Productivity growth, technological progress and efficiencychanges in Vietnamese high-tech industries.pdf

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
PRODUCTIVITY GROWTH,
TECHNOLOGICAL PROGRESS AND
EFFICIENCY CHANGES IN VIETNAMESE
HIGH-TECH INDUSTRIES
BY
DAO HOANG BINH THIEN
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
HO CHI MINH CITY, January 2015

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES
HO CHI MINH CITY THE HAGUE
VIETNAM THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
PRODUCTIVITY GROWTH,
TECHNOLOGICAL PROGRESS AND
EFFICIENCY CHANGES IN VIETNAMESE
HIGH-TECH INDUSTRIES
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS
By
DAO HOANG BINH THIEN
Academic Supervisor:
Dr. TRUONG DANG THUY
HO CHI MINH CITY, January 2015

iii
ABSTRACT
Recently, Vietnamese high-tech industries have been receiving attention from
both the government, foreign companies, as well as the private sector due to the
notable figures of export values (Ministry of Trade and Industry [MoIT] & United
Nations Industrial Development Organization [UNIDO], 2011). This thesis attempts
to estimate the productivity growth of Vietnamese high-tech manufacturers and its
sources of growth. Stochastic Production Frontier (SPF) approach is applied to the
2000-2012 panel dataset of Vietnamese high-tech manufactures, which are divided
in 5 sub-industries. Total Factor Productivity (TFP) is then measured and
decomposed to three sources, namely Technological progress (TP), Technical
efficiency changes (TEC), and Scale change effects (SCE). Three different technical
inefficiency effects models are also applied to investigate the determinants of
technical efficiency. The empirical results show considerable controversy in both
signs and magnitudes of TFP and its components, TE and its determinants across
models. However, in general, maximum likelihood estimates show that TFP is not the
main source of output increase. Furthermore, the productivity and efficiency of
Vietnamese high-tech manufacturers are unlikely to change largely over time.
Nevertheless, there are differences of technical inefficiency effects across regions,
sub-industries, firm sizes, and type of ownerships.
Keywords: Vietnam, High-tech, manufacturing, productivity, TFP,
Technological progress, Technical efficiency, Scale change effects

iv
ACKNOWLEDGEMENT
I have taken efforts in this thesis. However, it would not have been completed
without supports of many individuals and organizations. I would like to express my
appreciation to all of them.
I would like to give special thanks to my academic supervisor, Dr. Truong
Dang Thuy, whose comments and encouragement helped me to write this thesis.
Furthermore, I would also like to acknowledge the Scientific Committee and
the staff of Vietnam-Netherlands Programme for their guidance and support as well
as for providing necessary information regarding the thesis.
Lastly, my thanks also go to my family and my classmates for their precious
support which help me completing this thesis.

v
TABLE OF CONTENTS
ABSTRACT...........................................................................................................iii
ACKNOWLEDGEMENT......................................................................................iv
LIST OF FIGURES..............................................................................................viii
LIST OF TABLES ...............................................................................................viii
LIST OF APPENDICES ........................................................................................ix
ABBREVIATIONS.................................................................................................x
CHAPTER 1. INTRODUCTION ............................................................................1
1.1. Problem statement......................................................................................1
1.2. Research objectives and hypotheses ...........................................................5
1.3. Scope of study............................................................................................5
1.4. Structure of thesis.......................................................................................6
CHAPTER 2. LITERATURE REVIEW..................................................................7
2.1. Concepts ....................................................................................................7
2.1.1. Total factor productivity (TFP) ............................................................7
2.1.2. Technical change or Technological progress (TP)................................8
2.1.3. Technical efficiency (TE) and Technical efficiency change (TEC) ......8
2.1.4. Scale economies and Scale change effects (SCE).................................9
2.1.5. Allocative efficiency (AE) .................................................................11
2.2. Approaches to measure and decompose TFP growth ................................13
2.2.1. Primal or dual approach with production, cost, or profit function.......13
2.2.2. Stochastic and deterministic approaches ............................................14
2.2.3. Parametric and non-parametric methods ............................................14
2.3. A review of alternative Stochastic Production Frontier (SPF) models.......15

vi
2.3.1. Time-invariant models.......................................................................16
2.3.2. Time-varying models.........................................................................17
2.3.3. Exogenous inefficiency determinants.................................................19
2.3.4. TFP growth decomposition................................................................22
CHAPTER 3. OVERVIEW OF VIETNAMESE HIGH-TECHNOLOGY
MANUFACTURING SECTOR ............................................................................25
3.1. High-technology (HT)..............................................................................25
3.2. Overview of Vietnamese HT manufacturing sector ..................................26
CHAPTER 4. METHODOLOGY .........................................................................29
4.1. Empirical models .....................................................................................29
4.2. Functional form........................................................................................30
4.3. Estimation method....................................................................................34
4.4. Hypotheses and testing.............................................................................34
4.5. Variable measurement..............................................................................34
4.5.1. Variables in the frontier model...........................................................35
4.5.2. Determinants of Technical inefficiency..............................................36
4.6. Data source and filter process...................................................................39
CHAPTER 5. EMPIRICAL RESULTS.................................................................40
5.1. Data description .......................................................................................40
5.2. Maximum likelihood estimates.................................................................43
5.3. Results of hypothesis testing.....................................................................47
5.4. Results of TFP decomposition..................................................................49
CHAPTER 6. CONCLUSIONS ............................................................................54
6.1. Findings ...................................................................................................54

vii
6.2. Policy implications...................................................................................55
6.3. Limitations and future research ................................................................55
REFERENCES......................................................................................................57
APPENDICES.......................................................................................................64

