CURRENCY MANIPULATION PROBLEM - LEARNINGS FOR VIETNAM
3. DESCRIPTIVE STATISTICS OF VARIABLES
98 THE THIRD INTERNATIONAL CONFERENCE ON: “FINANCE AND ACCOUNTING FOR THE PROMOTION OF SUSTAINABLE...
controlling customs clearance brokers, improving NSW efficiency and reforming specialized border management.
Thus, it is possible to identify the mechanisms of ASEAN Single Window on improving the LPI of the ASEAN region according to the following research framework: Customs authorities carry out functional activities that positively impact on two criteria of customs clearance and logistics service quality through the effective implementation of ASW, thereby improving the LPI index.
CUSTOMS - ASEAN Single Window
Customs clearance criteria
in LPI
Quality criteria of logistics services
in LPI
National Logistics performance index
(LPI)
Research theoretical framework
- Exploratory factor analysis (EFA) results
Table 2: KMO measure and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .922
Bartlett’s Test of Sphericity
Approx. Chi-Square 636.857
Df 15
Sig. .000
Factor loading factor KMO (Kaiser-Meyer-Olkin) at 0.922>0.75 and sig value. 0.00 < 5%
suggests 6 criteria closely relating in factor formation (ensure the required number of observations for 10 countries is 56 observations).
Table 3: Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %
1 5.609 93.478 93.478 5.609 93.478 93.478
2 .170 2.829 96.307
3 .101 1.680 97.987
4 .064 1.059 99.046
5 .030 .494 99.540
6 .028 .460 100.000
Extraction Method: Principal Component Analysis.
The results show that 6 observed variables form a composite factor FT1. The Eigenvalues=5.6>1 and Variance=93.478% represent the factor formed from 6 criteria explains 93.478% of the variation of the composite factor.
Model analysis results in the matrix of component scores of the composite factor FT1 as follows:
FT1 = 0,172 C+0,175 IN+ 0,170 Sh+ 0,175 LS+ 0,174TR+0,168 Tm + εi (Model 2) - Model regression results and hypothesis testing:
The linear regression model is presented on the basis of the proposed theoretical model (Model 1). The dependent variable is LPI representing the scores of experts on the LPI index of member countries / scale of 5 (with 56 observations of 10 countries through 6 times published by the World Bank). The independent variables included in the model include: FT1 is a composite factor of 6 components forming the LPI index; The dummy variable Group takes the value 0 and 1, corresponding to the division into two groups of countries according to the score of the LPI index and the modernization of customs clearance and the quality of logistics services of the member countries.
The model is as follows: LPI = β0+ β 1FT1+ β 2 Group The expectation is βi>0 and are statistically significant.
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Table 4: Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 1.000a 1.000 1.000 .0063661 2.364
a. Predictors: (Constant), FT1, Group b. Dependent Variable: LPI
According to Table 4, the model has no autocorrelation (Durbin-Watson value = 2.364 near value 2) and the independent variables explain 100% of the variation of the LPI. (adjusted R-squared value = 100%).
Table 5: ANOVA
Model Sum of Squares Df Mean Square F Sig.
1
Regression 16.562 2 8.281 204326.237 .000b
Residual .002 53 .000
Total 16.564 55
a, Dependent Variable: LPI
b, Predictors: (Constant), FT1, Group
The table 5 shows that Sig. =0.00<5%, rejecting H0, which means the there is a significant correlation between LPI and independent variables.
Table 6: Regression results Model
B
Unstandardized Coefficients
Standardized Coefficients
T Sig.
Tolerance
Collinearity Statistics
Std, Error Beta VIF
1
(Constant) .032*** .006 5.38 .000
Group .005* .003 .005 1.92 .060 .44 2.25
FT1 .990*** .002 .997 424.03 .000 .44 2.25
a, Dependent Variable: LPI
*** Sig. <1%; * Sig. from 5% to 10%
The table 6 shows that the multicollinearity does not cause a concern in this model since VIF
<10. As such, the estimated regression is as follows:
LPI = 0.032+0.99FT1+ 0.003 Group +εi (Model 3)
Thus, the model for the first group with Group =1 (Singapore, Malaysia, Thailan, Indonesia, Philippines and Vietnam) is:
LPI = 0.035+0.99FT1 +εi (Model 3.1)
The model for group = 0 (Brulei, Campuchia, Laos, Myanmar):
LPI = 0.032+0.99FT1 +εi (Model 3.2)
α2=0.03 shows that on average, LPI of the first group is 0.003 point higher than the remaining, ceteris paribus.
α1= 0.99 reveals that on average, when FT1 changes 1 point, LPI will increase by 0.99 point in the likert scale 5, ceteris paribus.
Combined with the component score matrix equation (Model 2), the observed variable Customs clearance (C) and logistics service quality (LS) have a positive impact on FT1 with coefficients 0.172 and 0.175, respectively.
