Do vertical spillovers differ by investors’ productivity?
Theory and evidence from Vietnam∗
Bin Ni†

Hayato Kato‡

April 30, 2020

Abstract
Developing countries are eager to host foreign direct investment to receive positivetechnology spillovers to their local firms. However, what types of foreign firms are desirable for the host country to achieve spillovers best? We address this question using
firm-level panel data from Vietnam to investigate whether foreign Asian investors indownstream sectors with different productivity affects the productivity of local Vietnamese firms in upstream sectors differently. Using endogenous structural breaks, we
divide Asian investors into low-, middle-, and high-productivity groups. The results
suggest that the presence of the middle group has the strongest positive spillover effect. The differential spillover effects can be explained by a simple model with verticallinkages and productivity-enhancing investment by local suppliers. The theoreticalmechanism is also empirically confirmed.

Keywords: FDI spillovers; Heterogeneous productivity; Firm-level data; Endogenous structural break; Vertical Cournot model
JEL classification: D22; F21∗
This article is a substantially revised version of the paper previously circulated under the title “Productivity gaps and vertical technology spillovers from foreign direct investment: Evidence from Vietnam” (Niand Kato, 2017). We are grateful to two anonymous referees, the editor Andy McKay, as well as Mi Dai, Hiro
Ishise, Naoto Jinji, Fukunari Kimura, Eric Ramstetter and Shuichiro Urata for valuable suggestions. Our
thanks also go to the seminar and conference participants at the East Asian Economic Association (Bandung)
and the Asia Pacific Trade Seminars (Foreign Trade U) for their helpful comments. This research received
financial support from JSPS KAKENHI (JP16H07279; JP19K13693) and MEXT-Supported Program for theStrategic Research Foundation at Private Universities (JPS1391003), which we gratefully acknowledge.
†
Corresponding author. Faculty of Economics, Hosei University, 4342 Aiharamachi, Machida, Tokyo,
194-0298, Japan; E-mail: jiadaniel@hotmail.com.
‡
Graduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, 560-0043, Japan; Email: hayato.kato@econ.osaka-u.ac.jp.

1

Introduction

Hosting foreign direct investment (FDI) is essential for enhancing economic growth in developing countries. In addition to positive impacts on local economies such as expansion in local
sales and employment, one of the greatest benefits foreign multinationals could bring is technology spillovers to local firms. Through transactions and interactions with multinationals,
local firms learn multinationals’ sophisticated technology and thereby improve productivity.
One notable example is a US sewing-machine company, Singer, which started operations in
Taiwan in 1964, where there were small sewing-machine manufacturers with poor technology(Lall, 1996). To meet the local-content requirement forced by the government, the company sent several technical and management stuff to Taiwan to train local suppliers. The
forced local-content policy results in a significant technology transfer, helping local suppliers
become major exporters. Policymakers in developing countries expect FDI to bring such
positive spillovers and act as a catalyst for industrial development (Markusen and Venables,1999).
Previous studies on FDI spillovers have shown evidence of positive spillovers from backward industrial linkages (Javorcik, 2004; Blalock and Gertler, 2008; Liu, 2008; Wooster andDiebel, 2010 for meta-analysis).1 Specifically, an increase in FDI in downstream industries
improves the productivity of local firms in upstream industries. Using firm-level data in
Lithuania, Javorcik (2004) measured the presence of downstream FDI from the input-output
table and examined how it affects the productivity of local firms. She found that a onestandard-deviation increase in the foreign presence is associated with a 15-percent rise in
output of each local firm in supplying industries. Following Javorcik (2004), Blalock andGertler (2008); and Liu (2008) also found positive vertical spillovers in Indonesia and China,
respectively.
Recent studies show that the degree of vertical spillovers depends on both characteristics
of local firms and those of foreign investors (Blalock and Gertler, 2009; Javorcik and Spatareanu, 2008, 2011; Ni et al., 2017). For local firms to benefit better from vertical spillovers,
their capabilities measured by research and development (R&D) activities and the share of
educated workers are important (Blalock and Gertler, 2009 for Indonesia). Turning to foreign
investors’ aspects, the ownership structure in FDI projects (100% foreign ownership vs. joint
1

By contrast, the literature reports mixed evidence on spillovers within an industry, or horizontal spillovers.
In firm-level studies, Kokko (1994) found negative horizontal spillovers in Mexico, while Aitken and Harrison(1999) found positive ones in Venezuela. See Görg and Greenaway (2004); Smeets (2008); and Demena andvan Bergeijk (2017) for comprehensive surveys.

1domestic and foreign ownership) does matter (Javorcik and Spatareanu, 2008 for Romania).
Another important feature of foreign investors is their origin; those from particular source
countries bring more positive vertical spillovers (Javorcik and Spatareanu, 2011 for Romania;
Ni et al., 2017 for Vietnam).
We take one step forward from these studies by highlighting the role of foreign investors’
productivity. Specifically, we decompose downstream foreign investors into subgroups depending on their total factor productivity (TFP). We mainly focus on Asian investors, alongwith the two facts that (i) Asian investors have a larger presence in Vietnam than others(Figure 1) and that (ii) they tend to have a more significant effect (Ni et al., 2017).2 Thegrouping is not arbitrary but is based on a statistical method using endogenous structural
breaks (Lai et al., 2009). We use the thresholds to divide Asian investors into three groups:
those with high/intermediate/low levels of TFP. We find that foreign productivity has aninverted-U-shaped effect on the degree of vertical spillovers, i.e., the most significant positive
spillovers coming from the intermediate group.

4% 3%
11%

82%

Asia

EuropeNorth America

The others

Source: General Statistics Office, Vietnam.

FIGURE 1 Share of FDI firms by country of origin (years 2002-2011)

The inverted-U-shaped spillover effect can be explained by a simple Cournot oligopoly
model with downstream foreign firms and a local upstream firm (see also Appendix A2 for2

According to the Foreign Investment Agency in Vietnam, in terms of value, the FDI inflow from Asia
has reached 74% among all investment, whereas the second is the EU, with 15% by the end of 2017.

2

an extended model for horizontal spillovers). Before producing an intermediate good, thelocal supplier invests in reducing her marginal cost (i.e., doing R&D) while considering the
input demand by foreign customers. The local supplier’s productivity defined by the inverseof her marginal cost thus changes in response to changes in the number of foreign firms
through her investment, which we regard as vertical spillovers. It turns out that foreign firms
with intermediate productivity demand more inputs than those with high/low productivity,
inducing the local supplier to invest and thus increasing her productivity most effectively.
We further empirically test the mechanism and confirm it.
We believe that our focus on the different productivity levels of foreign investors is a
novel contribution to the literature on vertical spillovers. There have been some attempts toinvestigate the role of the productivity gap between local firms and foreign investors (Kokko,
1994 for Mexico; Blalock and Gertler, 2009 for Indonesia; Zhang et al., 2010 for China).3
These studies, however, define the gap as a distance between the productivity of each local
firm and that of the representative foreign investor (typically, the highest or the mean/medianproductivity). Foreign investors with different productivity are treated as one group and thus
their potential heterogeneous spillover effects are ignored.
Another contribution is our simple theory that explains the inverted-U-shaped effect. A
similar theoretical result can be found in Rodrı́guez-Clare (1996): hosting downstream FDI
would increase the productivity of local suppliers if the number of locally produced input
variety is close to the one produced in the origin country. His emphasis is on the foreign
investors’ input substitution between local and imported inputs, which is distinct from ourson their productivity-enhancing investment.4 We think that our theory is more suitable forfurther empirical checks because firm-level data on R&D or related activities can be obtained
more easily than data on input sourcing patterns.
Finally, it is worth noting our methodological contribution to identifying spillover effects.
Inspired by the identification strategy of Lu et al. (2017) in the context of China, we use therelaxation in FDI regulations upon Vietnam’s World Trade Organization (WTO) accession
as a quasi-natural experiment to conduct a difference-in-difference (DID) estimation. To be
specific, we compare firm performance in the treatment group (defined as the encouraged
industries) with that in the control group (i.e., the no-change industries) before and after3

For cross-country analysis, see Borensztein et al. (1998); Xu (2000); Li and Liu (2005); Shen et al. (2010);
and Baltabaev (2014). Theoretical explanations are given by Findlay (1978); Wang and Blomström (1992);
and Glass and Saggi (1998).
4
For the determinants of foreign investors’ local procurement in the host country, see Alfaro and Rodrı́guezClare (2004) for multinationals in Latin America and Kiyota et al. (2008); Baldwin and Okubo (2014) forJapanese multinationals across the world.