viii
LIST OF FIGURES
Figure 1: Value added of HT manufacturing industries of the world and selected
regions during 1997–2012 (in billions of current dollars)..........................2
Figure 2: High-tech exports of Vietnam & other countries in Asia (1997-2012) ......3
Figure 3: Production frontier, Technological progress, Technical efficiency, and
optimal Scale of production ....................................................................10
Figure 4: Technical efficiency and Allocative efficiency .......................................12
Figure 5: World exports & value-added of HT manufacturing sector (2001-2012) 27
Figure 6: Exports of Vietnamese HT manufacturing sub-industries .......................28
Figure 7: Proportions of HT firms operating in five sub-industries ........................41
Figure 8: Percentage of HT firms divided by regions.............................................41
Figure 9: Number of firms of different sizes, during 2000-2012 ............................42
Figure 10: Change of HT WFOEs and SOEs during 2000-2012 ............................42
Figure 11: Kernel density of TE (3 models BC92, BC95, and HL94) ....................43
Figure 12: List of major obstacles chosen by Vietnamese manufacturing firms (in
2009) ......................................................................................................50
LIST OF TABLES
Table 1: Contribution of Vietnamese HT in value added of manufacturing sector
during 2000–2012 (in percentage) ..........................................................27
Table 2: Some main characteristics of three models...............................................33
Table 3: Criterion to divide HT firms into three kind of sizes ................................36
Table 4: Definition and measurement of all variables in the study .........................38
Table 5: Descriptive statistics of production function variables .............................40
Table 6: Descriptive statistics of TI effects mean variables....................................40
Table 7: Maximum Likelihood estimates of translog production frontier...............45
Table 8: Maximum Likelihood estimates of technical inefficiency effects model
(Model BC95 and Model HL94).............................................................46
Table 9: LR Tests of hypotheses............................................................................48
Table 10: TFP & its decomposition in five HT sub-industries (model HL94) ........49
Table 11: TFP change & its sources of change over time (model HL94)................51
Table 12: Growth rate of production inputs across HT sub-industries....................52
Table 13: Returns to scale across HT sub-industries during 2000-2012 .................52

ix
LIST OF APPENDICES
Appendix 1: HT manufacturing industries in International and Vietnamese Standard
Industrial Classification....................................................................64
Appendix 2: Provinces and Cities of Vietnam divided by regions..........................65

x
ABBREVIATIONS
AE Allocative efficiency
AEC Allocative efficiency change
CRS Constant returns to scale
DEA Data envelopment analysis
DRS Decreasing returns to scale
GSO General Statistics Office
HT High-technology
IRS Increasing returns to scale
LR Likelihood-ratio
LS Least Squares
MFP Multi-factor Productivity
ML Maximum likelihood
MLDV Maximum Likelihood Dummy Variable
OLS Ordinary Least Squares
PIM Perpetual Inventory Method
SCE Scale change effects
SF Stochastic frontiers
SFA Stochastic Frontier Analysis
SOE State-owned enterprises
SPF Stochastic Production Frontier
TE Technical efficiency
TEC Technical efficiency change
TFP Total Factor Productivity
TI Technical inefficiency
TP Technological progress
VEC Vietnam Enterprise Census
VND Vietnam Dong
WFOE Wholly foreign owned enterprises

Dao Hoang Binh Thien Master’s Thesis VNP19 - 2014
1
CHAPTER 1. INTRODUCTION
1.1. Problem statement
Since last decades of 20th
century, the world has experienced the unexampled
evolution of advanced technology-intensive manufacturing industries such as
pharmaceuticals, computers, telecommunications, precision engineering, or aircraft.
Those high-technology (HT) industries have contributed considerably in promoting
human beings’ health and longevity, extending the ability of communication, and
improving the knowledge accessibility (Hamburg Institute for Economic Research
[HWWA], Kiel Institute for World Economics [IfW] & National Research Council
[NRC], 1996). Moreover, people are convinced that these HT industries will bring
the bright future of remarkable economic growth, including high value-added, high
wage employment. From the microeconomic perspective, HT firms are believed to
spend a large amount in R&D and innovation, which can lead to inventing new
products, gaining more market shares, using resources more productively, and
creating positive social returns that benefit other sectors (HWWA et al., 1996).
Figures of global value added of HT sector during recent years show clearly the
promising trend of its growth, especially in the dynamic Asia region (see Figure 1).

2
Figure 1: Value added of HT manufacturing industries of the world and
selected regions during 1997–2012 (in billions of current dollars)
Source: Appendix table 6-7 of Science and Engineering Indicators 2014
(National Science Board, 2014)
The power of nations is also believed to not influenced by heavy industries
like steels but the role is now played by HT manufacturing industries and knowledge-
based services, which means that the national autonomy can be improved by
developing these industries (HWWA et al., 1996). Indeed, the aging industrial
economy has bowed out to give way to the promising knowledge-based and
technology-intensive economy. Due to that importance, HT manufacturing industries
are the target of industrial policies in many countries and regions, including Vietnam.
HT manufacturing industries have been paid more attention in Vietnam recently with
many high-tech FDI projects built up (MoIT & UNIDO, 2011), together with new
Laws and Decisions approved to facilitate the science and technology activities.
Interestingly, even during the time of global crisis (2008-2009), this sector still had
an increase of export values with about USD 593 million, while low-tech and
medium-tech sectors experienced a reduction in exports (MoIT & UNIDO, 2011).
Indeed, during the period 2000 to 2009, over half of total exports are HT products
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
World North America European Union Asia

3
(MoIT & UNIDO, 2011). Although Vietnamese HT manufactures account for only a
small proportion of the world market share, its annual growth rate shows a potential
of development for this sector (see Figure 2). Moreover, the government expects HT
industries to play a key role in helping Vietnam economy develop and gain a higher
position in the global value chain (Strategy on exports and imports for 2011-2020,
with visions to 2030, 2011).
Figure 2: High-tech exports of Vietnam & other countries in Asia (1997-2012)
Bubble indicates HT exports in 2012. Annual growth rate is the geometric average annual
growth rate of exports during 1997-2012.
Source: Appendix table 6-21 of Science and Engineering Indicators 2014
(National Science Board, 2014)
It is obvious that the growth of Vietnamese HT manufacturing industries is
remarkable and it seems to be consistent with expectations. However, most of HT
firms in Vietnam are known to be operating as assembly lines rather than
concentrating on R&D and inventing new products. From another aspect, the large
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
-5% 0% 5% 10% 15% 20% 25% 30% 35%
Annual
growth
rate
Proportion in World Market share (2012)
China
India
Indonesia
Japan
Malaysia
Philippines
Singapore
South Korea
Taiwan
Vietnam
Thailand

