and poverty gap were 41.6 percent and 17.4 percent for poverty headcount and poverty gap, respectively. Ghana has the minimum level of headcount and poverty gap rate, with 0.13% and 0.015% respectively of its population. However, the Congo Democratic Republic has the highest headcount and poverty gap at 95.3 and 66.5%, respectively. For FDI inward stock, the average is 39.23%, with a minimum of 0.224 and a maximum of 1039 %. Institutional quality ranged from 77.40 to 1.182, with an average of 31.87. Congo Democratic Republic has the lowest institutional score, while Mauritius has the highest. The pairwise correlation measures the relative association among the dependent variables and regressors. The results indicate that except for labor (L), all the variables have statistically significant relationships with the poverty rate. However, the signs vary. A cursory look at Table 2also indicates that all correlation statistics are below 0.80. Hence, no evidence of multicollinearity among the covariates. Table 1. Summary Statistics. Variables N Mean Min Max Data Sources Headcount Ratio (% of Pop.) 1056 0.416 0.0013 0.953 W/B, Povcalnet Poverty Gap (% of Pop.) 1056 0.174 0.0002 0.665 W/B, Povcalnet FDI Inward Stock (% of GDP) 1056 39.23 0.224 1039 UNCTAD Financial Development Index 1056 0.141 0.0173 0.646 IMF Economic Growth 1056 1.847 −36.56 28.68 W/B, WDI Infrastructure 1056 40.54 0 165.6 W/B, WDI Institutional Quality 1056 31.87 1.182 77.40 WGI Labor Force ’000 1056 8042 1135 6.32 ×107W/B, WDI NB: United Nations Conference on Trade and Development (UNCTAD), World Bank World Development Indicator (W/B, WDI), International Monetary Fund (IMF) database, and World Governance Indicator (WGI). Economies 2022,10, 128 8 of 20 Table 2. Pairwise Correlation. HC PG FDI FD GDPC L Inst Infra HC 1 PG 0.958 *** 1 FDI 0.0694 * 0.0581 1 FD −0.473 *** −0.421 *** 0.0591 1 GDPC −0.669 *** −0.574 *** −0.0506 0.667 *** 1 L 0.0338 0.0177 −0.0896 ** 0.163 *** −0.0412 1 Inst −0.395 *** −0.380 *** −0.119 *** 0.609 *** 0.501 *** −0.196 *** 1 Infra −0.509 *** −0.479 *** 0.0441 0.432 *** 0.478 *** 0.0366 0.249 *** 1 *p< 0.05, ** p< 0.01, *** p< 0.001. HC = Headcount poverty, PG = poverty gap, FDI = foreign dirext investment, FD = financial development, GDPC = gross domestic product per capita, L = labor force, Inst = institutional quality, Infra = infrastructure. 3.2. Methodology This study follows other spatial studies like Uttama (2015), Do et al. (2021), and Chih et al. (2021) by initially using a simple regression model as the benchmark model. The Driscoll and Kraay (1998) robust-standard-errors-type approach, which accounts for heteroskedasticity, autocorrelation, and cross-sectional dependence is used in this study. This is nested into the fixed effect instrumental variable regression model (FE-2SLS). The choice of the instrument (lagged FDI) used in this study is based on the instrument relevance and exogeneity conditions. The empirical form of the model without spatial interaction is given as follows: FDIi,t=∅+X∗ i,tγ+τt+ϕi+vi,t(1) Povi,t=β0+β1FDIi,t+β2FDIi,t×insti,t+β3Xi,t+ϕi+µi,t(2) where Povi,t is poverty rate in country i at period t ; FDIi,t is FDI inward stock as a percentage of GDP in country i at period t ; Xi,t is the vector control variables which includes GDP growth, labor, institutional quality, financial development, and infrastructure; ϕi is country-specific effect that is time-invariant; and µi,t is the error term. Equation (1.0) is the first stage of the FE-2SLS model, while Equation (2) is the second stage. This study uses the probability value of the F-test in equation (1.0) as instrument relevance test. FDIi,t∗insti,t in Equation (2) is the interaction term of FDI with institutional quality. We included both FDI and institutional quality in Equation (2) to ensure that the interaction term does not proxy for either FDI or the inst . If β1> 0 and β2< 0, it suggests that FDI reduces poverty at high level of institutional quality. For robustness of our empirical estimate, we used the instrumental variables techniques nested within the generalized method of moments (IV-GMM) framework by Baum et al. (2007a,2007b). Since the estimate of Equation (2) is inconsistent and biased due to the possibility of spatial interdependence that exists in the independent(s) or dependent variable (Anselin 2009), we augment Equation (2) with spatial characteristics as shown in Equations (3) and (4). Spatial interdependence can be introduced into a simple regression model in three ways: as an additional covariate referred to as spatial autoregressive (SAR) ( WijPovj,t ) or spatial Durbin model (SDM) ( WijFDIj,t ; WijFDI ∗instj,t) or through the error structure known as the spatial error model (SEM) Eεiεj6=0 . The Wald and likelihood ratio test is used to determine the choice of the spatial autoregressive models, since the models are estimated using maximum likelihood (Anselin 1988;Anselin and Bera 1998). We follow LeSage and Pace (2009) by using two different hypotheses. The first is H0 : θ= 0. This hypothesis examines whether Equations (3) and (4) can be reduced to a SAR model. The second hypothesis is H0 : θ+ρβ2= 0, which suggests whether Equations (3) and (4) can be reduced to a SEM model. We further use the likelihood ratio (LR), which was initially proposed by Burridge (1980) to determine between the SAR and SDM model. According to Hao et al. (2020), if both the Wald and LR test are rejected, this suggest that the best model for the data is SDM. Economies 2022,10, 128 15 of 20 6. Conclusions and Recommendations In reducing the savings gap and achieving equitable and sustainable development, a large amount of quality foreign resources is required in Africa. Hence, foreign investments, such as FDI, are seen as one of the most important drivers of economic development in the region by policymakers. However, empirical studies examining the impact of FDI on poverty have reached varying results. While some studies argue that FDI reduces poverty, some studies believe that it increases poverty. Other studies posit that FDI’s impact is conditional on certain intermittent variables. The reason for the diverse findings on the impact of FDI is that the majority of these studies neglect the role of space. In contributing to the literature, this study assesses whether spatial interdependence/third-country effects matter in the impact of FDI on the incidence and intensity of poverty in Africa. In achieving this, the study employed the spatial Durbin model to quantify the impact of neighboring country FDIs on the poverty conditions of a host country. Before accounting for space in our model, the study conducted some pre-estimation tests to determine the existence of spatial spillover on the effect of FDI. The results indicate that neighboring country FDIs impact a host country’s poverty. Hence, neglecting spatial interdependence in the FDI model may result to biased estimates. This study’s empirical findings are as follows: (1) neighboring country FDIs have a significant and positive impact on the incidence and intensity of the host country, (2) neighboring countries’ institutional quality matters in the nexus between FDI and poverty reduction, since the positive impact of FDI on poverty is mitigated through a robust institutional quality, (3) there is a significant spatial spillover of neighboring countries’ poverty to a host country, (4) the marginal effect results indicate that countries within the region are no longer in isolation or independent; i.e., the level of poverty in a particular country is influenced by its determinants in the neighboring country. This result is robust to the different proximity matrix, which is the inverse distance. The empirical results of this study