IMPACT OF TRADE FACILITATION ON TRADE PERFORMANCE IN CASE OF VIETNAM, KENYA AND GERMANY

 

IMPACT OF TRADE FACILITATION ON TRADE PERFORMANCE IN CASE OF VIETNAM, KENYA AND GERMANY

Abstract

            The paper focuses on the comparative analysis of the impact of trade facilitation measures on the export performance of countries such as Vietnam, Kenya, and Germany. The countries have been selected based on the differences on their income, development levels, and locations in various continents but common in successful application of the e-custom clearance system to reduce the trade cost. The paper employs panel data analysis to assess the impact of trade facilitation on export performance across such countries from 2007 to 2017. The parameters denoting trade facilitation adoption include time for export (time_ex), cost for export (cost_ex), the Logistic Performance Index (LPI), and the e-clearance procedure of World Bank. Results obtained through empirical analysis indicated that trade facilitation measures i.e. electronic clearance, time for exports, cost of export cause, and impact exports significantly. The study suggested that the government of each nation should focus on adapting trade simplification and facilitation measures to help further in reduction of time and cost of exports. There is a need to implement electronic data interface for promotion of exports, especially in Vietnam.

Key Words: Trade Facilitation, Exports, Panel data, Logistics

JEL Classification F14, F15, F68

 

1. Introduction

Trade facilitation – the simplification, modernization, and harmonization of export and import processes has emerged as a key issue for the world trading system. Trade facilitation is critical in reducing trade costs, which remain high despite the steep decline in the cost of transportation, improvements in information and communication technology, and the reduction of trade barriers in many countries. Therefore, the WTO Agreement on Trade Facilitation (TFA), the most significant trade deal since the establishment of the World Trade Organization in 1995, has entered into force on February 22^nd of 2017.

Apart from the Trade Facilitation Agreements, to help nations improve their border procedures, reduce trade costs, boost trade flows and reap greater benefits from international trade, Organization for Economic Co-operation and Development (OECD) has developed a set of Trade Facilitation Indicators (TFIs) which identify areas for action and enable the potential impact of reforms to be assessed. Estimates based on the indicators provide a basis for governments to prioritize trade facilitation actions and mobilize technical assistance and capacity-building efforts for developing countries in a more targeted way.

The theory predicts that trade costs can have a disproportionately adverse impact on small developing economies. Typically, small developing economies have large agricultural or natural resource sectors typified by constant returns to scale, and only a small manufacturing sector. In contrast, big developed economies have a large manufacturing sector operating under increasing returns to scale. In this setting, trade costs lead both to less trade and to a disproportionate relocation of manufacturing to the big developed countries (the “home market effect”). Meanwhile, small developing countries become concentrated in the agricultural or natural resource sector. The key to explaining this result lies in the tension created between the consumer’s love of variety and increasing returns to scale. With open trade and zero trade costs, consumers in the big developed country will purchase both foreign and domestic manufactured goods because of their preference for variety. All things being equal, love of variety leads to more trade. On the other hand, increasing returns to scale gives a cost advantage to manufacturing firms in the developed country because of the size of the market and the larger scale of production that could be achieved by firms there. All things being the same, consumers in the developed country will prefer to purchase lower-cost domestic varieties than higher cost foreign varieties. Inefficient trade procedures that lead to higher trade costs upset this balance by making purchases (imports) of foreign varieties costlier. As a consequence, consumers in the developed country substitute away from foreign varieties towards domestic varieties. This shift in demand towards domestic manufactured goods gives greater scope for what are already powerful scale forces to operate. The manufacturing sector in the big developed country expands even more while it shrinks in the small developing country. This analysis suggests that small developing countries that want to diversify their economies have a strong interest in lowering trade costs, as this reduces incentives for manufacturing to concentrate in the biggest markets.

Thus, it becomes important to assess the impact of trade facilitation measures on trade facilitation and promotion of exports in case of Vietnam, Kenya and Germany. The following countries have been chosen considering their location in various continents and difference in scale of development. Vietnam and Kenya are at the same developing nations; meanwhile Germany is the developed country. Although there are various aspects on location, scale of development, they are common in successful application of the e-custom clearance system to reduce the trade cost. It is the reason which three countries are chosen in this study. To the overview of trade facilitation among 3 countries, there is the supporting information including in this paper.  

