Foreign Aid and Economic Growth in South Asian Countries

Abstract

This study employs panel data from 2000 to 2019 to examine and analyze the impact of foreign aid on growth in six South Asian nations, which are Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. The motivation of this study is to furnish empirical support for resolving the ongoing discourse on the relationship between foreign aid and economic growth. To this end, the article employs three panel regression models: pooled ordinary least-squares, random effect, and fixed effect, and estimates them using White cross-sectional standard errors and covariance. The empirical results reveal that foreign aid and population have a negative and considerable impact on Asian countries’ economic progress; whereas, gross capital formation has positive and significant effects. The empirical results of this study have important implications for both donors and aid recipient countries. It suggests that to get positive influence from aid, the recipient countries should enhance the quality of governance on the other side; the donor should provide a huge amount of aid for reforming the institutions’ quality and capacity building to developing countries. Furthermore, these countries should prefer to receive a grant as aid rather than a loan to reduce the debt burden. Government should develop the appropriate policies that help to increase government revenue and reduce the unnecessary current expenditure. To increase government revenue, the tax base should be regulated rather than being reliant on foreign loans. Also, advised channeling remittance into the productive sector and reducing the trade deficit by substituting imports through attracting foreign direct investment.

Keywords

Foreign aid economic growth panel data fixed effect random effect South Asian countries

Introduction

Foreign aid is expected to significantly contribute to the desirable growth rate of developing countries in various dimensions. This perspective is supported by various political philosophies and multiple economic theories, which are backed by empirical evidence. Harrod (1939) stated that both domestic and foreign capital are crucial sources of investment that can accelerate economic growth through capital accumulation. Solow (1956) growth model further emphasized the importance of the savings rate and capital stock in driving economic growth. Chenery and Strout (1966) extended this framework by introducing the concept of foreign aid as a means to address the foreign exchange gap and deficit finance issues faced by developing countries. In doing so, foreign aid can play a crucial role in promoting the economic growth of developing countries.

The slower pace of economic growth in less developed countries has resulted in a capital crunch due to various factors such as low-income levels, acute poverty, rapid population growth, and high unemployment rates. As a consequence, developing countries have struggled to achieve the desired level of economic growth with inadequate capital. Thus, foreign aid policies have become crucial to address the capital deficit and foreign exchange gap and to attain the targeted growth rate. Given the era of globalization, foreign aid has become a strategic issue for both donor and aid recipient countries. However, several studies have found empirical evidence suggesting a negative relationship between foreign aid and economic growth (Bräutigam & Knack, 2004; Liew, Mohamed, & Mzee, 2012; Rajan & Subramanian, 2008; Teboul & Moustier, 2001).

Foreign aid policy has a long history dating back to its implementation since World War II, and it continues to grow in scope and importance. However, many developing countries have not seen a significant impact of aid in their development journey, making the impact of foreign aid on economic growth a controversial issue in economic analysis. Despite numerous studies, no generally acceptable results regarding the impact of foreign aid on economic growth have been found. This motivates us to investigate the issue from a different perspective.

Using panel data for the years 2000 to 2019, this article aims to analyze and explain the effects of foreign aid on growth in six South Asian countries, namely Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. This study follows Solow’s growth model in the form of the Cobb–Douglas production function to find the impact of foreign aid on economic growth. To proxy economic growth, we use GDP per capita growth as the dependent variable, while the total capital formation share of GDP is employed as an indicator of domestic investment. Total population growth is employed as a proxy of labor growth rate. The total official development assistant (ODA) inflows as a share of Gross National Income (GNI) and Government consumption expenditure are taken as a proxy of total factor productivity. The panel regression models, pooled OLS (POLS), random effect, and fixed effect, are applied to find robust evidence in the aid–growth nexus.

The rest of this study is structured as follows. The literature review is presented in the second section. The empirical methodology, including a description of the data and an econometric model, is described in the third section. The fourth section examines the empirical findings and the fifth section concludes with final remarks.

