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Foreign Direct Investment and the Survival
of Domestic Private Firms in Viet Nam
ARI KOKKO AND TRAN TOAN THANG∗
Foreign direct investment (FDI) may benefit local firms in the host country
through various kinds of spillovers, but it may also raise competition and result in
the crowding out of domestic firms. Using detailed firm-level data for the period
2001–2008, this paper examines the aggregate effect of FDI on the survival of
domestic private firms in Viet Nam. We estimate the impact of both horizontal
and vertical FDI and explore how the presence of state-owned enterprises (SOEs)
influences the exit hazard for private firms. The results suggest that horizontal
and upstream FDI raise the exit hazard significantly, while downstream FDI
may reduce the hazard. The presence of SOEs has a direct negative effect on
the survival odds of local private firms in the same industry, but there is also an
indirect impact on the exit hazard from FDI. Local firms are more vulnerable
to foreign entry in sectors with high SOE shares. Looking at the net effects
of FDI during the period 2001–2008, we find that results vary between sectors
and over time but that the overall impact has been surprising small. The paper
also discusses policy conclusions and implications for empirical analyses of
spillovers from FDI.
Keywords: FDI, state-owned enterprises, exit hazard, survival, Viet Nam
JEL codes: F23, F21, L11
I. Introduction
Much of the academic literature on the host-country effects of foreign direct
investment (FDI) has focused on various types of external effects or spillovers
that may benefit or harm local firms. In particular, technology and knowledge
spillovers have been subject to extensive research. The overall evidence is mixed,
with several studies finding evidence of positive spillovers, but others arguing that
the impact of FDI on technology and productivity in local firms is insignificant
or even negative (Blomstr¨
om and Kokko 1998, G¨
org and Greenaway 2004, Meyer
and Sinani 2009, Wooster and Diebel 2010). One reason for the mixed findings
could be that the ability of local firms to absorb spillovers differs between countries
and industries, depending on the nature of competition between foreign and local
∗Ari Kokko is Professor at the Department of International Economics and Management at the Copenhagen Business
School and Associate Fellow at the Ratio Institute in Stockholm, Sweden. Tran Toan Thang is a researcher at the
Central Institute of Economic Management in Ha Noi. We are grateful for helpful comments by the participants at
the Asian Development Review conference held in Manila on 25–26 March 2013 and two anonymous referees.
Asian Development Review, vol. 31, no. 1, pp. 53–91 C
2014 Asian Development Bank
and Asian Development Bank Institute
54 ASIAN DEVELOPMENT REVIEW
firms, the development level of the host economy, the trade policy environment, and
various other market conditions.
However, the entry and presence of foreign investors does not only have an
impact on the technologies used in domestic firms, but it also affects various other
characteristics of the host-country market. Apart from their direct and indirect effects
on technology choice and knowledge, foreign investors may have some influence
on the nature and intensity of competition and on the demand, supply, and quality
of inputs and intermediate goods. These impacts may also influence the empirical
measurement of various spillovers and externalities. Some of the effects are likely
to be highly beneficial to local industry—it is hard to argue that access to new
knowledge and technology, better and cheaper intermediate goods, or increased
demand for locally produced inputs would be harmful to local firms—but other
consequences of foreign entry and presence may well be negative. For example, FDI
typically results in increased competition in both output markets and markets for
skilled labor and other inputs, which may result in the crowding out of domestic
firms.
Consequently, some studies have also focused on the crowding out or survival
effects of FDI. These studies typically try to estimate how the inflow of FDI affects
hazard rates or the likelihood that local firms are forced to leave the market. This
strand of literature tends to suggest that there is often a negative horizontal survival
effect, i.e., that the competition from foreign multinational corporations (MNCs)
raises the likelihood of exit for domestic firms in the industry. The pattern for
vertical FDI may be different since there is no direct competition effect when
foreign investors are engaged in upstream or downstream sectors. However, there
are relatively few studies on the survival effects of FDI in general and a particular
scarcity of studies on developing countries and transitional economies.
The purpose of this paper therefore is to explore the impact of FDI on the
survival of domestic private firms (DPFs) in Viet Nam. We will also discuss the
relationship between the survival of DPFs and the presence of state-owned enter-
prises (SOEs) in the local market. This question is worthy of investigation because
of the imperfect market competition in Viet Nam. Since private entrepreneurship
emerged only recently in the country, DPFs are in general relatively young and
small and have to compete with SOEs that are not only larger but also benefit from
various policy-related privileges. If the presence of foreign-owned firms is expected
to influence the survival odds of DPFs, the same is sure to hold also for the presence
of SOEs.
From a policy perspective, it is clear that questions about the survival of
firms are important. Together with firms’ entry and growth patterns, survival and
exit shape the dynamics of domestic industry development. In particular, there
are important political connotations in the short run if it is found that FDI forces
domestic firms out of business, possibly contributing to lower growth rates and
unemployment problems. These concerns may be especially relevant in the context
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 55
of Asian development, where the nature and motives of inward FDI has recently
shifted from cost savings to market penetration (Fujita 2011).
The paper will make at least four contributions to the extant literature. First,
we examine the survival effects from both horizontal and vertical FDI, in contrast to
earlier studies which focus on the survival impact of horizontal FDI alone. Second,
unlike most of the earlier studies that consider domestic firms as a homogenous
group, we highlight the role of SOEs for industry dynamics. Third, we explicitly
try to manage estimation problems related to the endogeneity of covariates. Fourth,
in addition to identifying the partly offsetting effects of horizontal and vertical
FDI, we also attempt to calculate the net effect of foreign presence. In addition,
our concluding discussion throws some doubt on the value of existing microlevel
estimates of the spillover effects of FDI, which are typically based on enterprise
samples that include firms that are crowded out (and by definition do not benefit from
technology spillovers) as well as firms that survive (and may benefit from spillovers).
Apart from this introduction, the paper consists of five sections. Section II
outlines the empirical setting for the study and describes some of the specific char-
acteristics of DPFs in Viet Nam. The context is obviously distinct from that in
developed countries. The heritage from central planning and the ongoing transi-
tion process also distinguishes Viet Nam from many other developing economies.
Section III provides a brief literature review. Section IV presents the data, empirical
model, and variables used in the regression analysis, while section V discusses the es-
timation results. Section VI concludes with a discussion of the policy consequences
and theoretical relevance of the findings.
II. Domestic Private Firms in Viet Nam
In 1986, Viet Nam launched an economic reform process to address some
of the weaknesses of central planning and to introduce elements of private en-
trepreneurship and market economics. This process known as Doi Moi or renovation
has resulted in the gradual liberalization and privatization of both input and output
markets, although SOEs remain important actors in many sectors of the economy.
By the mid-1990s, as part of the Doi Moi process, the Government of Viet Nam
had introduced a series of reforms supporting the development of the private sector,
including the promulgation of a Company Law and a Private Enterprise Law in
1990, changes in the Constitution recognizing the role of private enterprise in 1992,
a Domestic Investment Promotion Law in 1992, and a Bankruptcy Law in 1993.
After many years of being heavily restricted or even considered an illegal business
form, DPFs gradually gained recognition as important economic actors, with almost
the same status as SOEs and foreign owned firms.
However, the development of DPFs has not been straightforward. In the late
1990s, more than 10 years after the initiation of the reform process, domestic policy
56 ASIAN DEVELOPMENT REVIEW
Tab le 1 . Total Output Share by Sector, 2008 (%)
Sectors FDI Firms SOEs DPFs
Food processing 24.34 42.73 32.25
Textile, leather, wood 39.80 26.46 33.56
Gas, chemicals 35.54 44.28 20.03
Construction 7.36 35.08 57.44
Metal, machinery 51.26 24.47 24.13
Electricity, energy 13.83 58.12 27.94
Commerce, repairs 5.85 36.77 57.27
Transportation 22.50 22.29 55.13
Telecommunication 21.78 48.83 28.40
Financial services 19.92 50.38 29.55
Research and development 37.56 47.16 15.26
Real estate 28.85 28.39 42.71
Other services 13.20 55.42 31.34
DPF = domestic private firm, FDI =foreign direct investment, SOE =state-owned enterprise.
Source: Authors’ calculations from the enterprise census.
makers still had mixed views regarding DPFs (Hakkala and Kokko 2008). The
development of the private sector was therefore slow and cautious, and only around
40,000 private enterprises were in existence at the end of 2000, contributing less
than 10% of GDP (CIEM-UNDP 2010). The introduction of the Enterprise Law
in 2001, which greatly simplified the procedures for establishing DPFs, together
with political statements confirming the importance of private enterprise, became
a turning point for the development of the domestic private sector. The number of
newly established private firms started growing steeply from 2001.
By 2008, the number of DPFs had surpassed 150,000. They accounted for
27.3% of total output, with much higher shares in sectors such as construction (57%),
commerce (57%), and transportation (55%). Although SOEs maintain a dominant
position, DPFs and foreign firms jointly account for well over half of total output in
most broad industry groups (Table 1).
Despite their growing number, DPFs remain fairly weak in comparison with
SOEs and foreign enterprises. In 2008, almost three-quarters of DPFs were found in
labor-intensive industries with low technology and poor management. Most DPFs
also belong to the small-sized and medium-sized enterprise sector. While the average
number of employees in SOE was 425 and that in foreign owned firms about 325,
the average DPF had just 24 employees. Only 12% of Viet Nam’s DPFs had more
than 50 employees in 2008 (Table 2).
It is not surprising that affiliates of foreign multinational firms are larger and
stronger than DPFs. SOEs also hold a favored position in Viet Nam’s “socialist
market economy” and have special privileges in terms of access to output markets,
land, and credit sources (Hakkala and Kokko 2008). DPFs, by contrast, generally
struggle to manage the large sunk costs involved with the international market. Only
a small number of DPFs were engaged in direct exports in the early 2000s, and
most of them did not have appropriate strategies for the rapidly internationalizing
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 57
Tab le 2 . Firm Size Distribution, 2008 (%)
Firm Size SOEs DPFs FDI Total
0–50 20.97 87.20 29.46 76.61
50–100 17.29 6.27 18.10 8.12
100–200 21.09 3.59 18.03 6.36
200–300 11.11 1.17 9.66 2.75
300–1,000 22.21 1.44 18.52 4.72
1,000–3,000 6.05 0.30 4.71 1.19
3,000–5,000 0.89 0.02 0.90 0.17
5,000–10,000 0.35 0.02 0.38 0.07
>10,000 0.05 0.00 0.24 0.02
Total 100.00 100.00 100.00 100.00
DPF = domestic private firm, FDI =foreign direct investment, SOE =state-owned enterprise.
