INTRODUCTION

The agriculture sector is one of the major industry of the country’s economy and the government of Myanmar puts a high priority on it. The potential of agricultural development offers the most significant opportunity for inclusive economic growth in Myanmar. The vision of the agricultural policy in Myanmar is achieving sustainable development, international standard, food security, and climate smart agricultural system ensuring well-being of rural livelihood and the socio-economy development of smallscale farmers, and the country’s economic growth by 2030 (MoALI, 2018).

The economy of Myanmar is primarily based on three sectors that include agriculture, industry, and services. The agriculture sector includes crops, livestock, and fishery sub-sectors which contribute 21.9%, 26.6% to total export earnings in 2019 and 2020, respectively, and the sector employs 67.3% of the labor force (MoALI, 2020). The agriculture sector plays a critical role in all-inclusive development, rural poverty eradication, and food security enhancement for the country. It is also important to promote the employment and the livelihood of the rural population, especially for some 36% of those living below the poverty line (ADB, 2013).

Myanmar's government is dedicated to achieving the Sustainable Development Goals (SDGs), with the objective of delivering a balance of development in the economic, social, and environmental spheres. For these reasons, sustainability in every area should be considered such as cross-cutting, and integrated into all aspects of Myanmar’s Sustainable Development Plan (MSDP) implementation. Myanmar's fulfillment of the SDGs will be guided by the 2030 Sustainable Development Agenda throughout the same time frame (MoPF, 2018).

Myanmar receives international development assistance with largest amount nowadays (Carr, 2018). Many development agencies give a priority, especially, to social infrastructure and services. In recent years, Myanmar becomes the 3^rd largest recipient country of Official Development Assistance (ODA) per capita followed by Cambodia and Lao PDR, although it was the 7^th largest recipient country back in 2015. Therefore, it is necessary to analyze the effectiveness of the ODA for Myanmar and to find out better ways to enhance sustainability for the future cooperation.

There have been many studies to consider the effectiveness of ODA on agricultural development. Most of the studies analyzed the contribution and the impact of agricultural ODA for Myanmar and other countries utilizing various methodologies such as Auto-regressive Distributed Lag (ARDL) method, Vector Error Correction Model (VECM), and Generalized Method of Moments (GMM). These studies found out that the agricultural ODA had a positive and significant contribution, in particular, to value chain and technical transformation. Moreover, Korea’s ODA to agricultural production and marketing areas did not show significant effect but had a positive relationship with the regional economy (Htay and Lee, 2021). Nonetheless, Alabi (2014) and Ssozi et al. (2019) argued that bilateral aid in agriculture was more effective in productivity than multilateral aid. Zewide (2017) investigated on agricultural growth for Ethiopia using VECM and revealed that ODA had a negative and significant effect on agricultural growth in the short run, but it had a significant and positive influence in the long-run. In particular, Ighodaro & Chii analyzed the effectiveness of ODA on the agricultural sector growth in Nigeria applying ARDL and VECM and pointed out that ODA had an insignificant and negative relationship on the agricultural output both in the longrun and short-run. Oppositely, both technological trend and savings had a significant and positive relationship with agricultural output in terms of the short-run and the longrun (Ighodaro & Chii, 2015). The results of the previous researches showed there has been a positive relationship between agricultural growth and ODA. However, few studies investigated the effectiveness of ODA in the agricultural sector.

Korea’s Bilateral ODA Allocation

The Korean government has tried to establish its ODA implementation system more effective, transparent, and sustainable. Its implementing principle of ODA has ensured that ODA should contribute to the global community’s efforts to achieve SDGs. The significance of supporting ODA is aligned with the government’s international policies, such as diplomatic diversification and strategic economic cooperation, and a greater emphasis is being placed on the need for ODA to contribute to national interests of the partner countries (MoFA, 2021).