This paper aims at exploring a relationship between trade facilitation indicators and trade performance of each country. A major limitation of the study was non-availability of data pertaining to time and cost of export before 2006. Logistic Performance Index has also been recently adopted and the index gives ranking once in two years. Similarly, the adoption of e-clearance measures has also been recently adopted by most of the countries. The adoption of trade facilitation measures doesn’t usually ensure smooth and timely trade among countries because the procedures for export from each nation are not standardized and also not clearly informed.

 

2. Literature Review

Trade facilitation means to reduce the import and export cost, moderate the tariff and non-tariff barriers, and increase the infrastructure quality also competitiveness in service suppliers (Wilson et al., 2009). In the member countries of Association of Southeast Asian Nations the import and export costs vary considerably, from very low to moderately high levels. These countries possess tariff and non-tariff barriers are generally low to moderate. Infrastructure quality and services sector competitiveness range from fair to excellent. The paper indicates that trade flows in Southeast Asia are particularly sensitive to transport infrastructure and information and communications technology. The results suggest that the region could make significant economic gains from trade facilitation reforms. These gains could be considerably larger than those from comparable tariff reforms. Trade facilitation measures can be undertaken along two dimensions: a “hard” dimension related to tangible infrastructure such as roads, ports, highways, telecommunications, as well as a “soft” dimension related to transparency, customs management, the business environment, and other institutional aspects that are intangible (Perez and Wilson 2012). Estimates shows that trade facilitation reforms do improve the export performance of developing countries. This is particularly true with investment in physical infrastructure and regulatory reform to improve the business environment. The findings provide evidence that the marginal effect of the transport efficiency and business environment improvement on exports appears to be decreasing in per capita income. In contrast, the impact of physical infrastructure and information and communications technology on exports appears increasingly important the richer a country becomes.

Trade facilitation includes five main elements as follows: (1) simplification of trade procedures and documentation, (2) harmonization of the trade practices and rules, (3) more transparent information and procedures of international flows, (4) recourse to new technologies to promote international trade, and (5) more secured means of payment for international commerce (Zaki 2014). Gains derived from trade facilitation are more significant for developing economies (especially for the Middle East and North Africa region and Sub-Saharan countries) than for developed ones, whether in terms of welfare gain (either in the short or long run) or increase in trade. Clearly, long-run welfare effects of trade facilitation are much higher than in the short run. Trade facilitation helps boost both intra-regional trade and inter-regional trade. Fourth and most interestingly, it also helps improve export diversification, leading to an expansion in those sectors that are more sensitive to time, such as food, textiles, and electronics.

There are a number of studies which specify and analyze the relationship between inefficiencies of trade facilitation measures and trade performance. A major inefficiency in trade performance is the time taken to export which if longer leads to reduction in trade volume (Nordas et al., 2006). Trade time and cost may also increase while moving consignments from manufacturing sites to ports of shipment (Djankov et al., 2006), which at times attributes to fall in trade volumes by 1 percent. Trade facilitation measures include time taken to export (Djankov et al., 2006), reduction in costs of international trade, improvement in international transport and import (Wilson et al., 2003). Other major trade facilitation indicator is the World Bank’s LPI (Hertel and Mirza 2009). Other studies have enumerated indicators of trade facilitation as port efficiency, customs regulations and use of e-commerce (Wilson et al., 2005). The relationship between trade performance and these trade indicators has been established using econometric tools like multiple regression models (Felipe and Kumar, 2012) or single regression techniques (Hertel and Mirza, 2009; Puertas et al., 2013).

Country specific studies for Mexico (Soloaga et al., 2006) and Africa (Iwanow and Kirkpatrick 2009) have also indicated that adoption of trade facilitation measures enhances trade performance.

Other measures of trade performance can be the economy’s financial structure, interest rates, trade openness and growth (Sehrawat and Giri 2017). Human Development i.e. education also plays a major role in determining the trade performance of a country (Jawaid and Waheed 2017). Apart from parameters like time and cost of export and the LPI, such parameters are also useful in estimating the relationship between trade performance and trade facilitation.

