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THE ROLE OF LOGISTICS INFRASTRUCTURE TO ATTRACT FOREIGN DIRECT

INVESTMENT IN VIETNAM

Conference Paper · November 2019

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 THE ROLE OF LOGISTICS INFRASTRUCTURE
TO ATTRACT FOREIGN DIRECT INVESTMENT IN VIETNAM
VAI TRÒ C A H T I V I VI
TR C TI C NGOÀI VI T NAM

Hoang Manh Hung1*, Le Thy Ha Van2,

Tran Thi Tung Quyen3
1,2Faculty of Economics & Accounting, QuyNhon University

3
Faculty of Economics &Tourism, Quang Trung University

ABSTRACT
The purpose of thisstudy is to examine the impact of logistics infrastructure represented
by infrastructure in transport and information and communication technology on attracting
foreign direct investment (FDI) into the localities in Vietnam in the period 2005-2016 using
panel data models. The result shows that logistics infrastructure has a positive impact on FDI
which flows into localities, in which transport infrastructure has a stronger effect than
information and communication infrastructure. Some variables are added to the model
including market size, trade openness, human capital, to ensure the model's robustness. Most
of them are confirmed to have positive effects on FDI inflows.
Keywords: Foreign Direct Investment, Logistics Infrastructure, Vietnam

TÓM T T
M a bài vi t là nghiên c ng c h t ng logistics i di n b i
h t ng giao thông và công ngh thông tin và truy n thông i v i vi c thu hút dòng
v c ti a ph Vi n 2005-2016
b ng cách s d ng các mô hình d li u b ng. K t qu ch ra r h t ng logistics có
ng tích c c lên dòng v n FDI ch h t ng giao
ng m nh m h t ng công ngh thông tin và truy n thông. M t s
bi c b sung vào mô hình, bao g m quy mô th m i, v n nhân
l m b o tính v ng ch c cho mô hình c a chúng tôi. Các bi c xác nh n
có ng tích c i v i dòng v n FDI.
T c ti h t ng logistics, Vi t Nam
1. Introduction
Foreign direct investment (FDI) plays an important role and is a key in controlling
globalization process. For many developing countries, FDI is an important factor intheir
strategy of economics development. Romer (1993) showed that foreign investment makes it

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easier to transfer technology and business secretwhich is extremely essential for the growth in
these countries. Moreover, FDI increases employment opportunities, improves labor
productivity and supports developing countries to access foreign capital. In order to acquire
the benefits of FDI above, many developing countries have implemented policies and
strategies to promote and attract FDI such as abolishing trade and investment costs, improving
human capital and infrastructure.
There are many documents focusing on the determinants affecting FDI attraction. Most
of them concern on factors such as economic size, trade openness, exchange rates, labor costs
and political factors. Later, some scholars began to care about the role of infrastructure as well
as logistics services in attracting FDI. They argued that a country's logistics system brings the
specific advantages of location to help attract FDI from outside. Good infrastructure and
logistics services are essential conditions for foreign investors to operate effectively. Outdated
infrastructure and limited logisticsservices will increase the costs of companies.
Vietnam is considered a bright spot in FDI attraction because in recent years, FDI
inflows into Vietnam have continuously increased. Foreign investors have been present in all
63 provinces and cities nationwide and participated in 19/21 industries in the national
industrial classification system. Unsurprisingly, common destination for FDI are Ho Chi
Minh City, Binh Duong, Hanoi, etc whereare facilitated to develop logistics. Accordingly,
transport infrastructure, information and communication technology are always concerned
and improved. A survey of EuroCham in 2018 in which objects are enterprises showed that,
foreign investors considered the safety in using the infrastructure system in Vietnam as well
as the limitedcapability of these systems. This study was conducted to provide an empirical
evidence in examining whether infrastructure and the development of logistics services are
important factors of Vietnamese provinces in attracting FDI or not.
2. Literature review
the

structural resources in logistics systems, including warehouses, transport vehicles, conveyors,

