Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=riej20 International Economic Journal ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/riej20 Foreign aid—Economic Growth Nexus in Africa: Does Financial Development Matter? Isaac Appiah-Otoo, Alex O. Acheampong, Na Song, Camara Kwasi Obeng & Isaac K. Appiah To cite this article: Isaac Appiah-Otoo, Alex O. Acheampong, Na Song, Camara Kwasi Obeng & Isaac K. Appiah (2022) Foreign aid—Economic Growth Nexus in Africa: Does Financial Development Matter?, International Economic Journal, 36:3, 418-444, DOI: 10.1080/10168737.2022.2083653 To link to this article: https://doi.org/10.1080/10168737.2022.2083653 Published online: 06 Jun 2022. 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Isaac Appiah-Otoo a,b, Alex O. Acheampongc,d∗ , Na Songa, Camara Kwasi Obenge & Isaac K. Appiahf aSchool of Management and Economics, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China; bCenter for West African Studies, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China; cNewcastle Business School, University of Newcastle, Newcastle, Australia; dCentre for African Research, Engagement and Partnerships (CARE-P), University of Newcastle, Callaghan, Australia; eDepartment of Economic Studies, School of Economics, University of Cape Coast, Cape Coast, Ghana; fDepartment of Statistics, University of Ghana, Accra, Ghana ABSTRACT This study explored the role of financial development in foreign aid (measuredby agriculture, humanitarian, health, economic infrastruc- ture and services, and education aid) and economic growth rela- tionship for 37 African countries spanning the 2002–2018 period. Using the instrumental variable generalized method of moments model, our findings indicated that while foreign aid impedes Africa’s growth, financial development spurs economic growth. The condi- tional effect analysis showed that financial development conditions foreign aid to spur economic growth. The country-specific analy- sis further showed that foreign aid has a higher growth elasticity in countries with relatively better financial systems, such as Mauri- tius, South Africa, Gabon, Tunisia, and Botswana, whilst the growth elasticity of aid is smaller in countries with a relatively weak finan- cial system such as Malawi, Guinea Bissau, Sierra Leone, and the Democratic Republic of Congo. The study recommended the need for policymakers in Africa to implement innovative ways to improve domestic revenue mobilization. The study also recommended that policymakers in Africa should create an enabling environment that will enhance the development of Africa’s financial system tomitigate the adverse effect of aid on economic growth. ARTICLE HISTORY Received 29 September 2021 Revised 21 March 2022 Accepted 17 May 2022 KEYWORDS Foreign aid; financial development; economic growth; Africa JEL CODES F35; F43; G20; O55 1. Introduction Whether foreign aid (hereafter, aid) promotes economic growth in developing countries continues to dominate policy discussions and academic research. Developing countries have accrued high levels of aid in recent times, from US$ 127.3 billion in 2010 to US$ 145.7 billion in 2018; however, whether aid actually drives developing countries’ economic CONTACT Isaac Appiah-Otoo appiahotooi@yahoo.com School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China Center for West African Studies, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China ∗Present address: Bond Business School, Bond University, Gold Coast, Australia © 2022 Korea International Economic Association http://www.tandfonline.com https://crossmark.crossref.org/dialog/?doi=10.1080/10168737.2022.2083653&domain=pdf&date_stamp=2022-09-07 http://orcid.org/0000-0002-3683-0634 mailto:appiahotooi@yahoo.com INTERNATIONAL ECONOMIC JOURNAL 419 growth remains ambiguous (Maruta et al., 2020; Tahir et al., 2019). For instance, some scholars argue that aid drives economic growth by increasing domestic resources and foreign exchange reserves, human capital and physical investment, technical and man- agerial know-how, market access, research skills, and technology transfer (Ang, 2010; Burnside & Dollar, 2000; Kumi, 2020; Sethi et al., 2019), whilst others argue that aid impedes economic growth by driving corruption, hampering domestic production, exac- erbating conflicts, and promoting dependence syndrome (Ang, 2010; Burnside & Dollar, 2000; Kumi, 2020; Maruta et al., 2020; Sethi et al., 2019). These inconsistencies have been attributed mainly to the recipient countries’ conditions, such as the quality of institu- tions, geography, climate, financial liberalization, andmacroeconomic policies (Ang, 2010; Asongu, 2015; Burnside &Dollar, 2000;Maruta et al., 2020; Ram, 2004). However, whether the host countries’ financial system serves as a pre-condition for aid effectiveness remains underexplored in the aid-economic growth literature. The financial system plays an essential role in economic development by transferring funds from the surplus units to deficit units (Appiah-Otoo & Song, 2020). This facilitates savings and investment, thereby advancing economic growth (Appiah-Otoo&Song, 2020). Financial intermediaries also bring borrowers and lenders together and reduce the prob- lem of risks associated with the transfer of funds (Ang, 2008). Thus, the financial system reduces transactions cost and information asymmetry problems (Ang, 2008). Financial intermediaries also provide monitoring and corporate governance leading to economic growth (Botev et al., 2019). This implies that countries with a developed financial system will successfully allocate aid inflows to firms and households. Also, the financial system will effectively mitigate the undesirable effects of aid on economic growth, such as the appreciation of the real exchange rate. Nevertheless, the financial system in most developing economies, especially African countries, remains underdeveloped, and it is mainly dominated by government-owned banks, with a weak legal and regulatory system, high levels of corruption, insufficient accountingmeasures, and inefficient allocation of funds (Acquah and Ibrahim, 2019; Allen, Otchere and Senbet, 2011; Appiah-Otoo and Song, 2020; Chikalipah and Makina, 2019 2020). To this end, this study is motivated to explore the role of financial development in the aid-economic growth nexus in Africa for the period spanning 2002–2018. In this study, we seek to address the question: ‘does financial development serves as a pre-condition for aid effectiveness in Africa?’ Addressing this question is significant given that policymak- ers are interested in attaining high and sustainable development in Africa (Anyanwu, 2014, Asongu andOdhiambo, 2020). According to Anyanwu (2014) and Asongu and Odhiambo (2020), high and sustainable economic success propels investment, employment, improves household consumption andwelfare, reduces poverty, helps support health care and educa- tion that policymakers aspire to achieve. Thus, if aid is found to hamper economic growth and financial development mitigates the negative impact of aid on economic growth, then African countries can break out from their over-reliance on aid. On the contrary, if aid drives economic growth and financial development mitigates the positive impact of aid on economic growth, then this will imply that African countries will forever stay in underde- velopment. Thiswill help reforms to be initiated in the financial sector to stimulate financial development in Africa. This study contributes significantly to the aid-economic growth literature on the follow- ing fronts: First, we used five different measures of aid: agriculture, humanitarian, health, 420 I. APPIAH-OTOO ET AL. economic infrastructure and services, and education aid. As argued byMaruta (2019), ‘the question of aid effectiveness has shifted from “Is aggregated aid helpful?” to “Which type of aid is more helpful?” Also, donors have repeatedly stressed that they pursue multiple aims when delivering aid’. Thus, the need to explore the differential effect of different forms of aid on economic growth in Africa. Second, this study contributes to the literature by examining the moderating role of financial development proxied by domestic credit to the private sector and money supply in the aid-economic growth nexus. Besides, this study presents the country-specific marginal effect of the moderating role of financial develop- ment in the aid-growth nexus. Furthermore, to produce reliable results, this study relied on the instrumental variable generalized method of moments (IV-GMM) technique to esti- mate the effect conditional and unconditional effect of financial development and aid on economic growth in Africa. This econometric technique produces efficient results since it addresses endogeneity issues that emanate from reverse causality, variable omissions bias, and mismeasurements (Acheampong, Amponsah and Boateng, 2020; Appiah-Otoo and Song, 2021a, 2021b). Finally, the results from this study will provide new insights for policy reforms in Africa. Using the IV-GMM model, our findings indicated that foreign aid in the form of agriculture, humanitarian, health, economic infrastructure and services, and education aid impedes Africa’s growth, but a well-developed financial system enables aid management, thus enhancing aid effectiveness. However, the conditional analysis sug- gested that financial development conditions foreign aid to improve economic growth in Africa. The remainder of the study comprises the literature review in section 2, methodol- ogy in section 3, the results and discussion in section 4, whilst the conclusion and policy recommendations are in section 5. 2. Literature Review 2.1. Foreign aid and Economic Growth The Organization for Economic Co-operation and Development (OECD) defines foreign aid or official development assistance (ODA) to the government as planned to improve the welfare and the economic development of developing countries. Aid might be given bilaterally, alternatively channelling from donor to receiver through a multilateral devel- opment agency, the World Bank, or the United Nations. Aid incorporates grants, ‘‘soft’’ credits (where the grant component is at least 25% of the sum), and the provision of tech- nical assistance’’. ODA might be disbursed under various headings, namely, social (which incorporates education, health, water supply, sanitation, and social infrastructure), eco- nomic (communications and transport, energy, banking, business, and different services), production (agriculture, fishing, and forestry, industry,mining and construction, and trade and tourism), and humanitarian reasons. The links between aid and economic growth have been extensively studied, albeit the conflicting theoretical and empirical findings. For instance, some scholars documented that aid drives economic growth (Maruta, Banerjee and Cavoli, 2020; Tahir, Estrada and Afridi, 2019), while others have demonstrated that aid impedes economic growth (Babalola and Shittu, 2020; Sothan, 2018). The final group of studies has either documented an indi- rect or an inconclusive impact of aid on economic growth (Albiman, 2016; Alemu and Lee, INTERNATIONAL ECONOMIC JOURNAL 421 2015; Ang, 2010; Asongu, 2015; Burnside and Dollar, 2000; Ekanayake and Chatrna, 2017; Maruta, Banerjee and Cavoli, 2020; Nkusu and Sayek, 2004; Ram, 2004; Tsaurai, 2018). For instance, Tahir, Estrada and Afridi (2019) studied the aid-growth nexus for South Asian Association for Regional Cooperation (SAARC) countries from 2008 to 2015 and discovered that aid increases economic growth. Also, Maruta, Banerjee and Cavoli (2020) found that education aid drives South America’s economic growth; health aid drives Asia’s economic growth, whilst agriculture aid drives Africa’s economic growth for a panel of 74 developing countries. Nwaogu and Ryan (2015) further explored the aid-growth nexus using a panel of 53African and 34 Latin American countries. Using dynamic spatial econo- metrics models, the study indicated that aid drives economic growth in Africa, whilst the reverse exists in Latin American countries. Bird and Choi (2020) confirmed Nwaogu and Ryan’s (2015) findings using the fixed effects model. Karras (2006) also found aid to spur economic growth for 71 developing countries from 1960 to 1997, using the fixed effects and the ordinary least squares model. Conversely, using the pooled mean group estima- tor, Babalola and Shittu (2020) found aid to impede West African countries’ economic growth in the long run. Sothan (2018) reached the same conclusions for Cambodia. Albi- man (2016) studied the aid-growth nexus using Tanzania as a case study and found aid to have an insignificant effect on Tanzania’s economic growth in the short-run, while aid impedes Tanzania’s economic growth in the long-run. Also focusing on 39 African coun- tries further classified intomiddle-and low-income countries, Alemu and Lee (2015) found that aid has an insignificant effect onmiddle-income countries’ economic growth while aid enhances low-income countries’ economic growth. Nkusu and Sayek (2004) studied the role of financial development in the aid-growth nexus for 86 developing countries from 1970 to 1999 and found that aid impedes economic growth, but a well-developed financial system mitigates the negative impact while using the Ordinary Least Squares (OLS) esti- mator. Tsaurai (2018) reached the same findings while focusing on emerging markets and using the Fully Modified OLS technique. 2.2. Finance and Economic Growth Financial development refers to the process of getting information at the most reduced cost, realizing contracts, and undertaking transactions that bring about the development of financial contracts, markets, and intermediaries (Akinlo, Yinusa and Adejumo, 2020). Financial development assumes a significant part in countries’ economic development (Alexiou and Vogiazas, 2018). According to Sutton and Jenkins (2007), the financial sec- tor is arguably the most significant in the world as far as earning is concerned. It is the backbone of other economic sectors and the most regulated sector (Sutton and Jenkins, 2007). There exist a plethora of empirical studies on the relationship betweenfinancial develop- ment and economic growth. However, the empirical findings on the finance-growth nexus are ambiguous. Some studies document that finance boosts economic growth (Deme- triades and James, 2011; Peia and Roszbach, 2015; Pradhan et al., 2017; Salah, Sjö and Shahbaz, 2013; Seven and Yetkiner, 2016; Zhang, Wang and Wang, 2012), while others document that finance retards economic growth (Acquah and Ibrahim, 2019; Appiah- Otoo and Song, 2020; Swamy, 2018). The final group of scholars documents a threshold or insignificant impact of finance on economic growth (Caporale et al., 2014; Ductor 422 I. APPIAH-OTOO ET AL. and Grechyna, 2015; Hook and Singh, 2014; Jalil, Feridun