have produced important policy implications for African governments. First, since FDI does not reduce poverty from our empirical estimation, African countries need to embark on public sector reforms, as investment would not thrive when there is high corruption, low voice and accountability, government inefficiency, poor regulatory quality, low rule of law, and political instability. Second, since the empirical results of this study provide evidence of both direct and spillover effects of poverty determinants, we recommend that African countries consider their surrounding countries’ characteristics in their welfare policy formulation. The study also highlights the importance of joint task efforts toward building strong institutional quality. This is to permit African countries to have coordinated policies towards building a robust institutional framework. African governments through the African Union (AU) or other relevant agencies are encouraged to not only develop an institutional reform for Africa, but also establish a binding mechanism to ensure reform implementation. 7. Future Research This study has some shortcomings which can be addressed in future research. Other intermittent or mediating variables such as financial development, globalization, and International Financial Reporting Standards (IFRS) have all been shown to be crucial. Future research could investigate the impact of these variables on the nexus between FDI and poverty within spatial framework. Future studies could also extend the topic to other continents using spatial models. Moreover, this study only uses the income measure of poverty. Future studies are encouraged to consider other non-income poverty measures. Author Contributions: Conceptualized the major idea of this research paper, Prepared the methodology, data collection, analysis, interpretation, and conclusion section, S.A.; Prepared the literature review and general editing, M.B.; Prepared the introduction and formatting, S.B. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Economies 2022,10, 128 16 of 20 Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets used and/or analyzed during the current study are available from the author on reasonable request. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Moran’s IiResidual of Headcount Poverty Regression by Country *. Countries IiSd(Ii)Z-stat P-Value * Algeria 0.133 0.018 7.255 0.000 Angola 0.054 0.016 3.396 0.001 Benin 0.006 0.040 0.162 0.871 Botswana −0.133 0.025 −5.351 0.000 Burkina Faso −0.005 0.028 −0.130 0.896 Burundi 0.625 0.050 12.403 0.000 Cabo Verde −0.067 0.024 −2.733 0.006 Cameroon −0.088 0.020 −4.317 0.000 Central African Republic 0.103 0.017 6.180 0.000 Chad −0.001 0.016 −0.019 0.985 Comoros −0.276 0.023 −12.220 0.000 DRC 0.237 0.020 11.705 0.000 Republic of Congo 0.008 0.025 0.359 0.720 Cote d’Ivoire −0.092 0.029 −3.126 0.002 Egypt −0.045 0.015 −2.997 0.003 Ethiopia −0.104 0.019 −5.409 0.000 Gabon −0.274 0.026 −10.664 0.000 Gambia −0.183 0.061 −2.985 0.003 Ghana −0.186 0.036 −5.133 0.000 Guinea −0.121 0.040 −3.036 0.002 Guinea-Bissau −0.019 0.052 −0.345 0.730 Kenya −0.107 0.025 −4.288 0.000 Lesotho 0.023 0.039 0.607 0.544 Liberia 0.013 0.033 0.422 0.673 Madagascar 0.022 0.023 1.017 0.309 Malawi 0.277 0.028 9.999 0.000 Mali −0.001 0.023 −0.007 0.994 Mauritania −0.025 0.023 −1.030 0.303 Mauritius −0.393 0.023 −17.297 0.000 Morocco 0.147 0.019 7.734 0.000 Mozambique 0.170 0.028 6.130 0.000 Namibia −0.060 0.022 −2.729 0.006 Niger 0.002 0.017 0.154 0.878 Nigeria −0.005 0.021 −0.178 0.859 Rwanda 0.154 0.051 3.066 0.002 Senegal −0.021 0.057 −0.349 0.727 Sierra Leone −0.005 0.041 −0.106 0.916 South Africa −0.072 0.039 −1.839 0.066 Sudan −0.090 0.016 −5.675 0.000 Tanzania 0.071 0.025 2.864 0.004 Togo 0.003 0.043 0.095 0.925 Tunisia 0.156 0.017 9.286 0.000 Uganda −0.045 0.028 −1.555 0.120 Zambia 0.157 0.021 7.418 0.000 Measures of