Discussion Papers In Economics
And Business

Productivity Gap and Vertical Spillover: Evidence
from Vietnam
Bin NiDiscussion Paper 16-04

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANProductivity Gap and Vertical Spillover: Evidence
from Vietnam
Bin NiDiscussion Paper 16-04
March 2016

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANProductivity Gap and Vertical Spillover: Evidence
from Vietnam
Bin Ni†March 25, 2016

Abstract
Technology spillover induced by foreign direct investment has been proved to be an
important channel to boost the productivity growth of local firms in the host country,
especially in the context of developing economies. However, the empirical evidence
remains inconsistent as to what extent the scale of spillover is affected by the
productivity gap between foreign investors and local firms. This paper attempts tomake clear such mechanism by applying Vietnamese firm-level data. Focusing on
Asian investors, we show that the relationship between the productivity gap andvertical spillover takes an inverted-U shape. To be specific, we use stepwise chow test
to decide on the cutoff value of total factor productivity (TFP) as the grouping criteria,
and divide investors into low, middle and high-TFP groups. The results reveal that
local suppliers in Vietnam can benefit the most from the Asian investors withmiddle-level TFP, whereas the benefits from the other two groups fade away. The
finding is strongly robust even after we control the other spillover-influential factors
such as firms' own effort to innovate, foreign firms' ownership, country and industry
heterogeneity, and no matter whether we use stochastic frontier or Levinsohn & Petrin
measurement of TFP. It thus provides novel evidence that investors with advanced
technology do not necessarily diffuse their know-how to local partners. This implies it
is important that both Vietnamese local firms and investors with superior technology
work in the same direction to stimulate more corporations with each other.

Keywords: technology spillover, productivity gap, firm-level data, Vietnam.
JEL Classification Numbers: D22, F21, F64, Q56.
	
  

	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
†	
  

The Graduate School of Economics, Osaka University (jiadaniel@hotmail.com).	
  1Introduction
Empirical studies to explore the relationship between the technology gap and spilloverat the macro-level show contradictory results. Li and Liu (2005), discover that a highertechnology gap (measured as per capita income ratio) between the domestic economy andthe technology leader reduces the positive eﬀect from FDI on income growth. However,Baltabaev (2014) indicates that technology gap can enhance the spillover from FDI. Thishas been supported by Shen et al. (2010) who find that high income levels promote thepositive impact of FDI. In the meantime, micro-level studies also yield mixed findings. Liuet al. (2000), for example, find a negative significant impact from the technology gap in the
UK manufacturing industry. Blalock and Gertler (2009), on the other hand, demonstratethat Indonesian manufacturing firms with larger technological gaps gain from FDI. They
also show that firms with greater absorptive capacity benefit more from downstream FDI,
implying the necessity to control firms’ own absorbing eﬀort.
Based on previous literature, this study takes a step further to measure the spillover
impact from foreign investors by controlling the level of technology gap. To be specific, wegroup foreign firms by the TFP level while taking into account their geographical heterogeneity and ownership diﬀerence. We use diﬀerent thresholds as grouping criteria and divide
investors into low-, middle- and high-TFP ones. We pay special attention to the verticallinkage because it is most likely to occur (Blalock and Gertler, 2008). However, to take
into account the possible influence from the foreign competitors in the same industry, we
include horizontal spillover index in the estimation as well. After controlling firms’ own effort to absorb technology and other firm-industry-year fixed eﬀects, we show a robust resultthat local suppliers in Vietnam experience the most productivity gains in the presence ofdownstream Asian partners who have relatively similar TFP as the suppliers themselves, orin other words, the “peers”. It is thus in contrast with some of the existing studies whichindicate that only a larger technology gap between upstream local firms and downstream
foreign investors will lead to greater spillover (Findlay, 1978; Wang and Blomstrom, 1992;1

Borensztein et al., 1998).
This study is positioned as the first paper to apply firm-level data to verify how the levelof technology gap matters for spillover realization in the context of Vietnam. This paper
contributes to the previous literature by empirically presenting the consistent and robust
result to make clear the relationship. The rest of the paper will be organized as follows:
Section 2 describes the situation of inward FDI in Vietnam and why it is important toinvestigate this issue in Vietnam. Section 3 summarizes the previous literature concerning
the technology gap and spillover eﬀect of FDI. Section 4 describes the data and estimation
strategy. Section 5 presents the results, and examines the robustness. Section 6 concludes.