and Ma, 2010; Law and Singh, 2014; Shen and Lee, 2015; Wahyoe, Iftekhar and Nuruzzaman, 2017). For example, Seven and Yetkiner (2016), using the system generalized method of moments (sys-GMM) tech- nique, established that banking development spurs economic growth in low and middle- income countries. The study also revealed that stockmarket development drives economic growth in middle and high-income countries using 146 countries. Demetriades and James (2011) also documented that banking development promotes economic growth using 18 Sub-Saharan African countries from 1975 to 2006. Also, Pradhan et al. (2017), using the panel vector auto-regression technique, established that financial development drives economic growth in ASEAN Regional Forum countries. Beck and Levine (2004) used a panel of 40 countries spanning 1976–1998 to examine the effects of the stock market and banking development on economic growth. Using the sys-GMM estimator, the study indicated that the stock market and banks drive economic growth. Peia and Roszbach (2015) confirmed Beck and Levine’s (2004) findings using 22 developed countries. Salah, Sjö and Shahbaz (2013) also found that financial development contributes to economic growth in Kenya, in the long run, using the autoregressive dis- tributed lag (ARDL) model. Also, Zhang, Wang and Wang (2012) and Jalil, Feridun and Ma (2010) found the same results in the case of China. Conversely, Appiah-Otoo and Song (2020), using the ARDL model, documented that financial development (a composite index from banks, stocks, and insurance) impedes Ghana’s economic growth in the short and long runs. Swamy (2018) and Samargandi, Fidrmuc and Ghosh (2015) also reached the same findings for 24 and 52 countries. Also, Law and Singh (2014) indicated that financial development has a nonlinear effect on eco- nomic growth for 87 developed and developing countries from 1980 to 2010. Similarly, Hook and Singh (2014) found a threshold relationship between finance and economic growth for 87 countries covering the 1980–2010 period. The study of Wahyoe, Iftekhar and Nuruzzaman (2017) also revealed that financial deepening and financial intermedi- ation have an inverted U-shaped association with economic growth in Indonesia. Chen, Wu and Wen (2013) also examined the non-linearity between financial development and economic growth in China and found that financial development drives economic growth in high-income provinces but impedes economic growth in low-income provinces. For ten new European Union member countries, Caporale et al. (2014) revealed that the stock and credit market have an insignificant effect on economic growth, whilst liquid liabilities spur economic growth. Rousseau and Wachtel (2011) also concluded that financial deep- ening spur economic growth, given a stable financial environment; however, in periods of crisis, the positive effect disappears for 84 countries. Shen and Lee (2015) re-estimated the finance-growth links for 48 countries from 1976 to 2001 and found that stock mar- ket development spurs economic growth, whilst banking development impedes economic growth. Finally, Kar, Nazlioǧlu andAǧir (2011) examined the causal nexus between finance and economic growth for 15 MENA countries from 1980 to 2017 and found no evidence of causality between finance and economic growth. 2.3. Foreign aid and Financial Development It is acknowledged that foreign aid stimulates financial development by promoting cre- ativity and innovation, transparency, accountability and fairness, greater links with the INTERNATIONAL ECONOMIC JOURNAL 423 private sector, reducing bureaucracy, leveraging resource allocation via public and private partnership, and expanding skills and technical know-how (Maruta 2019). In a recent study, Maruta (2019) indicated that aid to the financial sector stimulates financial develop- ment using data for 70 developing countries from 1980 to 2016. Agapova and Vishwasrao (2020) also found that aid to the financial sector promotes credit to the public sector; however, it impedes credit to the private sector. From the literature review, it is obvious that a heterogeneous relationship exists between foreign aid and economic growth, financial development and economic growth, and for- eign aid and financial development. There are limited studies on the complementarity between foreign aid, financial development, and economic development. Accordingly, this study contributes to the scarce literature on the complementarity between foreign aid, financial development, and economic growth by focusing on a sample of 37 African coun- tries. This study also used five different measures of aid: agriculture, humanitarian, health, economic infrastructure and services, and education aid. Moreover, this study relied on domestic credit to the private sector and money supply as the financial development prox- ies. Furthermore, the country-specific marginal effect of the moderating role of financial development in the aid-growth nexus was presented. Finally, this study used the IV-GMM technique for the empirical analysis and a more recent dataset. 3. Data andMethodology 3.1. Data This study used a panel of 37 African countries1 from 2002 to 2018. These countries were selected given data availability on the various aid measures for the study period. Also, the study period was influenced by the availability of aid data from the OECD database. Given their dominant values, we used agriculture, health, education, humanitarian, economic infrastructure and services aids for our aid proxies. We used real GDP per capita to proxy economic growth, total labor force to represent labor and gross fixed capital formation as a proxy for investment. Also, following Acquah and Ibrahim (2019), we used domestic credit to the private sector (credit) and broadmoney supply as the financial development proxies. We obtained the aid data from the OECD database, while we sourced the data on eco- nomic growth, labor, investment, credit, and money supply from the World Development Indicators Database. Table 1 shows the original data’s variables, unit ofmeasurement, definition, and descrip- tive statistics before their transformation into a log form. Real GDP per capita has an aver- age of $1,967.506 with a maximum of $10,577.200. Credit recorded an average of 21.175%, with a maximum of 160.125%. At the country-level, South Africa (141.643%), Mauri- tius (84.274%), Tunisia (68.876%), Egypt (37.966%), Kenya (28.189%), and Botswana (26.852%) have a relatively highly developed financial system, whilst Guinea Bissau (7.117%), Guinea (5.938%), Chad (5.418%), Sierra Leone (5.093%), and DRC (3.951%) 1 Algeria, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Cote d’Ivoire, Demo- cratic Republic of Congo (DRC), Egypt, Eswatini, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Lesotho, Liberia, Madagascar,Malawi,Mali,Mauritania,Mauritius,Mozambique, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, SouthAfrica, Sudan, Tanzania, Togo, and Tunisia. 