




































Bangladesh Journal of Multidisciplinary Scientific Research 

 Vol. 5, No. 1; 2022 

ISSN 2687-850X   E-ISSN 2687-8518  

Published by CRIBFB, USA 

 

1 

ARE FOREIGN AID AND ECONOMIC GROWTH POSITIVELY 

RELATED? EMPIRICAL EVIDENCE FROM BANGLADESH 
 

 

Shahadat Hussain 

Assistant Professor 

Department of Finance and Banking 

University of Barishal, Barishal, Bangladesh  

E-mail: shahadathussain09@gmail.com 

https://orcid.org/0000-0003-0794-4277 

 

Md. Habibur Rahman 

Assistant Professor 

Department of Finance and Banking 

Jatiya Kabi Kazi Nazrul Islam University 

Trishal, Mymensingh-2224, Bangladesh 

E-mail: habiburfbjkkniu@gmail.com 

https://orcid.org/0000-0001-9763-6619 

 
 

Received: December 08, 2021         Accepted: January 31, 2022        Online Published: February 13, 2022  

 

DOI: 10.46281/bjmsr.v5i1.1611      URL: https://doi.org/10.46281/bjmsr.v5i1.1611 

 

 

ABSTRACT 

As an emerging country, the progress of Bangladesh is highly promising. Foreign aid may be 

one of the key players fueling such advancement. The note is an attempt to examine the effect of 

foreign aid on the economic growth of Bangladesh. With a view to fulfill our objective, the study 

employs annual time series data during the period of 1971 to 2019. It has used some 

econometric tools i.e., Unit Root Tests and OLS Methods to process the collected data. The 

dependent variable is Gross Domestic Product (GDP) while other independent variables i.e., 

Foreign Aid (ODA), Gross Capital Formation (GCF), Population (POP), and Education (EDU). 

The test results confirmed that GDP growth is positively related to foreign aid, gross capital 

formation, and education, but negatively related to population. In Bangladesh, if foreign aid 

increases by 1% then the GDP growth, gross capital formation, and education rate will 

accelerate to 0.1988%, 0.6015%, and 0.0652% respectively. So it is evident that foreign aid 

plays a propitious role to progress the economic growth of Bangladesh. It is a crying need to 

swell up effectiveness, transparency, proper accountability in allocation, and stronger 

management of aid inflows to speed up economic growth. 

 

Keywords: Foreign Aid, GDP Growth, OLS Methods, Bangladesh.  

 

JEL Classification Codes: F2, I2, O4. 

 

mailto:shahadathussain09@gmail.com
mailto:habiburfbjkkniu@gmail.com


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INTRODUCTION 

Having independence in 1971 Bangladesh, a poorest and mostly densely populated country, is 

facing multiplex challenges with extensive corruption, acute poverty, educational barriers and 

poor healthcare facilities. In addition to constant natural calamities intensify the severity that 

crying need of foreign aid in the development of many sectors. Basically Aid is for uplifting the 

people around 64 million living here who are lying under the international poverty line at $1.25 

per day, dropping poverty rate by 19% both for rural and urban areas for last few decades. The 

poverty headcount ratio is $1.90 per day was at 24.5% in 2005 go down to 18.5% in 2010 

(Chowdhury, 2018). The World Bank reported that the economy of Bangladesh is growing up at 

nearly 6% per year over the past decades. Aid from the International Development Association 

(IDA) $16 billion in 2013 supports to reform the policies as well as various investment projects 

in which more than $12.5 billion had been used for the purpose of healthcare, promoting 

education and infrastructural development that ultimately triggering the growth. Around $40,488 

million aid has been invested to develop infrastructures and services i.e., energy, transportation, 

communication and financial institutions from 2001-19. On the other hand, social infrastructure 

and service (i.e., health and sanitation, education and water supply) got expenditure $25,710 

million. But the dependency on aid (in terms of development projects) is almost weakened closer 

to 2019. In the fiscal year 2001-02 around half (48%) of the Annual Development Program 

(ADP) budgets was the aid funded that has been decreased to 32% in the fiscal year 2019-20 

(Rafi & Khan, 2021).  