3. Methodology

The present study is an attempt to examine the causal relationship between trade performance and trade facilitation in the case of Vietnam, Kenya and Germany at the national level. Time series data over 2007-2017 has been taken into consideration. The data has been extracted from Trade map and World Bank. Trade stands for export and import. The export performance is measured by the percentage of export in trade. The economic indicators include domestic credit (cr) and inflation (inf).

Trade facilitation is defined in terms of time for export (time_ex), cost for export (cost_ex), the Logistic Performance Index (LPI) and the e-clearance procedure as extracted from Doing Business Reports, World Bank.  The study uses the methodology of Doing Business 2014 and calculates time for export in number of days rather than hours.

The model has been modified to suit the present scenario of the Economies of Vietnam, Kenya and Germany based on the literature review. To Ahmed and Ismail, 2015, the relationship between trade performance and trade facilitation along with other variables for panel estimation is expressed as:

Ln (ex_tr) _it = β₀+β₁ln (time_ex) _it+β₂ln (cost_ex) _it +β₃lncr _it +β₄LPI_it +β₅ECL _it +β₆lninf _it + ε _{it ---} (1)
for t=1, 2, N and i=0, 1

Where LPI and ECL are dummy variables; they will be 0 if there is no application in the Logistic Performance Index or E clearance declaration. They will be 1 if there is an application in the Logistic Performance Index or E clearance declaration.

All the variables have been transformed into natural logs in order to overcome the problem of hetereoscedaticity.

In this present study, availability of data is limited and it has been suggested by econometricians to use panel estimates in such cases. It is expected that panel estimates handle issues of measurement bias and limited degrees of freedom efficiently. The current data series consists of 51 cross section spread over 17 years with 3 countries (Vietnam, Kenya and Germany). Thus, panel estimation method is suitable in our case.

Test for non-stationary

It is important to understand that in case of a non-stationary series, the results and inferences from regression are spurious and hence meaningless. Thus, the data series are checked for stationary through panel root tests. Four panel root tests have been applied to check the robustness. The Levin, Lin and Chu test, a panel based version of the ADF test will be applied in this case. It is represented by equation 2 below

∆Xit = αi +β Xi, t −1+ ∑ j=1θij ∆Xi, t − j + εi, t  (2)

Where, Δ is the first difference operator, X_it is the variable being tested and ε_it is the white noise disturbance at time t in this test, β is identical across sectors and hence restrictive. It tests the null hypothesis β=0 and acceptance of null hypothesis implies non-stationarity (World Bank, 2010).

In case of Im, Pesaran and Shin test, β varies across all sectors relaxing the assumption of Levin, Li and Chu test of identical first-order autoregressive coefficients. This test is based on mean group approach and can be represented as equation (3) below

                                                                             (3)

Where, _βi,  and   are the mean and variance of t _βi.

In this test, the null hypothesis which is tested is β₁=β₂=… =0. The other two tests applied are ADF and Phillips Peron Chi square tests. In both these tests, the null hypothesis is same as the IM, Pesaran test but individual roots are tested by them.

Panel co-integration tests

In practice, non-stationary series are transformed by differencing into stationary series for empirically analyzing the series. In economic theory, questions are raised about the model after differencing. Engel and Granger are of the view that to analyze non stationary series at level, all the data series are integrated at same order and co-integrated. As per their study, in case of co-integrated series, long run equilibrium relationship may exist even in case of non-stationary data. Thus, panel co-integration tests are applied to the data series.                       

Kao test for assessing panel co-integration is applied to the data series. As per Kao test, the null hypothesis indicates that the residual series should be non-stationary if no co-integration exists. Kao’s test is based on panel regression model and uses DF and ADF test statistic.

Granger causality test

Granger causality test for panel data is carried out to examine the causal relationship between Logistics performance and trade. The granger causality test is carried out by running bivariate regression in the panel data as per equation 4 and 5 although FE and RE is the best choice to evaluate effect of the parameters.  This study expects the causality more than the fixed effect and random effect.

y_i,t = α_0,i+α_1,iy_i,t−1+… +αl,iyi,t−1+β1,ixi,t−1+… +βl,ixi,t−1+∈i,t (4)

xi,t = α0,i+α1,ixi,t−1+… +αl,ixi,t−1+β1,iyi,t−1+… +βl,iyi,t−1+∈i,t      (5)

Where t is the time period and I are the cross sections.

