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Abstract

Though the impact of foreign aid on economic growth has been examined in several studies, the roles of human and physical capital in aid-growth nexus has not been investigated. This is important because the effectiveness of foreign aid could be contingent upon the absorption capacity of the recipient country. We fill this research gap by examining the moderating roles of human and physical capital on aid-growth nexus in Nigeria during 1980 to 2020. Our study deploys the Autoregressive Distributed Lag approach on the interaction models. It shows that the variables are cointegrated. Though aid has a positive impact on growth, human and physical capital have significant moderating roles on the impact of aid on growth. Precisely, the marginal effect of aid on growth varies with the levels of human and physical capital. However, if human and physical capital surpass the threshold levels of 55% and 65%, respectively, an increase in aid cannot accelerate growth. These findings are robust to alternative proxies, structural breaks as well as diagnostic tests. This research implies that, given the requisite levels of human and physical capital, foreign aid can complement domestic capital to enhance growth. Hence, for nations to obtain the long-term benefits of aid, they should create the enabling environment by adequately investing in human and physical capital to attain sustainable economic development.

JEL Classification: O47; F35; E22; J24

Keywords

economic growth foreign aid physical capital human capital

Introduction

The nexus between foreign capital inflows and economic growth can be traced to the endogenous and neoclassical growth theories which emphasize that capital accumulation and technological progress accelerate economic growth (Ehigiamusoe & Lean, 2019a). Foreign capital inflows provide funds to the productive sectors to improve the marginal productivity of capital particularly in countries with insufficient domestic capital. Foreign capital inﬂows close the gap between saving and investment in most developing nations (Babalola et al., 2018; Lipsey, 1999). Besides, foreign capital inflows serve as the key channels through which managerial expertise, technology transfers, and production eﬃciency flow to many developing countries. One of the main constituents of foreign capital inflows is foreign aid (hereafter referred to as aid). It is the movement of resources from one nation to another (provided by non-governmental organizations, governments, or individuals) to support economic development process in the recipient countries (Bhavan et al., 2010; Yiew & Lau, 2018). Most developing countries depend on aid to drive economic growth (hereafter referred to as growth) probably because the theoretical literature supports the growth-enhancing effect of aid (Bhattacharyya & Intartaglia, 2021; Cai et al., 2018; Haldar & Sethi, 2022). However, the impact of aid on growth in the recipient economy may differ from one country to another.

Furthermore, physical capital has been considered as a fundamental determinant of growth. For example, the Harrod and Domar growth model stresses the importance of saving and investments in the process of growth. To replace worn-out capital stock, a country needs to save a certain percentage of her income and embark on new investments (in form of net additions to capital stock) to boost growth. The more the savings and investments a country has, the faster the economy will grow (Domar, 1946; Ehigiamusoe & Lean, 2019a). The production function indicates that real output would increase if capital or labor increases (Arndt et al., 2015; Solow, 1956). Thus, investments in physical and human capital can lead to external economies and improvement in technology which enhance long-run growth. Besides, there is a link between foreign aid and capital accumulation since foreign capital inflows increase capital accumulation while the latter enhances the utilization of the former. Therefore, the first research question is: Does physical capital have a significant moderating role on the impact of aid on growth in Nigeria?

The endogenous growth model posits that sustained growth is mainly caused by endogenous factors which are inside the system (e.g., innovation, human capital) instead of exogenous variables. It argues that improvement in productivity arises from faster innovation, knowledge accumulation as well as greater investments in human capital (Lucas, 1988; Romer, 1986; Sothan, 2018). The theory emphasizes the positive externalities and spillovers effect of a knowledge-based economy that boost economic development. Besides, some studies have shown that human capital and foreign capital inflows (e.g., foreign aid) have dynamic relationship. For instance, Kosack and Tobin (2006) and Yogo (2017) argued that foreign aid and human capital have dynamic relationship, while Serieux (2011) opined that aid is ineffective in some nations because of the diversion of the funds from investment that can spur growth (e.g., human capital growth). Though some scholars have studied the impact of human capital on economic expansion (Ang, 2010; Ehigiamusoe & Lean, 2017), no empirical study (based on our knowledge) have investigated whether the interaction between aid and human capital affect growth. Hence, the second research question is: Does human capital have a significant moderating role on the impact of aid on growth in Nigeria?

This study focuses on Nigeria because the country has experienced large inflows of foreign aid in the last four decades. For instance, the net official aid received increased from USD76.04 million in 1980 to USD3.75 billion in 2020 (World Development Indicators, 2022). Similarly, the contribution of aid relative to Gross National Income (GNI) increased from 0.06% in 1980 to 0.81% in 2020. During this period, Nigeria’s real GDP per capita rose from USD2159.9 to USD2396.3 while the average GDP growth rate was 3.05%. Besides, the available statistics show that the average physical capital was 36.60% whereas the average human capital was 32.26% (World Development Indicators, 2022). The trends of these statistics suggest the need to use econometric analysis to unravel the connection between these variables.

Therefore, the specific objectives of this study include: (i) To examine the moderating role of physical capital on the impact of aid on growth in Nigeria (ii) To determine the moderating role of human capital on the impact of aid on growth in Nigeria. Hence, the motivation for this study emerged from the observed gaps in previous studies. For instance, most previous empirical works on the connection between aid and growth used only one proxy to measure foreign aid, and they did not account for structural breaks and other important growth determinants (e.g., human capital, physical capital). Our research fills this gap by using two different proxies to measure foreign aid in a framework that accounts for structural breaks and other conventional determinants of growth. Second, some scholars have contended that the impact of aid on growth could be affected by the policy environment, institutional quality as well as financial liberalization in the recipient country (Ang, 2010; Babalola & Shittu, 2020; Burnside & Dollar, 2000). To the best of our knowledge, the moderating role of physical capital on the impact of aid on growth has not been empirically investigated. Besides, our study shows the marginal effect of aid on growth at different levels of physical capital, a subject that has yet to be empirically determined. The empirical outcomes on how the interaction between aid and physical capital influence growth can assist the formulation of the appropriate policies to boost foreign aid, physical capital, and growth. Third, our research epitomizes a novel idea which investigates the moderating role of human capital on the impact of aid on growth in Nigeria. The findings of this research will enhance policy formulations. For example, the marginal effect of aid on growth at diverse levels of human capital will indicate how a simultaneous rise in both aid and human capital will accelerate economic expansion.

