Abstract
The study investigates the impacts of official development assistance (ODA) on the
inflows of foreign direct investment (FDI) in 64 provinces of Vietnam in 2002–2004. These
impacts are examined using both the two-stage least squares method and the fixed
effects/random effects model. The regression results give evidence of the positive impacts of
ODA in infrastructure on FDI inflows, not only by direct channels, but also by indirectchannels through improving the human capital base of respective provinces. However, theshort term impacts of the current level of ODA disbursement are ambiguous. Robust evidence
only proves the long-term impacts of ODA in infrastructure on FDI attraction. At the donor
level, we found that Japanese ODA has a positive and significant impact on the allocation of
the FDI of Japanese private investors in both the short-run and long-run.

1

I.

I TRODUCTIO

To achieve a sustainable economic development target, the major issue for developing
countries is to attain an adequate foreign financial source to escape from the vicious circle oflow savings and low economic growth. Developing countries usually start their economic
development at a low level of income per capita and, thus, low levels of domestic savings.
According to Hayami and Godo (2006): “For such developing countries, in which theinvestment needs for development tend to exceed domestic saving capacities, net capital
import from abroad represents a possible escape from the vicious circle of slow economic
growth and low savings” (pp.45-46). Therefore, in developing countries, of which financial
sectors are yet to be well developed, foreign direct investment (FDI) and official development
assistance (ODA) not only represent the major source of finance, but also provide a chancefor human capital accumulation and technology transfer in recipient countries.
FDI, compared to ODA, is preferable due to its characteristics of politicalindependence. The determinants of FDI have become a major source of recent economic
studies to unveil the reasons why some regions are more successful than others in attracting
FDI. So far, many empirical studies have identified various factors that can affect the inflowof FDI including the level of development, market size and policy, and infrastructure, of
which infrastructure is considered the key element to improve the investment environment for
promoting FDI.
Countries with high levels of infrastructure investment like Japan, Korea and later
China and Vietnam, have experienced a high and sustainable economic growth. In thesecountries, infrastructure and its related services reduce the transaction costs, raisecompetitiveness, support productivity and efficiency in manufacturing and distribution, and2

create a sustainable domestic market while linking them to the international market. To
further attract FDI, the challenge now is to find ways to finance infrastructure. Infrastructure,
due to its specificity of high capital intensity and long life cycle tends to be prone to market
failures. Therefore, investment in infrastructure usually relies heavily on public finance. In
developing countries with low levels of domestic saving as mentioned above, ODA
represents the main source of finance.
In the literature, many studies have focused on figuring out the relationship among
FDI, infrastructure, ODA, and economic development. While most studies find a positive
relationship between infrastructure and FDI inflow and between FDI and economic growth,
studies on the relationship between ODA in infrastructure and FDI attraction is still lacking1.
According to the World Bank (2006), this is probably due to the fact that in most developing
countries, ODA in infrastructure does not contribute significantly to the total funds forinfrastructure investment. In fact, ODA in developing countries, is usually not an importantsource of financing infrastructure investment in low- and middle-income countries of East
Asia. However, it is not the case in Vietnam.
Over the past decade, Vietnam has achieved spectacular continuous GDP growth withan annual per capita growth rate averaging 7.3%, the eighth highest in the world, from 1992
to 2002. A critical part of this success is contributed by the high level of FDI. In fact, FDI has
contributed as much as 14% of the total Gross Domestic Product (GDP) of Vietnam and 54%
of its total exports (Freeman, 2002). Among various factors, the high level of infrastructure
investment, which has been mainly financed by ODA, has been considered to contributesignificantly to attract FDI to Vietnam. The importance of ODA in funding infrastructure has
been recognized widely in the 5-year plan and 10-year socio-economic strategy by the1

The studies include Glickman and Woodward, 1988; Hill and Munday, 1991, 1992; Wheeler and Mody, 1992;
Lorre and Guisinger, 1995.

3

Vietnamese government. In the Socio-economic Development Strategy of 2006–2010, theVietnamese government has specified the target of future annual infrastructure investment
summing up to 11.4% of the GDP, i.e., an increase of 2% of the GDP over the recent levels.
In order to meet the government's infrastructure goals, this financing gap will need tobe filled with an increase in ODA in infrastructure. The key issue for recent discussion is tofind a positive relationship between increasing ODA in infrastructure and improving the FDI
inflow. However, so far, there is no empirical study about the linkage between ODA in
infrastructure and FDI inflow in Vietnam. This linkage, once proved, can not only affectlargely the governmental policies toward mobilizing and utilizing this important fund fordevelopment, but also can provide a strong argument for persuading donors to invest in
infrastructure projects.
To provide more evidence on the debate about the importance and the role of ODA in
infrastructure and FDI in Vietnam, this paper focuses on analyzing the impacts of ODA in
infrastructure as one of the most important determinants of FDI inflow. This paper aims to
explore the interrelationship between ODA in infrastructure and FDI inflow. Morespecifically, this paper can produce a new perspective on the discussion of the impacts offoreign aid on FDI promotion to persuade donors in infrastructure projects.
This paper is divided into 6 main sections. Section II contains definitions of important
terms and the linkages among them. Section III review the past studies on the targeted issue.
Section IV describes the overall picture of ODA and FDI in Vietnam and the proposed
hypothesizes. Section V summarizes the econometric model with expected results. Section VI
shows the regression results. Finally, Section VI presents the conclusion and policy
implications.