Panel Granger causality test has been performed by treating the panel data as a large stacked set and then performing Granger causality test with the exception of limiting the entry of data from one cross section into lagged values of data from the next cross section. It is assumed in this case that all the coefficients are same across all cross sections and is represented in equation 6.

(6)

                                                               

The details have been summarized in the section on empirical analysis

4. Findings  

In this part we present the analysis and result of the panel data across the 3 countries which include Vietnam, Kenya and Germany. In order to assess the basic feature of the data, descriptive statistics are calculated. The descriptive statistics are depicted in Table-1.

The table 1 shows that the variables chosen are normally distributed. The mean to the median ratio of each variable is approximately one.

Table 1

Descriptive Statistics

Parameter	EX_TR	TIME_EX	COST_EX	CR	INF	LPI	ECL
Mean	3.75	2.78	7.08	4.34	4.49	0.62	0.35
Median	3.84	3.09	6.83	4.56	4.57	1.00	0.00
Maximum	4.02	3.80	8.26	5.10	5.11	1.00	1.00
Minimum	3.21	1.53	5.81	3.43	3.68	0.00	0.00
Std. Dev.	0.26	0.57	0.62	0.57	0.37	0.48	0.48
Skewness	-0.73	-0.54	0.35	-0.21	-0.55	-0.51	0.60
Kurtosis	2.03	1.81	2.20	1.32	2.43	1.26	1.37
Jarque-Bera	6.19	5.19	2.26	5.96	3.14	8.14	8.27

 

The standard deviation is also low compared to the mean, showing a small coefficient of variation. The range of variation between minimum and maximum is also reasonable. The coefficient of skewness of each variable is small and is mildly negative skewed. The figure for kurtosis in each variable is under 3 which illustrates near normality. The Jarque-Bera statistics also accept the null hypothesis of normal distribution in each variable with changing probabilities. Therefore, the normal distribution is completely true.

In empirical analysis, if the panel data series are non-stationary there is a risk of obtaining spurious results. Thus, the present study checks the stationary of the data through individual, trend and common test. In this view, the stationary properties of panel data are examined and transformation of non-stationary series in to stationary series in undertaken. The log transformed data for export as a percentage of trade (EX_TR), time required for exports (TIME_EX), cost of export (COST_EX), e-clearance from customs (ECL), availability of domestic credit (CR), inflation (INF) and Logistics Performance Index (LPI) were tested for stationary. The results indicated in table-2 suggest that all the above stated variables have unit root at level and are non-stationary. While at the first difference level, none of the variables have a unit root and hence are stationary.

Table 2

Summary of panel root test

Test		Levin, Li and Chu t test for common unit root	Im, Pesaran and Shin W-test	ADF –Fisher Chi Square	PP-Fisher Chi Square
Variable	Level	Statistic	Statistic	Statistic	Statistic
Export as percentage of Trade	Level	-0.05	-0.02	6.25	7.30***
	First Diff	-1.79**	-3.15***	21.75***	28.08***
Time taken for Export	Level	-2.20***	0.956	13.46	23.24
	First Diff	-7.05**	-4.52**	44.91**	51.60**
Cost of Export	Level	6.07	4.46	0.99	6.41
	First Diff	4.07**	0.77**	4.03**	19.61***
E-clearance	Level	0.78	1.48	1.14	1.17
	First Diff	-1.92**	-1.73**	12.36**	24.84***
Domestic Credit	Level	-1.54	-0.93	7.92	6.38
	First Diff	-2.19**	-2.39***	15.77**	16.31**
Logistics Performance Index	Level	-0.85	0.33	3.08	2.71
	First Diff	-1.74**	-1.731**	12.36**	24.84*
Macroeconomic Stability	Level	1.42	3.22	0.99	0.90
	First Diff	-3.93***	-3.01***	19.33***	19.52***

 

* Significant at 10%, ** Significant at 5% and *** Significant at 1%

 

In the Kao test for co-integration it can be seen that the null hypothesis is accepted as the probability is < 0.05 and hence there is the co-integration existed in the data set. Table-3 implies that exports as a percentage of trade and trade facilitation have a long run equilibrium relationship. Thus, now it is advised to use regression model in equation 1 for estimating the relationship exports and trade facilitation.