global spatial autocorrelation 0.927 0.003 −21.681 0.000 * The probability level is two-tail test. NB: the LISA test of countries in the sample is estimated using year 2019, which is the end of our study period. Economies 2022,10, 128 17 of 20 Table A2. United Nation Regional Classification. Algeria Comoros Guinea Morocco Sudan Angola Congo, Democratic Republic of Guinea-Bissau Mozambique Tanzania Benin Congo, Republic of Kenya Namibia Togo Botswana Cote d’Ivoire Lesotho Niger Tunisia Burkina Faso Djibouti Liberia Nigeria Uganda Burundi Egypt Madagascar Rwanda Zambia Cabo Verde Ethiopia Malawi Senegal Zimbabwe Cameroon Gabon Mali Seychelles Central African Republic Gambia Mauritania Sierra Leone Chad Ghana Mauritius South Africa Table A3. Spatial impact of FDI on Poverty in Africa (Spatial Weight: Distance). (1) (2) (3) (4) (5) (6) VARIABLES SAR Model SDM Model SEM Model Headcount Poverty Gap Headcount Poverty Gap Headcount Poverty Gap FDI 0.0001 5.47 ×10−50.0001 7.11 ×10−50.0002 ** 0.0001 ** (7.78 ×10−5) (5.79 ×10−5) (7.72 ×10−5) (5.70 ×10−5) (7.85 ×10−5) (5.92 ×10−5) Financial Devt. 0.0881 0.159 ** 0.0473 0.105 0.115 0.155 ** (0.0920) (0.0684) (0.0919) (0.0680) (0.0893) (0.0657) Infrastructure −0.0007 *** −0.0003 *** −0.00071 *** −0.0002* −0.0008 *** −0.0003 *** (0.0001) (9.16 ×10 -5)(0.0001) (9.61 ×10 -5)(0.0001) (7.10 ×10 -5) Labor −0.206 *** −0.149 *** −0.232 *** −0.163 *** −0.184 *** −0.156 *** (0.0281) (0.0210) (0.0309) (0.0237) (0.0179) (0.0128) Inst −0.0014 *** −0.0016 *** −0.0013 *** −0.0016 *** −0.0014 *** −0.0017 *** (0.0005) (0.0004) (0.0005) (0.0003) (0.0005) (0.0003) Economic Growth −0.0018 *** −0.0013 *** −0.0017 *** −0.0012 *** −0.0021 *** −0.0016 *** (0.0006) (0.0004) (0.0006) (0.0004) (0.0006) (0.0004) FDI ×Inst −4.62 ×10−6−3.31 ×10−6−4.94 ×10−6*−3.93 ×10−6*−4.62 ×10−6−3.54 ×10−6 (3.01 ×10−6) (2.24 ×10−6) (2.99 ×10−6) (2.21 ×10−6) (2.96 ×10−6) (2.19 ×10−6) W∗FDI 0.0017 *** 0.00162 *** (0.0004) (0.0003) W∗FDI ×Inst − 3.84 × 10 −5 ** −5.99 ×10−5 *** (1.80 ×10−5) (1.33 ×10−5) W∗Poverty −0.0548 0.0148 −0.153 −0.221 ** (0.0931) (0.0983) (0.101) (0.111) Lambda (λ)−0.597 *** −0.684 *** (0.145) (0.154) Number of Countries 44 44 44 44 44 44 Prob > χ2 a 721.63 *** 525.77 *** 753.55 *** 525.77 *** 1860.49 *** 1356.69 *** SEM vs. SDM b4.26 *** 5.36 *** (0.0004) (0.0003) SAR vs. SDM (LR test) c18.12 *** 32.78 *** Standard errors in parentheses, *** denotes significance at 1%, ** at 5%, and * at 10%. The Hausman test suggests fixed effect over random effect model; the report is available on request. All regressions are estimated using maximum-likelihood estimator. a: Joint significance of all the variables. b: Wald test of spatial terms. c: Log likelihood ratio test. Notes 1As measured by head count poverty as a percentage of population in 2019. 2As measured by the average of the six dimensions of institutional quality in 2019. Economies 2022,10, 128 18 of 20 3See Elhorst (2014) for more on the mathematical derivation. References Agarwal, Manmohan, Pragya Atri, and Srikanta Kundu. 2017. Foreign direct investment and poverty reduction: India in regional context. South Asia Economic Journal 18: 135–57. [CrossRef] Agbloyor, Elikplimi Komla, Agyapomaa Gyeke-Dako, Ransome Kuipo, and Joshua Yindenaba Abor. 2016. Foreign direct investment and economic growth in SSA: The role of institutions. Thunderbird International Business Review 58: 479–97. [CrossRef] Ajide, K. Bello, and Ibrahim Dolapo Raheem. 2016. Institutions-FDI nexus in ECOWAS countries. Journal of African Business 17: 319–41. [CrossRef] Ali, Muhammad, Muhammad Nishat, and Talat Anwar. 2009. Do foreign inflows benefit Pakistani poor? [with comments]. The Pakistan Development Review 48: 715–38. [CrossRef] Anselin, Luc. 1988. Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geographical Analysis 20: 1–17. [CrossRef] Anselin, L. 2009. Spatial regression. In The SAGE Handbook of Spatial Analysis. Newcastle upon Tyne: Sage, Volume 1, pp. 255–76. Anselin, Luc, and Anil K. Bera. 