Source: Authors’ calculations from the enterprise census.
economy (Kokko and Sj¨
oholm 2005). DPFs also struggle to compete with the SOEs
in factor markets. Hansen, Rand, and Tarp (2009) and Carlier and Son (2004)
concluded that the main problem related to the funding of business in Viet Nam
was not the shortage of capital but rather unequal access to capital. In 2003, SOEs
accounted for less than 4% of total employment but received nearly half of total
official credit.
DPFs face problems in the formal credit market because of the unwillingness
of banks to extend credit to private firms, which are often considered to be more
risky than state-backed SOEs and rarely able to provide collateral (generally land).
The constrained access to bank credit limits access to land markets, and vice versa,
creating a vicious cycle for the DPFs. Shortages of investment capital also limit
DPFs’ ability to upgrade technology, which often leads to slow productivity growth.
This is a severe handicap for both the growth and survival of DPFs.
III. Literature Review
A term commonly used in discussions about survival and exit hazards is the
“crowding out effect.” An early argument for this effect is provided by Grossman’s
(1984) occupational choice model, which suggests that in an open economy, inward
FDI may lead to the failure of domestic firms because foreign firms typically pay
higher wages than DPFs. He argued that the best potential entrepreneurs are also
the best workers. Therefore, by paying higher wages, foreign firms may discourage
individuals from entrepreneurship. The higher wages paid by foreign firms may
also force local companies to compete for the most productive labor, adding to
wage costs and raising the exit hazard (Driffield and Girma 2003, Pesola 2006).
However, effects operating through the labor market are not the only channels
through which FDI influences the survival odds of local firms. Later studies have
gone beyond arguments based on labor markets and conceptualize the crowding
58 ASIAN DEVELOPMENT REVIEW
out effect of inward FDI as the sum of competition, spillovers, and production
linkages.
Looking at the horizontal impact of inward FDI in the local market, the
expected effect on domestic firms is largely negative. Foreign entry does not only
add to increased competition for labor and other inputs but also reduces output
prices—the price effects on both the input and output sides raise the exit hazard for
domestic firms, all other things being equal. The variable that adds some uncertainty
is the possible presence of horizontal productivity or technology spillovers. Those
local firms that are able to learn from the technologies or practices employed by
their foreign competitors may be able to improve their efficiency and productivity.
This positive spillover effect may in some cases be strong enough to mitigate the
increased exit hazard, but it is clear that many local firms will mainly be influenced
by increasing competition rather than positive spillovers, at least in the short run
(Aitken and Harrison 1999; Blomstr¨
om, Kokko, and Zejan 2000; Caves 2007;
Crespo and Fontoura 2007). Several factors have been identified as determinants
of spillovers, including the complexity of foreign technology and the technology
gap between domestic and foreign firms (Kokko 1994), the absorptive capability
of domestic firms (Cohen and Levinthal 1990, Kinoshita 2001), and the strategic
role of the foreign affiliates in the network of their parent company (Kokko and
Kravtsova 2008, 2012).
The expected effects of FDI in upstream and downstream sectors are not
equally clear-cut because vertical FDI does not include any direct competition ef-
fect. Instead, impacts are largely determined by the nature of the linkages between
local firms and their foreign suppliers and customers. Vertical FDI is often expected
to generate various productivity spillovers, e.g., through the increase in product
varieties and the use of specialized inputs from backward linkages, or the tech-
nical support and guidance provided through forward linkages (Rodriguez-Clare
1996, Markusen and Venables 1999). However, negative effects are also possible,
in particular when FDI results in changes in technological standards and quality
requirements. FDI in upstream sectors may crowd out competing local firms that
supply inputs with lower price and quality, forcing downstream firms to adjust tech-
nologies. Downstream FDI may crowd out the traditional customers of local firms
operating in upstream sectors. Downstream FDI may also stimulate the entry of
foreign suppliers, adding to the competition in the upstream sectors. The aggregate
impact of vertical FDI on the exit hazard for domestic firms is therefore hard to
predict on theoretical grounds, although it is reasonable to expect a priori that the
impact of downstream FDI should be less negative than that of horizontal FDI.
More generally, the positive effects from linkages seem to depend on the
characteristics of the incoming FDI (Veugelers and Houte 1990, Pradhan 2006).
For example, depending on entry mode, foreign firms can influence the number of
domestic firms by directly replacing domestic firms or by inducing domestic firms
to merge in order to manage tougher competition. In a longer term perspective,
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 59
foreign entries with high research and development (R&D) investment may crowd
out research and development investment of domestic firms, reducing the domestic
firms’ long-term survival odds (Haller 2005). Similarly, and in line with Grossman
(1984), foreign firms can trigger brain drain in domestic firms in upstream or
downstream sectors.
Using social network analysis, Giuliani (2008) showed that backward link-
ages do not necessarily create information linkages and knowledge transfer. In his
empirical analysis of Costa Rica, only 21% of the backward linkages overlapped
with information linkages, which is below the expectation from theoretical litera-
ture. Lin and Saggi (2007) suggested that the backward linkages between foreign
firms and suppliers can result in the delinking of existing connections between local
producers and their local suppliers, hence making some local producers worse off.
Other studies (Navaretti and Venables 2004, Carluccio and Fally 2010, Markusen
and St¨
ahler 2011) also suggest the possibility that fierce competition in factor mar-
kets due to the foreign entry may harm domestic firms in upstream and downstream
sectors. The entry of foreign firms may result in the entry of new suppliers, lead to
tougher competition, and induce more exits in the intermediate goods market.
A. Theoretical Models of FDI and Survival
Markusen and Venables (1999) provide one of the first formal models that
combine these different effects. They note that FDI generates a competition effect,
which is likely to be particularly strong in final goods markets and will lead to lower
market prices that may force less efficient domestic firms out of the market. At the
same time, they posit that foreign firms in downstream industries may foster the
formation of local suppliers through production linkages, as they demand inputs
for their production processes. They may therefore induce domestic firms to enter
the intermediate goods market, which in turn leads to reductions in input prices.
Such price reductions suggest two effects: increased entry of domestic firms in the
downstream sectors and more exits in the upstream sectors. Hence, Markusen and
Venables (1999) predict both horizontal and vertical impacts, with entry as well as
exit effects following from the presence of foreign firms in the domestic market.
More recent studies have extended the theoretical analysis. Navaretti and
Venables (2004) use a monopolistic competition model to derive the welfare effects
of multinational entry on domestic firms. Under the assumption that foreign firms
produce at a similar marginal cost as domestic firms, they suggest that the entry
of foreign firms replaces domestic firms one by one. This prediction is repeated by
Markusen and St¨
ahler (2011) under assumptions of fixed and endogenous domestic
market structures. They suggest that if the market structure is endogenous, changes
in the foreign firm’s output level will not change aggregate production and the size
of active domestic firms, but instead result in market entry or exit. If the market
structure is fixed, foreign investment will lead to an increase in aggregate output but
60 ASIAN DEVELOPMENT REVIEW
a reduction in the output and the profit of domestic firms. That reduction may in the
longer term result in further exit of domestic firms.
Kosova (2010) links the survival question to the firm’s growth and suggests
that the determinants of growth and survival may be similar. Her static model is
the dominant-fringes model, in which the dominant firms are foreign and the fringe
firms are domestic. Foreign firms operate as market leaders and select an output level
(where the marginal revenue equals marginal cost) which determines the market
price and hence the quantity sold by the fringe firms. A proportion of fringe firms
will be crowded out if their marginal costs are substantially larger than those of
foreign firms. The dynamic version of Kosova (2010) is based on a combination of
the dominant-fringes model and dynamic industrial models (Jovanovic 1982) and
technological shock models (Sun 2002). The results suggest that the exit hazard of
domestic firms decreases with higher output prices, positive technology shocks, and
the expectation of higher efficiency. Moreover, a higher growth rate of foreign firms
leads to a higher exit rate of domestic firms. This effect is described as the dynamic
crowding out effect.
B. Empirical Evidence on FDI and Survival
There are few empirical studies on the impact of FDI on the survival of
domestic firms. Appendix 1 briefly summarizes the results from the most well-known
studies, and shows that the empirical evidence is contradictory. Studies by Iurchenko
(2009) for manufacturing in the Ukraine; Ferragina, Pettiglio, and Reganati (2009)
for the service sector in Italy during the period 2005–2007; Burke, G¨
org, and Hanley
(2008) for the United Kingdom (UK) manufacturing in 1997–2002; and Girma and
G¨
org (2003) for Ireland in 1973–1996, all find evidence of positive survival effects
from FDI. These results stand in contrast to negative or nonsignificant effects of
horizontal FDI found by Louri, Peppas, and Tsionas (2006) for Greece in 1997–2003
and Girma and G¨
org (2003) for the UK in 1973–1996.
One explanation for the contradictory results could be that none of the studies
mentioned above examine the survival effect from vertical FDI. To the best of our
knowledge, Wang (2010) is the only study to explicitly consider the effect of vertical
FDI on the survival of domestic firms. He analyzes the survival probability of 47,000
manufacturing firms in Canada during the period 1973–1996. The findings suggest
that the competition from horizontal FDI shortens the domestic firms’ expected
survival span, but that FDI in forward and backward sectors have positive effects as
a result of positive technology spillovers. Overall, the conclusion is that the positive
effects outweigh the negative effects, but it should be noted that this conclusion is
based only on a comparison of marginal effects.
There are no earlier studies of the impact of FDI on firm survival in Viet Nam.
Vijverberg and Haughton (2002) examine the life spans of household enterprises
using the Viet Nam Living Standard Survey. Carlier and Son (2004) discuss survival
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 61
and exit of firms in a qualitative study that is based on a small sample of enterprises.
Hansen, Rand, and Tarp (2009) use the Cox proportional hazard function to estimate
the impact of government support on the survival and growth of small and medium-
sized enterprises for a sample of 2,500 enterprises. All three studies focus on local
and sectoral conditions as determinants of firm survival, but do not include FDI
among the covariates.
C. Other Determinants of Firm Survival
FDI is obviously not the only variable influencing firm survival, and recent
literature has identified a number of other key determinants of the survival and exit
of firms. The relationship between a firm’s survival and its age and size is described
in a number of industrial organization studies (Jovanovic 1982, Hopenhayn 1992,
Ericson and Pakes 1995, Lambson 1991). A core argument in this line of research
is that firms do not know about their “true” efficiency before entering the market.
Upon entry, they find out about their true efficiency and respond accordingly when
faced with uncertainty and productivity shocks; some firms survive and grow while
others exit. There is a learning process (Jovanovich 1982) so that older firms, which
have had more learning opportunities, are more likely to survive until the next time
period. There is also a correlation between firm size and survival, meaning that
larger firms have a higher propensity to survive.