In 2010, the Committee on International Development Cooperation (CIDC) prioritized 26 partner countries for a targeted and concentrated approach to improve development effectiveness. Korea's bilateral aid to 24 priority partner countries continued to be selected in 2019, with a total amount of USD 1,060.37 million for a five-year period from 2015 to 2019 (55.7 percent). Bangladesh and Lao PDR, two of the top ten recipients of ODA, received a total of USD 78.81 million. Bangladesh was the largest recipient country in 2019, with the amount of USD 98.38 million, followed by Vietnam ($93.21 million), Myanmar ($83.39 million), and Ethiopia ($82.15 million), as shown in Table 1 (OPC, 2020).

Korea’s bilateral ODA was primarily focused on Asian countries in 2019, with USD 1.0 billion. Africa was received USD 516.3 million was contributed to African countries, accounting for 25.2% of the bilateral ODA. Moreover, Asia and Africa were also the major regional recipients of Korea’s multilateral aid, based on its policy priorities of overall strategy. However, gross bilateral ODA was not broken down by region in 2019. Furthermore, Korea also committed USD 1.4 billion (40.4% for bilateral aid) to promote assistance for commerce and trade performance of developing countries and combine into the world economy (OECD, 2021).

International Development Cooperation of Myanmar

Distribution and allocation policies of resources has been considered as a key component for poverty reduction and equitable economic growth, and Myanmar also has a similar policy that emphasizes on the role of greater access to public goods and services to reduce poverty. Development partners should also ensure that their technical advice is explicitly provided to assist the government policy that efficiently utilizes resource allocation for the small-scale agriculture.

In 2016, the total amount of ODA from international organizations to Myanmar was approximately $1.5 billion, of which ODA for the agriculture and forestry sector was only 5.9%. The amount of ODA was $235 million from Japan, $183 million from U.S., $124 million from Korea, $72 million from Germany, and $48 million from France. The international organizations were the World Bank with $300 million and Asian Development Bank with $263 million for various development programs and projects.

In general, Myanmar receives bilateral and multilateral development ODAs. These assistances focus on various areas. For instance, Japan provides technical cooperation with private partners, the U.S. provides ODA programs for agricultural production of smallholders to enhance productivity and marketing capability in response to climate change. Furthermore, ACIAR assists for agricultural research, ADB focuses on promoting agricultural value chains and technical assistance for rural development, the World Bank Group supports financial access for rural development, and FAO focuses on promoting agricultural value chain development through mitigating natural disasters coping with climate change (Moore, 2016).

In the meantime, Korea designated Myanmar as one of the priority partner countries since 2010. Korea is also one of the major donors to Myanmar with various ODA projects. In addition, there are a number of assistant organizations of Korea in Myanmar, such as KOICA (Korea International Cooperation Agency) and KOPIA (Korea Program on International Agriculture). KOICA focuses on new village development programs, post-harvest research and agricultural marketing. KOPIA provides technical assistance for productivity enhancement in the agricultural sector. Regarding rural development of Myanmar, the government has benchmarked Korea’s experience of ‘Saemaul Undong, SMU’, which the Korean government launched a comprehensive rural development policy in the early 1970s, with a focus on rural livelihood and agricultural infrastructure such as village development, irrigation, farmland expansion, and arable land development. SMU entails the experience and lessons learned from enhancing agricultural productivity, building infrastructure, establishing systems of processing and distribution of agricultural products, and developing advanced agriculture technologies. Moreover, the Korean government would continue to share its development experience and knowledge with Myanmar to support the socioeconomic development strategies of the nation.

Regarding Korea’s Country Partnership Strategy with Myanmar, Korean government is willing to promote sustainable and inclusive rural development, agriculture infrastructure, and value chain development by providing necessary ODA programs. The strategy also highlights enhancing agricultural productivity and promoting exports of Myanmar’s agricultural products. The details of the strategy focus on the followings: 1) promotion of efficient irrigation management for predictable and systematic agricultural production, flood prevention, and water management; 2) policy consultation and technical assistance for agricultural product distribution and exports; 3) formation of independent farmers’ cooperatives to improve their bargaining power in the markets; 4) rural community development while reflecting the lessons learned from SMU; and 5) establishment of accessibility to agricultural financial services (CPS, 2021).