4
proportion of imported components in HT products exported from Vietnam may
affect considerably on the production of HT firms. Thus, the question is whether
Vietnamese HT manufacturing industries perform as well as they look like or not. To
answer it, productivity, which indicates how well the firms perform using given factor
inputs, should be investigated carefully. Indeed, productivity is considered an
important indicator of development, playing a key role to firms’ survival (Duong,
Lai, Nguyen, Le, & Hua, 2014; National Science Board, 2014; Syverson, 2011). More
specifically, empirical researchers often estimate total factor productivity (TFP), a
measures of firms’ overall productivity, in their analysis. Moreover, they do not stop
at measuring TFP and its growth only, some authors try to examine what drives TFP
growth. Theoretical literature indicates that TFP growth of HT firms stem mainly
from the progressive technological change (Sun & Kalirajan, 2005). However, if
governments only focus on attracting investments to enhance technological progress
of HT sector, they may ignore the contribution of other important sources such as
effects from changes in scale of production (Hamit-Haggar, 2011; Kim & Han, 2001).
Besides, empirical studies show evidence that firms can also obtain higher TFP gains
if they apply best practice methods of the given technology, such firms are considered
“technically efficient” (Kalirajan, Obwona, & Zhao, 1996). In this circumstance,
technological progress may be absent; instead, effects from improving technical
efficiency are the key source contributing to TFP growth. Thus, such components
should be taken into account when modelling the production function and measuring
TFP. They will provide more comprehensive insights of HT sector’s status for policy
makers in taking HT development policies in consideration.
Nevertheless, there are very few papers analyzing the status of TFP change of
Vietnamese HT manufacturing industries as well as its decomposition. The study of
Nguyen, Pham, Nguyen, and Nguyen (2012), which can be the only paper touching
that field of TFP growth’s decomposition for Vietnamese manufacturing sector until
now, is not focused on HT manufacturing industries. Other studies, if conducted in
analysis of HT sector, stop at measuring TFP (Newman & Narciso, 2009), or

5
investigate only one source of TFP change, namely technical efficiency with analyses
on its various determinants (firm size, firm location, ownership…) (Le & Harvie,
2010; Nguyen, Giang, & Bach, 2007). Obviously, the literature of empirical
researches on TFP of Vietnamese high-tech manufacturing industries and its sources
of change is rather poor.
Thus, with longer timespan (2000-2012) and narrower research object (high-
tech industries), besides estimating determinants of technical inefficiency, this paper
attempts to measure TFP growth of Vietnamese HT manufacturers as well as its
decomposition. The results of the study may provide some information to understand
the performance of Vietnamese HT sector and be helpful for HT sector development
policies.
1.2. Research objectives and hypotheses
This paper aims to investigate the productivity and efficiency of Vietnamese
high-tech manufacturing sector, namely three objectives to attain:
 To measure TFP growth of Vietnamese high-tech manufacturers.
 To decompose TFP growth into Technological progress, Scale change effects,
and Technical efficiency change.
 To examine determinants of technical inefficiency.
1.3. Scope of study
The unbalanced panel data in this research includes 5822 observations of 2403
Vietnamese high-tech manufacturing firms through 13 years from 2000 to 2012. The
selected sector includes five sub-industries:
(i) Pharmaceuticals;
(ii) Computers and peripherals;
(iii) Radios, TVs, and communication equipment;
(iv) Precision instruments;
(v) Aircrafts.

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Firms in the sample include various sizes from small to large, different
ownerships from state owned, foreign owned, to private owned, with their
headquarters located nationwide in six regions of Vietnam.
1.4. Structure of thesis
The thesis is divided in six chapters with the following structures:
Chapter 2 presents the literature of productivity and efficiency measurement
and decomposition. Starting with the definitions of key concepts such as high-
technology, productivity, and efficiency, various approaches dealing with the
productivity measurement are then reviewed. Moreover, different models of
productivity decomposition and efficiency estimation are also discussed with
advantages and disadvantages of each own.
Chapter 3 provides a brief overview of Vietnamese HT manufacturing sector
after discussing about definitions and classifications of high technology firms and
industries.
Chapter 4 describes the specific research methodology, in which the
parametric approach and regression technique are expressed in details. This chapter
also discusses the seven hypotheses mentioned in the second part of chapter
Introduction more clearly with the testing methods.
Chapter 5 presents the empirical results in two parts, namely descriptive
statistics of the data and results of the regression. Based on empirical evidence from
econometric models, the inference and analysis is then drawn and discussed about
productivity and efficiency of Vietnamese high-tech sector.
Chapter 6 concludes main findings of the study as well as policy and
managerial implications stemmed from the results presented in Chapter 5. This
chapter also point out limitations of the thesis and then refer to directions for
researches in the future.

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CHAPTER 2. LITERATURE REVIEW
This chapter provides some definitions of key concepts such as total factor
productivity and kinds of efficiency. In addition, various approaches measuring and
decomposing productivity change are also discussed in this chapter. Especially,
stochastic production frontier analysis (SPF) is the main focus of this chapter.
2.1. Concepts
2.1.1. Total factor productivity (TFP)
Productivity of a firm implies the ratio of outputs over inputs in production
(Coelli, Rao, O’Donnell, and Battese (2005). In other words, it shows how well the
outputs can be produced from given amounts of inputs. Productivity is often used to
compare performance between firms or industries: the larger the ratio is, the better
the firm (or industry) performs. In case there are multiple outputs and multiple inputs
involving the production, partial productivity measures, which only take one factor
of production into account, may be selected to estimate to simplify the estimation
process. There are many partial measures of productivity such as labor productivity,
land productivity, or fuel productivity. Meanwhile, Total Factor Productivity (TFP)
is the measure of overall productivity, which involves all factors of production. In
this case, TFP is defined as a ratio of aggregate output produced over aggregate input
used (Coelli et al., 2005). It is a better choice of performance measurement than
partial measures because partial productivity measures can misrepresent the
performance of a firm (Coelli et al., 2005). Because we may never take into account
all the factors affecting the output level, Multi-factor Productivity (MFP) is the more
precise term that should be used in empirical calculation. However, researches tend
to use those two terms interchangeably in their studies, which is also applied in this
thesis. Over time, TFP tends to change, usually positively, which is believed to be an
important factor contributing to the survival of firms (micro perspective) and
economic growth (macro perspective) in the long-run.