Exits of firms are not only caused by the characteristics of individual firms,
but also by sectoral characteristics. For example, Lambson (1991) posits that firm
performance depends on prevailing market conditions such as input prices and
market demand. If market conditions change frequently and sunk costs are large,
both the entry and exit of firms will be influenced by changes in input prices. Industry
dynamics are also affected by demand shocks that influence firms’ expectations
about future demand.
The role of competition has also been highlighted in many studies (Agarwal,
Sarkar, and Echambadi 2002; Nelson and Winter 1982). Competition does not only
lead to the crowding out of inefficient firms, but exerts a more complex effect.
Market concentration may stimulate collusion, creating more scope for profits and
therefore a higher probability to survive. Market concentration may also result in
the establishment of barriers to entry, which could allow some inefficient incumbent
firms to survive longer than would otherwise be the case.
A factor closely related to competition is technological change. Klepper
(2002) and Klepper and Simons (2005) highlight the importance of technological
events that may lead to a shakeout in the industry. These events may originate within
or outside the industry and affect both potential new entrants and incumbents.
Externally generated innovations will result in a race to adapt to or take advantage
of the new technologies. Firms that manage to adopt new technologies gain lower
unit costs and expand to a greater optimal size, while firms that fail to adjust become
62 ASIAN DEVELOPMENT REVIEW
unprofitable and exit. Internally generated innovations developed by incumbent firms
often set new standards for products. Consumer demand shifts to such standardized
designs, and firms compete to produce the standard product at the lowest possible
cost. Exit risk rises as firms shift from a past regime of product innovation to a new
regime of process improvement. Firms that do not succeed at process innovation are
driven out of business.
The empirical evidence for the determinants of survival is reasonably con-
sistent with the theoretical predictions. Manjon-Antolin and Arauzo-Carod (2008)
provide a comprehensive summary that classifies the firm’s survival determinants
into internal and external categories. For internal determinants, most empirical
studies find that size and age, R&D, and ownership of firms are key determinants.
Evidence suggests that the effects of age and size are not uniform: size may have
a nonlinear impact, and there may also be an inverse U-shaped impact of age. The
role of R&D has been confirmed in many studies, e.g., by Audretsch (1995) for
the US and Esteve-Perez and Manez-Castillejo (2005) for Spain. The evidence of
ownership focuses mainly on the distinction between foreign and domestic firms.
Most studies, including Mata and Portugal (2002), Kimura and Fujii (2003), G¨
org
and Strobl (2003b), and Esteve-Perez, Manez-Castillejo, and Sanchis-Llopis (2008)
report findings suggesting that foreign firms are more footloose than domestic
firms, meaning their threshold for exit from the host market is lower. Esteve-Perez
and Manez-Castillejo (2005) find no difference in the hazard rate for limited and
unlimited liability companies.
For the external factors, the most prevalent determinants are industry char-
acteristics, spatial factors, and the business cycle. Agarwal, Sarkar, and Echambadi
(2002) and Esteve-Perez, Manez-Castillejo, and Sanchis-Llopis (2008) both point
to a higher hazard rate for firms in high-tech industries. This is explained by the
rapid obsolescence of the firms’ technological endowment in rapidly changing high-
tech sectors. In addition, the entry rate (Mata and Portugal 2002, Lopez-Garcia and
Puente 2006) and the minimum efficient scale (MES) of production (Audretsch and
Mahmood 1995, Strotmann, 2007) determine the probability of a firm’s survival.
A high rate of entry puts pressure on incumbents, while a high MES acts as a bar-
rier to both entry and exit. The evidence for spatial factors is mixed. For example,
Strotmann (2007) found that rural firms are more likely than urban firms to survive.
Louri and Barbosa (2000) and Fritsch, Brixy, and Falck (2006) report contradictory
results.
Survival is also related to fluctuations in the business cycle (Manjon-Antolin
and Arauzo-Carod 2008). In many studies, sectoral growth is an important deter-
minant of survival: firms fail more often in recessions. Researchers using cohort
dummies and time dummies generally confirm the importance of macroeconomic
conditions (Lopez-Garcia and Puente 2006; Esteve-Perez, Manez-Castillejo, and
Sanchis-Llopis 2008; Disney, Haskel, and Heden 2003).
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 63
IV. Data, Estimation Issues, and Variables
A. Data
The data for this study are extracted from Viet Nam’s yearly enterprise census,
which is conducted by the General Statistical Office (GSO). The census includes
all known active firms in the economy in each year. The current dataset covers the
period 2001–2008.
The data are merged to form a panel dataset. Each firm has an identifier that
is the tax code. The tax codes are not always available immediately to new firms,
which means that some of the newly established firms have instead been identified
using telephone/fax numbers. Over 5,000 firms had to be excluded from the sample
due to the lack of information necessary to identify them from the annual data.
After merging all annual observations and dropping the firms that could not
be identified over time, the dataset makes up an unbalanced panel dataset containing
a total of 86,108 individual firms, starting with 28,358 firms in the year 2001 and
ending with 55,701 firms at the end of 2008. Three types of firms can be identified
in this dataset: DPFs, SOEs, and foreign-owned firms. A large number of firms
enter and exit during this period. Among the DPFs operating at the beginning of
the period, only 45.6% survived until 2008. The corresponding number for foreign
firms is 52.6%.
The survival function of a firm is defined as the probability that the firm
survives past time tgiven that the firm has survived until time t. In this context, time
tis defined as the length of a year. Therefore, firms at risk of failure at time tare
firms in their t’th year in the dataset. From 2001 to 2008 there are 7 time-intervals
coded from 1 to 7, and time tin this analysis is considered as an interval-discrete
time period rather than continuous.
A firm’s exit or death at time interval tis identified when the firm is observed
in interval tbut does not exist in subsequent intervals. This means that the time of a
firm’s exit is not exactly known: only the interval in which the firm exited is known.
Similarly the firm’s entry into the market is not known exactly until it is observed in
the interval time period tin the dataset. Both cases require the use of discrete time
models instead of continuous time models.
After arranging the data in order to implement the discrete time model, the
data is expanded according to time intervals, so that each firm has one observation
or more than one observation, depending on how long they survive. For example, a
firm surviving through the entire sample period will have seven observations in the
final dataset, which altogether has 312,506 observations. There were 53,109 firms
in the dataset at the end of the analysis period. Since we do not have information
about events after 2008, we do not know how long they survive afterwards. Hence,
they are classified as right censored.
64 ASIAN DEVELOPMENT REVIEW
B. Model Specification
The survival function S(t)ofafirmattimetis defined as the probability of
that firm remaining in the market beyond time t(see Jenkins 2005 for the details):
S(t)≡Pr(T>t)=1−F(t)(1)
where F(t)=Pr(T≤t) is called the failure function, representing the areas below
the density function f(t) of time spells. A related concept—the hazard rate or hazard
function—is defined as:
θ(t)=f(t)
1−F(t)=f(t)
S(t)(2)
The hazard rate is not expressed exactly, but as the conditional probability of
the firm to exit shortly after surviving up to time t. Because of the close relationship
between the survival function and the hazard function, it is common to estimate the
hazard rate instead of estimating survival time.
Survival literature distinguishes two types of hazard functions: the propor-
tional hazard model (PH) and the accelerated failure time model (AFT). With a few
exceptions (e.g., Wang 2010), most of the existing analyses of survival use variants
of the PH model, such as the cloglog and lognormal models (Bandick and G¨
org
2010, Kosova 2010) or models based on the Cox proportional hazard function (G¨
org
and Strobl 2003b, Taymaz and ¨
Ozler 2007; Burke, G¨
org, and Hanley 2008). The
choice of hazard function is based on the assumption of how the firm’s survival
odds change over time. For the PH type, the typical characteristic is a separability
assumption stating that:
θ(t,X)=θ0(t)exp(βX)(3)
where θ(t,X) is the hazard rate at survival time tfor a firm with covariate vector
X;θ0(t) is called a baseline hazard function, depending on tbut not X(expressing
a common exit pattern for all firms in the dataset); and exp(βX) is a non-negative
function of covariates X. This assumption implies that the absolute difference in X
reflects the proportionate difference in the hazard at each time t.
If it is assumed that time is continuous, then it is appropriate to use continuous
models like the Cox proportional hazard model. If time is instead defined as a discrete
variable, it is more appropriate to use discrete models like logit or complementary
logit models (cloglog). In the current dataset, it can be argued that although time is
continuous, the spell length is measured only in 1-year intervals (from July to July).
Firms can exit the market at any time within the interval. In survival language, this
means the failure can occur within a specific interval, but it is not known exactly
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 65
when it happens. Datasets with this nature can be described as censored interval data,
and in combination with the proportional hazard assumption, it is most appropriate
to use the cloglog model for estimation purposes. Therefore, the hazard function for
interval jused below will take the form:
log(−log[1 −hj(X)]) =βX+γjor h(aj,X)=1−exp[−exp(βX+γj)] (4)
where Xis the vector of covariates and γjis the log of the difference between the
integrated hazard θ0(t) evaluated at the end and the beginning of the interval.
A further issue is the fact that the dataset is right-censored. At the end of the
period of analysis, there is a group of firms that remain active, but it is unknown how
long they will survive. OLS estimation for this type of censored data can be biased.
We solve this problem by using standard survival estimation procedures. To do that,
the dataset is rearranged into a particular form by splitting the observations by the
number of spells (years) in the dataset. As shown by Jenkins (2005), this makes it
possible to use a standard binary estimation procedure. In all, four steps are done
before the estimations:
i. We expand the data for each firm in accordance with its survival time. This
means that each firm will have more than one observation in the dataset if it
survives for more than one time interval. We end up with 312,506 observations
out of which 51,710 are right-censored observations.
ii. We construct the time-varying covariate vector (X) and merge it with the firm-
year based data.
iii. We select the functional form for the hazard function. As noted above, we
focus on models with PH properties, where the clogclog model is our preferred
choice.
iv. We estimate the model using binary dependent variable regression models.
C. Construction of Covariates
The vector Xfor the hazard function (4) is constructed on the basis of the
findings in the literature review as well as the availability of information in the current
dataset. It includes three components: sectoral characteristics, firm characteristics,
and dummy variables.
D. Sectoral Characteristics
HFDI, DownFDI, and UpFDI represent the foreign presence within a sector
as well as in downstream and upstream sectors. These variables are the key variables
66 ASIAN DEVELOPMENT REVIEW
for testing the hypotheses related to the impact of foreign presence. The calculation
for foreign presence variables is based on the output share of foreign firms in 3-digit
sectors, as follows:
HFDI jt =iFDIijt
iRijt
(5)
where FDIijt are the output values of foreign firm iin 3-digit VSIC sector jat time
twhile the denominator (Rijt) is the total output of all firms in the sector.