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. Statistics Textbooks and Monographs 155: 237–90. Anselin, Luc, and Julie Le Gallo. 2006. Interpolation of air quality measures in hedonic house price models: Spatial aspects. Spatial Economic Analysis 1: 31–52. [CrossRef] Anyanwu, John C., and Andrew EO Erhijakpor. 2009. The Impact of Road Infrastructure on Poverty Reduction in Africa. New York: Poverty in Africa, Nova Science Publishers, Inc., pp. 1–40. Arabyat, Yaser. 2017. The Impact of FDI on poverty reduction in the Developing Countries. International Finance and Banking 4. [CrossRef] Arogundade, Sodiq, Mduduzi Biyase, and Joel H. Eita. 2021. Foreign Direct Investment and Inclusive Human Development in Sub-Saharan African Countries: Domestic Conditions Matter. Economia Internazionale/International Economics 74: 463–98. Arogundade, Sodiq, Adewale Samuel Hassan, and Santos Bila. 2022. Adewale Samuel Hassan, and Santos Bila. 2022. Diaspora Income, Financial Development and Ecological footprint in Africa. International Journal of Sustainable Development & World Ecology, 1–15. [CrossRef] Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. 2007a. Enhanced Routines for Instrumental Variables/Generalized Method of Moments Estimation and Testing. Stata Journal 7: 465–506. [CrossRef] Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. 2007b. ivreg2: Stata Module for Extended Instrumental Variables/2SLS, GMM and AC/HAC, LIML, and k-Class Regression. Boston: Boston College Department of Economics, Statistical Software Components, p. S425401. Bharadwaj, Ashish. 2014. Reviving the globalization and poverty debate: Effects of real and financial integration on the developing world. Advances in Economics and Business 2: 42–57. [CrossRef] Brahim, Mariem, and Houssem Rachdi. 2014. Foreign direct investment, institutions and economic growth: Evidence from the MENA region. Journal of Reviews on Global Economics 3: 328–39. Burridge, Peter. 1980. On the Cliff-Ord test for spatial correlation. Journal of the Royal Statistical Society: Series B (Methodological) 42: 107–8. [CrossRef] Campos, Nauro, and Kinoshita Yuko. 2003. Why Does FDI Go Where it Goes?: New Evidence from the Transition Economies. IMF Working Paper, WP/03/228. Washington, DC: International Monetary Fund. Chenery, Hollis, and A. Stout. 1966. Foreign assistance and economic development. American Economic Review 56: 679–733. Chih, Yao-Yu, Ruby P. Kishan, and Andrew Ojede. 2021. Be good to thy neighbors: A spatial analysis of foreign direct investment and economic growth in sub-Saharan Africa. The World Economy 45: 657–701. [CrossRef] Cleeve, Emmanuel. 2012. Political and institutional impediments to foreign direct investment inflows to sub-Saharan Africa. Thunderbird International International Business Review 54: 469–77. [CrossRef] Colen, Liesbeth, Miet Maertens, and Johan Swinnen. 2009. Foreign direct investment as an engine for economic growth and human development: A review of the arguments and empirical evidence. Hum. Rts. & Int’l Legal Discourse 3: 177. Do, Quynh Anh, Quoc Hoi Le, Thanh Duong Nguyen, Van Anh Vu, Lan Huong Tran, and Cuc Thi Thu Nguyen. 2021. Spatial Impact of Foreign Direct Investment on Poverty Reduction in Vietnam. Journal of Risk and Financial Management 14: 292. [CrossRef] Donou-Adonsou, Ficawoyi, and Kevin Sylwester. 2016. Financial development and poverty reduction in developing countries: New evidence from banks and microfinance institutions. Review of Development Finance 6: 82–90. [CrossRef] Driscoll, J. C., and A. C. Kraay. 1998. Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics 80: 549–60. [CrossRef] Elhorst, Paul. 2014. Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Heidelberg: Springer, vol. 479, p. 480. Fowowe, B., and M. I. Shuaibu. 