The structure of this paper proceeds as follows: the first section presents the introduction; the second section reviews the related literature; the third section displays the methodology; the fourth section presents the discussion of the results; and the fifth section displays the policy implications.

Literature Review

The empirical literature on the impact of foreign aid on economic growth has produced mixed outcomes. A strand of the literature posits that aid promotes economic growth (Bhattacharyya & Intartaglia, 2021; Cai et al., 2018; Haldar & Sethi, 2022; Hansen & Tarp, 2001; Juselius et al., 2014). For instance, Hansen and Tarp (2001) examined the impact of aid on economic growth in 56 countries and revealed that aid enhances economic growth regardless of the policy environment of the recipient country. Juselius et al. (2014) also investigated the impact of aid on economic growth in 36 countries and reported that aid promotes economic growth. They posited that aid accelerates economic growth by boosting the other determinants of economic growth (e.g., physical and human capital). In a panel data analysis of 47 countries, Cai et al. (2018) found that aid boosts economic growth. They noted that the growth-enhancing effect of aid is contingent upon the political stability of the recipient country. Hence, they advocated for a stable political environment for countries that want to obtain the economic benefits of aid. Maruta et al. (2020) noted that aid to the education and health sectors enhances economic growth in 74 developing countries albeit aid to the agricultural sector has tenuous effect on growth. Bhattacharyya and Intartaglia (2021) noted that diversified aid accelerates economic growth in 126 countries. Boateng et al. (2021) also revealed that aid promotes economic growth albeit aid volatility hinders economic growth in 45 African countries. Abate (2022) revealed that aid contributes to economic growth in 48 developing countries while Haldar and Sethi (2022) reported that aid to the agricultural and social sectors accelerate economic growth in 32 countries.

Another strand of the literature opined that aid is not a significant driver of economic growth (Adedokun, 2017; Bird & Choi, 2020; Duodu & Baidoo, 2022; Easterly et al., 2004; Liu & Li, 2022). In a panel data analysis of 62 countries, Easterly et al. (2004) noted that aid has insignificant impact on economic growth, even in a good policy environment. Adedokun (2017) found that aid does not contribute to economic growth in 47 countries. Sethi et al. (2019) revealed that aid does not contribute to economic growth in Sri Lanka. Bird and Choi (2020) also noted that aid is not a significant driver of economic growth in 76 countries. Duodu and Baidoo (2022) also showed that aid does not promote economic growth in Ghana regardless of the quality of institutions. Liu and Li (2022) reported that neither aid nor its volatility influences economic growth in 78 countries

At the extreme, some empirical works have indicated that aid inhibits economic growth (Nwosa & Akinbobola, 2016; Rajan & Subramanian, 2008; Shao & Wang, 2022). Specifically, Rajan and Subramanian (2008) reported that different forms of aid inhibit economic growth, even under good policy and different geographical environments. Nwosa and Akinbobola (2016) noted that aid hinders economic growth albeit macroeconomic policy plays a significant moderating role. Sothan (2018) found that aid has a negative long-run impact on economic growth in Cambodia while Shao and Wang (2022) showed that aid impedes economic growth in China.

Apart from the direct impact of aid on economic growth, some studies have investigated the variables that moderate the impact of aid on economic growth such as institutional quality, financial market development, foreign direct investment, macroeconomic policy environment, and political stability (Ang, 2010; Babalola & Shittu, 2020; Baharumshah et al., 2017; Burnside & Dollar, 2000; Harb & Hall, 2019; Teboul & Moustier, 2001; Younsi et al., 2021). For instance, Burnside and Dollar (2000) revealed that good policy environment has a positive moderating role on the impact of aid on economic growth in 56 countries. Teboul and Moustier (2001) reported that domestic saving and foreign direct investment favorably moderate the impact of aid on economic growth in 6 countries. Ang (2010) showed that financial liberalization has a favorable moderating role on the impact of aid on economic growth in India. Harb and Hall (2019) also disclosed that the impact of aid on economic growth in 25 countries depends on the level of economic development, albeit corruption has no significant moderating role. Babalola and Shittu (2020) found that institutional quality favorably moderates the impact of aid on economic growth in 16 countries. Younsi et al. (2021) revealed that domestic and foreign direct investment have a positive moderating role on the impact of aid on economic growth in 41 countries. Table 1 presents the summary of some empirical studies on the impact of aid on economic growth and the variables that moderate it.

Table 1.

Summary of Studies on the Impact of Aid on Growth and the Variables that Moderate it.