UpFDI and DownFDI are calculated as the product of horizontal FDI in
downstream and upstream sectors weighted by the coefficients of the IO table αst
and its transposed matrix δst:
UpFDI jt =αstHFDI jt and DownFDI jt =δstHFDI jt (6)
HFDI is expected to raise the hazard of exit and hence reduce the survival
odds of DPFs (a static crowding out effect), while DownFDI and UpFDI are also ex-
pected to influence DPFs through spillovers and demand creation and may therefore
either raise or reduce the hazard.
In some of the regressions, we will also use the corresponding measures for
SOEs. They are denoted HSOE,UpSOE, and DownSOE, and they are defined simi-
larly as the FDI variables. GFDIjreflects the output growth of FDI in the sector j.
Based on Kosova (2010), we would expect the variable to have a negative effect
on the survival of domestic firms. However, Audretsch (1991, 1995) argues that
demand and output growth could elevate price above average cost, allowing firms
to improve their price cost margins and their survival probability. Because of these
contradictory prior expectations, we have no firm expectation for the sign of this
variable in the model. The output growth rate of SOEs, GSOE, is defined in an
analogous manner.
EXPORT is the ratio of exports to total sales for each 3-digit sector. This
variable is intended to control for the fact that firms in export-oriented sectors may
have better survival odds thanks to the demand from the world market.
Another reason for expecting exports to be important is that exporting is
likely to influence the competition between DPFs and foreign-owned firms. More
specifically, holding the volume of FDI-generated output constant, it can be expected
that the competitive pressure felt by DPFs is lower when the foreign firms are
export-oriented rather than focused on the local market. We therefore include the
variable EXPRATIO, which reflects the export to sales ratio of foreign-owned firms
in each 3-digit sector. To explore the relation between export oriented FDI and local
competition further, we also interact EXPRATIO with HFDI.
IMPORT is the ratio of imports to total sales for each 3-digit sector. In the
short term, an increase in imports of final goods is supposed to raise the exit hazard
of DPFs.
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 67
The Herfindahl index represents the concentration in the market. It is calcu-
lated as the sum of squares of the output shares in the sector (see Tirole 1988):
HERF jt =xijt
Xjt 2
(7)
where xijt is the output of firm iin sector jat time t.Xjt is total output of sector j.
The Herfindahl index is included in the model because it is closely correlated with
the market power of larger firms. The effect of this variable on the survival of DPFs,
however, is not unambiguous. High concentration typically results in high price-cost
margins, which means that incumbent firms should have a lower hazard rate and a
higher probability of survival. At the same time, high concentration suggests that
less efficient firms face pressure to leave the market.
MSCALE is the MES of the industry, measured as the log of median employ-
ment size in the 3-digit sector. Audretsch (1995) argued that a firm may be forced
to exit the market if its production scale is below the technically efficient minimum
level required by the industry. Sectors which have a high minimum scale are believed
to have high price-cost margins and hence ensure a higher survival rate for those
firms that can reach this scale. The average effect, however, is unclear. New firms
in sectors with high MSCALE also encounter more difficulties than firms that enter
other sectors (G¨
org and Strobl 2003b).
ENTRY is the entry rate in 3-digit sectors, computed as the ratio between
the total number of firms entering into the sector and the total number of firms
operating at that time. A high entry rate reflects a low cost of entry to the market.
In addition, a high entry rate also reflects high competition and may lead to slower
growth for individual firms as well as a high exit rate. In fact, Siegfried and Evans
(1994) suggest that there is a direct relationship between the entry and exit rates
because inefficient incumbents will be replaced by more efficient entrants. The
ENTRY variable is included to test for this replacement hypothesis.
E. Regional Characteristics
NBR and DIVER are geographical variables. They are included to capture
the impact of density and therefore competition in a geographical context and to
capture the agglomeration effect on firms’ survival. The inclusion of these variables
is motivated by the heterogeneity across different provinces in Viet Nam.
NBR is a proxy for neighborhood concentration. It is computed as the neighbor
agglomeration index:
NBR jr =
61
k=r
Cjk
d2
k
(8)
68 ASIAN DEVELOPMENT REVIEW
where Cjk is the total output of sector jof province k, and dkis the distance
(in kilometers) from province kto province r. In other words, this variable is the
sum of the distance-weighted outputs of other provinces. If a province is located in a
more concentrated region, the value of NBR is higher. Hence, this indicator reflects
the local competition and is expected to raise the hazard of exit.
DIVER is the diversity index computed as:
Dr=jqjr2(9)
where qjr is the share of output from sector jin province r. Diversity comes into
effect through the availability of complementary goods and services and choices,
and it is assumed to reduce the vulnerability to external shocks and the exit hazard.
F. Firm Characteristics
REL SIZE is measured as the ratio between the firm’s employment and the
average size of firms in its industry (3-digit VSIC). Larger firms are expected to
have a lower hazard of exit because they may benefit from scale economies and have
more capacity to do R&D as well as to expand their networks and diversify their
products.
CAP INT denotes the capital intensity of the firm and is measured as the
ratio of fixed assets (deflated by the gross domestic product [GDP] deflator) to the
total number of employees. The variable is included to capture the effect of specific
capital costs as well as the underlying efficiency level as analyzed by Kejzar and
Kumar (2006).
AGE is the age of the firm, measured as the number of years since their
establishment. Firms’ age reflects the experience of the firm in the market, also
covering the learning process that could be either passive or active (Jovanovic 1982,
Hopenhayn 1992). The older the firm, and the longer the learning process, the lower
the hazard of exit.
In addition, to control for sectoral/regional heterogeneity, dummies for sectors
and regions are included. There are 10 sectoral dummies that are based on the 1-
digit VSIC, capturing all subsectors in agriculture, mining, manufacturing, and
services. Seven regional dummies are constructed on the basis of the standard
regional classification in Viet Nam. They capture both geographical and economic
development differences between regions.
It should be noted that like many other survival studies, the present model does
not include time dummies. A first reason is that they would be highly correlated with
the variable AGE in the model. Second, the business cycle, technological progress,
and other temporal shocks are already included in the time varying covariates in
the model, like the growth of SOEs and FDI, as well as changes in the market
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 69
Tab le 3 . Variable Definitions
Expected
Variables Computation Mean Std. Dev. Signa
HFDIjt Foreign presence at time interval tin sector j,
measured as total output value of FDI firms in
sector j/total output value of the sector
0.220 0.267 +
UpFDIjt Foreign presence at time interval tin upstream
sectors relative to sector j, computed from HFDI
and transposed IO table coefficients
0.260 0.133 +/–
DownFDIjt Foreign presence at time interval tin downstream
sectors relative to sector j, computed from HFDI
and IO table coefficients
0.307 0.128 –
HSOEjt SOE presence at time interval tin sector j, measured
as total output value of SOEs in sector j/total
output value of the sector
0.478 0.303 +
UpSOEjt SOEs presence at time interval tin upstream sectors,
computed from HSOE and transposed IO table
coefficients
0.381 0.156 +/–
DownSOEjt SOEs presence at time interval tin downstream
sectors, computed as sum product of HSOE and
IO table coefficients
0.328 0.125 –
GFDIjt Sales growth of FDI firms in sector jat time t1.255 0.819 +/–
GSOEjt Sales growth of SOEs in sector jat time t1.043 0.693 +
EXPORTjt Export ratio of sector jat time t0.062 0.165 –
EXPRATIOjt Export ratio of foreign-owned firms in sector jat
time t
0.186 0.284 –
IMPORTjt Import ratio of sector jat time t0.1202 0.214 +
HERFjt Herfindahl index, proxy for concentration in the
market, calculated as the sum of squares of
employment share in sector j
+/–
MSCALEjt MES of sector j, computed as the median
employment of sector j
32.514 35.164 +/–
ENTRYjt Entry rate in sector jat time t0.241 0.082 +
NBRrt Neighborhood concentration index, measured as
the spatial market concentration of provinces
surrounding province rat time interval t
448,445.4 68,421 +/–
DIVERrt Spatial diversity index, measured as the sum of
squares of sectoral output shares in province rat
time t
9.198 66.906 –
REL_SIZEit Relative size of the firm iat time t, measured as the
output of firm ito median output in sector j
1.157 3.813 –
CAP_INTit Capital intensity, measured as total value of fixed
assets to number of employees of firm iat time t
140.80 371.97 –
AGE it Age of firm iat time tmeasured as number of years
since establishment to time t
4.525 4.151 –
aExpected effect of the variable on the hazard of exit.
Source: Authors’ computations.
structure. Furthermore, due to the close correlation between the growth of SOEs
and sectoral growth (which could be used to proxy the growth of market demand),
we drop sectoral growth from the estimations to avoid multicollinearity. Table 3
summarizes the variables included in the regression models, as well as the mean,
70 ASIAN DEVELOPMENT REVIEW
standard deviation, and expected impact on the exit hazard. A correlation matrix is
provided in Appendix 1.
G. Endogeneity
There is some potential endogeneity in the model specification noted above.
A first source of endogeneity is unobservable heterogeneity caused by the business
cycle, institutional reform, regional and industry factors, and other variables that
are not included in the model but that may influence both the survival of domestic
firms and foreign entry into the market. A second source of endogeneity is the
interdependence or simultaneous causality between survival and some covariates
in the model. The entry and exit of firms may be simultaneously determined, as
new entrants force less efficient incumbents out of the market (Manjon-Antolin and
Arauzo-Carod 2008). The exit of incumbents may also generate a “vacuum” of local
input supplies or customers that motivate or allow new actors to enter the market.
In other words, entry and exit can be simultaneous not only because all firms
are faced with similar market barriers but also because one can cause the other. In
addition, foreign firms may prefer to enter sectors that have high (or low) exit rates,
which may be seen as an indication of more (or less) competition. Neglecting these
endogeneities may obviously cause spurious estimation results.
Responses to the endogeneity of covariates are hard to find in the survival
literature, and even more scarce in studies examining the survival effects of inward
FDI. Earlier studies have employed different strategies to handle the problem. Many
studies have ignored it (for example Wang 2010; Ferragina, Pittiglio, and Reganati
2009; Iurchenko 2009; and Burke, G¨
org, and Hanley 2008), some have addressed
it by introducing lags or sectoral dummies (Kosova 2010, G ¨
org and Strobl 2003b),
while others have used more advanced methods such as instrumental variables
(Girma and G¨
org 2003, Bandick and G¨
org 2010). Ignoring potential endogeneity
or simply using sectoral dummies or lags of potentially endogenous variables may
not be sufficient to ensure unbiased estimation. We will therefore use a two-stage
instrumental variable model to address this problem.
