Conclusions
This paper empirically studies impacts of
technology spillover on convergence among firms in
three sectors of the economy: (i) agricultural, forestry,
and fishery industry, (ii) manufacturing industry, and (iii)
services. The results are summarized as follows. Firstly,
we employ the semi-parametric method to estimate
TFP. A TFP series is estimated by using LevinshonPetrin method and the second one is estimated by using
Olley-Pakes method. Using dynamic I-O table (2005-
2007), we construct channels of technology spillover in
the horizontal and vertical dimensions and combining
them with convergence model. Using two TFP series
and variables of technological spillover, we examine two
groups of convergence models. On the basic of
specified convergence model, we estimate the group of
unconditional convergence model and conditional
convergence one (the condition of technological
spillover). The estimation results show that the impacts
of technology spillover in two dimensions- horizontal
and vertical- are quite complicated, depending on type
of model and the studied period. There is not one-way
impact on speed of convergence, i.e. they have both
positive and negative impacts. However, the estimation
results show that the technology spillover significantly
raise the speed of convergence among firms in all three
sectors of the economy. The evidence is that the speed
of convergence of the conditional convergence model
(with technology spillover variables) is faster than
unconditional convergence one (without technology
spillover variables).
The explanation of the role of technology
spillover in the convergence process is very meaningful
for policy-makers. To induce the development and
progress, not only the technological innovation but also
technology spillover are very important sources of
productivity growth. Along with policies to foster
technological innovation, however, we also should
emphasize the importance of technological spillover,
thanks to which firms need not create new technologies
themselves. The combination of technological
innovation and spillover would allow us to more
efficiently employ our resources in the process of
developing all sectors of the whole economy.
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© 2014. Nguyen Khac Minh, Nguyen Viet Hung, Nguyen Viet Hwng & Tran Thi Thu Ha. This is a research/review paper,
distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License
permitting all non-commercial use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Global Journal of Management and Business Research: B
Economics and Commerce
Volume 14 Issue 9 Version 1.0 Year 2014
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 2249-4588 & Print ISSN: 0975-5853
How Does Technology Diffusion Increase Speed of TFP
Convergence at Firm Level? Exploring the Effects of High
Technology Firms on Linkages: Evidence from Vietnam
Enterprises
By Nguyen Khac Minh, Nguyen Viet Hung, Nguyen Viet Hwng
& Tran Thi Thu Ha
National Economics University, Viet Nam
Abstract- The objective of this research is to give an explanation why low-technology enterprises
can catch up with high-technology ones even when they are unable to invest in R&D. The answer
is the existence of technology diffusion, however, how does technology diffusion take place and
can we quantify this process? To answer this question, we structure variables which represent
the transmission channel of technology diffusion from high-technology enterprises to low-
technology ones, then quantifying impacts of technology diffusion and applying this
methodology into the analysis of impacts of technology diffusion on total factory productivity
(TFP) convergence of Vietnamese enterprises.
Keywords: technology diffusion, horizontal spillover, vertical spillover, TFP convergence,
convergence under the technology diffusion.
GJMBR - B Classification : JEL Code : D92
HowDoesTechnologyDiffusionIncreaseSpeedofTFPConvergenceatFirmlevel?ExploringtheEffectsofHighTechnologyFirmsonLinkagesEvidencefromVietnam
Enterprises
Strictly as per the compliance and regulations of:
How Does Technology Diffusion Increase Speed
of TFP Convergence at Firm Level? Exploring the
Effects of High Technology Firms on Linkages:
Evidence from Vietnam Enterprises
Nguyen Khac Minh α, Nguyen Viet Hung σ, Nguyen Viet Hwng ρ & Tra
Abstract- The objective of this research is to give an
explanation why low-technology enterprises can catch up with
high-technology ones even when they are unable to invest in
R&D. The answer is the existence of technology diffusion,
however, how does technology diffusion take place and can
we quantify this process? To answer this question, we
structure variables which represent the transmission channel
of technology diffusion from high-technology enterprises to
low-technology ones, then quantifying impacts of technology
diffusion and applying this methodology into the analysis of
impacts of technology diffusion on total factory productivity
(TFP) convergence of Vietnamese enterprises. We establish
two TFP series in accordance with the methods developed by
Olley-Pakes [7] and extended by Levinsohn and Petrin [5]. On
the basis of two constructed TFP series, we estimate the
unconditional convergence model and the convergence model
under the effects of technology diffusion. The estimation
results of two models show that the impacts of technology
diffusion occur complicatedly but the total effect of the
variables representing for impacts of technology diffusion on
TFP convergence is positive and the speed of convergence in
the model including the variables of technology diffusion is
faster than one in the model excluding this variables.
Keywords: technology diffusion, horizontal spillover,
vertical spillover, TFP convergence, convergence under
the technology diffusion.