The Official Development Assistance which is known as foreign aid plays an effective 

role to foster economic development and erode the poverty from the third world countries over 

the few centuries. The welfare of a nation could be measured in the form of how it impacts on 

economic growth. The World Bank and IMF are prominent to stretch their helping hand to uplift 

the world economic affairs. Though many years have been passed, it is still now a question for 

the recipient countries whether foreign aid is an effective way to lower their economic hardness 

and boost up the economic growth. A plenty of empirical studies tried to unveil the connection of 

foreign aid and economic growth in various way. But there is no uniform ground regarding the 

findings of the actual effectiveness of foreign aid for the recipient nations. .  

Two well-known researcher Griffin and Enos (1970) had broken the capital inflows 

through aid, private capital and other inflows for the convenience of testing that revealed the aid 

coefficient is significant and positively influence the economic growth. Burnside and Dollar 

(2000) explored that foreign aid leads the growth in the 56 developing countries only for good 

policies rather than poor policies. Good policies should be incorporated with the proper 

management of small budget deficits, effective control over inflation and focusing on 

international trade, recommending that aid would extend efficiency if it had been allocated 

systematically over good policies. Using a large sample of 68 developing countries by Durbarry 

et al. (1998), confirmed that the positive interaction of aid on growth, conditioning on a stable 

macroeconomic policy applying the Augmented Fischer-Easterly type model. In contrast, 

Voivodas (1973) detected a negative correlation of aid and growth by studying 12 years’ time 

series data of 22 Least Developed Countries from 1956-1968. The calculated aid coefficient was 

not also statistically significant. Analyzing the pooled data of 13 Asian countries, Dowling Jr and 

Hiemenz (1983) examined aid-growth nexus and concluded that a significant and positive effect, 

adding trade, finance and intervention of local government as a policy variables. Levy (1988) 

also got positive results for the Sub-Saharan African Countries during 1968-82. Ram (2004) 

noted an important question that is there any influence of aid on growth based on the recipient 



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country’s policies? Employing the Burnside-Dollar policy index with two other broader measure, 

he noted that good policy has no effect on aid and alleviation of poverty in developing countries. 

A more formal attempt by Ghura et al. (1995) to detain the probable side effect of aid, i.e., 

“Dutch-Disease effect” and some policy variables to economic growth with the more 

sophisticated model sampling 41 countries from 1986-92 found the positive results with 

enormous evidence. A different conclusion of Burnside and Dollar (1997) was that aid effect 

policy variables strongly, but the ratio of aid-GDP does not significantly influence on the 

economic growth in the LDCs. Mosley (1980) reported a weedy inverse relation on aid-growth 

nexus developing a simultaneous equation model, but he got positive and significant statistical 

results from the poorest countries’ sample. The test results of Boone (1996) reported that foreign 

aid has no effect on the investment growth as well as income to uplift the poor one rather than 

make healthier the political elites because they are receiving benefits continuously irrespective of 

liberal democratic, or highly repressive, suggesting the effectiveness of a short term aid targeted 

the program for LDCs. After having a wider sample of 120 countries, Lohani (2004) unveiled the 

relationship between foreign aid and development. Incorporating knowledge index, heath index 

and standard of living index for measuring development, this paper captured a negative link of 

aid-growth prescribing that the government should emphasize largely on foreign direct 

investment and domestic investment to boost up growth. Moreira (2005) addressed foreign aid 

enhances growth in the developing countries analyzing macroeconomic data for 48 developing 

countries from 1970-98 arguing the importance of time lags effective in short-run rather than 

long-run. Chheange (2009) documented aid triggers corruption rather than growth, even using 

panel data sampling 67 developing countries from 1986 to 2005 suggesting two lessons: avoid 

aid or utilize it for good governance by the recipient countries. Analyzing cross country data, 

Knack (2001) noted foreign aid corrodes the institutional quality of the public sector stimulating 

rent seeking behavior and corruption. Heavy aid dependency impedes good governance through 

raising bureaucratic complexity and misusing laws. Tait et al. (2015) studied aid-growth nexus 

on 25 Sub-Saharan African countries during 1970-2012. By testing the fixed effect model, they 

showed aid, in the form of grant, extend the economic growth positively only in the long run. 

Sahoo (2016) uncovered similar results studying the South Asian countries, especially for Sri-

Lanka, India and Pakistan applying co-integration test and vector error correction model. By 

contrast, Fatima (2014) cast doubt to link up the issues for Pakistan both in aggregate and 

disaggregate level.  