Logistics infrastructure is commonly divided into two groups: transportation infrastructure

and information and communication infrastructure. These are two of the most important
factors that are commonly used to develop national logistics performance indicators. For
example, in 2007, World Bank released the Logistics Performance Index (LPI) based on six
basic factors: Efficiency of customs and bodermanagement clearance; Quality of trade and
transport infrastructure; Ease of arranging competitively priced shipments; Competence and
quality of logistics services; Ability to track and trace consignments; Frequency with which

778
shipments reach consignees within scheduled or expected delivery times. Lu et al (2010) have
developed a logistics capability index (LCI) consisting of four factors: Physical infrastructure
and ICT infrastructure, Logistics Service Excellence; Efficiency of import-export procedures;
Regulatory Environment. Memedovic et al. (2008) proposed another indicators set is LOCAI
(Logistics Capability Index) used to measure logistics capability with 5 basic elements
including modern infrastructure; traditional infrastructure adapted for multi-modal
transportation; trade facilitation; quality of logistics services; and soft infrastructure. Within
the limited data sources, we can only use the infrastructure of ICT and transport as a criterion
for evaluating the logistics capability of provinces in Vietnam. These are two factors that are
expected to have a strong impact on FDI attraction in many countries and regions.
The role of infrastructure in FDI attraction has increasingly received scholars' attention
recently. The initially remarkable studies are of Root & Ahmed (1979), Wheeler &
Mody(1992). Accordingly, they studied on some developing countries and found that the
more adequate infrastructure countries are, the higher flows of foreign investmentwill they be
received. In contrast, the low infrastructure condition in some countries has caused big
obstacles in foreign direct investment. Investors seem to care about the quality of
infrastructure than other incentives such as tax cut. Many laterstudies which also recognized
the outstanding importance of infrastructure development are Loree & Guisinger (1995)
(studied the United States), Kinoshita (1998) (studied the Asian region) and Kumar (2001)
(studied sixty-six countries). In a empirical study by Escribano et al. (2005) using enterprises data
from the World Bank's ICA survey on seven countries also showed the importance of infrastructure to
productivity and FDI. In another study was complemented in 33 African countries, Khadaroo &
Seetanah (2009) used the length of the paved road per square kilometer to represent the transport
infrastructure and the number of phones per 1,000 people to represent the information and
communication infrastructure. Results from static and dynamic panel data models showed that both of
these factors have a positive impact on FDI.
Some research from the Chinese economy which attracts the largest amount of foreign
direct investment has the same conclusion. Accordingly, besides that other factors,
infrastructure improvement has created attraction for foreign companies (Liu et al., 1997;
Cheng & Kwan, 2000; Bransteller & Feenstra, 2002).
In a recent study, Blyde & Molina (2015) examined the impact of logistics
infrastructure on foreign investors' location selection in 230 countries and territories. Logistics
infrastructure data used are divided into two groups. The first one concerns the quality of port
and airport infrastructure. Improvements in the quality of port and airport infrastructure lead
to a reduction in transportation costs, waiting times, especially in the lower costs in handling
incidence from the transport process. The second one is the infrastructure in information and
communication technology (ICT infrastructure), which is an important platform to support

