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Sustainability 2018, 10(12), 4491; https://doi.org/10.3390/su10124491

Article
Dynamic Relationships between Mega Projects and Official Development Assistance: Case of South Korean Infrastructure Construction Projects in ASEAN’s Developing Countries
1
Seoul Housing & Communities Corporation, 621, Gaepo-ro, Gangnam-gu, Seoul 06336, Korea
2
Korea Institute of S&T Evaluation and Planning, 68, Mabang-ro, Seocho-gu, Seoul 06775, Korea
*
Author to whom correspondence should be addressed.
Received: 16 October 2018 / Accepted: 27 November 2018 / Published: 29 November 2018

Abstract

:
In developing countries, infrastructure construction projects are sometimes recognized as mega projects that are important for national development. Such projects are usually implemented as official development assistance (ODA). However, the correlation between the size of ODA and the infrastructure construction project orders gained cannot be completely confirmed. South Korea gives constant aid to developing countries and increases the budget of ODA simultaneously in order to select which country’s construction market to advance. The largest portion of South Korea’s aid continues to be provided to Asian countries. This study analyzes the dynamic relationship between South Korea’s ODA to the Association of Southeast Asian Nations (ASEAN) developing countries and the economic fluctuations represented by infrastructure construction projects, gross domestic product (GDP), gross national income (GNI), and foreign direct investment (FDI). The analysis result shows that the scale of ODA is synergistic to the number of infrastructure construction project orders awarded. In other words, as ODA increases, developing countries can continue to construct infrastructure, and South Korean companies can improve their performance in overseas construction projects in cooperation with those countries. The results of this study can be used as basic data for the decision-making of donor countries when selecting a country’s construction market to advance.
Keywords:
mega project; official development assistance; infrastructure construction project; developing country; economic fluctuation; vector error correction model

1. Introduction

For developing countries, mega projects (including infrastructure construction projects), which require an enormous amount of resources, have difficulty in attracting the required finances (investment costs) [1]. As developing countries generally have low credit ratings, it is difficult for them to acquire large-scale funding in international capital markets. As a result, developing countries receive long-term loans at low interest rates to support funding for securing infrastructure. This makes it difficult for developing countries to promote infrastructure projects through government finances alone [2]. In particular, as most developing countries lack public funds, project finances for infrastructure construction projects for roads, harbors, railways, power generation, etc., are very risky. For this reason, infrastructure construction projects of developing countries are usually conducted through ODA. Accordingly, the scale of ODA is related to the success of the infrastructure construction project.
In addition, the efficient allocation of a limited ODA budget may be connected to the sustainability of infrastructure construction projects in developing countries with different economies and development stages.
From the perspective of the donor countries, direct investment in recipient countries is a kind of reciprocal benefit situation based on the anticipation of improving their endowment with infrastructure in roads, railroads, ports, and finance [3].
In 2017, the Export-Import Bank of Korea, which manages the economic development cooperation fund, held a joint seminar with the Export-Import Bank of China, JICA (Japan International Cooperation Agency), and NEDA (Neighboring Countries Economic Development Cooperation Agency), all of which are aid organizations for infrastructures in Southeast Asian countries. The four organizations discussed financial cooperation to intensively assist Southeast Asian countries where the demand for large-scale infrastructure development is on the rise. As a consequence, the South Korean government has expanded the budget for ODA.
As the paradigm of ODA is currently changing, some donor countries including South Korea are gradually increasing the amount of ODA, and are utilizing ODA to win overseas construction contracts [4].
For instance, Chinese companies have increased their market shares through China’s ODA. For projects in which Japanese companies have participated, the Japanese government has supported a portion of the construction project fund since 2014 [5]. Various studies have been conducted to explore the correlations between the ODA from donor countries and the national economic growth of developing countries. However, the scope of these studies is limited to the correlations between the ODA from the donor countries and the GDP, GNI, and FDI originating from the developing countries. In case a donor country intends to establish strategies for expanding its contribution to the economic growth of a developing country, it may be necessary to predict demands for ODA in the budgets.
Therefore, several questions have been raised regarding the effects of ODA on both parties (developing and donor countries): (1) Does ODA influence the general economic status of developing countries? If so, then (2) does an infrastructure construction project of a developing country have a correlation to the amount of ODA? Finally, (3) can ODA be continuously influential in gaining and implementing infrastructure construction projects in developing countries?
This study analyzes the relationship between ODA and the number of infrastructure construction project orders awarded by developing countries to a donor country. Several studies have focused on ODA and its impact on the economic development of developing countries [3]. However, only a few studies have paid attention to the impact of ODA, which is the investment cost of infrastructure construction projects in a developing country on the number of project orders awarded to the donor country [6]. In the existing literature, most studies focus on economic indicators from the viewpoint of recipient countries (developing countries). Therefore, studies on willingness to advance infrastructure construction project of developing countries and decision-making for ODA budget allocation need to be conducted from the viewpoint of donor countries.
Of course, ODA reflects the policy stances of each government. As the budget of ODA is limited, it is difficult to statistically show the political relationship between a donor country and a recipient country. However, it is necessary to predict the requirements for ODA in terms of budget, which can help the governments of donor country in assigning financial resources. In this regard, the performance of a mega construction project can indicate the trend of demand for ODA and serve as the basis of budgeting ODA in a donor country.
The objective of this research is to present an analytical model that considers the correlations between the competitiveness of ODA donor countries in the construction markets of developing countries, and the general economic fluctuations of developing countries.
The model is validated by analyzing the performance of South Korea in terms of infrastructure project orders awarded by them, and the variables representing economic fluctuations of developing countries where South Korea has provided ODA. A model to explore the relations is established by introducing the vector error correction modeling method. To evaluate and verify the characteristics of the time-series data obtained, a unit root test, Granger causality test, time-lag test, and co-integration test are conducted.

2. Research Background

2.1. Status of South Korean Overseas Infrastructure Construction Business

The number of overseas construction project orders received by South Korea reached about US $71.60 million in 2010, and has continued to decrease. This is far below the annual average growth rate of 10% of the global construction market for the same period [7]. Figure 1 illustrates the values of overseas construction project orders awarded to South Korean companies during the last 10 years.
However, the rate of increase of project orders awarded by South Korean construction companies after 2011 is 3.7%, which is significantly lower than the annual growth rate of the international construction business, which is estimated to be 10% [9]. In the first half of 2017, the total value of overseas construction project orders awarded to South Korean companies increased by 7.2% over the same period last year. As the global economy tended to recover, infrastructure investments increased and the oil prices were lowered in the Middle East. Thus, South Korean construction companies moved forward, and the number of overseas construction project orders increased.
However, according to Global Insight (2015), since Middle Eastern countries are sensitive to fluctuations in oil prices, the dependence on Asian countries in the global construction market will increase [10]. In this situation, the ASEAN Connectivity Forum (2013), which was organized by the International Contractors Association of Korea and the ASEAN-Korea Centre, explored the possibility of connecting businesses to the infrastructure markets of ASEAN countries, where many direct investments of South Korea have been made.
Figure 2 illustrates the total value of infrastructure construction project orders received by South Korean companies in ASEAN countries over the last 10 years. It turns out that South Korean companies’ performance in the infrastructure construction business in these countries continues to improve. Since 2006, the total value of infrastructure construction project orders received by South Korean companies has not decreased below US $4 billion. The majority of the orders have been concentrated in public construction projects such as bridges, harbors, power plants, and dams in developing countries.
Until recently, South Korean companies experienced difficulty in the overseas construction business, which included a lack of financial support, manpower shortages, cultural differences, and uncertainty in local countries. Accordingly, strategies for gaining construction project orders in each target country need to be seriously discussed at the national level.