Literature Review

Many economic theories support the role of capital accumulation in economic growth. Harrod (1939) and Domar (1946) stated that capital accumulation enhances economic growth whether it is generated domestically or acquired from outside. Solow’s growth model (1956) stated that the saving rate and capital stock play an important role in promoting economic growth. The “Two Gap” model developed by Chenery and Stout (1966) originally explained how foreign aid affects economic growth. The hypothesis argues that the lack of savings and foreign exchange forces emerging nations to remain underdeveloped. However, Bacha (1990) and Taylor (1994) argued that underdeveloped nations also suffer from a “fiscal deficit,” whereby governments require additional funding to finance public investments in infrastructure, education, and other critical social sectors necessary for development. Therefore, foreign aid is seen as a means to fill these gaps and boost economic growth.

There has been numerous empirical research that identified the relationship between foreign aid and growth, but the findings have not been consistent. Studies by Arndt, Jones, and Tarp (2015), Brückner (2013), Clemens, Radelet, Bhavnani, and Bazzi (2012), Fayissa and El-Kaissy (1999), Hansen and Tarp (2000), and Lensink and White (2000) have reported a positive and significant impact of aid on growth. In contrast, other studies have found that ODA has a negative and significant impact on growth. These studies argue that ODA replaces domestic capabilities, diverts funding to ineffective technologies, affects income equality, leads to increased dependency, mismanagement, and corruption, and has a negative association with economic growth (Easterly, 2003; Easterly, Levine, & Roodman, 2004; Knack, 2001; Kosack, 2003; Rajan & Subramanian, 2008; Svensson, 2000). Nevertheless, some studies have reported mixed or uncertain effects of foreign aid on growth (Birdsall, Rodrik, & Subramanian, 2005; Boone, 1994; Burnside & Dollar, 2000). Recent empirical analyses related to South Asian countries are presented below.

Jena and Sethi (2020) investigated the impact of foreign aid and economic growth of eight south Asian countries during the period 1996–2017. Using fully modified ordinary least-squares (FMOLS) and panel dynamic ordinary least-squares estimation techniques, they found a positive and significant association between aid and growth. Based on panel data from 1996–2018, Dash (2021) employed the FMOLS model to analyze the effectiveness of foreign aid and economic growth of South Asian countries and found a positive and significant nexus between both short-run and long-run. In contrast, Weerasingha and Mustafa (2019) identified a negative relationship between foreign aid and the economic growth of South Asian countries, during the period of 1977–2017, utilizing the panel ordinary least squares model. Similarly, Rao, Sethi, Dash, and Bhujabal (2020) examined the interrelationship between foreign aid and economic growth in South Asian countries, from 1980 to 2016, using the system GMM and found that foreign aid had a negative association with growth. However, Shah and Hwang (2022) empirically investigated the impact of foreign aid on the growth of six South Asian countries using time-series annual data over the period 1980–2019. They applied variance decomposition and impulse response function and discovered that Bhutan and India had a positive impact on growth, while Bangladesh, Nepal, Pakistan, and Sri Lanka had a negative impact in the short run.

The relationship between foreign aid and economic growth remains a subject of ongoing debate due to the fact that foreign aid comprises both grants and loans, and while the former does not require repayment, the latter accrues interest and can result in a high debt burden that has a negative impact on the economy. However, some empirical studies have shown that foreign aid can have a positive impact on economic growth by providing capital and technological support for growth in developing countries. The lack of agreement among existing studies in the literature has led to the motivation for conducting the present study. The objective of this research is to provide a significant contribution to the existing literature by examining the influence of foreign aid on the economic growth of six South Asian countries and offering pertinent policy suggestions to enhance the efficacy of foreign aid in promoting economic growth.

Research Methodology

This empirical analysis uses six South Asian countries’ panel data over the period 2000–2019. Solow’s growth model in the form of the Cobb–Douglas production function is used for determining the impact of foreign aid on economic growth.

Y_{i t} = A_{i t} K_{i t}^{α_{3}} L_{i t}^{α_{4}}

(1)

After, logarithmic transformation, we get

{EGR}_{i t} = α_{3} {GCF}_{i t} + α_{4} {POP}_{i t} + A_{i t}

(2)

where EGR is GDP per capita growth which indicates economic growth. GCF is the gross capital formation and POP is the population growth which represents capital and labor, respectively. A is the growth rate of total factor productivity. Subscript “i” and “t” represent the number of countries and periods. Parameters α3 and α4 are the elasticity of output with respect to capital and labor, respectively. This study assumed the total factor productivity “A” as

