Does FDI inflow crowd in private investment? Empirical evidence for the Southeast region of Vietnam from the panel quantile regression approach
Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
127
Does FDI inflow crowd in private investment? Empirical evidence
for the Southeast region of Vietnam from the panel quantile
regression approach
Nguyen Van Bon1*
1Faculty of Finance - Banking, University of Finance - Marketing, Vietnam
*Corresponding author: [email protected]; [email protected]
ARTICLE INFO
ABSTRACT
DOI:10.46223/HCMCOUJS.
econ.en.11.2.1802.2021
Well-known to be the most dynamic economic region in
Vietnam with the establishment of more export processing zones,
high technology parks, and industrial zones, the Southeast region is
increasingly attracting more capital flows from all over the world.
Does FDI inflow crowd in private investment in this region? To
answer this research question, the study examines the effect of FDI
inflow on private investment for a sample of 6 provinces/cities of
the Southeast region between 2005 and 2019 using the panel
quantile regression approach, the difference GMM Arellano-Bond,
and the FE-IV estimator. The results show that FDI inflow crowds
in private investment in this region. In addition, public
expenditure, inflation, and population also promote private
investment. These findings suggest some crucial policy
implications for local governments in this region to receive more
FDI inflows as well as promote private investment.
Received: April 14th, 2021
Revised: June 07th, 2021
Accepted: June 15th, 2021
Keywords:
FDI inflows; panel quantile
regression; private investment;
Southeast region of Vietnam
1. Introduction
The effect of Foreign Direct Investment (FDI) inflow on private investment is hotly
debated among economists. From the cited work by Agosin and Machado (2005), a strand of
studies tries to examine complementarity or substitutability between them. FDI inflow plays a
crucial role in the process of economic development and growth in many countries, especially
developing countries. This kind of capital is the fixed form of across-border economic activities
conducted by the multinational corporations in which FDI companies receive benefits from
internationalizing their brand name, selling and marketing their products and services in other
countries (Agosin & Machado, 2005) while the private sector’s investment capital significantly
contributes to promoting economic growth, creating more jobs, and thus stabilizing the social
security (Khan & Reinhart, 1990).
Despite its significant contribution to economic development in host countries, FDI
inflows have certain influences on private investment. On the one hand, if FDI investors use
domestic credit in host countries to finance their business operations, they will stress high
pressure on interest rates in host countries, which makes domestic investors give up business
opportunities. It is a crowding-out effect of FDI inflows on domestic investment (Delgado &
McCloud, 2017). On the other hand, domestic investors can link to FDI investors through
cooperation opportunities such as an investment joint-venture between private companies and
foreign firms. In some cases, domestic investors can supply raw materials for FDI investors and
get the transfer of appropriate technologies to decrease costs. It is a crowding-in effect of FDI
inflows on domestic investment (Agosin & Machado, 2005).
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Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
The Southeast region is well-known as the most dynamic and developed economic area
in Vietnam, with the most development of Ho Chi Minh City. HIDS (2020) notes this region
contributes more than 2/3 of the annual government revenue with an urbanization level of 50%.
Attracting FDI flows from several countries is a good solution to compensate investment capital
in this region through local governments’ appropriate regulations and policies. As a result, more
export processing zones, industrial zones, and high technology parks are established in this
region. However, under incentive policies (cheap land lease, tax reduction, and appropriate
administrative procedures), whether FDI inflow crowds out or crowds in private investment in
this region will be the core purpose of this study.
So far, no related studies have been conducted for the Southeast region in Vietnam. So,
the main objective of this study is to empirically investigate the effect of FDI inflow on private
investment for a group of 6 provinces/cities of the Southeast region in Vietnam from 2005 to
2019 using the panel quantile regression approach. The FE-IV estimator is employed to check
the robustness of estimates.
The structure of the study is presented in the following way. Section 2 presents the
literature review that notes the effect of FDI inflow on private investment. The methodology and
research data are given in Section 3 that suggests the empirical equation and describes the
applied estimators. The empirical results and discussion in Section 4 show the main results and
robustness check. Finally, section 5 concludes and suggests some crucial policy implications
based on the findings in Section 4.
2. Literature review
The most contribution to the literature is the research works by Morrissey and
Udomkerdmongkol (2012) and Farla, De Crombrugghe, and Verspagen (2016). They apply the
one-step system GMM Arellano-Bond estimator to examine the effects of FDI, governance, and
interaction term on domestic investment for a group of 46 developing countries. They come to
the same conclusion that FDI inflow stimulates domestic investment.