4

II.

1.

CO CEPTUAL FRAMEWORKDefinition of terms

1.1. Foreign aid
According to the Glossary of Statistical Terms of the Organization for Economic Cooperation and Development (OECD), ODA refers to “the flow of official financing
administered with the promotion of economic development and welfare of developing
countries as the main objective, and which are concessional in character with a grant elementof at least 25 percent (using a fixed 10 percent rate of discount). By convention, ODA flows
comprise contributions of donor government agencies, at all levels, to developing countries(“bilateral ODA”) and of multilateral institutions. ODA receipts comprise disbursements bybilateral donors and multilateral institutions”.

1.2. Infrastructure
According to the definition in investorworld.com, “Infrastructure is the basic physicalsystems of a country's or community's population, including roads, utilities, water, sewage,etc”2. These systems have been considered significant in improving the dynamic developmentof economies. Infrastructure in this study is limited to transport, electricity andtelecommunications and water supply because these services are capital-intensive and tend to
exhibit substantial economies of scale. Moreover, the features of infrastructure services
including space and use specificity, long-lasting life time and lumpiness make it typical typeof a public good. With the characteristics of a public good, including non-excludability andnon-rivalness and, more importantly the phenomenon of free-riding, infrastructure services
2

http://www.investorwords.com/2464/infrastructure.html

5suffer from market failures, which make it difficult to allocate appropriate privatevfinancial
sources for infrastructure investment. According to World Bank (2006, pp. 10-15), “In someinfrastructure sectors, there are activities in which private sector financing can help unlock
resources. However, in most cases, private sector interest is likely to be limited and private
sector participation is more difficult to structure”. In these situations, official financing plays
an important role in unlocking a country’s own resources to meet infrastructure challenges.
Official funds, especially ODA, may play a significant role in financing large–scale
infrastructure projects. This is particularly important because through ODA, the government
can also encourage private sector initiation so as to maximize national and international
welfare.

1.3. FDI
According to the Government of Vietnam (1999), “FDI is defined as a long-term
investment by a foreign direct investor in an enterprise resident in an economy other thanwhere the foreign direct investor is based”.

2. The linkage between ODA and FDI
Infrastructure affects FDI inflow in both direct and indirect ways (Figure 1).
Infrastructure in transport systems provides better conditions for foreign investors to accesstheir markets of both outputs and inputs. The establishment of other crucial facilities
including electricity, water, and gas allows mass production of output. In addition, workers
involved in infrastructure construction have chances to increase their income and, in turn,raise the aggregate disposal income and consumption, which eventually increases market sizeand potential sales of investors. Also, better access to schools and hospitals and the increase6

in income have positive effects in human capital formulation, which in turn, supplies a highly
qualified labor force for the investors.

7

III.

REVIEW OF PAST STUDIESIn the literature, much effort has been spent on investigating the relationship between
infrastructure and investment, focusing mainly on FDI. These studies can be divided into
three categories: (1) the effects of infrastructure on investment and economic growth; (2) the
effects of foreign aid on investment and aggregate growth; (3) the determinants of FDI suchas political stability, development indicators, market size, and economic growth.

3.1. The effects of infrastructure on investment and economic growth
According to Estache (2004), there are 102 studies that have examined the impacts ofinfrastructure on growth, investment, and productivity. The majority finding is the positive
impact of infrastructure on economic growth and investment. In the case of developing
countries, many studies have confirmed the significant impact on aggregate output of thedevelopment of telecommunications and roads3. In particular, Canning and Bennathan (1999)
estimated the social rates of return to electricity and paved roads, relative to the returns togeneral capital. They found that both types of infrastructure are highly complementary withthe other physical and human capital, especially in a few countries, where there is evidence ofacute shortages of electricity and paved roads. Thus, there are excess returns to infrastructure
investment. While infrastructure is important, Devarajan, Swarroop and Zhou (1996),
ironically, found a negative relationship between the share of infrastructure expenditures intotal public expenditure.
Prud’homme (2004) found that most of these studies, which follow the production
function-based approach, suffer from a number of drawbacks. Firstly, many infrastructure
3

The studies include studies of Roller and Waveman, 2001; Demetriades and Mamuneas, 2000; Canning and
Bennathan, 1999; Fernald, 1999.

8

investment decisions are not based solely on the consideration of maximizing economic
growth but much weight is taken on the political and social aspects. Secondly, measuring theinfrastructure stock is difficult, both conceptually and empirically, because what affects
investment are infrastructure services, while the available information is related toinfrastructure supply. Thirdly, the efficiency with which infrastructure services is supplied
depends on the regulatory framework governing service provision.