Thus, it is concluded that when there is an improvement in customs clearance and logistics service quality, the LPI index will be improved through the component score matrix equation and linear regression (model 3).
Testing the difference between member countries in LPI: Perform a test to see if there is a difference in the LPI index value between the two groups. Test hypothesis H0: there is no difference and hypothesis H1: there is a difference in the scores of LPI. The result of p-value = 0.031<5% rejects the hypothesis H0, suggesting the difference in LPI scores between these groups of countries in ASEAN.
- Challenges for ASEAN Single Window in improving LPI index of ASEAN countries
The need for stronger reform in the Customs factor is not only stated in the Protocols of ASEAN, of the Agreement on International Trade Facilitation of the WTO, but also analyzed and proposed by existing studies. Thuy Nguyen et al. (2014) argue that: “Customs procedures are a barrier to international trade facilitation and logistics in ASEAN member countries”. Meanwhile, Sanchita Basu Das (2017), which compiles related research and survey reports, shows that: the survey of 246 European companies in 2017 found that 67% of respondents are worried about the burden of customs formalities in ASEAN, hindering the development of their supply chains in the region. A 2015 survey of 5,545 Japanese companies operating in Southeast Asia uncovered that simplified customs clearance was recognized as the highest requirement of trade facilitation measures in the AEC. Regarding US companies, about 50% of the 451 companies from ASEAN are looking forward to reducing transaction costs more and have high expectations for the ASEAN trade facilitation program and customs development.
Table 7: Average score of national LPI index and customs clearance and service quality criteria of 10 ASEAN countries in 6 times published by the World Bank.
Unite: Likert-scale 5
No. Countries LPI C LS
1 Brunei Darussalam 2.79 2.70 2.64
2 Campuchia 2.59 2.40 2.49
3 Indonesia 2.99 2.65 2.92
4 Lao 2.40 2.26 2.33
5 Malaysia 3.44 3.20 3.38
6 Myanmarr 2.26 2.13 2.19
7 Philippines 2.94 2.68 2.86
8 Singapore 4.09 4.02 4.09
9 Thai Lan 3.31 3.08 3.21
10 Viet nam 3.04 2.79 2.96
Mean 2.99 2.79 2.90
Souce: World Bank
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Table 7 shows the average score of the indicators in the six publications of each ASEAN member country. The statistics describing the general average score of the LPI index and the Criteria for Customs clearance and quality of logistics services indicate that: (1) the average score of all three indexes is lower than 3 points/scale of 5 average), suggesting that the ASEAN region’s LPI scores are just below the world average; (2) two criteria of customs clearance and quality of logistics services are two of the three criteria with the lowest average scores among the six criteria that make up the region’s LPI; (3) the above two indicators are not evenly distributed across countries: Singapore excels and separates with scores of 4.02 and 4.09 out of 5 (high and very high). While Thailand and Malaysia are in the middle and high, Vietnam, Philippines and Indonesia, Brunei Darussalam are in the middle and low. The remaining three countries, Laos, Cambodia and Myanmar are at low levels ranging from 2.13 to 2.4 points/scale of 5 (much lower than Singapore).
Table 8: Cross-border trade statistics of 10 ASEAN countries in 2019
Countries
Cross-border Trade
Export time Export costs Import time Import costs
Document preparation
(hours)
Cross-border compliance
(hours)
Document preparation
(USD)
Cross-border compliance
(USD)
Document preparation
(hours)
Document preparation
(hours)
Document preparation
(hours) (USD)
Cross-border compliance
(USD) Brunei
Darussalam 155 117 90 340 132 48 50 395
Cambodia 132 48 100 375 132 8 120 240
Indonesia 61 53 139 254 106 99 164 383
Laos PDR 60 9 235 140 60 11 115 224
Malaysia 10 28 35 213 7 36 60 213
Myanmarr 144 142 140 432 48 230 210 457
Philippines 36 42 53 456 96 120 50 580
Singapore 2 10 37 335 3 33 40 220
Thailand 11 44 97 223 4 50 43 233
Vietnam 50 55 139 290 76 56 183 373
Source: World Bank (2020), Doing Business Report Table 8 shows the time and cost of customs clearance for import and export goods in the national business environment index (DB). Basically, the data is consistent with the customs clearance scores of the countries in the LPI index. The following observations can be drawn: (i) the time and cost of cross-border trade between countries in the region is still high in the world and uneven (there is a big difference between the three countries of Singapore, Thailand and Malaysia with the rest of the countries); (ii) the time and financial costs for customs compliance at the border are higher than for customs clearance preparation; (iii) Time and financial costs for imports are higher than for exports.
4. POLICY IMPLICATIONS OF IMPLEMENTING ASEAN SINGLE WINDOW TO IMPROVE LPI OF ASEAN MEMBERS