424 I. APPIAH-OTOO ET AL. Table 1. Descriptive Statistics. Variable Definition Unit of Measurement Obs. Mean Std. Dev. Min Max gdp Economic growth Constant US$ 629 1967.506 2334.849 210.804 10577.200 k Investment % of GDP 623 21.153 7.828 3.949 59.723 l Labor Total 629 8000000 1.00e+07 300000 5.84e+07 bm Money supply % of GDP 620 31.601 21.377 2.917 115.327 dcps Domestic credit to the private sector % of GDP 619 21.175 26.339 0.491 160.125 health Health aid Constant US$ 624 1.845 2.725 0.000 34.499 educ Education aid Constant US$ 629 3.843 3.615 0.086 20.275 econ Economic infrastructure and services aid Constant US$ 628 2.806 4.888 −1.496 55.730 agric Agriculture aid Constant US$ 628 1.726 1.828 0.000 13.674 huma Humanitarian aid Constant US$ 614 3.955 8.357 0.000 80.892 Note: The values in this table are the raw values of the variables. However, for the empirical estimation, we follow Maruta, Banerjee and Cavoli (2020), the aid proxies were divided by real GDP and multiplied by 100. Figure 1. Country-specific averages of education aid from 2002-2018. have a weak financial system. Concerning the aid proxies, humanitarian aid has the high- est average, followed by education aid, economic infrastructure and services aid, health aid, with agriculture aid having the lowest average. At the country-level, Algeria has the highest average of education aid, whilst Tanzania, Egypt, Ghana, and Sudan have the high- est averages of health, economic infrastructure and services, agriculture, and humanitarian aid, respectively (see Figures 1–5) Figure 6 shows the correlation between financial development (credit, broad money supply), foreign aid (health aid, agriculture aid, education aid, humanitarian aid, economic infrastructure, and services aid), and economic growth. The results show that financial development (credit and broad money supply) has a strong positive correlation with eco- nomic growth, whilst aid (health, agriculture, education, humanitarian, and economic infrastructure and services aid) has a strong negative correlation with economic growth. Thus, an increase in credit and broad money supply will raise economic growth, whilst an INTERNATIONAL ECONOMIC JOURNAL 425 Figure 2. Country-specific averages of health aid from 2002-2018. Figure 3. Country-specific averages of economic infrastructure and services aid from 2002-2018. Figure 4. Country-specific averages of agriculture aid from 2002-2018. 426 I. APPIAH-OTOO ET AL. Figure 5. Country-specific averages of humanitarian aid from 2002-2018. Figure 6. Bivariate relationship between credit, broadmoney supply, health aid, agriculture aid, educa- tion aid, humanitarian aid, economic infrastructure and services aid, and economic growth. increase in health, agriculture, education, humanitarian, and economic infrastructure and services aid will decrease economic growth. 3.2. Theoretical Model and Hypothesis Development The neoclassical Aggregate Production Function (APF) of Solow (1956) serves as the the- oretical basis of this study. The APF examines the relationship between output and inputs used in production (Appiah-Otoo and Song, 2021b; Peprah, Ofori and Asomani, 2019). INTERNATIONAL ECONOMIC JOURNAL 427 The model is stated as Eq. (1) Yit = Ait × f (kit , lit) (1) Where Yit depicts output, kit depicts capital stock, and lit depicts labor. Ait depicts total factor productivity (TFP). i represents country, and t signifies time. In this study, capital stock is denoted by investment as a share of GDP given the unavailability of capital stock data for developing countries (Albiman, 2016). This proxy has also been extensively used in economic growth studies (see Appiah-Otoo and Song, 2021b; Ekanayake and Chatrna, 2017; Maruta, Banerjee and Cavoli, 2020; Peprah, Ofori and Asomani, 2019). To examine foreign aid, financial development, and the interaction of foreign aid and financial develop- ment influence on economic growth, we substitute foreign aid, financial development and the interaction between foreign aid and financial development intoAit : Eq. (2). The jus- tification for substituting foreign aid, financial development and the interaction between foreign aid and financial development intoAit stems from the fact that Aitdenotes other factors that drive capital stock and labor to enhance economic growth and foreign aid, financial development and the interaction between foreign aid and financial development have been shown to influence economic growth (see Nkusu and Sayek, 2004; Seven and Yetkiner, 2016; Tsaurai, 2018). Ait = f (aidit , f dit , aidit × f dit) (2) Substituting Eq. (2) into Eq. (1) gives Eq. (3) Yit = f (kit , lit , aidit , f dit , aidit × f dit) (3) Stating Eq. (3) in econometrics gives Eq. (4) yit = bo + b1kit + b2lit + b3aidit + b4f dit + b5aidit × f dit + εit (4) Transforming Eq. (4) into log form gives Eq. (5) ln yit = bo + b1 ln kit + b2 ln lit + b3 ln aidit + b4 ln f dit + b5 ln aidit × ln f dit + εit (5) From Eq. (5), we employ the following growth equation: Eq. (6) ln gdpit = bo + b1 ln kit + b2 ln lit + b3 ln aidit + b4 ln f dit + b5 ln aidit × ln f dit + εit (6) From Eqn. (6), we obtained the following marginal effect of aid on economic growth Eq. (7) �lngdpit �lnaidit = b3 + b5 ln f dit (7) We test the following conditional hypotheses: • If b3 >0 and b5 >0, Aid measured by agriculture, health, education, humanitarian, and economic infrastructure and services have a positive impact on economic growth and financial development measured by credit and money supply affect favorably that positive impact. 428 I. APPIAH-OTOO ET AL. • If b3 >0 and b5 <0, Aid has a positive effect on economic growth, whilst financial development reduces the positive effect. • If b3 <0 and b5 >0, Aid harms economic growth, whilst financial development miti- gates the negative effect. • If b3 <0 and b5 <0, Aid harms economic growth, and financial development worsens the negative effect. 3.3. Estimation Strategy We employed the Baum, Schaer and Stillman (2002) IV-GMM technique to estimate the above-stated equations. This technique produces efficient results since it addresses endo- geneity sources such as omitted variable issues, measurement errors, or reverse causality. Also, since the IV-GMM technique applies the orthogonality condition, its estimates are robust to autocorrelation and efficient in the presence of unknown heteroscedasticity (Baum, Schaer and Stillman, 2002). This method is also appropriate since the study period is smaller than the countries studied (Stock, Wright and Yogo, 2012). For post estima- tion analysis, we relied on the Hansen statistics and the Cragg-Donald/Kleibergen-Paap F-statistics to test the appropriateness of the instruments. The Hansen statistics test the assumption that the null hypothesis of instrument over-identification restriction should not be rejected (Baum, Schaffer and Stillman, 2003). This technique has recently been uti- lized inmuch-applied research (seeAcheampong, Appiah-Otoo, et al., 2021a; Appiah-Otoo et al., 2021; Appiah-Otoo and Song, 2021b) Also, we applied the Lewbel (2012) two-stage least squares (2SLS) estimator to test the robustness of the IV-GMM estimates. The Lewbel 2SLS technique is applied when appropriate instruments are not available or weak for identifying structural parameters in the regression models with endogenous or mismeasured regressors. The Lewbel 2SLS technique includes internally constructed heteroskedasticity-based instruments generated from the residuals of the auxiliary equation, which is multiplied by each of the included exogenous variables in mean-centered form (Lewbel, 2012). One usefulness of the Lew- bel 2SLS technique is that it does not rely on satisfying standard exclusion restrictions and has recently been utilized in many empirical studies (see, for instance, Acheampong, Erdiaw-Kwasie, et al., 2021c Awaworyi Churchill and Smyth, 2017; Mishra and Smyth, 2015). 