8
2.1.2. Technical change or Technological progress (TP)
According to neo-classical economists, due to the law of diminishing returns,
the firm cannot increase its output levels forever if it keeps accumulating factors of
production, given the current technology (Sharma, Sylwester, & Margono, 2007).
Thus, when a firm is observed to increase its TFP in the long-run, they argue that the
only reason for TFP growth is that the firm has adopted more advanced technology,
implying that there is technological progress (TP).
In Solow (1957)’s model, positive technical change (or TP as in some
reviews), which is exogenous and unexplainable by the model, is the only source of
long-run growth of per capita income. Graphically, TP is expressed as the upward
shift of the production frontier. In other words, with the presence of TP, a firm can
increase its potential productivity beyond previous limits (see Figure 3 for
illustration).
However, arguing against Solow (1957)’s, later studies of other authors have
proved that not only TP is the main source of TFP growth, the improvement of
technical efficiency, the exploitation of scale economies or allocative efficiency also
drive TFP growth (Coelli et al., 2005).
2.1.3. Technical efficiency (TE) and Technical efficiency change (TEC)
A producer is considered as technically efficient “if and only if it is impossible
to produce more of any output without producing less of some other output or using
more of some input” (Koopmans, cited in Kumbhakar & Lovell, 2000). Despite the
popularity of Solow (1957)’s, this model has a critical weakness when assuming that
the firms are operating with full efficiency, i.e. the firms are operating along with the
production frontier (see Figure 4). If ignoring the potential contribution of efficiency
changes to TFP growth, the estimate of productivity may be biased and misleading
(Hamit-Haggar, 2011). Nishimizu and Page (1982) were the pioneers in introducing
efficiency change as a source of productivity growth. The assumption of full
efficiency is also unrealistic while it is likely that many firms’ productions are
inefficient, which means that there is the gap between the production frontier and the

9
firm’s actual production level. Kim and Han (2001) argued that improvements in
technical efficiency (TE) can cause TFP growth for firms that are not fully utilize
existing technology due to some restraints such as organizational factors. The
literature also shows more evidence that positive (negative) technical efficiency
change (TEC) can contribute to progressive (regressive) TFP change. For instance,
Nguyen et al. (2012) and Kim and Han (2001), after measuring and decomposing
TFP change, drew a conclusion about the positive contribution of TEC into TFP
growth, whereas findings of Kim and Shafi'i (2009) and Hamit-Haggar (2011)
confirmed that TFP of manufacturing industries can be hurt with worsen technical
efficiency.
2.1.4. Scale economies and Scale change effects (SCE)
According to theoretical background, which are clearly reviewed in Coelli et
al. (2005) and Kumbhakar and Lovell (2000), a firm is exploiting scale economies
when the ray from the origin is at a tangent to the production frontier and thus defines
the point of maximum possible productivity, i.e. the point of optimal scale (see Figure
3). They also indicate that when a firm production is technically efficient, it can still
increase productivity by exploiting scale economies, which is called scale change
effects (SCE). More exactly, when the production function exhibits increasing returns
to scale (IRS), the contribution of SCE to TFP growth will be positive, whereas the
decreasing returns to scale (DRS) will worsen the TFP growth. Obviously, if constant
returns to scale (CRS) exist in the production, there will be no scale effects on the
improvement (decline) of TFP.
Estimating sources of TFP growth in manufacturing industries of Korea during
1980-1994, Kim and Han (2001) show that the almost scale components are negative
or close to zero, which results in a decrease in TFP growth. In other words, Korean
manufacturers were operating at DRS or CRS during the period of study. Kim and
Shafi'i (2009) when estimating TFP growth for the case of Malaysian producers also
confirmed that SCE influence significantly on the overall productivity; however, the
impact is different across industries.

10
Figure 3: Production frontier, Technological progress, Technical efficiency,
and optimal Scale of production
Source: Coelli et al. (2005)
Where
F’0: Production frontier at time 0
F’1: Production frontier at time 1
x: input y: output
A: the firm has technical efficiency at time 0
B: the firm has technical inefficiency at time 1
C: the optimal scale at time 0
From F’0 to F’1: technological progress from time 0 to time 1
A
C
B
Technological progress

11
2.1.5. Allocative efficiency (AE)
Allocative efficiency, which involves the mix of input selection (capital and
labor, for example), is also another component of TFP growth. Indeed, while the
factor prices and the production technology hold constant, selecting the mix of inputs
in optimal proportions such that costs of production reach minimum can also increase
the productivity of the firm (Coelli et al., 2005). According to Schmidt and Lovell
(1979), allocative inefficiency exists when the factor price, i.e. the marginal cost of
an input is not equal to its marginal revenue product. This leads to the inefficient
production process. Thus, a firm is called “allocatively efficient” when it can
minimize costs of production by selecting the right proportions of inputs. The
decrease in costs of production may result in the increase in output levels (while the
input levels and costs unchanged) or the decrease in input levels (while the output
level is fixed); both cases lead to the improvement in productivity. Graphically, there
are circumstances that firms are fully technically efficient (the output value lies on
the efficient isoquant) or allocatively efficient (output value lies on the isocost line).
Combining AE with TE, they create a new measure called economic efficiency, or
overall efficiency. Economic efficiency occurs when the output value is the point of
contact between isoquant curve and isocost line (see Figure 4).
Empirically, most of Canadian manufacturers benefited from AE according to
the research of Hamit-Haggar (2011). On the other hand, estimation of Kim and Han
(2001) for Korean firms expressed that AE had a negative impact on TFP growth. In
other words, there were an inefficient allocation of inputs in production. They also
implied that the degree of capital market distortion might be the cause of AE
difference across industries: The allocative inefficiency was more clearly observed in
industries supported by the government. However, allocative efficiency is almost
unlikely to be estimated in empirical studies when there is the unavailability of data
on costs and prices (Sharma et al., 2007).

