ODA and economic growth in Vietnam in 1992 – 2020

(1)

ODA and economic growth in Vietnam in 1992 – 2020

Do Thi Hai^1*

1 VietNam Institute of Economics, Ha Noi, Viet Nam

*Corresponding Author: [email protected] Accepted: 15 September 2022 | Published: 1 October 2022

DOI:https://doi.org/10.55057/ijaref.2022.4.3.17

_________________________________________________________________________________________

Abstract: This paper studies the impact of officiall development association (ODA) on Vietnam's economic growth, with research data from 1992 - 2020 through the use of error correction model (ECM - Error Correction Model) to determine the extent of the trend long - term and short - term effects with the use of tests for stationarity (Unit Root Test), cointegration (Cointegration Test) and Granger Causality Test. Research results show that in the long term, ODA has had a positive impact on growth, through additional capital for domestic savings and foreign currency capital for imports. However, the research results also show that ODA has over-substituted and replaced domestic savings in the long run, thereby hindering the motivation to invest with domestic capital and making the economy dependent on domestic capital. foreign capital sources.

Keywords: ODA, economic growth, Vietnam

___________________________________________________________________________

1. Introduction

In the early stage of Vietnam's economic development, foreign investment capital sources play an important role to improve the infrastructure and development of the economy, especially ODA capital because of its low cost. Looking back on the past, it could be seen that in the nearly 30 years (1992 - 2020), remarkable successments have been achieved such as: the average annual gross domestic product (GDP) growth rate is over 6%, the people's living standard has been enhanced, many aspects of socio - cultural life, education, health care have been improved, stable political situation and national security are maintained, international cooperation relations are expanded.

In the recent years, the trend of ODA capital in Vietnam is increasing with a higher speed and ODA capital contributed in the country’s development of social economic process. The ODA capital allocated in many fields in every proviences of the country. Vietnam has actively intergrated into the world economy and strengthened cooperation with multilateral organizations as well as bilateral partners. ODA plays an important role in promoting economic growth, contributing to the GDP growth rate. Therefore, it is necessary to study the impact of ODA on economic growth in Vietnam in order to contributing to reviewing and evaluating the contribution of ODA to the Vietnamese economy in the period 1992 - 2020.

2. Literature review

There are many studies in the world on the impact of ODA on economic growth in both qualitative and quantitative. These studies are very different, even when studying within the

(2)

same territory also reveals many different points of view. Chenery and Strout (1966) analyzed the relationship of foreign investment and economic growth with data for 50 developing countries in the period 1957 and 1962. The authors measured the gross national product (GNP) growth rate, the investment rate, domestic saving rate, export - import trend in each country and found that: in poor countries, the level of domestic saving is low and has a shortage of foreign currency reserves, hence, the level of investment investment is low and leads to low growth. Therefore, they argued that aid will support capital accumulation, thus increasing the level of investment, increasing the growth rate.

Dowling and Hiemenz (1983) examined the aid - growth relationship for Asia in more than 13 countries by using aggregate data and found the impact of aid on growth. They also controlled for a number of policy variables such as trade, finance and government intervention. Singh (1985) obtained similar results for a broader sample of 73 countries for the period 1960 - 1970 and 1970 - 1980. In sub - Saharan Africa, Levy (1988) reported a significantly positive relationship in a regression model including aid (as a percentage of GDP) and income per head people for the period 1968 - 1982. Hadjimichael and partners (1995) found positive evidence for the period 1986 to 1992 by using a sample of 41 countries. Their model is more complex than most of its predecessors by trying to capture the potential side effects of foreign aid (such as the effect of “Dutch disease”) and policy variables are thought to have an effect on growth.

Burnside and Dollar (1997) used a model that includes multiple policy variables and found that although aid - to - GDP ratios generally does not significantly affect growth in developing countries but aid does interact with policy variables. However, Boone (1996) was doubt on the growth effects of aid. He argued that, for some of these countries, aid had no impact on investment or income growth. Research by Gupta (1975) and Gupta and Islam (1983) have shown that if indirect effects are included, initial estimates of the negative effects of foreign capital may be upside down.

In contrast, Mosley (1980) used simultaneous equation model and found a weak negative correlation between aid and growth, although he did find a significant, positive relationship for the the “poorest” country in our sample. Another study by Mosley (1987) pointed out a paradox: aid can be effective in the area where the project is funded, but it has no specific impact on the whole of economy.