Similar to Morrissey and Udomkerdmongkol (2012) and Farla et al. (2016), some studies
such as Kim and Seo (2003), Titarenko (2006), Mutenyo and Asmah (2010), Eregha (2012), and
Szkorupová (2015) indicate that FDI inflow reduces domestic private investment. Wang (2010)
confirms that the FDI inflow crowds out domestic private investment while the cumulative FDI
stimulates it using the estimators of random effects, fixed effects, and GMM Arellano-Bond. In
the same vein, Pilbeam and Oboleviciute (2012) apply the one-step difference GMM estimator
for a group of 26 EU member states over the period 1990 - 2008 and find a crowding-out effect
of FDI on domestic private investment for the older EU14 countries.
In contrast, some studies such as Desai, Foley, and Hines (2005), Tang, Selvanathan, and
Selvanathan (2008), Ndikumana and Verick (2008), Ang (2009, 2010), and Prasanna (2010)
present the “crowd-in hypothesis.” Al-Sadig (2013) notes that FDI inflow promotes private
investment via the system GMM estimator for panel data of 91 developing countries over the
period from 1970 to 2000. The findings in Al-Sadig (2013) show that the positive effects of FDI
in the sample of low-income countries are conditional on the availability of human capital in the
receipt countries. Similarly, Munemo (2014) indicates that the complementarity between FDI
inflow and domestic investment is strongly dependent on business start-up regulations in receipt
countries for a group of 139 countries from 2000 to 2010 with the two-step difference GMM
estimator. Meanwhile, Munemo (2014) states that reforms in these regulations can enhance the
complementarity between FDI inflow and domestic private investment. In the same vein,
Boateng, Amponsah, and Annor (2017) support the crowding-in effect of FDI inflow on
domestic investment for a sample of 16 sub-Sahara African countries from 1980 to 2014 using
Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
129
fixed effect, FMOLS, and pooled OLS techniques. Recently, Jude (2019) shows that FDI inflow
crowds in domestic investment for a sample of 10 Central and Eastern European economies over
the period 1995 - 2015 through the one-step system GMM estimator.
Unlike the above-mentioned studies, some such as Mišun and Tomšk (2002), Agosin and
Machado (2005), Apergis, Katrakilidis, and Tabakis (2006), Onaran, Stockhammer, and Zwickl
(2013), Ahmed, Ghani, Mohamad, and Derus (2015), Nguyen (2021a, 2021b) show mixed
evidence for the relationship between FDI inflow and domestic investment. Lin and Chuang
(2007) note that FDI inflow promotes the domestic private investment of the larger firms, but
FDI inflow reduces it in the smaller firms in Taiwan for the periods of 1993 - 1995 and 1997 -
1999 suing the Heckman 2SLS estimator. In the long run, Tan, Goh, and Wong (2016) find that
FDI inflow has a crowding-in impact on gross domestic investment for a group of 08 Asean
countries over the period 1986 - 2011 via the PMG estimator. Similarly, Chen, Yao, and
Malizard (2017) find a neutral relationship between FDI inflow and domestic private investment
in China over the period from 1994Q1 to 2014Q4 through the ARDL test. Regarding the entry
mode set up by FDI enterprises, they report that equity joint venture promotes domestic private
investment but wholly foreign-funded enterprises decrease it. More recently, Nguyen (2021a,
2021b) emphasize that the FDI inflow - private investment relationship strongly varies based on
the institutional quality in the host countries. Nguyen (2021a) finds that FDI inflow crowds out
private investment in 25 developed countries (good governance environment) but crowds in it in
72 developing countries (bad governance environment) using the two-step GMM Arellano-Bond
estimators for 97 countries over the period 2000 - 2013. In contrast, by using the two-step GMM
Arellano-Bond estimator for a group of 52 provinces in Vietnam over the period 2004 - 2015,
Nguyen (2021b) indicates FDI inflow crowds in private investment under good institutional
quality but crowds out under poor institutional quality.
In short, from the literature review perspective, in addition to the difference between
GMM Arellano-Bond estimator and the FE-IV estimator, this study is the first to use the panel
quantile regression approach to examine the effect of FDI inflow on private investment for the
Southeast region in Vietnam. It is the research gap that this study focuses on.