2

Foreign direct investment in Vietnam
Vietnam is an ideal setting for us to investigate the relationship between FDI and tech-

nology spillover, for several reasons. To begin with, Vietnam has experienced remarkableeconomic growth mainly due to two major events—the adoption of a major economic reform
called Doi Moi in 1986, and the accession to the World Trade Organization (WTO) in 2006.
According to the “Vietnam country profile” made by Library of Congress Federal Research
Division, a high growth rate of around 7% was observed from the late 1990s, and this period
is characterized as being a period of rapid increase in inward FDI to the country (see Figure3-2). By year 2000, China had been the world s most popular destination for FDI for along time, however, ever since then, the trend has shifted to emerging South-East Asian
countries, among which Vietnam is becoming one of the most successful countries in theregion in attracting FDI across the world both because of its labor abundance and low wage
rate1 and the success of Doi Moi in liberalizing trade and investment.
In the meantime, for most of the foreign investors who are entering the Vietnamese
1

For example, in the apparel industry, the average wage in Vietnam is approximately half that in China(the Wall Street Journal, May 1st, 2013). Also, Samsung is shifting their production base to Vietnam inorder to maintain profit margins by saving labor costs as growth in sales of high-end handsets has slowed
down, according to a Bloomberg report in December 2013 (Lee and Folkmanis, 2013).

2

Figure 1: Number of FDI projects and implemented FDI (Bill. Dongs) in Vietnam.
14000.0	
  

1800	
  
1600	
  

12000.0	
  

1400	
  
10000.0	
  

1200	
  

8000.0	
  

1000	
  

implemented	
  capital	
  6000.0	
  

800	
  

Number	  of	
  projects	
  

600	
  

4000.0	
  400	
  2000.0	
  

1995	
  
1996	
  
1997	
  
1998	
  
1999	
  
2000	
  
2001	
  
2002	
  
2003	
  
2004	
  
2005	
  
2006	
  
2007	
  
2008	
  
2009	
  
2010	
  

0.0	
  

200	
  
0	
  

Source: General Statistics Office, Vietnam.

market, they have far better technology than their Vietnamese counterparts do, in termof TFP. Ni et al. (2015) has shown that the average TFP levels of Asian, European andNorth American firms are all higher than that of the Vietnamese firms (See Figure 2), andsuch technology disadvantage gives Vietnamese firms more potential to catch up. Sincetechnology spillover from foreign investors turns out to be an important channel to boost
the productivity of domestic firms in Vietnam (Nguyen, 2008), Vietnamese government
has committed to improve its investment environment and tries to use more policy tools2
to attract FDI. However, the targets are not limited to foreign investors with advanced
technology. For instance, the newly passed 2005 Investment law in Vietnam distinguishes thesectors in which FDI is encouraged, including both labor-intensive and high-tech industries.
Such actions increase the uncertainty as what kind of FDI will enter and lead to random
technology gap between new foreign investors and domestic firms, which leaves room for us
to explore how the spillover can be aﬀected by technology diﬀerences. The findings can thus
provide possible implications for the decision-makers in Vietnam to adjust further policies.

2

Policies’ influence on the technology spillover might also be substantial, it is beyond the scope of
discussion in this paper.

3

Figure 2: Average firm TFP by region.
0.650	
  0.640	
  
0.630	
  
0.620	
  0.610	
  0.600	
  0.590	
  0.580	
  0.570	
  
0.560	
  0.550	
  
0.540	
  Vietnam	
   Europe	
   North	
   total	
  America	
   Asia	  ASEAN	
   Japan	  &	
   other	  Korea	
   Asia	  Source: Ni et al. (2015). 

3Conceptual framework and literature review
To investigate how the productivity gap can aﬀect the level of technology spillover that

local suppliers receive, there are several questions that need to be answered: How can technology spillover be measured, and how is it determined? In terms of vertical spillover, why
does the technology gap matter?
The technology spillover eﬀect has always been a noticeable topic regarding the impact of
FDI3 . The prevailing perception of such eﬀect has been based on domestic firms’ productivity,and in empirical works mixed results have been observed concerning the spillover eﬀect, asin the survey of Smeets (2008). However, the phenomenon might be induced by factors suchas the channels for transferring technology via FDI and local firms’ capabilities to absorb
the knowledge. With regard to the former, as suggested in Saggi (2002), there are threepotential channels of spillover: demonstration eﬀect, labor switching and vertical linkage.
1. Demonstration eﬀect. Local firms may imitate multinational firms (MNEs) because

3

Rigorously speaking, we are defining the technology spillover as the knowledge transmission between
firms with diﬀerent ownership, in comparison to the one that occurs in between a firm’s headquarter and its
oversea subsidiaries. In Saggi (2002), spillover alone has been defined as the pure externalities accompanying
FDI, such as facilitation of technology adoption.