I. Introduction
here have been a lot of researches exploring the
productivity convergence among countries at both
the national level and the firm-level. The results,
however, are not consistent with each other, and in
many cases they are opposite to each other. Bernard
and Jones [1], for instance, could not find any evidence
of the convergence in manufacturing industry. Others
acquire results supporting for the convergence in
countries which have low-productivity at the first stage of
development but quickly grow in the subsequent
periods. Nishimura e t al. [6] provides evidences of
Author α : National Economic University (NEU),207 Giai Phong Street,
Hai Ba Trung District, Hanoi Vietnam; Water Resources University 175
Tay Son, Don Da Street, Ha Noi Vietnam.
Author σ ρ : National Economic University,207 Giai Phong Street, Hai
Ba Trung District, Hanoi Vietnam.
Author Ѡ : Pedagogical University No 2, Xuan Hoa, Phuc Yen, Vinh
Phuc.
productivity convergence among Japanese enterprises.
Pascual et al. [8] study the productivity convergence in
manufacturing-processing industry in Europe. They
analyze in detail sub-industries which belong to the
manufacturing-processing industry to make a
comparison among industries having similar
characteristics of technology. They find out that in some
industries, there exists a productivity convergence while
in others as well as the whole manufacturing-procession
industries, there does not exist a productivity
convergence. Their results put an emphasis on the
importance of making a comparison among industries
having similar technologies when we analyze the
productivity convergence. Minh et al. [9] studies effects
of FDI spillover on efficiency convergence of
Vietnamese manufacturing enterprises. They employ the
dynamic input-output (I-O) tables to construct the
structure of relationship between domestic and FDI
enterprises through spillover effects of FDI enterprises
on domestic ones. Using the data of Vietnamese
manufacturing industry in the period 2000-2011, they
point out the positive effects of FDI spillover on the
efficiency convergence of manufacturing enterprises.
However, one issue arising here is that why productivity
(or technical efficiency) convergence is so important.
One reason is that the technology spillover behind the
productivity convergence process can create
opportunities for low-technology enterprises to catch up
with high technology ones, even when they could not
invest into R&D or purchase new technologies due to
high investment cost, especially for new market-comers
and small and medium enterprises. The recent
researches show that the R&D spillover is one important
explanatory variable for convergence. For one nation
which has international trade of goods and services,
investment, exchange of information and knowledge,
firms’ productivity would depend on its own R&D as well
as others’ R&D because technology spillover is not
restricted by geographical boundaries. Technology is
the root cause of long-term economic growth. The
economic performance of one nation has a strong
relationship with the capacity of inventing new
knowledge and applying these knowledge as well as the
T
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ones invented by other nations. This research would
answer the question how technological knowledge
spillovers from advance-technology enterprises to low-
technology one, and how we can quantify this spillover
effect.
This research is structured into five sections.
The next one will present the methodology framework
including models measuring total factor productivity to
estimate TFP; constructing convergence model; and
integrate transmission channels of technology progress
into the convergence model. The third section provides
data and estimation results. The conclusions would be
in section 4.
II. The Methodology Framework
a) Measurement of total factor Productivity
To construct the convergence model from TFP
series estimated from a unified dataset by two different
methods, we will briefly present the methodology basis
of the Olley-Pakes method, in which investment is used
as a controlling variable. The second method is quite
similar, except using the intermediate input as a
controlling variable.
The efficiency estimates at the firm-level in this
research are attained by using Olley and Pakes method.
This method has been developed to point out the
possibly simultaneous bias when estimates the
production function. This can be illustrated by examining
logarithm version of Cobb-Douglas function below (at
point of time t for firm i):
LnYt =β0 + βl lnLt+ βk LnKt+ βt LnIt+ωt +ηt (1)
In which, Yt is denoted for output, Lt for labor
and Kt for capital, It for intermediate inputs.
The individual error component of firm ωt, and
error component follows i.i.d ηt. The component ηt has
no determinant impact on firms’ decisions. The
productivity component, ωt, is assumed to be
unobservable in the eyes of econometrists, but firms’
managers have information, and this component has
impact on the rule of making decision in the firm. The
simultaneity problem arises when there exists
simultaneous correlation, both within firm i and between
periods t, between εt and inputs of firms in separate
series of firms. To show the simultaneity problem, OP
employs investment as a representative for latent
productivity that is serially correlated. The investment
function, therefore, can be rewritten as follows:
it = it(ωt, kt)
In order to take positive value for investment, it
= it(ωt, kt) is converted to inverse function to get ωt as a
function of capital and investment ωt =(it , kt ). To
facilitate the analysis, we denote: yt=lnYt , lt=lnLt ,
kt=lnKt and it=lnIt. Then, the first equation gives use the
output as a function of observable variables:
yt = βl lt+ βk kt+ βt ιt+φt (it , kt ) +ηt , (2)
in which, (it , kt )= β0 + βk kt+ φt (it , kt ). The robust
estimates of input variables can be attained by using the
semi-parametric estimation. Assuming ωt following first-
order Markov and capital does not instantaneously
respond to creativeness in productivity – in which
invention in productivity can be defined as follows:
ξt = ωt – E[ ωt|ω t-1]. (3)
With this assumption, the robust estimates of βk
can be derived from estimation of this function:
yt* = yt –ltβl – βl ιt = β0 + βkkt + E[ ωt|ω t-1]+ ηt* (4)
in which, yt* is the net output after eliminating the
contribution of labor and ηt*=ξt +ηt. Because the result
of the first stage is one estimate of ωt – one robust
estimate of E[ ωt|ω t-1] which can be obtained, and the
estimates of the equation (3) give us the robust estimate
of βk. Total factor productivity (TFP) of firm i in year t is:
tfpit = yit - llit - ιit - kkit (5)
in which, tfpit is the logarithm of TFP (lnTFP) s and the
appropriate indicators are estimates of parameters
attained from production function estimation.