Ann Veiderpass Per- Ake Andersson (2007), this study attempts to partially explain aid effectiveness by assessing aid effectiveness in the context of production theory. The first step is to determine the extent to which the country is using its resources. It was measured by an efficiency index that reflects a country's productive capacity. The second step is to examine whether any systematic correlation can be found between a country's performance and its inflows in the form of aid. The study included activity from 60 countries in the period 1995 and 2000. China, followed by Nigeria, displayed the highest relative efficiency values for the study period. The results showed that aid effectiveness ranged from 14 to 15 percent per year in the period 1995 and 2000, found in India, Indonesia and Pakistan.

Uttam Golder, Md. Imran Sheikh, Fatema Sultana (2021), this study evaluated the influence of foreign aid on the country's economic growth by using annual data, covering the period 1989 - 2018. The Autoregressive distributed lag (ARDL) model was applied to achieve the research objective and the empirical results show a significant and strong impact of foreign assistance on economic growth. The results further showed that domestic investment also contributes significantly to economic development. However, trade openness plays a significant positive

(3)

role in the short term, although the impact is immaterial in the long run. Empirical findings indicated that the association of aid, domestic investment and growth is reliably significant at the 1% level in the long run, while aid has a larger effect than investment in country. However, in the short term, aid, domestic investment, trade opening and growth showed a positive and remarkable response also at 1%. This assessment performs a detailed analysis of the country's economic growth and based on its results. This work suggests that more focus should be placed on generating investment, push more exports and aid allocation must be determined according to the relative needs of the country.

In Vietnam, paper researchs on ODA are quite few, mostly qualitative research (operational efficiency, management and use of ODA, ...) and almost paper research on the impact of ODA on Vietnam's economic growth is still limited.

Pham Hoang Mai (1996) presented the need for the government to actively intervene in the process of managing and using ODA. Specifically, the government needs to restructure ODA capital flows, attract donor partners to increase government spending, thereby stimulating private sector investment and increasing the amount of capital disbursed, focusing on projects of building social infrastructure directly towards social goals (poverty eradication) instead of economic goals.

Quantitative study by Pham Thu Hien (2008) estimated the impact of ODA for infrastructure development projects on FDI inflows in 64 provinces and cities of Vietnam in the period 2002 - 2004 by using regression method 2 phase (2SLS) and fixed effect/random effect (Fixed Effect/Random Effect) methods. Research results show the positive impact of ODA on FDI (Foreign Direct Investment) flows, not only through direct channels, but also through indirect channels through improving human capital in aid - receiving provinces. However, the study only finds the long - term impact of ODA on FDI, while the short - term impact of the level of ODA disbursement is not clear. Furthermore, the author finds that Japanese ODA has a positive and significant effect on the distribution of Japanese FDI in both the long and short run.

Bui Dinh Vien (2009) gived a brief overview of Australia's ODA for Vietnam, in which the author points out four reasons for the low rate of ODA disbursement: (i) disbursement speed depends on project design quality. The design of the project is not consistent with the actual implementation, (ii) the difference in disbursement procedures between donors and the project implementation localities, (iii) the limited capacity of project staff, (iv) the project management organization structure depends on Australian contractors. At the same time, the author also offers solutions, namely harmonizing procedures, balancing priority areas for ODA funding with Vietnam's goals, and requesting the donor to grant autonomy project manager for the Vietnamese side, suggested that Australia consider supporting Vietnam through preferential credits in the future when the ODA source is decreasing.

3. Research method

3.1 Research paradigm

Based on the model of Chenery and Strout (1966); Boone (1996), the author identifies a regression model to estimate the impact of aid on Vietnam's economic growth in the period 1992 - 2020 as follows:

GDPR = f (ODA, OPENR, POPR, SAV) (1) In there

+ GDPR: real GDP growth rate;

(4)

+ ODA: ODA/GDP rate;

+ OPENR: growth rate of foreign trade openness. In which, trade openness = (total export and import turnover)/GDP

+ POPR: population growth rate;

+ SAV: domestic saving to GDP ratio

From equation (1), it could be written as:

GDPR = β0 + β1ODA + β2OPENR + β3POPR + β4SAV + u(t) (2)

3.2 Data resource

The data collected here is secondary data. The data includes: real GDP growth rate, total net disbursed ODA, total domestic savings, population growth rate, foreign trade openness. The data is collected from the databases of the World Bank, IMF and OECD for the period from 1992 to 2020. Research data is collected from statistical websites such as data.worldbank.org, data.imf .org… The time series of this data is 28 years, which is consistent with the conclusions of Chenery and Strout (1966); Boone (1996).