3. Methodology and research data
3.1. Methodology
In this study, the fixed effects panel quantile regression approach is used to examine the
effect of FDI inflow on private investment. This approach makes the study focus on the entire
conditional distribution of private investment and determine the effect of FDI inflow on private
investment. In particular, most related literature uses panel data models based on conditional
mean regression. The heterogeneity in such models is often ignored. Ignoring this
heterogeneity in panel data models can lead to bias in estimates. The seminal work by Koenker
and Bassett (1978) developed the quantile regression approach. Compared with the conditional
mean regression, this approach can bring more robust estimation results (Koenker & Bassett,
1978). Therefore, based on the work by Agosin and Machado (2005), the empirical equation is
extended as follows:
푃퐼푁 = 훽0 + 훽1퐹퐷퐼푖푡 + 푋푖푡훽′ + 휀푖푡
(1)
푖푡
where subscript i and t are the province and time index, respectively. PINit is private investment,
FDIit is net FDI inflow. Xit is a set of control variables such as public expenditure, inflation, and
population; εit is an error term; β0, β1, and β’ are estimated coefficients. Public expenditure can
stimulate or reduce domestic private investment through mechanisms of complementarity or
substitutability. So, Ahmed and Qayyum (2007) indicate a positive impact of public expenditure
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Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
on private investment in Pakistan. Meanwhile, inflation can raise the transaction cost and reduce
enterprises’ profit, resulting in a decrease in private investment (Jin & Zou, 2005). Similarly, the
effect of population on domestic private investment can stem from labor supply to enterprises.
Regarding the fixed effects panel quantile regression approach, the econometric model is
presented as:
′
( |
)
( )
푄푦 휏 푋푖푡 = 푋푖푡훽 휏 , 푖 = 1, 푛, 푡 = 1, 푇
(2)
푖푡
( |
)
( )
( )
where 푄푦 휏 푋푖푡 is the conditional τ-quantile of yit given Xit; 훼푖 휏 and 훽 휏 are conditional on
푖푡
τ. The key challenge in this approach is the random parameters problem stemming from the
substantial amount of fixed effects (Galvao, 2011; Koenker, 2004; Lamarche, 2010). One reason
for literature on the panel quantile regression approach is impossible to remove unobserved fixed
effects in the quantile regression model. Koenker (2004) suggested an appropriate method to
deal with these problems. Accordingly, the fixed effects estimator is applied to reduce a
weighted sum of K ordinary quantile regression objective functions corresponding to K values of
τ. The slope coefficients of objective function and coefficients of fixed effects are assigned to be
dependent and independent with τ, respectively. A penalty term penalizes the coefficients of
fixed effects to reduce them to zero. The parameters are estimated as follows:
퐾
푁
푇
푁
푇
( )
| |
min ∑ ∑ ∑ 휓푘휒휏 (푦푖푡 − 훼푖푡 − 푋푖푡훽 휏푘 ) + 휙 ∑ 훼푖
(3)
푘
(
훼,훽
)
푘=1 푖=1 푡=1
푖=1
where k, i, and t are the quantile, country, and time index, respectively; X is the matrix of
independent variables, 휒휏 is the quantile loss function, and 휓푘 is the weight corresponding to
푘
kth quantile. The study uses equally weighted quantiles, as suggested by Lamarche (2010). The
penalty parameter 휙 improves the estimates of 훽 by reducing individual effects to zero.
To deal with the endogeneity phenomena and the autocorrelation of errors, the study uses
the difference GMM Arellano-Bond estimator for the following empirical equation:
푃퐼푁 = 훼0 + 훼1푃퐼푁푖푡−1 + 훼2퐹퐷퐼푖푡 + 푋푖푡훼′ + 휇푖 + 휁푖푡
(4)
푖푡
where PINit-1 is a proxy for the initial level of private investment, 휁푖푡 is an observation-specific
error term while μi is an unobserved time-invariant, country-specific effect; α0, α1, α2, and α’ are
estimated coefficients.
The GMM (General Method of Moments) Arellano and Bond (1991) estimator first
proposed by Holtz-Eakin, Newey, and Rosen (1988) is employed for estimation. To remove
country-specific effects, the first difference is taken in Equation (4). Then, for the assumption
that time-varying white noises in original models are not serially correlated, regressors in the
first difference are used as instrumented by their lags (Judson & Owen, 1999). This strategy is
known as the difference GMM Arellano-Bond estimator (D-GMM), which can handle
simultaneity biases in regressions.