Author	Country/Region	Methodology	Main findings
Burnside and Dollar (2000)	56 countries (1970–1993)	OLS	Good policy environment positively moderates the influence of aid on growth.
Hansen and Tarp (2001)	56 countries (1974–1993)	IV Regression	Aid improves growth in countries with both favorable and unfavorable policy environment.
Teboul and Moustier (2001)	6 countries (1960–1996)	OLS	Domestic saving and foreign direct investment favorably moderate the effect of aid on growth.
Easterly et al. (2004)	62 countries (1970–1997)	OLS, 2SLS	Aid has insignificant influence on growth, even in good policy environment.
Azarnert (2008)	41 nations (1980–2000)	Endogenous growth model	Aid has detrimental impact on incentive for human capital accumulation.
Williamson (2008)	208 countries (1973–2004)		Aid is ineffective in promoting human capital development.
Shirazi et al. (2009)	Pakistan	VECM	Aid has direct relationship with human capital.
Rajan and Subramanian (2008)	83 countries (1960–2000)	OLS, IV, GMM	Different forms of aid inhibit growth, even under good policy and different geographical environment.
Ang (2010)	India (1966–2005)	ARDL	Financial liberalization favorably moderates the aid-growth relationship.
Juselius et al. (2014)	36 countries (1960–2007)	VAR	Aid boosts growth via the determinant of growth (i.e., human capital as well as physical capital).
Slesman et al. (2015)	80 nations 1,975–2,005	Threshold Regression	Foreign capital inflows enhance growth, and high institutional quality favorably moderates it.
Nwosa and Akinbobola (2016)	Nigeria. (1970–2013)	ARDL	Aid hinders growth, but macroeconomic policy plays significant moderating role.
Adedokun (2017)	47 nations (1996–2012)	GMM	Aid does not contribute to growth, albeit governance boosts growth.
Baharumshah et al. (2017)	80 nations (1975–2007)	Threshold Regression	The economic benefits of foreign capital inflows are contingent upon financial market development level.
Guillaumont et al. (2017)	Panel data (1977–2007)	2SLS, GMM	Human capital and economic performance matter for aid-growth nexus.
Cai et al. (2018)	47 nations (1980–2013)	Fixed Effect	Aid promotes growth, albeit the effect is contingent upon political stability of the recipient nation.
Harb and Hall (2019)	25 countries	PSTR	Influence of aid on growth depends on economic development level, but corruption has no moderating role.
Wang and Zhuang (2019)	86 nations (1970–2015)	IV, Fixed and Random effect	Aid is most effective in nations that have low human capital development.
Anetor (2020)	28 countries (1999–2017)	GMM	Interaction term between human capital and FDI has harmful effect on growth.
Babalola and Shittu (2020)	16 nations (1996–2017)	Panel ARDL	Quality of institution favorably moderates the influence of aid on growth.
Bird and Choi (2020)	76 economies (1976–2015)	GMM	Aid does not contribute to growth.
Bhattacharyya and Intartaglia (2021)	126 nations (1965–2010)	Fixed Effect	Diversified aid network accelerates growth.
Shao and Wang (2022)	China	OLS, GTWR	Aid impedes growth in recipient nation.
Younsi et al. (2021)	41 nations (1990–2016)	GMM	Domestic and foreign direct investment favorably moderate the influence of aid on growth.
Duodu and Baidoo (2022)	Ghana (1984–2018)	ARDL	Aid has insignificant effect on growth, and institutional quality does not moderate the nexus.
Liu and Li (2022)	78 nations (2003–2017)	GMM	Neither aid nor its volatility influences growth.

Note. GMM = generalized method of moments; ARDL = autoregressive distributed lag model; GTWR = geographically and temporally weighted regression; 2SLS = two stage least squares; IV = instrumental variable; OLS = ordinary least squares; VAR = vector autoregression; PSTR = panel smooth transition regression model; VECM = vector error correction model.

This literature review indicates that the empirical works on the direct impact of aid on growth are mixed and inconclusive. Besides, the few empirical works that have investigated the variables that moderate the impact of aid on growth focused on institutions, financial development, foreign direct investment, domestic saving, and government policy, while the moderating roles of physical and human capital have not been empirically determined. It is fundamental to investigate this issue because some studies have asserted that aid, physical capital, human capital, and economic growth have dynamic relationships (Azarnert, 2008; Guillaumont et al., 2017; Shirazi et al., 2009; Wang & Zhuang, 2019; Williamson, 2008). Our study fills this research gap using empirical strategy that enables us to show whether physical and human capital have favorable or adverse moderating roles on the impact of aid on economic growth. It also shows the marginal effect of aid on economic growth at various levels of physical and human capital. To the best of our knowledge, these issues have not been empirically investigated. By showing how a simultaneous rise in aid and physical capital (or human capital) influence economic growth, this study will enhance policy formulations.

Methodology

Model Specification

The relationship between foreign aid and economic growth is based on the neoclassical growth theory which emphasizes the importance of capital accumulation, labor, and technological progress in promoting economic growth (Ehigiamusoe & Lean, 2019a; Solow, 1956). Foreign aid provides funds to the productive sectors to improve the marginal productivity of capital, particularly in countries with insufficient domestic capital. Foreign aid closes the gap between saving and investment, as well as serves as the key channel through which managerial expertise, technology transfers, and production eﬃciency flow to many developing countries (Babalola et al., 2018; Lipsey, 1999). Besides, Mankiw et al. (1992) noted that an augmented Solow model which comprises physical and human capital accumulation can explain economic growth. In line with the theoretical and empirical literature, our baseline model indicates that economic growth is a function of foreign aid, physical capital, human capital, and other determinants (Ang, 2010; Arndt et al., 2015; Mallik, 2008; Sothan, 2018):

$\begin{matrix} GD P_{t} = α_{0} + α_{1} AI D_{t} + α_{2} CA P_{t} + α_{3} HC A_{t} + α_{4} GO V_{t} \\ + α_{5} IN F_{t} + α_{6} TO P_{t} + ε_{t} \end{matrix}$ (1)

where: GDP = economic growth (real GDP per capita in constant 2015 USD), AID = foreign aid (net official development assistance as well as official aid received in constant 2018 USD), CAP = physical capital (proxied by gross fixed capital formation relative to GDP), HCA = human capital (proxied by gross secondary school enrollment rate), INF = inflation rate, GOV = government consumption expenditures relative to GDP, TOP = trade openness relative to GDP. Before conducting the analysis, all the variables except inflation rate were transformed into natural logarithm.

To establish the moderating role of physical capital on the impact of aid on growth, we add the interaction term between aid and physical capital to the model as follows:

$\begin{matrix} GD P_{t} = ϕ_{0} + ϕ_{1} AI D_{t} + ϕ_{2} (AID * CAP)_{t} + ϕ_{3} CA P_{t} \\ + ϕ_{4} HC A_{t} + ϕ_{5} GO V_{t} + ϕ_{6} IN F_{t} + ϕ_{7} TO P_{t} + ε_{t} \end{matrix}$ (2a)

where $AID * CAP$ = interaction term between aid and physical capital.