b) The Role of Technology Spillover in Convergence
This section is to answer this question: “How
does technology diffusion from high-tech to low-tech
firms take place?” We would construct several channels
through which high-tech firms can have impact on
productivity and productivity convergence of low-tech
ones. Herein, we structure channels allowing horizontal
and vertical technological spillovers.
To implement this task, we need give some
assumptions at first:
1. Assumption 1: There is the relationship between
firms’ technology and productivity. It means that
technology and productivity (TFP) of any firm have a
strongly positive correlation (high-tech firm has high
(TFP) productivity).
2. Assumption 2: Firm i is called a high-tech one in
year t if this firm has TFP being double or more than
the average TFP of that industry in the same year.
We use LHit as a variable capturing the
existence of firm i which has advance technology in the
industry under consideration in year t, and J is a set of
firms having advance technology:
LHit = 1 if i J∈ and LHit = 0 if i J∉ (6)
The horizontal technology spillover variable
jtLHh tells us the extent of participation of high-tech
firms in that industry and it can be measured by the
weight of actual output of high-tech ones in total output
of the whole industry:
How Does Technology Diffusion Increase Speed of TFP Convergence at Firm level? Exploring the Effects of
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1
it it
it n
jt
j
LH XLHh
X
=
∗
=
∑
(7)
In which, Xit is the real output of firm i, n is the
number of firms in questions.
The variable jtLHb measures the backward
spillover effect, exhibiting the extent of participation of
high-tech firms in the downstream industry; therefore, it
reflects the linkages between low-tech suppliers and
high-tech clients. So, we can measure jtLHb as
follows:
jt jkt kt
k if k j
LHb LHhγ
≠
= ∗∑ (8)
In which, γjkt is the ratio of output of industry j
selling to industry k in the period t. The values of γ can
be computed from I-O table. When computing γ, we
eliminate firms’ inputs sold within the industry (k ≠ j)
because this component has already been captured by
ktLHh . We can avoid the endogeneity problem by
using the ratio of output sold to downstream industry k
with a certain level existence of foreign firms ktLHh . In a
similar way, we can define the forward spillover variable
LHfit as follows:
jt jlt lt
l if l j
LHf LHhδ
≠
= ∗∑ (9)
Herein, I-O table provides us δjlt, the ratio of
inputs of industry j purchased from the upstream
industry l. Inputs purchased within the intra-industry (l ≠
j) are also eliminated because these are already
captured by LHh.
c) The model of productivity convergence among firms
The simple model of productivity convergence
developed by Bernard and Jones [1] has been widely
used in researches of cross-country productivity
convergence. This is the basis for the model of long-
term average productivity growth convergence (TFP) as
a function of the initial productivity, and we can specify
the general model as follows:
( ), , , 0 1 ,1ln ln ln lni final i final i initial i initial iTFP TFP TFP TFP uT β β∆ = − = + + (10)
In which, T shows the length of the period, final
denoted for the final year, initial for the initial year (in this
sample, the initial year is 2000 while the final year is
2012). The catching-up variable can be exhibited by a
negative value of the coefficient β1 = -{1 – (1 - λ)T}/T.
We assume that uit ∼ N(0, σ). The convergence model
(10) can be applied for two TFP series computed from
two distinctive methods on the same dataset. Therefor,
we would two types of model to estimate convergence:
model (10.1) is the model 10 in which TFP (denoted by
pm) can be estimated by using Levinshon-Petrin
procedure while model (10.2) is the model 10 in which
TFP (denoted by pi) would be estimated by using Olley-
Pakes procedure.
d) The impact evaluation model of technology spillover
on TFP convergence
The impact evaluation model of technology
spillover can be specified as follows:
2012
, , .
2000
2012 2012 2012
2000 2000 2000
1ln [ln ln ] lniT i final i initial i initial t jt
t
t jt t jt t jt iT
t t t
TFP TFP TFP TFP LH
T
LHh LHb LHf
α β δ
γ χ χ µ
=
= = =
∆ = − = + +
+ + + + +
∑
∑ ∑ ∑
(11)
The convergence model (11) can be applied for
two TFP series computed by using two different
methods on the same dataset. Therefor, we would have
two types of model to estimate convergence under
impacts of technological spillover: model (11.1) is the
model 1 in which TFP (denoted by pm) can be
estimated by using Levinshon-Petrin procedure while
model (11.2) is the model 11 in which TFP (denoted by
pi) would be estimated by using Olley-Pakes procedure.