3

Vietnam’s WTO accession. We did this to reduce the influence of self-selected FDI inflow
into particular industries, and pin down the pure effect of foreign presence on local suppliers.
The method, to our knowledge, is the pioneer one of its kind.
Related studies in the context of Vietnam
Among other countries, Vietnam provides us with an ideal setting to investigate the relationship between FDI and technology spillover for two reasons. First, Vietnam experienced
remarkable economic growth thanks to the adoption of a major economic reform called Doi
Moi in 1986, and the accession to the WTO in 2006.5 It is becoming one of the most successful countries in the region to attract FDI across the world. The secrets lie in its laborabundance, low wage rate, and the successful liberalization of the investment environment.6
Second, the average productivity level of most Vietnamese local firms is lower than thatof foreign investors entering Vietnam (Ni et al., 2017), which gives Vietnamese firms morepotential to catch up. How such a technology gap can be filled is one of the challenges thatdeveloping countries face.
There are important contributions on the spillover effect of FDI on Vietnamese firms, suchas Nguyen (2008); Anwar and Nguyen (2014); Le and Pomfret (2011); and Newman et al.
(2015).7 Nguyen (2008); and Anwar and Nguyen (2014) highlight the location of local firms
and report a positive effect of downstream FDI on local firms’ TFP only in some regions. Le
and Pomfret (2011) found mixed effects of the labor-productivity gap between foreign andlocal firms on the degree of vertical spillovers. Using survey data of over 4,000 Vietnamese
manufacturers, Newman et al. (2015) observed whether a local firm has foreign firms as
customers. Such a direct linkage, however, is found to have no significant effect on the local
firm’s TFP.
We attempt to advance these studies in two ways.8 First, more careful attention is paid
5
Although we focus on the manufacturing sector, these two events also had significant impacts on the
agricultural sector. See Cazzuffi et al. (2018) for the welfare implications of agricultural commercialization.
6
In the apparel industry, for example, the average wage in Vietnam is approximately half of that in China(The Wall Street Journal, May 1st, 2013: http://www.wsj.com/public/page/archive-2013-5-01.html,
accessed on 25 October 2017). UNCTAD (2008) evaluates the impacts of the Doi Moi policy and the WTO
accession on FDI.
7
Apart from these firm-level studies, there are also studies using more aggregate data. Anwar and Nguyen(2010b) used province-level panel data from 1996 to 2005 to examine the effect of (aggregate) FDI on regional
economic growth. They found that FDI contributes to the regional economy in general, but the contribution
becomes smaller if the region is not equipped with a good financial system. Anwar and Nguyen (2010a)
distinguished between vertical and horizontal FDI and looked at their impact on the growth rate of local
manufacturing sectors.
8
Nguyen (2008); Anwar and Nguyen (2014); Le and Pomfret (2011); and we use the Vietnam Enterprise4

to TFP measures and the identification of spillover effects. Nguyen (2008); and Anwar
and Nguyen (2014) computed the local firms’ TFP from the Solow residuals of the CobbDouglas production function. Such a TFP measure is subject to an endogeneity bias in thesense that factor inputs are correlated with the error term (TFP measure) when applying
simple ordinary least squares (OLS) to estimate the Cobb-Douglas production function.9 We
apply the stochastic frontier method to estimate TFP, which overcomes this drawback by
separating technical efficiency from the statistical noise. The TFP measure is then regressedon the foreign presence to see spillover effects. Our identification strategy using Vietnam’s
WTO accession as a quasi-natural experiment is most likely to sort out the self-selection biasof FDI entry.
Second, as emphasized before, we consider potential heterogeneity in spillover effects from
Asian investors. While the above-mentioned studies highlight the characteristics of local firms
such as their location (Nguyen, 2008; and Anwar and Nguyen, 2014), they did not look at
the role of origin countries, nor that of heterogeneous productivity among foreign investors.
By contrast, we focus on these aspects and find an inverted-U-shaped relationship between
the TFP of downstream Asian investors and that of local suppliers. We further proposea plausible theoretical mechanism for the findings and empirically confirm it. Our results
would give more precise policy implications on which types of foreign firms bring the largest
spillovers.
The rest of the paper is organized as follows. Section 2 describes the data and estimation
strategy. Section 3 presents the results and examines the robustness. Section 4 develops a
simple model to explain the empirical results. The theoretical mechanism is further empirically checked. The final section concludes the paper.
Survey data, which will be explained in the next section. However, our data span a longer period and cover
more recent years, i.e., from 2002 to 2011. The data source is used in other recent studies on Vietnam, e.g.,
Ha et al. (2016) on resource misallocation; Trinh and Ha (2018) on small and medium-sized firms; Nguyen
et al. (2020) for employment.
9
Van Biesebroeck (2007) took five TFP estimation methods including OLS, the stochastic frontier approach and the semiparametric approach (Olley and Pakes, 1996; Levinsohn and Petrin, 2003), and checked
their robustness to measurement error and to differences in production technology using simulated data. He
concluded that “(G)iven the well-known simultaneity problem between inputs and unobserved productivity, estimating a production function by least squares (OLS) is generally not advisable (p.531).” See also
Van Beveren (2012) for the recent development of TFP estimation.

5

2

Data and estimation strategy

2.1

Data

This paper applies a firm-level panel dataset constructed from the Vietnam Enterprise Survey, collected annually by the General Statistics Office (GSO) of Vietnam for all industrial
sectors as of March 1 of each year. The general objectives of this survey are (i) to collect thebusiness 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. The
majority of the firms in the dataset can be found in the list of Vietnam Standard Industrial
Classification (VSIC) codes, including all 22 manufacturing sectors out of 42 in total.10
Profiles of firms concerning ownership, labor, capital stock, turnover, assets, total wage,
materials inputs, and information on FDI are provided. In our estimation model, we measurecapital and labor by fixed assets and total labor at the end of the year. Output and capitalare deflated using annual GDP.11
This panel dataset covers 10 years, from 2002 to 2011, whereas the census is conducted
for firms with more than 10 employees (over 20 employees in 2010 and 2011). The GSO
surveyed all multinational enterprises, which are defined as firms that have foreign capital.12
An advantage of this dataset is that for foreign-owned firms, GSO also reports the countryof origin of the largest shareholders of the targeting firm. In practice, we only count theforeign ownership with the largest share because more than 96% of the firms have only asingle shareholder. For example, if Japan’s share of investment is the largest, we consider
the targeting firm to be a Japanese-invested firm. Each firm is given a unique “enterprisecode,” which is used together with the province code to identify firms and construct the panel
dataset (unbalanced).
To achieve more accurate estimation results, following Javorcik and Spatareanu (2011),
we eliminate the missing observations and outliers by deleting samples in the top and bottom
one percentile of all firm-specific output and input variables (in the means of annual growth).
The top and bottom 1% of output/capital and output/labor are also excluded. This gives us
10

We use the first 2-digits indicated in the VSIC coding system. There are two types of codes, namely VSIC
code 2007 and VSIC code 1993, where VSIC code 2007 is applied since the year 2007. We also construct aconcordance table to unify these two systems. For simplicity we aggregate some sectors. See Appendix Table
A2 for detailed categorization of industries.
11
Even though Producer Price Index in the sector level is a preferred deflator, such data are not available
for Vietnam.
12Though in the standard FDI literature, foreign firms are defined as the ones whose foreign share is morethan 10%. We stick to the definition by GSO.