12
Figure 4: Technical efficiency and Allocative efficiency
Source: Coelli et al. (2005)
where
SS’: The isoquant curve, representing the possible input
combinations to produce a given output level when the firm
is fully efficient
AA’: The isocost line, representing the combinations of inputs
that minimize the costs
x1, x2: input
q: output
R: The firm is allocative efficient but not technically efficient
Q: The firm is technically efficient but not allocative efficient.
Q’: The firm is both technically and allocative efficient,
indicating that it reaches full economic efficiency.

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2.2. Approaches to measure and decompose TFP growth
2.2.1. Primal or dual approach with production, cost, or profit function
From neo-classical perspective, TFP growth can be measured as the residual
factor by deducting input growth (labor and capital) from output growth, expressed
in the famous model of Solow (1957), known as “growth accounting”. The original
Solow model is also called the “primal residual” approach. Studies adopting primal
approach often use product functions to measure TFP. The production function of a
firm is considered the technological possibilities of that firm to produce an output
using some amounts of inputs. A production function should have several properties
such as non-negativity, weak essentiality, monotonicity, and concavity (Coelli et al.,
2005).
Meanwhile, the cost function is set out to find the input quantities that
minimize costs from the set of all technically feasible input-output combinations,
given the current technology. Similarly, the profit function solves the problem of
profit maximization obtained from given amounts of input. The methods adopting
cost frontiers or revenue (profit) frontiers can be more useful because they can
measure economic efficiency, not only technical efficiency (Coelli et al., 2005).
Using cost or profit function also means adopting the “dual approach”. For example,
Hsieh (2002) used the “dual residual” approach including both quantities and costs
of factors of production to calculate TFP growth of four East Asian countries during
1966-1991. His estimates seem to explain more exactly the economic growth of these
Asian countries because costs of factors reflect actual market conditions better than
quantities. However, the dual approach is data demanding; it requires the information
of factor prices of production, which are often difficult to obtain in Vietnamese young
industries like high-tech. For that reason, primal approach using production function
is preferred when studying TFP growth of Vietnamese high-tech manufacturer
because it only needs data on quantities of inputs and outputs.

14
2.2.2. Stochastic and deterministic approaches
Deterministic production functions do not allow random events or other
factors to impact on output, i.e. any deviation from the production frontier will be
considered as an inefficiency (Coelli et al., 2005). Obviously, this approach will
overestimate the technical inefficiency (TI), leading to underestimating the
contribution of technical efficiency change into TFP growths (or shrinks). To solve
the problem, another random variable is introduced to represent statistical noise. That
kind of frontier is called stochastic frontier. Indeed, stochastic frontier models take
both inefficiency and statistical noise in account to explain why the production (or
cost or profit) is not going along with the frontier (Sharma et al., 2007). In other
words, it allows for calculating TFP changes and its component sources in a stochastic
environment.
2.2.3. Parametric and non-parametric methods
TFP improvements (recession) and its decomposition can be calculated by
adopting parametric or non-parametric methods. Popular non-parametric approaches
that can be mentioned are index number techniques and data envelopment analysis
(DEA). Regarding index number techniques such as Fisher’s (1922) or Törnqvist’s
(1936) indices, the main advantages are that they are easy to calculate and need only
two observations (two firms/industries or two periods of time of the same firm)
(Kumbhakar & Lovell, 2000). However, those techniques cannot answer the question
about sources of TFP change. To solve this, one can conduct DEA, which has strong
points that it does not need the specific functional form of the production (or cost or
profit) function. Nevertheless, DEA estimators cannot separate the impacts of random
shocks and inefficiency from the change of TFP and also not applicable for time series
dataset (Coelli et al., 2005).
Meanwhile, parametric approaches need the distributional form of the
inefficiency term and statistical noise as well as the restrictions on the underlying
technology (Coelli et al., 2005). There are several parametric methods that
researchers often apply, which are Least Squares (LS) econometric production

15
models, or Stochastic frontiers (SF). Each of them has its own strengths and
drawbacks. For instance, LS models are simple but they are built based on the
deterministic approach, which means all variation in output not associated with
variation in inputs will be considered TI (Kumbhakar & Lovell, 2000). Meanwhile,
SFs can separate that variation to random events and technical efficiency.
SF estimators including exogenous inefficiency determinants can be obtained
by using Ordinary Least Squares (OLS) or Maximum likelihood (ML). SFs can also
be estimated in two-step procedure (OLS at first to estimate the slope parameters then
ML at second to estimate the intercept and the variances of error terms) or one-step
procedure (ML only).
OLS is the easier method to calculate than the latter. There is, however, the
downward bias in the normal OLS estimators of intercept coefficients, Modified
Least Squares or Corrected Least Squares should be applied to shift up the biased
OLS intercept parameter (Kumbhakar & Lovell, 2000). Meanwhile, despite the
requirements of distributional forms and computational issues in reaching
convergence, ML is argued to be the better choice when analyzing large sample
(Coelli et al., 2005).
2.3. A review of alternative Stochastic Production Frontier (SPF) models
It can be easily recognized that panel data is more informative than cross-
sectional data. One can adapt panel data estimation techniques to relax some strong
assumptions of cross-sectional data such as the distribution or independence of the TI
error terms (Kumbhakar & Lovell, 2000). The literature on panel data stochastic
production frontiers can be divided into two main categories based on the assumption
that TI effects change or unchanged over time. Representatives of time-invariant
models are Pitt and Lee (1981), Schmidt and Sickles (1984) and Battese and Coelli
(1988), whereas papers written by Cornwell, Schmidt, and Sickles (1990),
Kumbhakar (1990), Battese and Coelli (1992), Lee and Schmidt (1993), and Greene
(2005) can exemplify time-varying models. Besides, TI can be specified as a function
of exogenous factors such as the models of Battese and Coelli (1995) and Huang and