A_{i t} = α_{0} + α_{1} {AID}_{i t} + α_{2} {GCEX}_{i t} + ε_{i t}

(3)

where ${A I D}_{i t}$ is the foreign aid in the form of net ODA received as a percentage of GNI and ${G C E X}_{i t}$ is government consumption expenditure as a share of GDP. The parameters α0 is a constant, and α1 $α_{1}$ , $α_{2}$ are the elasticity of output with respect to ${A I D}_{i t}$ and ${G C E X}_{i t}$ . εit is the error term. Here, it is assumed that the inflow of foreign aid enhances the total factor productivity which alternatively increases economic growth. Morrissey (2001), stated foreign aid supports the economic growth of recipient countries by increasing human and capital investment with technological transfer and so on. Thus, by substituting Equation (3) with Equation (2), we get

{E G R}_{i t} = α_{0} + α_{1} {A I D}_{i t} + α_{2} {G C E X}_{i t} + α_{3} {G C F}_{i t} + α_{4} {P O P}_{i t} + ε_{i t}

(4)

Equation (4) is the main multivariate regression model and the objective of this study is to find the coefficient value of the explanatory variable $α_{1} {, α}_{2}, α_{3}, α_{4}$ α1,α2,α3,α4. For further analysis, all the variables are expressed in a natural logarithm. Further, Table 1 provides a summary of variables, proxy, measurement, and source of data.

Table 1.

Summary of Variables.

Variable	Proxy	Measurement	Source
Economic growth	EGR	GDP per capita growth (annual percent)	World Bank (WDI)
Foreign aid	AID	Net ODA received (percent of GNI)	World Bank (WDI)
Government expenditure	GCEX	General government final consumption expenditure (percent of GDP)	World Bank (WDI)
Capital	GCF	Gross capital formation (percent of GDP)	World Bank (WDI)
Labor	POP	Population growth (annual percent)	World Bank (WDI)

Source: The data were obtained from World Development Indicators 2020, World Bank, and can be downloaded from https://databank.worldbank.org.

This study utilized advanced panel data techniques to estimate the outcomes. Different panel modeling approaches were considered, including POLS, fixed effects model (FEM), and random effects model (REM), and the Breusch–Pagan (BP) test was employed to determine the most suitable model. The rejection of the null hypothesis favored FEM/REM, and the Hausman test was then conducted to select the best-performing model between FEM and REM. The acceptance of the null hypothesis indicated that REM is a better fit for the data than FEM.

The fixed effect model accounts for individual characteristics by including individual dummy variables for each entity and developing over time but not time-specific effects, which can overcome some of the limitations of OLS estimators. The equation for the FEM is

Y_{i t} = α_{0 i} + α_{1} X_{i t} + ε_{i t}

(a)

where $Y_{i t}$ is the dependent variable; Xit is the vector of regressors; α1 is the vector of coefficients; and εit is the error term, while the intercept, $α_{0 i}$ , varies across countries, and remains constant across time. It is time-invariant and thus has no “t” subscript.

The REM, also known as the error components model, accounts for entity heterogeneity in the error term instead of defining it as a dummy variable, allowing for a common intercept. The equation for the random effect model is

Y_{i t} = α_{0} + α_{1} X_{i t} + ꭃ i + ε_{i t}

(b)

where $Y_{i t}$ is the dependent variable; $X_{i t}$ is the vector of regressors; αi is the vector of coefficients. $(ϑ_{i t} = ꭃ i + ε_{i t})$ is the error term that is distributed normally with zero mean and constant variance allowing for evaluation Jit. $ꭃ i$ αi is assumed to represent the unobservable and unmeasurable country-specific characteristics.

Table 2 presents a summary of the descriptive statistics based on 120 observations for all variables. The results indicate that the average value of the EGR is 4.19 with a standard deviation of 2.52, suggesting moderate variability in the data dispersion. The mean value of AID is reported as 2.89, indicating a lower inflow of foreign aid to support economic growth through technological transfer. Similarly, variables such as GCEX, GCF, and POP fluctuate from 4.85–22.70, 14.12–69.48, and −0.27–2.65 with standard deviations of 4.74, 12.59, and 0.59, respectively, suggesting lower variability in the data.

Table 2.

Descriptive Statistics.