2. The effects of foreign aid on investment and aggregate growth
Doucouliagos and Paldam (2005) report that there are 43 papers containing
accumulated estimates of the impacts of aid on savings. Aid is found to have a positive
impact on savings and investment and, thus, savings and investment promote aggregate
economic growth. Also, Sturm (2001) found that foreign aid has made a statisticallysignificant contribution to public investment, which in turn contributes to economic growth.
However, studies such as Hansen and Tarp (2000) and Harms et al. (2004) conclude that
foreign aid may depress domestic savings.

3. The determinants of FDI
Edward Crenshaw (1991) found in a number of studies the positive effects of the levelof development, market size and economic growth on FDI4. On the other hand, Bennett andGreen (1972) and Schneider and Frey (1985) found a negative relationship between politicalinstability and FDI.
Harms and Lutz (2006) and Karakaplan et al. (2005) examine directly the relation
between foreign aid and FDI using data from developing countries. Harms and Lutz (2006)
4

These studies include study of Dunning (1981), Bollen and Jones (1982), and Scheneider and Frey (1985).

9found two major effects of ODA on FDI flows: (1) ODA improves infrastructure system ofrecipient countries and, thus, enhances the marginal product of capital in the recipient
countries; (2) ODA encourages rent-seeking behavior, which leads to a decrease in the factor
productivity in the country and may discourage FDI inflows. The effects of aid on FDI isgenerally insignificant but significantly positive for countries in which private agents face
heavy regulatory burdens. Karakaplan et al. (2005) also found an insignificant effect of aid on
FDI, but in contrast to the finding of Harms and Lutz (2006), their results suggested that good
governance and developed financial markets lead to a positive effect of aid.

IV.

FDI A D ODA I VIET AM: A OVERVIEW

Since the start of economic reforms in Vietnam in 1986 up until 2006, FDI inflow in
Vietnam has increased considerably. In 2005, Vietnam attracted US$5.8 billion of FDI. Apartfrom the shock to the world or regional economy such as the Asian financial crisis, thenumber of registered FDI projects has increased strikingly (Figure 2) (in the first 11 monthsof 2007, the amount of FDI has achieved 15.03 bil USD). We can also notice a similar trend
in the level of ODA disbursement. In 2005, the donor community has commited the amountof 3,74 bil.USD to Vietnam (the commitment reached 4.45 bil USD in 2007) . ODA resources havemostly been allocated in accordance with the priority of the Government. Infrastructure has
been the largest beneficiary of ODA: 22.6 per cent of the source was allocated to transport,
20.3 percent to electricity generation and transmission and another 8.3 per cent to watersupply, drainage and urban infrastructure (Ministry of Planning and Investment, 2003).
Interestingly, the distribution of ODA among provinces of Vietnam is not biased toward thepoor areas of the country (Table 1). Regions with low poverty rates such as Red River Delta
10and South East received much more aid than other poor regions. This can be explained by thehigher efficiency of development spending, mainly through encouraging FDI in relatively
more developed areas in generating economic growth. Therefore, a large amount of ODA wasallocated to the relatively prosperous urban centers rather than to the rural and mountainous
areas, where the poverty concentration is high.
To see more clearly the trend of correlation between FDI and ODA, let have a lookin Red River Delta and the South East region, which are ahead of the rest of the country interms of the amount of FDI they attract. These regions also have the nation's highest numberof new FDI enterprise registrations per capita and are also recorded to be regions with highest
amount of ODA commitment and disbursement. In particular, provinces with a highconcentration of ODA including Hochiminh City, Hanoi, Dongnai, Binhduong, and BariaVungtau have attracted large inflows of FDI (Figure 3). Since their local economies continue
to be reinforced by large ODA inflows, we can expect to find a strong correlation between
FDI inflow and ODA in infrastructure in the provinces of Vietnam.
On the basis of current situation of Vietnam, the null hypothesises are:-H 0 : ODA in infrastructure has no positive impact on FDI

H 1 : ODA in infrastructure has positive impact on FDI-

H 0 : ODA in infrastructure of one donor has no positive and strong impact on

FDI from that particular country.

H 1 : ODA in infrastructure of one donor has positive and stronger impact on
FDI from that particular countryV.

ECO OMETRIC MODEL11

We use the two stage least squares (2SLS) and the fixed effects/random effects(FE/RE) model in the estimation.
1. The 2SLSIn the first stage of 2SLS, the ODA in infrastructure is regressed on a set of
exogenous variables including 2 instrumental variables that affect ODA, but do not affect the
FDI. In the second stage, the predicted values of ODA enter as one of the regressors of the
FDI.
There are two reasons why the 2SLS is justifiable. Firstly, infrastructure, which ismainly financed by ODA, acts as a prerequisite for the establishment of FDI projects. ODA isalso considered an endogenous variable in the FDI regression and instrumental variables areneeded to correct for endogeneity using two-stage estimation procedure. Secondly, the error
term in the FDI regression contains unobserved province-specific effects so that the error
term may be correlated with the variations in some of the regressors. For example, shocks in
the Gross Domestic Product Per Capita (GDPPC) may be highly correlated with the shocks
that affect FDI. The tests for endogeneity, the test for over-identification, and the test forstrength of instruments are conducted to ensure the robustness of the coefficients, of which
the results are all in favor of 2SLS.
In both stages, all variables are transferred into logarithm form to minimize theexcessive variations in the value of ODA and FDI. Also, as some provinces are recorded tohave zero values, those observations are excluded when they are transferred into logarithm
form and hence reduce the number of observations for the regression analysis. In order toavoid this problem, a small value of 0.001 is added to each observation when they are beingtransformed into logarithm form (Pham Hoang Mai, 2005).
12