2.2. Status of South Korean Official Development Assistance

In 1987, South Korea cancelled its status as a recipient of the World Bank, and began to raise an economic development cooperation fund. KOICA (Korea International Cooperation Agency) was founded in 1991, and South Korea became the first donor country among OECD members in the 2000s, where it used to be a recipient country [12]. The global financial crisis of 2008 made OECD members reduce the budget of international aid. However, South Korea continued to increase its aid budget in order to support the growth of developing countries. In 2010, it joined the OECD DAC (Development Assistance Committee).
South Korea continuously increased its ODA budget, recording an annual rate of increase of 21.7% from 2006 to 2011. As for types of South Korea’s ODA, bilateral aid continues to account for over 70% of the total ODA except in 2008. In other words, South Korea tends to focus its ODA on bilateral relationships. South Korea’s bilateral grant aid occupied only about 20% of the total ODA in the 1990s but increased to 58.1–73.1% between 2006 and 2015. This change seemed to be affected by transparency and evaluation criteria of South Korea’s ODA [13].
According to OECD statistics, the ODA/GNI ratio of South Korea, which indicates the proportion of ODA to the economy of each donor country that is also an OECD member, was 0.14% in 2015, ranking South Korea at 16th among 29 members. The South Korean government continues to increase its aid budget in order to raise the ODA/GNI ratio to 0.20% by 2020.
The regional allocation of South Korea’s ODA over the last 10 years shows that ASIA has received about 64% of the total ODA, thereby being the most benefited (see Figure 3). Moreover, the amount of ODA for Southeast Asia continues to increase.

2.3. Previous Research on Official Development Assistance in Developing Countries, and Its Effects

A literature review was conducted to extract variables for establishing a model. This model was established to explore the relationship between the performance of the international infrastructure construction business (form ODA donor countries) and the economic status of developing countries.
Blaise (2005) [15] analyzed the effect of Japan’s ODA in order to promote the introduction of FDI into China. Based on the analysis result, the Chinese government concentrated ODA projects in infrastructures to solve serious bottlenecks in economic development. In other words, Japan’s ODA was effective in promoting direct investments of Japanese companies in China. Driffield (2013) [16] analyzed the relative contributions of FDI, ODA, and immigrant remittances to the economic growth of developing countries. It turned out that FDI, ODA, and immigrant remittances had a positive effect on the economic growth of developing countries. However, as each recipient had a restrictive FDI, the relationship between FDI and ODA was shown to be negative.
Won (2013) [17] examined the impact of infrastructure ODA projects on the urbanization and economic growth of developing countries. The analysis showed that when the ODA budget for one economically active person increased by 1%, the GDP per capita increased by 0.0105%. This indicated that infrastructure ODA projects can promote the economic growth of developing countries. Isabel (2016) [18] analyzed the relationship between ODA and social capital and economic development in Latin American countries. It was revealed that the reliability of social capital determined the effect of ODA on economic growth. The development of social capital was important to attract ODA, thereby escaping poverty.
Hong (2016) [19] focused on the effect of ODA on the economic growth of Asian countries with respect to changes in public investment and government revenue. It turned out that the effect of ODA was maximized when ODA was 19–56% of the GDP. Suphian (2017) [20] analyzed the effect of ODA on trade conditions, FDI, education, and economic growth in East African countries such as Tanzania, Uganda, and Kenya, which benefited from a large-scale ODA. In the long term, ODA had a positive impact on the economic development of these three countries.
Choi (2007) [21] clarified the role of ODA as a method to support overseas construction business. However, only the ODA donations by South Korea are analyzed, and the target markets or companies are not investigated.
Suh (2011) [22] analyzed the correlation between the economic growth and ODA of Indonesia, the Philippines, and Vietnam, recipient countries of ODA from South Korea. According to the results, ODA had a positive relationship with per capita GDP at a 10% significance level.
Our literature review revealed that ODA affected the economic growth of recipient countries, and both ODA and FDI influenced infrastructure development. Accordingly, the amount of ODA needs to continuously increase for economic growth and infrastructure development in developing countries.
It is practical to assume that the effects of ODA on each recipient country’s economic growth vary. Such variety indicates that the economic fluctuations of a recipient country owing to ODA may affect the number of infrastructure construction project orders received by a donor country.
On the basis of the above results, this study selected analysis variables that indicate the economic situation of South Korea’s recipient (developing) countries (see Table 1).
The impact of ODA on the economic fluctuations of a developing country can be measured by using the GDP, GNI, exchange rate, and interest rate. In addition, the impact of ODA on the infrastructure construction projects of a developing country can be measured based on the FDI.
Review of theory and previous literature has confirmed that ODA has a positive impact on the economic growth of recipient countries (i.e., ASEAN developing countries). This study formulated the following hypotheses: (1) The economic fluctuations of the developing country affects infrastructure construction projects. (2) Increase in ODA of the developing country has a positive impact on the continued promotion and acquisition of infrastructure construction projects.

3. Modeling Procedure

3.1. Variables and Unit Root Test

As has been discussed so far, our literature review on the trends of ODA of donor countries showed that an increase in the amount of ODA was influential on the economic status of a recipient developing country. This correlation may affect the number of infrastructure construction projects received by a donor country. To identify the dynamic relationship between variables over time, this study set up a VECM (vector error correction model) to analyze multivariate time series data. The entire test process is illustrated in Figure 4.
Economic variables tend to follow an unstable random walk. In case a time series is nonstationary, an independent time series may appear to have a high correlation in a regression analysis. This problem is referred to as a spurious regression. Accordingly, in order to prevent such a spurious regression and judge the stability of a time series, a unit root test is performed. If a time series turns out to be unstable and has unit roots, a difference operation is conducted to stabilize and use the time series for an analysis.
The ADF (augmented Dickey-Fuller) test, which is a representative unit root test, is an extension of the Dickey-Fuller test [23]. A null hypothesis implies that a unit root exists. If the t-statistic is greater than the ADF critical value, the null hypothesis is not rejected; this implies that the unit root exists. In this study, therefore, the existence of the unit root was investigated for the time-series data of each analytical variable for developing countries in ASEAN.

