4.1. Descriptive Statistics
illustrates the descriptive statistics for each variable in the disaggregated and aggregated regions. The mean value of GDP for the three aggregated regions is 6.65% with a standard deviation of 3.26%. Within the three regions, the highest mean GDP is found for East Asia, with a value of 8.49% and standard deviation of 3.71%. This proves that East Asia has a fast-growing economy. In East Asia, China has recently become well-known for its resilient growth and has appeared as the second largest economy in the world after the United States. Therefore, it is not surprising that the average GDP of East Asia is higher than that of Central Asia and ASEAN countries. However, the most volatile economic situation is also found in East Asia, as reflected by its having the highest standard deviation. This is due to the fact that East Asia possesses larger financial debt than other countries. In contrast, the lowest value of GDP (−2.53%) is found in ASEAN. Following the global financial crisis, ASEAN countries, and especially Malaysia, have been sluggish in their economic recovery.
The statistics show that transportation infrastructure has average value of 210,946.8 ton-km with a standard deviation of 608,120.20 ton-km. The maximum value of transportation infrastructure refers to China in East Asia with 2,562,635 ton-km. This is due to China being heavily engaged in the development of infrastructure in line with the OBOR program. In December 2008, the Chinese government announced a budget of 4 trillion RMB to enhance the domestic economy [66
]. Of these funds, 1.5 trillion RMB is accounted for the development of infrastructure, including railways, roads, irrigation, and airport construction.
The mean education index is 0.67 with a standard deviation of 0.06. This indicates that education plays an important role in Asian countries where more than 90% of Asia population can read and write. This is in line with the aspiration to produce more educated and skilled labor for the next generation. ASEAN is the leading region in the education system, which has the mean of 0.68. The education in the three studied regions has an average performance as there is not much difference between the maximum and minimum value. This indicates that Asian countries are conscious of the importance of education in order to provide a better future for the next generation.
The inflation rate in the Asian countries has a high mean and standard deviation of 10.87% and 10.16%, respectively. The highest mean inflation is found in Central Asia with a value of 15.55% and a standard deviation of 11.52%. The rise in the price of food and energy is believed to cause high inflation. The maximum value of inflation rate is found to be 59.74%. This is because the global crisis caused Turkmenistan to suffer from a deficiency in export routes and crucial external debt [67
]. Additionally, the prices of necessary food were also accelerated by 30% in 2008.
The average performance of trade in the studied countries amounted to 104.57% with a standard deviation of 42.78%. The highest trade performance was 220.41% in ASEAN, while the lowest was 39.05% in East Asia. Bilateral trade between China and ASEAN have been growing over the past 10 years; the exports of ASEAN goods have expanded rapidly against the imports from China [68
The mean value for the labor force amounted to 91,693,392 people with a standard deviation of 217 million people. Among the three regions, East Asia is claimed to have more job opportunities to drive economic expansion as their countries have the largest mean labor force. According to Statistics Times [69
], the population of China is 1.42 billion, the largest population in the world; thus, the participation in the workforce is higher than other countries. This also explains the observation that East Asia has the maximum and minimum size of labor force.
4.2. Panel Data Regression Model
shows the correlation matrix among the explanatory variables. The correlation coefficients range from 0.0410 to 0.5361 in absolute value. This indicates that there is no multicollinearity problem in the model, as the correlation coefficients are less than 0.80. To enhance the accuracy of multicollinearity detection, we also calculate the VIF in Table 4
. All the VIF values fall between 1.3534 and 2.2635 (i.e., less than 10), indicating that there is no multicollinearity problem in the model.
shows the results of the panel unit root test using Fisher-PP. When the intercept is included, the variables are found to be stationary at level, except for transportation infrastructure. On the other hand, a panel unit root existed in education, transportation infrastructure, and labor force at level, but turned out to be stationary at first difference. It is concluded that the panel data is stable where the variables have no unit root problem.
shows that the null hypothesis of the Hausman test is rejected, as the random effect (p
= 0.0077) is significant at the 1% level. Therefore, the fixed effects model is the best estimation model to be adopted based on the Hausman test. Using the Poolability F-test helps to increase the accuracy of the result by determining whether either POLS or the FEM is more suitable for a model. The result of the Poolability F-test indicates that the FEM is preferable, as the p
-value (0.0026) is less than the 5% significance level. In conclusion, the FEM should be employed in this study instead of REM or POLS in order to generate more appropriate outcomes. Besides, the FEM is able to control the stable characteristic of individuals in the model and eliminate the key source of omitted variable bias.