6

1,780,508 observations in total, but the observations for each variable can vary substantially
due to data availability. See Appendix Table A1 for the statistical summary.

2.2

Estimating firm productivity

TFP is the most commonly used measure of the effect of FDI spillover on a firm’s performancein the literature (e.g., Javorcik, 2004). Although there are many ways to estimate TFP,
we choose the stochastic frontier method, which can isolate statistical noise from genuine
productivity.13
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:ln Yit = α0 + αk ln K it + αl ln Lit + 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 OLS, TFP
is calculated by normalizing the exponential transformation of the residual. The well-known
drawback of this approach is its inability to isolate genuine productivity from statistical noise.
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 (2003), the model is specified as:ln Yit = α0 +X

αn ln xni + vi + ui ,(2)nwhere xni is a vector of inputs. vi is the noise component and ui is the nonnegative technicalinefficiency component. Here, technical efficiency derived by inverting the technical inefficiency estimate is the measure of TFP. Half-normal, exponential and Gamma distributions
are often assumed on ui to ensure nonnegativity of productivity estimates whereas a full normal distribution is assumed on vi as is common for random noise. The conditions for the error
components for the half-normal model are (i) vi ∼ i.i.d. N (0, σv2 ), (ii) ui ∼ i.i.d. N + (0, σv2 ),and (iii) vi and ui are distributed independently of each other, and of the regressors.
13

Meanwhile, the Levinsohn and Petrin (2003) method has been considered a standard way to calculate
TFP because it alleviate the bias caused by correlation between unobservable productivity shocks and input
levels. However, the lack of information on intermediate input, which is essential for the calculation, prevents
us from applying the approach.

7

This model is estimated using 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(−b
ui ),

(3)

where ubi is E(ui | εi ). Alternative distributional assumptions on ui can be accommodated
simply by replacing (ii).

2.3

Estimating the spillover effectNow, 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, namely horizontal spillover and vertical spillover, respectively. We focus on Asianinvestors’ impact for the reasons aforementioned, while controlling for investors from othermajor areas. The baseline estimation specification is as follows:
ln T F Pijt = Horizontal Originjt−1 + βV ertical Originjt−1 + Herf indaljt−1+αi + ηt + uijt ,(4)

where the coefficients of explanatory variables except for V ertical Asia are omitted forbrevity. In (4), the dependent variable ln T F Pijt is the logarithm of TFP of a local firm
i in sector j, at time t. All spillover variables are lagged by one period to take into accountthe possible delay for the spillover to take effect. Following the formula developed by Javorcik and Spatareanu (2011), we define Horizontal Originjt as the share of sector j’s output
produced by foreign firms in year t, differentiated by their origin. Because we focus on foreign
investors from Asia, Europe and North America, Horizontal Origin will be a vector including Horizontal Asia, Horizontal Europe and Horizontal N orthAmerica. If we want to
explore the horizontal spillover impact by all the foreign firms, then Horizontal Origin will
be replaced with Horizontal total.
In the meantime, V ertical Originjt measures the foreign presence in downstream industries. Following the literature, vertical spillovers in this study refer only to backward
spillovers to upstream suppliers. In addition, we include in the regression the Herfindahl
index of industry concentration. Time dummies are included to control for a time-specific8shock. Firm fixed effects αi are included to control for firms’ heterogeneity.
The variable V ertical Originjt is defined as:
V ertical Originjt =X

γjkt Horizontal Originkt .

(5)

k6=j

Here γjkt is the coefficient representing the proportion of sector j’s output used by sector
k in year t.14 All the coefficients are taken from the Vietnamese Input-Output Table (IO
Table) 2007. Because the Enterprise Survey follows the VSIC code industry classification,
we have to match the industries in our dataset with those used in the IO Table (see Ni et al.,2017 for the detailed matching procedure). We end up with 42 two-digit industries, which
are listed in the Appendix Table A2. Furthermore, the VSIC code system changed fromVSIC code1993 to VSIC code 2007 in the year 2007, and therefore, the industry codes prior
to 2007 are converted in accordance with VSIC code 2007 by using a 1993-2007 concordancetable.15
TABLE 1 TFP level and significance of vertical spillover by country origin

RegionVietnam
EuropeNorth America
total Asia
ASEAN
Japan & Korea
other Asia

Mean_TFP Vertical_Spillover
0.576
0.622
×
0.608
×
0.594
○
0.637
×
0.592
×
0.586
○

Source: Ni et al. (2017)

V ertical Originjt , our key variable of interest, captures the potential interaction between
foreign firms in j and local suppliers in k. In accordance with the construction of horizontal
spillovers, we include V ertical Asia, V ertical Europe and V ertical N orthAmerica as well.
In the previous study, Ni et al. (2017) found different backward vertical spillovers induced
14
15

When we calculate γjkt , sector j’s output sold for final consumption was excluded.
The table is formed based on the content description of the sector.

9

by investors from various regions. Even among Asian investors, the vertical spillover isheterogeneous. We then use the same dataset to calculate the mean TFP of the firms fromdifferent regions and summarize the relationship between the mean TFP and the significanceof vertical spillover. As shown in the upper panel of Table 1, among all firms, Asian ones
are the most likely to induce vertical spillover to local suppliers. This motivates us to focus
on Asian investors and further explore whether technology difference within Asian investors
affects the magnitude of spillover. We first divide Asian investors into subgroups basedon different TFP thresholds, and then construct new vertical spillover variables using thesesubgroups of samples. The revised specification is written as:ln T F Pijt = Horizontal totaljt−1 +PX

β ϕ V ertical Asiaϕjt−1 + V ertical Europejt−1

ϕ=ϕ0+V ertical N orthAmericajt−1 + Herf indaljt−1 + αi + ηt + uijt , (6)where the coefficients of explanatory variables except for V ertical Asia are omitted forbrevity; ϕ indicates a specific TFP threshold; and P stands for the total number of TFP
thresholds we use. In the next section, we will elaborate on the procedure of how to determine
ϕ and P .

2.4

Dividing the Asian investors into subgroups

When it comes to the grouping of the Asian investors, we need to find the TFP threshold,ϕ, below or above which the subgroups of investors tend to have a structurally different
spillover impact on domestic firms located in the upstream industry. For example, after wehave decided on a single threshold ϕ0 , we divide Asian investors into two subgroups: the firstgroup of firms whose TFP is above ϕ0 and the second group of firms whose TFP is below
ϕ0 .16 Guided by the previous studies that found heterogeneous spillover effects from foreign
investors with different characteristics, we would expect the first group to induce different
spillover from the second group. Such an argument can be extended to multiple thresholds
case, in which more than one structural transition should be observed.
Then the next question is: how can we determine the number of thresholds, or P as
noted previously? Rather than choosing the thresholds arbitrary, we adopt a modified stepwise Chow test and conduct statistical verification that can help us capture the “structural
16

In the sample, we have firms from 32 different Asian countries, excluding Vietnam. The countries withthe largest numbers of investors are Taiwan, South Korea, Japan, Singapore, China, Hong Kong and Malaysia(more than 1,000 firms throughout the period).