V. Results and Discussion
A. Descriptive Statistics: Survival of DPFs
As a first step, we investigate the survival of firms using nonparametric
methods. Table 4 provides a first glance at the data in survival format. The first
column shows the number of intervals, that is, the number of years of survival. The
second column is the total number of firms at risk of failure during each interval.
It shows that for the first interval 79,852 DPFs were at risk. It should be noted that
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 71
Tab le 4 . Survival of DPFs and Foreign Firms
Interval Beg. Total Deaths Lost Survival Std. Error
95% Confidence
Interval
DPFs
1 79,852 9,376 9,027 0.8755 0.0012 0.8732 0.8779
2 61,449 5,080 7,347 0.7986 0.0015 0.7956 0.8015
3 49,022 3,434 7,230 0.7382 0.0017 0.7348 0.7415
4 38,358 3,286 6,339 0.6692 0.0019 0.6654 0.6730
5 28,733 6,311 4,998 0.5082 0.0023 0.5037 0.5127
6 17,424 655 5,072 0.4859 0.0024 0.4813 0.4905
7 11,697 0 11,697 0.4560 0.0024 0.4813 0.4905
Foreig n
1 6,256 540 435 0.9106 0.0037 0.9031 0.9175
2 5,281 398 405 0.8392 0.0048 0.8295 0.8484
3 4,478 264 511 0.7867 0.0055 0.7757 0.7973
4 3,703 260 444 0.7280 0.0062 0.7157 0.7399
5 2,999 769 388 0.5284 0.0076 0.5134 0.5432
6 1,842 34 396 0.5175 0.0077 0.5023 0.5324
7 1,412 0 1,412 0.5175 0.0077 0.5023 0.5324
Log-rank test: =30.02; P=0.0000
Likelihood-ratio test =30.6092; P=0.0000
Source: Authors’ computations.
this is the total of all DPFs established at any time during the period 2001–2008.
At the end of the interval, there were 9,376 firms that failed or died as shown in
the third column named “Deaths.” This is the sum of firms that did not survive
after their first year (appearance in the dataset). The fourth column, “Lost,” gives
the number of firms that were censored or that were out of risk. This indicates that
9,027 firms that were established in the last year of the sample survived until the
end of the sample period, i.e., were right-censored. Correspondingly, the data for
the seventh interval shows that 11,697 enterprises recorded seven spells of survival.
Since they survived through the whole sample period, there were no observations
in the “Deaths” column. Moreover, all of them were right-censored, and survived
beyond the sample period. Hence, they are all included in the “Lost” column.
The estimation of the survival function and its statistics are presented in the
remaining columns. As shown in Jenkins (2005), the rate of survival at interval jis
estimated by:
Sj=
f
k=1Nk−1
2mk−dk
Nk−1
2mk(10)
where Nkis the number of firms at the start of the interval, mkis the number of firms
censored, and dkis the number of firms that died. The “Survival” column records
the estimated survival rates for all intervals. As shown in the table, only 45% of the
72 ASIAN DEVELOPMENT REVIEW
firms remained after 7 years. The table also reveals that the median of the survival
duration is approximately 5 years.
The estimated survival rates of foreign firms are somewhat higher than those
of DPFs at every interval: foreign firms have a median survival time of around
6 years. The last two rows in the table provide tests for the equality of survival
functions. Both the log-rank test and the likelihood test indicate that the differences
in survival propensity are statistically significant. This finding contradicts G¨
org and
Strobl’s (2003a) results for Irish manufacturing, which suggested that foreign firms
seemed to be more footloose than domestic firms. However, it should be noted that
most DPFs in Viet Nam during this period were relatively young and small—both
of these characteristics raise the likelihood of exit.
Earlier survival studies in Viet Nam have shown somewhat lower survival
rates, but these studies focused on firms established before 2001 (Hansen, Rand,
and Tarp 2009) and household enterprises (Vijverberg and Haughton 2002). It is
likely that the survival rates for firms established during the 1990s were lower
because of the less favorable regulatory environment. Moreover, the lower survival
rates of household enterprises are partly explained by the fact that they were even
smaller than the DPFs established after 2001.
B. Econometric Results
As a first step of the econometric analysis, we have tested whether the assump-
tions for the proportional hazard model hold. Finding that this is the case (results
not reported here but available on request), we proceed to estimate the PH model
(equation 4). To handle the possible endogeneity of covariates HFDI and ENTRY,
we complement the base equation (which assumes no endogeneity) with a variant
where the potentially endogenous variables are lagged, as well as an estimation using
the instrumental variable method (2SCML).
Table 5 shows the results of the model for all DPFs. All specifications are
stratified at the 1-digit sector level. This procedure allows for differences in the
baseline hazards. This kind of specification is supported by the Wald test presented
at the bottom rows of the table. Column (1) of Table 5 is the estimation where HFDI
and ENTRY are assumed to be completely exogenous. Column (2) presents the
results of the estimation using the first lags of the endogenous explanatory variables.
Column (3) shows the results with the 2SCML correction factors. It can be noted
in column (3) that the correction factors in the first stage of the 2SCML estimation
are statistically significant at the 1% level, confirming the prior suspicion that the
HFDI and ENTRY variables are endogenous. Column (3) is therefore the preferred
estimation equation.
Although the hazard ratio is commonly used to present the hazard function
estimation, Table 5 reports the coefficient forms. The reason is that the signs of the
coefficients are also the signs of the effects: a negative coefficient means a lower risk
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 73
Tab le 5 . Estimation Results—Impact of FDI
Exogenous Lag 1 2SCLM
(1) (2) (3)
HFDI 0.253∗∗∗ 0.341∗∗∗
(42.16) (26.16)
Lag1.HFDI 0.262∗∗∗
(32.03)
UpFDI –0.198∗∗∗ –0.208∗∗∗ 0.469∗∗∗
(17.42) (11.32) (14.41)
DownFDI 0.228∗∗∗ 0.202∗∗∗ –0.408∗∗∗
(12.77) (7.59) (10.26)
ENTRY 0.543∗∗∗ 0.219∗∗∗
(16.91) (5.67)
Lag1.ENTRY 0.871∗∗∗
(19.77)
GFDI –0.009 –0.013 0.256∗∗∗
(1.0718) (1.067) (17.15)
EXPORT 0.742∗∗∗ –0.695∗∗∗ –1.083∗∗∗
(9.82) (5.76) (8.65)
EXPRATIO 0.00 0.001 –0.004∗∗∗
(0.10) (1.80) (4.37)
HFDI ∗EXPRATIO –0.006 –0.004∗∗∗ –0.005∗∗∗
(26.47) (9.37) (9.07)
IMPORT –0.187∗∗ 0.040∗∗ –0.314∗∗∗
(17.02) (2.63) (17.76)
HERF –0.198∗∗∗ –0.352∗∗∗ –0.593∗∗∗
(24.55) (24.77) (32.74)
MSCALE –0.383∗∗∗ –0.444∗∗∗ –1.218∗∗∗
(16.79) (12.60) (23.20)
NBR –0.126∗∗∗ –0.051∗∗∗ –2.588∗∗∗
(16.93) (4.71) (29.52)
DIV ER –0.047∗∗∗ 0.002 –0.060∗∗∗
(15.27) (0.46) (11.69)
REL SIZE –0.175∗∗∗ –0.134∗∗∗ 0.159∗∗∗
(18.23) (9.62) (9.20)
CAP INT –0.008 0.013 –0.001
(1.02) (1.17) (0.07)
AGE –0.423∗∗∗ –0.416∗∗∗ –0.079∗∗
(34.30) (16.33) (2.88)
Constant –1.116∗∗∗ –0.860∗∗∗ 16.96∗∗∗
(8.39) (3.49) (20.87)
HFDI correction 0.263∗∗∗
(9.68)
ENTRY correction –8.878∗∗∗
(31.72)
N 306,477 225,796 217,284
Log pseudo likelihood –56,701.25 –27,722.85 –24,934.99
Wald tests 7,043.85 2,973.58 3,321.32
∗=significant at the 10% level, ∗∗ =significant at the 5% level, ∗∗∗ =significant at the 1% level.
Note: Cloglog model, dependent variable: dead1 (1 =firm exit, 0 =otherwise). All estimations are in coefficient
form rather than as hazard ratios. The HFDI correction and ENTRY correction variables are the error terms
from the instrument equations of the 2SCLM procedure. Numbers in parentheses are z-values. Coefficients for
regional/sectoral dummies are not shown.
Source: Authors’ computations.
74 ASIAN DEVELOPMENT REVIEW
of exit. With a few exceptions, the variables have the expected sign in the estimation
reported in column (3). However, several variables have the opposite sign in the first
two columns of the table. These large differences between the estimations can be
explained by the possible biases caused by ignoring endogeneity or inappropriately
using lagged variables as instruments.
Focusing first on the variables of interest in column (3), it can be seen that
there is a significant relationship between FDI and the exit hazard faced by DPFs.
The presence of foreign firms in the same sector (HFDI) raises the probability of
exit very notably. More specifically, the coefficient βHFDI =0.341 suggests that a
1 percentage point increase in HFDI, ceteris paribus, will induce an increase in the
hazard of exit by 100(e0.341 −1) =40.6%.
This aggregate impact of horizontal FDI on the exit hazard is, as noted earlier,
the sum of two effects: the negative competition effect and the potentially positive
productivity spillover effect. However, since learning is generally not instantaneous,
it is the former that dominates in the short run. The result confirms the static
crowding out effect described in Kosova (2010). Louri, Peppas, and Tsionas (2006)
and Wang (2010) found similar effects for DPFs in Greece and Canada, respectively,
but G¨
org and Strobl (2003b) and Backer and Sleuwaegen (2003) found the opposite
for Ireland and Belgium.
Kosova (2010) distinguishes the static crowding out effect from a dynamic
crowding out effect that is related to the output growth of foreign firms in the same
sector. The coefficient of the variable GFDI in column 3 is positive and significant,
which suggests that an increase in foreign output will raise the exit hazard for
domestic private firms. Hence, the dynamic crowding out effect also seems to be
confirmed.
UpFDI and DownFDI reflect the impact of FDI from upstream and down-
stream sectors. The estimated coefficients show that they are somewhat larger (in
absolute terms) than the replacement/competition effect of HFDI, but they have
the opposite impacts. FDI in upstream sectors raises the exit hazard, but FDI in
downstream sectors seems to reduce it. The finding that βDownFDI =−0.408
means that, ceteris paribus, a given 1 percentage point increase in the share of
foreign presence in downstream sectors would lead to a decrease in the hazard
rate by (1 −e−0.408)∗100 =33.5%. By contrast, an increase in foreign presence in
upstream sectors by 1 percentage point raises the exit hazard by nearly 60%.