779
companiesto have quick information exchange ability at a long distancebut it is still cheap and
reliable. This help cost reduction of coordinating blocks across borders. The authors showed
that countries with adequate infrastructure in port, airport, information and communication
technology are attractive locations for multinational companies (MNCs).
While most studies confirm the importance of infrastructure for FDI attraction, there are
other studies that failed to confirm this hypothesis. For example, in a study by Quazi (2007) to
examine the determinants affecting FDI attraction in East Asian countries period 1995-2000,
the author mentioned that information and communication infrastructure is represented by
number of telephone and internet subscribers over 1000 people. Nonetheless, the author could
not find a positive and significant relationship between infrastructure and FDI. Accordingly,
the reason for not finding the impact of ICT infrastructure on FDI is that this representative
variable could not grasp the actual effectiveness of infrastructure for FDI. The author
recommended the use of other representative variables in subsequent studies. Kumari &
Sharma (2015) studied some developing countries in Asia period 1990-2012 and gave an
surprising result ofa negative impact of infrastructure on FDI inflows.
In Vietnam, plenty of studies evaluate the impact of logistics infrastructure in attracting
FDI. For example, Hans-Rimbert & Nguyen (2002) used the length of asphalt roads in the
provinces as a representative variable. The result was no effect of the variable on FDI attraction.
Meyer & Nguyen (2005) chose local passengers volume to be representative variable and showed a
positive impact except for new FDI inflow. In terms of information technology infrastructure, the widely
used representative variable is the number of telephone per 1000 people. There are differences in results
between the studies. While Nguyen & Nguyen (2007) could not findthe relationship between information
technology infrastructure and FDI attraction, Nguyen (2006) found a positive impact on most models.In
another study, Malesky (2007) examined the factors affecting additional FDI for projects as well as the
number of newly licensed FDI projects in 2006. The number of fixed telephone per capita in 1995 has a
positive impact and the transport distance has a negative impact on FDI attraction, respectively. Similar to
Malesky (2007), Nguyen (2015) also usedthe number of fixed telephone per 100 people to represent the
development levelof information and communication technology. The research was conducted in 43
provinces in Vietnam and the result showeda positive and strong impact of this factor on FDI inflows to
these provinces.
Thus, the role of infrastructure in general and logistics infrastructure in particular for
FDI attraction has been studied and confirmed in many previous studies. Although there are
conflicting results, most of studies supported the positive role of infrastructure for FDI. The
results depend on choosing representative variables as well as the approach method, and
model. In this study, we mention logistics infrastructure including infrastructure in transport
and infrastructure in information and communication with the representative variablesare the
amount of capital invested in transport infrastructure and the number of telephone and internet

780
subscribers per capita, in turn. Static and dynamic panel data models was used with different
estimation methods to examine the consistence of the results.
3. Research method
3.1. Models
The static panel data model is specified as follows:

Where is foreign direct investment into province/city i in year t.;

are infrastructure in transport and infrastructure in information and
communication of province/city i in year t, respectively; is a vector of other control
variables including:
SIZE: Market size. Most studies used population, GDP and GDP per capita to represent
the market size because it reflects the purchasing power of people in those countries. Foreign
investors prefer investment into a larger market size because the large-size market is
considered to bring higher levels of development and income per capita. We use GDP per
capita to be representative for market size. Because this variable is used as an indicator of
market potential of foreign investors, the authors expect it has a positive impact on FDI
attraction.
TRADE: Trade openness. The openness of an economy is a major factor determining
the level of import and export relations that a country will have with another country.
Openness is not only important for exports but also imports because many investors require
intermediate inputs to import from other countries. In general, it is accepted that greater
openness in trade provides new investment opportunities and strengthens the link between
domestic and international markets. Many empirical studies commonly consider the ratio of
trade (export + import)/GDP to be a representative variable for trade openness. We also
expect a positive relationship between trade openness and FDI attraction.
LABOR: Human capital. Human capital is often considered an additional factor for
physical capital and one of the essential elements of investment strategy. It refers to skilled
labor which acquired by individuals through a process of investing in education and training.
Therefore, human capital has been added as a new production factor and becomes one of the
main motive power of innovation and development. In this study, the authors identify human
capital based on the average number of students enrolling in high schools and expect a
significantly positive relationship between human capital and FDI inflows.
PCI: The quality of economics management are representative by the provincial
competitiveness index (PCI). PCI includes a number of components indices that reflect the
business environment, the quality of economicsmanagement as well as the administrative
reform efforts of Vietnam's provincial and municipal governments. These are important
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factors to attract FDIinflows, which has been demonstrated in a number of studies in Vietnam
(for example, Malesky, 2008; Nguyen, 2015).
The static model (1) has not allowed to grasp the idea whether the level of FDI
attraction in the past will affect the current FDI inflows or not. And if so, what is its impact
level? This can only be answered by estimating dynamic panel data model with the presence
of a lag variable of the dependent variableas a independent variable. The dynamic panel data
model is considered as follows:

The presence of the lag variable will create a dynamic model.This can cause the
endogenous problem in the model by the correlation of the lag variable with random error
components. Using the least squares method in this case will provide biased and inconsistent
results (Baltagi, 2008). Arellano and Bond (1991) recommended that the lag of dependent
variables and independent variables are instrumental variables. Accordingly, the system
GMM method is used in this study to solve the endogenous problem in dynamic models.
3.2. Estimation method
For the panel data, the commonly used estimation methods are Pooled OLS model
(POLS), fixed effect model (FEM) and random effect model (REM). For the POLS model, the
provinces areconsidered identically, which often does not reflect the reality because each
province has its own unique characteristics that will affect FDI attraction, such as geographic
location. Failure to control these separate effects may cause biased OLS estimation. In FEM
or REM model, this problem can be controlled because , in which represents

the separate effects that are constant over time and are not observed in each province.
Thus, the choice between POLS or FEM, REM depends on considering the existence of .

If they exist, FEM and REM are appropriate, whereas POLS is suitable. Both FEM and REM
models accept the existence of , but when correlates with the independent variables, the FEM

model is selected, whereas the REM model is more suitable. The problem of selecting the suitable
model is made easy through LM tests (in case choosing POLS or REM) and Hausmantest (in
case choosing FEM or REM).
However, one of the weaknesses of the above models is the endogenous problem of the
explanatory variables, which makes the estimation results biased. Endogenous problem in the
above models may arise from three main reasons. The first reason is concurrent errors because
some variables are linked together. The second one is the problem of omitting explanatory
variables. When this happens, there will exist a correlation between the error component and
the explanatory variables. The last one is the measurement error of the explanatory variables.
This is entirely possible because the limited statistical capability of local governments in
Vietnam. Particularly for dynamic model (2), the endogenous problem exists because of the
782
 presence of the lag variable, which creates the correlation between the error component and
the lag variable (Baltagi, 2008).
To solve the endogenous problem, we use the GMM system estimation method (S-
GMM) which developed by Blundell and Bond (1998). The advantage of this method is that it
is possible to control the local fixed impact without the assumption of no correlation between
the fixed effect and the explanatory variables. Another advantage of GMM system is dealing
with the endogeneity of all explanatory variables by using their lag variables as instrument
variables.
4. Empirical analysis
4.1. Overview of LPI index and FDI attraction in Vietnam
LPI is the national logistics performance index published by the World Bank (WB),
evaluated on a 5-point scale with 6 criteria. LPI evaluates a nation's international logistics
performance based on surveys in more than 160 countries following standardized questions,
including two international and domestic sections. 6 criteria include: Customs, Infrastructure,
International shipments, Logistics quality and competence, Tracking and Tracing, Timeliness.
Table 1. Vietnam's LPI value and ranking over time

Logistics
International Tracking
overall LPI Customs Infrastructure quality and Timeliness
Year shipments and tracing
competence
score rank score rank score rank score rank score rank score rank score rank
2010 2.96 53 2.68 53 2.56 66 3.04 58 2.89 51 3.10 55 3.44 76
2012 3.00 53 2.65 63 2.68 72 3.14 39 2.68 82 3.16 47 3.64 38
2014 3.15 48 2.81 61 3.11 44 3.22 42 3.09 49 3.19 48 3.49 56
2016 2.98 64 2.75 64 2.70 70 3.12 50 2.88 62 2.84 75 3.50 56
2018 3.27 39 2.95 41 3.01 47 3.16 49 3.40 33 3.45 34 3.67 40
Source: World Bank
Table 1 and Figure 1 describe the overall LPI index and the componentscriteria for evaluating
the LPI of Vietnam, thereby showing that the overall LPI tends to increase over time (except for
2016). This shows that Vietnam's logistics capability has improved significantly. Among the

3.67 points in 2018. The criterionwhich evaluates the timeliness of goods transferred to recipients
during delivery time is expected the best. Two criteria have a relatively low

783
 Figure 1. Vietnam's LPI index over time

respectively. The value of these two indicators evaluate the efficiency of the customs clearance
process.The speed, simplicity and predictability of procedures as well as the infrastructure related to
trade and transport quality are still weak. In fact, in recent years, the infrastructure of roads and

overall planning is not yet available and overlapping management, did not facilitate for
infrastructure for the flow of goods. Information technology application is still weak and has not
been properly invested.
In 2016, all criteria fell sharply leading to the decline of the overall LPI. However, in

strongest increase with 0.61 points (from 2.84 to 3.45).