3.3 Methodology

Using the error correction model ECM and cointegration analysis method proposed by Engle and Granger (1987) to determine the long-run relationship between the variables. If the variables are co-integrated (or co-integrated), it means that there exists a long-run relationship between them.

The general model of cointegration is as follows:

Y = β1 + βiXi + Ut (3)

Where Xi and Y are non-stationary or non-stationary variables at its order I (0), but stationary at first difference: I (1) and Ut is the model residual.

In the OLS method, it is not forced to use the stationary series, however, if the R2 value > the Durbin - Watson statistic, it means that the model has signs of being spoofed. But according to Engle and Granger (1987), in addition to the condition that R2 < Durbin-Watson statistic value, if it is a stationary series I (0), then the model is not a spurious model, and Xi and Y are co- affiliation, it means that they have a long-term relationship.

The general model of ECM is as follows:

D(Y) = β2 + βiD(Xi) + t-1 + V (4)

In which, D(Xi) and D(Y) are the first difference of Xi and Y; V is white noise 4. Result

Based on the model of Engle and Granger, the author estimates the long-term impact for the model (2). The results of model (2) are presented in Table 2 as follows:

Table 2: Result of long-run equilibrium estimation model (2)

Dependent Variable: GDPR Method: Least Squares

Date: 01/03/22 Time: 22:28 Sample: 1992 2020

Included observations: 28

Variable Coefficient Std. Error t-Statistic Prob.

(5)

C 0.084392 0.026864 3.141523 0.0056

ODA 0.621042 0.269719 2.302550 0.0335

OPENR 0.051541 0.019044 2.706422 0.0145

POPR -1.587022 1.126259 -1.409109 0.1758

SAV -0.121832 0.052913 -2.302491 0.0335

R-squared 0.583586 Mean dependent var 0.065487

Adjusted R-squared 0.491049 S.D. dependent var 0.014534 S.E. of regression 0.010369 Akaike info criterion -6.110372 Sum squared resid 0.001935 Schwarz criterion -5.863526 Log likelihood 75.26928 Hannan-Quinn criter. -6.048291

F-statistic 6.306544 Durbin-Watson stat 1.519773

Prob(F-statistic) 0.002354 Source: Author's calculation from eview 8 software

Table 2 shows that: R2 value < Durbin – Watson statistic value, thus meeting the necessary conditions for regression is not fake. However, R2 = 58.36%, so the model can only explain to a good extent. From the above analysis, we get the regression equation describing the fluctuations of the variables affecting the GDP growth rate as follows:

GDPR = 0,0844 + 0,621ODA + 0,0515OPENR – 1.587POPR - 0,1218SAV In summary, the results of model (2) show that ODA and OPENR have a positive impact on GDP growth in the long run, because a 1% change in ODA and OPENR will change GDPR by 0.621, respectively 0.621% and 0.0515% in the same direction. Meanwhile, POPR and SAV have a negative impact on GDP growth in the long run, because a 1%

change in POPR and SAV will make GDPR change respectively of 0.314% and 0.146%

in negative directions.

Some model tests:

- Fisher test: This test is used to examine the validity of the model with the following hypothesis:

• Ho: Xi = 0 (all Xi variables do not affect Y)

• H1: Xi ≠ 0 (one of the Xi variables affect Y)

Through the regression results obtained from Table 3, we can determine F*=6,30; Looking up the Fisher distribution table at 5% significance level, we have Fk-1,n-k = 3.01 (With k = 4 degrees of freedom, n=28)

So F*> Fk-1,n-k, it means that reject the hypothesis Ho, accept the hypothesis H1. This means that the variation of the dependent variable (Y) is explained by the independent variables according to the regression model.

- Check for autocorrelation (multicollinearity) Breusch - Godfrey test of order 1

(6)

Table 3: Breusch – Godfrey test of order 1 . autocorrelation

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.767236 Prob. F(1,17) 0.4523 Obs*R-squared 0.895478 Prob. Chi-Square(1) 0.2358

Source: Author's calculation from eview 8 software

With significance level α = 0.05. We have P_ value = 0.2358 > 0.05. rejecting hypothesis H1:

There is first order autocorrelation (hypothesis Ho: There is no autocorrelation). That is, the Model has no first order autocorrelation.