10

changes.”(Lai et al., 2009) We first assume the following baseline estimation model:
ln T F Pijt = δ0 + δ1 V ertical Asiajt−1 + uijt .

(7)

We want to verify that apart from the total vertical spillover induced by Asian investors,
will it cause substantial variation to the estimation system if we include an additional vertical
spillover variable using the subgroup of Asian investors? In the next step, we construct anaugmented model:
ln T F Pijt = δ0 + δ1 V ertical Asiajt−1 + δ ϕ V ertical Asiaϕjt−1 + uijt ,(8)

V ertical Asiaϕ captures the additional impact from the subgroup of Asian investors, whereas
ϕ is the threshold based on which we divide the samples. To locate the spike better evenduring trivial transitions, we sort all Asian firms according to their TFP level. After doing
this, (a) we use the lowest 5% of the samples to calculate V ertical Asiaϕ5% ; (b) we estimate(7) and (8), retrieve the sum of squared residuals from each result to test the null hypothesis
H0 : δ ϕ = 0; (c) during this process, we calculate the F -statistics as follows:
F =SSR1 − SSR2 N − k
·
.
SSR1
q

(9)

q is the number of restrictions, k is the number of parameters and SSR1 comes from (8).
The larger the F -statistics are, the less likely that δ ϕ = 0.
We then repeat the process of (a)-(c) introduced above, except that we replace V ertical Asiaϕ5%with V ertical Asiaϕ10% , i.e., using the lowest 10% of the samples to calculate the additional
term. The rest will be the same as the previous one. We continue this practice using thelowest 15%, 20%, and so forth, until 100%. Finally, we plot the F -statistics extracted eachtime against the percentage level of the TFP. The results are shown in Figure 2.

11

160

80%

140
120

35%

100
80
60
40
20
0

Source: Author's own calculation based on Vietnamese Enterprise
Survey data.
FIGURE 2 F -statistics by TFP level (the lowest % of all Asian samples)

As we can see, at the 80% threshold there is a huge spike, which indicates that theinclusion of V ertical Asiaϕ80% brings about the most structural variation compared with theoriginal specification (see (7)). Thus, we first use 80% TFP cutoff as our main criterion anddivide Asian investors into those whose TFP is above the lowest 80% of the total distribution
and those below the lowest 80%.
Besides, at the 35% threshold, another spike is observed, but the magnitude is not aslarger as at the 80% threshold. This guides us to use the 35% cutoff further to divide the“<80%” group of Asian investors into “<35%” and “35%<80%” subgroups. We will show
the results for both practices in the following section.

12

3

Estimation results

3.1

Results using the 80% TFP cutoff

Table 2 shows the baseline estimation results using (6). Columns (1) and (2) present theresults excluding industry control variables, whereas columns (3) and (4) report results withindustry controls. We find negative signs for Horizontal Group throughout the models,
indicating the presence of a strong replacement effect by investors in the same industry. As
for the variable of interest—V ertical Asia—only the variable constructed using the sampleof Asian investors whose TFP level is “<80%” shows consistent and significant results. In
addition, the coefficient is larger than that of the spillover index induced by the “>80%”
group. This reveals that Asian investors endowed with a relatively lower TFP level have themost spillover effect on their upstream Vietnamese suppliers.

3.2

Results using both the 35% and 80% TFP cutoffs

When we decompose the “<80%” group by adding the 35% TFP cutoff, the result is evenmore explicit. As Table 3 shows, among the low-, middle-, and high-TFP Asian investors,
only those within the middle-TFP range (35%-80%) induce the most positive and significant
vertical spillover in all specifications. Meanwhile, Asian investors within the low-TFP range(<35%) have a negative impact on Vietnamese suppliers’ TFP. This is because Asian investors
with the most similar technology to that of local firms are likely to purchase the same parts
that local firms will also use. Under certain circumstances, it is difficult for the spillover
to occur, and on the contrary, these Asian investors will pose as a “threat” to their local
suppliers and thus suppress their TFP growth.

13

TABLE 2 Baseline grouping (80% TFP cutoff)(1)
Dependent Variable
Horizontal_total (lag 1)

LnTFP-0.0285***(0.0103)
Vertical_Asia (lag 1) (<80%) 0.0409***(0.0136)
Vertical_Asia (lag 1) (>80%) -0.00330(0.00760)
Vertical_Europe (lag 1)
0.220**(0.0998)
Vertical_NorthAme (lag 1)-1.686**(0.668)Herfindal Index-0.0309(0.0357)
Observations
1,230,433
R-squared
0.046
Horizontal_origin_control
No
Time FE
Yes
Firm FE
Yes
Industry control
No

(2)
(3)
Stochastic Frontier
LnTFP
LnTFP
-0.0109(0.00923)
0.0387*** 0.0445***(0.0136)(0.0124)-0.000231-0.00861(0.00780) (0.00885)
0.239**
0.257***(0.0989)(0.0942)-1.840*** -1.576***(0.684)(0.487)-0.0650**-0.0231(0.0280)(0.0288)
1,228,710 1,229,073
0.046
0.053
Yes
No
Yes
Yes
Yes
Yes
No
Yes

(4)
LnTFP

0.0449***(0.0129)-0.00805(0.00917)
0.263***(0.0953)-1.630***(0.506)-0.0215(0.0290)
1,228,688
0.053
Yes
Yes
Yes
Yes

Note: Robust standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Horizontal_origin
includesHorizontal_EU,
Horizontal_NorthAme,
Horizontal_Asia (<80%) and Horizontal_Asia (>80%).
Industry control includes the number of foreign firms in an industry (in log form)
and industry-level capital ratio of State-owned firms.

14

TABLE 3 Baseline grouping (35% and 80% TFP cutoffs)(1)
Dependent Variable
Horizontal_total (lag 1)

LnTFP-0.0283***(0.0101)
Vertical_Asia (lag 1) (<35%)-0.0340(0.251)
Vertical_Asia (lag 1) (35%~80%) 0.0511***(0.0173)
Vertical_Asia (lag 1) (>80%)-0.00420(0.00775)
Vertical_Europe (lag 1)
0.213**(0.0993)
Vertical_NorthAme (lag 1)-1.644**(0.660)Herfindal Index-0.0323(0.0358)
Observations
1,230,433
R-squared
0.046
Horizontal_origin_control
No
Time FE
Yes
Firm FE
Yes
Industry control
No

(2)
(3)
Stochastic Frontier
LnTFP
LnTFP
-0.0111(0.00913)-0.143-0.0560(0.250)(0.200)0.0530*** 0.0545***(0.0181)(0.0161)-0.000999-0.00946(0.00794)(0.00903)
0.237**
0.254***(0.0981)(0.0951)-1.822*** -1.521***(0.677)(0.479)-0.0660**-0.0244(0.0284)(0.0287)
1,228,710 1,229,073
0.046
0.053
Yes
No
Yes
Yes
Yes
Yes
No
Yes

(4)
LnTFP

-0.0955(0.196)
0.0567***(0.0168)-0.00878(0.00934)
0.261***(0.0955)-1.585***(0.499)-0.0249(0.0289)
1,228,688
0.053
Yes
Yes
Yes
Yes

Note: Standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Horizontal_origin
includesHorizontal_EU,
Horizontal_NorthAme,
Horizontal_Asia (<35%), Horizontal_Asia (35%~80%) and Horizontal_Asia(>80%); Industry control includes the number of foreign firms in an industry (inlog form) and industry-level capital ratio of State-owned firms.