16
Liu (1994) to permit the pattern of TI effects to not only change over time but also
vary across firms.
2.3.1. Time-invariant models
Pitt and Lee (1981) are considered the pioneers in extending SPF to panel data.
Allowing the TI vary across firms but unchanged through time, they estimated
parameters using ML and assumed the TI error component to follow normal-half
normal distribution, namely:
𝑦𝑖𝑡 = 𝑥𝑖𝑡𝛽 + 𝜀𝑖𝑡, 𝑖 = 1, … , 𝑁, 𝑡 = 1, … , 𝑇, (1)
𝜀𝑖𝑡 = 𝑣𝑖𝑡 − 𝑢𝑖, (2)
𝑣𝑖𝑡~𝑁(0, 𝜎𝑣
2
), (3)
𝑢𝑖~𝑁+
(0, 𝜎𝑢
2
), (4)
where 𝑦𝑖𝑡 is the actual output level of the ith
producer at time tth
, 𝑥𝑖𝑡 is a 1xK
input vector and 𝛽 is a Kx1 parameter vector to be estimated.
Afterward, this model is generalized to normal-truncated normal case in
Battese and Coelli (1988) with (4) is replaced by 𝑢𝑖~𝑁+
(𝜇, 𝜎𝑢
2
) (𝜇 = 0 is the special
case of the distribution function, which makes it become Pitt and Lee’s (1981)
model).
SPF estimators can also be performed using fixed-effects techniques like the
model in Schmidt and Sickles (1984):
𝑦𝑖𝑡 = 𝛼𝑖 + 𝑥𝑖𝑡𝛽 + 𝑣𝑖𝑡, 𝑖 = 1, … , 𝑁, 𝑡 = 1, … , 𝑇, (5)
𝑤ℎ𝑒𝑟𝑒 𝛼𝑖 = 𝛼 − 𝑢𝑖, (6)
𝑢𝑖 ≥ 0 𝑎𝑛𝑑 𝑣𝑖𝑡 𝑖𝑖𝑑 (0, 𝜎𝑣
2), (7)
𝛼𝑖s are producer-specific intercepts, which can be estimated by suppressing
the constant then adding dummy variables for each firms, or applying the within
transformation (running OLS regression after substituting 𝑦𝑖𝑡 by 𝑦𝑖𝑡 − 𝑦𝑖
̅ and so on).

17
After estimating the parameters, the estimators of the fixed-effects 𝑢𝑖 can be
obtained from:
𝑢𝑖
̂ = 𝛼
̂ − 𝛼𝑖
̂ , (8)
𝑤ℎ𝑒𝑟𝑒 𝛼
̂ = max(𝛼𝑖
̂ ), (9)
Estimates of 𝑢𝑖 obtained from the fixed-effects panel data model prove to be
consistent (Kumbhakar & Lovell, 2000). However, 𝑢𝑖 also capture the effects of other
events that vary across producers that may or may not the cause of TI. For that reason,
Schmidt and Sickles (1984) also proposed the random-effects SPF model:
𝑦𝑖𝑡 = [𝛼 − 𝐸(𝑢𝑖)] + 𝑥𝑖𝑡𝛽 + 𝑣𝑖𝑡 − [𝑢𝑖 − 𝐸(𝑢𝑖)]
= 𝛼∗
+ 𝑥𝑖𝑡𝛽 + 𝑣𝑖𝑡 − 𝑢𝑖
∗
, 𝑖 = 1, … , 𝑁, 𝑡 = 1, … , 𝑇,
(10)
Where 𝑢𝑖s (𝑢𝑖 ≥ 0) are randomly distributed with constant mean and variance
but uncorrelated with the regressors and 𝑣𝑖𝑡s.
If N is large and T is small, GLS can be used to estimate the regressors, then
𝑢𝑖 can be obtained from:
𝑢𝑖
̂ = max(𝑢𝑖
∗
̂) − 𝑢𝑖
∗
̂, (11)
𝑤ℎ𝑒𝑟𝑒 𝑢𝑖
∗
̂ =
1
𝑇
∑(𝑦𝑖𝑡 − 𝛼∗
− 𝑥𝑖𝑡𝛽)
𝑡
, (12)
However, the assumption of TI effects to be time-invariant seems to be
unrealistic with long panel datasets when firms can improve their performance via
learning-by-doing over time. That is the reason why some authors try to specify
different functions of 𝑢𝑖𝑡 to allow for time-varying TI effects.
2.3.2. Time-varying models
Cornwell et al. (1990) replaced the firm-specific effects 𝛼𝑖 in (5) by a function
of time allowing parameters to vary across firms:
𝛼𝑖𝑡 = 𝜃𝑖1 + 𝜃𝑖2𝑡 + 𝜃𝑖3𝑡2
, (13)

18
This specification allows the TI to change through time while still having firm-
specific characteristics. However, this pattern requires Nx3 parameters to estimate
firm effects, which leads to the impact on degree of freedom (Belotti, Daidone, Ilardi,
& Atella, 2012).
To reduce the numbers of parameters, alternative of time-varying TI effects
were proposed by Kumbhakar (1990), Battese and Coelli (1992), and Lee and
Schmidt (1993) with the general function of 𝑢𝑖𝑡 as following:
𝑢𝑖𝑡 = 𝑔(𝑡)𝑢𝑖, 𝑢𝑖 ≥ 0, (14)
where g(t), depending on the authors, can be specified as:
Kumbhakar (1990): 𝑔(𝑡) = [1 + exp(𝑏𝑡 + 𝑐𝑡2)]−1
, (15)
Battese and Coelli (1992): 𝑔(𝑡) = exp[−𝜂(𝑡 − 𝑇𝑖)], (16)
Lee and Schmidt (1993): 𝑔(𝑡) = θ𝑡,
where θ𝑡 is a set of time dummy
variables.
(17)
In (15), there are only two parameters b and c to estimate. It allows 0 ≤
𝑔(𝑡) ≤ 1 and 𝑔(𝑡) can be concave or convex, monotonically increasing or
decreasing. However, the estimation and inference of technical efficiency change can
be complicated.
In (16), 𝜂 is the unknown scalar parameter representing the rate of change in
TI. If 𝜂 > 0, 𝑢𝑖𝑡 decreases as t increases, and vice versa; while 𝜂 = 0 means time-
invariant TI, i.e. the firm-specific effects. This model has the advantage that there is
only one parameter 𝜂 that needs to be estimated and it is easy to conclude about the
technical efficiency as well as its change over time. However, this specification only
allows TE to increase at a decreasing rate (𝜂 < 0) or decrease at an increasing rate
(𝜂 > 0).
In (17), the parameters can be easily obtained because it does not need any
parametric form of 𝑢𝑖𝑡. Nevertheless, this model should not be applied in cases of
large T because the number of parameters to be estimated is also a large figure.