While the positive impact of downstream FDI on the survival of DPFs could
possibly be explained by demand creation (which may be particularly strong when
foreign investors are export oriented) and the spillovers that come about when foreign
firms buy local inputs and provide support for their local suppliers, it is more difficult
to explain why upstream FDI seems to strongly reduce the life expectancy of DPFs.
One possible channel of influence could be that foreign firms in upstream sectors
are likely to crowd out local firms in the same sector, which in turn could harm
the domestic firms in downstream sectors. This would be particularly serious if the
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 75
foreign firms in upstream sectors use technologies and manufacture intermediate
goods that do not match the technologies and input requirements of local firms in
later stages of the value chain.
Regarding the other survival determinants, it should be noted that interna-
tional trade tends to lower exit hazards. The variable EXPORT has a large neg-
ative coefficient, and a higher export ratio for foreign-owned firms also tends
to reduce the exit hazard. These results are as expected. The interaction variable
HFDI * EXPRATIO also records a negative sign. The interpretation is that the hori-
zontal crowding out effect is weaker in sectors where foreign firms are more export
oriented. However, it is somewhat surprising that the variable IMPORT also records
a significant negative coefficient, given that import competition was hypothesized
to raise the competitive pressure on DPFs.
It is possible that the relatively high correlation between imports and exports
at the 3-digit level makes it difficult to disentangle the separate effects of these two
variables—the fact that the signs of several of the trade-related variables change
between the different estimations could indicate collinearity. It can also be hypoth-
esized that a high import ratio is a characteristic of sectors where domestic firms
have already learned to manage tougher competition. The DPFs in these sectors can
perhaps be described either as firms that have survived import competition for some
time or new entrants that are aware of the tough market conditions. To explore the
impact of imports in closer detail, it would be interesting to check whether there
are any differences between sectors depending on whether their imports consist of
final goods or intermediate goods. Unfortunately, lack of data on the use of imports
makes it impossible to examine this distinction.
Market concentration, proxied by the Herfindahl index reduces the exit haz-
ard; the more concentrated the market, the lower the probability that domestic firms
will have to exit the market. A likely reason for this result is that incumbents have
some market power that allows them to respond positively to foreign entry. Since
high concentration is often a sign of high entry barriers, it is possible that the number
of vulnerable firms—newly established young firms that could easily be squeezed
out from the market—is also relatively small. The variable NBR, which proxies local
concentration, also seems to reduce the exit hazard. This could be an indication of
an agglomeration effect. The geographic variable measuring diversity, DIVER, also
appears to reduce the exit hazard.
The explanation for the negative coefficient estimate of the variable MSCALE
is similar to that for market concentration. MSCALE has a positive effect on survival
that is consistent for all three specifications, confirming the argument by Audretsch
(1995) that firms in sectors with high minimum scale seem to enjoy higher price-cost
margins due to high entry barriers, raising survival rates. At the same time, high
minimum scale is likely to mean that exit costs are also high, and that firms will not
respond quickly to negative demand shocks or cost increases.
76 ASIAN DEVELOPMENT REVIEW
Turning to the variable ENTRY, the results indicate that a high entry ratio
raises the exit hazard. This finding is consistent with Backer and Sleuwaegen (2003)
and Wang (2010) and confirms the replacement hypothesis stating that the entry of
a new firm will force inefficient older firms out of the market. In addition, a high
entry rate is an indicator of low sunk cost, which suggests not only low entry costs
but also low exit costs.
Looking at firm characteristics, there are some interesting points to be noted.
Capital intensity does not have any significant impact on firms’ survival, which
may appear counterintuitive: high capital intensity could be seen as an indication of
relatively high barriers to entry and hence high price-cost margins that reduce the
exit hazard. However, Viet Nam is a labor-abundant rather than a capital-abundant
country. DPFs in general are not likely to have strong competitive advantages related
to capital intensive technologies. It is also notable that the relative size of the firm
seems to raise the exit hazard, despite the a priori expectation that larger firms would
be more resilient. A possible reason is that larger firms may be more vulnerable,
e.g., because of higher debt levels, but we do not have access to the financial data
needed to explore this further. The variable AGE has the expected positive impact
on survival in all three estimations.1
C. Do SOEs Matter?
As mentioned earlier, the economy of Viet Nam is distinguished by the
dominance of SOEs in many sectors. SOEs are not only focused on the provision of
public services, but they also hold prominent positions in many other industries. This
motivates an analysis of the role of SOEs in determining the survival of DPFs. One
obvious reason is that SOEs may influence the survival of DPFs in the same way as
FDI does—SOEs can also be assumed to be larger and stronger firms that dominate
the smaller and weaker private actors. Moreover, the presence of SOEs may have
a conditioning impact on the relation between FDI and DPF survival. Hence, we
define four variables—HSOE, DownSOE, UpSOE, and GSOE—to represent SOEs
presence in horizontal, downstream, and upstream sectors, as well as the growth of
SOE output.
The expected effects of the SOE variables on DPFs are similar to those of
the FDI variables, but not necessarily identical. The reason is that unlike foreign
firms, SOEs do not always exhibit higher efficiency or higher productivity than
DPFs, and they have fewer unique technological assets that could spill over to local
firms (see further Nguyen et al. 2006 and Tran 2013). The backward and forward
linkages between DPFs and SOEs may also differ from those between DPFs and
1To test the robustness of the results, we have also estimated the hazard function with some parametric models
that also have PH properties, including Weibull, Exponential, and Cox models. The results of these tests (available
on request) indicate that the findings discussed above are fairly robust to alternative assumptions about the hazard
function.
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 77
foreign firms. In particular, DPFs may be more likely to select SOEs rather than
foreign firms as suppliers or customers due to lower technical requirements and the
similarity in business culture. This means that the linkages may be stronger or more
extensive, although the potential technological advantage of SOEs—and hence the
potential for learning and spillovers—is likely to be weaker.
Furthermore, because SOEs have existed in the market longer than the foreign
firms and because they hold substantial market power, the foreign firms may choose
different entry strategies and operational strategies in different sectors, depending
on the market share of SOEs. This suggests the hypothesis that the survival effects
from FDI may vary with the share of SOEs in the sector. This hypothesis is tested by
introducing some interaction terms between SOEs and FDI in the empirical model.
In Table 6, column (1) presents the estimation results in which only the
presence of SOEs and control variables are included. Column (2) includes both
SOEs and FDI. Column (3) focuses on the FDI variables but adds a dummy variable
for the quintile of sectors with the highest SOE shares and interacts it with the FDI
variables, while column (4) adds a corresponding interaction variable for the quintile
of sectors with the lowest SOE shares. These interaction variables are introduced in
order to explore how the presence of SOEs influences the impact of FDI on local
firms.
The control variables are robust across estimations, but are not included in
Table 6 to save space (results are available on request).2
The results in column (1) show that SOEs have a significant effect on the
survival odds of DPFs. The variable HSOE has the expected positive coefficient,
but UpSOE records a negative coefficient, suggesting that relations with SOEs in
upstream industries may benefit local firms. This is in contrast to the results for
upstream FDI, which was found to raise the exit hazard. The difference presumably
reflects the smaller technology gap between DPFs and SOEs. The coefficient for
DownSOE is positive but not significantly different from zero, which is also some-
what surprising, given that the coefficient of DownFDI in Table 5 was negative and
significant.
Another surprising result is that increases in SOE output (GSOE) seem to
have dynamic crowding in effects on DPFs. The coefficient of GSOE in column (1) is
–0.208, indicating that the hazard of exit declines by 18.7% for a 1 percentage point
increase in the growth of SOEs. This impact becomes even stronger when foreign
presence is included in column (2). This result is not easily explained unless the
growth of SOE output is highly correlated with overall demand growth. The result
could also be connected to the fact that SOEs have lost market shares in several
industries and gone through a gradual privatization process during the period under
study, which means that there are many sectors where SOEs record negative growth.
2One exception is the variable AGE, which is insignificant in some of the estimations reported in Table 6.
78 ASIAN DEVELOPMENT REVIEW
Tab le 6 . The Impact of SOEs and FDI
High SOE Low SOE
SOE FDI & SOE Dummy Dummy
(1) (2) (3) (4)
HFDI 0.394∗∗∗ 0.710∗∗∗ 0.753∗∗∗
(9.63) (32.67) (18.61)
UpFDI 0.715∗∗∗ 0.774∗∗∗ 0.889∗∗∗
(16.91) (20.06) (19.22)
DownFDI –0.951∗∗∗ –0.624∗∗∗ –0.449∗∗∗
(19.97) (15.02) (9.62)
HSOE 0.432∗∗∗ 0.381∗∗∗
(24.43) (11.54)
UpSOE –0.168∗∗∗ –0.258∗∗∗
(7.96) (9.04)
DownSOE –0.060 0.333∗∗∗
(1.663) (9.67)
GFDI 0.430∗∗∗ 0.390∗∗∗ 0.456∗∗∗
(22.85) (18.49) (23.83)
GSOE –0.207∗∗∗ –0.354∗∗∗
(13.80) (23.50)
HFDI ∗SOE_DUM 0.397∗∗∗ –0.153∗∗∗
(4.64) (5.77)
UpFDI ∗SOE_DUM –0.204∗∗∗ –0.613∗∗∗
(3.91) (11.05)
DownFDI ∗SOE_DUM 1.232∗∗∗ –0.288∗
(6.00) (2.55)
SOE_DUM 2.530∗∗∗ –2.857∗∗∗
(4.88) (7.50)
N 217,284 217,284 217,284 217,284
Log pseudo likelihood –24,896.03 –24,530.54 –24,619.21 –24,539.41
Wald-test 3,697.232 5,518.01 4,521.25 4,537.00
∗=significant at the 10% level, ∗∗ =significant at the 5% level, ∗∗∗ =significant at the 1% level.
Note: Cloglog model, dependent variable: dead1 (1 =firm exit, 0 =otherwise). All estimations are in coefficient
form, not hazard ratios. Numbers in parentheses are z-values. Coefficients for regional/sectoral dummies are
not shown. The High SOE dummy identifies the quintile of 3-digit sectors with the highest SOE shares of
output. The Low SOE dummy marks the quintile with the lowest SOE shares. Coefficients for control variables
are not shown to save space.
Source: Authors’ computations.
Having established that SOEs do have an impact on local firms, it is interesting
to examine whether the presence of SOEs may moderate or condition the survival
effect of FDI. Column (2) adds the FDI variables to the estimation equation. The
signs of the FDI variables remain unchanged, but the absolute size of the estimated
coefficients increases: in particular, the vertical impacts of FDI appear to grow
stronger. The impact of SOEs is also influenced by the inclusion of the FDI variables.