Some empirical studies stressed the need to use the interaction model to capture the conditional hypothesis between three or more variables (Brambor et al., 2006; Ehigiamusoe, 2020). A conditional hypothesis is a situation in which the relationship between two variables depends on the value of another variable. Some previous studies have examined the interaction effect between two variables regardless of their stationarity properties (e.g., Ang, 2010; Babalola & Shittu, 2020; Teboul & Moustier, 2001; Younsi et al., 2021). In our research, the relationship between aid and growth could be contingent upon the level of physical capital since some studies have showed that aid and physical capital have dynamic relationship (Juselius et al., 2014). Through the interaction term, we determine whether physical capital has a favorable or adverse moderating role on the impact of aid on growth. The marginal effect of aid on growth can be computed by taking the partial derivatives of Equation 2a with respect to aid as follows:

$\frac{\partial GD P_{t}}{\partial AI D_{t}} = ϕ_{1} + ϕ_{2} CA P_{t}$ (2b)

Our emphasis is on the significance and sign of the coefficients of aid ( $ϕ_{1}$ ) and interaction term ( $ϕ_{2}$ ). If $ϕ_{1} > 0$ and $ϕ_{2} < 0$ , it suggests that aid accelerates growth, but physical capital adversely affects that favorable impact. If $ϕ_{1} < 0$ and $ϕ_{2} > 0$ , it indicates that aid impedes growth, but physical capital weakens that unfavorable impact. However, a positive marginal effect ( $ϕ_{1} + ϕ_{2} CAP$ ) indicates that more aid and physical capital facilitate growth, while a negative marginal effect suggests otherwise. Besides, if $ϕ_{1}$ and $ϕ_{2}$ have different signs, it suggests that a threshold level of physical capital exists beyond which aid cannot accelerate growth (Ehigiamusoe et al., 2019; Olaniyi & Oladeji, 2021). Hence, it would be necessary to compute the threshold level of physical capital using the following Equation:

$CAP > - (\frac{ϕ_{1}}{ϕ_{2}})$ (2c)

It is essential to ascertain the significance of the marginal effect by computing the t-statistic and the standard error (Brambor et al., 2006; Ehigiamusoe, 2020). First, we use the coefficient covariance matrix to find the variance. Second, the standard error is computed as the square root of the variance, while the marginal effect divided by the standard error produces the t-statistic. The marginal effect is significant if the t-statistic is large.

Moreover, to establish the moderating role of human capital on the impact of aid on growth, we add the interaction term between aid and human capital to the following model:

$\begin{matrix} GD P_{t} = β_{0} + β_{1} AI D_{t} + β_{2} (AID * HCA)_{t} + β_{3} CA P_{t} \\ + β_{4} HC A_{t} + β_{5} GO V_{t} + β_{6} IN F_{t} + β_{7} TO P_{t} + ε_{t} \end{matrix}$ (3a)

where $AID * HCA$ = interaction term between aid and human capital. The interaction term allows us to establish the favorable or adverse moderating role of human capital on the impact of aid on growth. The marginal effect of aid on growth can be computed by taking the partial derivatives of Equation 3a with respect to aid as follows:

$\frac{\partial GD P_{t}}{\partial AI D_{t}} = β_{1} + β_{2} HC A_{t}$ (3b)

Again, we focus on the significance and sign of the coefficients of aid ( $β_{1}$ ) and the interaction term ( $β_{2}$ ). Essentially, a positive marginal effect ( $β_{2} + β_{2} HCA$ ) indicates that greater aid and human capital will accelerate growth, whereas a negative marginal effect indicates the contrary. Also, if $β_{1}$ and $β_{2}$ have different signs, it indicates the presence of a threshold level of human capital beyond which aid cannot boost growth. Therefore, we compute the threshold level of human capital using the following Equation:

$HCA > - (\frac{β_{1}}{β_{2}})$ (3c)

Finally, our research calculates the respective t-statistic and standard error to ascertain the significance of the marginal effect.

Justification of Variables

Our study proxy economic growth using real GDP per capita, which is consistent with the extant literature (Demetriades & Hook Law, 2006; Ehigiamusoe & Lean, 2018a, 2019b). Although there is no consensus on the best proxy for foreign aid, several empirical studies have used the net inﬂows of foreign aid (Goh et al., 2017; Moolio & Kong, 2016). Besides, some studies have also employed net official development assistance received as a ratio of GNI (Ehigiamusoe & Lean, 2019a). Therefore, our study follows the extant literature and proxy aid as net official development assistance as well as official aid received. For robustness checks, it uses net official development assistance received as a ratio of GNI as an alternative proxy. From a priori expectation, aid is expected to positively influence growth.

Moreover, the human capital and physical capital are included in the model based on growth theory. Their coefficients are expected to have positive sign. The standard neoclassical growth model considers a rise in overall capital stocks (public and private) as a vital determinant of growth. The capital stock could result from savings and investment in equipment, machinery, utilization of novel production technologies as well as public infrastructure development (Solow, 1956). This study uses gross fixed capital formation relative to GDP as a measure for physical capital growth, which is consistent with some previous studies (Bird & Choi, 2020; Duodu & Baidoo, 2022; Olaniyi & Oladeji, 2021). Besides, human capital is included in our model to capture the role of human capital on economic expansion. It is proxied by secondary school enrollment rate, which agreed with past empirical research (Aghion et al., 2009; Young & Sheehan, 2014). For robustness check, it uses the mean years of schooling as an alternative proxy which agreed with some research (Arndt et al., 2015; Ehigiamusoe et al., 2019; Ehigiamusoe & Lean, 2018a).

Regarding the control variables in the model, government consumption expenditure is employed to measure government policy, whereas trade openness measures the extent of openness in a country. Inflation rate captures the effect of macroeconomic instability. The literature often used these control variables (Ehigiamusoe et al., 2017; Ehigiamusoe & Lean, 2018b, 2019b), which are projected to have positive impacts on growth, except inflation rate which could have a negative effect.

Estimation Technique

Our research deploys the Autoregressive Distributed Lag technique suggested by Pesaran et al. (2001) to unravel the relationship between growth and the independent variables. The justifications for choosing the ARDL bound testing method are premised on its advantages over other approaches. First, the ARDL specification allows for a concurrent estimation of both long-run and short-run coefficients, which provides viable alternatives for policy formulations. Second, compared to other types of cointegration testing methods, the ARDL accommodates a model in which some variables are stationary at levels [I(0)] and some variables are stationary after first differenced [I(1)]. In other words, it can accommodate mixed-order variable integration. Third, the ARDL method allows for the use of different lag lengths for different variables in the model. Fourth, the ARDL technique produces unbiased estimates and valid t-statistic regardless of the inclusion of endogenous regressors. Finally, the ARDL bounds test typically produces unbiased parameters and valid t-statistic since it can control for endogeneity and autocorrelation. To apply the ARDL estimation technique, we transformed equations (1), (2a), and (3a) into ARDL models as respectively shown in equations (4), (5), and (6) below:

$\begin{matrix} Δ GD P_{t} = α_{0} + \sum_{h = 1}^{p} α_{1 h} Δ GD P_{t - h} + \sum_{i = 0}^{q} α_{2 i} Δ AI D_{t - i} \sum_{j = 0}^{r} α_{3 j} Δ CA P_{t - j} \\ + \sum_{k = 0}^{s} α_{4 k} Δ HC A_{t - k} + \sum_{l = 0}^{t} α_{5 l} Δ GO V_{t - l} + \sum_{m = 0}^{u} α_{6 m} Δ IN F_{t - m} \\ + \sum_{n = 0}^{v} α_{7 n} Δ TO P_{t - n} + α_{8} GD P_{t - 1} + α_{9} AI D_{t - 1} + α_{10} CA P_{t - 1} \\ + α_{11} HC A_{t - 1} + α_{12} GO V_{t - 1} + α_{13} IN F_{t - 1} + α_{14} TO P_{t - 1} + ε_{t} \end{matrix}$ (4)

$\begin{matrix} Δ GD P_{t} = ϕ_{0} + \sum_{h = 1}^{p} ϕ_{1 h} Δ GD P_{t - h} + \sum_{i = 0}^{q} ϕ_{2 i} Δ AI D_{t - i} \sum_{j = 0}^{r} ϕ_{3 j} Δ (AID * CAP)_{t - j} \\ + \sum_{k = 0}^{s} ϕ_{4 k} Δ CA P_{t - k} + \sum_{l = 0}^{t} ϕ_{5 l} Δ HC A_{t - l} + \sum_{m = 0}^{u} ϕ_{6 m} Δ GO V_{t - m} \\ + \sum_{n = 0}^{v} ϕ_{7 n} Δ IN F_{t - n} + \sum_{o = 0}^{w} ϕ_{8 o} Δ TO P_{t - o} + ϕ_{9} GD P_{t - 1} + ϕ_{10} AI D_{t - 1} \\ + ϕ_{11} (AID * CAP)_{t - 1} + ϕ_{12} CA P_{t - 1} + ϕ_{13} HC A_{t - 1} + ϕ_{14} GO V_{t - 1} \\ + ϕ_{15} IN F_{t - 1} + ϕ_{16} TO P_{t - 1} + ε_{t} \end{matrix}$ (5)

$\begin{matrix} Δ GD P_{t} = β_{0} + \sum_{h = 1}^{p} β_{1 h} Δ GD P_{t - h} + \sum_{i = 0}^{q} β_{2 i} Δ AI D_{t - i} \sum_{j = 0}^{r} β_{3 j} Δ (AID * HCA)_{t - j} \\ + \sum_{k = 0}^{s} β_{4 k} Δ CA P_{t - k} + \sum_{l = 0}^{t} β_{5 l} Δ HC A_{t - l} + \sum_{m = 0}^{u} β_{6 m} Δ GO V_{t - m} \\ + \sum_{n = 0}^{v} β_{7 n} Δ IN F_{t - n} + \sum_{o = 0}^{w} β_{8 o} Δ TO P_{t - o} + β_{9} GD P_{t - 1} + β_{10} AI D_{t - 1} \\ + β_{11} (AID * HCA)_{t - 1} + β_{12} CA P_{t - 1} + β_{13} HC A_{t - 1} + β_{14} GO V_{t - 1} \\ + β_{15} IN F_{t - 1} + β_{16} TO P_{t - 1} + ε_{t} \end{matrix}$ (6)

From equation (4), the first set of parameters ( $α_{1}, α_{2}, α_{3}, α_{4}, α_{5}, α_{6}, α_{7}$ ) focus on the short-run impact while the second set of parameters ( $α_{8}, α_{9}, α_{10}, α_{11}, α_{12}, α_{13}, α_{14}$ ) focus on the long-run impact of the regressors on growth. The null hypothesis of no cointegration between growth and independent variables is tested against the alternative hypothesis as follows:

$H_{0} : α_{8} = α_{9} = α_{10} = α_{11} = α_{12} = α_{13} = α_{14} = 0$ (6)

Data

Our study employs annual data of Nigeria covering 1980 to 2020. The data for all the variables (except mean years of schooling) were collected from the World Development Indicators (2022) published by the World Bank. The data of the mean years of schooling were sourced from the Human Development Report (2021) published by the United Nations Development Programmes (UNDP). The software used for data analysis is EViews 12.

Results and Discussion

Descriptive Statistics

Table 2 displays the descriptive statistics and correlation analysis. It shows that the mean real GDP per capita, physical capital, aid, and human capital were USD1881.15, 36.60%, USD1.44billion and 32.26%, respectively. The corresponding standard deviation were USD465.08, 19.74%, USD2.2billion, and 9.84%. This implies the existence of wide variations between the variables, and the data are widely dispersed from their averages. In addition, the correlation analysis depicted in Table 2 shows that the entire variables have positive correlation with GDP except inflation (i.e., negatively correlated with GDP). It also reveals that foreign aid has positive relationship with physical capital and human capital.

Table 2.

Descriptive Statistics and Correlations.

	GDP	AID	AID_GNI	CAP	HCA	GOV	INF	TOP
Mean	1881.15	1,440,000,000	0.637	36.608	32.260	3.800	18.771	32.529
Median	1685.20	394,000,000	0.469	34.049	27.556	2.091	12.554	34.023
Maximum	2688.26	11,900,000,000	4.939	89.386	56.205	9.448	72.835	53.277
Minimum	1388.53	76,000,000	0.024	14.168	13.679	0.911	5.388	9.135
Std. Dev.	465.08	2,200,000,000	0.918	19.741	9.845	2.882	16.718	12.406
Skewness	0.469	3.3037	3.709	0.964	0.539	0.766	1.862	−0.352
Kurtosis	1.602	13.963	16.602	3.409	2.366	2.077	5.313	2.236
GDP	1
AID	0.499	1
AID_GNI	0.174	0.906	1
CAP	0.629	0.454	0.287	1
HCA	0.837	0.494	0.198	0.796	1
GOV	0.817	0.456	0.197	−0.650	0.784	1
INF	−0.374	−0.222	−0.002	0.174	−0.284	−0.342	1
TOP	0.150	0.133	0.182	−0.439	0.044	0.172	−0.067	1

Note. AID_GNI = Net aid received in % of GNI.