The first equation in the 2SLS is
Eq. (1)lnODA = f(lnlagODA, ln ODAhat × ln SCHOOL , lnINDUS, lnSCHOOL,

lnGDPPC, lnPOP, lnTAX, , D year 03 , D yeear 04 , lnPATIENTBED, DMoutainous ).
The endogenous variable ODA is measured by the current disbursement value of
ODA in infrastructure from all donors. This represents the direct, short-term impact of ODA
on the attraction of FDI. ODA is measured in real domestic currency, deflated by the CPI
using 2000 as the base year and converted into US dollar (US$) using the official exchange
rate of the respective year. In this paper, ODA values include ODA in roads, telephones andother telecommunication, water supply and electricity, which come from the report ofDevelopment Assistance Database (DAD) project of the Ministry of Planning andInvestment.5
Two instrumental variables are included to correct for the endogeneity of ODA. The
first instrumental variable is the number of patient beds per person in local hospitals(PATIENTBED), which represents the level of basic public service in that province. The dataof this variable come from Ministry of Labor and Social Affairs. The second instrumental
variable is the dummy for mountainous province ( DMoutainous ), which takes the value of unity,
if the province is mountainous, and zero, if otherwise.
The second-stage equation of the 2SLS is
Eq. (2)

lnFDI = f(lnODAhat, lnlagODA, ln ODAhat × ln SCHOOL , lnINDUS,
lnSCHOOL, lnGDPPC, lnPOP, lnTAX, , D year 03 , D yeear 04 ).

5

For this study, foreign aid data are measured in gross which is yet to subtract the repayment from thegovernment to loan aid. The gross value is justifiable because this paper analyzes the effects of ODA atprovincial level, which does not relate closely to repayment at the national level.

13Since there are no strong reasons to assume any functional forms other than a linear
relationship, a simple linear regression analysis is applied. The structural regression equation
follows the model used by Kimura and Todo (2007).
The dependent variable is measured by the registration value of FDI in the provinces.
The commitment value, rather than disbursement value, is an appropriate proxy because thisstudy focuses on the influence of ODA in infrastructure and the improvement of FDI inflow.
The data of registered FDI come from the data of provincial committed FDI flows for the
2002-2004 period, which are collected by the Foreign Investment Agency of the Ministry ofPlanning and Investment.
The variable lnODAhat represents the short-term impact of ODA in infrastructure on
FDI, whereas the variable lnlagODA represents the long-term impact. The indirect effect of
ODA on FDI is captured by the interaction term between ODA and the secondary school
enrollment ( ln ODAhat × ln SCHOOL ). The variable lnINDUS represents the area ofindustrial and export-processing zones, where basic infrastructure for FDI projects isprovided.
One motivation of foreign investors to invest in developing countries is theavailability of a cheap and well-educated labor force. The variable lnSCHOOL is used as aproxy for the quality of local labor force, which represents the potential productivity in eachprovince. The income per capita of each province (ln GDPPC ) is used as a proxy for the levelof wage rate in Vietnam, which represents the cost of labor in Vietnamese provinces.
The effects of provincial market size are represented by the population of the province(lnPOP). Population has been considered a proxy for market size and many studies have
proved the statistically significant positive effects of population on FDI flows in both national

14

and regional levels, for both developed and developing countries (Lecraw 1991; Wheeler andMody 1992).
To examine the impacts of governmental policies on FDI inflow, we include thetaxratio variable (lnTAX), which represents the favorable policies of government towards
mountainous provinces. The variable is calculated by the average tax ratio, based on the ratioof turnover tax and profit tax over total turnover of FDI projects. The larger the ratio, the lessfavorable the governmental policy toward the province.
The year dummies for 2003 and 2004 are included to assess any secular increase in
FDI compared to 2002 (the base year).
2. The FE/RE model
The FE/RE model is appropriate because there are unobserved factors that affect local
FDI attraction, the values of which are constant across time and cannot totally be covered in
the ordinary least squares (OLS) model. In this case, the OLS regression may suffer from theomitted variables problem which causes the coefficients to be inconsistent and biased. Inorder to correct for biases arising from omitted variables and the possible correlation between
the error term and explanatory variables, we employ the FE/RE method to correct for thecorrelation between the error term and the independent variable. The superiority of the FE/RE
model is evaluated based on the result of Hausman test.
The FE/RE equation is:
Eq. (3)lnFDI = f(lnODAhat, lnlagODA, ln ODAhat × ln SCHOOL , lnSCHOOL,
lnGDPPC, lnPOP, lnTAX, , ai ).
The FE/RE effects model includes all the exogenous variables in the second-stage

equation of the 2SLS model, except for the variable lnINDUS, the value of which is constantthroughout the period of 2002-2004. All time-invariant factors are captured by ai .
15