III. Empirical Results
a) Data
The micro-data basis is derived from annual
business survey undertaken by General Statistical Office
(GSO) from 2000 to 2012. This research employs all
firms from all industries including cultivation, animal
husbandry, mining, manufacturing and service
industries, however, these firms must be available in all
thirteen years of GSO surveys.
The main information of firms includes type of
firm, field of business, number of labors (the average
number in the year), assets, capital allowance, fixed
How Does Technology Diffusion Increase Speed of TFP Convergence at Firm level? Exploring the Effects of
High Technology Firms on Linkages: Evidence from Vietnam Enterprises
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assets, labor’s earnings, salary and bonus and social
security contribution, financial obligations, profits (in
term of VND million).
Inputs and outputs are corrected for inflation.
This research uses balanced panel data, including all
firms appearing in 13 years from 2000 to 2012. We
eliminate firms whose age, revenue, assets, and labor
do not take positive values. In this research, the added
value is used to estimate total factor productivity.
However, these data are not available and must be
indirectly computed from other related indicators. The
dataset consists of 10767 observations for each year
and the whole sample period is 13 years. These
observations are categorized into three sectors, namely:
1. Sector 1 consists of firms in the agricultural, forestry,
fishery and mining industries, calling mining
industry;
2. Sector 2 consists of firms in the manufacturing
industry such as food processing, garment and
textiles, shoe & leather, wooden, paper, chemical,
rubber, plastic, non-metal, metal, machinery
production, wooden furniture and recycling
industries, calling manufacturing industry.
3. Sector 3 consists of other industries such as
construction, transportation, post and
telecommunication, banking and finance and they
are called as service industry.
So, corresponding to models (10) defined
above, we have the following cases:
Model (10.1T) is the model (10.1) applied for
the whole sample dataset; model (10.1K) is the model
(10.1) applied for dataset of mining industry; model
(10.1C) is the model (10.1) applied for dataset of
manufacturing industry; model (10.1D) is the model
(10.1) applied for dataset of service industry.
Corresponding to models (11) defined above,
we have the following cases:
Model (11.1T) is the model (11.1) applied for
the whole sample dataset; model (11.1K) is the model
(11.1) applied for dataset of mining industry; model
(11.1C) is the model (11.1) applied for dataset of
manufacturing industry; model (11.1D) is the model
(11.1) applied for dataset of service industry.
b) Testing assumptions of Olley-Pakes approach
Before continuing our discussion about
estimation of parameters of production function by OP
method, we must test if the main assumptions of OP
approach are satisfied, i.e. if investment monotonically
increases with respect to TFP measured using strictly
positive investment observations. We estimate the fixed
effect model at the firm level, in which logarithm of
investment and TFP and year dummy variable are used
as explanatory variables and would be adjusted for
group of any variables at the four-digit industry code.
The estimate of logarithm of TFP ranges from 0,7 to 0,8
for the whole sample and sectors, statistically significant
at 1%. The estimate implies that a 1% shock of TFP at
firm-level will cause investment to increase by 0,7- 0,8%
in the whole sample. Thereby, with the given dataset of
considered firms, using OP approach to estimate
production function is an appropriate method.
c) The unconditional convergence
Table 1 records estimation results of models
(10.1) and (10.2). These results are derived from OLS
regression. To compute the speed of convergence, we
firstly estimate β, then computing λ based on the
following formula: β1
= -{1 – (1 - λ)T}/T. The estimated
coefficients have expected sign and are highly
statistically significant.
Table 1 : Estimation results of two groups of unconditional convergence models.
Dependent variable dlnpm Dependent variable: dlnpi
Model 10.1T 10.1K 10.1C 10.1D 10.2 T 10.2 K 10.2 C 10.2.D
Constant 0,0807*** 0,1008*** 0.0565*** .0902*** 0,0722*** 0,1007*** 0.0523*** 0.0796***
(0,0025) (0,0101) (0.0045) (0.0031) (0,0021) (0,0100) (0.0038) (0.0025)
β -0,047*** -0,057*** -.0367*** -0,0521*** -0,051*** -0,057*** -0,0406*** -0,0559***
(0,0012) (0,0047) (0,0019) (0,0015) (0,0011) (0,0047) (0.0523) (0.0015)
R2 0,1299 0,2400 0,2113 0,1368 0,1560 0,2113 0,1208 0,1634
0,1298 0,2370 0,2099 0,1367 0,1559 0,2099 0,1205 0,1632
No. obs. 10767 3185 547 7035 10767 3185 547 7035
F-statistics 1606,76 348,32 146,03 1114,38 1989,96 437,40 146,03 1373,21
(0,0000) (0,0000) (0,0000) (0,0000) (0,0000) (0,0000) (0,0000) (0,0000)
Speed of
convergence (%)
6,7 8,96 4,7 7,73 7,55 8,96 5,38 8,67
Half-life time 14,27 11,81 18,54 12,95 13,16 11,81 16,72 12,05
Source: the author estimates from business surveys of GSO
We can draw out two comments from the
estimation results given in Table 1. Firstly, in both two
groups of models, we can see a clear evidence for
productivity convergence. However, the speed of
convergence in cases is slightly different. The speed of
convergence attained from models (10.1T), (10.1K),
(10.1C) and (10.1D) are correspondingly 6,7%;8,96%
;4,7% and 7,73%. Meanwhile, the speed of convergence
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obtained from models (10.2T), (10.2K), (10.2C) and
(10.2D) are respectively 7,55%; 8,96% ; 5,38% and
8,67%. Thereby, the speed of convergence computed
from the group (10.2) are higher than ones from the
group (10.1), except the case of models 10.1K and
10.2K which have a same speed at 8,96%. Secondly,
the speed of convergence is higher than one obtained in
the research at the nation-level. For instance, while
Dorwick and Nguyen [2] reports the result of speed of
convergence cross countries being around 2.5%
annually, our results collected from both models 10.1
and 10.2 point out that the speed of convergence does
not exceed 9%. On the other hand, these results are
lower than ones provided by Nishimura et al. [6].