3.3

Endogeneity

Identification strategy.
A crucial assumption for obtaining an unbiased estimate as in (6) is that the regressor ofinterest, namely, the vertical spillover variables, are uncorrelated with the error term. However, this assumption can likely be violated in our setting. For example, foreign downstream
firms with higher productivity are more likely to enter industries/places with high productive
domestic suppliers. This leads to a positive correlation between foreign presence and productivity of domestic firms resulting simply from the location decision by foreign investors rather
than the positive spillover effects of their investment. In such a case, failure to control foreign15

investors’ self-selection into particular local industries would lead to a biased (upward) estimation of foreign investors’ positive effect on domestic firms’ TFP. We can consider similar
scenarios for investors with low and middle productivity.
To deal with the identification problem, we follow Lu et al. (2017) and use the relaxationin FDI regulations upon Vietnam’s WTO accession to conduct a DID estimation. The“Foreign Investment Law” in Vietnam was issued first in December 1987 to create morefavorable conditions for foreign investors and underwent two major amendments in 2000
and 2005. Though Vietnam has been officially encouraging foreign investment as part of its
development strategy since the Doi Moi economic reform, it was not until the Investment Law
of 2005 that Vietnam provided a more detailed and legal framework for foreign investment.
The law distinguishes four types of sectors: (1) prohibited sectors, (2) encouraged sectors,(3) conditional sectors applicable to both foreign and domestic investors, and (4) conditional
sectors applicable only to foreign investors (UNCTAD, 2008). The list of encouraged, forexample, includes high-technology, agriculture, labor-intensive industries (employing 5000 or
more employees), and infrastructure development (for the detailed categorization, please refer
to Appendix Table A2). Foreign investors in the encouraged sectors are considered to havebetter access to Vietnam’s domestic market and engage more with the local firms comparedwith investors in other categories.
On January 11, 2007, Vietnam became the 150th member of the WTO. Upon its WTO
accession, Vietnam committed to gradually open more sectors.17 The relaxation of the FDI
regulations can be considered an exogenous economic shock, which gives us the setting inwhich to conduct DID.18 The treatment group includes the sectors that experienced thechange in FDI regulations after 2006, that is, the prohibited and conditional sectors mentioned above; while the control group contains the sectors that were already encouraged prior
to WTO entry. We then apply firm-level data covering 2002 to 2011 to compare the spillover
effect between the treatment group and control group, before and after Vietnam’s entry into
the WTO.
17
Vietnam excluded certain products from its WTO distribution services commitments, including rice,sugar, tobacco, and crude and processed oil (UNCTAD (2008)). Distribution of alcohol, cement and concrete,
fertilizers, iron and steel, paper, tires, and audiovisual equipment had opened to foreign investors by 2010.
18
The timing of the FDI deregulation can be considered as random because the negotiation of the Vietnam’s
WTO accession was a lengthy way (11 years) and uncertain prior to 2006. See Hanh (2011) for details.

16

Estimation specification.
We adopt an augmented version of (6):ln T F Pijt = T reatmentj × P ost06t + Horizontal totaljt−1 + V ertical Originjt−1+Herf indaljt−1 + αi + ηt + uijt , (10)where the coefficients of explanatory variables are omitted for brevity. Our main interest liesin the coefficient of the interaction term in (10). T reatmentj indicates whether industry j
belongs to the treatment group, as we defined before. P ost06t is a dummy variable indicating
the post-WTO period, that is, P ost06t = 1 if t > 2006 and 0 if t ≤ 2006. While controlling
for the other trends that might affect domestic firms’ TFP, it is assumed that the Asian firms
located in FDI-prohibited industries will induce a larger spillover effect after Vietnam’s entry
into the WTO. Because of the relaxation of the regulations, there will be more interaction
between Asian investors and local suppliers, which is why we are expecting a positive signfor the interaction term.
Another point worth mentioning is that because we create vertical spillover variables using different subgroups of Asian investors, the interaction term T reatmentj × P ost06t might
overestimate the impact induced by Asian investors with middle-range TFP. Thus, it is necessary to control for the level of interaction between a specific subgroup of Asian investors
and their local suppliers. Antràs (2003) argued that the costs of physical capital are easier toshare than the costs of labor inputs, and that headquarters provides affiliates with machineryand equipment or assists their suppliers in the acquisition of capital equipment. It is reasonable to assume that capital-intensive firms will have a higher propensity to interact actively
with local suppliers. Previous studies use the scale of industry-specific capital intensities tomeasure sourcing intensity (e.g., Kohler and Smolka, 2015). To follow this practice, we first
calculate the average capital-labor ratio using the samples of Asian investors whose TFPlevels are between 35% and 80% of the total distribution, in each industry. Then, we interact this ratio with T reatmentj ×P ost06t , and use it as a robustness for our DID verification.19
Estimation results.
As the results in Table 4 show, the Horizontal total is always negative, which is consistent
with the previous findings that foreign investors have an even more substantial replacement
19
In place of T reatmentj × P ost06t , we use mean capital labor ratio(35% − 80%Asian)j × T reatmentj ×
P ost06t . The new interaction term remains positively significant. We do not present these results in thepaper, but they are available upon request.

17

impact on domestic firms in the same industry after Vietnam’s entry to the WTO. In thecase of vertical spillovers, when we divide Asian firms into low- and high-TFP groups, ourregressor of interest, T reatmentj × P ost06t , is positive and significant in all specifications.
These results show that domestic suppliers in industries with FDI inflows encouraged after
the WTO accession experienced an increase in their productivity levels compared with thosein industries without much change in FDI regulations. This confirms our previous findingthat the vertical spillovers are mainly due to the low-TFP group of Asian firms located indownstream industries.
In addition, when we further divide the samples by 35% and 80% thresholds, the results shown in Table 5 show a similar trend as in the previous ones. Horizontal total
still has negative signs, as expected. On the other hand, regardless of whether we include
V ertical Asia(35% − 80%) or not, the variable of interest, T reatmentj × P ost06t , is positiveand significant in all specifications. Combining the results from Table 4, we can infer thatamong the low-TFP group of investors, it is Asian firms in the middle-TFP range (35%-80%)
that induce the most significant vertical spillover to the local suppliers. Thus, by applying the
supplementary robustness checks using DID, we are reassured of the significant productivitypromoting influence of Asian investors whose TFP level is within the middle range of thetotal distribution.