4

it is too costly to adopt the new technology owned by MNEs. Through this process, the setof technologies available to local firms can be expanded, leading to a positive externality.
But in the meantime, the incremental technologies may also increase the competition. If
the competition eﬀect outweighs the positive influence, the total net eﬀect of the technology
spillover due to FDI will turn out to be negative, which is known as the “crowding out” or“market stealing eﬀect” (Aitken and Harrison, 1999; Caves, 1996; Sleuwaegen and Backer,
2003). This eﬀect is observed mostly in the same industry where competition is more common
and addressed as “horizontal spillover”.
2. Labor switching. The knowledge embodied by the workers can transmit across firms
through physical movement. Those who have worked in multinational firms will bring theknowledge to the next employer, and such intangible asset can also become a source oftechnology spillover. However, since it is diﬃcult to track the individuals’ carry-on from old
to new employers and we focus mainly on the spillover eﬀect derived from firms’ interaction,
we will not emphasize on this factor in the current study.
3. Vertical linkage. This is the most widely recognized channel through which the technology spillover might occur. During the process when foreign investors in the downstream
industries source from the local suppliers in the upstream industries, the know-how as well as
the demanding requirements by foreign firms will push up the productivity level of the suppliers, resulting in the backward vertical spillover. Evidence of technology transfer throughvertical linkage is well observed. For example, Blomstrom and Kokko (1998), Gorg andGreenaway (2004) all find positive vertical spillover. Javorcik (2004) comes up with similar
results in Lithuania. Recent literature to support the positive spillover argument includes
Blalock and Gertler (2008), Indonesia/ Javorcik and Li (2013), Romania. Combining thethree factors together, since vertical linkage is most likely to benefit local firms, it will be of
the utmost interest in our study.
For the spillover to take eﬀect, apart from the supply side of technology as mentioned
above, we also need to look into the demand side catered for the technology upgrading.

5That is, if a local supplier wants to benefit from the advanced technology owned by foreign
investors, there are two major determinants: the supplier’s own eﬀort to innovate, such as
the proportion of expenditure on R&D to the total revenue, and its compatibility to absorb
the technology, or the “capability to catch up”. Especially for the latter, it to a great extent
determines the diﬀusion level a local firm might receive and has been explored by a numberof previous studies. Carluccio and Fally (2013) provide a decent theoretical explanationon the mechanism of how compatibility aﬀects technology spillover: The more compatible a
supplier is to adopt the foreign technology, the wider the choice set of compatible intermediate
inputs will be, thus reducing the production unit cost and driving up the productivity of the
supplier. Such variation becomes the source of diﬀerence in technology spillover eﬀect. Morepractically, technology gap is used to capture the incompatibility between local suppliers
and foreign investors in the downstream industries. Findlay (1978), Wang and Blomstrom(1992), Borensztein et al. (1998) all theoretically show that larger technology gap is related
to more vertical spillover. Nevertheless, the empirical research trying to investigate thisissue is rarely seen. Although several studies are aimed at quantifying the influence thetechnology gap might have on the spillover level4 , none of them take into the account thevertical interaction across industries. Le and Pomfret (2010) is the most similar to ourstudy in the sense that they consider both vertical spillover and technology (measured bylabor productivity) gap, but the result is inconclusive. Also, since the “technology gap”
term enters the model separately from the spillover index, the assumption is that these twovariables will have a linear correlation, while this might not always be correct (Perez, 1997).
In this study, we would like to relax the above assumption and take a diﬀerent approachby studying how foreign investors with diﬀerent TFP levels aﬀect the spillover they might
4

In the macro level, Baltabaev (2014) shows technology gap is positively associated with TFP growth
whereas Lai et al. (2009) use Chinese provincial data to present a non-linear relationship between thetechnology gap and spillover level. As for micro-level evidence, Liu et al. (2000) use UK data to show thatafter controlling firms’ own stock of indigenous knowledge (intangible assets per worker in locally-owned
firms to those in foreign-owned firm), technology gap (value added per employee in a foreign firm/local
firm in each industry) is found to be negatively correlated with the labor productivity of local firms in thesame industry. In contrast, Blalock and Gertler (2009) apply Indonesian data and indicate that within theindustry, the larger the technology gap, the more productivity gain local firms will receive.

6

induce to the Vietnamese suppliers located in the upstream industries. We will group foreign
investors based on diﬀerent thresholds of ex ante productivity level and see how each grouppromotes the local suppliers’ TFP. The detailed methodology will be described in the next
section.

4

Data and estimation strategy

4.1

Data

This paper uses a panel dataset constructed from the Vietnam Enterprise Survey at firmlevel. The Vietnam Enterprise Survey data is collected annually by the General Statistics
Oﬃce (GSO) of Vietnam for all industrial sectors as of March 1st of each year. The generalobjectives of this survey are: (i) to collect the business information needed to compile national
accounts; (ii) to gather up-to-date information on business registrations; and (iii) to develop
a statistical database of enterprises. This panel dataset covers ten years, from 2002 to
2011, in which Vietnam experienced two major economic changes, namely WTO accession
and the global economic crisis. The majority of the firms in the dataset can be foundin the list of Vietnam Standard Industrial Classification (VSIC) codes5 , including all 22
manufacturing sectors out of 42 in total. Profiles of firms concerning ownership, labor, capitalstock, turnover, assets, FDI, wage, materials inputs and other information are provided6 . In
our estimation model, we measure capital and labor by fixed asset and total labor at the endof year. Output and capital are deflated using annual GDP7 . Above that, the GSO surveyed
all multinational enterprises (MNEs), which are defined as firms that have foreign capital8 .
An advantage of this dataset is that the country that represents the ownership of the firm is

5

We use the first 2-digits indicated in VSICcode2007 and VSICcode1993 to identify industries. For
simplicity we aggregate some sectors. The details are available upon request.
6
Census is taken for firms with more than 10 employees (over 20 employees in 2010 and 2011).
7
Producer Price Index in the sector level is a preferred deflator but such data are not available forVietnam.
8
The sampling methods varied for private firms across years.