19
The three above models (15) - (17) can solve the problem of reducing the
parameters but the changes of 𝑢𝑖𝑡 over time are the same between producers, which
is a restriction that makes it less flexible than Cornwell et al.’s (1990) model. It also
means the bias in 𝑢𝑖𝑡 estimation (Belotti et al., 2012). Arguing that taking account of
time-invariant latent variables into the inefficiency effects while those factors are not
associated with the production process can create a misspecification bias, Greene
(2005) introduced the “true” fixed-effects and “true” random-effects model to
separate the heterogeneity from efficiency:
“True” fixed-effects model: 𝑦𝑖𝑡 = α𝑖 + 𝑥𝑖𝑡β + 𝑣𝑖𝑡 − 𝑢𝑖𝑡, (18)
where α𝑖 is the group specific constant.
“True” random-effects
model:
𝑦𝑖𝑡 = α + 𝑤𝑖 + 𝑥𝑖𝑡β + 𝑣𝑖𝑡 − 𝑢𝑖𝑡, (19)
where 𝑤𝑖 is the constant term varying across
firms.
Belotti et al. (2012) gave advice that model (18) requires a long panel (𝑇 ≥ 0)
to make the estimated parameters α𝑖 consistent. Moreover, α𝑖 in (18) creates a large
dimension of parameters if the number of firms is large, which leads to a
computational issue. Thus, a Maximum Likelihood Dummy Variable (MLDV)
approach should be used with caution (Belotti et al., 2012; Greene, 2005).
Despite its flexibility among other time-varying models, one can argue against
model (19) that the some portions of latent heterogeneity do impact on inefficiency
(Belotti et al., 2012). Therefore, the decision to disentangle those two parts or not
depends on the characteristics of data.
2.3.3. Exogenous inefficiency determinants
One can argue that time-varying technical efficiency may not only stem from
the passage of time but also from other variables, which may be or may be not
production inputs and outputs but can still affect the productivity of the firm
(Kumbhakar & Lovell, 2000). Examples of those exogenous variables are the firm’s

20
characteristics (ownership, firm age, firm size, location), or policy variables
(government regulations). If that observable heterogeneity is not controlled in the
SPF, the estimated parameters of technical efficiency can be biased and affect the
post-estimation inference (Belotti et al., 2012). Nevertheless, there is no common
agreement about the impacts of those determinants. For instance, Nguyen et al. (2007)
showed that there was a positive relationship between firm size and technical
efficiency of small and medium manufacturing firms in Vietnam, whereas estimation
results in the study of Badunenko, Fritsch, and Stephan (2006) about German firms
indicated that larger firms tend to be less technically efficient than small ones.
Another example of the controversy in effects of exogenous variables is findings
about effects of foreign ownership on TE: negative in findings of Pham, Dao, and
Reilly (2009) but positive in estimates of Zhou (2014). Thus, inference about
exogenous sources of TE should be made with caution.
Regarding the estimation methods, from the early approach, the two-step
procedure was used, which means inefficiency then exogenous effects are estimated
in sequence. However, as pointed out in Wang and Schmidt (2002), there is strong
evidence in favor of the one-step procedure (exogenous effects are estimated
simultaneously with other parameters of the model).
The models of Huang and Liu (1994) and Battese and Coelli (1995) are
considered the most well-known ones that apply one-step approach to incorporate
exogenous effects into efficiency by parameterizing the mean 𝜇𝑖𝑡 of the pre-truncated
distribution, i.e. 𝑢𝑖𝑡 𝑖𝑖𝑑 𝑁+
(𝑧𝑖𝑡𝛿 + 𝑧𝑖𝑡
∗
𝛿∗
, 𝜎𝑢
2
) . The general function of technical
efficiency effects is defined as:
𝑢𝑖𝑡 = 𝑧𝑖𝑡𝛿 + 𝑧𝑖𝑡
∗
𝛿∗
+ 𝑤𝑖𝑡, (20)
where 𝑧𝑖𝑡 is a 1xM vector of exogenous determinants.
𝛿 is an Mx1 vector of unknown parameters that need to be estimated.
𝑧𝑖𝑡
∗
is a vector of interaction between exogenous variables 𝑧𝑖𝑡 and input
variables 𝑥𝑖𝑡.
𝛿∗
is a vector of unknown parameters to be estimated.