18

TABLE 4 Results using the DID method (80% TFP cutoff)(1)
Dependent Variable
Horizontal_total (lag 1)
Vertical_Asia (lag 1) (<80%)
Vertical_Asia (lag 1) (>80%)
Vertical_Europe (lag 1)
Vertical_NorthAme (lag 1)
Treatment ×Post06
Herfindal Index
Observations
R-squared
Time FE
Firm FE
Industry control

LnTFP-0.0288**(0.0118)0.0395*(0.0208)-0.00701(0.00789)
0.219(0.137)-1.734**(0.807)0.00503**(0.00198)-0.0247(0.0280)
1,230,432
0.046
Yes
Yes
No

(2)
(3)
Stochastic Frontier
LnTFP
LnTFP
-0.0117-0.0296**(0.0112)(0.0119)0.0431***(0.0141)-0.0118-0.00933(0.0102)(0.00744)
0.256**
0.323**(0.124)(0.149)-1.617***-1.507**(0.543)(0.710)0.00465*
0.00578**(0.00250)(0.00222)
0.0164-0.0254(0.0526)(0.0275)
1,229,072
1,230,432
0.053
0.045
Yes
Yes
Yes
Yes
Yes
No

(4)
LnTFP
-0.0132(0.0107)-0.0141(0.0104)
0.369***(0.130)-1.367***(0.493)0.00551**(0.00272)
0.0150(0.0528)
1,229,072
0.052
Yes
Yes
Yes

Note: Robust standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Industry control includes the number of foreign firms in an industry (in log form)
and industry-level capital ratio of State-owned firms.

19

TABLE 5 Results using the DID method (35% and 80% TFP cutoffs)(1)
Dependent Variable
Horizontal_total (lag 1)
Vertical_Asia (lag 1) (<35%)
Vertical_Asia (lag 1) (35%~80%)
Vertical_Asia (lag 1) (>80%)
Vertical_Europe (lag 1)
Vertical_NorthAme (lag 1)
Treatment ×Post06
Herfindal Index
Observations
R-squared
Time FE
Firm FE
Industry control

LnTFP-0.0288**(0.0117)-0.0185(0.192)0.0490*(0.0254)-0.00775(0.00795)
0.211(0.140)-1.692**(0.794)0.00489**(0.00193)-0.0250(0.0277)
1,230,432
0.046
Yes
Yes
No

(2)
(3)
Stochastic Frontier
LnTFP
LnTFP
-0.0119-0.0304**(0.0109)(0.0123)-0.03990.220(0.161)(0.226)
0.0523***(0.0192)-0.0125-0.00881(0.0103)(0.00757)
0.253*
0.297*(0.127)(0.149)-1.562***-1.535**(0.541)(0.717)0.00452*
0.00586**(0.00260)(0.00220)0.0153-0.0244(0.0519)(0.0278)
1,229,072
1,230,432
0.053
0.045
Yes
Yes
Yes
Yes
Yes
No

(4)
LnTFP
-0.0140(0.0109)0.217(0.157)-0.0136(0.0106)
0.343**(0.133)-1.395***(0.492)0.00560**(0.00258)
0.0161(0.0530)
1,229,072
0.052
Yes
Yes
Yes

Note: Standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Industry control includes the number of foreign firms in an industry (in log form)
and industry-level capital ratio of State-owned firms.

3.4

Other robustness checks

Several issues are worth extra care to confirm the robustness of our findings. One mightargue that the differing spillover impact is due to geographical heterogeneity. For instance,
Vietnam has close business connections with Japan and China, and this bond can enhance
the interaction between investors from these countries and local suppliers. However, this is
not the case for investors from other Asian countries. If the distribution of Asian investors
within the 35%-80% range is not random, then it will contaminate our estimation of the sole
influence of the technology gap on vertical spillover.
To alleviate this concern, we decompose Asian investors in the “middle” subgroup. We
find that investors with middle-level TFP are not limited to a particular country; rather,
20

they are scattered, ranging from East Asia to South Asia. This gives us reason to believethat geographical (or cultural) differences might not be as serious as we considered, though
we should take further effort to justify this point.
Another issue is that foreign investors’ ownership can affect the spillover they induce
to domestic firms because joint ventures may face a lower cost to find 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 foreign investors’ ownership (full or partial ownership) and re-estimate (6).
The inclusion of the new indexes does not change our prediction.
In addition, there might be a concern with measurement error for the TFP thresholds.
To confirm this, we use 25% or 50% TFP cutoffs to replace 35% when dividing the <80%group. We still come up with the same results, regardless of the threshold value we use.

4

A simple model

We have found that the effect of foreign presence on the local firms’ productivity differs
depending on the productivity of foreign investors. We here provide a simple theoreticalmodel to explain a potential mechanism of our empirical findings. Our emphasis is on thelocal firm’s choice of productivity-enhancing investment. We are exclusively concerned withvertical spillovers; see Appendix A2 for an extended model that explains negative horizontal
spillovers.
Consider a partial-equilibrium Cournot model with upstream and downstream sectors.
In a host country, there are one local upstream firm and N ∗ > 1 number of symmetricforeign downstream firms, each of whom produces a homogeneous intermediate and final
good, respectively.20 N ∗ represents the presence of foreign firms or the variable V ertical Asia
in the empirical section. Foreign firms source 1/ϕ units of a homogeneous input per unitof production from a local supplier at the price of w. ϕ captures their productivity or atheoretical counterpart of their TFP. In addition to input production, the local supplier makes
a cost-reducing investment, which can be thought of as an R&D. Her marginal cost, denotedby cU , is thus endogenously determined. The inverse of the marginal cost, 1/cU , corresponds
to the productivity (or TFP) of the local supplier, which is the dependent variable of our
20

Because competition among local suppliers would not change our qualitative results, we assume thatthe number of local firms is one. In Appendix A2, we introduce foreign firms into the upstream sector andexamine the intra-industry spillover effect from them.

21empirical specification.
The timing of actions proceeds as follows. First, the local supplier decides on how muchthey invest in marginal-cost reduction. Second, she sells inputs to foreign firms and finallythey serve the final-good market. The problem is solved backward.
We will illustrate how the effect of foreign presence in the downstream sector on the local
supplier’s productivity, i.e., d(1/cU )/dN ∗ , varies with the productivity of foreign firms ϕ.

4.1

Final stage: Downstream sector

Let us first see the decision of foreign firms in the final stage. They face the linear demandof p = a − Q, where p is the price of the final good and Q is the total demand. Letting qi∗ beP ∗ ∗the quantity supplied by foreign firm i, the market-clearing condition implies Q = N
i=1 qi .
The profit of foreign firm i is the sales from selling the final product minus the input cost:πi∗ = pqi∗ − (w/ϕ)qi∗ ,where 1/ϕ is the unit input requirement and thus ϕ is the TFP of foreign firm i.
The optimal output that maximizes πi is given byqi∗ =a − w/ϕ.
N∗ + 1

The total output is thenN ∗ (a − w/ϕ),N∗ + 1→ w = ϕ[a − (N ∗ + 1)Q/N ∗ ].

Q = N ∗ qi∗ =(11)

Accordingly, the total input demand is Q/ϕ.

4.2

Second stage: Upstream sector

We turn to the second stage, where the local supplier chooses quantity Q. Her gross profit isw(Q/ϕ) − cU (Q/ϕ)= [a − (N ∗ + 1)Q/N ∗ − cU /ϕ]Q,22

where w is given in (11). cU is the marginal cost, exogenously given at this stage. The
supplier maximizes the gross profit to obtainQ/ϕ =N ∗ (aϕ − cU ).
2ϕ2 (N ∗ + 1)

(12)

It is worth noting here that the above input demand has an inverted-U-shaped relationshipwith the foreign productivity ϕ. Foreign firms with very low ϕ produce little and do not need
many inputs. Those with very high ϕ can produce much from a tiny amount of inputs. It
is foreign firms with middle ϕ who demand inputs most. This nonmonotonic relationshiptranslates into interesting interactions between the foreign productivity and the supplier’s
investment and productivity, as we will see below.