7also reported9 . Each firm is given a unique “enterprise code”, and it is used together withthe province code to identify firms and construct the panel dataset.
To achieve more accurate estimation results, we eliminate the missing observations andoutliers. Firms in the top and bottom one percentile of all firm-specific output and input
variables (in the means of annual growth) were deleted from the sample. Also the top andbottom 1% of output/capital and output/labor are excluded.

4.2

Estimating the firm productivity

TFP is the most commonly used measure of the eﬀect of FDI spillover on a firm’s performance in the literature (see, for example, Javorcik 2004). Although there are many ways toestimate TFP, we choose two alternative approaches that are suitable to our data situation,
namely stochastic frontier estimation, and Levingsohn and Petrin’s (2003) firm-level productivity estimation. The former has the advantage of isolating statistical noise from genuine
productivity whereas the latter has the advantage of incorporating explicitly the correlation
between unobservable productivity shocks and input levels.
Let us begin by using the traditional econometric approach to estimate TFP to illustrate
the advantages of our approaches. The Cobb-Douglas production function is written as:

lnYit = α + βk lnK it + βl lnLit + εit(1)where Yit stands for firm i’s net revenue in year t. K and L represent capital and labor
respectively, εit is the unobserved error term. Once this model is estimated using ordinary
least squares (OLS), TFP is calculated by normalizing the exponential transformation of
the residual10 . The well-known drawback of this approach is its inability to isolate genuine
productivity from statistical noise.
9

We only count the foreign ownership with the largest share. For example, if Japan’s share of investment
is the largest, we consider the firm to be a Japanese-invested firm.
10
The intercept is usually corrected make the estimated TFP to fall within the appropriate range.

8The stochastic frontier analysis overcomes this drawback by including two error components representing both (the inverse) technical eﬃciency and statistical noise. According to
Kumbhakar and Lovell (2000), the model is specified as:

lnYit = β0 +∑βn lnxni + vi + ui

(2)

where xni is a vector of inputs. vi is the noise component and ui is the nonnegative technical ineﬃciency component. Here, technical eﬃciency derived by inverting the technicalineﬃciency estimate is the measure of TFP. A half normal, exponential and Gamma distributions are often assumed on ui to ensure non-negativity of productivity estimates whereasa full normal distribution is assumed on vi as is common for random noise. The conditions
for the error components for the normal-half normal model are: (i) vi ∼ iid N (0, σv 2 ) (ii)ui ∼ iid N + (0, σv 2 ) (iii) vi and ui are distributed independently of each other, and of theregressors.
This model is estimated by maximum likelihood estimation. Once estimates of ui are
obtained from the residual of the model, the technical eﬃciency of the firm can be obtainedby:
T Ei = exp{−ûi }(3)where ûi is E(ui | εi ) 11 . Alternative distributional assumptions on ui can be accommodated
simply by replacing (ii).
Meanwhile, the Levinsohn and Petrin (2003) method tries to alleviate the bias causedby correlation between unobservable productivity shocks and input levels. The detailed
discussion can be found in Ni et al. (2015). When the method is applied, however, thelack of data on intermediate input, is a critical constraint for us. We do not have a direct
measure of intermediate input, instead, we useϕ(−µ∗ /σ ∗ )work-in-process

as a proxy variable forεi λ∗ ϕ(εi λ/σ)i
E(ui | εi ) = µ∗i + σ ∗ 1−Φ(−µ∗ /σ ∗ ) = σ [ 1−Φ(ε λ/σ) − σ ], σ and λ are σu and λv ; ϕ and Φ are densityiiand cumulative density functions respectively.
11

9intermediate input.

Work-in-process

is an appropriate proxy because products that areuncompleted in the previous period are to be brought into the production line in the current
period and to be completed. Also, it has to be noted that we interpolate input variables
to avoid losing too many observations due to the use of the lagged inputs in the Levinsohn
and Petrin model. These caveats are thought to reduce reliability of our estimation using
this structural approach. Thus, we rather use this model as a supplementary tool for thestochastic frontier analysis.