The validity of instruments in S-GMM will be assessed by the Sargan test as well as the
Arellano-Bond test. The Sargan and Hansen tests are used to check the endogeneity phenomena,
while the Arellano-Bond test is applied to search the autocorrelation of errors in the first
difference. So, the test result of the first autocorrelation of errors, AR(1) can be ignored while the
second autocorrelation of errors, AR(2), is checked on the first difference series of errors to
eliminate the phenomenon of AR(1).
For further check, the study uses the FE-IV estimator. FE-IV is the instrumental variable
estimation with fixed effects for panel data in which some variables can be endogenous (Baum,
Schaffer, & Stillman, 2003). The Sargan statistic is used to assess the validity of instruments.
Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
131
3.2. Research data
The main data are private investment, FDI, public expenditure, consumer price index,
and population, which all are taken from the General Statistics Office of Vietnam (GSO, 2020).
The research sample consists of 06 provinces/cities in the Southeast region (Binh Duong, Binh
Phuoc, Ba Ria - Vung Tau, Dong Nai, Tay Ninh, and Ho Chi Minh City) from 2005 to 2019. The
variables are defined as follows:
Private investment (PIN): The share of domestic private investment in GDP (%);
FDI inflow (FDI): The ratio of net FDI inflow to GDP (%);
Public expenditure (EXP): The share of public expenditure in GDP (%);
Inflation (INF): Consumer price index, average annual (logarithm);
Population (POP): The population of a province (logarithm).
The descriptive statistics of the data are given in Table 1. The results indicate the average
private investment in the Southeast region from 2005 to 2019 is 15.196%, with the lowest of
0.731% in 2007 (Ba Ria - Vung Tau) and the highest of 36.971% in 2005 (Binh Duong).
Meanwhile, in the same period, the average FDI inflow into this region is 10.643%, with the
lowest of 0.49% in 2016 (Ho Chi Minh City) and the highest of 48.46% in 2006 (Binh Duong).
Meanwhile, the matrix of correlation coefficients is described in Table 2. Public expenditure and
population are positively connected with private investment, while FDI and inflation
insignificantly. Correlation coefficients among independent variables are relatively low, which
helps to remove the co-linearity among these variables.
Table 1
Descriptive statistics
Variable
Private investment (%)
FDI inflow (%)
Obs
90
Mean
15.196
10.643
5.380
Std. Dev.
8.878
Min
0.731
0.49
Max
36.971
48.46
90
9.661
Public expenditure (%)
Consumer Price Index (value)
Population (value)
90
3.021
1.02
12.09
90
107.750
5.970
99.7
125.4
90
2,510.727 2,411.406
799.6
8,673.87
Source: Processed by Software
Table 2
The matrix of correlation coefficients
PIN
FDI
EXP
INF
POP
PIN
FDI
EXP
INF
POP
1
0.160
0.651***
0.163
0.184*
1
-0.099
0.164
-0.142
1
0.060
-0.346***
1
-0.102***
1
Note: ***, **, and *denote significance at 1 percent, 5 percent, and 10 percent respectively
Source: Processed by Software
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4. Empirical results and discussion
The results by panel quantile regression are given in Table 3. In the table, 10th, 20th,
30th, 40th, 50th, 60th, 70th, 80th, and 90th percentiles are applied for analysis. OLS estimate is
also included in the table for comparison. The results across all quantiles and OLS estimator note
that FDI inflow crowds in private investment. In addition, public expenditure, inflation, and
population promote private investment. It is easy to see that the estimated coefficients do not
vary much across the quantiles.
Unlike Morrissey and Udomkerdmongkol (2012) and Farla et al. (2016), we find out that
FDI increases private investment, validating the “crowd-in hypothesis” in the prior findings (Al-
Sadig, 2013; Ang, 2009, 2010; Desai et al., 2005; Ndikumana & Verick, 2008; Tang et al.,
2008). So, FDI flows to the Southeast region are complementary to domestic private investment.
It can be originated from the fact that domestic companies can cooperate with foreign enterprises
as subcontractors to foreign businesses, partners in investment joint ventures, or suppliers of
supply raw materials. It also implies that regulations and policies related to attracting FDI
inflows in the Southeast region are effective and appropriate in improving the economic
activities of the private sector.