Therefore, it is necessary to examine the effectiveness of ODA, in this case, between Myanmar and Korea to make sure that the future development cooperation take a further step toward the right direction for both countries and possibly for others driven by implications of the study. This study aims to build a win-win situation for both countries and to promote an effective way for development cooperation to contribute to SDGs. The main objective of the study is to analyze the effectiveness of bilateral development cooperation in the agricultural sector between Myanmar and Korea.

Analytical Framework and Model Specification

To achieve the main objective of the study, this study utilizes VECM to examine the existence of co-integration among the relevant factors investigating the effectiveness of bilateral cooperation in the agriculture sector. The methodological approach of the study is depicted in the Figure 1. The reason for using the model is that it is relevant due to its closeness to reality and its ability to produce better short-term forecasts and, more importantly, long-term effect can be aggregated in economically meaningful respects for macroeconomic perspectives.

Data Source

Regarding the general objective, this study utilizes the secondary data. The data used is annual time series from 1991 to 2018 (28 years) from different reliable sources such as Korea International Cooperation Agency (KOICA) and Central Statistical Organization (CSO) of Myanmar for different variables for ODA and the agricultural sector in Myanmar.

Time series analysis is explored in this study. To analyze the effectiveness of bilateral development cooperation of Korea on the agricultural development of Myanmar, the interest variables used in this study are various. For example, the influential factors on agricultural development, agriculture GDP(AGDP) can be explained by agricultural official development assistance (AODA), government expenditure on agriculture sector (GEA) and foreign direct investment (FDI).

Stationary Test

Doing empirical analysis with time series data, it is essential to determine whether data is stationary or not. The data with unit roots cause problems of statistical inference and unpredictable results might occur in time series. There are several ways to test a unit root. Among these, this study uses ADF test. It is more powerful than the Dickey-Fuller (DF) test and can handle more complex models. The ADF test determines that y_t is I (1) when compared to the alternative hypothesis of I (0), it can be assumed y_t is an Autoregressive Integrated Moving Average (ARMA) process and it has not only autoregressive but also moving average terms (Mccarthy & Permanente, 2015).

Using the ADF test for testing the existence of a unit root, the estimation of regression is required as follows:

Where

If the absolute value of computed t statistics is larger than the critical values of ADF, we reject the null hypothesis, β₁ = 0, in which there is no unit root and it is stationary and vice versa.

Co-Integration Test

As the second stage in the VECM model, determining the existence of cointegration among the time series variables is critical. For assessing long-run relationship in the series, the Johansen’s (1988) initial multivariate procedure is considered for the study. As a part of the procedure, a maximum likelihood procedure is used to determine the existence of cointegrating vectors in non-stationary time series dataset. We can accept the null hypothesis if there is no cointegration between the variables. Otherwise, if the series have at least one cointegration, we can accept the alternative.

If the interest variables are cointegrated, this suggests the presence of a long-term or equilibrium relationship. There is a long-run relationship in the system if there is co-integration among the variables in non-stationary variables. Since differencing the variables to become stationary in the regression does not reflect long-run behavior of the variables, checking for the presence of co-integration is crucial. To assess whether or not non-stationary variables in a particular model have a long-run equilibrium relationship, empirical analysis for evaluating co-integration is required. In order to conduct co-integration test, Johanson maximum likelihood estimation procedure is applied.

In the VECM modeling, the first step in Johansen cointegration analysis defines an unrestricted vector autoregression (VAR). It is critical to choose the correct VAR order for a cointegration test. The following form of VAR is considered:

where Y_t is vector of I(1) variables, A_t is matrix of parameters, X_t is vector of stationary I(0) exogenous variables, A_X is matrix of parameters and U_t is white noise errors.