4.3

Estimating the spillover eﬀect

Now we proceed to the methodology to estimate the eﬀect of FDI on the estimated TFP.
A standard reduced form is used where a firm’ s TFP is regressed on measures of the FDI
spillover and other covariates, as in Javorcik and Spatareanu (2011). The FDI spillover
variables are built based on the influence of FDI within the same industry and downstream
industries. Since Ni et al. (2015) have shown that only investors from Asian regions tend to
induce significant spillover eﬀect, we will focus on Asian investors’ impact while controllingfor investors from other major areas12 . The estimation model becomes:

lnT F Pit = Horizontal Groupjt−1 + V ertical Groupjt−1 + Herf indaljt−1+αi + Xit + ηt + uit(4)

where the variable V ertical Group is defined as:

V ertical Groupjt =∑αjkt Horizontal Groupkt

(5)

k̸=j

lnT F Pit is the logarithm of TFP of a local firm i, at time t. Following the formula

12

Another advantage is that the geographical heterogeneity which might aﬀect the spillover level can also
be ignored. However, for the robustness check, we also conduct the analysis to include the factor of countryof origin, and the results remain unchanged

10

developed by Javorcik and Spatareanu (2011), we define Horizontal Group as the share of
the output produced by foreign firms within sector k in year t, and αjkt is the coeﬃcient
representing the proportion of sector js output used by sector k in year t13 . All the coef-

ficients are taken from the Vietnamese Input-Output Table (IO Table) 2007. The “Group”
term attached to each spillover variable depends on how we group foreign investors. Since wefocus on how the productivity diﬀerence for Asian investors matters, we will choose diﬀerent
TFP threshold “φ” to divide Asian investors into subgroups. Thus the above equation canbe rewritten as:

lnT F Pit = Horizontal Groupjt−1 +P∑V ertical Asiaφjt−1 + V ertical Europejt−1

φ=1+V ertical N orthAmericajt−1 + Herf indaljt−1 + αi + Xit + ηt + uit(6)

where P stands for the number of thresholds we use. The potential spillover induced by
European and North American investors are also included14 . Since there might be a time
lag for spillover to occur, we use the one-year lags of each variable as independent variables.
Xit represents firm covariates. In particular, we need to control a local firm’s own eﬀort
to absorb the technology, which is calculated as R&D expenditure/Net turnover. We also
include the industry-level Herfindahl index. Firm fixed eﬀect αi and year dummy ηt are
being controlled.
For the industry classification, we follow that of the IO Table 2007 because we need to
explore the industry linkage to construct vertical spillover variables. However, because theEnterprise Survey follows VSICcode industry classification, we had to match the industries
in the dataset with those used in the IO Table. In the end, our industry categories reduced
from 138 to 42 (Half of them are manufacturing sectors). Furthermore, the VSICcode systemchanged from VSICcode1993 to VSICcode2007 in year 2007, and therefore, the industry codes

13

When we calculate αjkt , sector j s output sold for final consumption was excluded.
Since investors from Asia, Europe and North America occupy more than 90% of the observations in thedataset, we ignore the influence of investors from other regions for the time being.
14

11

used in prior to 2007 are converted in accordance with VSICcode2007 by using a 1993-2007
concordance table15 .

4.4Grouping the Asian investors

When it comes to the grouping of the Asian investors, we need to choose the TFP
thresholds that might cause the structual change of the influence that the investors might
have on the spillover level. In order to determine such thresholds, statistical tests have to
be conducted and we adopt a modified Stepwise Chow Test16 . The idea is that suppose wehave a baseline estimation model:

yt = β0 + β1 V ertical Asia + ut

(7)

We want to verify that apart from the total vertical spillover, whether there is substantialchange if we include an additional term which reflects the partial influence of Asian investors.
We then run an augmented model:

yt = β0 + β1 V ertical Asia + βφ V ertical Asiaφ + ut

(8)

where we use the sum of squared residuals from equations (8) and (7) to test the null
hypothesis H0 : βφ . The F statistics are calculated as follows:

F =SSR1 − SSR2 N − k
·
SSR1
q

(9)

q is the number of restrictions and k is the number of parameters. We replace the term
V ertical Asiaφ each time we change the value of φ. In practice, we will use percentiles ofthe φ distribution among Asian investors and start from the lowest (i.e. from 1% cutoﬀ
to 100% cutoﬀ). The purpose is to find out the largest F statistics and determine the15
16

The table is made based on the content description of the sector.
We made reference to Lai et al. (2009).

12

correspondent φ. The result of the test is illustrated in Figure 2.
Figure 3: Chow F statistics by TFP percentage (Asian).
160

80%

140
120

35%

100
80
60
40
20
0

Source: Author's own calculation based on Vietnamese Enterprise
Survey data.

As we can see, at the 80% cutoﬀ there is a huge spike, which indicates the potential
structural change starting from this value. At the 35% another spike is observed, but lesssteeper. Thus we first use 80% TFP cutoﬀ as our main criteria, and divide Asian investors
into “>80%” and “<80%” groups. Then we need to construct the vertical Asian spillover
indexes based on the observations within each range respectively. In the next attempt, we
will use the 35% cutoﬀ to further divide “<80%” group into lower and middle subgroups.
Both estimation results will be shown in the following section.