Unit Root Tests

The outcomes of the unit root tests displayed in Table 3 indicate that inflation rate is stationary at level [I(0)], while the other variables are stationary after first differenced [I(1)]. This suggests that the variables in the model are a combination of [I(0)] and [I(1)]. Besides, the unit root with break test confirmed that the variables are a mixture of [I(0)] and [I(1)]. Since the variables are integrated, it is important to ascertain the cointegration relationship between the dependent and independent variables using the appropriate technique.

Table 3.

Unit Root Tests Results.

	Augmented Dickey Fuller		Phillips-Perron		Unit root with break test
Variables	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
GDP	−1.508	−3.119**	−0.958	−4.230***	−6.255***	−4.445**
AID	−1.151	−6.200***	−1.182	−7.106***	−4.949**	−8.715***
AID_GNI	−2.892**	−5.946***	−1.910	−5.804***	−3.834	−7.604***
CAP	−2.225	−5.094***	−1.516	−5.133***	−2.961	−6.638***
HCA	−2.585	−6.518***	−2.535	−6.526***	−5.561***	−8.225***
GOV	−1.055	−6.353***	−1.055	−6.361***	−6.761***	−8.0501***
INF	−3.042**	−5.915***	−2.909**	−12.219***	−5.769***	−7.498***
TOP	−2.239	−7.503***	−2.514	−7.619***	−6.079***	−8.110***

Note.*** and ** indicate statistical significance at 1% and 5% levels respectively. Therefore, the null hypothesis which states that the variable has a unit root is rejected.

Cointegration Tests and Estimation Results

The ARDL bounds test results displayed in Table 4 disclose a cointegration relationship between growth and the regressors in the three models. The computed F-statistics of the three models (9.227, 12.430, and 12.668) are larger than the upper bound value of 3.99 at 1% significant level. Consequently, the null hypothesis of no cointegration is rejected. Hence, it is fundamental to establish the short-run and long-run impacts of the regressors on economic growth.

Table 4.

Results of ARDL Estimation.

Variables	Model 1	Model 2	Model 3
Bound test	9.227***	12.430***	12.668***
Long-run coefficients
AID	0.162*** (0.019)	0.719*** (0.180)	0.851*** (0.097)
CAP	0.149*** (0.051)	0.142*** (0.035)	4.345*** (0.560)
HCA	0.310*** (0.096)	4.164*** (1.133)	0.397*** (0.074)
GOV	0.013 (0.037)	−0.022 (0.025)	−0.088*** (0.018)
INF	−0.006*** (0.001)	−0.005*** (0.001)	−0.001 (0.001)
TOP	0.080*** (0.023)	0.074*** (0.018)	0.105*** (0.022)
AID*CAP		−0.173*** (0.052)
AID*HCA			−0.216*** (0.028)
Convergence coefficient	−0.889*** (0.116)	−0.704*** (0.094)	−0.874*** (0.123)
Short-run coefficients
$Δ$ AID	0.075*** (0.014)	0.478*** (0.107)	0.065 (0.093)
$Δ$ CAP	−0.093 (0.060)	0.036*** (0.041)	0.076 (0.0561)
$Δ$ HCA	0.061 (0.063)	2.737*** (0.668)	0.270*** (0.059)
$Δ$ GOV	−0.052*** (0.015)	−0.077*** (0.014)	−0.050*** (0.012)
$Δ$ INF	−0.001 (0.001)	−0.001*** (0.001)	−0.001(0.001)
$Δ$ TOP	−0.010 (0.018)	−0.002 (0.001)	−0.030* (0.009)
$Δ$ AID*CAP		−0.122*** (0.031)
$Δ$ AID*HCA			−0.008 (0.027)
Constant	2.462*** (0.752)	−9.904*** (3.922)	−11.348*** (1.921)
R-squared	0.998	0.998	0.999
Diagnostic tests
Autocorrelation test	3.120 (0.097)	1.607 (0.244)	4.063 (0.366)
Homoscedasticity test	0.430 (0.959)	0.899 (0.604)	2.716 (0.201)
Normality test	1.059 (0.588)	2.050 (0.358)	1.202 (0.548)
Ramsey RESET	0.193 (0.851)	0.936 (0.367)	1.294 (0.231)
Marginal Effects
Minimum level		0.260***	0.285***
Mean level		0.119***	0.110***
Maximum level		−0.058***	−0.019***
Threshold level		65%	55%

Note.*** and ** connote significance at 1% and 5% levels, correspondingly. Values reported in parentheses are standard errors (except in diagnostic tests in which probability values are shown in parentheses). Economic growth is the dependent variable.

The ARDL estimations showed in Table 4 (Column 1) depict that aid has a significantly positive impact on growth, indicating that changes in aid can explain changes in growth in both long-run and short-run periods. Specifically, one per cent rise in aid will raise growth by 0.162% points in the long-run, and 0.075% points in the short-run. Hence, this empirical outcome provides evidence that aid has a significantly positive effect on growth in Nigeria. This finding agreed with some empirical research which revealed that aid boosts growth (Arndt et al., 2015; Babalola et al., 2018; Boateng et al., 2021; Clemens et al., 2012; Maruta et al., 2020). As for the relevant transmission channels, aid probably accelerates growth in Nigeria because it enhances infrastructural development, alleviates poverty, reduces inequality, and improves speciﬁc social sectors (e.g., education and health) and economic sectors (e.g., agricultural, industrial and services sectors).

Besides, the estimations disclose that human capital, physical capital, and trade openness have significantly positive long-run impacts on growth in Nigeria. These outcomes agreed with the theoretical and empirical literature (Aghion et al., 2009; Ehigiamusoe & Lean, 2018a). However, no evidence that government consumption expenditure boosts economic growth. This is probably due to the ineﬃciency of government consumption expenditure in Nigeria (Samargandi et al., 2015). Additionally, inflation rate has adverse influence on growth in Nigeria. This agreed with the empirical and theoretical literature (Ehigiamusoe et al., 2019). The convergence coefficient is significantly negative, indicating the speed of adjustment of the system from short-term deviation to long-term equilibrium. The R-squared indicates that variations in the regressors explain a reasonable proportion of the variation in economic growth in Nigeria.