3.2. Granger Causality Test

In the case of a typical regression analysis, if causality is unclear, the Granger causality test is performed to identify causes and effects by using a distributed lag model [24]. As the Granger causality test assumes a stable time series, a unit root test needs to be conducted beforehand. “X Granger-causes Y” means that, in addition to a historical value of Y, that of X explains Y as an exogenous variable. The results of the Granger causality test provide important information for determining the arrangement of endogenous variables when a VECM is constructed [25].
This study conducted a Granger causality test to identify the degrees of causality between variables in each developing country, which were converted to stable time series data by a unit root test.

3.3. Co-Integration Test

Engle & Granger (1987) argued that if individual time series with unit roots are linearly combined with each other to create a stable time series, such a regression model has a co-integration relation. In other words, there is a long-term balance between variables [26]. Based on such a co-integration relation, unstable time series data need not be differenced and can be applied to a VECM.
The Johansen test, which estimates the number of co-integration relations and a model for them by expanding the ADF unit root test to multivariate data, is conventionally used as a co-integration test. The Johansen test checks a co-integration relation by using a VECM and estimates a co-integration coefficient. Co-integration vectors are tested based on likelihood ratios. Both trace and max tests are mainly used, and the statistics of each test are expressed as follows:
λ t r a c e ( r ) = T i = r + 1 n ln ( 1 λ ^ )
λ m a x ( r , r + 1 ) = T l n ( 1 λ ^ r + 1 )
where the null hypothesis of λ t r a c e is “the number of co-integration vectors is equal to or smaller than r ”, and that of λ m a x is “the number of co-integration vectors is smaller than r ”. Generally, a null hypothesis is rejected if the continuous statistic from r = 0 (the number of co-integration vectors is 0) to r = k 1 , where the null hypothesis fails to be rejected, is calculated to exceed the threshold under a significance level.
This study utilized the Johansen test to check the co-integration relations between time series variables in each developing country.

3.4. Time Lag Test

After judging the stability of a time series through the unit root test, an appropriate time lag needs to be determined to construct a VECM. If the time lag is too short, the critical region for rejecting the null hypothesis becomes wider. On the other hand, if the time lag is too long, the reliability of the test is weakened [27]. Either the AIC (Akaike information criteria) or the SIC (Schwartz Bayesian criteria) is conventionally used to determine an appropriate time lag. The time lag is determined at a point where the information criteria are minimized [28]. The equations of the information criteria are as follows:
AIC ( p ) = ln | Ω ^ p | + 2 n 2 p T
SIC ( p ) = ln | Ω ^ p | + n 2 p ( l n T ) T
It is reported that, unlike SIC information criteria, AIC information criteria tend to overestimate the number of parameters [29]. Accordingly, this study adopted SIC information criteria to test appropriate time lags.

3.5. Variance Decomposition Analysis

Variance decomposition analysis identifies the relative effect of other variables on the variation of a specific variable in a VAR model. In other words, the relative importance of other variables with regard to the change of the main variable can be measured [30]. The variance decomposition proceeds as follows.
First, the error in predicting the future value y t s by using a VAR model is calculated as follows:
e t , s = y t + s y ^ t + s = ϵ t + s + C 1 ϵ t + s 1 + C 2 ϵ t + s 2 + + C s 1 ϵ t + 1
where the variance of predictive value y ^ t + s can be expressed as follows:
MSE ( y ^ t + s ) = MSE ( e t s ) = E [ ( y t y ^ t + s ) ( y t y ^ t y ^ t + s ) ]
The contribution of each component of an orthogonal error for the mean square error (MSE) can be identified. If ϵ t is expressed by the orthogonalized error U t , the following formula is obtained:
Ω = E ( ϵ t ϵ t ) = a 1 a 1 V a r ( u 1 t ) + + a n a n V a r ( U n t )
MSE ( y ^ t + s ) = j = 1 n V a r ( u j t ) [ a j a j + C 1 a j a j C 1 + + C s 1 a j a j C s 1 ]
When (7) is put into the (6), then (8) is derived, which shows the contribution of the j-th orthogonal error to the MSE, usually in a percentage.
In this study, we analyzed the relative importance of economic variables and ODA for the number of construction project orders received by South Korean companies in each developing country.

3.6. Impulse Response Analysis

An impulse response analysis can measure the response to the effects of other variables over time when a unit impulse is applied to a specific variable in a VAR model. The correlation between the variables and the ripple effect can be identified based on such a response [31].
In a VAR model (p), which is a stable vector process, X t can be expressed as follows:
X t = c + Φ 1 X t 1 + + Φ p X t p + ϵ t
The above formula can be expanded to a vector time series in order to build an infinite vector moving average (VMA) model as follows:
X t = e t + Γ 1 e t 1 + Γ 2 e t 2 + = μ + Γ ( L ) ϵ t
Γ s = ϑ X t + s ϑ ϵ t
Γ s = n × n
Γ s ( i , j ) , which is the element at point (i, j), measures the effect of the impact error term ( ϵ t ) for the variable X i on the variable X i , t + s at point (t + s). Accordingly, the impact response function takes Γ s ( i , j ) as the function of s.
This study analyzed the responses of the number of construction project orders received by South Korean companies when a certain impact was applied to economic variables and South Korea’s ODA in each developing country.

4. Data Analysis and Interpretation

4.1. Data Collection

The purpose of this study is to examine the correlations between the infrastructure construction project orders awarded by South Korean companies in ASEAN (the recipient countries of its ODA), the variation of ODA donations, and economic fluctuations in these recipient countries. For the variables selected in this study, time-series data from between 1987 and 2016 were used. The time-series data on the total amount of ODA donations from South Korea were collected from the OECD/DAC. Further, the time-series data on the number of infrastructure construction projects orders awarded by its companies were collected from the International Contractors Association of Korea, while data on the economic variables (GDP, GNI, FDI, interest rate, and official exchange rate) were collected from the data bank of the World Bank.
The impact variations of a developing country’s economic and infrastructure construction project status according to ODA can be assessed by means of the time series data described in Figure 5.
The GDP, GNI, FDI, interest rate, and official exchange rate, which are the basic indicators of economic growth, are variables for analyzing the economic status of each recipient country. Variables relevant to the donor country are the amount of ODA and number of infrastructure construction project orders awarded to its companies by ASEAN developing countries (Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Thailand, and Vietnam).

4.2. Results of Unit Root Test

In this study, the null hypothesis “a unit root exists” was judged at a significance level of 5% or 10% in order to judge the existence of the unit root [32]. Accordingly, an Augmented Dickey-Fuller test was conducted to see whether a unit root existed in the time series of variables in each developing country of ASEAN. The results are presented in Table 2.
In the unit root test, p-values of the time series of variables in each developing country of ASEAN could not reject the null hypothesis that the “unit roots exists at a significance level of 5% or 10%.” For this reason, a primary difference was determined. Among ASEAN countries, Laos, Malaysia, and Myanmar were excluded from the analysis since the number of infrastructure construction project orders received by South Korean companies in ASEAN developing countries, which is one of the key variables of this study, did not reject the null hypothesis. This is because the performance of South Korean companies in gaining infrastructure construction project orders was discontinuous in these three countries. Cambodia was also excluded since the interest rate did not reject the null hypothesis.