Compare the components criteria of LPI in Vietnam with some countries in Southeast
Asia in 2018 through the spider diagram (Figure 2), the wider the contour was, the higher the
score of the criterion was; therefore, the level of logistics development in that country was
stronger. The chart is more balanced thatrepresents the more uniform component criteria of
LPI. From the chart in Figure 2, it is easy to see the significant difference of Singapore
compared to other countries. Singapore's contour is quite wide and uniform, reflecting
Singapore's outstanding logistics development level. Myanmar is contrary with the narrowest
contour. It means that the points of the criteria arethe lowest and imbalance. The remaining

The other 4 criteria achieve better points which are just lower than Thailand.
Table 1 and Figure 1 describe the overall LPI index and the componentscriteria for
evaluating the LPI of Vietnam, thereby showing that the overall LPI tends to increase over
time (except for 2016). This shows that Vietnam's logistics capability has improved
784
ranging from 3.44 points in 2010 to 3.67 points in 2018. The criterionwhich evaluates the
timeliness of goods transferred to recipients during delivery time is expected the best. Two

Figure 2. Vietnam LPI chart compares with some Southeast Asian countries

Regarding the situation of attracting FDI in Vietnam, Vietnam has attracted 349.1
billion USD by the end of 2018, the implemented capitalis 191.4 billion USD with 27,353
FDI projects from foreign investors, participated in 19/21 industries in the national economic
classification system, of which processing and manufacturing industry is 195.3 billion USD
and accounts for 57.4%. The FDI attraction in real estate business is 57.9 billion USD,
accounting for 17.0%. The production and distribution of electricity and gas is 23.0 billion
USD, making up 6.7% of authorized capital.
According to the General Statistics Office, there are 130 countries and territories with
valid investment projects in Vietnam, of which the top is Korea with a authorized capital of
62.5 billion USD, accounting for 18.3%, followed by Japan with 57.0 billion USD,
accounting for 16.7% of the total investment. Next came Singapore, Taiwan, Britishvirgin
Islands, Hong Kong, successively. In terms of space allocation, most 63 provinces and cities
have FDI projects, in which Ho Chi Minh City leading with 45.0 billion USD, accounting for
13.2%. The second place is Hanoi with 33.1 billion USD, accounting for 9.7%, followed by
Binh Duong with 31.7 billion USD which be amounted to 9.2% of authorized capital.
Currently, FDI is an important additional source, accounting for about 25% of the total
social investment, contributing 20% of the country's GDP. FDI has contributed to
restructuring the economy in the growth model based on modern technology and high quality
human resources, creating jobs for millions of workers with growing incomes, contributing to
building the labor force meeting the demands of industrialization and the fourth industrial
revolution. FDI has also made an important contribution to Vietnam's exports. In recent years,
785
exports of FDI sector accounted for over 70% of the export turn-over total of the country, in
which the key products are high-tech industrial goods. In 2018, the trade surplus of the FDI
sector compensated the trade deficit of the domestic business sector and created the largest
trade surplus ever with 7.2 billion USD.
Thus, FDI has increasingly asserted an extremelyimportant role in Vietnam's economy.
Improving investment environment, enhancing competitive capability are essential to attract
FDI and raise development and investment capital, in which enhancing the capability of the
national logistics infrastructure can be a one of the most important factors. This is proved in
the estimation results from the models in the next section.
4.2. Results
Table 1 summarizes the results of static model estimation from POLS, FE and RE
methods, successively. The F-test and Wald test show that the estimated static models are
suitable at 1% significance level. However, comparing POLS and FE models, F test = 19.96
which rejects the null hypothesis (the null hypothesis said that all of equal to zero or there
is no difference between groups). Therefore, FE model is selected.
In addition, Hausman test supports the selection of the FE model when rejecting the null
hypothesis which said that the RE model is appropriate. Thus, in case of static model, FE is
the most suitable model for discussing the results.
Table 2. Static panel results
POLS FE (Robust) RE (Robust)
INV 0.0710 0.9011* 0.7840
(0.17) (1.77) (1.56)
ICT 2.6892*** 1.8163** 1.7976**
(2.98) (2.16) (2.23)
GDPCAP 1.4679*** 1.7369*** 1.6949***
(7.90) (4.62) (4.81)
TRADE 0.0694*** 0.0271*** 0.0323***
(9.11) (2.79) (3.68)
LABOR 0.5355*** 0.4765** 0.5391***
(9.53) (2.09) (3.85)
PCI 0.0390*** 0.0318** 0.0335**
(2.71) (2.39) (2.53)
Constant -1.5816* -1.5447 -1.6654
(-1.78) (-1.27) (-1.49)
Number of observations 720 720 720
F test 122.03*** 14.44***
122.63***
F test that all u_i=0 19.66***
12.17*
Note: t statistic in parentheses; * p<0.1, ** p<0.05, *** p<0.01
Source: Authors' calculations