The above studies indicate that the aid-growth relationship is yet inconclusive that should 

welcome us to continue further research. 

 

OBJECTIVES OF THE RESEARCH 

 To identify the relationship of GDP growth and foreign aid. 

 To verify the effect of gross capital formation (GCF), population (POP) and education 

(EDU) on GDP of Bangladesh. 

 To recommend some policies for ODA, GCF. POP and EDU to strengthen economic 

growth. 

 

DATA AND METHODOLOGY 
In this empirical study, the causality of foreign aid and economic growth in Bangladesh is 

examined by the econometric model in which Gross Domestic Product (GDP) is dependent 

variables against the other independent variables i.e., Foreign Aid (ODA), Gross Capital 



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Formation (GCF), Population (POP) and Education (EDU). The econometric models (i.e., Unit 

root tests and Ordinary Least Square (OLS) Method) have been applied for processing variables.  

Annual time series data from 1971 to 2019 was collected from the website of World Bank. The 

econometric equation has been built to the hypotheses in this study is as follows: 

 

GDP =  α + β1ODA + β2GCF + β3POP + β4EDU+∈  
 

Where,  

  GDP = Log of Gross Domestic Product 

ODA = Log of Foreign Aid 

GCF = Log of Gross Capital Formation 

POP = Log of Population 

EDU = Log of Education 

α = Intercept 

β = Coefficient 

∈ = Error Term 

 

β1, β2, β3 and β4 are the coefficients of the respective variables. In this 

model GDP is the dependent variable while ODA, GCF, POP and EDU 

are considered as the independent variables. 

 

Hypotheses are as follows: 

H1:Foreign Aid has positive relation with GDP 

H2:Gross Capital Formation has positive relation with GDP 

H3:Population has positive relation with GDP 

H4:Education has positive relation with GDP 

 

EMPIRICAL RESULTS 

Stationary Test 

Augmented Dickey-Fuller (ADF) Test 

The unit root test has been applied to verify the data used in the study either stationary or not. 

The ADF test has been used for this purpose. If the data is stationary only in that case results are 

more reliable, but if time series data is not stationary, then the results will no longer be valid.  

Table 1 reports results of the Augmented Dickey-Fuller (ADF) test. The t-statistics are to 

be calculated for both intercept and, trend and intercept cases. The hypotheses are:H0: The 

variables have unit root, i.e., non-stationary, H1: The variables have no unit root, i.e., stationary. 

If t-statistics are greater than ADF critical values or p-values are greater than 5% level of 

significance, then the null hypothesis can’t be rejected. That means the variables have unit root 

or the variables are non-stationary. On the other hand, if t-statistics are less than ADF critical 

values or p-values are less than 5% level of significance, then we can reject null hypothesis. 

Therefore, the unit root does not exist in the variables. So the variables will be stationary.  

 



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Table 1. Results of Augmented Dickey-Fuller (ADF) Test 

 

 Intercept Trend and intercept 

Variables Level First Difference Level First Difference 

GDP 0.114 -7.333* -3.277 -7.223* 

ODA -2.688 -10.947* -3.502 -10.523* 

GCF -0.164 -5.651* -2.574 -5.462* 

POP -0.412 -4.202* -1.732 -3.411* 

EDU 1.038 -5.319* -0.817 -5.832* 

Note: * denotes 5% level of significance. 

 

Here computed ADF test statistics 0.114 is for GDP at level with intercept which is less 

than the critical values and p-value is not significant at the 5% level. That dictates the null 

hypothesis could not be rejected. So, it has the unit root problem and series are non-stationary at 

level. When the series turns into first difference, then the value (-7.333) becomes significant at 

5% level and turns into stationary. Similarly at level with trend and intercept, the test statistics -

3.277 is not significant but at first difference series becomes stationary. 

In the same way, the computed ADF test (at level with intercept) statistics are -2.688, -

0.164, -0.412 and 1.038 for ODA, GCF, POP and EDU respectively. All these values are 

insignificant at 5% level of significance. When they transformed into first difference, then all the 

variables become stationary. In case of the trend and intercept all the selected variables are 

stationary at the first difference but not level form.  