4.3. Results of Granger Causality Test

In this study, a Granger causality test was conducted to identify the causality between variables in Cambodia, Indonesia, the Philippines, Thailand, and Vietnam, excluding the above three countries for which a stable time series was obtained by the unit root test. The results are presented in Table 3.
The causal relations rejected the null hypothesis that “there is no causality with the amount of infrastructure construction project orders received” within a significance level of 5% or 10%. According to the test results, there was causality with FDI, GDP, GNI, ODA, the official exchange rate, and the interest rate in Cambodia, with GNI and ODA in Indonesia, with FDI and ODA in the Philippines, with the interest rate and ODA in Thailand, and with FDI, GNI, the interest rate, and ODA in Vietnam. Consequently, the amount of South Korea’s ODA in ASEAN developing countries was causally related to the number of infrastructure construction project orders received by South Korean companies in those countries.

4.4. Results of Time-Lag Test

This study also conducted a time-lag test to construct a VECM including difference variables obtained by the unit root test. The results are presented in Table 4.
Based on the SIC criteria, the appropriate time lags were 2 for Cambodia and Vietnam and 1 for Indonesia, the Philippines, and Thailand. We applied the appropriate time lags of each country to the VECM.

4.5. Results of Co-Integration Test

In this study, the unit root test showed that Cambodia, Indonesia, the Philippines, Thailand, and Vietnam had unstable time series variables. Accordingly, a co-integration test was performed to confirm the long-term balance of unstable time series variables for each country. The Johansen test, which was most widely used, was conducted as the co-integration test. The results are presented in Table 5.
The co-integration test revealed that Cambodia had four co-integration relations between variables. Indonesia, Thailand, and Vietnam had three co-integration relations, and the Philippines had two co-integration relations. Based on this result, we constructed a VECM and performed a variance decomposition analysis and impulse response analysis.

4.6. Results of Variance Decomposition Analysis

After basic testing for a time series of variables, a VECM was constructed. The relative influence of marginal variables on key variables of each country were measured by a variance decomposition analysis. Accordingly, the relative influence of South Korea’s ODA support and the economic variables on the variations of the number of orders by South Korea’s infrastructure construction projects in Cambodia, Indonesia, the Philippines, Thailand, and Vietnam was measured. The results are presented in Table 6, Table 7, Table 8, Table 9 and Table 10.
For Cambodia, ODA showed a higher relative influence than other variables during each period. In addition, the influence of the initial variation continued to increase until the end of the period. In other words, the change in South Korea’s ODA was more influential on the number of infrastructure construction project orders received by South Korean companies than the economic fluctuations of Cambodia (see Table 6).
Additionally, in the case of Indonesia, the influence of ODA on the initial variation continued to increase until the end of the period. However, FDI had the highest relative influence during the period. In other words, the change in FDI was more influential on the number of infrastructure construction project orders received by South Korean companies than the change in South Korea’s ODA in Indonesia (see Table 7).
Moreover, in the case of the Philippines, ODA was most influential among the variables during the period. However, the amount of ODA tended to decrease over time. In other words, the change in South Korea’s ODA was more influential on the number of infrastructure construction project orders received by South Korean companies than the economic fluctuation of the Philippines (see Table 8).
In addition, in the case of Thailand, FDI had the highest relative influence during the period. In addition, ODA had the second highest relative influence among the variables. Accordingly, FDI was most influential on the number of infrastructure construction project orders received by South Korean companies (see Table 9).
Further, in the case of Vietnam, ODA had a significantly higher relative influence than the other variables in the beginning. However, the influence of ODA tended to decrease over time. In other words, the change in South Korea’s ODA was very influential on the number of infrastructure construction project orders received by South Korean companies (see Table 10).

4.7. Results of Impulse Response Analysis

In this study, considering the amount of ODA support by South Korea to Cambodia, Indonesia, Philippines, Thailand and Vietnam, as well as the impact of economic variables, the dynamic response results of the number of infrastructure construction project orders awarded to the South Korea were measured. The results are presented in Figure 6.
In the case of Cambodia, for the change in the amount of South Korea’s ODA, the number of infrastructure construction project orders received by South Korean companies increased in the positive (+) direction from the beginning, and a rate of increase of about 0.48% was maintained in the final period. In addition, for the change in FDI, the number of infrastructure construction project orders received by South Korean companies increased in the negative (−) direction from the beginning, and a rate of increase of about 0.08% occurred in the final period. Consequently, the amount of South Korea’s ODA had a positive (+) correlation with the number of infrastructure construction project orders received by South Korean companies. These two variables produced a synergistic effect.
Moreover, in the case of Indonesia, for the change in FDI, the number of infrastructure construction project orders received by South Korean companies showed a positive (+) response of 0.41% in the beginning. The change rate was maintained at about 0.60% over time. In addition, for the change in the amount of South Korea’s ODA, the number of infrastructure construction project orders received by South Korean companies continued to increase in the positive direction (+) from the beginning. Accordingly, both the FDI and South Korea’s ODA had a positive (+) correlation with the number of infrastructure construction project orders received by South Korean companies, indicating a synergistic effect between variables.
In addition, in the case of the Philippines, for the change in the amount of South Korea’s ODA, the number of infrastructure construction project orders received by South Korean companies gradually increased in the positive (+) direction from the beginning, and a rate of increase of about 1.41% was maintained in the final period. On the other hand, for the change in FDI, the number of infrastructure construction project orders received by South Korean companies increased in the negative (−) direction over time, and a change rate of about 1.89% occurred in the final period. Consequently, the amount of South Korea’s ODA had a positive (+) correlation with the number of infrastructure construction project orders received by South Korean companies. These two variables produced a synergistic effect.
Additionally, in the case of Thailand, for the change in FDI, the number of infrastructure construction project orders received by South Korean companies changed by about 0.13% in the negative (−) direction in the beginning, and then gradually increased in the positive (+) direction. The final period showed a rate of change of about 0.91%. In addition, for the change in the amount of South Korea’s ODA, the number of infrastructure construction project orders received by South Korean companies gradually increased in the positive (+) direction from the beginning, and the final period showed a change rate of about 0.66%. Consequently, both the FDI and South Korea’s ODA showed a positive (+) correlation with the number of infrastructure construction project orders received by South Korean companies, indicating a synergistic effect between variables.
Further, in the case of Vietnam, for the change in the amount of South Korea’s ODA, the number of infrastructure construction project orders received by South Korean companies increased in the positive (+) direction from the beginning, and the final period showed a change rate of about 0.32%. In addition, for the change in FDI, the number of infrastructure construction project orders received by South Korean companies changed in the positive (+) direction from the beginning, but decreased over time. The final period showed a change rate of about 0.03%. Consequently, both the FDI and South Korea’s ODA showed a positive (+) correlation with the number of infrastructure construction project orders received by South Korean companies, indicating a remarkable synergistic effect between the variables in the beginning.