4. Results and Discussion 4.1. IV-GMMResults Table 2 presents our baseline results without the interaction effect. The results suggest that all the foreign aid measures (health, agriculture, education, humanitarian, and economic infrastructure and services aid) have a negative and significant effect on economic growth. The estimates show that a 1% increase in health, agriculture, education, humanitarian, and economic infrastructure and services aid reduces economic growth by 0.30, 0.36, 0.53, 0.27, and 0.19%, respectively, at the 1% significance level. Thus, foreign aid in any other forms stymies Africa’s economic growth, and this could be due to African countries’ weak insti- tutions such as corruption, political instability, lack of transparency, and poor economic INTERNATIONAL ECONOMIC JOURNAL 429 Table 2. Baseline results without interaction effects (IV-GMM Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 lnk 0.599∗∗∗ 0.526∗∗∗ 0.515∗∗∗ 0.407∗∗∗ 0.789∗∗∗ 0.608∗∗∗ 0.642∗∗∗ (0.075) (0.076) (0.071) (0.071) (0.091) (0.092) (0.090) lnl −0.154∗∗∗ −0.130∗∗∗ −0.199∗∗∗ −0.104∗∗∗ −0.104∗∗∗ −0.188∗∗∗ −0.164∗∗∗ (0.028) (0.025) (0.025) (0.027) (0.037) (0.030) (0.033) lnhealth −0.297∗∗∗ (0.012) lnagric −0.364∗∗∗ (0.015) lneduc −0.532∗∗∗ (0.020) lnhuma −0.274∗∗∗ (0.012) lnecon −0.190∗∗∗ (0.023) lndcps 0.644∗∗∗ (0.035) lnbm 0.962∗∗∗ (0.051) Constant 6.792∗∗∗ 6.538∗∗∗ 7.751∗∗∗ 6.644∗∗∗ 5.820∗∗∗ 6.386∗∗∗ 4.478∗∗∗ (0.421) (0.394) (0.411) (0.494) (0.587) (0.462) (0.564) Observations 541 548 551 522 542 538 538 R2 0.500 0.550 0.563 0.517 0.219 0.401 0.413 RMSE 0.668 0.646 0.644 0.636 0.856 0.759 0.752 F 273.137 248.898 315.493 225.467 61.733 164.795 173.658 J 14.628 5.678 0.454 1.824 0.952 7.314 8.525 JP 0.001 0.058 0.500 0.177 0.329 0.007 0.004 Notes: To address endogeneity concerns, the lag values of the aid proxies were treated as instruments in the models with aid, whilst the lag values of the financial development proxies were treated as instruments in the models with financial development. Hansen J-statistics J, Hansen J-statistics p-value JP, F-statistics represent weak instrument identification. RMSE is the root mean squared error. Robust standard errors in parentheses. The post estimation statistics suggest that the IV-GMMmodels pass the instrument over-identification restriction in Models 2-5, except Models 1, 6, and 7; however, the F-statistics show that the instruments used are not weak. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01 policies, which adversely affect the effectiveness of foreign aid in the region (Burnside & Dollar, 2000; Dollar & Easterly, 1999). The negative effect of aid on economic growth could also imply that aid promotes dependency syndrome and probably represents a small percentage of total financial resources needed to facilitate Africa’s economic growth. This supports the findings of Babalola and Shittu (2020) and Sothan (2018). The results indicate that financial development proxied by credit and money supply has a positive and significant effect on economic growth. The estimates show that a 1% increase in credit and money supply increase Africa’s economic growth by 0.64% and 0.96%, respectively, at the 1% significance level. The positive and significant impact of financial development on economic growth supports the findings of (Aluko and Ibrahim, 2020; Demetriades and James, 2011; Olalekan, Sunday and Oladeji, 2021; Peia and Roszbach, 2015; Pradhan et al., 2017; Salah, Sjö and Shahbaz, 2013; Seven and Yetkiner, 2016; Zhang, Wang andWang, 2012). These findings could be attributed to the following: First, a devel- oped financial system lessens information asymmetry, which helps channel savings into a profitable investment (Ang, 2008). Second, a developed financial system enhances cor- porate governance and thus increases investment projects productivity (Botev, Egert and Jawadi, 2019). Also, a developed financial system enhances transparency and thus improves hedging against risk (Murinde, 2012). Furthermore, a developed financial system enhances capital accumulation through savings mobilization (Emara and El Said, 2021). Finally, an 430 I. APPIAH-OTOO ET AL. Table 3. Foreign aid, credit, and economic growth (IV-GMM Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 lnk 0.456∗∗∗ 0.295∗∗∗ 0.349∗∗∗ 0.308∗∗∗ 0.459∗∗∗ (0.074) (0.076) (0.071) (0.073) (0.101) lnl −0.185∗∗∗ −0.166∗∗∗ −0.216∗∗∗ −0.112∗∗∗ −0.145∗∗∗ (0.026) (0.021) (0.021) (0.026) (0.030) lndcps 0.435∗∗∗ 0.880∗∗∗ 0.571∗∗∗ 0.534∗∗∗ 1.337∗∗∗ (0.088) (0.154) (0.077) (0.115) (0.260) lnhealth −0.276∗∗∗ (0.066) lnhealth×lndcps 0.003 (0.022) lnagric −0.688∗∗∗ (0.134) lnagric×lndcps 0.121∗∗∗ (0.043) lneduc −0.654∗∗∗ (0.101) lneduc×lndcps 0.064∗∗ (0.033) lnhuma −0.432∗∗∗ (0.092) lnhuma×lndcps 0.073∗∗ (0.032) lnecon −0.874∗∗∗ (0.259) lnecon×lndcps 0.244∗∗∗ (0.091) Constant 6.623∗∗∗ 5.476∗∗∗ 7.065∗∗∗ 5.810∗∗∗ 3.736∗∗∗ (0.374) (0.450) (0.388) (0.579) (0.857) Observations 534 541 544 515 535 R2 0.620 0.653 0.694 0.501 0.383 RMSE 0.584 0.569 0.539 0.648 0.763 F 333.877 349.397 407.912 126.647 104.474 J 2.910 0.161 0.017 1.865 2.582 JP 0.088 0.688 0.897 0.172 0.108 Notes: To address endogeneity concerns, the lag values of the aid proxies were treated as instruments. Hansen J-statistics J, Hansen J-statistics p-value JP, F-statistics represent weak instrument identification. RMSE is the root mean squared error. Robust standard errors in parentheses. The IV-GMMmodels are robust to instrument over-identification restriction since the probability values of the Hansen test are above 5%. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. expanded financial system facilitates trade and enhances specialization that boosts labor productivity (Levine, 2005). This study also established that investment significantly increases economic growth in all themodels. An increase in investment increases Africa’s economic growthwithin 0.41% to 0.79% at the 1% significance level. This supports the endogenous growth theory and the findings of Olalekan, Sunday and Oladeji (2021), Sepehrdoust and Ghorbanseresht (2019), Haftu (2019) and Adeleye and Eboagu (2019), which showed that investment spurs economic growth. There are various reasons for this result. First, investment such as the transport infrastructure contributes to economic growth directly by promoting easier access to inputs at a reduced transaction cost. It indirectly facilitates extra input to dif- ferent sectors. Also, a developed transport network gives faster, affordable, more efficient, and flexible transport facilities that can enable greater productivity in manufacturing and production (Meersman and Nazemzadeh, 2017). The findings show that labor has a negative and significant effect on economic growth in all the models at the 1% significance level. The results show that a 1% rise in labor INTERNATIONAL ECONOMIC JOURNAL 431 Table 4. Foreign aid, money supply, and economic growth (IV-GMM Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 lnk 0.506∗∗∗ 0.507∗∗∗ 0.427∗∗∗ 0.338∗∗∗ 0.644∗∗∗ (0.069) (0.082) (0.067) (0.092) (0.097) lnl −0.162∗∗∗ −0.140∗∗∗ −0.187∗∗∗ −0.065∗ −0.146∗∗∗ (0.026) (0.024) (0.023) (0.037) (0.035) lnbm 1.038∗∗∗ 1.443∗∗∗ 0.940∗∗∗ 1.511∗∗∗ 2.133∗∗∗ (0.226) (0.430) (0.244) (0.370) (0.565) lnhealth −0.614∗∗∗ (0.197) lnhealth×lnbm 0.104∗ (0.056) lnagric −1.208∗∗∗ (0.429) lnagric×lnbm 0.260∗∗ (0.123) lneduc −0.947∗∗ (0.406) lneduc×lnbm 0.145 (0.119) lnhuma −1.274∗∗∗ (0.355) lnhuma×lnbm 0.317∗∗∗ (0.106) lnecon −1.612∗∗ (0.676) lnecon×lnbm 0.432∗∗ (0.199) Constant 3.890∗∗∗ 2.129 4.849∗∗∗ 1.649 −0.130 (0.852) (1.523) (0.930) (1.494) (1.833) Observations 534 541 544 515 535 R2 0.593 0.566 0.676 0.204 0.308 RMSE 0.606 0.637 0.556 0.820 0.809 F 325.218 197.143 372.641 63.715 75.721 J 1.500 0.073 0.016 1.455 2.096 JP 0.221 0.787 0.898 0.228 0.148 Notes: To address endogeneity concerns, the lag values of the aid proxies were treated as instruments. Hansen J-statistics J, Hansen J-statistics p-value JP, F-statistics represent weak instrument identification. RMSE is the root mean squared error. Robust standard errors in parentheses. The IV-GMM models pass the instrument over-identification restriction given the probability values of the Hansen test. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. decreases Africa’s economic growth within the ranges of 0.10% to 0.20%. This conflicts with the endogenous growth theory, which claims that labor is an essential factor of pro- duction. This could be attributed to brain drain, low productivity, and unskilled labor (Acheampong, Boateng, et al., 2021b). Table 3 presents our interaction effect results when credit was used as the financial development proxy. The results suggest that agriculture, education, humanitarian, and economic infrastructure and services aid interact with credit to boost economic growth significantly. However, health aid and credit interaction is positive but not statistically significant. Generally, this finding implies that financial development measured by credit mitigate the adverse influence of health, agriculture, education, humanitarian, and eco- nomic infrastructure and services aid onAfrica’s economic growth. Thus, Africa’s financial system could isolate the negative effect of aid on economic growth when it ensures good corporate control, provides risk management and ensures efficient resource allocation. In 432 I. APPIAH-OTOO ET AL. Table 5. Foreign aid, credit, andeconomicgrowthwithout SouthAfrica,Mauritius, Tunisia, Egypt, Kenya, and Botswana (IV-GMM Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 lnk 0.620∗∗∗ 0.585∗∗∗ 0.596∗∗∗ 0.482∗∗∗ 0.797∗∗∗ 0.689∗∗∗ 0.678∗∗∗ (0.076) (0.075) (0.074) (0.070) (0.088) (0.100) (0.097) lnl −0.204∗∗∗ −0.173∗∗∗ −0.202∗∗∗ −0.132∗∗∗ −0.153∗∗∗ −0.193∗∗∗ −0.174∗∗∗ (0.034) (0.029) (0.028) (0.029) (0.038) (0.041) (0.044) lnhealth −0.245∗∗∗ (0.012) lnagric −0.291∗∗∗ (0.016) lneduc −0.451∗∗∗ (0.022) lnhuma −0.236∗∗∗ (0.013) lnecon −0.163∗∗∗ (0.020) lndcps 0.368∗∗∗ (0.049) lnbm 0.605∗∗∗ (0.075) Constant 7.504∗∗∗ 7.095∗∗∗ 7.587∗∗∗ 6.854∗∗∗ 6.428∗∗∗ 6.833∗∗∗ 5.589∗∗∗ (0.463) (0.418) (0.408) (0.497) (0.547) (0.569) (0.688) Observations 476 476 476 463 470 463 463 R2 0.474 0.471 0.469 0.437 0.264 0.232 0.252 RMSE 0.616 0.618 0.619 0.593 0.731 0.750 0.743 F 188.157 158.501 174.586 139.632 61.082 58.244 60.054 J 11.143 2.351 0.247 1.543 0.555 5.817 6.144 JP 0.004 0.309 0.619 0.214 0.456 0.016 0.013 Notes: To address endogeneity concerns, the lag values of the aid proxies were treated as instruments. Hansen J-statistics J, Hansen J-statistics p-value JP, F-statistics represent weak instrument identification. RMSE is the root mean squared error. Robust standard errors in parentheses. other words, implementing favorable financial regulatory policies that ensure the stabil- ity and effectiveness of the African financial system could condition aid to spur economic growth. InTable 4,we usedmoney supply as the financial development indicator. The interaction effect results suggest that health, agriculture, humanitarian, and economic infrastructure and services aid interact with money supply to enhance economic growth significantly. However, the interaction between education aid and credit is positive but not statis- tically significant. These results indicate that financial development proxied by money supply mitigates the negative impact of health, agriculture, education, humanitarian, and economic infrastructure and services aid on Africa’s economic growth. These findings are not different from our results reported in Table 3. 4.2. Sensitivity Checks From the individual country descriptive statistics, it is observed that the financial system in South Africa, Mauritius, Tunisia, Egypt, Kenya, and Botswana is relatively well devel- oped compared to their counterparts in Africa. Thus, their presence might bias the results derived in Tables 2–4. Accordingly, we omitted these countries and re-estimated our anal- ysis. The results presented in Tables 5–7 are not significantly different from those obtained in Tables 2–4. This implies that our results are robust irrespective of outliers. INTERNATIONAL ECONOMIC JOURNAL 433 Table 6. Foreign aid, credit, andeconomicgrowthwithout SouthAfrica,Mauritius, Tunisia, Egypt, Kenya, and Botswana (IV-GMM Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 lnk 0.478∗∗∗ 0.364∗∗∗ 0.399∗∗∗ 0.451∗∗∗ 0.519∗∗∗ (0.082) (0.081) (0.080) (0.084) (0.114) lnl −0.212∗∗∗ −0.186∗∗∗ −0.229∗∗∗ −0.115∗∗∗ −0.159∗∗∗ (0.034) (0.029) (0.031) (0.034) (0.040) lndcps 0.399∗∗∗ 0.960∗∗∗ 0.682∗∗∗ 0.644∗∗∗ 1.184∗∗∗ (0.099) (0.266) (0.186) (0.180) (0.340) lnhealth −0.343∗∗∗ (0.082) lnhealth×lndcps 0.035 (0.029) lnagric −0.853∗∗∗ (0.270) lnagric×lndcps 0.203∗∗ (0.100) lneduc −1.018∗∗∗ (0.351) lneduc×lndcps 0.210 (0.134) lnhuma −0.804∗∗∗ (0.191) lnhuma×lndcps 0.230∗∗∗ (0.075) lnecon −0.955∗∗∗ (0.346) lnecon×lndcps 0.298∗∗ (0.131) Constant 7.043∗∗∗ 5.422∗∗∗ 6.832∗∗∗ 5.228∗∗∗ 4.207∗∗∗ (0.443) (0.675) (0.462) (0.861) (1.003) Observations 469 469 469 456 463 R2 0.514 0.505 0.540 0.154 0.210 RMSE 0.594 0.599 0.577 0.728 0.759 F 220.964 124.052 121.504 48.705 43.214 J 2.545 0.000 0.129 1.195 1.643 JP 0.111 0.984 0.719 0.274 0.200 Notes: To address endogeneity concerns, the lag values of the aid proxies were treated as instruments. Hansen J-statistics J, Hansen J-statistics p-value JP, F-statistics represent weak instrument identification. RMSE is the root mean squared error. Robust standard errors in parentheses. 4.3. Robustness Check Using the Lewbel 2SLS stimator We used Lewbel’s 2SLS model to re-estimate our results. The results are presented in Tables 8–10. Consistent with our results in Tables 2–4, all the aid measures (health, edu- cation, humanitarian, and economic infrastructure and services aid) significantly retard economic growth while the financial development indicators significantly enhance eco- nomic growth. The interaction effect results also indicate that most foreign aid measures interact with credit andmoney supply to significantly increase economic growth, except for the interaction effect of education and humanitarian aid and money supply on economic growth, which showed an insignificant positive impact. 4.4. Country-specific Analysis Given the differences in financial development and aid inflows, we used the IV-GMM results in Table 3 to present the country-specificmarginal effect of aid on economic growth. 