Table 3

Kao residual Co- integration Test

Parameters	Null hypothesis	Maximum lag	t-statistic	P
All sectors	No co-integration	Automatic lag length selection based on AIC with a max lag of 3	-2.546	0.0054

 

The results of regression model for estimating the impact of trade facilitation on trade performance specifically exports are presented in Table-4.

 

Table 4

Result of regression model for impact of Trade facilitation on Export

 

Variable	Coefficient	Std. Error	t-Statistic	Probability
C	4.8147	0.6046	7.963	0.0000
Cost_Ex	-0.294	0.1201	-2.447	0.0190***
ECL	-0.071	0.0353	-2.0267	0.0496***
LPI	0.6254	0.1322	4.7280	0.5342
CR	0.3093	0.1555	1.9881	0.0151***
INF	-0.169	0.0687	-2.4649	0.0182***
Time_Ex	0.3504	0.1611	2.17447	0.0358***
R-squared	0.9301	Prob(F-statistic)	0.000
Adjusted R-squared	0.9157	Durbin-Watson stat	1.0809

 

*** Significant at 10%, ** Significant at 5% and * Significant at 1%

 

The results indicate that cost for export, time for export and e-clearance procedure have significant impact on export performance. Time has a positive impact on export performance; meanwhile cost for export has a negative impact on exports. Credit available in each of these countries and inflation prevalent too has a significant impact on exports.

Apart from analyzing the impact of trade facilitation on export performance and vice versa, it is also important to examine causality amongst the variables. As per table-5 results, it can be inferred that cost of doing exports causes export performance.

Table 5

Panel Ganger Causality Test

Null Hypothesis	F-statistic	Prob
Time_ex does not cause cost_ex cost_ex does not cause time_ex	0.01914 0.25256	0.8906 0.6179
Ex_tr does not cause cost_ex cost_ex does not cause ex_tr	2.24276 5.79256	0.1417 0.0206***
ecl does not cause cost_ex cost_ex does not cause ecl	5.18055 0.04763	0.0280*** 0.8283
LPI does not cause cost_ex cost_ex does not cause LPI	3.64625 0.65628	0.0630* 0.4224
INF does not cause cost_ex cost_ex does not cause INF	7.17869 5.01797	0.0105*** 0.0304***
cr does not cause cost_ex cost_ex does not cause cr	0.52051 8.13154	0.4746 0.0067**
Ex_tr does not cause time_ex Time_ex does not cause ex_tr	1.34157 1.45418	0.2533 0.2346
ecl does not cause time_ex Time_ex does not cause ecl	4.23787 0.13216	0.0458*** 0.7180
LPI does not cause time_ex Time_ex does not cause LPI	1.61543 0.44894	0.2107 0.5065
INF does not cause time_ex Time_ex does not cause INF	5.07256 21.3752	0.0296*** 4.E-05
Cr does not cause time_ex Time_ex does not cause Cr	0.07599 0.99491	0.7842 0.3243
ecl does not cause ex_tr ex_tr does not cause ecl	0.00015 0.00021	0.9902 0.9884
LPI does not cause ex_tr ex_tr does not cause LPI	0.01825 0.00866	0.8932 0.9263
Inf does not cause ex_tr ex_tr does not cause Inf	0.01442 8.24265	0.9050 0.0064**
Cr does not cause ex_tr ex_tr does not cause Cr	5.12019 2.24243	0.0289*** 0.1417

***Significant at 1% level of significance

**Significant at 5% level of significance

*Significant at 10% level of significance

 

Automation of clearance procedures and Logistics Performance Index also causes cost of exports. Automation of clearance procedures also causes time of exports. It is also seen that in order to enhance the position of the nation in the Logistics Performance Index, automation of customs clearance is usually adopted.