4.8. Interpretation

The dynamic relationship between the number of infrastructure construction project orders received by South Korean companies and South Korea’s ODA in ASEAN developing countries such as Cambodia, Indonesia, the Philippines, Thailand, and Vietnam was analyzed. It turned out that these two variables had a positive (+) correlation with each other (see Table 11).
The analysis indicated that the number of infrastructure construction project orders received by South Korean companies was most sensitive to changes in the amount of South Korea’s ODA. The positive (+) correlation between these two variables was seen in Cambodia, the Philippines, and Vietnam. This indicates that a simple increase in the amount of South Korea’s ODA can increase the number of infrastructure construction project orders received by South Korean companies in those developing countries. In other words, South Korea may consider the improvement of the number of infrastructure construction project orders awarded to South Korean companies and the long-term continuity of those projects from the standpoint of a donor country. In this regard, the above countries can be selected as attractive places into which South Korea can advance in the area of mega projects of infrastructure construction.
In the case of Indonesia and Thailand, which are more sensitive to the influence of and changes in the FDI, the number of infrastructure construction project orders received by South Korean companies depends on economic fluctuations. For this reason, a mere increase in ODA is not sufficient to conclude that South Korean companies need to advance into the infrastructure construction markets of these countries. Of course, the amount of South Korea’s ODA increases in ASEAN developing countries. However, since the ODA/GNI ratio of this region is still lower than the average value of ODA/GNI, such a simple increase in the amount of ODA can be a strategy. Nevertheless, as the above analysis result indicates, an increase in the amount of ODA does not guarantee an increase in the number of infrastructure construction project orders received by South Korean companies in Indonesia and Thailand. It seems that the economic fluctuations of these countries will have the same effect on other donor countries that provide a large amount of ODA.
As for the relationship between variables, the analysis revealed that the change in South Korea’s ODA was positively (+) proportional to that of the GDP or GNI in Cambodia, Indonesia, Thailand, and Vietnam. This means that the infrastructure construction projects implemented by South Korean companies in those countries were connected to major industries for which ODA funds were used. Accordingly, donor countries advance into these countries through ODA projects that have a strong characteristic of investment development. On the other hand, in the case of the Philippines, the change in South Korea’s ODA was inversely (−) proportional to the major economic indicators.
In other words, the infrastructure construction projects implemented by South Korean companies in this country were not significantly connected to major industries, but were literally national infrastructure development projects supported by ODA funds. Consequently, donor countries advance into such a developing country through ODA projects that have a strong characteristic of aid for developing national infrastructures.

5. Discussion

In this study, a VECM was used to analyze the relationship between ODA and infrastructure construction projects in developing countries. South Korea, which is experiencing continuously expanding ODA and a growing ASEAN construction market entry, is deemed to be significant to other donor and recipient countries [33]. Continuous future demand for infrastructure projects through ODA is expected in developing countries and infrastructure projects appear to be essential for their economic growth [34]. ODA refers to non-profit aid projects as basic purposes. Mutual growth between the donor and recipient country is necessary for the continued success of these projects.
The study results indicate that an increase in the ODA of South Korea would have a positive impact on the promotion of infrastructure construction projects in developing ASEAN countries. In other words, the performance of South Korea’s overseas construction businesses can also be improved. Based on the results, we confirmed the hypotheses of this study (i.e., Section 2.3) that (1) the economic fluctuations of the developing country affect infrastructure construction projects, and (2) an increase in the ODA of the developing country has a positive impact on the continued promotion and acquisition of infrastructure construction projects.
However, the countries delivering more aid than South Korea, including China, Japan, and top OECD DAC donors, will be similarly impacted. Therefore, it is necessary to identify economic variables that have a greater impact on infrastructure construction project orders than ODA.
For developing countries in which the impact of FDI is larger than that of ODA, the orders for infrastructure construction projects will be more responsive to economic fluctuations, even with a simple increase in ODA. From the perspective of the donor country, increasing ODA to enter the market of the developing country is deemed an unreasonable strategy. ODA-related policy directions, economic scale, and industry trends based on the developing country should be considered [35].
In case of the developing country, it is necessary to establish partnerships or consortiums between the ODA support institutions of donor countries and companies through ODA projects based on international agreements. These partnerships or consortiums will be able to facilitate the progress of infrastructure construction projects of the developing county through funding [36]. In addition, the donor country’s companies can reduce risk through the project’s stable earnings.

6. Summary and Conclusions

This study represents basic research for continuing infrastructure construction projects that are mega projects in developing countries benefiting from ODA and for helping donor countries select which countries are attractive enough to be integrated into the global construction market. To achieve these goals, we analyzed the dynamic relationships between ODA and economic fluctuations with respect to the number of infrastructure project orders awarded to donor countries. South Korea was selected as the target to be analyzed since it is a member of OECD DAC, which continuously increases the amount of ODA and attempts to advance into overseas markets owing to unstable domestic markets.
Among the ODA recipient countries, Cambodia, Indonesia, the Philippines, Thailand, Vietnam, Laos, Malaysia, and Myanmar were selected for analysis. Time-series data from 1987 to 2016, obtained from the OECD/DAC, International Constructors Association of Korea, and World Bank were selected as variables representing variations of the economic status of ASEAN developing countries. Laos, Malaysia, and Myanmar were excluded at the general testing stage for time-series data.
After conducting variance decomposition and impulse response analyses, ASEAN developing countries (Cambodia, Indonesia, the Philippines, Thailand, and Vietnam) showed that the amount of infrastructure construction project orders received by South Korean companies was positively (+) proportional to the change in South Korea’s ODA. In addition, except for the Philippines, the amount of ODA was positively (+) proportional to the GDP or GNI in all of the other developing countries. This means that infrastructure construction projects that are considered to be “mega projects” were closely related to major industries in these developing countries, and were also significantly supported by ODA funds. In other words, as the increase in the amount of ODA was related to the economic growth and the development of major industries, this not only has a long-term positive impact, but also ensures stable short-term funds for infrastructure construction projects. Such a cycle seems to enable mega projects, which aim for self-sustained development of developing countries, to be continuously and actively implemented.
On the other hand, an increase in the amount of ODA may result in the improvement of a donor country’s gaining orders for mega projects in a developing country. In practice, ODA is greatly influenced by political and diplomatic relations between countries. There are limitations to quantitatively analyzing this impact. Accordingly, in competition for infrastructure construction projects, there is a need to consider the political and diplomatic relations between developing countries and top OECD DAC donor countries with larger ODA than South Korea. For this reason, the participation of donor countries in infrastructure construction projects of developing countries may be restricted. Accordingly, it is absolutely necessary to increase the proportion of grant aid. Thus, an improvement of the quality of ODA will enhance the image of the donor country, which can increase the probability of gaining subsequent mega project orders. Also, it will be necessary for donor countries to establish cooperative measures between countries at an early stage by contributing to the development of infrastructure in the recipient countries (developing countries) through ODA projects, wherein free and paid support measures are interconnected. As a result, donor countries can expect to increase contributions to the international community and improve the efficiencies of ODA projects.
Future comparative analyses among top ODA donor countries regarding the developing countries’ awarded infrastructure projects based on type of construction will be required.