The positive impact of public expenditure on private investment can stem from the fact
that public spending in healthcare and education will develop human capital. High-quality
human resources contribute to the development of the private sector by providing a skilled
workforce to the private sector. Meanwhile, the potential benefit of inflation is to promote
savings - investments (Jin & Zou, 2005). Inflation is also a factor that stimulates the price level,
increasing private sector projects’ investment capital. It can be found in Adams (2009) for the
case of Sub-Saharan African countries. However, high inflation can lead to social instability.
Similarly, population growth contributes to both providing human resources for the development
of the private sector and setting up a market large enough to consume the private sector’s
products and services, thus boosting private investment.
The results by D-GMM and FE-IV are given in Table 4. In the estimation procedure by
D-GMM, FDI is detected to be endogenous; thus FDI is used as instrument in the GMM-style
while the remaining variables such as private investment, public expenditure, inflation, and
population as instruments in the IV-style. In line with those by the panel quantile regression
approach, the results by D-GMM and FE-IV show that FDI crowds in private investment, re-
validating the “crowd-in hypothesis.” In addition, public expenditure, inflation, and population
also promote private investment. In addition, inflation and population also promote private
investment.
Table 3
FDI and private investment: Panel quantile regression and OLS
Dependent variable: Private investment (%)
Quantiles
Variables
OLS
10th
20th
30th
40th
50th
60th
70th
80th
90th
FDI
0.278*** 0.413*** 0.431*** 0.461***
0.491*** 0.501*** 0.518*** 0.530*** 0.552*** 0.574***
(0.053)
(0.145)
(0.124)
(0.091)
(0.071)
1.957*** 1.996*** 2.055*** 2.098*** 2.180***
(0.588) (0.560) (0.572) (0.619) (0.775)
(0.067)
(0.068)
(0.074)
(0.092)
(0.114)
EXP
2.530***
(0.179)
1.676
1.743*
1.852**
(0.767)
2.256**
(0.963)
(1.231)
(1.044)
Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
133
Quantiles
Variables
OLS
10th
20th
30th
40th
0.161*** 0.158*** 0.152**
(0.063) (0.060) (0.061)
50th
60th
70th
80th
90th
INF
0.190**
(0.094)
0.188
0.182*
0.171**
(0.082)
0.148**
(0.066)
0.140*
0.133
(0.132)
(0.112)
(0.083)
(0.103)
POP
0.001***
(0.000)
0.002*
0.003**
(0.001)
0.003***
(0.001)
0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003***
(0.001)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.001)
Note: ***, **, and *denote significance at 1 percent, 5 percent, and 10 percent respectively
Source: Processed by Software
Table 4
FDI and private investment: D-GMM and FE-IV
Dependent variable: Private investment (% GDP)
Variables
D-GMM
FE-IV
PIN (-)
-0.194
-----------
(0.126)
FDI
0.853***
(0.205)
0.427***
(0.063)
EXP
-0.629
2.074***
(0.57 4)
(1.552)
INF
0.056
0.188***
(0.066)
(0.086)
POP
0.005***
(0.001)
0.003***
(0.001)
Observation
AR(2)
78
84
0.116
0.736
-------------
0.3195
Sargan test
Note: ***, **, and *denote significance at 1 percent, 5 percent, and 10 percent respectively
Source: Processed by Software
5. Conclusion and policy implications
Driven from the fact that the Southeast region is well-known to be the most dynamic
economic area in Vietnam that increasingly attracts more FDI inflows from all over the world,
the study uses the panel quantile regression approach, D-GMM, and FE-IV to empirically
examine the relationship between FDI inflow and private investment for balanced panel data of
06 provinces in this region from 2005 to 2019. The estimated results show that FDI crowds in
private investment. Also, public expenditure, inflation, and population are positively significant
determinants of private investment in this region.
The findings in this study suggest some crucial policy implications for this region in
attracting more FDI inflows to stimulate domestic private investment. However, some problems
such as tax evasion, transfer pricing, and pollution by FDI enterprises also cause concerns.
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Nguyen Van Bon. HCMCOUJS-Economics and Business Administration, 11(2), 127-136
Therefore, local governments in the Southeast region, as well as the central government in
Vietnam, should reform the regulations and policies to attract more green-FDI inflows to keep
sustainable development in the future. Future research can address the effect of FDI inflows on
private investment by industry/sector. In particular, these studies should examine the role of
institutional quality in the FDI inflow – private investment relationship by industry/sector.
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