3. Expected sign of the coefficients
We expect lnODA and lnlagODA to assume positive signs because the level ofinfrastructure is a critical determinant of FDI inflow. The coefficient of the variable
ln ODAhat × ln SCHOOL is expected to have a positive sign to prove the positive indirect

effect of ODA on FDI by enhancing human capital resources. The area of industrial andexport-processing zones (lnINDUS), which reflects the level of infrastructure development, isalso expected to have a positive coefficient. LnSCHOOL is expected to have a positivecoefficient, which implies that the higher rate of secondary school enrollment attracts more
FDI. Workers who completed secondary school are likely to understand new technology
more easily and be able to better participate in industrial production.
The expected sign of the coefficient of lnGDPPC is ambiguous. GDPPC represents
the prevailing wage rates in the provinces so that the higher the GDPPC, the lower the FDI.
On the other hand, GDPPC represents consumption demand and, thus, a higher GDPPC
means a higher potential sale for the firms. The variable lnPOP, which is a proxy for the
potential market size, is

expected to have a positive coefficient. Provinces with larger

population size are expected to attract more FDI.
Government tax incentives in mountainous and remote areas and in areas withdifficult natural, economic and social conditions are expected to promote FDI inflow. The
Foreign Direct Investment Law of Vietnam set two encouraging tax levels, i.e., 10% and 15%,
instead of the normal 28%. The coefficient of the variable lnTAX is expected to have anegative sign, i.e., the smaller the tax ratio the higher the FDI inflow. The dummy variable2003 and 2004 is expected to have positive sign to indicate a secular rise in the FDI vis a vis
2002 (control).
16

VI . REGRESSIO RESULTS

The results of the first stage regression are summarized in Table 2. As expected, thelevel of ODA disbursement is positively affected by the level of disbursement in the previous
year. More specially, a 1% increase in the previous-year disbursement leads to about a 1%
increase in the level of current disbursement, which is statistically significant at 1%. ODA in
infrastructure is negatively correlated with the number of patient beds in provincial hospitals,
implying that aid allocation is influenced by the level of development of public service in thatprovince. Provinces receiving less public service will receive more attention and more aid
from donors. However, the dummy variable ( DMoutainous ) has minor positive coefficient (nearly0), implying that there is minor bias of aid allocation towards mountainous provinces. In
other words, ODA in infrastructure in Vietnam is not significantly higher in provinces withspecial difficulty. In addition, GDPPC, also does not have a significant impact on the level of
ODA in infrastructure. GDPPC is the proxy of the level of living in the provinces. Thisimplies that ODA in infrastructure does not necessarily flow to poor provinces.
The results of the second stage regression are summarized in Table 3. The effect of
the total stock of current ODA in infrastructure on FDI flow is positive but insignificant in the
2SLS model. This suggests that the total direct short-term effects of foreign aid on FDI are
not substantial. However, after controlling for provincial fixed effects, we find in the FEmodel that the effect of current aid in infrastructure positively affects the FDI inflow. In
particular, a 1% increase in ODA in infrastructure leads to a 4% increase in FDI inflow. Thisis consistent with the expectation that ODA in infrastructure enhances the attraction of FDI.
In other words, infrastructure depends, to a large extent, on the specific characteristics of each17

province. These results imply that there is evidence of the direct, short-term impact of ODA
in FDI inflows.
The positive coefficients of lnlagODA in infrastructure are statistically significant
both in the 2SLS model and the FE model. This proves the direct, long-term impact of ODA
in infrastructure on FDI inflows, which can be explained by the specific characteristics oflong lifetime cycle of infrastructure.
In the 2SLS model, the coefficient of the interaction term between ODA in
infrastructure and secondary school enrollment ( ln ODAhat × ln SCHOOL ) is significant,which means that ODA, through increasing the secondary school enrollment rate, has
positively contributed to the attraction of FDI. This suggests that ODA in infrastructureenhances FDI inflow through indirect channels by improving the human capital in theprovinces.
In the FE model, the coefficient of the interaction term is, however, not significant,suggesting that the indirect impact of ODA in infrastructure on FDI attraction is not robust.
The area of industrial zones also has a significant impact on FDI inflows with the coefficient
being statistically significant in the 2SLS model. The number of pupils enrolled at secondary
schools has a positive and significant coefficient both in the two stage least squares modeland the FE model. The magnitudes of the coefficients in both model are comparatively large,
implying that the quality of labor force of each province has played a decisive role inattracting FDI flows.
The positive correlation of GDPPC to FDI inflows implies that FDI does not flow toprovinces with low income per capita or low average wage rate. Rather, it supports theargument that GDPPC, which represents the potential market size of the province, has apositive impact on the FDI inflow, i.e., the higher the income, the larger the local market; and18