2014. Is foreign direct investment good for the poor? New evidence from African countries. Economic Change and Restructuring 47: 321–39. [CrossRef] Gani´c, M. 2019. Does Foreign Direct Investment (FDI) contribute to poverty reduction? Empirical evidence from Central European and Western Balkan Countries. Scientific Annals of Economics and Business 66: 15–27. [CrossRef] Gnangnon, S. K. 2020. Does Poverty deter Foreign Direct Investment flows to Developing Countries? Preprints. [CrossRef] Economies 2022,10, 128 19 of 20 Gohou, G., and I. Soumare. 2012. Does foreign direct investment reduce poverty in Africa and are there regional differences? World Development 40: 75–95. [CrossRef] Gutiérrez-Portilla, P., A. Maza, and J. Villaverde. 2019. A spatial approach to the FDI-growth nexus in Spain: Dealing with the headquarters effect. International Business Review 28: 101597. [CrossRef] Hansen, H., and J. Rand. 2006. On the causal links between FDI and growth in developing countries. The World Economy 29: 21–41. [CrossRef] Hao, Y., Y. Wu, H. Wu, and S. Ren. 2020. How do FDI and technical innovation affect environmental quality? Evidence from China. Environmental Science and Pollution Research 27: 7835–50. [CrossRef] Hayat, A. 2019. Foreign direct investments, institutional quality, and economic growth. The Journal of International Trade & Economic Development 28: 561–79. Jalilian, H., and C. Kirkpatrick. 2019. Financial development and poverty reduction in developing countries. International Journal of Finance & Economics 7: 97–108. Javorcik, B. S. 2015. Does FDI bring good jobs to host countries? The World Bank Research Observer 30: 74–94. [CrossRef] Jilenga, M. T., and X. Helian. 2017. Foreign direct investment and economic growth in Sub-Saharan Africa: The role of institutions. Turkish Economic Review 4: 378–87. Jude, C., and G. Levieuge. 2017. Growth effect of foreign direct investment in developing economies: The role of institutional quality. The World Economy 40: 715–42. [CrossRef] Kaulihowa, T. 2017. Foreign Direct Investment and Welfare Dynamics in Africa. Ph.D. thesis, Development Finance, Faculty of Economic and Management Sciences at Stellenbosch University, Stellenbosch, South Africa. Kelejian, H. H., G. S. Tavlas, and G. Hondroyiannis. 2006. A spatial modelling approach to contagion among emerging economies. Open Economies Review 17: 423–41. [CrossRef] Lehnert, K., M. Benmamoun, and H. Zhao. 2013. FDI inflow and human development: Analysis of FDI’s impact on host countries’ social welfare and infrastructure. Thunderbird International Business Review 55: 285–98. [CrossRef] LeSage, J., and R. K. Pace. 2009. Introduction to Spatial Econometrics. London: Chapman and Hall/CRC. Madariaga, N., and S. Poncet. 2007. FDI in Chinese cities: Spillovers and impact on growth. World Economy 30: 837–62. [CrossRef] Magombeyi, M. T, and N. M. Odhiambo. 2018. Dynamic impact of FDI inflows on poverty reduction: Empirical evidence from south Africa. Sustainable Cities and Society 39: 519–26. [CrossRef] Mankiw, N. G., D. Romer, and D. N. Weil. 1992. A contribution to the empirics of economic growth. Quarterly Journal of Economics 107: 407–37. [CrossRef] Moran, P. A. 1950. A test for the serial independence of residuals. Biometrika 37: 178–81. [CrossRef] Nunnenkamp, P. 2004. To what extent can foreign direct investment help achieve international development goals? World Economy 27: 657–77. [CrossRef] Perera, L. D. H., and G. H. Lee. 2015. Have economic growth and institutional quality contributed to poverty and inequality reduction in Asia? Journal of Asian Economics 27: 71–86. [CrossRef] Peres, M., W. Ameer, and H. Xu. 2018. The impact of institutional quality on foreign direct investment inflows: Evidence for developed and developing countries. Economic Research-Ekonomska Istraživanja 31: 626–44. [CrossRef] Qin, C., and W. Zhang. 