Author Contributions

S.-W.L. designed the research, developed the concept, and supervised the overall work. J.-K.J. analyzed the data and revised the manuscript. All authors discussed the research, cooperated with each other to revise the paper, and read and approved the final manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ahn, S.M. Development of Budget-Constrained Rescheduling Method in Mega Construction Project. Ph.D. Thesis, INHA University, Incheon, Korea, 2015. [Google Scholar]
  2. Kim, T.H. Development Roadmap for Development Cooperation through PPP and Development Finance; Ministry of Economy and Finance: Phnom Penh, Cambodia, 2017. Available online: http://www.training.go.kr/htmi/Report/TD0392TR0221016/20180419N7183pLC.pdf?saveas= (accessed on 31 May 2017).
  3. Lee, S.W. Reciprocal relations between official development assistance recipient and donor countries: Case of South Korean overseas construction business and southeast Asian Countries’ economy. Sustainability 2017, 9, 2274. [Google Scholar]
  4. Axel, M.; Jadir, S. South Korea’s transition from recipient to DAC donor: Assessing Korea’s development cooperation policy. Grad. Inst. Int. Dev. Stud. 2013, 4, 107–142. [Google Scholar]
  5. Korea Trade Investment Promotion Agency. Japan’s Active Support for SMEs that Utilizes ODAs. KOTRA Global Window: Trade and Investment Information Portal. 2015. Available online: http://news.kotra.or.kr/user/globalAllBbs/kotranews/album/2/globalBbsDataAllView.do?dataIdx=153310&column=&search=&searchAreaCd=&searchNationCd=&searchTradeCd=&searchStartDate=&searchEndDate=&searchCategoryIdxs=&searchIndustryCateIdx=&page=1&row=0 (accessed on 24 February 2015).
  6. Lee, S.W. Analysis on the Dynamic Relationship between South Korea’s Official Development Assistance and Construction Order in Asia. Ph.D. Thesis, Hanyang University, Seoul, Korea, 2018. [Google Scholar]
  7. The Export-Import Bank of Korea. World Construction Market Trends and Implication; Overseas Economic Research Institute, 2015; Available online: http://keri.koreaexim.go.kr/site/program/board/basicboard/view?boardtypeid=200&menuid=007005002&boardid=26678 (accessed on 31 May 2015).
  8. International Constructors Association’s Database. Scale and Trends of Number of Overseas Construction Orders by Korean Companies. Available online: http://www.icak.or.kr/sta/sta_1401.php (accessed on 31 December 2017).
  9. Hyundai Research Institute. Korea’s current account structural change. Wkly. Econ. Rev. 2014, 597, 14–26. [Google Scholar]
  10. Construction Management Association of Korea’s Database. The Export Planning of Construction Management in Overseas Construction Market. 2016. Available online: http://www.cmak.or.kr/upload/es/160908_2.pdf (accessed on 31 December 2016).
  11. Scale and Trends of Number of Overseas InfraStructure Construction Project Orders by Korean Companies. Available online: http://www.icak.or.kr/sta/sta_0101.php (accessed on 31 December 2017).
  12. Kim, K.H. Comparisons between Countries around the World, and Implications for Effectiveness of ODA; SERI Economic Focus; Samsung Economic Research Institute: Seoul, Korea, 2011. [Google Scholar]
  13. Lee, J.W. Analysis of the Determinants of Resource Allocation of Korea’s ODA. The Export-Import Bank of Korea. Available online: http://keri.koreaexim.go.kr/site/program/board/basicboard/view?boardtypeid=208&menuid=007001004001&boardid=45779 (accessed on 7 October 2005).
  14. Organization for Economic Co-Operation and Development Statistics, Economic Development Cooperation Fund Database. Official Development Assistance Recipients and Scale of Support. Available online: http://stats.oecd.org/viewhtml.aspx?datasetcode=TABLE2A&lang=en# (accessed on 12 December 2017).
  15. Severine, B. On the link between Japanese ODA and FDI in China: A microeconomic evaluation using conditional logistic analysis. J. Appl. Econ. 2005, 37, 51–55. [Google Scholar]
  16. Nigel, D. Impact of FDI, ODA and migrant remittances on economic growth in developing countries: A systems approach. Eur. J. Dev. Res. 2013, 25, 173–196. [Google Scholar]
  17. Won, J.J. Assessing the links between Infra-Capital Accumulation through ODA, Urbanization Process, and Economic Growth in Developing Countries. Ph.D. Thesis, Seoul National University, Seoul, Korea, 2013. [Google Scholar]
  18. Neira, I. Official development assistance, social capital and growth in Latin America. CEPAL Rev. 2016, 2016, 31–45. [Google Scholar] [CrossRef]
  19. Chen, H. Official development assistance, public investment and economic growth in Asia and the Pacific. Int. J. Dev. Confl. 2016, 6, 73–96. [Google Scholar]
  20. Robert, S. Official development assistance and economic growth in east African countries. Korean Assoc. Peace Stud. 2017, 18, 143–168. [Google Scholar] [CrossRef]
  21. Choi, J.W. Strategy for Expansion of Overseas Construction Competitive Power by Status Study and Analysis Official Development Assistance (ODA). Master’s Thesis, Hanyang University, Seoul, Korea, 2007. [Google Scholar]
  22. Suh, W.T. An Empirical Analysis of the Effect of ODA on the Economic Growth of Developing Countries. Master’s Thesis, Hong-ik University, Seoul, Korea, 2011. [Google Scholar]
  23. Sang, J.; Lee, S.W.; Kim, J.H.; Kim, J.J. An analysis of the relationship between financial technology market variables and housing prices by type of housing using VECM. AIK 2013, 29, 83–91. [Google Scholar]
  24. Lee, H.S. A Study on the Influence of Macroeconomic Factors upon the Housing Transaction and the Jeonse Rental Index. Ph.D. Thesis, Gachon University, Seongnam-si, Korea, 2007. [Google Scholar]
  25. Lee, S.H.; Lee, S.H.; Kim, J.J. Empirical analysis on the characteristics of overseas construction business by regions. AIK 2013, 29, 97–104. [Google Scholar]
  26. Jun, H.M. Analysis on the Determinants of Bank Profitability in Korea. Master’s Thesis, Soongsil University, Seoul, Korea, 2012. [Google Scholar]
  27. Jeon, J.K. Impact Analysis of Economic Fluctuation of Saudi Arabia on Korean Overseas Construction Business. Master’s Thesis, Hanyang University, Seoul, Korea, 2016. [Google Scholar]
  28. Cho, W.S. A Study on the Effects of the Open Macroeconomic Variables on the Economic Growth. Ph.D. Thesis, Dongguk University, Seoul, Korea, 2011. [Google Scholar]