Breusch - Godfrey test of order 2

Table 4: Breusch - Godfrey test of autocorrelation of order 2

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.756324 Prob. F(2,16) 0.2932 Obs*R-squared 4.365223 Prob. Chi-Square(2) 0.2852 Source: Author's calculation from eview 8 software

With significance level α= 0.05. We have P_value = 0.2852 > 0.05. We reject the hypothesis: There is 2nd order autocorrelation. That is, the model does not have 2nd order autocorrelation.

- Test of variance variance (White test)

Table 5: Verification of variance of variable error

Heteroskedasticity Test: White

F-statistic 1.234667 Prob. F(14,8) 0.4397

Obs*R-squared 16.34654 Prob. Chi-Square(14) 0.3552

Scaled explained SS 13.63432 Prob. Chi-Square(14) 0.6318 Source: Author's calculation from eview 8 software

The results of the test of variance of variance according to the White method give a P_value of 0.355 > 0.05. We reject hypothesis H1: There is a phenomenon that the variance of the model changes (Hypothesis Ho: There is a phenomenon that the variance of the model is constant).

That is to say that the model has a constant (invariant) Variance.

(7)

- Ramsey RESET test for error model

Table 6: Ramsey Reset test

Ramsey RESET Test

Value df Probability

F-statistic 0.232675 (2, 16) 0.7308

Likelihood ratio 0.522537 2 0.7465

The results of the Ramsey Reset test on the wrong model give a P_value of 0.7308 > 0.05. We reject the hypothesis H1: The model omits the variable (Hypothesis Ho: The model does not omit the variable). That is, it is confirmed that the model has a model that does not omit variables.

From the long-term equilibrium estimation results, the author continues to use the Error Correction Model (ECM) to analyze the short-term impact of the aid variables on the GDPR dependent variable. Based on model (4), model (2) is rewritten as follows:

DGDPRt = β0 + β1DODA + β2DOPENR + β3DPOPR + β4DSAV + t-1 + V (5)

Table 7: ECM model estimation results (5)

Variable Coefficient Std. Error t-Statistic Prob.

DODA -0.116769 0.186924 -0.624683 0.5422

DOPEN 0.033271 0.013543 2.456706 0.0277

DPOPR -0.254818 0.828382 -0.307610 0.7629

DSAV

t – 1

-0.056824 0.068414 -0.830591 0.4201

-0.696547 0.246708 -2.823363 0.0135

C -0.000769 0.001898 -0.405187 0.6915

R-squared (R²) 0.492270 Mean dependent var -0.001325 Adjusted R-squared 0.310937 S.D. dependent var 0.009932 S.E. of regression 0.008244 Akaike info criterion -6.515278 Sum squared resid 0.000952 Schwarz criterion -6.216558 Log likelihood 71.15278 Hannan-Quinn criter. -6.456964

F-statistic 2.714739 Durbin-Watson stat 1.715578

Prob(F-statistic) 0.064402

Table 7 shows that, R² (0.492270) < Durbin - Watson statistic value (1.715578), so the regression results can be considered as not tampered with. R² = 49.22%, so the model can only explain at an average level. In addition, the estimated results also pass diagnostic tests on residuals.

(8)

So the short-run equilibrium equation for model (5) is as follows:

DGDPRt = -0.0008 – 0.1168DODA – 0.0333DOPENR – 0.2548DPOPR - 0.0568DSAV – 0.6965t – 1 (6)

According to the estimation results, in the short term, OPENR has a positive impact on GDPR, while ODA, POPR and SAV have a negative impact on GDPR. In addition, t – 1 has a coefficient of -0.697 < 0 thus reinforcing the stability of the long-run model and reflecting a rather high adjustment towards equilibrium of GDPR. However, in this short-term model it is shown that the probability value of the F-statistic is > 5%, so the short-term model is not statistically significant.

5. Discussion

The regression equation describing the variation of variables affecting the GDP growth rate is as follows:

GDPR = 0,0844 + 0,621ODA + 0,0515OPENR – 1.587POPR - 0,1218SAV The coefficient of determination adjusted R² (Adjusted R-squared) = 0.491 shows that the independent variables explained 49.1% of the variation of GDP growth rate. Based on the estimated results and the current situation of Vietnam's economy, we can see:

Firstly, the population growth rate negatively affects economic growth in the aid - affected model, this result is similar to the study of Vu Minh Duc (2006) and Boone (1996). According to research by Boone (1996), donors are often timid in giving aid to countries with high population size, because of political reasons and the effectiveness of aid. And in the research model of Mosley (1987) shows that, countries with small and medium population size often bring better aid efficiency. However, Boone (1996) also found that increased labor force growth could help offset this negative effect.