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 For dynamic models, the results of the FE and S-GMM estimation are summarized in
Table 3. Sagan test for dynamic model shows that the use oflag variables as instrument
variables is suitable. In addition, the Arellano-Bond test for second-order autocorrelation also
accepts the null hypothesis which said that there is no second-order autocorrelation in the
dynamic model. The impact direction of the independent variables on the dependent variable
is consistent between the estimation methods as well as between static and dynamic models.
The signs of the estimated coefficients areas authors' expectation. However, there are
significant differences in the magnitude of the coefficients. The lag variable of FDI in
dynamic model are highly statistically significant confirming the appropriateness when using
dynamic model. From the above analysis, in the next section, we will discuss the results based
on the dynamic model from S-GMM method.
Table 3. Dynamic panel results

FE System-GMM
L.FDI 0.5089*** 0.6604***
(15.17) (5.68)
INV 0.7804** 2.9786***
(2.5) (4.32)
ICT 0.8139* .9021*
(1.33) (1.74)
GDPCAP 0.8128*** 0.5425**
(4.23) (2.21)
TRADE 0.0120* 0.0230**
(1.87) (2.48)
LABOR 0.2078* 0.1458*
(1.76) (1.67)
PCI 0.0239** 0.0224***
(2.43) (2.9)
constant -1.1236* -2.1179***
(-1.71) (-2.97)
Number of observations 720 720
Number of groups 60 60
Number of instruments 32
F test 101.75***
F test that all u_i=0 3.03***
171.20***
Arellano-Bond test for AR(2) in first differences (p 0.005
value)
Arellano-Bond test for AR(2) in first differences (p 0.943
value)
Sargan test of overid. Restrictions (p value) 0.269
Hansen test of overid. restrictions (p value) 0.246
Note: t statistic in parentheses; * p<0.1, ** p<0.05, *** p<0.01
Source: Authors' calculations
787
 Variable INV which represents the amount of capital invested in logistics transport
infrastructure has a very highlystatistical significance and a positive estimated coefficient.
This demonstrates the special importance of transport infrastructure in attracting FDI into
Vietnam. Information and communication technology infrastructuredoes not have much
impact on FDI attraction. Accordingly, the coefficient of ICT variable is positive but only
statistically significant at 10% level. This result is consistent with the results of many studies
mentioned above. In fact, many provinces in Vietnam have limited infrastructure, especially
transport infrastructure. This is identified as one of the main causes of invisible barriers in
attracting foreign investment. In contrast, provinces with synchronous transport infrastructure,
especially relatively adequate logistics infrastructure, have attracted a large amount of FDI.
Nowadays, in a highlycompetitive world, the quality of logistics services can have a
significant impact on the decision-making of businesses in selecting countries to invest in
production, selecting suppliers to purchase and selecting consumer markets to penetrate. High
logistics costs and especially poor services are a barrier to not only trade but alsoforeign direct
investment. The infrastructure and technical facilities of transportation have a huge role in the
development of logistics services. At the same time, another fundamental thing is advances in
information technology. The use of electronic information exchange systems with the support
of communication networks and information processing technology plays an important role in
managing storage of goods, circulation and documentation of that shipment. The application
of modern informatics technology into logistics activities will supportfinding customers,
management, monitoring and solving all logistics problems with the most reasonable costs.
Therefore, the development of logistics infrastructure is an important factor to attract FDI.
The lag variables of FDI are highly statistically significant, indicating the possibility of
losing dynamic information when using static models. As Quazi (2007) argued that foreign
investors often dislike risk and tend to favor familiar territories. The author noted the need for