Considering the empirical studies and theoretical framework for trade facilitation, it is seen that trade facilitation parameters like time to export, cost of export, logistics performance, adoption of e-clearance mechanisms and other parameters like inflation, availability of credit enhance trade performance of the countries. It can be inferred from the empirical analysis of the current paper that time to export, cost of export, adoption of e clearance measures, availability of credit and inflation have a significant relationship with enhancing exports from Vietnam, Kenya and Germany. Ease of doing business which is a culmination of various trade facilitation indicators leads to trade promotion under Hoekman and Nicita  (2011), Weerahewa (2009), Lee and Kim (2016), Perez and Wilson (2012), Behar and Venables (2001) along with reduction of cost. As it can be assessed from the results of the current study, adoption of online clearance procedures, less inflation and availability of credit along with reduction in time and cost of exports leads to increase in exports of a country. Although there are differences in economic scale and various locations on continents, three nations have

It can be thus concluded that each nation should focus on adoption of trade facilitation parameters in order to increase its trade performance.

5. Conclusions

It is widely believed and has been validated theoretically and empirically that trade facilitation (e-clearance; cost for export and time for export) has a significant impact on export performance. In the present study an attempt has been made to examine the cause and impact of trade facilitation on trade performance. The results obtained through empirical analysis indicate only time has positive impact on export while two elements of cost for export and e-clearance have the negative effects on trade performance.

The study suggests that three nations including developed (Germany) and developing countries (Vietnam and Kenya) should focus on reduction of cost for export; improve the e-clearance system and simultaneously consider the time for export.  In term of cost for export, Vietnam and Kenya has a declining cost for export during 2007 to 2016, but Germany has the opposite trend. The time for export in Germany did not change significantly. This clearly explains the positive significant of time required for export clearance on export performance. The present study also suggests that the nation would promote the export performance if they have the efficient trade policies in place. These trade policies should mainly be directed to understand global requirements of trade harmonization and standardization and thus adoption of trade facilitation measures.

 

 

References

 

1. Ahmed, S., Ismail, S. (2015), Economic growth and military expenditure linkages: A panel data analysis, International Economic Policy, 23, 48-72.
2. Duval Y. (2007), Trade Facilitation Beyond the Doha Round of Negotiations, Bangkok: Asia-Pacific Research and Training Network on Trade (ARTNeT), Working Paper Series No.50
3. Nordås, H, E Pinali & M Geloso Grosso (2006), ‘Logistics and Time as a Trade Barrier’, OECD Trade Policy Working Papers 35
4. Perez A. P., Wilson J. (2012), Export Performance and Trade Facilitation Reform: Hard and Soft Infrastructure, World Development, 40 (7), 1295-1307
5. Persson M.(2013), Trade Facilitation and the Extensive Margin, The Journal of International Trade & Economic Development, 22(5), 658-693
6. Puertas, R., Marti, L. and García, L. (2013) Logistics performance and export competitiveness: European experience, Empirica Journal of European Economics, on line, DOI 10.1007/s10663-013-9241-z
7. Sehrawat, M. and Giri, A.K. (2017), Financial Structure, Interest Rate, Trade Openness and Growth: Time Series Analysis of Indian Economy, Global Business Review, 18 (5), 1278-1290
8. Wilson, J (2003), ‘Trade Facilitation: New Issues in a Development Context’, Trade Note No 12 World Bank, Washington DC
9. World Trade Report (2015), The theory and measurement of trade facilitation, https://www.wto.org/english/res_e/booksp_e/world_trade_report15_e.pdf
10. Zaki C. (2014), An Empirical Assessment of the Trade Facilitation Initiative: Econometric Evidence and Global Economic Effects, World Trade Review 13(1), 103-130

 

 

 

 

Supporting Information

S1. Detail Information of trade facilitations among 3 countries

 

In order to assess the macroeconomic indicators across Vietnam, Kenya and Germany, it is important to analyze the trends and patterns in exports, ranking in trading across borders, the time and cost of exports and adoption of trade facilitation measures.

Export performance of Vietnam, Kenya and Germany from 2001- 2016

From figure-1 it can be seen that Vietnam’s export has gradually increased, Germany indicates fluctuating patterns in exports while that of Kenya has been stable during the last 15 years. Vietnam in 2007, on becoming the 130th member of WTO recorded exports worth 48000 million US$. Ten years after joining WTO, Vietnam has now become the emerging economy in Asia region. The highest volume of exports is over 175000 million US$ in 2016 for Vietnam. With unstable political conditions Kenya’s export remains stable over 5000 million US$ per year. Meanwhile, Germany’s export indicated high growth rates and the export performance reached its peak in 2008 with about 1500000 million US$.