	Mean	Median	Maximum	Minimum	Std Dev.
EGR	4.19	4.18	17.03	−2.24	2.52
AID	2.89	1.54	12.34	−0.29	2.11
GCEX	10.90	10.13	22.70	4.85	4.74
GCF	31.71	29.28	69.48	14.12	12.59
POP	1.35	1.22	2.65	-0.27	0.59

Source: These descriptive statistics are calculated based on panel data of six South Asian countries taken over the period 2000–2019 from the World Bank.

Table 3 shows a simple correlation matrix. In terms of correlation, the explanatory variables are not severely correlated. Further, we find that EGR has negative relation with AID and POP, while it has a positive relation with GCEX and GCF. Among the explanatory variables, AID and GCF have a negative correlation with POP, while the other variables have a positive correlation.

Table 3.

Matrix of Correlation.

	EGR	AID	GCEX	GCF	POP
EGR	1.00
AID	−0.03	1.00
GCEX	0.12	0.24	1.00
GCF	0.36	0.25	0.39	1.00
POP	−0.33	−0.01	0.03	−0.25	1.00

Notes: The matrix of correlation has been calculated by using panel data of six South Asian countries taken over the period 2000–2019 from the World Bank.

This calculation is done after log transforming.

Figures 1 and 2 represent the economic growth and net inflow of aid, respectively. The maximum EGR of 17.03 was observed in Bhutan in 2007, while the lowest was recorded in Sri Lanka in 2001, with a value of −0.24. Similarly, the maximum net inflow of aid was observed in Bhutan in 2003, with a value of 12.34, while the lowest was recorded in Sri Lanka in 2018, with a value of −0.28. Figure 1 does not reveal any discernible trend in economic growth over time.

Figure 1.

Notes: The line graph is plotted by using panel data of six South Asian countries over the period 2000–2019 from the World Bank.

Figure 2.

Notes: The line graph is plotted by using panel data of six South Asian countries over the period 2000–2019 from the World Bank.

Empirical Result

The findings of the panel data regression are presented in Table 4, which reports the results for all three panel models: POLS, FEM, and REM. The coefficient of AID is consistently negative and significant across all three models, indicating a significant negative impact of foreign aid on the economic growth of the six South Asian countries. Additionally, among the control variables, GFC has a positive and significant impact on growth, while POP has a negative and significant impact. However, the coefficient of GCEX is positive but not statistically significant.

Table 4.

Summary of Pooled OLS, Fixed Effect, and Random Effect Model Results.

	Dependent Variable: Economic Growth
Variable	POLS	Fixed Effect	Random Effect
AID	−0.05**	−06*	−0.05***
AID	(−2.10)	(−1.70)	(−3.63)
GCEX	0.03	0.46	0.01
GCEX	(0.28)	(1.37)	(0.09)
GCF	0.94***	0.74**	0.85***
GCF	(5.60)	(2.54)	(4.02)
POP	−0.21**	−0.18*	−0.20**
POP	(−2.47)	(−1.81)	(−2.71)
Constant	−1.69***	−2.17*	−1.50**
Constant	(−3.28)	(−1.79)	(−2.82)
Observation	111	111	111
R ²	0.39	0.34	0.33
Adj. R²	0.37	0.30	0.31
Prob. (F-statistic)	0.00	0.00	0.00
D-W Stat.	1.99	1.95	1.94

Notes: Panel least-squares method is used with white cross-sectional standard errors and covariance (d.f. corrected) for estimation.

Robust t-statistics are provided in parentheses.

***, **, and *: Significant at 1 percent, 5 percent, and 10 percent levels, respectively.

It is important to find a suitable model for the relevancy of the regression model. The Breusch–Pagan Lagrange multiplier (BPLM) test is applied to select the best model between POLS and FEM/REM. The BPML test rejected the null hypothesis, indicating that the FEM/REM model is appropriate since the probability value is significant at the 1 percent level. Furthermore, the Hausman test (RE vs. FE) was applied to select and find appropriate model. The acceptance of the null hypothesis provides validity of the results of FEM. The summary of the BPLM and Hausman tests is reported in Table 5.

Table 5.

Summary of Breusch–Pagan and Hausman Test.

Test	Statistics	Remarks
Breusch–Pagan test (POLS vs. REM/FEM)	85.02***	Rejection of null hypothesis(FEM/REM appropriate)
Hausman test (REM vs. FEM)	6.55	Acceptance of null hypothesis(REM is appropriate)

Notes: ***, **, and *: Significant at 1 percent, 5 percent, and 10 percent levels, respectively.