  29. Baeck, S.K. A Study on the Relation between Macroeconomic Factors and Real Estate Public Auction Market. Ph.D. Thesis, Mokwon University, Daejeon, Korea, 2016. [Google Scholar]
  30. Ahn, M.K.; Moon, H.; Kim, J.J. The impacts of construction investment related building permit area indicator. AIK 2006, 22, 155–164. [Google Scholar]
  31. Lee, Y.H. A Business Model Combining Lease and Transaction to Hedge Risks of Housing Market Fluctuation. Ph.D. Thesis, Hanyang University, Seoul, Korea, 2017. [Google Scholar]
  32. Said, E.D.; Dickey, D.A. Testing for unit roots in autoregressive moving average models of unknown order. Biometrica 1984, 71, 599–607. [Google Scholar] [CrossRef]
  33. The Export-Import Bank of Korea. Understanding Public Private Partnerships (PPP) business in Developing Countries. EDCF Issue Paper. 2012. Available online: https://www.edcfkorea.go.kr/site/program/board/basicboard/list?boardtypeid=312&menuid=004002001006&pagesize=10&currentpage=3 (accessed on 31 August 2012).
  34. Kim, H.R.; Jang, H.S. Analysis of Competency Assessment Using BSA for ODA Loan Construction Projects. KJCEM 2012, 13, 45–53. [Google Scholar][Green Version]
  35. Kim, M.H. A Study on the Potential Risk Factors and Management Guideline for Overseas Construction Project Types; Construction & Economy Research Institute of Korea: Seoul, Korea, 2016; Available online: http://www.cerik.re.kr/01/report_view.asp?page=1&idx=1912&pcls=2&subject=&team= (accessed on 5 August 2016).
  36. Ministry of Land, Infrastructure and Transport. A Study on the Strategy for Activating Overseas Construction through ODA; Korea Institute for International Economic Policy, 2009. Available online: http://www.ndsl.kr/ndsl/search/detail/report/reportSearchResultDetail.do?cn=TRKO201800027712 (accessed on 31 December 2009).
Figure 1. Total values of overseas construction project orders received by South Korean companies from 2006 to 2016 [8].
Figure 1. Total values of overseas construction project orders received by South Korean companies from 2006 to 2016 [8].
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Figure 2. Total value of infrastructure construction project orders received by South Korean companies between 2006 and 2016 in ASEAN countries [11].
Figure 2. Total value of infrastructure construction project orders received by South Korean companies between 2006 and 2016 in ASEAN countries [11].
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Figure 3. Regional allocation of South Korea’s ODA and its trend in Southeast Asia between 2006 and 2016 [14].
Figure 3. Regional allocation of South Korea’s ODA and its trend in Southeast Asia between 2006 and 2016 [14].
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Figure 4. Entire test process.
Figure 4. Entire test process.
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Figure 5. Time series data and relation.
Figure 5. Time series data and relation.
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Figure 6. Result of variance decomposition analysis.
Figure 6. Result of variance decomposition analysis.
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Table 1. Variables in this study.
Table 1. Variables in this study.
VariablesDefinition
GDPGDP by developing country in ASEAN
GNIGNI by developing country in ASEAN
FDIFDI by developing country in ASEAN
Interest rateInterest rate by developing country in ASEAN
Official exchange rateOfficial exchange rate by developing country in ASEAN
Amount of ODA supportAmount of ODA support by Korea’s ODA developing country (in ASEAN)
Infrastructure construction project ordersAmount of Korea’s infrastructure construction project orders by Korea’s ODA developing country (in ASEAN)
Table 2. Results of unit root test.
Table 2. Results of unit root test.
CountryVariableLevelPrimary Difference
t-Stat.p-Valuet-Stat.p-Value
CambodiaAmount of ODA support−6.1874440.0001−4.7747920.0026
FDI−1.4582440.5343−1.0708000.7072
GDP−3.4701000.0191−6.8359770.0000
GNI−2.9887970.0532−6.8486460.0000
Interest rate−3.3013702.0245−5.9942791.0621
Official exchange rate−5.3822160.0005−2.7310130.0894
Infrastructure project orders−12.054760.0000−12.162410.0000
IndonesiaAmount of ODA support−5.5071640.0001−3.7371540.0112
FDI−2.5524860.1150−5.3887860.0002
GDP−5.4135510.0001−6.8346420.0000
GNI−5.2814610.0002−6.6146010.0000
Interest rate−6.5297410.0000−5.7650260.0002
Official exchange rate−5.2675030.0003−6.3787490.0000
Infrastructure project orders−6.7391830.0000−5.5781070.0001
PhilippinesAmount of ODA support−4.1762190.0033−6.6504670.0000
FDI−1.4893460.5242−3.3855580.0206
GDP−4.4579840.0015−8.1307250.0000
GNI−4.2933120.0023−8.1657770.0000
Interest rate−6.0940010.0000−6.2571640.0000
Official exchange rate−4.0887490.0043−5.9792520.0001
Infrastructure project orders−5.2216930.0003−5.6136390.0001
ThailandAmount of ODA support−4.2858820.0027−11.464070.0000
FDI−6.3489540.0000−4.0845060.0050
GDP−3.3409410.0224−6.0616370.0000
GNI−3.3394250.0225−6.2476430.0000
Interest rate0.3875970.9713−9.2350460.0000
Official exchange rate−3.6974120.0106−6.9258340.0000
Infrastructure project orders−6.7258470.0000−6.3838080.0000
VietnamAmount of ODA support−6.4849320.0000−4.5291710.0014
FDI3.1660281.0000−4.6915990.0008
GDP−3.7050790.0160−3.8963840.0061
GNI−3.6748040.0170−5.9887120.0000
Interest rate−3.8648430.0081−4.2997700.0044
Official exchange rate−4.6418890.0011−5.3667140.0003
Infrastructure project orders−4.0676220.0045−6.0814820.0001
Table 3. Results of Granger causality test.
Table 3. Results of Granger causality test.
CountryCausalityLagF-Stat.p-Value
CambodiaAmount of ODA support → FDI16.36130.0327
FDI → Infrastructure project orders11.97320.0000
GDP → Infrastructure project orders10.02760.0000
Amount of ODA support → GNI10.00000.0000
GNI → Infrastructure project orders10.00000.0000
Amount of ODA support → Infrastructure project orders10.00000.0000
IndonesiaGNI → Infrastructure project orders19.99090.0057
Amount of ODA support → Infrastructure project orders10.00000.0000
PhilippinesFDI → Infrastructure project orders22.92750.0756
Amount of ODA support → Infrastructure project orders20.55320.0583
ThailandAmount of ODA support → FDI 16.66950.0163
Interest rate → Infrastructure project orders13.35040.0886
Amount of ODA support → Infrastructure project orders20.89980.0363
VietnamAmount of ODA support → FDI14.19660.0521
FDI → Infrastructure project orders13.03010.0957