fear of investing in an unknown l

stamp out concern of foreign investors when investing in an unknown location). According to
Cheng & Kwan (2000), FDI is one of the least volatile forms of foreign investment. In fact,
once FDI has been implemented, it is less likely to withdraw capital from the invested country
because of its relatively high investment costs. Therefore, FDI may persist over time. This
result is similar to the evidence provided by Noorbakhsh et al.(2001), who showed that many
MNCs consider their new markets relying on their previous investments.
The estimated coefficient of market size measured by GDP per capita (GDPCAP) is
positive and statistically significant. The positive relationship between GDPCAP and FDI
reinforces the hypothesis that foreign capital is positively correlated with market size in the
invested country. This result shows that foreign companies investing in the provinces concern
not only export but also domestic market.
788
 Labor resource is another important factor affecting investment decisions, which clearly
describes in the positive and statistically significant coefficient of LABOR. In fact, in the
provinces of Vietnam, there are many FDIflowing in labor-intensive sectors; therefore,
investors often have to consider the quality of the labor force that they intend to recruit at the
locality including the availability, cost and education level. In addition, TRADE and PCI also
have positive impacts on FDI, suggesting that an effective environment with better trade
openness is likely to attract foreign investors.
5. Conclusion
Logistics capability to support the global circulation of goods as well as in the local area
will be a big challenge and an important factor bringing successfulness for multinational
companies. Logistics supports and shapes network and supply chain cohesion. Logistics
capability is assessed based on measures of transport and information infrastructure, supply
chain management and trade facilitation capabilities. According to Dorni
benefits arising from globalized supply chains cannot be realized without co-developments in

capability that has a great impact on attracting FDI into Vietnam, which is infrastructure
including infrastructure in transport and information and communication. The study's results
provide evidence about the important role of logistics infrastructure in attracting foreign
investors into Vietnam, in which transport infrastructure is considered to have a stronger
impact. In addition, other factors such as market size, trade openness, human capital and the
quality of economics management also make an important contribution to boosting FDI into
provinces in Vietnam.
Although it is identified as the leading factor affecting FDI inflows, logistics
infrastructure in Vietnam has the lowest score compared to other indicators that constitute the
logistics capacity index (LPI). In recent years, it can be said that the logistics infrastructure
system of Vietnam has been much more invested; however, the achievement is still limited,
the transport infrastructure is still inconsistent. This makes the price and cost of production of
goods in general be high; therefore, the competitiveness of the economy is much reduced,
leading to a decrease in attraction for foreign investors. Therefore, in order to enhance FDI
inflows, Vietnam needs to improve its competitiveness by investing in the development of
infrastructure, especially in connected infrastructure, because current global supply chains
areoperated in a very tight and timely manner. Therefore, the infrastructure must meet those
requirements in both tangible and visible sides, (such asseaport systems, railways and
transportation networks, the process of handling customs and administrative procedures).
In addition, the provinces should continue to focus on improving the investment and
business environment by limiting barriers to investment and business, tax cut and improving
tax administration systems as well as process of handling customs procedures; boosting

789
investment in improving the level of labor to meet the increasing requirements of foreign
investors. Accordingly, the economy need to prioritize in the repression of bureaucracy and
corruption, as well as simplify procedures and eliminate unnecessary steps, improve the local
competitiveness index.

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