Vector Error Correction Model (VECM)

If the variables are co-integrated or have a long run relationship, an error correction mechanism can be proceeded. The reason for using VECM is that it is relevant due to its closeness to reality and its ability to produce better shortterm projection and, more importantly, long-term forecasts aggregated in economically meaningful respects for macroeconomic policy analysis. In addition, the error correction mechanism (ECM) is used to accurate the variables of any deviation in short-run from the condition of long-run equilibrium. If there is the existence of long-term or equilibrium relationship between the two variables of Y and X, ECM can be used. The ECM can be expressed as follows:

Where Δ indicates the first difference operator, εt is a random error term, and U_t−1 = Y_{t− 1} − β₁ − β₂X_t−1, the oneperiod lagged value of the error term.

The above equation indicates that ΔY_t depends on ΔX_t and the equilibrium error term. The model is out of equilibrium condition when the error term is nonzero. Therefore, the study assume that ΔX_t is zero and U_t−1 is positive. Since b₂ is expected to be negative, the term b₂U_{t− 1} is negative and ΔY_t would be negative to restore the equilibrium.

It is necessary to check unbiased estimation and robustness of the model by conducting diagnostics tests. The robustness of estimated coefficients can be determined by conducting Diagnostic tests. Lagrange multiplier (LM) test, residual diagnostics is utilized in this study for testing residual. Then the diagnostics test for stability also applied to examine whether the parameters are stable or not in the estimated model across various data used.

Empirical Results

Testing the presence of unit root in time series estimation is necessary before running the regression. For testing stationarity, ADF unit root test is applied. The test finds that there are no variables that are stationary. Therefore, first difference of the variables is used to examine the presence of stationarity. Based on the ADF test statistics at first difference, the absolute values of all variables are statistically significant at the 5% level. As a result, the variables are stationary at first difference and we can accept alternative hypothesis. It means that the data and variables can be used for the analysis. The empirical test for ADF does not include the constant term for the simplicity due to the equivalent results.

In time series analysis, the error correction model is crucial due to better understand long-run dynamics. Since VECM is the multivariate extension of the ECM, the existence of cointegration is essential for the long-run relationships in time series data. Therefore, we should choose the appropriate lag length to consistently test for cointegration. The three major tests that can be applied to choose appropriate lag length are Akaike Information Criteria (AIC), the Hannan-Quinn Information Criteria (HQIC) and the Schawarz Information Criteria (SBIC). For determining the optimal lag length for the study, AIC is used in this study and the result indicates that the optimal lag length is lag order 2 as shown in the Table 3.

Cointegration tests identify situations in which two or more non-stationary time series variables are integrated so as not to deviate from the long-run equilibrium. To identify the long-term relationships between sets of variables, this study used Johansen test, it allows for more than one cointegrating relationship. Based on the results of the Table 4, the values of trace statistic are larger than 5% critical value at 0 co-integration equation, we can accept the alternative hypothesis. In this model, there is one cointegrating equation and this indicates that there is cointegration among the variables and they have long run relationship, so we can apply VECM for the further analysis based on the Johansen’s test result of maximum rank of 1 with the eigenvalue of 0.69.

The model equation is as follows:

ECT_t−1= lnAGDP − 1.775215 lnAODA − 1.186621lnGEA + 1.965333 lnFDI − 16.08611

When ECT_{t− 1} = 0, the normalized co- integration equation can be written as:

In Table 5, the results indicate that the coefficient of Korea’s ODA for the agriculture sector (lnAODA) is negative (-1.775215) and government expenditure on the agriculture sector (lnGEA) is also negative (-1.186621) and statistically significant at 1% level, respectively. However, the coefficient of foreign direct investment (lnFDI) is positive (1.965333) and it is also statistically significant at 1% level.