hence, the more attractive the province is to FDI flows. This also proves that the quality,
rather than the cost of labor, matters in the decision of foreign investors in FDI allocation.
The only variable which has the opposite sign with the expectation (negative signinstead of positive) is population. For the 2SLS model, the coefficient is statisticallysignificant at 1% but it is insignificant in the FE model. This reveals that the impact of
population on FDI inflows is ambiguous. Apart from Hanoi and Hochiminh city, where thehigh density of population is associated with the higher level of income and expenditure,other big-population provinces are mainly characterized with larger geographical areas, but
low levels of expenditure on consumption goods. Therefore, population does not
appropriately represent the purchasing power or the potential market size of the provinces.
Tax ratio has a statistically significant, positive correlation with FDI inflows in the
2SLS model but has no statistically significant impact on the FDI inflows in the FE model.
This result may imply that the government tax incentives have not been effective in attracting
FDI inflows in mountainous or remote provinces. From 2002 to 2004, provinces such as
Quang Tri province in Central Coast, Kon Tum province in Central Highlands, and Bac Kan
province in Northern Uplands, which are among poorest provinces in Vietnam, received no
FDI projects and very few ODA in infrastructure projects.
Now, we examine the disbursement value of ODA in infrastructure in Japan andEuropean Union (EU), the two major bilateral donors in Vietnam. Japan has been by far thelargest donor in Vietnam since the resumption of aid to Vietnam in 1992. During the 19942005, Japan has committed JPY 1.4 trillion of which JPY 965 billion is in the form of soft
loans with the main area of investment in infrastructure projects such as national road, tradeports, and large-scale power plants.

19

We apply the similar techniques of 2SLS and FE/RE model to explore thedeterminants of FDI in the two countries. In the case of Japan, the Hausman test (Pro>chi2== 21.05%) shows that RE is superior to FE so that we report the results of the RE model. Our
results indicate that ODA from Japan has a positive and significant effect on the inflow ofJapanese FDI, but this does not hold true in the case of EU (Table 4). It might be because
Japanese ODA is allocated mainly for infrastructure development: the proportion of ODA in
infrastructure investment accounts for 85% of the total Japanese ODA (Ministry of Planningand Investment, 2006). The transportation sector and power industry account for the largest
chunk of Japanese ODA, which comprise 38% and 29% of the total ODA, respectively. And,according to CONCETTI (2002), there is a high tendency for Japanese investors to invest inprovinces which received much ODA in infrastructure.
In the case of EU, the major factors that affect FDI are secondary school enrollment
and the area of industrial zones. This implies that ODA in infrastructure attracts FDI throughits indirect effect on human capital accumulation and the presence of complementaryindustrial zones. Overall, our results show that the major distinct feature of Japanese ODA is
its emphasis on infrastructure development, which, in turn, significantly attracts FDI to theprovinces of Vietnam.

VII.

CO CLUSIO S A D POLICY IMPLICATIO S

Our results provide evidence that ODA in infrastructure has contributed to theattraction of FDI in the provinces of Vietnam, not only by direct channels through the current
ODA disbursement, but also by indirect channels through improving the human capital baseof the respective provinces. The impact of ODA in infrastructure on FDI inflow is ambiguous
20

because of the specific characteristics of infrastructure such as use specificity, long gestation
period, and lumpiness.
Our results also show an unequal allocation of FDI across provinces, which can be
attributed to differences in the infrastructure development, quality of labor force, and size ofthe local market. Provinces with a higher level of infrastructure development, higher qualityof labor force, and larger local market tend to attract a significantly larger amount of FDI.
Japanese ODA on infrastructure has a positive and significant impact on Japanese FDI but
this does not hold true in the case of EU.

Japanese ODA is heavily biased toward

infrastructure development.
This study has three policy implications. First, the Vietnamese government should
adjust its priority policy in ODA mobilization towards attracting more ODA in infrastructure
particularly from the Japanese government. Second, in order to increase the relevance andefficiency of infrastructure projects, the government should establish a concrete socioeconomic strategic development plan geared towards increasing returns to infrastructuredevelopment. Finally, the government should focus on strengthening secondary school andvocational training programs, rather than providing tax incentive policies, because skilledhuman capital base is a major key for attracting FDI to Vietnam.