4.3

First stage: Productivity-enhancing investment

In the first stage, the supplier engages in productivity-enhancing investment, or R&D investment. The marginal cost, cU , or the inverse measure of productivity, depends on theinvestment level x. We simply specify cU as an decreasing function of x:cU = c − x,
where c is a positive constant.
The supplier chooses x to maximize the profit net of a quadratic investment cost:
π U = w(Q/ϕ) − cU (Q/ϕ) − x2 /2,
The optimal investment level and the resulting marginal cost are respectively
N ∗ (aϕ − c),
2ϕ2 (N ∗ + 1) − N ∗ϕ[2cϕ(N ∗ + 1) − N ∗ a]cU =.
2ϕ2 (N ∗ + 1) − N ∗x=

The second-order condition (SOC) requires 2ϕ2 (N ∗ + 1) − N ∗ < 0 or ϕ >(13)p
N ∗ /[2(N ∗ + 1)].

To ensure both x > 0 and cU > 0 as well as the SOC, we assume (a)ϕ > ϕmin ≡ c/a andp(b)c/a > N ∗ /[2(N ∗ + 1)]. x has an inverted-U-shaped relationship with ϕ because thesame relationship holds between ϕ and the input demand Q/ϕ, as indicated before.

23

4.4

Inverted-U-shaped relationship

We see that the foreign presence N ∗ in the downstream sector increases the supplier’s productivity 1/cU :∂x∂(1/cU )1=∗
2∂N(c − x) ∂N ∗2(aϕ − c)=> 0,[2cϕ(N ∗ + 1) − N ∗ a]2(14)

where x is given in (13). An increase in the foreign presence raises the input demand andmakes R&D investment more rewarding.
The magnitude of the above effect, however, depends on the productivity of foreign firms,
ϕ:b
2[4c (N + 1) − N a − 2acϕ(N + 1)] ≥ 0 if ϕ ≤ ϕd ∂(1/c )
,
=
∗
2
∗
∗
3
< 0 if ϕ > ϕdϕ∂N[2ϕ (N + 1) − N ]bUwhere ϕ
b≡



2

∗

∗ 2

∗

4c2 (N ∗ + 1) − N ∗ a2,
2ac(N ∗ + 1)

noting ϕ∗ > 0 because of assumption (b). The positive effect of foreign presence on the local
supplier’s productivity first increases and then decreases as the foreign firms’ productivity
rises.
An increase in foreign productivity, ϕ, leads to an inverted-U-shaped effect on the degreeof spillovers ∂(1/cU )/∂N ∗ in the following steps, as illustrated in quadrant I in Figure 3. As
the second-stage result suggests, foreign productivity has an inverted U-shaped relationshipwith the input demand Q/ϕ (quadrant IV). A larger input demand makes stronger the
local supplier’s incentive to improve productivity and thus leads to more R&D investment x(quadrant III).21 A higher level of investment then magnifies the degree of vertical spillovers
21

We can check that the incentive to invest increases with the input demand. In the first stage, the FOC

is
∂π U
∂
=
[w(Q/ϕ) − cU (Q/ϕ) − x2 /2]∂x∂x= Q/ϕ − x,which increases with Q/ϕ.

24∂(1/cU )/∂N ∗ (quadrant II).22 In sum, the degree of vertical spillovers is linked with theforeign productivity through investment decisions in response to input demand.

FIGURE 3 Foreign productivity and vertical spillovers

The empirical implication key to our theory is that foreign productivity also has aninverted-U-shaped relationship with the optimal R&D investment level, as quadrant I in
Figure 4 shows. The local suppliers invest most at the intermediate value of ϕ, which gives
the highest input demand.
22

Using (13) and (14), we have∂(1/cU )2[2ϕ2 (N ∗ + 1) − N ∗ ]
=
· x,
∗
∂N
[2cϕ(N ∗ + 1) − N ∗ a]2

which increases with x.

25

FIGURE 4 Foreign productivity and R&D investment

4.5

Empirical evidence on the mechanism

The mechanism illustrated in our model implies that downstream foreign investors with different productivities have different demands for local inputs: Foreign firms with very low
productivity produce little and thus demand few inputs, and those with very high productivity can produce much from a tiny amount of inputs. Foreign firms with intermediate
productivity demand input the most. As such foreign customers increase their presence, local suppliers engage in R&D investment and thus improve their productivity most effectively.
To verify whether local suppliers increase the productivity through the channel of R&D
investment, as in the quadrant I of Figure 4, we conduct the following analysis:ln R&Dijt = Horizontal totaljt−1 +X

β ϕ V ertical Asiaϕjt−1

ϕ∈{low, middle, high}+ V ertical Europejt−1 + V ertical N orthAmericajt−1 + Herf indaljt−1 + αi + ηt + uijt ,where the coefficients of explanatory variables except for V ertical Asia are omitted forbrevity. We use two variables—a firm’s own investment in machinery or its investment
in repairing fixed assets alternatively as a proxy for their effort on R&D—and regress iton the same set of spillover variables. As we can see from the results in Table 6, the coefficient of V ertical Asia(35% − 80%), β middle , has the most significant and positive result.
26

This indicates that the presence of Asian investors with the middle TFP level is driving up
the local firms’ own investment in R&D, and this effort, in turn, will lead to an increase inproductivity. In contrast, the groups of Asian investors with low- and high-TFP levels are
not inducing as much vertical spillover as the group with a middle-TFP level does.

TABLE 6 Results using the DID method (35% and 80% TFP cutoffs)
Dependent Variable
Horizontal_total (lag 1)
Vertical_Asia (lag 1) (<35%)
Vertical_Asia (lag 1) (35%~80%)
Vertical_Asia (lag 1) (>80%)
Vertical_Europe (lag 1)
Vertical_NorthAme (lag 1)
Herfindal Index
Observations
R-squared
Time FE
Firm FE
Industry control(1)
Investment_machinery
0.112(0.0986)-6.750***(1.931)0.590***(0.190)-0.0311(0.0946)-2.436**(1.158)13.79*(7.235)0.965(1.289)418,655
0.010
Yes
Yes
No

(2)
Investment_fixed_assets-0.0211(0.457)-27.36**(13.67)3.563***(1.109)1.553*(0.917)0.731(6.082)84.54***(22.30)17.70***(2.226)53,440
0.150
Yes
Yes
No

Note: Robust standard errors in parentheses, clustered at industry level.
*** p<0.01, ** p<0.05, * p<0.1
Industry control includes the number of foreign firms in an industry (in log form)
and industry-level capital ratio of State-owned firms.

5

Conclusion

The spillover impact of FDI has been widely investigated in the existing literature. In thisstudy, we examine how the 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 a statisticalmethod, the endogenous structural break approach, to divide Asian investors by different
27

TFP thresholds, we showed that the relationship between the productivity gap and vertical
spillover has an inverted-U shape, that is, Vietnamese suppliers can achieve the most TFP
gains from the diffusion of Asian investors with middle-level TFP.
We use the economic shock of Vietnam’s entry to the WTO in 2007 to conduct a DID
estimation to identify the spillover effect further, which did not change the predictions. The
empirical results are also robust to several sensitivity checks, thus providing evidence that
not all the foreign investors with the most advanced technology can benefit local firms in
Vietnam. To understand the empirical findings better, we proposed a simple theoreticalmodel to highlight a possible mechanism. The model focuses on local firms’ own efforts on
R&D, and it is affected by the TFP level of Asian investors located in the downstream sectors.
Consequently, middle productive Asian investors induce local firms to exert the most effort
on R&D-related activities, and thus increase local firms’ productivity. Such a mechanism
is also verified empirically. Nevertheless, there might be other channels through which thepresence of foreign investors can induce different levels of spillover, and we will leave theverification for our future study.