d) Impacts of technology spillover on convergence
The estimation results of two unconditional
convergence models (10.1) and (10.2) prove there exists
productivity convergence among firms in three sectors
in Vietnam. In this section, we examine convergence
under the impact of technological spillover. The
estimation results of models (11.1) and (11.2) are
provided in Table 2 and 3. Because results from
unconditional and conditional models using two
estimated productivity series by Levinshon- Petrin and
Olley-Pakes algorithms tend to have the same direction
and are nearly indifferent, therefore, we put focus on
making a comparison impacts of technology spillover
between unconditional model and conditional one using
estimated productivity series by using Levinshon- Petrin
algorithm. Comparing impacts of technology spillover
between unconditional model and the conditional one
using Olley – Pakes gives us a similar result. Table 2
gives us impacts of technology spillover on firms in
three sectors, in which estimated productivity series are
based on Levinshon- Petrin algorithm.
Table 2 : Impacts of technology spillover on convergence in the model with dependent variable being dlnpm.
(11.1T) (11.1K) (11.1C) (11.1D)
Dlnpm Coef. Coef. Coef. Coef.
Lnpm -0.0512***
(0.0012)
-0.0608***
(0.0047)
-0.0428***
(0.0020)
-0.0572***
(0.0016)
LH2001 -0.0126
(0.0107)
-0.1110*
(0.0618)
LHh2001 0.0204
(0.0150)
Lhb2001 -0.1691
(0.1344)
6.7889***
(1.9386)
-.3657*
(.1991)
-.3256*
(.1766)
LH2002 0.0385**
(0.0187)
.0286
(.0270)
.0696**
(.0279)
LHh2002 0.0336**
(0.0159)
-.1156**
(.0530)
.0411
(.0329)
.0803***
(.0208)
LHb2002 -1.238**
(.5623)
-.3145***
(.1139)
.2071**
(.1051)
LHf2002 -1.581***
(0.3037)
4.9953***
(1.8458)
-.6765
(.5016)
-2.5973***
(.4700)
LH2003 0.0471***
(0.0146)
.0521***
(.0163)
LHh2003 -0.0058*
(0.0034)
Lhb2003 -0.0276**
(0.0123)
Lhf2003 -0.0339***
(0.0079)
-.0737***
(.0101)
LH2004 -0.0018
(0.0012)
-.0909
(.0789)
.0375*
(.0219)
-.0021*
(.0012)
LHh2004 .0667**
(.0302)
Lhb2004 -0.0474
(0.0456
-1.0480**
(.4159)
.24996***
(.0731)
Lhf2004 0.0533
(0.0387)
-2.2548***
(.6674)
.0553
(.0445)
-.4658**
(.1864)
LHh2005 -0.00001
(6.28e-06)
.23671***
(.0843)
-.00008**
(.00004)
Lhb2005 0.0331***
(0.0103)
-.1974***
(.0661)
.0396***
(.0102)
LH2006 0.0356** .13625 .0308 .0443*
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(0.0151) (.1029) (.0206) (.0254)
LHh2006 .0021*
(.0011)
Lhb2006 -0.0788
(0.0617)
-.1568
(.1048)
-.1931***
(.0726)
Lhf2006 -0.2347**
(0.0759)
.1097
(.1131)
-.8500***
(.1282)
LHh2007 .0064***
(.0020)
Lhb2007 .0355**
(.0169)
LH2008 .0373***
(.0140)
.0832***
(.0199)
LHh2008 -.0071
(.0062)
-.0885
(.05691)
-.0114
(.0075)
Lhb2008 .2039***
(.0589)
-.8207*
(.4846)
.1271*
(.0717)
Lhf2008 -.0668
(.0475)
-.8434***
(.3169)
-.1516
(.0993)
LH2009 .1163
(.0937)
.0185
(.0175)
-.0166
(.0104)
LHh2009 .3428***
(.1271)
Lhb2009 -.0411***
(.0121)
-.0261*
(.0152)
.0417
(.0282)
Lhf2009 .0904**
(.0402)
LH2010 .0166*
(.0092)
-.2163
(.1577)
.0179*
(.0107)
LHh2010 .1507**
(.0743)
.6489*
(.3700)
Lhb2010 -.1802***
(.0468)
-.3026***
(.1129)
Lhf2010 .9184***
(.1600)
2.0739***
(.5532)
1.0739***
(.2518)
LH2011 .0508***
(.0091)
.0531***
(.0137)
Lhb2011 -.1051**
(.0442)
-.1480
(.1129)
Lhf2011 .0548**
(.0278)
.0941***
(.0340)
LH2012 .0053***
(.0016)
.3029**
(.1195)
.0288
(.0241)
.0055***
(.0016)
LHh2012 .0022***
(.0007)
.0014**
(.0007)
Lhb2012 .0471***
(.0069)
.0496
(.0307)
.0277**
(.0128)
Lhf2012 .0202
(.0160)
.0473
(.0434)
.0342***
(.0090)
_cons .1190***
(.0049)
.1173***
(.0221)
.0580***
(.0108)
.1595***
(.0063)
Speed of convergence
(%)
7.53 10.04 5.78 9.02
Half-life time 13.18 11.05 15.84 11.76
Source: the author estimates from business surveys of GSO
From estimation results in table 2, we have
following comments. The value of β coefficient
estimated from convergence model under the impact of
technology spillover variable is negative and highly
statistically significant.