434 I. APPIAH-OTOO ET AL. Table 7. Foreign aid, money supply, and economic growth without South Africa, Mauritius, Tunisia, Egypt, Kenya, and Botswana (IV-GMM Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 lnk 0.532∗∗∗ 0.596∗∗∗ 0.518∗∗∗ 0.532∗∗∗ 0.634∗∗∗ (0.077) (0.098) (0.081) (0.092) (0.106) lnl −0.182∗∗∗ −0.154∗∗∗ −0.188∗∗∗ −0.091∗∗∗ −0.147∗∗∗ (0.034) (0.031) (0.030) (0.035) (0.042) lnbm 0.911∗∗∗ 1.378∗∗ 1.175∗∗∗ 1.337∗∗∗ 1.826∗∗∗ (0.259) (0.583) (0.409) (0.296) (0.705) lnhealth −0.655∗∗∗ (0.234) lnhealth×lnbm 0.122∗ (0.069) lnagric −1.339∗∗ (0.623) lnagric×lnbm 0.318∗ (0.187) lneduc −1.653∗∗ (0.809) lneduc×lnbm 0.379 (0.255) lnhuma −1.500∗∗∗ (0.326) lnhuma×lnbm 0.411∗∗∗ (0.102) lnecon −1.558∗∗ (0.788) lnecon×lnbm 0.432∗ (0.240) Constant 4.505∗∗∗ 2.357 3.893∗∗∗ 2.126∗ 0.964 (1.049) (2.174) (1.414) (1.244) (2.266) Observations 469 469 469 456 463 R2 0.496 0.437 0.504 0.052 0.176 RMSE 0.606 0.641 0.602 0.774 0.778 F 251.338 69.214 120.946 36.773 32.330 J 1.503 0.001 0.096 1.576 1.582 JP 0.220 0.976 0.757 0.209 0.208 Notes: To address endogeneity concerns, the lag values of the aid proxies were treated as instruments. Hansen J-statistics J, Hansen J-statistics p-value JP, F-statistics represent weak instrument identification. RMSE is the root mean squared error. Robust standard errors in parentheses. The results are presented in Figures 7–11. The results show that the mean marginal effect of aid on economic growth is positive in all the countries. However, we observed thatMau- ritius, South Africa, Gabon, Tunisia, and Botswana have a high effect of aid on economic growth. This can be ascribed to their developed financial system (Allen, Otchere and Sen- bet, 2011). We also found that Malawi, Guinea Bissau, Sierra Leone, and DRC have the lowest effect. This could also be attributed to their underdeveloped financial system (Allen, Otchere and Senbet, 2011). These are novel findings that have significant implications for practice in Africa. 5. Conclusion and Policy Recommendation This study examines the role of financial development in foreign aid (measured by agri- culture, humanitarian, health, education, and economic infrastructure and services aid) and economic growth nexus in Africa using a more recent dataset for 2002–2018. First, this study contributes to the literature by using five different measures of aid: agriculture, INTERNATIONAL ECONOMIC JOURNAL 435 Table 8. Baseline results (Lewbel 2SLS Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 lnk 0.620∗∗∗ 0.585∗∗∗ 0.541∗∗∗ 0.444∗∗∗ 0.783∗∗∗ 0.690∗∗∗ 0.621∗∗∗ (0.076) (0.075) (0.069) (0.065) (0.083) (0.097) (0.095) lnl −0.204∗∗∗ −0.173∗∗∗ −0.193∗∗∗ −0.141∗∗∗ −0.153∗∗∗ −0.193∗∗∗ −0.151∗∗∗ (0.034) (0.029) (0.028) (0.028) (0.038) (0.039) (0.043) lnhealth −0.245∗∗∗ (0.012) lnagric −0.291∗∗∗ (0.016) lneduc −0.451∗∗∗ (0.022) lnhuma −0.235∗∗∗ (0.013) lnecon −0.161∗∗∗ (0.020) lndcps 0.385∗∗∗ (0.046) lnbm 0.648∗∗∗ (0.072) Constant 7.504∗∗∗ 7.095∗∗∗ 7.609∗∗∗ 7.120∗∗∗ 6.441∗∗∗ 6.777∗∗∗ 5.243∗∗∗ (0.463) (0.418) (0.406) (0.451) (0.535) (0.519) (0.680) Observations 476 476 476 463 470 463 463 R2 0.474 0.471 0.468 0.438 0.263 0.231 0.248 RMSE 0.616 0.618 0.619 0.593 0.732 0.751 0.745 Heteroscedasticity robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 Table 9. Foreign aid, credit and economic growth (Lewbel 2SLS Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 lnk 0.109∗∗ 0.155∗∗∗ 0.191∗∗∗ 0.222∗∗∗ 0.341∗∗∗ (0.055) (0.046) (0.039) (0.046) (0.056) lnl −0.125∗∗∗ −0.080∗∗∗ −0.089∗∗∗ −0.078∗∗∗ −0.104∗∗∗ (0.018) (0.012) (0.013) (0.017) (0.016) lndcps 0.331∗∗∗ 0.291∗∗∗ −0.023 0.255∗∗∗ 0.217∗∗∗ (0.030) (0.026) (0.033) (0.037) (0.037) lnhealth −0.772∗∗∗ (0.027) lnhealth×lndcps 0.258∗∗∗ (0.012) lnagric −0.811∗∗∗ (0.033) lnagric×lndcps 0.249∗∗∗ (0.016) lneduc −0.807∗∗∗ (0.018) lneduc×lndcps 0.217∗∗∗ (0.010) lnhuma −0.654∗∗∗ (0.036) lnhuma×lndcps 0.192∗∗∗ (0.016) lnecon −0.674∗∗∗ (0.032) lnecon×lndcps 0.222∗∗∗ (0.014) Constant 5.860∗∗∗ 5.004∗∗∗ 6.094∗∗∗ 5.360∗∗∗ 5.258∗∗∗ (0.290) (0.204) (0.208) (0.343) (0.277) Observations 534 541 544 515 535 R2 0.771 0.848 0.865 0.706 0.798 RMSE 0.454 0.377 0.358 0.497 0.436 Heteroscedasticity robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 436 I. APPIAH-OTOO ET AL. Table 10. Foreign aid, money supply and economic growth (Lewbel 2SLS Results). Variables Model 1 Model 2 Model 3 Model 4 Model 5 lnk 0.443∗∗∗ 0.444∗∗∗ 0.446∗∗∗ 0.389∗∗∗ 0.682∗∗∗ (0.062) (0.066) (0.061) (0.069) (0.088) lnl −0.139∗∗∗ −0.134∗∗∗ −0.196∗∗∗ −0.105∗∗∗ −0.111∗∗∗ (0.025) (0.022) (0.022) (0.025) (0.031) lnbm 1.455∗∗∗ 0.867∗∗∗ 0.707∗∗∗ 0.558∗∗∗ 1.218∗∗∗ (0.189) (0.140) (0.095) (0.128) (0.106) lnhealth −1.025∗∗∗ (0.163) lnhealth×lnbm 0.223∗∗∗ (0.046) lnagric −0.629∗∗∗ (0.146) lnagric×lnbm 0.095∗∗ (0.041) lneduc −0.560∗∗∗ (0.131) lneduc×lnbm 0.032 (0.039) lnhuma −0.280∗∗ (0.113) lnhuma×lnbm 0.022 (0.034) lnecon −0.585∗∗∗ (0.105) lnecon×lnbm 0.123∗∗∗ (0.029) Constant 2.324∗∗∗ 4.132∗∗∗ 5.709∗∗∗ 5.090∗∗∗ 2.271∗∗∗ (0.683) (0.557) (0.484) (0.629) (0.614) Observations 534 541 544 515 535 R2 0.536 0.628 0.692 0.579 0.468 RMSE 0.647 0.590 0.542 0.597 0.709 Heteroscedasticity robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 humanitarian, health, economic infrastructure and services, and education aid. The use of these different forms of aid measures is backed by Maruta (2019) call that ‘the question of aid effectiveness has shifted from “Is aggregated aid helpful?” to “Which type of aid is more helpful?” Also, donors have repeatedly stressed that they pursuemultiple aims when delivering aid’. Thus, the need to explore the differential effect of different forms of aid on economic growth in Africa. Second, this study extends the literature by probing the conditional effect relationship between financial development and foreign aid on Africa’s economic growth. Finally, our study contributes to the literature by examining the country-specific marginal effect of the moderating role of financial development in the aid-growth nexus. Using the IV-GMM technique, our findings indicated that (i) while foreign aid (mea- sured by agriculture, humanitarian, health, education, and economic infrastructure and services aid) impedesAfrica’s growth, financial development (measured by domestic credit to the private sector, and money supply) spurs economic growth. (ii) the conditional effect analysis showed that financial development conditions foreign aid to spur economic growth. (iii) the country-specific analysis further showed that foreign aid has a higher growth elasticity in countries with relatively better financial systems, such as Mauritius, South Africa, Gabon, Tunisia, and Botswana, whilst the growth elasticity of aid is smaller in countries with a relatively weak financial system such as Malawi, Guinea Bissau, Sierra INTERNATIONAL ECONOMIC JOURNAL 437 Figure 7. Marginal effect of health aid. Figure 8. Marginal effect of agriculture aid. 438 I. APPIAH-OTOO ET AL. Figure 9. Marginal effect of education aid. Figure 10. Marginal effect of humanitarian aid. INTERNATIONAL ECONOMIC JOURNAL 439 Figure 11. Marginal effect of economic infrastructure and services aid. Leone, and the Democratic Republic of Congo. (iv) these results are robust to outliers and alternative econometric estimation approaches. These findings offer important practical implications for policymakers in Africa. The conclusions of this study show that aid specificity such as agriculture, humanitarian, health, education, and economic infrastructure and services aids hamper economic growth in Africa. This observation could be attributed to the general observation that foreign aid inhibits economic growth in Africa since the region has weak institutions due to a high level of corruption, lack of transparency, poor regulatory system, government ineffective- ness, and poor economic policies. This suggests that improving aid effectiveness in Africa requires policymakers to strengthen and enhance existing domestic institutions. Thus, institutional reforms and implementation of good domestic economic policies remain critical for enhancing