To justify the result obtained from REM several diagnostic tests are conducted. The summary of multicollinearity, endogeneity, and cross-sectional dependence test are reported in Table 6.

Table 6.

Residual Diagnostic Test.

Multicollinearity Test		Endogeneity Test		Cross-Sectional Dependence Test
Variable	Central VIF	Variable	t-statistic	Test	Statistic
AID	1.24	Resid_AID	−0.79(0.43)	Breusch–Pagan LM	16.06(0.37)
			GCEX		1.90	Resid_GCEX	1.64(0.01)	Pesaran scaled LM	0.19(0.84)
GCF	1.76	Resid_GCF		1.82(0.07)			Pesaran CD		0.82(0.40)
			POP	1.13	Resid_POP	−1.22(0.22)

Note: The probability values of respective statistics are provided in parentheses.

The central VIF values, which indicate the presence of multicollinearity, are below 2, suggesting that this issue does not exist in the model. Moreover, the insignificant values of residual coefficients for each variable suggest that there is no endogeneity problem. To assess cross-sectional dependence, we employed various test statistics such as the Breusch–Pagan LM, Pesaran scaled LM, and Pesaran CD, all of which reject the null hypothesis of cross-sectional dependence (correlation) in residuals. The probability value greater than 5 percent further confirms the validity of the results. Heteroskedasticity was assessed using the Breusch–Pagan LM test, which reveals that the Chi-square value is greater than the LM value (9.48 > 8.45), indicating no evidence of heteroskedasticity. Furthermore, insignificant lag values of residual [t stat. 1.21 (prob. 0.22)] indicate that there is no significant autocorrelation. Based on the results of the residual diagnostic tests, we conclude that the model is stable and the results are reliable.

Conclusion

The findings of this study suggest a negative and significant relationship between foreign aid and economic growth, consistent with previous research (Bräutigam & Knack, 2004; Liew et al., 2012; Rajan & Subramanian, 2008; Teboul & Moustier, 2001; Yahyaoui & Bouchoucha, 2020; Zardoub & Sboui, 2021). As a result, the hypothesis that foreign aid has a positive impact on growth in South Asian countries is rejected. Previous research studies have identified poor governance and high corruption as possible reasons for foreign aid, particularly loans, to hinder economic development in some countries (Qayyum, Din, & Haider, 2014). Senadza, Fiagbe, and Quartey (2017) have further pointed out that loan inefficiencies can arise due to loan terms, such as repayment terms and loan costs, misuse of loan funds, and loan distribution/payment in highly corrupt countries.

To achieve a positive impact from aid, recipient countries should improve their governance quality, while donors should provide significant aid to enhance institutional quality and capacity building in developing countries. Additionally, these countries should prioritize receiving grants instead of loans to minimize the debt burden. Governments should develop policies to boost revenue and reduce unnecessary current expenditure. To increase government revenue, it is advisable to regulate the tax base rather than relying on foreign loans. The study recommends channeling remittances into the productive sector and reducing the trade deficit by attracting foreign direct investment.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Arndt

, Jones

, & Tarp

(2015). Assessing foreign aid’s long-run contribution to growth and development. World Development , 69, 6–18.

Bacha

E. L.

(1990). A three-gap model of foreign transfers and the GDP growth rate in developing countries. Journal of Development Economics , 32(2), 279–296.

Birdsall

, Rodrik

, & Subramanian

(2005). How to help poor countries. Foreign Affairs , 84(4),136–152.

Boone

(1994). The impact of foreign aid on savings and growth . (CEP Working Paper No. 677). London School of Economics.

Bräutigam

D. A.

, & Knack

(2004). Foreign aid, institutions, and governance in sub-Saharan Africa. Economic Development and Cultural Change , 52(2), 255–285.

Brückner

(2013). On the simultaneity problem in the aid and growth debate. Journal of Applied Econometrics , 28(1), 126–150.

Burnside

, & Dollar

(2000). Aid, policies, and growth. American Economic Review , 90(4), 847–868.

Chenery

H. B.

, & Strout

A. M.

(1966). Foreign assistance and economic development. The American Economic Review 56(4), 679–733.