Figure 1.

Export performance of Vietnam, Kenya and Germany from 2001- 2016

                                                                              Source: Trade Map

The reason for high export performance may be attributed to Germany’s association with OECD and is also a high-income country as per World Bank. Germany marked the lowest volume of export performance at 600000 million US$ in 2001. This number is 3.5 times higher than that of Vietnam export and 120 times of Kenya’s export in 2016. Though the initial exports from Germany were quite high as compared to Kenya and Vietnam, it cannot be concluded that the reason was absence of trade facilitation measures.

Ranking Trading Across Border

The significant measures of trade facilitation which have emerged in recent years include the doing business indicators and its significant sub category of trading across borders along with the Logistics Performance Index by World Bank. As cost and time are the trade facilitators to promote international commerce activities among countries. To World Bank, Doing Business measures the time and cost (excluding tariffs) associated with three sets of procedures—documentary compliance, border compliance and domestic transport—within the overall process of exporting or importing a ship­ment of goods. In case of Doing Business Rankings of trading across borders for Germany, Vietnam and Kenya, it is seen that at the beginning of the ranking period i.e. 2009, Germany ranked 11 while by 2018, it dropped to 39 rank facing declines mainly from 2015 as depicted in Figure-2.

 

Figure 2

Ranking Trading Across Border (2009-2018)

 

 

 

Source: Doing Business Reports 2009-2018

The reason for this decline can be attributed to other countries focusing on facilitating trade and reducing the regulatory procedures. In case of Kenya, its ranking in trading across borders is lowest but the pace of increase in ranks is highest. This indicates that Kenya has taken major steps in simplification of trade and thus facilitating it. In case of Vietnam, the rank of Vietnam is fluctuated around 70 during the initial six years but declined to 90s after that.

Time and cost of export

In order to assess the time and cost of export, doing business indicators are again observed. It is seen that there are a few differences in the methodology between Doing Business 2014 and 2016. Doing Business 2106 includes time in terms of hours for documentary and border compliance; the cost in terms of US dollar. Figure-3 and Figure-4 depict the time and cost of trading across borders specifically for exports. A number of hours taken for export are converted into days. It is seen that the time and cost to export in 2006 was highest for Kenya and lowest for Germany. Over the 10 year time period it is observed that in case of Germany the time to export decreased from 8-9 days to 5. In case of Kenya, the reduction has been phenomenal from 45 days to just 5 days equivalent to Germany despite being a low-income country. While in case of Vietnam the time to export decreased from 24 days to 13 days. In case of Vietnam it is seen that the decrease has not been as rapid as that of Kenya as depicted in Figure -3.

Figure 3

Time of export (in Days 2006-2018)

 

Source: Doing Business Reports 2006-2018

 

 

 

 

 

 

Figure 4

Cost of export (in US$ 2006-18)

 

Source: Doing Business Reports 2006-2018

 

On assessing the cost of export from 2006-2018 across Germany, Kenya and Vietnam, it is observed that all three nations focused on cost reduction but Kenya again emerged in terms of remarkable initiatives in cost reduction for exports and by 2016 was less than Germany. In case of Vietnam, the reduction in cost was observed but it still remains higher than Kenya and Germany.

Logistic Performance Index (LPI)

Logistic Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. The Logistics Performance Index overall score reflects assessments of a country's logistics based on efficiency of the customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time.

As it can be seen from Figure-5, Germany was amongst the top four since 2007 and ranked number one since 2014. Kenya has showed tremendous performance by reaching forty second positions from 122 while Vietnam was stagnant at around 50-60.

 

Figure 5

Logistic Performance Index 2016

 

Source: World Bank Logistic Performance Indicators 2007-2018

The Logistics Performance Index is formulated by measuring various indicators like infrastructure, timeliness, shipments, logistics competence and tracking. From Figure-6, it can be seen that in case of Germany all the parameters rank quite high thus assisting Germany to be a top ranker. In case of Kenya, the main laggard is timeliness and shipments though they too fare well on the ranking list.