5

Estimation results

5.1

Results for using 80% TFP cutoﬀ

The baseline estimation results using equation (6) are shown in Table 1. We observe
negative signs for Horizontal Group throughout the models, and this indicates the presenceof a strong replacement eﬀect by investors in the same industry. As for the variable ofinterest—vertical Asia, only the one that is constructed using the “<80%” group of samples
13

Table 1: Baseline grouping (80% TFP cutoﬀ)

(1)

(2)

Stochastic
Dependent Variable
Horizontal_total (lag 1)

Vertical_Asia (lag 1) (>80%)

LnTFP

LnTFP

(3)

(4)

Frontier
LnTFP-0.0425***-0.0136(0.00804)(0.00843)

LnTFP

0.00937*

0.0127**-0.00173-0.00390(0.00477)(0.00501)(0.0183)(0.0195)

0.0330***0.0277***0.0370***0.0371***(0.00873)(0.00819)(0.00957)(0.00991)-0.149*-0.116-0.0123-0.0283(0.0896)(0.0876)(0.104)(0.106)

0.338

0.332

-0.151-0.0426(0.338)(0.337)(0.321)(0.339)-8.09e-05-0.000105-7.37e-06**-7.32e-06**(0.000142)(0.000148)(3.50e-06)(3.59e-06)

0.224**

0.269***-0.139-0.0271(0.0912)(0.0980)(0.145)(0.125)Observations

421,438

420,810421,535

420,908

R-squared

0.044

0.044

0.055

0.055

Horizontal_origin control

No

Yes

No

Yes

Time FE

Yes

Yes

Yes

Yes

Industry FE

No

No

Yes

Yes

Vertical_Asia (lag 1) (<80%)

Vertical_Europe (lag1)

Vertical_NorthAme (lag1)

Own_effort

Herfindal Index

Note: Standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Horizontal_origin includes Horizontal_EU, Horizontal_NorthAme, Horizontal_Asia (<80%)
and Horizontal_Asia (>80%).

14

shows consistent and significant results. Also the coeﬃcient is larger than that of the spillover
index induced by the “>80%” group. This reveals that it is the Asian investors that areendowed with relatively lower TFP level, have the most spillover eﬀect on their upstream
Vietnamese suppliers.

5.2

Result for using both 35% and 80% TFP cutoﬀs

When we decompose the “<80%” group by adding the 35% TFP cutoﬀ, the result is evenmore explicit. As can be seen in Table 2, among the three groups, i.e. low, middle and highTFP Asian investors, only the ones within middle TFP range (35%-80%) induce the mostpositive and significant vertical spillover. In the meantime, Asian investors within low TFP
range (<35%) have a negative impact on Vietnamese suppliers’ TFP. The explanation forthis is that since Asian investors that have the most similar technology as local firms do arelikely to purchase the same parts that local firms will also use. Under certain circumstance,
it is diﬃcult for the spillover to occur, and on the contrary, these Asian investors will poseas a “threat” to their local suppliers and thus suppress the TFP growth of the latter.

5.3

Robustness checks

Several issues are worth extra care to confirm the robustness of our findings. One mightargue that the diﬀerence of spillover impact is due to geographical heterogeneity. For instance, Vietnam has close business connection with Japan and China, and such special bondwill enhance the interaction between investors from these countries and local suppliers. But
it is not the case for investors from other Asian regions. If the distribution of Asian investors
within 35%-80% range is not random, then it will contaminate our estimation of technology
gap’s sole influence on the vertical spillover.
To alleviate this concern, we give the list of Asian investors in the “middle” subgroup. As
shown in Table 4, investors with middle-level TFP are not limited to a particular country,
rather, they are scattered, ranging from East Asia to South Asia. This gives us the reason
15

Table 2: Baseline grouping (35% and 80% TFP cutoﬀs)

(1)

(2)

Stochastic
Dependent Variable
Horizontal_total (lag 1)

Vertical_Asia (lag 1) (<35%)

LnTFP

LnTFP

(3)

(4)

LnTFP

LnTFP

Frontier-0.0445***-0.0104(0.00832)(0.00767)-0.375**-0.486***-0.373**-0.386**(0.145)(0.149)(0.150)(0.152)0.0486***0.0471***0.0278**0.0276**(0.0139)(0.0135)(0.0118)(0.0119)0.005280.00886*-0.0111-0.0123(0.00501)(0.00534)(0.0190)(0.0194)-0.160*-0.1150.01090.00443(0.0881)(0.0849)(0.103)(0.100)0.567*

0.525

0.09130.101(0.330)(0.325)(0.325)(0.333)-3.69e-05***-3.71e-05***

5.78e-06

5.56e-06(5.91e-06)(6.11e-06)(6.36e-06)(6.30e-06)-0.176***-0.0975*-0.0208-0.0156(0.0573)(0.0553)(0.0576)(0.0721)Observations

421,450

420,822421,428

420,801

R-squared

0.043

0.044

0.054

0.054

Horizontal_origin control

No

Yes

No

Yes

Time FE

Yes

Yes

Yes

Yes

Industry FE

No

No

Yes

Yes

Vertical_Asia (lag 1) (35~80%)

Vertical_Asia (lag 1) (>80%)

Vertical_Europe (lag1)

Vertical_NorthAme (lag1)

Own_effort

Herfindal Index

Note: Standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Horizontal_origin includes Horizontal_EU, Horizontal_NorthAme, Horizontal_Asia (<35%),
Horizontal_Asia (35%~80%) and Horizontal_Asia (>80%).