Most of coefficients of technology spillover in all
three models are statistically significant at 1%-10%,
however, their sign are different in models.
The sign of LH variable in 2001 is negative but
not highly significant. It is also negative in 2003 and
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significant at 5%. In 2004, it is negative but insignificant
while it is positive in 2010 and significant at 10%. In 2002
and 2006, it is positive and significant at 5% while it is
positive and significant at 1% in 2003, 2008, 2011, and
2012. The total impact of this coefficient is positive and
statistically significant. This can explain that the
technology spillover from high-tech to low-tech firms is
significant.
The sign of variables Lhh, Lhb and Lhf are
opposite to each other depending on the year we
consider. This can be explained as follows. The
horizontal technology spillover (Lhh) implies the spillover
from high-tech firm to low-tech firm in the same industry.
There are two main channels for this transmission: the
mobility of trained labors in high-tech firm and
technology imitation (positive Lhh). The presence of
high-tech firms also stimulates competitiveness in the
market. The stronger competitiveness will force low-tech
firms to either apply high technology, new management
method, or employ the existing resources more
efficiently, and this is also an important channel for
horizontal spillover (positive Lhh). However, none of
these impacts must be positive. The movement of labor
market can generate a negative spillover effects such as
brain drain from low-tech to high-tech firm putting a
harmful effect on productivity in low-tech firm, or paying
higher wage without requiring an improvement in
productivity due to the higher wage in high-tech firms.
The high-tech firms can prevent costs concerned with
high technology leakage from happening by restricting
technology transfer or keeping know-how in secret.
These policies apparently hinder opportunities for
horizontal spillover through performance impact. Higher
productivity in high-tech firms can put the downward
pressure on the price or lower demand for products of
low-tech firms (negative Lhh). If low-tech firms could not
adapt with more fierce competition and raise the
productivity, they could not only lag behind but also be
kicked out of business due to the existence of high-tech
firms.
The vertical spillover is the backward spillover
from high-tech firms to low-tech firms in the upstream
industries (positive Lhb). Even in the case that high-tech
firms try to minimize the technology leakage to low-tech
firms in the same industry (horizontal impact), they still
want to support their suppliers (low-tech one) in order to
help them provide good quality input and high-tech
firms can benefit from this outcome (positive Lhb). In
other words, if high-tech firms decide to purchase inputs
from low-tech ones (possibly due to location), they can
transfer technology to low-tech firms which provide them
with inputs, and stimulate the spread of technology to
the upstream industries to break the stagnation (positive
Lhb). The impact of backward linkages also can be
harmful for low-tech firms (negative Lhb).
The forward spillover (Lhf) is from high-tech
firms to low-tech ones in the downstream industries. The
availability of better inputs from high-tech firms can raise
the productivity of firms using these inputs (positive Lhf).
However, inputs produced by high-tech firms are usually
more expensive and less appropriate for requirements
of low-tech firms (negative Lhf). In this case, there would
be a negative spillover.
Table 3 : A comparison of speed of convergence between unconditional convergence model and convergence
model under the impact of technology spillover.