In Column 2, we add the interaction term between aid and physical capital in the model to establish the moderating role of physical capital on the impact of aid on growth. The estimations indicate that the interaction term has a negative and significant coefficient whereas the coefficient of aid remain positive. This suggests that physical capital has an important moderating role on the impact of aid on growth. To capture the total effect of changes in both physical capital and aid on growth, it is essential to calculate the marginal effect. Therefore, the marginal effect of aid on growth computed at the minimum, mean and maximum levels of physical capital are 0.260, 0.119 and −0.058, correspondingly. The positive marginal effects at the minimum and mean levels of physical capital implies that a simultaneous increase in aid and physical capital will enhance growth in Nigeria. However, the negative marginal effect at the maximum level of physical capital suggests the presence of a certain threshold level after which a simultaneous rise in both aid and physical capital cannot accelerate growth. Specifically, the computed threshold level reveals that the marginal effect of aid on growth becomes negative if physical capital exceeds 65% threshold level. Below this threshold level, a rise in both aid and physical capital will enhance growth in Nigeria.

Thus, this study emphasizes a dynamic connection between physical capital and aid. Hansen and Tarp (2001) and Arndt et al. (2015) have shown that aid is positively related to physical capital (i.e., investment). Given that the average physical capital in Nigeria from 1980 to 2020 is 36.6%, the variable is still within the threshold level. For instance, when physical capital was 75.75% in 1983, a simultaneous increase in aid and physical capital inhibits growth by 0.014% points. But when physical capital was 28.64% in 2020, a simultaneous increase in aid and physical capital will raise growth by 0.138% points. A critical look at the available statistics indicate that physical capital has always fallen below the threshold level (i.e., 65%) since 1984 in Nigeria. Therefore, the country still has ample of space to expand her physical capital without inhibiting economic growth.

In Column 3, we add the interaction term between aid and human capital in the model to establish the moderating role of human capital on the impact of aid on growth. The estimations disclose that the interaction term has a negative and significant coefficient, whereas the coefficient of aid is significantly positive. This indicates that human capital plays a diminishing moderating role on the impact of aid on growth. To capture the total effect of changes in both human capital and aid on growth, it is essential to calculate the marginal effect. Therefore, the marginal effects of aid on growth computed at the minimum, mean and maximum levels of human capital are 0.285, 0.110 and −0.019, correspondingly. Since the marginal effects are positive at the minimum and mean levels but negative at the maximum level of human capital, we compute the threshold level. We find a threshold level of 55%, beyond which a simultaneous increase in both aid and human capital cannot accelerate growth. Below this threshold level, a simultaneous increase in both aid and human capital boosts growth in Nigeria. We also employ the mean years of schooling as an alternative proxy of human capital and obtained similar outcomes.

During the 1980 to 2020 period, the average human capital in Nigeria was 32.26%. This value is below the threshold level. Precisely, human capital was consistently below the threshold level during the entire period under review except in 2013. For instance, when human capital was 56.2% in 2013, a simultaneous increase in aid and human capital impedes growth by 0.019% points. But when human capital was 43.5% in 1980, a simultaneous increase in aid and human capital boost growth by 0.036% points. Arndt et al. (2015) opined that the impact of aid on growth is difficult to explain in the absence of a strong connection between aid and vital growth determinants (e.g., investment and human capital). They posited that aid could contribute to both intermediate results (i.e., accumulation of human capital) and ﬁnal results (e.g., economic growth). They showed a positive nexus between aid and human capital growth.

The results of the control variables show evidence that trade openness promotes growth while inflation rate inhibits growth. The negative sign and significance of the convergence coefficient indicate the speed of adjustment, while the R-squared shows that variations in the independent variables explain a reasonable proportion of the variation in economic growth.

Robustness Checks

Diagnostic Tests

For robustness check of the estimation results, this study conducts some diagnostic tests. Firstly, it uses the Breusch-Godfrey LM test to ascertain the presence of serial correlation in the models. The outcomes displayed in Table 4 reject the null hypothesis of autocorrelation, implying absence of autocorrelation in the models. Secondly, the study employs the Breusch-Pagan-Godfrey heteroskedasticity test to ascertain the existence of heteroskedasticity in the models. The outcomes reported in Table 4 suggest that the models have no heteroskedasticity. Third, the study conducts normality test with Jarque-Bera statistic, and the outcomes displayed in Table 4 indicate normal distribution. Fourth, the Ramsey RESET reveal appropriate functional form of the model as well as absence of omitted variable bias. Finally, the study conducts stability tests, and the graphs displayed in Figure 1 indicate that CUSUM and CUSUM of squares fall within the border lines at 5% significant level, implying model stability and appropriate specification.

Figure 1.

Stability tests.

Issue of Structural Breaks

For further robustness check of the estimation results, this study conducts structural break test using the test proposed by Bai and Perron (2003) to ascertain the presence of structural break in the model. This is important because some studies (e.g., Ehigiamusoe & Lean, 2018a; Narayan & Smyth, 2008) claimed that the long-run link between economic variables may be deceptive if structural break is disregarded. The test detected a structural break in 1999. To account for the structural break, our research adds a dummy variable in the model and redo the estimation. The dummy variable takes a value of 1 from the break year but a value of 0 otherwise (Ehigiamusoe & Lean, 2018a; Wallack, 2003). The estimations with structural break dummy displayed in Table 5 are analogous to the outcomes presented in Table 4 in terms of the coefficients’ signs and significance (though the size slightly differ). Also, the results reveal that the coefficient of the structural break is negative albeit insignificant. This implies that structural break has insignificant impact on growth in Nigeria.

Table 5.

Robustness Check of Estimation Results (with Structural Break).