28

Appendix
A1. Additional tables
TABLE A1 Statistical summary
Main Variables

Obs

Mean

S.D.

Min

Max

Total number of labor

168166743.31377

389.4886

1

88275

Net turnover

127207322495.07

369967.2

1

1.85E+08

Fixed assets

16816677421.772

3176780

2.16E+08

Foreign capital

1780508

1143772637455901.61E+08

TFP_SF

1272073

0.577142

0.119471

0.043346 0.7869591

Total investment

5025764906.839

324552.3

0 224000000

Investment for capital

3912062214.798

361815.3

0

2.24E+08

Investment for machinery

437119548.7237

22750.5105620648

Invest for fixed assets

36061193.01869

11582.92

0

6.70E+06

Horizontal spillover_total

1272073

0.142614

0.17438

0 0.9757611

Backward vertical spillover_Asia

1272092

0.168748

0.093517

0.005015 0.4736987

Backward vertical spillover_EU

1272092

0.042771

0.018266

0.001202 0.1525457

Backward vertical spillover_North America

1272092

0.007068

0.003548

0.000148

Backward vertical spillover_Asia (TFP<35%)

1272092

0.008674

0.006718

0.000176 0.1645835

Backward vertical spillover_Asia (TFP∈[35%, 80%])12720920.23291

0.093561

0.016153 0.7696869

Backward vertical spillover_Asia (TFP>80%)

1272092

0.451289

0.181828

0.015756 0.8360114

Herfindal Index

1780508

1.68E-05

0.002019

0 0.9940184

Number of foreign firms by industry

178050839265.88

37088.492125166

SOE capital share by industry

12707130.132711

0.11812900.8

Firm level

Industry level29

0.046097

TABLE A2 Categories of industries by FDI regulation as of 2005 Investment law inVietnam
No. Industry nameCategory

1 Agriculture

The others

2 Mining

Encouraged

3 FoodThe others

4 Beverages

The others

5 Tobacco

The others

6 Textiles

Encouraged

7 Apparel

Encouraged

8 Leather products

Encouraged

9 Wood products

Encouraged

10 Paper products

The others

11 Printing products

The others

12 Coke products

The others

13 Chemical products

Encouraged

14 Pharmaceuticals

The others

15 Rubber and plastic

Encouraged

16 Non-metallic products

The others

17 Metals

The others

18 Electronics

Encouraged

19 Electrical equipment

Encouraged

20 Machinery

Encouraged

21 Vehicles

Encouraged

22 Transportation equipment

Encouraged

23 Furniture

The others

24 Other manufacturing

The others

25 Repair and installation

The others

26 Electricity and water

Encouraged

27 Construction

Encouraged

28 Wholesale and retailThe others

29 Transportation

Encouraged

30 Accommodation and restaurants

The others

31 Information

The others

32 Finance

The others

33 Real estate

The others

34 Professional activity

The others

35 Support services

The others

36 Communist party

The others

37 Education

The others

38 Hospital and social work

The others

39 Arts and entertainment

The others

40 Lottery

The others

41 Other services

The others

42 Household services

The others

Source: Vietnamese Enterprise Survey, GSO & Investment promotion
center for central Vietnam.

30

A2. An extended model with horizontal spillovers
We here examine horizontal spillovers by modifying the model developed in the main text.
We consider foreign firms in the upstream industry as well as in the downstream industry.
There are N U ∗ > 1 number of symmetric foreign upstream firms with zero marginal cost.
while the local supplier does q U .
Each of them, indexed by j, produces qjU ∗ units of inputs, P
The input market must clear, implying that Q/ϕ = q U + j qjU ∗ . In the following, we willshow that the presence of foreign firms in the upstream industry has a negative effect on the
productivity of the local firm in the same industry, i.e., ∂(1/cU )/∂N U ∗ < 0.
In the second stage, we solve for optimal levels of inputs that foreign and local suppliers
produce. The profit of foreign supplier j is
πjU ∗ = wqjU ∗= [a − (N ∗ + 1)Q/N ∗ ]qjU ∗ .
The FOC gives his best-response function:∂πjU ∗ /∂qjU ∗ = 0,"!#

→ ϕ a − (N ∗ + 1) q U +

X

qjU ∗

qjU ∗ − ϕ2 (N ∗ + 1)qjU ∗ /N ∗ = 0.

j

Similarly, the best-response function of the local supplier is given by∂π U /∂q U = 0,"!#

→ ϕ a − (N ∗ + 1) q U +

X

qjU ∗

q U − cU − ϕ2 (N ∗ + 1)q U /N ∗ = 0.

j

We exploit the symmetry of foreign suppliers, i.e., qjU ∗ = qkU ∗ = q U ∗ for all j 6= k, and solvethese equations to obtainN ∗ (aϕ2 − cU ),ϕ2 (N ∗ + 1)(ϕ + N U ∗ + 1)
N ∗ [aϕ2 − (ϕ + N U ∗ )cU ]qU = 2 ∗.
ϕ (N + 1)(ϕ + N U ∗ + 1)
qU ∗ =

As can be seen, the larger number of foreign suppliers makes competition tougher, thereby
reducing the quantity of the local supplier: ∂q U /∂N U ∗ < 0. We assume ϕ > 1 to ensure thatthe local supplier’s gross profit is non-negative at the maximum level of R&D.23
The optimal investment level x can be obtained by solving the local supplier’s maximiza23

Using the optimal quantities in the second stage, we havemax (w − cU )q U = (w − cU )q U |x=c =31

aϕ[1 + (ϕ − 1)N U ∗ ].
N U∗ + ϕ + 1

tion problem in the first stage, as we did in the main text. The SOC is satisfied as long as thenumber of foreign downstream firms is not extremely large.24 To show ∂(1/cU )/∂N U ∗ < 0(or equivalently ∂x/∂N U ∗ < 0), however, we do not have to solve for optimal x itself. Instead, wee only have to check whether the incentive of investment responds negatively to theintra-industry foreign presence: U∂πN ∗ (Γx − Θ)∂=
∂N U ∗ ∂x
ϕ2 (N ∗ + 1)(N U ∗ + ϕ + 1)3
N ∗ (Γ · c − Θ)< 2 ∗ϕ (N + 1)(N U ∗ + ϕ + 1)3aN ∗ [ϕ3 + (N U ∗ − 1)ϕ2 + (2N U ∗ + 1)ϕ − N U ∗ − 1]=−
< 0,
ϕ(N ∗ + 1)(N U ∗ + ϕ + 1)3
whereΓ ≡ 2(ϕ2 + N U ∗ ϕ + N U ∗ + 1) > 0,
Θ ≡ a(1 − N U ∗ )ϕ3 + (2aN U ∗ + 2c + a)ϕ2 + [(a − 2c)N U ∗ + a]ϕ − 2c(N U ∗ + 1) < 0.
That is, the greater presence of foreign suppliers discourages the investment of the local supplier and thus decreases her productivity, which corresponds to negative horizontal spillovers.
A sufficient condition for this to be positive is ϕ > 1.
24
The condition is given by N ∗ (N U ∗ − 2) − (N U ∗ + 2)2 < 0 and obtained as follows. First, we can seethat ∂ 2 π U /∂x2 decreases with ϕ, implying that it takes the infimum at ϕ = 1. We then check under what
condition the infimum takes a negative value, which is a sufficient condition for the SOC i.e., ∂ 2 π U /∂x2 < 0.

32

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