Model Unconditional convergence model Conditional convergence model under the
impact of technology spillover
10.1 T 10.1 K 10.1C 10.1 D 12.1 T 12.1 K 12.1C 12.1 D
β -0.047*** -0.057*** -.0367*** -0.052*** -0.051*** -0.061*** -0.043*** -0.057***
Speed of
convergenc
e
6.7 8.96 4.7 7.73 7.53 10.04 5.78 9.02
A half-life
time
14.27 11.81 18.54 12.95 13.18 11.05 15.84 11.76
Source: the author estimates from business surveys of GSO
Despite complexity of these effects, however,
the total impact of technology spillover is positive. This
can be shown by a comparison of results in table 3. It
shows a strong evidence for impacts of technology
spillover on productivity convergence among firms in all
three sectors (because of negative β and highly
statistically significant). This once again confirms the
positive impacts of technology spillover. It can be
proved by the higher absolute values of coefficient β in
all three models taking technology spillover into
consideration. The empirical evidence is shown in the
table 3.
e) A comparison of estimation of convergence under
the impact of technology spillover between model
(11.1) and (11.2)
This section compares the estimation results
from two models: the versions (11.1) and (11.2) of
conditional convergence model. The estimation results
of model (11.1) are given in table 2 while the ones of
model (11.2) are given in table 4 below.
How Does Technology Diffusion Increase Speed of TFP Convergence at Firm level? Exploring the Effects of
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Table 4 : Impact of technology spillover on convergence in the model with dlnpi as the dependent variable (The
conditional convergence model)
Independent
variable\Model
(11.2T) (11.2K) (11.2C) (11.2D)
Coefficient Coefficient Coefficient Coefficient
Lnpi -.0548***
(.0012)
-.0628***
(.0044)
-.0466***
(.0020)
-.0605***
(.0015)
LH2001 -.0115
(.0099)
-.0958*
(.0569)
LHh2001 .0182
(.0139)
Lhb2001 -.1659
(.1242)
6.3036***
(1.7804)
-.3708**
(.1824)
-.3496**
(.1639)
LH2002 .0405**
(.0173)
.0297
(.0247)
.0700***
(.0259)
LHh2002 .0275*
(.0147)
-.1033**
(.0487)
.03443
(.0302)
.0712***
(.0193)
Lhb2002 -1.0190**
(.5163)
-.2900***
(.1044)
.2447**
(.0975)
Lhf2002 -1.776***
(.2807)
4.9552***
(1.6954)
-.6599
(.4598)
-2.3787***
(.4364)
LH2003 .0442***
(.0135)
.0488***
(.01496)
LHh2003 -.0047
(.0031)
Lhb2003 -.0234**
(.0114)
Lhf2003 -.0278***
(.0074)
-.0645***
(.0097)
LH2004 -.0015
(.0011)
-.0895
(.0725)
.0356*
(.0201)
-.0017
(.0012)
LHh2004 .0616**
(.0278)
-.3601**
(.1730)
Lhb2004 -.0323
(.0422)
-1.0229***
(.3819)
.2344***
(.0670)
Lhf2004 .0594*
(.0358)
-2.0947***
(.6129)
.0544
(.0407)
LHh2005 -.00001*
(5.81e-06)
.2268***
(.0774)
-.00007*
(.00004)
Lhb2005 .0278***
(.0095)
-.1797***
(.0606)
.0355***
(.0094)
LH2006 .0332**
(.0141)
.11887
(.0946)
.0295
(.0189)
.0410*
(.0235)
LHh2006 .0017*
(.0010)
Lhb2006 -.0625
(.0570)
-.1381
(.0961)
-.1672**
(.0674)
Lhf2006 -.21081***
(.0702)
.1138
(.1036)
-.8021***
(.1190)
LHh2007 .0059***
(.0018)
Lhb2007 .0322**
(.0155)
LH2008 .03473***
(.01298)
-.0734
(.0522)
LHh2008 -.0073
(.0057)
.0750***
(.0185)
LHh2008 -.0114
(.0070)
Lhf2008 .1358**
(.0666)
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Lhb2008 .1743***
(.0545)
-.7266
(.4453)
Lhf2008 -.0583
(.0439)
-.8140***
(.2907)
-.1326
(.0910)
Lhb2009 -.0402***
(.0112)
.1043
(.0860)
LHh2009 .31365**
(.1167)
.0177
(.0160)
-.0120
(.0096)
Lhb2009 -.0250*
(.0139)
.0314
(.0262)
Lhf2009 .0858**
(.0369)
LH2010 .01574*
(.0085)
-.2087
(.1448)
.01572
(.0098)
LHh2010 .14643**
(.0682)
Lhb2010 -.1745***
(.0432)
-.3142***
(.1048)
Lhf2010 .8599***
(.1477)
1.9524***
(.5079)
.6414*
(.3392)
.8879***
(.2338)
LH2011 .0475***
(.0084)
.0492***
(.0126)
Lhb2011 -.0811**
(.0409)
-.1103
(.1035)
Lhf2011 .0422
(.0258)
.0815***
(.0311)
LH2012 .0051***
(.0015)
.2836***
(.1097)
.0290
(.0221)
.0052
(.0015)
LHh2012 .0021***
(.0006)
.0014**
(.0007)
Lhb2012 .0432***
(.0064)
.0451
(.0282)
.0241**
(.0118)
.0342***
(.0084)
Lhf2012 .02425
(.01481)
.0426
(.0398)
_cons .1048***
(.0044)
.0971***
(.0199)
.0517***
(.0097)
.1399***
(.0056)
Speed of convergence (%) 8,39 10,67 6,53 9,95
Half-life time 12,3 10,69 14,52 11,11
Source: the author estimates from business surveys of GSO
From estimation results given in table 4, we can
draw out same conclusions as what we have got in table
2. The coefficient β is estimated from the conditional
convergence model under the impact of technology
spillover are all negative and highly statistically
significant. Most of coefficients of the technology
spillover variable are statistically significant at 1%-10%,
however, their signs vary across models. Other analyses
are similar what we present above. This section is about
to make a comparison of speed of convergence
between two models (11.1) and (11.2). The results can
be summarized in table 5 below.