GDP → Infrastructure project orders13.81420.0657
Amount of ODA support → GNI10.00000.0000
GNI → Infrastructure project orders17.27510.0143
Amount of ODA support → Interest rate10.00000.0000
Interest rate → Infrastructure project orders10.00000.0000
Amount of ODA support → Infrastructure project orders10.00000.0000
Note: Null hypothesis is rejected if p < 0.05 or 0.1.
Table 4. Results of time lag test.
Table 4. Results of time lag test.
CountryLagAICSICHQ
Cambodia023.9350424.2205224.02232
118.1590920.1574018.77000
215.11502 *18.82616 *16.24955 *
Indonesia026.5040226.8370726.60584
121.9315824.59599 *22.74611
220.86746 *25.8632322.39471 *
Philippines016.5172516.8503016.61906
110.4012913.06569 *11.21582
28.914555 *13.9103210.44181 *
Thailand013.0027213.3357713.10453
17.374238 *10.03865 *8.188774 *
28.25554413.251319.782800
Vietnam030.7426731.0757230.84449
123.5342626.1986724.34880
220.32688 *25.32265 *21.85414 *
Note: AIC = Akaike information criteria; SIC = Schwartz Bayesian information criteria; HQ = Hannan-Quinn information criteria; * = appropriate time lag for each standard.
Table 5. Results of co-integration test.
Table 5. Results of co-integration test.
CountryNull HypothesisTest Statistics0.05 Critical Valuep-Value
Cambodiar = 0222.951595.753660.0000
r ≤ 1118.847069.818890.0000
r ≤ 269.4089847.856130.0002
r ≤ 334.0744429.797070.0151
Test indicates 4 cointegrating eqn(s) at the 0.05 level
Indonesiar = 0160.9644125.61540.0001
r ≤ 1105.038295.753660.0099
r ≤ 271.2408269.818890.0384
Test indicates 3 cointegrating eqn(s) at the 0.05 level
Philippinesr = 0208.2475125.61540.0000
r ≤ 1114.276095.753660.0015
Test indicates 2 cointegrating eqn(s) at the 0.05 level
Thailandr = 0188.1120125.61540.0000
r ≤ 1115.277195.753660.0012
r ≤ 269.9607369.818890.0487
Test indicates 3 cointegrating eqn(s) at the 0.05 level
Vietnamr = 0267.6569125.61540.0000
r ≤ 1133.216995.753660.0000
r ≤ 276.3509769.818890.0137
Test indicates 3 cointegrating eqn(s) at the 0.05 level
Table 6. Result of variance decomposition analysis (Cambodia).
Table 6. Result of variance decomposition analysis (Cambodia).
Variance Decomposition of Infrastructure Construction Project Orders
PeriodODAFDIGDPGNIOfficial Exchange RateInfra Project Orders
19.26420.00710.19660.01581.080590.9342
210.81400.09490.21060.14051.009589.8619
311.72400.14410.21690.22940.976889.3266
412.36180.17180.22030.28140.959289.0331
512.86140.18880.22240.31370.948588.8535
613.28000.20020.22380.33540.941488.7334
713.64570.20830.22480.35090.936388.6476
813.97390.21440.22550.36250.932488.5834
914.27410.21920.22610.37150.929588.5335
1014.55220.22300.22660.37880.927188.4937
Table 7. Result of variance decomposition analysis (Indonesia).
Table 7. Result of variance decomposition analysis (Indonesia).
Variance Decomposition of Infrastructure Construction Project Orders
PeriodODAFDIGDPGNIOfficial Exchange RateInterest RateInfra Project Orders
11.948131.92940.06744.22520.35161.917860.9609
29.750328.17884.41005.71510.41144.378948.7295
39.048329.16223.03098.38190.85644.975446.4578
411.367832.44223.28039.38180.72774.548640.4543
513.001333.91062.56989.11180.68544.190339.0303
614.197633.59913.17869.30930.68114.316837.3916
714.367733.94402.77919.68500.67214.575236.8397
814.900534.29633.27059.91670.59374.439335.6383
915.377134.89042.89769.87210.56794.439035.2078
1015.901234.58013.69149.93870.54644.372434.3775
Table 8. Result of variance decomposition analysis (Philippines).
Table 8. Result of variance decomposition analysis (Philippines).
Variance Decomposition of Infrastructure Construction Project Orders
PeriodODAFDIGDPGNIOfficial Exchange RateInterest RateInfra Project Orders
131.95423.78470.885223.94624.44145.031931.7071
226.05745.18708.093226.33612.82599.318824.3843
330.52786.14978.666523.87592.91168.334322.1836
427.88596.43709.846223.96463.226410.125321.5128
527.05246.77149.482824.28352.779711.704921.2072
629.79457.09379.758822.94453.581410.956019.5007
728.30337.278610.368722.35205.230711.369519.0265
826.60727.44439.891922.88704.944913.027119.3448
929.50527.74849.740722.15484.672712.331518.2723
1028.65327.929610.378120.83858.116611.597517.2509
Table 9. Result of variance decomposition analysis (Thailand).
Table 9. Result of variance decomposition analysis (Thailand).
Variance Decomposition of Infrastructure Construction Project Orders
PeriodODAFDIGDPGNIOfficial Exchange RateInterest RateInfra Project Orders
12.68972.94245.75240.646613.48730.340775.8177
28.72179.42637.06043.458911.61950.213061.7406
312.509016.40627.50763.607310.21960.149552.3184
414.822120.65207.71463.66889.37740.115046.7812
516.371323.33697.83483.70298.84640.093843.3115
617.500425.15427.91423.72528.48720.079740.9688
718.377426.45877.97083.74108.22950.069539.2884
819.091227.43908.01333.75288.03580.061938.0259
919.692728.20228.04643.76207.88500.056037.0429
1020.213128.81328.07283.76947.76430.051236.2560
Table 10. Result of variance decomposition analysis (Vietnam).
Table 10. Result of variance decomposition analysis (Vietnam).
Variance Decomposition of Infrastructure Construction Project Orders
PeriodODAFDIGDPGNIOfficial Exchange RateInterest RateInfra Project Orders
147.68408.38581.94070.81840.20810.913240.7598
245.11426.05551.65690.58840.12306.447640.9595
342.28804.60061.43440.88850.088111.781340.0454
440.01953.67551.27451.26030.073315.939139.0404
538.33263.05591.16011.58100.066319.027138.1995
637.09172.61871.07641.83550.062621.325137.5403
736.16892.29631.01362.03420.060323.067037.0281
835.47062.04960.96522.19070.058724.418136.6260
934.93231.85530.92682.31570.057525.490336.3049
1034.51031.69830.89572.41740.056626.359036.0441
Table 11. Analysis results for relationships between number of infrastructure construction project orders received by South Korean companies and influential variables.
Table 11. Analysis results for relationships between number of infrastructure construction project orders received by South Korean companies and influential variables.
CountryResult of Analysis
Directly Proportional (+) VariableInversely Proportional (−)
CambodiaODA, GDP, official exchange rateFDI, GNI
IndonesiaFDI, ODA, GDP, official exchange rateGNI, interest rate
PhilippinesODAFDI, GDP, GNI, official exchange rate, interest rate
ThailandFDI, ODA, GDP, GNIOfficial exchange rate, interest rate
VietnamODA, FDI, GNIGDP, Official exchange rate, interest rate

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