The most notable change is that the coefficient linked to downstream SOEs becomes
positive and significant, suggesting that the exit hazard increases if SOEs raise their
share among the customers of DPFs. This result is not easily explained, and may be
due to the gradual retreat of SOEs from some of the downstream industries. If so,
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 79
there could be a link between the coefficient estimates for DownSOE and DownFDI,
although the correlation matrix in Appendix 3 suggests that they are not highly
correlated.3
To explore the relations between the impacts of FDI and SOEs in somewhat
closer detail (in a context where the possible correlation between the FDI and SOE
variables is less of a concern), columns (3) and (4) add dummy variables for the
3-digit industries with the highest and the lowest (horizontal) SOE shares. In column
(3), the dummy SOE DUM distinguishes the quintile of sectors with the highest SOE
shares. The direct effect is an increase in the exit hazard for DPFs, as seen from the
positive and significant coefficient for SOE DUM. All three interaction variables
combining SOE DUM with HFDI,UpFDI, and DownFDI are also significant.
For HFDI, the results suggest that the strong direct effect of horizontal FDI is
even stronger in industries with high SOE shares—DPFs that are already pressured
by SOEs are particularly vulnerable to further competition from foreign-owned
firms. For vertical FDI, the direct effects seem to be smaller or even reversed in
sectors with high SOE shares. In particular, it appears that the beneficial effects of
downstream FDI are absent in the sectors that are most strongly dominated by SOEs.
In column (4), where the dummy variable identifies the sectors with the lowest
SOE shares, the effects are of a different nature. First, the coefficient of SOE DUM
is negative and significant, suggesting that the exit hazard is smaller in these sectors.
Second, the inclusion of the interaction term reduces the impact of HFDI. The direct
effect of horizontal FDI is still an increase in the exit hazard, but this effect is
somewhat weaker in the sectors with low SOE shares. Third, the effects of vertical
FDI are less harmful (upstream FDI) or more beneficial (downstream) in the sectors
with low SOE shares.
For the impact of horizontal FDI, the theoretical interpretation of the con-
ditioning role of SOEs appears straightforward. The higher the share of SOEs, the
tougher the baseline competition and the stronger the additional negative effect of
HFDI on the survival odds of DPFs. It is more difficult to make any strong gen-
eralizations about how SOEs influence the vertical effects. The results for sectors
with high SOE shares are unclear, both theoretically and empirically, and the results
probably reflect differences in the capabilities of both DPFs and SOEs across indus-
trial sectors. Further work is clearly needed to better understand these interactions.
Yet, the observations that the presence of SOEs has an impact on the exit hazard
for DPFs and that they also influence the impact of FDI on DPFs are important and
have rarely been made in extant literature.
3If the market share of SOEs in downstream industries falls, it is possible that this could be reflected in an
increase in the market share of foreign-owned firms (although the SOEs could also be replaced by DPFs). If this shift
in market shares has an impact on the exit hazards for DPFs, it would be recorded as opposite effects for the retreating
(SOEs) and expanding (FDI) investor groups.
80 ASIAN DEVELOPMENT REVIEW
Tab le 7 . Net Effect, Change in Hazard of Exit 2001–2008 (%)
FDI SOE
Low High Low High
Export Export Avg. Export Export Avg.
Food processing Horizontal –5.62 5.92 2.83 7.04 1.39 2.92
Backward –2.49 9.37 6.52 0.57 13.87 10.15
Forward – 2.52 –35.72 –29.38 –0.32 –4.14 –2.61
Tota l –10.63 –20.43 –20.03 7.29 11.12 10.46
Textile, leather, wood products Horizontal 0.43 –5.50 –4.23 1.78 0.50 0.77
Backward –0.31 7.61 5.66 –2.19 5.39 3.52
Forward 2 .50 –1.20 –0.33 –0.79 –1.82 –1.58
Tota l 2.62 0.91 1.10 –1.20 4.07 2.71
Metal products, machinery Horizontal 2.19 –0.40 0.26 –1.30 1.72 0.80
Backward 2.27 1.41 1.61 –0.62 –3.68 –2.39
Forward 1 .41 –5.08 –2.84 –1.27 –3.21 –2.70
Tota l 5.87 –4.07 –0.97 –3.19 –5.17 –4.29
Electricity, energy Horizontal –6.91 15.54 3.35 6.08 8.55 6.47
Backward –2.13 –1.26 –1.98 –2.71 –2.57 –2.69
Forward 1 0.85 14.14 11.26 –0.19 –1.66 –0.50
Tota l 1.81 28.42 12.63 3.18 4.32 3.28
Other services Horizontal –0.01 –0.56 –0.24 –4.05 6.96 0.33
Backward –2.08 3.61 0.40 –2.43 1.21 –0.87
Forward – 5.79 –7.08 –6.15 –0.31 –3.18 –0.88
Tota l –7.88 –4.03 –5.99 –6.79 4.99 –1.42
All industries Horizontal –0.54 –0.13 –0.28 0.48 2.97 1.81
Backward –1.48 4.13 1.57 –1.95 5.55 2.03
Forward – 1.43 –8.25 –4.68 –0.43 –2.90 –1.60
Tota l –3.45 –4.25 –3.39 –1.90 5.62 2.24
Source: Authors’ computations.
D. Net Effects of Changes in FDI and SOE Shares
It should be noted that all discussions so far have focused on marginal effects.
The results show that effects of foreign presence on the survival of DPFs are
remarkably large, they vary depending on whether the foreign firms are in the
same or upstream/downstream sectors, and they are influenced by the presence of
SOEs. In particular, there seems to be a strong and robust crowding out effect of
horizontal FDI and SOE presence. To compute the net effects of changes in FDI
during the period under study, the estimated coefficients from the model must be
combined with the actual changes in the various forms of FDI and SOEs included
in the model.4
Table 7 illustrates these net effects based on the coefficients in column (2)
of Table 6. The effects from changes in both FDI and SOEs on the survival of
DFPs are calculated. To take into account the heterogeneity of DFPs, we present the
effects for five major sectors as well as the average effect on the domestic industry.
4Although cloglog is a nonlinear model, its PH property allows us to compute the net effects to the hazard
ratio.
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 81
Moreover, noting the significant impact of international trade on exit hazards,
Table 7 also presents separate estimations for the quintiles of 3-digit industries
with the lowest and highest export shares in each industry group. All estimations
are based on FDI and SOE shares at the 3-digit level. To facilitate an overview of
results, the table shows the change in exit hazards between 2001 and 2008, rather
than data for individual years. Because of the relatively large changes in FDI and
SOE shares between individual years, with increases as well as decreases, there
is substantial variation over time and across more disaggregated sectors, which
complicates interpretation.
A first point to note is that the estimated net effects on changes in hazard rates
are relatively small, considering the large marginal effects found in Table 6. The
main reason is that the changes in FDI shares over the whole period have not been
very large—both FDI and domestic industry have grown substantially, and a large
share of the year-to-year fluctuations disappears when we look at the end points in the
dataset.5Second, although the average impact of FDI is relatively small—a reduction
in the exit hazard by about 3%—there are differences between the broad industry
groups, as well as differences between more and less export-oriented subgroups of
industries.
Generalizing, it appears that FDI has contributed more to reduce exit hazards
in relatively simple industries like food products, while there has been some crowding
out of local firms in more advanced industries such as electricity and energy. The
effects also seem more beneficial in the more export-oriented industry groups, with
the exception of the electricity and energy sector. Third, the average impact of
changes in SOE shares is a small increase in the hazard of exit, although there are
differences across sectors. There does not seem to be any immediate relationship
between the technical complexity of the sector and the net impact of SOEs, nor is
there any obvious link to the export orientation of the industry. Although the results
confirm that SOEs do have an impact on the exit hazards facing DPFs, it is clear
that further work in needed to gain deeper insights into this relationship.
VI. Conclusion
This paper has examined the survival effect of inward FDI on DPFs in Viet
Nam. Recent literature suggests that the survival effects of FDI come from differ-
ent sources that may sometimes have contradictory impacts. Firms that manage to
absorb positive technological spillovers will face lower exit hazard thanks to im-
proved productivity and efficiency. Positive effects also come from demand creation
5It should be noted that we do not include the dynamic crowding out effect from growth in foreign production
in these estimations. The dynamic effect is a short-term phenomenon, and aggregating growth rates over many years
yields results that are obviously not realistic.
82 ASIAN DEVELOPMENT REVIEW
connected to the presence of foreign firms in downstream sectors. In such cases,
domestic firms may gain from increased possibilities to exploit economies of scale.
However, the most frequently noted effect in the survival literature is the negative
competition effect that occurs as foreign firms take market shares and force local
enterprises to reduce output or cut prices in order to maintain their market shares.
In either case, less efficient DPFs are likely to be forced to exit the industry.
The paper makes the following four contributions to extant literature. First,
we have examined the survival effects from both horizontal and vertical FDI, while
most of earlier studies focus on the survival impact of horizontal FDI alone. The
results confirm that horizontal FDI is likely to crowd out local firms, but also suggest
that the vertical effects are important and that omitting these effects may result in
inappropriate conclusions about the overall impact of FDI.
Second, unlike earlier studies that consider domestic firms as a homogenous
group, we highlight the role of SOEs for industry dynamics. The presence of SOEs
apparently has a direct effect on the survival of DPFs—which is not surprising, con-
sidering the significant market shares and market power of SOEs in Viet Nam—but
they also seem to have a conditioning impact on the relationship between FDI and
the survival of DPFs. These preliminary findings stress the need for further study on
the interactions between FDI, SOEs, and DPFs, particularly in transition economies
where SOEs still play an important economic role.
Third, we have explicitly tried to manage estimation problems related to
the endogeneity of covariates. In particular, we have found indications that both
foreign presence and entry ratios may be endogenous. Earlier studies have generally
assumed that covariates are exogenous or used lagged variables to try to control for
endogeneity.
Fourth, apart from pointing to the partly offsetting effects of horizontal and
vertical FDI on the survival of DPFs, we also attempt to calculate the net effect
of foreign presence. Although the marginal effects are large and vary by year and
industry group, we find a surprisingly small net effect.
What policy conclusions does a finding about increased exit hazards imply?
There are two general interpretations of the possible welfare effects of the changes in
industrial structure generated by FDI. A first perspective focuses on the vulnerability
of DPFs in Viet Nam. The findings show that DPFs can suffer from both a remarkably
large short-term crowding out effect and negative longer term effects caused by
changes in upstream and downstream sectors. An almost instinctive policy response
is to call for measures to strengthen the competitiveness of DPFs, in order to maintain
a strong domestic industry sector and a high level of employment in domestic firms.