Table 5 : A comparison of speed of convergence between models of conditional convergence under the impact of
technology spillover.
Model Convergence model under the impact of technology spillover (11.1)
Convergence model under the impact of
technology spillover (11.2)
11.1 T 11.1 K 11.1 C 11.1 D 11.2 T 11.2 K 11.2C 11.2 D
Coefficient β
-
0.051***
-
0.061***
-
0.043***
-
0.057***
-
0.055***
-
0.063***
-
0.047***
-
0.061***
Speed of
convergence 7.53 10.04 5.78 9.02 8.39 10.67 6.53 9.95
Half-life time 13.18 11.05 15.84 11.76 12.3 10.69 14.52 11.11
Source: the author estimates from business surveys of GSO
How Does Technology Diffusion Increase Speed of TFP Convergence at Firm level? Exploring the Effects of
High Technology Firms on Linkages: Evidence from Vietnam Enterprises
© 2014 Global Journals Inc. (US)
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Following the definition given above, model
(11.1) is the one in which TFP (denoted pm) is estimated
by using Levinshon-Petrin procedure while model (11.2)
is the one in which TFP (denoted pi) is estimated by
using Olley-Pakes procedure. The results in table 5
show that the speed of convergence of convergence
model under the impact of technology spillover
estimated by using Levinshon-Petrin procedure is
slightly smaller than one derived from Olley-Pakes
procedure. This implies that these approaches can be
substituted for each other.
We come to the general comments as follows.
The above results show that speed of convergence
when taking technology spillover into consideration is
larger than the case without taking this effect into
consideration. Besides, the speed of convergence at the
firm-level is larger than one at the nation-level.
Theoretically, we can see that the spillover of
technological knowledge among firms within one nation
is quicker than one across nations due to “national
boundaries effect”. The technology spillover behind
productivity convergence can create opportunities for
lagging firms to catch up with leading ones. If there
does not exist technological spillover, lagging firms
could not catch up with leading ones if they do not
invest into R&D or purchase new technologies, patents,
and costs of these investments are huge for new-market
comers or small and medium firms.
However, we should notice that a quick
technology spillover also can create its own problem. If
this can be done easily, then no firms have incentive to
invest into R&D. However, our results show that the
process of technology spillover does not occur
immediately but takes a quite long time to take place.
Thereby, the advantage of technology of leading firms
can be maintained in a certain period of time, and this
can help firms have incentive to introduce more advance
technology.
IV. Conclusions
This paper empirically studies impacts of
technology spillover on convergence among firms in
three sectors of the economy: (i) agricultural, forestry,
and fishery industry, (ii) manufacturing industry, and (iii)
services. The results are summarized as follows. Firstly,
we employ the semi-parametric method to estimate
TFP. A TFP series is estimated by using Levinshon-
Petrin method and the second one is estimated by using
Olley-Pakes method. Using dynamic I-O table (2005-
2007), we construct channels of technology spillover in
the horizontal and vertical dimensions and combining
them with convergence model. Using two TFP series
and variables of technological spillover, we examine two
groups of convergence models. On the basic of
specified convergence model, we estimate the group of
unconditional convergence model and conditional
convergence one (the condition of technological
spillover). The estimation results show that the impacts
of technology spillover in two dimensions- horizontal
and vertical- are quite complicated, depending on type
of model and the studied period. There is not one-way
impact on speed of convergence, i.e. they have both
positive and negative impacts. However, the estimation
results show that the technology spillover significantly
raise the speed of convergence among firms in all three
sectors of the economy. The evidence is that the speed
of convergence of the conditional convergence model
(with technology spillover variables) is faster than
unconditional convergence one (without technology
spillover variables).
The explanation of the role of technology
spillover in the convergence process is very meaningful
for policy-makers. To induce the development and
progress, not only the technological innovation but also
technology spillover are very important sources of
productivity growth. Along with policies to foster
technological innovation, however, we also should
emphasize the importance of technological spillover,
thanks to which firms need not create new technologies
themselves. The combination of technological
innovation and spillover would allow us to more
efficiently employ our resources in the process of
developing all sectors of the whole economy.
V. Acknoledgement
We have received supports from National
Foundation for Science and Technology Development
(NAFOSTED), code No II 2.2-2012.18.
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How Does Technology Diffusion Increase Speed of TFP Convergence at Firm level? Exploring the Effects of
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