Variables	Model 1	Model 2	Model 3
Bound test	6.448***	13.623***	12.732***
Long-run coefficients
AID	0.115***(0028)	1.312*** (0.343)	0.812*** (0.193)
CAP	0.061 (0.075)	0.124** (1.116)	3.986** (1.156)
HCA	0.443***(0.142)	7.923*** (2.138)	0.295** (0.086)
GOV	−0.019 (0.046)	−0.006 (0.028)	−0.081 (0.018)
INF	−0.005*** (0.002)	−0.005*** (0.001)	−0.001** (0.001)
TOP	0.098** (0.041)	0.111*** (0.025)	0.091** (0.027)
AID*CAP		−0.349*** (0.098)
AID*HCA			−0.204*** (0.055)
SBK	−0.013 (0.069)	−0.061 (0.036)	−0.061 (0.059)
Convergence coefficient	−0.662*** (0.178)	−0.710*** (0.119)	−0.755*** (0.108)
Short-run coefficients
$Δ$ AID	0.056** (0.025)	0.931*** (0.198)	−0.099 (0.101)
$Δ$ CAP	−0.101 (0.064)	0.088** (0.038)	−0.892 (0.607)
$Δ$ HCA	0.078 (0.097)	5.625*** (1.227)	0.314*** (0.068)
$Δ$ GOV	−0.024 (0.022)	−0.079*** (0.020)	−0.061*** (0.012)
$Δ$ INF	−0.001** (0.001)	−0.001* (0.001)	0.001 (0.001)
$Δ$ TOP	0.015 (0.027)	0.009 (0.019)	0.019 (0.015)
$Δ$ AID*CAP		−0.255*** (0.056)
$Δ$ AID*HCA			0.038 (0.029)
$Δ$ SBK	−0.053 (0.038)	0.033 (0.034)	0.018 (0.034)
Constant	3.242*** (0.863)	−22.578*** (7.486)	−9.757*** (4.038)
R-squared	0.997	0.997	0.999
Diagnostic tests
Autocorrelation test	0.195 (0.826)	2.523 (0.107)	2.885 (0.277)
Homoscedasticity test	0.975 (0.547)	1.132 (0.414)	0.578 (0.859)
Normality test	0.076 (0.962)	0.448 (0.741)	0.413 (0.813)
Ramsey RESET	0.150 (0.883)	0.561 (0.584)	1.301 (0.240)
Marginal effects
Minimum level		0.386**	0.278**
Mean level		0.103**	0.112**
Maximum level		−0.256**	−0.009**

Note.*** and ** connotes significance at 1% and 5% levels, correspondingly. The values in parentheses are standard errors. Economic growth is the dependent variable, SBK = dummy variable to account for structural break.

Alternative Proxies

For robustness check of the estimations, the study uses alternative proxies of foreign aid (i.e., foreign aid relative to GNI) and redo the analysis. This is necessary because the impact foreign aid has on growth might be contingent upon the proxy used to capture the variable (Ehigiamusoe & Lean, 2019a). The empirical outcomes (not shown due to space constraint but available upon request) agreed with the estimations depicted in Table 4. A summary of the outcomes indicates that the coefficient of aid is positive whereas the interaction terms have negative coefficients. The marginal effects of aid on growth are positive at minimum as well as mean levels of physical capital (human capital) but negative at the maximum levels. This suggests that our empirical outcomes are insensitive to the proxy employed to measure foreign aid.

Policy Implications and Conclusion

This study focuses on two main research questions: (i) Does physical capital moderate the effect of aid on growth in Nigeria? (ii) Does human capital moderate the effect of aid on growth in Nigeria? Utilizing ARDL technique on annual data of Nigeria covering the 1980 to 2020 period, this study discloses that aid has a significantly positive impact on growth in Nigeria. The economic implication of this finding is that Nigeria should create the enabling environment that attract aid, as well as formulate the appropriate policies and programs that enhance the economic benefits of aid. Such policies include the promotion of macroeconomic stability, security, institutions, and financial development. This is fundamental because some empirical studies (Ang, 2010; Babalola & Shittu, 2020; Burnside & Dollar, 2000) have shown that good policy environment, institutional quality and developed financial system are fundamental for aid to boost growth.

Secondly, our study indicates that physical capital has a significant moderating role on the impact of aid on growth. The marginal effect of aid on growth varies with the levels of physical capital. We unveil a threshold level of 65% beyond which an increase in aid cannot spur growth. Below the threshold level, a simultaneous rise in both aid and physical capital will enhance growth. The implication of this ﬁnding is that adequate physical capital in the recipient nation is a critical requirement for the effectiveness of aid. The empirical outcomes regarding the variables which moderate the aid-growth relationship are essential for formulation of policies. A country should quicken the growth of physical capital to gain the long-run economic benefits of aid. Hence, policies that can increase physical capital (capital accumulation) should be made a priority in the development agenda of Nigeria.

Thirdly, our study reveals that human capital has a significantly moderating role on the impact of aid on growth in Nigeria. Specifically, the marginal effect of aid on growth is positive at the minimum and mean levels of human capital, but negative at the maximum level. It unveils a threshold level of 55% beyond which a rise in aid will not enhance growth. The implication of this finding is that policy makers should pay adequate attention to the development of human capital through the provision of qualitative education and health care services. Policies and activities that can enhance human capital development (e.g., investment in education, health, research and development) should be vigorously pursued in Nigeria.

Finally, the findings are robust to alternative proxies, diagnostic tests as well as structural breaks. The policy implication of our research is that foreign aid enhances economic growth in Nigeria. Hence, policies that can support the effective utilization of foreign aid should be vigorously pursued to achieve the desired growth. In addition, since foreign aid can promote growth if moderated by the requisite physical and human capital, there is need for government to invest heavily in education to reform the standard of education and improve capital accumulation.

This research has succeeded in showing the moderating roles of human and physical capital on the impact of aid on growth in Nigeria. However, this study is unable to show the asymmetric effects of aid, human capital, physical capital, and their interactions on growth. This is important because the asymmetric effect will indicate how a positive or negative change in these variables influence growth. Consequently, we recommend that future research should utilize the estimation techniques (e.g., Nonlinear Autoregressive Distributed Lag approach) that can reveal the asymmetric effects of aid, human capital, physical capital, and their interactions on growth. We also suggest that further research should be conducted on this issue in other developing nations for the purpose of comparison.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

This project was supported by the University of Southampton Malaysia Sustainable Development Goals Fund UoSM/SDG2022/2 awarded by the University of Southampton Malaysia.

ORCID iDs

Kizito Uyi Ehigiamusoe

Sikiru Jimoh Babalola

Data Availability Statement

The data underlying this study are openly available in World Development Indicators at

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