21

REFERE CES
Bennett, Peter D. and Robert, T.Green (1972) “Political instability as a determinant of Direct
foreign investment” Journal of Marketing Research 9:182-86.
Canning, D. and Benethan, E. (1999) “The social rate of return on infrastructure investment”
Policy research working paper 8o 2390, World Bank, Washington DC.
CONCETTI (2002) “Perceptions of Japan’s ODA impact in Vietnam: A survey report”,
prepared for the International Development Centre of Japan (IDCJ).
Crenshaw, Edward M. (1991) “Foreign Investment as a Dependent Variable: Determinants ofForeign Investment and Capital Penetration in Developing Nations, 1967-1978” Social Force69: 1169-1182.
Demetriades, P. and Mamuneas, T. (2000) “Intertemporal output and employment effects ofinfrastructure capital: Evidence from 12 OECD economies” The Economic Journal 110: 687712.
Devarajan, S., Swaroop, V. and Zhou, H., (1996) “The composition of public expenditure andeconomic growth.” Journal of Monetary Economics 37: 313-44.
Doucouliagos, H. and Paldam, M. (2005) “Aid effectiveness on accumulation. A meta study”,
Working paper 2005-12, University of Aarhus. Denmark.
Estache, A. (2004) “A survey of the recent economic literature on selected emerging
infrastructure policy issues in LDCs,” Background paper for the October 2004 Berlin
meeting of the POV8ET Infrastructure Working group.
Fernald, J.G. (1999) “Roads to prosperity? Assessing the link between public capital andproductivity.” The American Economic Review 89: 619-38.
Freeman, N. J. (2002) “Foreign direct investment in Vietnam: An overview” Paper prepared
for the Department for International Development - DFID Workshop on Globalisation andPoverty in Vietnam-Hanoi, 23-24th.
Government of Vietnam (1999) “Investment Law”: 2.
Government of Vietnam (2005) “Socio – economic development strategy 2006–2010”.
Hasen, H. and Tarp, F. (2000) “Aid effectiveness disputed” Journal of International
Development 12 (3).
Hayami, Y. and Godo, Y. (2005) “The poverty to the wealth of nations development
22

economics” Oxford, Third Edition: 45-46.
Harms et al. (Harms, P. and Lutz M.) (2004) “Aid, governance, and private foreign
investment: Some puzzling findings and a possible explanation.” Working paper 8o. 03.04,
Study Center Gerzensee and the Swiss National Bank.
Harms, P. and Lutz, M. (2006), “Aid, governance, and private foreign investment.”
Economic Journal 116(513): 773-790.
Hill, S. and Munday, M. (1991) “The determinants of inward investment: a Welsh analysis”,
Applied Economics 23: 1761-1769.
Hill, S. and Munday, M. (1992) “The UK regional distribution of foreign direct investment:
analysis and determinants”, Regional Studies 26(6): 535-544.
Karakaplan, Ugur M., Neyapti, Bilin, Sayek and Selin (2005) “Aid and foreign direct
investment: International evidence.” Bilkent University Discussion Papers 05-05, AnkaraTurkey: Bilkent University.
Kimura, H. and Todo, Y. (2007) “Is foreign aid a vanguard of FDI? A gravity equation
approach” Research paper, Research Institute of Economy, Trade and Industry (RIETI),Tokyo, Japan.
Lecraw, D.J. (1991) “Factors influencing FDI by TNCs in host developing countries: apreliminary report” in Buckley P.J. and Clegg J” Multinational Enterprises in Less Developed
Countries, Macmillan.
MPI (Ministry of Planning and Investment) (2006) “Evaluation Study of Japanese ODA for
Vietnam: Summary”, Hanoi: 23.
MPI (Ministry of Planning and Investment) (2003), “10 years of ODA in Vietnam: Overview
on Implementation of the comprehensive poverty reduction and growth strategy and the newchapter of large scale infrastructure”, Report at the Consulative Group Meeting, Hanoi.
Pham Hoang Mai (2005) “Regional Economic Development and Foreign Direct Investment
Flows in Vietnam, 1988-98” Journal of the Asia Pacific Economy 7 (2): 182 - 202
Prud’homme R. (May 2004) “Infrastructure and development” Paper to Annual Bank
Conference on Development Economics, Washington,.
Roller, L-H and Waveman, L. (2001) “Telecommunication infrastructure and economic
development: A simultaneous approach” American Economic Review 91: 909 – 23.
Schneider, F. and Bruno, S. Frey (1985) “Economic and political determinants of Foreign
direct investment” World Development 13: 161-75.
Sturm, Jan-Egbert (2001) "Determinants of public capital spending in less-developed
23

countries," CCSO Working Papers 200107, University of Groningen, CCSO Centre for
Economic Research.
Wheeler, D. and Mody, A. (1992) “International investment location decision: the case of US
firms” Journal of International Economics 33: 57-76.
World Bank (2006) “Infrastructure strategy – Cross sectoral issues”.

24

Table 1
Regional Distribution of Official Development Assistance, 1993-2004

Regions
North West

Poverty Ratein 200268

Per capita ODA (US$)
Average in 1993-2004
120

North East

38

117

Red River Delta

22

130

Hanoi 1

5

440

Central Coast

39

128

Central Highlands

5282South East

11

157

2

274

23

50

Hochiminh City

2

Mekong Delta

1

Hanoi is located in Red River delta but is cited separately because the ODA level in thisprovince is much higher than the average level in the region.
2
Hochiminh City is located in South East but is cited separately for the same reason.

Source: World Bank, Ministry of Planning and Investment (MPI), Ministry of Labor,
Invalids and Social Affairs (MOLISA) and Government Statistics Office (GSO),
2003.