The results of the FEM, shown in Table 7
, indicate that the independent variables such as trade, education, transportation infrastructure, and labor force are significant in explaining GDP in the selected Asian countries within the OBOR initiative, however inflation rate is not. The transportation infrastructure is found to have a positive effect on GDP. When there is a 1% increase in transportation infrastructure, GDP is increased by 0.9131%. The results indicate that further investment in transportation infrastructure, and especially in railways, would increase the nations’ GDP. This is in line with the OBOR initiative, where transportation infrastructure is one of the main concentrations. A railway network is planned to be assembled in the Asian countries. Rodrigue and Notteboom [70
] stated that the implementation of railway systems facilitates more flexible and high-capacity inland transportation. Substantial economic and social opportunities are provided through the extraction of resources, the settlement of regions, and the growing mobility of freight and passengers. Dowell [71
] also claimed that the expansion of transportation infrastructure is able to increase productivity due to the reduction in travel time and enhancement of infrastructure.
Zou et al. [19
] and Yang and Ma [20
] also found that transportation infrastructure is positively related to GDP. More investment in roads and railways is expected to explore more economy of scale and lead to economic growth in China. Additionally, Demurger [72
] found a positive nonlinear and concave relationship between transport infrastructure and GDP, while Villafuerte et al. [29
] supported that an improvement in the transportation infrastructure along the OBOR route can raise the GDP growth in Central, West, and South Asia.
The tension between transportation infrastructure and economic growth is in line with Salient Paradox Theory. This tension refers to the mainstream discourse in economics where further investment in transportation infrastructure will increase the efficiency and profitability of the business sector, in turn enhancing the economic growth of the countries [73
]. However, Ansar et al. [31
] found that the investment in transportation infrastructure does not necessarily lead to economic growth, due to overinvestment in underperforming projects that fails to deliver a positive risk-adjusted return.
The positive coefficient of trade indicates that a 1% increase in trade raises GDP by 0.0149%. This is because many countries are expected to engage in trade and share the resources through OBOR. With participation in OBOR, the gap between the developed and developing countries is abridged and the nations’ economies are improved. This is supported by the studies of Ruankham and Jongsureyapart [48
], Cui et al. [40
], Haggai [46
], and Villafuerte et al. [29
] who also found a positive relationship between trade and GDP. Mutual benefit is believed to exist when the countries engage in trading through OBOR.
Surprisingly, education is found to negatively affect GDP; an increase in the human development index leads to a fall in GDP of 14.5001%. This might be due to the inadequacy of the education system in the country. For instance, Central Asia faces several serious challenges in its education systems, such as low school-enrollment (especially by girls), less allocation of education budgets, a lack of qualified teachers, and corruption in the system [74
]. For example, one Central Asian country, the Kyrgyz Republic, has too many higher education institutions without any effective quality control [75
]. Moreover, corruption present in the education system, with “informal payments” securing university admission or excellent examination grades. This is believed to shrink the nation’s GDP, as the education system does not contribute in cultivating the potential human capital for economic growth despite the large amount of investment provided by government.
Additionally, the education system in ASEAN countries also appeared to be inadequate. According to UNESCO [76
], ASEAN tends to focus on academic performance instead of the relevant curriculum. The relevant curriculum is able to provide students with the skills to be well prepared for the working environment. Otherwise, the graduates are not able to maximize their productivity, which results in a drop in GDP. Moreover, there is also lack of available facilities, teachers, and budget for education in Southeast Asia [77
]. The uneven spread of population and the geographical location also impact the development of education systems. For instance, students from remote areas have been found to have difficulty in attending classes due to the insufficiency of transportation systems.
Furthermore, labor force also possesses a negative relationship with GDP in the selected countries. This is due to the fact that the demand for labor force is outstripping its supply, especially in China [78
]. The low increase in labor force supply is unable to provide sufficient productivity and increase the nation’s GDP. Zou et al. [19
] also supported these findings by asserting that the economy of country is exacerbated if the labor growth does not contribute to reducing the income inequality.
The results also indicate that that there is no relationship between inflation rate and GDP. This is supported by Aria et al. [54
] who performed a Generalized Method of Moments (GMM) estimator to examine the cyclical and casual patterns of inflation and economic growth in 115 countries. Moreover, Semuel and Nurina [55
] also found that inflation is not significantly related to economic growth in Indonesia due to its mild inflation stage. When the inflation rate is anticipated, it does not have a large effect on GDP. This is because consumers are still proactive in their purchasing power [79
]. Yii et al. [56
] also claimed that inflation has no effect on GDP, due to the implementation of effective economic policies by government in order to control inflation.
Last but not least, the R-squared values indicate that 22.45% of the variation in GDP can be explained by the variation in trade, education, transportation infrastructure, inflation rate, and labor force. The remaining 77.55% of the variation in GDP is explained by other factors.