Clemens

M. A.

, Radelet

, Bhavnani

R. R.

, & Bazzi

(2012). Counting chickens when they hatch: Timing and the effects of aid on growth. The Economic Journal , 122(561), 590–617.

10.

Dash

A. K.

(2021). Does foreign aid influence economic growth? Evidence from South Asian countries. Transnational Corporations Review , 1–14.

11.

Domar

E. D.

(1946). Capital expansion, rate of growth, and employment. Econometrica , 14, 137–147.

12.

Easterly

(2003). Can foreign aid buy growth? Journal of Economic Perspectives , 17(3), 23–48.

13.

Easterly

, Levine

, & Roodman

(2004). Aid, policies, and growth: Comment. American Economic Review , 94(3), 774–780.

14.

Fayissa

, & El-Kaissy

M. I.

(1999). Foreign aid and the economic growth of developing countries (LDCs): Further evidence. Studies in Comparative International Development , 34(3), 37–50.

15.

Hansen

, & Tarp

(2000). Aid effectiveness disputed. Journal of International Development , 12(3), 375–398.

16.

Harrod

R. F.

(1939). An essay in dynamic Theory. The Economic Journal , 49(193), 14–33.

17.

Jena

N. R.

, & Sethi

(2020). Foreign aid and growth nexus: Empirical evidence from South Asian countries. South Asian Journal of Business Studies , 10(1), 1–28.

18.

Knack

(2001). Aid dependence and the quality of governance: Cross-country empirical tests. Southern Economic Journal , 68(2), 310–329.

19.

Kosack

(2003). Effective aid: How democracy allows development aid to improve the quality of life. World Development , 31(1), 1–22.

20.

Lensink

, & White

(2000). Aid allocation, poverty reduction and the assessing aid report. Journal of International Development , 12(3), 399–412.

21.

Liew

C.-Y.

, Mohamed

M. R.

, & Mzee

S. S.

(2012). The impact of foreign aid on economic growth of East African countries. Journal of Economics and Sustainable Development , 3(12), 129–188.

22.

Morrisey

(2001). Does aid increase growth? Progress in Development Studies , 1(1), 37–50.

23.

Qayyum

, Din

, & Haider

(2014). Foreign aid, external debt and governance. Economic Modelling , 37, 41–52.

24.

Rajan

R. G.

, & Subramanian

(2008). Aid and growth: What does the cross-country evidence really show? The Review of Economics and Statistics , 90(4), 643–665.

25.

Rao

D. T.

, Sethi

, Dash

D. P.

, & Bhujabal

(2020). Foreign aid, FDI and economic growth in South-East Asia and South Asia. Global Business Review , 24(1), 31–47.

26.

Senadza

, Fiagbe

, & Quartey

(2017). The effect of external debt on economic growth in Sub-Saharan Africa. International Journal of Business and Economic Sciences Applied Research (IJBESAR) , 11(1), 61–69.

27.

Shah

B. N.

, & Hwang

(2022). A comparative study on foreign aid and growth in six South Asian countries. International Journal of Public Finance , 7(2), 305–318.

28.

Solow

R. M.

(1956). A contribution to the theory of economic growth. Quarterly Journal of Economics , 70(1), 65–94.

29.

Svensson

(2000). Foreign aid and rent-seeking. Journal of International Economics , 51(2), 437–461.

30.

Taylor

(1994). Gap models. Journal of Development Economics , 45(1), 17–34.

31.

Teboul

, & Moustier

(2001). Foreign aid and economic growth: The case of the countries south of the Mediterranean. Applied Economics Letters , 8(3), 187–190.

32.

Weerasingha

W. R. D.

, & Mustafa

A. M. M.

(2019). Foreign aid and economic growth in South Asia. In 8th annual International Research Conference - 2019, on Sustainable through Business, Humanities and Technologies (pp. 106–112). Management and Commerce, South Eastern university of Sri Lanka.

33.

Yahyaoui

, & Bouchoucha

(2020). Foreign aid–growth nexus in Africa: Do institutions matter? Journal of the Knowledge Economy , 11(4), 1663–1689.

34.

Zardoub

, & Sboui

(2021). Impact of foreign direct investment, remittances and official development assistance on economic growth: Panel data approach. PSU Research Review , 7(2), 73–89.