Secondly, the sign of the estimated coefficient of SAV is negative (-). This goes against the conclusion of Chenery and Strout (1966). Considering Vietnam's domestic savings and investment in the period 1993 - 2015, the amount of domestic savings is almost always smaller than the amount of domestic investment, which means that the amount of domestic savings is not enough to meet the demands for total investment. However, the results of the regression model imply that, in the long run, aid has not only offset domestic saving, but even over-compensated and replaced domestic saving. In other words, the economy's investment in the long run is financed by external capital (aid and other sources) rather than by domestic savings.

According to Mosley (1987) and based on model results, although aid has a positive effect on growth in the long run, replacing domestic saving is not good for the economy, because it will erode the domestic saving incentive (in other words, reduce the investment incentive of domestic investors), further making the economy dependent on aid. It is an unsustainable form of economic development. Meanwhile, as Vietnam has become a middle-income country, aid loan terms will become increasingly difficult, and future aid amounts will decrease according to donor community practice, then, if the dependence on aid is too great, the economy will be in trouble.

Thirdly, trade openness positively affects growth in the aid-affected model. This is

(9)

similar to the conclusion of Vu Minh Duc (2006), which implies that aid helps to supplement foreign currency sources for the growth of international trade.

References

Ann Veiderpass Per-Åke Andersson (2007) Foreign aid, economic growth and efficiency development A Data Envelopment Analysis approach, https://www.oecd.org/derec/sweden/foreignaid.pdf

Bui Dinh Vien (2009) Improving the efficiency of management and use of the Australian Government's ODA for Vietnam, State Management Review, No. 161, June 2009, pp. 28- 32

Boone (1996) Politics and the Effectiveness of Foreign Aid, European Economic Review,

Vol. 40, pp. 289-328.

Chenery, H.B. and Strout, A.M. (1966) Foreign Assistance and Economic Development.

American Economic Review, 56, 679-733.

Dowling, J.M. and U. Hiemenz (1983), Aid, Savings, and Growth in the Asian Region, The Developing Economies, Vol. 21, No.1, pp. 1-13.

Engle, R. and Granger, C. (1987) Cointegration and Error Correction: Representation, Estimation and Testing. Econometrica, 55, 251-276.

Gupta, K.L. (1975), Foreign Capital Inflows, Dependency Burden and Savings Rates in Developing Countries: A Simultaneous Equation Model’, Kyklos, Vol. 28, pp. 358- 374.

Gupta, K. L. and M. A. Islam (1983), Foreign Capital, Savings and Growth: An International Cross-Section Study, Boston, D.Riedel Publishing Company

Hadjimichael, M.T et al. (1995), Sub-Saharan Africa Growth, Savings, and Investment, 1986- 1993, International Monetary Fund Occasional Paper No. 118, Washington D.C Levy, V. (1988), Aid and Growth in Sub-Saharan Africa: The Recent Experience,

European Economic Review, Vol. 32, pp. 1777-1795

Mosley, P. (1980), Aid, Savings and Growth Revisited, Bulletin of the Oxford University Institute of Economics and Statistics, Vol. 42, pp. 79-85.

Mosley, P (1987), Overseas aid: Its defence and reform – at available https://uknowledge.uky.edu/upk_economics/8/

Pham Hoang Mai (1996), The role of government in the utilization of official development assistance in Vietnam. Contemporary Southeast Asia: a quarterly journal of international and strategic affairs, 18(1), 81-99.

Pham Thu Hien (2008), The effects of ODA in infrastructure on FDI inflows in provinces of Vietnam, 2002-2004. Paper presented at the Vietnam Development Forum.

Uttam Golder, Md. Imran Sheikh , Fatema Sultana (2021) The Relationship Between Foreign Aid and Economic Growth: Empirical Evidence from Bangladesh, Journal of Asian Finance, Economics and Business Vol 8 No 4 (2021) 0625–0633

Vu Minh Duc (2006), Foreign aid and economic growth in the developing countries: A cross-country empirical analysis. Paper presented at the Discussion Forum