2022. Green, poverty reduction and spatial spillover: An analysis from 21 provinces of China. Environment, Development and Sustainability 10: 1–20. [CrossRef] Rewilak, J. 2017. The role of financial development in poverty reduction. Review of Development Finance 7: 169–76. [CrossRef] Ragoubi, Hanen, and Sana El Harbi. 2018. Entrepreneurship and income inequality: A spatial panel data analysis. International Review of Applied Economics 32: 374–422. [CrossRef] Rye, Suzanna-Zora. 2016. Foreign Direct Investment and its Effects on Income Inequality. Master’s thesis, Economics Institute, University of Oslo, Oslo, Norway. Schoch, M., and C. Lakner. 2020. African Countries Show Mixed Progress Towards Poverty Reduction and Half of Them Have an Extreme Poverty Rate Above 35%. World Bank Blogs. Available online: https://blogs.worldbank.org/opendata/africancountries-show-mixed-progress-towards-poverty-reduction-and-half-them-have-extreme (accessed on 20 December 2021). Seetanah, B., S. Ramessur, and S. Rojid. 2009. Does infrastructure alleviate poverty in developing countries. International Journal of Applied Econometrics and Quantitative Studies 6: 31–36. Sikandar, F., V. Erokhin, W.H. Shu, S. Rehman, and A. Ivolga. 2021. The Impact of Foreign Capital Inflows on Agriculture Development and Poverty Reduction: Panel Data Analysis for Developing Countries. Sustainability 13: 3242. [CrossRef] Siriopoulos, C., A. Tsagkanos, A. Svingou, and E. Daskalopoulos. 2021. Foreign Direct Investment in GCC Countries: The Essential Influence of Governance and the Adoption of IFRS. Journal of Risk and Financial Management 14: 264. [CrossRef] Sobhee, S. K. 2017. The effects of poor institutional quality on economic growth–investigating the case of Sub-Saharan and Latin American economies prior to the world economic downturn. Journal of Economic Thought 1: 83–95. Son, H. H., and N. Kakwani. 2004. Economic Growth and Poverty Reduction: Initial Conditions Matter. Brasília: International Policy Centre for Inclusive Growth. Soumare, I. 2015. Does FDI improve economic development in North African countries? Applied Economics 47: 5510–33. [CrossRef] Stiglitz, J. 2002. Globalization and Its Discontents. London: Allen Lane. Economies 2022,10, 128 20 of 20 Sukhadolets, T., E. Stupnikova, N. Fomenko, N. Kapustina, and Y. Kuznetsov. 2021. Foreign Direct Investment (FDI), Investment in Construction and Poverty in Economic Crises (Denmark, Italy, Germany, Romania, China, India and Russia). Economies 9: 152. [CrossRef] Sustainable Development Goals (SDG). 2019. Sustainable Development Report. Available online: https://sdgindex.org/reports/ sustainable-development-report-2019/ (accessed on 1 December 2021). Ullah, K, T. Majeed, and G. Mustafa. 2020. Exploring Spatial Patterns and Determinants of Poverty: New Evidence from Pakistan. The Pakistan Development Review 53: 439–59. United Nations. 2003. Financing for development. In Monterrey Consensus of the International Conference on Financing for Development. Mexico: Monterrey. UNCTAD Statistics. 2020. Statistical Release. Available online: https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx (accessed on 10 December 2021). Utesch-Xiong, F., and U. S. Kambhampati. 2021. Determinants of Chinese Foreign Direct Investment in Africa. Journal of African Business, 1–18. [CrossRef] Uttama, N. P. 2015. Foreign direct investment and the poverty reduction nexus in Southeast Asia. In Poverty Reduction Policies and Practices in Developing Asia. Berlin/Heidelberg: Springer, vol. 281. World Bank. 2019. World Bank Povcal Database. Available online: http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx (accessed on 1 November 2021). Yeboua, Kouassi. 2020. Foreign Direct Investment and Economic Growth in Africa: New Empirical Approach on the Role of Institutional Development. Journal of African Business, 1–18. [CrossRef]