 

Phillips-Perron (PP) Test 

To testify unit root, an alternative test was suggested by Phillips in 1987 that was modified by 

Perron in 1988, and both Philips and Perron in 1988. Actually, it is a non-parametric statistical 

test that considered serial correlation of error terms without lagged difference terms. The 

asymptotic distribution of PP test is analogous to ADF test statistics. 

 

Table 2. Results of Phillips-Perron Unit Root Test 

 

 Intercept Trend and intercept 

Variables Level First Difference Level First Difference 

GDP 1.266 -12.993* -3.466 -12.696* 

ODA -6.852 -12.942* -7.871 -12.236* 

GCF 0.477 -11.267* -3.530 -11.446* 

POP -0.176 -3.921* -1.661 -4.441* 

EDU 1.190 -5.335* -0.817 -5.745* 

Note: * denotes 5% level of significance. 

 

Table 2 represents results of Phillips-Perron tests for five variables- Gross Domestic 

Product (GDP), Foreign Aid (ODA), Gross Capital Formation (GCF), Population (POP) and 

Education (EDU) in logarithmic form. The hypotheses are:H0: The variables have unit root, i.e., 

non-stationary, H1: The variables have no unit root, i.e., stationary. If t-statistics are greater than 

PP critical values, then we can’t reject null hypothesis. That mean the variables have unit root or 



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the variables are non-stationary. If t-statistics are less than PP critical values or p-values are less 

than 5% level of significance, then we can reject null hypothesis. So the variables become 

stationary. Computed PP test statistics are not statistically significant for all variables at level 

irrespective of both cases i.e., intercept or trend and intercept. The variables contained unit root 

at level form, but when they are transformed into the first difference then they become 

stationary. 

 

Regression Model 

Table 3 represents the results of multiple regression analysis. It has been seen that the R2 value 

0.997110 indicates that there is more than 99% variation of the dependent variable is caused by 

independent variables. Again the p-values for the most of the variables are less than 5% that 

indicates the coefficients are statistically significant. In addition, the F-statistic 2587.646 is also 

significant at 5% significance level. All these characteristics indicate that the econometric model 

is fitted well. Using the value of the coefficients, the econometric model could be expressed as 

follows: 

 

D(GDP) = 6.514123 + 0.198894 D(ODA) + 0.601543 D(GCF) − 0.279842 D(POP)
+ 0.065203 D(EDU) 

 

The regression results indicate that GDP is positively related with ODA, GCF and EDU, 

but negatively related to POP. If foreign aid increases by 1% then the GDP growth will 

accelerate to 0.1988% in Bangladesh. In the same way, a 1% addition to gross capital formation 

and education rate will lead to change GDP growth by 0.6015% and 0.0652% respectively. In 

contrast, if the number of population increases to 1%, it will negatively affect the GDP growth 

by 0.2798% in Bangladesh.  

 

Table 3. The Results of Regression Analysis 

 

Dependent Variable: D(GDP) 

Variable Coefficient Std. Error t-Statistic Prob. 

D(ODA) 0.198894 0.044388 4.480824 0.0001 

D(GCF) 0.601543 0.040328 14.91636 0.0000 

D(POP) -0.279842 0.055341 -5.056703 0.0000 

D(EDU) 0.065203 0.081217 0.802820 0.4284 

C 6.514123 0.510187 12.76810 0.0000 

R-squared 0.997110   Mean dependent var. 24.65915 

Adjusted R-squared 0.996725   S.D. dependent var. 1.008611 

S.E. of regression 0.057723   Akaike info criterion -2.734743 

Sum squared resid. 0.099960   Schwarz criterion -2.512550 

Log likelihood 52.85800   Hannan-Quinn criter. -2.658042 

F-statistic 2587.646   Durbin-Watson stat 1.270732 

Prob(F-statistic) 0.000000 

 

 



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Diagnostic Tests 

Normality Test  

From the Figure 1 it is to be seen that the probability value of Jarque-Bara statistic is larger than 

5% which is 0.6057. So the null hypothesis of residuals follows normal distribution could be 

accepted. Therefore, residuals of this model follow normal distribution.  