While in case of Vietnam, infrastructure and tracking are major laggards and thus reduce the ease of doing business in Vietnam.  This can be attributed to the fact that a number of roads, bridges, and ports have been initiated by the Ministry of Transport, Vietnam but there is no well laid down master plan for logistics centers, LCDs, seaports and river ports. The management is overlapped and does not facilitate goods flows[1]

 

 

 

Figure 6

Indicators of Logistic Performance Index 2016

 

Source: World Bank Logistic Performance Indicators 2007-2018

 

Custom electronic data interchange

Customs electronic data interchange plays the important role in the custom management and administration of any nation. All the three countries have adopted e-clearance procures from customs of their respective countries. In case of Germany there is an Automatic Rate and Local Customs Clearance System, for Kenya a platform has been created by Kenya Revenue Authority and for Vietnam an Automated Cargo Clearance System. In 2015 almost all countries have applied EDI after the Kyoto Convention[2]. The advantage of Information and Communication Technology (ICT) has contributed to the successful establishment of Single Window in Germany and Kenya. The barriers of ICT with restrictions on infrastructure, technical matters, the mechanism of legal documents, and professional custom providers have caused delay in operationalizing single window clearance in Vietnam. With the trade plan of 2020, Vietnam will complete the National Single Window and aims to connect with ASEAN Single Window[3].

 

 

S2. More references

1. Djankov, S, C Freund & C Pham (2006), ‘Trading on Time’, World Bank Policy Research Working Paper
2. Doing Business Reports by World Bank, http://www.doingbusiness.org/reports/global-reports/doing-business-2018
3. Ease of Doing Business report 2016, http://www.doingbusiness.org/rankings
4. Germany Customs website, http://www.zoll.de/DE/Home/home_node.html
5. Grainger A. (2011), Trade Facilitation: A Conceptual Review, Journal of World Trade 45(1), 36-62
6. Hertel T. and and Mirza, T... (2009), "The Role of Trade Facilitation in South Asian Economic Integration." Study on Intraregional Trade and Investment in South Asia. ADB, Mandaluyong City.
7. Iwanow, T. and Kirkpatrick C. (2009) Trade Facilitation and Manufactured Exports: Is Africa Different?, World Development, 37, 1039-1050
8. Jawaid, S.T. and Waheed, A. (2017), Contribution of International Trade in Human Development of Pakistan, Global Business Review, 18 (5), 1155-1177
9. Kenya Customs website, https://www.kentrade.go.ke/
10. Limão, Nuno and A. J. Venables. 2001. "Infrastructure, Geographical Disadvantage, Transport Costs, and Trade." The World Bank Economic Review 15(3):451–479.
11. Logistics Performance Indicators by World Bank, https://lpi.worldbank.org/
12. Overall Trade Facilitation Index, https://www.oecd.org/
13. Shepherd B., Wilson J. (2009), Trade facilitation in ASEAN member countries: Measuring progress and assessing priorities, Journal of Asian Economics, 20(4), 367–383
14. Siddiqui A. A., Ahmed S. (2017), Impact of Foreign Direct Investment on Sectoral Growth of Indian Economy, International Journal of Economics and Financial Issues, 7(3), 477-488
15. Soloaga, I., Wilson, J.S. and Mejía, A. (2006) Trade facilitation reform and Mexican competitiveness. World Bank Policy Research Working Paper 3953, June.
16. Vietnam Economy Overview, http://www.vlr.vn/en/news/news/economy-overview/2826/vietnam-logistics-2016-goes-down-.vlr
17. Vietnam News, http://vietnamnews.vn/economy/420928/viet-nam-to-connect-to-asean-single-window.html#lt5EDS21E2rc9EXv.97
18. Wilson, J.S., Mann C. and Otsuki, T. (2005) Assessing the Benefits of Trade Facilitation: A Global Perspective, the World Economy, 28, 841-871.
19. Trade Map https://www.trademap.org/

 

 

 

 

 

[1] http://www.vlr.vn/en/news/news/economy-overview/2826/vietnam-logistics-2016-goes-down-.vlr

 

[2] DAKOSY Datenkommunikations system; Germany: http://www.zoll.de/DE/Home/home_node.html

Kenya Trade Net System: https://www.kentrade.go.ke/

Vietnam National Single Window: link not available

[3] http://vietnamnews.vn/economy/420928/viet-nam-to-connect-to-asean-single-window.html#lt5EDS21E2rc9EXv.97