However, an alternative interpretation is based on an industrial efficiency
perspective. How should the structure of domestic industries develop if domestic
enterprises are to become more competitive in an increasingly open and interna-
tionally oriented market? It seems clear that the strength of the local private sector
is not only measured by the number of DPFs in individual industries, but also by
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 83
Tab le 8 . Survival Time and Size (Employment)
Years of Survival Relative Size
10.87
21.00
31.12
41.27
51.47
61.71
72.03
Note: Size relative to 3-digit industry average.
Source: Authors’ computations.
the size, productivity, and competitiveness of these firms. Moreover, flexibility and
dynamism are increasingly important characteristics in the internationalized market
place. Entrepreneurs need to be able to respond to market signals, moving towards in-
dustries and activities where market conditions are favorable and away from sectors
where the returns to investment and work effort are lower.
Seen from this perspective, it is not obvious whether an increased exit hazard
due to inward FDI is good or bad for domestic industry. In fact, an increased exit rate
could even be favorable if it is part of a dynamic restructuring process, where weak
firms exit and leave room for more efficient and productive enterprises that are able
to grow faster. This suggests that the key questions are “Who are the survivors?”
and “Are there enough survivors to maintain a high level of employment?”
A detailed analysis of the survivors lies beyond the objectives of this pa-
per, but Table 8 provides a quick glance at one of the characteristics of surviving
firms—size. The table presents the relative size of firms (based on the number of
employees) across firms with different survival times. There is a consistent pattern
where surviving firms quickly grow larger: the typical DPF that has survived 7 years
is more than twice as large as the average firm in its 3-digit industry. This suggests
that survivors have an opportunity to grow stronger and larger over time, and that
the restructuring process that is triggered by FDI inflows is perhaps not detrimental
to the domestic economy as a whole. At the same time, it is appropriate to recognize
that more detailed studies of the dynamic effects of FDI are needed to better under-
stand the differences between failing and surviving firms, particularly on whether
and how resources used in failing firms are transferred to surviving companies.
A final point relates to the theoretical consequences of the finding that there is
a systematic crowding out effect from FDI. As noted earlier, studies of the technology
spillovers from FDI have resulted in contradictory findings, with positive as well as
negative results reported in the extensive literature (Blomstr¨
om and Kokko 1998,
G¨
org and Greenaway 2004). One reason could be that the analyses are performed
on samples that include both surviving firms and firms that are crowded out because
of the competition from FDI. It is possible that the spillover effects estimated in
such samples are poor descriptions for both types of firms. The enterprises that are
84 ASIAN DEVELOPMENT REVIEW
crowded out per definition do not benefit from any positive technology spillovers,
and their inclusion in the sample may also obscure the true impact on surviving
firms. This suggest that estimations of spillovers should perhaps be performed in
two stages, with a first stage estimating survival and a second stage estimating
spillover effects for those firms that are not crowded out by foreign presence.
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Appendix 1: Recent Studies on Survival Effects of FDI
Covariates Controls for
Authors Countries Models (Firm Variables) Endogeneity Data Results
Wang (2010) Canada AFT FDI, Export, Import, Entry rate, Industry
dummies, Cohort dummies (Size,
Ownership dummy, Multi-plant dummy)
No Manufacturing
1973–1996
(–) Horizontal (+)
Backward (+)
Forward
Ferragina,
Pittiglio, and
Reganati
(2009)
Italy Cox Herfindahl, MES, FDI (Size, Age, Relative
labor productivity, Ownership dummy)
No Manufacturing
and services
2005–2007
(+) Services, (?)
Manufacturing
Iurchenko (2009) Ukraine Cox FDI, Export, Concentration, Region
dummies, Sector dummies, Time dummies
(Size, Capital intensity, Wages, Number of
subsidiaries, Profitability)
Lag(1) for
Herfindahl
Manufacturing
2001–2007
+
Bandick and
G¨
org (2010)
Sweden Cloglog Industry dummies, Time dummies
(Ownership, Size, Age, Multi-plant
dummies, R&D intensity, Exports)
Scoring
propensity and
IVs
Manufacturing
1993–2002
+/?
Kosova (2010) Czech Lognormal FDI, Reform dummies, Industry dummies,
Region dummies (Foreign capital share,
Sales growth, Age, Size, Intangible assets,
Technology gap, Solvency)
Dummies for
time, region,
sector
1994–2001 (–) Short term
Burke, G¨
org, and
Hanley (2008)
UK Cox FDI, Concentration, Sectoral growth (Size) No Manufacturing
1997–2002
(+) Overall (–)
Dynamic and (+)
Static industries
Taymaz and
¨
Ozler (2007)
Turkey Cox FDI, Entry rate, Growth, Prices, Imports,
Exports, Herfindahl, MES, Time dummies,
Industry dummies (Size, Employment
growth, K/L, Advertising, Contracted input
share, Contracted output share, Interest
payments, Profit margin, Bonuses)
No Manufacturing
1983–2001
(?)
Continued.
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 89
Appendix 1: Continued.
Covariates Controls for
Authors Countries Models (Firm Variables) Endogeneity Data Results
Louri, Peppas,
and Tsionas
(2006)
Greece Weibull, Cox,
Exponential
FDI, Herfindahl (Inefficiency, Ownership,
Age, K/L, Total assets, Leverage, Liquidity,
Profit, Debt)
No Manufacturing
1997–2003
(–)
G¨
org and Strobl
(2003b)
Ireland Cox FDI, MES, Herfindahl, Employment growth
(Size, Tech level, Age, Foreign dummy)
Sectoral
dummies
Manufacturing
1973–1996
(+)All(+) High tech
(–) Low tech
Backer and
Sleuwaegen
(2003)
Belgium System (OLS)
equation
Imports, Foreign entry, Foreign exit (Price
cost margin, Sales growth)
No Manufacturing (–) Short run, (+) Long
run
Alvarez and
G¨
org (2005)
Chile Probit FDI, MES, Herfindahl (Size, Age,
Productivity, Export dummy, Foreign
dummy)
No Manufacturing
1990–2000
(+) With productivity
improvement
Girma and G¨
org
(2003)
UK Cox Industry growth, Herfindahl, Region dummies
(Age, Size, Ownership, Age at acquisition)
IV method Electronic and
Food
industries
1980–1993
(−) Electronics (?) Food
Mata and
Portugal
(2002)
Portugal Exponential
Hazard
FDI, MES, Employment growth, Entry rate,
Concentration, Industry growth (Labor
quality, Size, Ownership form)
No All firms
1983–1991
(?)
Dries and
Swinnen
(2004)
Poland Probit (FDI dummy, Vertical links, Size, Age,
Education, Household characteristics)
Yes Dairy farm data
(1996–2000)
(+) Backward effect
AFT =accelerated failure time model, FDI =foreign direct investment, K/L =capital/labor, MES =minimum efficient scale, R&D =research and development, UK =United
Kingdom.
Source: Authors’ summary of the literature.
90 ASIAN DEVELOPMENT REVIEW
Appendix 2: Survival Probabilities of Domestic Firms (%)
Ye a r O E C D aUSbUKcUKdTurkey eViet Na m
1 – 93 99.2 75 83 87
2 – – 86.0 – 78 79
3 71 – 76.0 – 69 73
4 – – 69.7 55 60 66
5–67––– 50
10 – 54 – – 40 –
–=not available, OECD =Organisation for Economic Co-operation and Development, UK =United Kingdom,
US =United States.
aOECD. 2011. Entry, Exit, and Survival. In OECD Science, Technology and Industry Scoreboard 2011.Paris:OECD
Publishing; average for firms in the cohort 2004.
bAgarwal, Rajshree, and David Audretsch. 2001. Does Entry Size Matter? The Impact of the Life Cycle and
Technology on Firm Survival. Journal of Industrial Economics 49(1): 21–43; for US in period 1906–1990.
cHelmers and Rogers (2010); for UK in the period 2001–2006.
dSaridakis, George, Kevin Mole, and David Storey. 2008. New Small Firm Survival in England. Empirica 35(1):
25–39; for small sample survey in 1996–2001.
eTaymaz, Erol, and Sule ¨
Ozler. 2007. Foreign Ownership, Competition, and Survival Dynamics. Review of Industrial
Organization 31(1): 23–42; for the case of Turkey for the period 1983–2001.
FDI AND THE SURVIVAL OF DOMESTIC PRIVATE FIRMS IN VIET NAM 91
Appendix 3: Correlation Matrix for Variables Used in the Model (simple correlations)
123456789101112131415161718
1HFDI 1.000
2UpFDI –0.471 1.000
3DownFDI –0.366 0.392 1.000
4HSOE 0.385 0.048 –0.150 1.000
5UpSOE –0.392 0.355 0.281 –0.138 1.000
6DownSOE –0.185 0.457 0.331 –0.015 0.307 1.000
7GFDI –0.144 0.106 0.041 –0.057 0.043 0.015 1.000
8GSOE –0.213 0.228 0.165 0.008 0.090 0.088 0.289 1.000
9EXPORT 0.258 –0.051 –0.032 0.074 –0.185 0.020 –0.044 –0.060 1.000
10 IMPORT 0.450 –0.338 –0.216 0.060 –0.274 –0.080 –0.140 –0.106 0.651 1.000
11 EXPRATIO 0.528 –0.531 –0.206 0.012 –0.373 0.036 –0.120 –0.264 0.012 0.299 1.000
12 HERF 0.213 –0.171 –0.253 –0.172 –0.170 0.015 –0.024 –0.048 0.073 0.160 0.191 1.000
13 NBR 0.181 –0.124 –0.096 0.017 –0.107 –0.050 0.073 0.036 0.041 0.097 0.138 0.114 1.000
14 DIVER –0.045 0.006 0.034 –0.041 –0.051 0.018 –0.142 –0.042 0.025 0.046 –0.001 0.079 –0.507 1.000
15 MSCALE 0.411 –0.271 –0.071 0.054 –0.258 –0.195 –0.159 –0.091 0.096 0.291 0.314 0.096 0.088 0.106 1.000
16 RELSIZE –0.130 0.068 0.028 –0.107 0.104 0.028 0.069 0.014 0.020 –0.037 –0.130 –0.012 0.072 0.133 –0.251 1.000
17 CAPINT –0.003 0.038 0.007 –0.014 0.012 0.060 0.012 0.039 0.024 –0.005 –0.018 0.059 0.090 –0.007 –0.058 0.075 1.000
18 AGE 0.063 –0.028 –0.025 –0.014 –0.073 0.015 –0.013 –0.060 0.256 0.264 0.040 0.028 0.011 0.055 0.025 0.157 0.013 1.000
19 ENTRY –0.295 0.282 0.318 –0.026 0.347 0.223 0.171 0.185 –0.122 –0.240 –0.244 –0.179 –0.012 –0.112 –0.146 0.091 0.026 –0.107
Source: Authors’ computations.