In an empirical sense, there is elasticity among variables; a percentage change in AODA and GEA results in a 1.78% and 1.19% increase in AGDP, respectively. But a 1% change in FDI causes a 1.97% decrease in AGDP. The output of the long-run relationship of the vector error-correction model states that independent variables AODA and GEA have a positive impact and are statistically significant on AGDP. On the other hand, FDI is statistically significant but has a negative impact on AGDP.

As it is expected, the outcomes confirm that the effect of Korea’s ODA for agricultural growth and government expenditure on the agriculture sector have a positive influence on the agriculture sector. Nevertheless, FDI has a negative relationship with agricultural sector growth. The coefficients of all variables are statistically significant at the 1% level.

The results in Table 6 indicate that there is a long-run causal effect in AGDP at a statistically significant level of 5%. The coefficient is -.186 and the p-value is 0.025. That shows the presence of a long-run causality effect in the AGDP equation. In the short run, the coefficient of FDI is positive (.311) and it has a causality effect on AGDP at a 5% level of significance. But the coefficient of AODA is negative (-.245) and GEA is positive (.003) and they do not have a causal effect on AGDP in the short run as they are not statistically significant. It refers that Korea’s ODA for the agriculture sector has a negative effect and is not statistically significant on the agricultural GDP, and government expenditure on the agriculture sector has a positive impact but is not significant in the short run. Nevertheless, foreign direct investment has a positive impact and is statistically significant on the agricultural GDP in the short run.

Autocorrelation problem can be occurred arises in a regression model when error terms correlate over time. Lagrange multiplier (LM) test is used to determine the existence of autocorrelation problem in Table 7 and the outcome indicate that there is no serial correlation in both lag order one and two as p-value is larger than 10% critical value and we can accept the null-hypothesis.

The Jarque-Bera test is usually use for testing normality. It is always positive and if not close to zero, it shows that the data used do not have a normal distribution. As shown in Table 8, the results show that in D_lnAGDP and D_ln- GEA equations, the residuals are less than 5% critical level and not normally distributed. Only D_lnAODA and D_lnFDI, the residuals are greater than 5% critical level, which means that the residual are normally distributed. Therefore, we have to accept the alternative hypothesis and the result shows that the residuals are not normally distributed in this scenario.

The VECM specification imposes 3-unit moduli. The model’s stability demonstrates the estimated model's validity; thus, it should be tested before proceeding. The longterm stability of the parameters was also checked by plotting recursively limited to 95% of the critical values. As seen in the Table 9, the model satisfies with the stability condition.

CONCLUDING REMARKS

The agriculture sector is critical in boosting rural people's social welfare and overall economic development. It is also important in poverty reduction and ensuring inclusive development of a nation’s economy in the long term. Myanmar is an agriculture-based country and more than half of rural people rely on agriculture for their livelihoods and employment. In that sense, international development cooperation is essential in promoting economic transformation and in accelerating progress towards the SDGs.

This study explores the effectiveness of Korea’s bilateral development cooperation on the agriculture sector of Myanmar using a co-integrated VECM to show both the long-run and short-run relations. The significant coefficients show that both the explanatory variables of Korea’s ODA and Myanmar’s expenditure on the agriculture sector have a positive effect on the sector's growth in the longrun. In addition, foreign direct investment has a significant impact, but its long-term impact on agriculture is negative. In the meantime, only foreign direct investment has a significant and positive impact in the short run while Korea’s ODA and government expenditure on the sector have a negative effect and are not significant on the agricultural GDP in the short run.

Although Korea's development cooperation to the Myanmar's agricultural sector has been effective, it still needs to be improved efficiently. On the other hand, the government of Myanmar needs to be more transparent, timely, and manageable in order to implement a more efficient and sustainable way. Even though the Myanmar's agricultural sector has great potential to be developed, there are still weaknesses on policies and a lack of access to foreign aid compared to other ASEAN countries due to a lack of stability in investment legislation. If they formulate better agricultural policies, laws, and legislation, there would be more support from the international community including Korea.

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