aid effectiveness in Africa. It is also argued that foreign aid has promoted dependence syndrome in Africa, thus hampering its domestic revenue mobi- lizations. The policy implication is that African countries should implement innovative ways to improve domestic revenue mobilization and stay away from foreign aid. Increas- ing domestic revenuemobilization requires digitizing African economies. Also, enhancing and empowering domestic tax mobilization authorities remains imperative for increasing tax revenue in Africa. The findings suggest that financial development direct facilitates economic growth in Africa since a developed financial system lessens information asymmetry, channels savings into a profitable investment, promote good corporate governance, enhances transparency and hedging against risks, increases capital accumulation, and facilitates trade and labor productivity (Ang, 2008; Botev, Egert and Jawadi, 2019; Emara and El Said, 2021; Levine, 440 I. APPIAH-OTOO ET AL. 2005). These suggest that any impediments to Africa’s financial system would be detri- mental to the region’s economic growth. Therefore, implementing policies that will ensure financial liberalization and stability in Africa is vital for driving the technological innova- tion needed to promoteAfrica’s economic growth. It should be noted that creating enabling environment that will enhance the development of Africa’s financial systemwouldmitigate the adverse effect of aid on economic growth in the region. This study focused on Africa; thus, future investigations should extend this study to other developing countries such as Asia and South America. Future studies should also use private demand as an alternative measure of economic growth since aid’s overall goal is to augment the private sector’s economic performance. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This workwas supported byMinistry of Education ofHumanities and Social ScienceGrant of China: [Grant Number 17YJC790127]. Notes on Contributors Isaac Appiah-Otoo received his Ph.D. degree in finance from the University of Electronic Science and Technology of China, Chengdu, China. His research interest lies in financial economics, finan- cial technology (fintech), applied econometrics, development economics, energy and environmental economics, and international finance. Alex O. Acheampong is an Assistant Professor of Economics at Bond Business School, Bond Univer- sity, Australia, and a sessional academic at the University of Newcastle Business School, Australia. Alex’s research interest lies in applied econometrics, energy economics, environmental economics, macroeconomics, and development economics. Na Song received her Ph.D. degree in applied mathematics from the University of Hong Kong, Hong Kong, in 2013. She is currently an Assistant Professor of mathematical finance at the Uni- versity of Electronic Science and Technology of China, Chengdu, China. Her research interests include mathematical finance, quantitative risk management, stochastic calculus, as well as their applications. Camara Kwasi Obeng is a Senior Lecturer at the School of Economics, University of Cape Coast, Ghana.His research interests are international trade and finance, fiscal implications of trade reforms, environment and poverty studies with a particular focus on the use of Computable General Equi- librium (CGE), Product Space Analysis, Randomised Control Trials methodologies, and currently, Machine Learning. Isaac K. Appiah is currently a Senior Research Officer at the Commission of Human Rights and Administrative Justice, Ghana. He studied at the University of Cape Coast and the University of Ghana and conducts research in the areas of Applied Statistics, Econometrics, and Surveys. ORCID Isaac Appiah-Otoo http://orcid.org/0000-0002-3683-0634 http://orcid.org/0000-0002-3683-0634 INTERNATIONAL ECONOMIC JOURNAL 441 References Acheampong, A. O., Amponsah, M., & Boateng, E. (2020). Does financial development mitigate carbon emissions? Evidence from heterogeneous financial economies. 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(2012). Financial development and economic growth: Recent evi- dence from China. Journal of Comparative Economics, 40(3), 393–412. https://doi.org/10.1016/j. jce.2012.01.001 https://doi.org/10.1016/j.econmod.2013.08.031 https://doi.org/10.1016/j.worlddev.2014.11.010 https://doi.org/10.1016/j.kjss.2018.01.008 https://doi.org/10.1016/j.eap.2019.07.002 https://doi.org/10.1016/j.ecosys.2015.09.004 https://doi.org/10.1353/mcb.2006.0095 https://doi.org/10.2307/1884513 https://doi.org/10.1080/09638199.2017.1349167 https://doi.org/10.1198/073500102288618658 https://doi.org/10.1002/ijfe.1604 . https://doi.org/10.1016/j.ecosys.2019.100702 https://doi.org/10.2478/cer-2018-0026 https://doi.org/10.1016/j.inteco.2016.11.003 https://doi.org/10.1016/j.jce.2012.01.001 1. Introduction 2. Literature Review 2.1. Foreign aid and Economic Growth 2.2. Finance and Economic Growth 2.3. Foreign aid and Financial Development 3. Data and Methodology 3.1. Data 3.2. Theoretical Model and Hypothesis Development 3.3. Estimation Strategy 4. Results and Discussion 4.1. IV-GMM Results 4.2. Sensitivity Checks 4.3. Robustness Check Using the Lewbel 2SLS stimator 4.4. Country-specific Analysis 5. Conclusion and Policy Recommendation Disclosure statement Funding ORCID References << /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles false /AutoRotatePages /PageByPage /Binding /Left /CalGrayProfile () /CalRGBProfile (Adobe RGB \0501998\051) /CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.3 /CompressObjects /Off /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages false /CreateJobTicket false /DefaultRenderingIntent /Default /DetectBlends true /DetectCurves 0.1000 /ColorConversionStrategy /sRGB /DoThumbnails true /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 524288 /LockDistillerParams true /MaxSubsetPct 100 /Optimize true /OPM 1 /ParseDSCComments false /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo false /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings false /StartPage 1 /SubsetFonts true /TransferFunctionInfo /Remove /UCRandBGInfo /Remove /UsePrologue false /ColorSettingsFile () /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 150 /ColorImageMinResolutionPolicy /OK /DownsampleColorImages true /ColorImageDownsampleType /Bicubic /ColorImageResolution 300 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages false /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.90 /HSamples [2 1 1 2] /VSamples [2 1 1 2] >> /ColorImageDict << /QFactor 0.40 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 15 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 15 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 150 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages false /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.90 /HSamples [2 1 1 2] /VSamples [2 1 1 2] >> /GrayImageDict << /QFactor 0.40 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 15 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 15 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages true /MonoImageDownsampleType /Average /MonoImageResolution 300 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict << /K -1 >> /AllowPSXObjects true /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /Description << /ENU () >> >> setdistillerparams << /HWResolution [600 600] /PageSize [493.483 703.304] >> setpagedevice