16

to believe that geographical (or cultural) diﬀerence might not be as serious as we haveconsidered, though further eﬀort is needed to justify this point.
Another issue is that the ownership of the foreign investors can aﬀect the spillover they
induce to domestic firms, since joint ventures may face lower costs of finding local suppliers ofintermediates and thus be more likely to engage in local sourcing than wholly owned foreignsubsidiaries (Javorcik and Spatareanu, 2008). We thus generate the new vertical spillover
indexes based on the foreign investor’s ownership (fully or partially owned) and include themin equation (6). The estimation results remain unchanged17 .
Also there might be concern on the measurement error of the TFP cutoﬀs. To confirm
this, we use 25% or 50% TFP cutoﬀ to replace 35% when dividing the <80% group. We
still come up with the same result regardless of which cutoﬀ value we use. It is the Asianinvestors with middle-level TFP that induce the most significant vertical spillover to theirVietnamese suppliers.
To sum up, we can find a nonlinear correlation between the technology level that Asianinvestors own and the vertical spillover they might have on the local suppliers, which can bedepicted in Figure 4. The horizontal axis indicates the average TFP level of Asian investors(or shown as percentile) and the vertical axis reveals the induced vertical spillover. The
vertical spillover keeps increasing but insignificant until φ reaches point a. Before φ reachesb the vertical spillover will be significant or even be maximized in some point above the line“L”. Taking into account the fact that most Asian investors have a higher average TFP level
than Vietnamese suppliers do, we can describe the relationship between the technology gap(for Asian investors and Vietnamese suppliers) and the vertical spillover as an inverted-U
shape.

17

We do not include the results due to space constraint, but they are available upon request.

17

Table 3: List of Asian investors (between 35% and 80% TFP cutoﬀ)
Countryname
TaiwanKorea, Republic
Japan
Singapore
China
Hongkong
Malaysia
ThailandPhilippine
Indonesia
India
BruneiIsraelBangladesh
Sri Lanka

Countrycode
1320
1311
1307
1108
1304
1305
1105
1109
1107
1103
1306
1101
1204
1302
1318
Total

Number of firms
5,306
3,106
2,906
1,218
1,009
647
591
384
90
47
41
22
21
14
13
15,415

Source: General Statistics Office, Vietnam.

18

Percent (%)
34.33
20.1
18.8
7.88
6.53
4.19
3.82
2.48
0.58
0.3
0.27
0.14
0.14
0.09
0.08

Figure 4: Concept of implication.
VerticalSpillover

Significance Zone

L	
0	

6

a	

b	

ϕ asia (%)

Conclusions
By far the spillover impact of FDI has been widely investigated. In this paper, we examine

how productivity gap and vertical spillover are correlated in the context of Vietnam. In
particular, we focus on Asian investors, which are most likely to induce vertical spillover
to local suppliers, as shown in the previous literature. After applying statistical method to
divide Asian investors by diﬀerent TFP cutoﬀs, we show that the relationship between the
productivity gap and vertical spillover takes an inverted-U shape, i.e. Vietnamese suppliers
can achieve the most TFP gains from the diﬀusion of the Asian investors with middle-level
TFP.
The empirical results are robust against several sensitivity checks, thus providing theevidence that not all the foreign investors with the most advanced technology can bringabout the benefits to the local economy. In some scenario, high-tech foreign investors mightwant to protect their “core” competence and only outsource the minor components to the
local processing firms or suppliers. If that is the case, Vietnamese government should foster
Vietnamese firms to improve their technology level and adopt new policies to promote thecorporation between foreign investors and local firms.
19

7

Appendix

Table A1: TFP comparison among different methods
TFP scores
OLS
SF
LP

N
513913513913513913

mean
0.001
0.513
0.028

sd
0.004
0.165
0.046

max
1
0.81
1



min
0
0.008
0

Source: Author’s own calculation based on Enterprise Survey,
GSO, Vietnam

References
Aitken, B. J. and Harrison, A. E. (1999) “Do Domestic Firms Benefit from Direct Foreign
Investment? Evidence from Venezuela”, The American Economic Review, 89(3): 605-618.
Baltabaev B. (2014) “Foreign Direct Investment and Total Factor Productivity Growth: New
Macro-Evidence”, The World Economy, doi: 10.1111/twec.12115.
Blalock, G. and Gertler, P. J. (2008) “Welfare Gains From Foreign Direct Investment
Through Technology Transfer to Local Suppliers”, Journal of International Economics, 74(2):402-21.