25

Table 2
Determinant of Official Development Assistance in Infrastructure
The First Stage in Two Stage Least Squares Model

Variables

8ame of the variables

Lag of ODA in infrastructurelnlagODA

Coefficients(2SLS)
Coef.
t-value
0.68**
3.75

lnSchool*lnODAhat

ln ODAhat × ln SCHOOL

0.45**

4.01

Area of industry zones

LnINDUS

0.02

1.69

Secondary school enrollment

lnSCHOOL

-0.81

1.02

Income per capita

lnGDPPC

-1.14

-0.12

Population

lnPOP

2.15

0.89

Tax ratio

lnTAX

0.61

0.78

Year Dummy for 2003

D year 03

23.01

1.01

Year dummy for 2004

D year 03

21.67

1.23

Number of patient beds perperson in hospitals of theprovince

PATIENTBED-2.26**-1.97

Dummy for mountainous
province

DMoutainous

0.08*

1.96

Constant termCons

-14.52*-1.68

Adjusted R 2

0.87

Number of observation

192* = statistically significant at 5% level.
** = statistically significant at 1%level.

26

lnPOPlnTAX
D year 03
D year 04
Cons

Population

Tax ratio

Year Dummy for 2003

Year dummy for 2004

Constant term

* = statistically significant at 5% level** = statistically significant at 1%level.

27

lnGDPPC-19.201.29

2.91-1.51**-1.98**

0.45
192

1.87*

LnSCHOOL

Secondary school
enrollment
Income per capita

Adjusted R 2
Number of observations

2.03**

lnINDUSArea of industry zones

-0.54

0.19

0.93

3.00

-2.24

1.96

2.42

1.69

1.99

0.14*

ln ODAhat × ln SCHOOL

0.32

1.97

0.85*

lnlagODA

Lag of ODA in
infrastructure
lnSchool*lnODAhat

Two-stage Least Squares
Coef.
t-value
1.36
1.48

lnODAhat

8ame of the variables

Predicted ODA

Variables-19.48

0.87

-1.86

1.26*

3.36**

0.135

1.50**

0.49
192

-0.59

0.02

-1.29

1.98

2.33

0.81

2.12

Fixed Effects
Coef.
t-value
2.04*
1.97

The Determinant of Foreign Direct Investment Inflows-The Structural Equation in Two Stage Least Squares Model

Table 3

3.15** 2.30

lnGDPPC

lnPOP

lnTAX

D year 03

D year 04

Cons

Population

Tax ratio

Year Dummy for 2003

Year dummy for 2004

Constant term

Adjusted R 2
Number ofobservation* = statistically significant at 5% level

1.63

LnSCHOOL

Secondary school
enrollment
Income per capita

1.39

0.35

1.55

-4.32

0.063

1.91

-4.24

0.024

0.53
192

3.45**

2.73**

0.09

-0.75

0.77

2.31

3.67

1.13

2.34

2.03

-1.48

-1.28

0.18

2.35

3.27

2.92

1.60

2.78

-1.66

-3.04

0.78
128

-89.11

8.94

-4.28

-0.16

4.50

2.47**

0.74**

0.20**

0.68

1.30**

2SLS
Coef.
tvalue** = statistically significant at 1%level.
28

0.51
192

0.07

0.11

0.76

-1.04

0.42

0.56

0.15

lnINDUSArea of industry zones

1.60

3.31**

2.35** 1.90

ln ODAhat × ln SCHOOL

0.69*

2.03**

2.00** 3.55

lnlagODA

Lag of ODA in
infrastructure
lnSchool*lnODAhat

2.52

lnODAhat

1.47**

1.03 *

lnFDI

Foreign Direct
Investment inflows
Predicted ODA

0.88

Japan
2SLS
Random Effects
Coef
t-value Coef.
t-value

8ame of the variables

Variables

25.13

-3.51

0.20

0.39

2.47**

0.92**

0.06**

0.21

1.64

0.79
128

0.83

-0.58

0.91

0.16

3.38

2.22

2.92

0.96

0.24

Fixed Effects
Coef.
t-valueEU

The Regression Results For Japanese and European Union (EU) Official Development Assistance

Table 4.

Schools,
electricity
and roads

Infrastructure(From ODA)

Human capital
accumulation

IncomeIncrease

29

Consumption
and market
size increase

Improvement
in investment
environment

The Relationship Between Infrastructure and Foreign Direct Investment Inflows

Figure 1.

FDI

Registered

30

Year
Number of Projects

0

100

1000

0

200

300

400

500

2000

3000

4000

5000

6000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

700

8000

600

800

9000

7000

900

10000

The Amount of Registered Foreign Direct Investment in Vietnam, 1991 – 2004

Source: Ministry of Planning of Investment.

US$ Millions

Figure 2

Projects

y
cit

oi
n
Ha
Do

i
na
g
n

31

g
g
n
g
VT
ga
on
on
on
n
u
h
d
a
i
d
Lo
aip
am
ar
nh
i
H
L
B
B
Registered FDI
ODA disbursement

Source: Ministry of Planning and Investment, 2004.

M
HC

0

2000

4000

6000

8000

10000

12000

Ha

g
on
u
id

Vi

c
hu
p
nh

Registered Foreign Direct Investment and Committed Official Development Assistance in Provinces of Vietnam, sum in 1988-2004.

Figure 3