 

 

0

1

2

3

4

5

6

7

8

9

-0.10 -0.05 0.00 0.05 0.10

Series: Residuals
Sample 1973 2019
Observations 35

Mean       3.68e-15
Median   0.011297
Maximum  0.112893
Minimum -0.097743
Std. Dev.   0.054222
Skewness   0.038243
Kurtosis   2.174370

Jarque-Bera  1.002626
Probability  0.605735

 
Figure 1. Histogram normality test 

 

Serial Correlation Test 

Table 4 reported the results of Breusch-Godfrey serial correlation LM test. The observed R-

squared value is 1.381875 and p-value of Chi-Square 0.5011 is greater than 5%, indicating that 

there is no serial correlation.  

 

Table 4. Breusch-Godfrey serial correlation LM Test 

 

 

F-statistic 0.575471   Prob. F(2,28) 0.5690 

Obs*R-squared 1.381875   Prob. Chi-Square(2) 0.5011 

 

Heteroskedasticity Test 

Table 5 reported results of the Breusch-Pagan-Godfrey heteroskedasticity test. It can be noted 

that the observed R-squared value is 3.351028 and p-value is 0.5009>0.05 indicating the 

residuals have no heteroskedasticity problem. 

 

Table 5. Heteroskedasticity Test: Breusch-Pagan-Godfrey 

 

F-statistic 0.794108   Prob. F(4,30) 0.5384 

Obs*R-squared 3.351028   Prob. Chi-Square(4) 0.5009 

Scaled explained SS 1.445637   Prob. Chi-Square(4) 0.8362 

 

 



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Stability Test 
To test out the stability of our model it is better to perform the cumulative sum of recursive 

residual (CUSUM) test. Following the Figure 2 it has been seen that the recursive error falls 

between the two critical lines that indicate the estimated model and parameters are stable during 

the sample period in our study. 

 

 

-16

-12

-8

-4

0

4

8

12

16

8384 86 88 9098 00 02 04 06 08 10 12 1516 18

CUSUM 5% Significance  
 

Figure 2. The Plot of CUSUM test 

 

CONCLUDING REMARKS AND RECOMMENDATIONS 

The study explores the linkage of foreign aid and economic growth in Bangladesh using OLS 

method. Specifically, it attempts to find out whether the aid and growth are positively or 

negatively related. Both ADF and PP tests have been employed to verify the pattern of time 

series data either stationary or not. Data for all the variables i.e., Gross Domestic Product (GDP), 

Foreign Aid (ODA), Gross Capital Formation (GCF), Population (POP) and Education (EDU) 

are not stationary at level. After transforming into the first difference they changed into 

stationary which is a precondition of regression analysis. The regression model is fitted well 

passing normality, serial correlation, heteroskedasticity and stability tests and confirmed that 

GDP growth has positive association with foreign aid, gross capital formation and education, but 

negative association with the population. It is to be noted that a 1% increment in aid promotes 

the economic growth around 0.1988% based on sampling data. However, the study is suggesting 

that Government should focus on the proper allocation of aid funding through transparent and 

effective management. Additionally, an aid effectiveness program launched by UNDP could be 

strictly followed. However, how the foreign aid impacts in the development of specific sector 

could be a key concern for further research. 

 

 



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AUTHOR CONTRIBUTIONS 

Conceptualization: Shahadat Hussain, Md. Habibur Rahman 

Data Curation: Shahadat Hussain, Md. Habibur Rahman 

Formal Analysis: Shahadat Hussain, Md. Habibur Rahman 

Funding Acquisition: Shahadat Hussain, Md. Habibur Rahman 

Investigation: Shahadat Hussain, Md. Habibur Rahman  

Methodology: Shahadat Hussain 

Project Administration: Shahadat Hussain 

Resources: Shahadat Hussain, Md. Habibur Rahman 

Software: Shahadat Hussain 

Supervision: Shahadat Hussain 

Validation: Shahadat Hussain, Md. Habibur Rahman 

Visualization: Shahadat Hussain, Md. Habibur Rahman 

Writing – Original Draft: Shahadat Hussain 

Writing – Review & Editing: Shahadat Hussain, Md. Habibur Rahman 

 

CONFLICT OF INTEREST STATEMENT 

The authors declare that they have no competing interests.  

 

ACKNOWLEDGEMENT 

All authors contributed equally to the conception and design of the study. 

 

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