20

Blalock, G. and Gertler, P. J. (2009) “How Firm Capabilities Aﬀect Who Benefits From
Foreign Technology”, Journal of Development Economics, 90(2): 192-99.
Blomstrom, M. and Kokko, A. (1998) “Multinational corporations and spillovers”, Journalof Economic Surveys, 12(3): 247-277.
Borensztein, E., Gregorio, J. D. and Lee, J. W. (1998) “How Does Foreign Direct Investment
Aﬀect Economic Growth?” Journal of International Economics, 45(1): 115-135.
Carluccio, JF and Fally, T (2013) “Foreign Entry and Spillovers with Technological Incompatibilities in the Supply Chain”, Journal of International Economics, 90 (1): 123-135.
Caves, R. E. (1996),

Multinational Enterprise and Economic Analysis , Second Edition.

New York, NY: Cambridge University Press.
Findlay, R. (1978) “Relative Backwardness, Direct Foreign Investment, and the Transfer ofTechnology: A Simple Dynamic Model”, Quarterly Journal of Economics, 92(1): 1-16.
Gorg, H. and Greenaway, D. (2004) “Much Ado About Nothing? Do Domestic Firms Really
Benefit from Foreign Investment?” World Bank Research Observer, 19(2): 171-197.
Javorcik, B. S. (2004) “Does Foreign Direct Investment Increase the Productivity of Domestic
Firms? In Search of Spillovers Through Backward Linkages”, The American Economic
Review, 94(3): 605-631.
Javorcik, B. S. and Li, Y. (2013) “Do the biggest aisles serve a brighter future? Global retailchains and their implications for Romania”, Journal of International Economics, 90(2):348363.
Javorcik, B. S. and Spatareanu, M. (2008) “To Share or Not to Share: Does Local Participation Matter for Spillovers from Foreign Direct Investment?” Journal of Development
Economics, 85 (1-2): 194-217.

21

Javorcik, B. S. and Spatareanu, M. (2011) “Does it matter where you come from? Vertical
spillovers from foreign direct investment and the origin of investors”, Journal of Development
Economics, 96: 126-138.
KlausE, M. and E. Sinan (2009) “When and Where Does Foreign Direct Investment Generate
Positive Spillovers? A Meta-analysis”, Journal of International Business Studies, 40(79):1075-1094.
Kumbhakar, S. C. and Lovell, C. A. K. (2000) “Stochastic frontier analysis”, Published by
the Press Syndicate of the University of Cambridge.
Lai, M. Y., Wang, H. and Zhu, S. J. (2009) “Double-edged Eﬀects of the Technology Gap
and Technology Spillovers: Evidence from the Chinese Industrial Sector”, China Economic
Review, 20(3): 414-424.
Le, Q. H. and Pomfret, R. (2010) “Foreign Direct Investment and Wage Spillovers in Vietnam: Evidence from Firm Level Data”, ASEAN Economic Bulletin, 27(2): 159-172.
Levinsohn, J. and Petrin, A. (2003) “Estimating Production Functions Using Inputs toControl for Unobservables”, Review of Economics Studies, 70: 317-341.
Li, X. and Liu, X. (2005) “Foreign Direct Investment and Economic Growth: An Increasingly
Endogenous Relationship”, World Development, 33(3): 393-407.
Liu, X. M., Siler, P., Wang, C. Q. and Wei, Y. Q. (2000), “Productivity Spillovers from
Foreign Direct Investment: Evidence from UK Industry Level Panel Data”, Journal of International Business Studies, 31(3): 407-425.
Liu, Z. (2008) “Foreign Direct Investment and Technology Spillovers: Theory and Evidence”,
Journal of Development Economics, 85(1/2): 176-93.

22

Ni, B., Spatareanu, M., Otsuki, T., Yamada, H. and Manole, V. (2015) “How Will the Originof FDI Aﬀect Domestic Firms

TFP? Evidence from Vietnam”, FREIT Working Paper,

No. 74.
Nguyen, L. P. (2008) “Productivity Spillovers from Foreign Direct Investment: Evidence
from Vietnamese Firm Data”, Available at SSRN: http://ssrn.com/abstract=1101203
Perez, T. (1997) “Multinational Enterprises and Technological Spillovers: An Evolutionary
Model”, Evolutionary Economics, 7(2): 169-192.
Saggi, K. (2002) “Trade, Foreign Direct Investment, and Technology Transfer: A Survey”,
The World Bank Research Observer, 17(2): 191-235.
Shen, C.-H. and Lee, C.-C. (2010) “What Makes International Capital Flows Promote Economic Growth? An International Cross Country Analysis”, Scottish Journal of Political
Economy, 57(5): 515-46.
Sleuwaegen, L. and Backer, K. D. (2003) “Does Foreign Direct Investment Crowd Out Domestic Entrepreneurship?” Review of Industrial Organization, 22: 67-84.
Smeets, R. (2008) “Collecting the Pieces of the FDI Knowledge Spillovers Puzzle”, WorldBank Research Observer, 23(2): 107-138.
Wang, J. Y. and Blomstrom, M. (1992) “Foreign Investment and Technology Transfer”,
European Economic Review, 36(1): 137-55.

23