21
𝑤𝑖𝑡 𝑖𝑖𝑑 𝑁+
(0, 𝜎𝑤
2
) is the random unobserved component such that 𝑢𝑖𝑡 ≥ 0.
In the study of Battese and Coelli (1995), there is no inputs variables in the
specification of 𝑢𝑖𝑡 . Therefore, all estimators of 𝛿∗
in (20) are equal to zero.
Meanwhile, Huang and Liu (1994) indicated that TI comes from two main parts: firm-
specific characteristics and the intensity of input use. They argued that producers may
gain “more information, knowledge, and experience with respect to one input
productivity than another”. Thus, the coefficients of 𝛿 and 𝛿∗
in (20) are different
from zero. The model of Huang and Liu (1994) is called non-neutral stochastic
because the effects of TI effects on productivity of firms will be biased toward some
inputs other than others.
Instead of the mean parameterization, parameterizing the variance 𝜎𝑖𝑡
2
of the
pre-truncated distribution, i.e. 𝑢𝑖𝑡 𝑖𝑖𝑑 𝑁+
(𝜇, 𝜎𝑖𝑡
2
) where 𝜎𝑖𝑡
2
= exp(𝑧𝑖𝑡𝛾), is also a
way to analyze the effects of exogenous determinants on inefficiency as well as solve
the problem of heteroscedasticity (Kumbhakar & Lovell, 2000). It can be understood
that those determinants affect on the production risks, representing by the variance
𝜎𝑖𝑡
2
(Coelli et al., 2005; Kumbhakar & Lovell, 2000). Caudill, Ford, and Gropper
(1995) and Hadri, Guermat, and Whittaker (2003) are among the authors supporting
this approach.
As stated in Wang (2002), the approach of Battese and Coelli (1995) and
Caudill et al. (1995) can be combined, which implies that
𝑢𝑖𝑡 𝑖𝑖𝑑 𝑁+(𝜇𝑖𝑡, 𝜎𝑖𝑡
2), 𝑤ℎ𝑒𝑟𝑒 𝜇𝑖𝑡 = 𝑧𝑖𝑡 𝛿 + 𝑧𝑖𝑡
∗
𝛿∗
𝑎𝑛𝑑 𝜎𝑖𝑡
2
= exp(𝑧𝑖𝑡
1
𝛾). 𝑧𝑖𝑡 and 𝑧𝑖𝑡
1
are
not necessarily the same vector of variables.
Caudill et al. (1995) also propose 𝜎𝑣
2
= exp(𝑧𝑖𝑡 𝜆) to solve the problem of
heteroscedasticity of random error term component 𝑣𝑖𝑡. However, as clearly stated in
Kumbhakar and Lovell (2000), the heteroscedasticity of 𝑣𝑖𝑡 does not cause severe
consequences on the estimators. Thus, the parameterization of 𝜎𝑣
2
seems to be
neglected in the literature of efficiency measurement.

22
2.3.4. TFP growth decomposition
As mentioned in the concepts part, TFP growth can stem from many sources
such as Technological progress (TP), Technical efficiency change (TEC), Scale
change effects (SCE), and Allocative efficiency change (AEC).
The decomposition process starts with the general production frontier:
𝑦𝑖𝑡 = 𝑓(𝑥𝑖𝑡, 𝑡) exp(−𝑢𝑖𝑡) , (21)
From now on, the ‘it’ subscripts are omitted for simplicity.
Taking logarithm of both sides of (21) and totally differentiating with respect
to time, the output growth rate is yielded as:
𝑑𝑙𝑛𝑦
𝑑𝑡
=
𝜕𝑙𝑛𝑓(𝑥, 𝑡)
𝜕𝑡
+ ∑
𝜕𝑙𝑛𝑓(𝑥, 𝑡)
𝜕𝑥𝑗
𝑑𝑥𝑗
𝑑𝑡
𝑗
−
𝑑𝑢
𝑑𝑡
,
𝑜𝑟 𝑦̇ = 𝑇𝑃 + ∑ 𝜀𝑗𝑥̇𝑗
𝑗
+ 𝑇𝐸𝐶, (22)
where a dot over a variable means the time rate of change of that variable.
𝑇𝑃 = 𝜕𝑙𝑛𝑓(. )/𝜕𝑡 is the primal rate of technological change. 𝜀𝑗 = 𝜕𝑙𝑛𝑓(. )/𝜕𝑥𝑗 is the
output elasticity of the jth input. 𝑇𝐸𝐶 = −𝑑𝑢/𝑑𝑡 is change in technical efficiency.
Then, TFP change is defined as output growth unexplained by input growth:
𝑇𝐹𝑃
̇ = 𝑦̇ − ∑ 𝑆𝑗𝑥̇𝑗
𝑗
, (23)
where 𝑆𝑗 is input j’s share in total production costs, 𝑆𝑗 = 𝐶𝑗/𝐶 (C is total cost,
𝐶𝑗 is costs of input jth).
Substituting (23) into (22), TFP change is yielded:
𝑇𝐹𝑃
̇ = 𝑇𝑃 + ∑ (𝜀𝑗 − 𝑆𝑗)𝑥̇𝑗 + 𝑇𝐸𝐶
𝑗
= 𝑇𝑃 + (𝑅𝑇𝑆 − 1) ∑ 𝜆𝑗𝑥̇𝑗
𝑗 + ∑ (𝜆𝑗 − 𝑆𝑗)𝑥̇𝑗
𝑗 + 𝑇𝐸𝐶
= 𝑇𝑃 + 𝑆𝐶𝐸 + 𝐴𝐸𝐶 + 𝑇𝐸𝐶, (24)

23
where 𝑅𝑇𝑆 = ∑ 𝜀𝑗
𝑗 is the estimates of returns to scale, 𝜆𝑗 = 𝜀𝑗/𝑅𝑇𝑆; 𝑆𝐶𝐸 =
(𝑅𝑇𝑆 − 1) ∑ 𝜆𝑗𝑥̇𝑗
𝑗 measures the change in scale of production; and 𝐴𝐸𝐶 =
∑ (𝜆𝑗 − 𝑆𝑗)𝑥̇𝑗
𝑗 is the change in allocative efficiency. However, when it is impossible
to collect the price data, allocative efficiency cannot be estimated. In that case,
according to Kumbhakar and Lovell (2000), it is assumed that 𝑆𝑗 =
𝜀𝑗
𝑅𝑇𝑆
= 𝜆𝑗, then
(24) becomes:
𝑇𝐹𝑃
̇ = 𝑇𝑃 + (𝑅𝑇𝑆 − 1) ∑ 𝜆𝑗𝑥̇𝑗
𝑗
+ 𝑇𝐸𝐶 = 𝑇𝑃 + 𝑆𝐶𝐸 + 𝑇𝐸𝐶, (25)
(25) is the decomposition of TFP growth, which is consistent with the equation
from Kumbhakar, Denny, and Fuss (2000). It can be easily seen (25) has three
components: technological progress (TP), change in technical efficiency (TEC), and
scale change effects (SCE).
TP can be positive (negative) under progressive (regressive) technological
change. It will be eliminated if the technology does not change over time. This term
can be decomposed to neutral (autonomous change) and biased components
(depending on the intensity of input use).
Regarding the effects of changing scale of production, the sign of SCE
depends on signs and magnitude of both output elasticity and rates of input change
over time. It is positive (negative) if there is IRS (DRS) and increasing (decreasing)
use of inputs. If the assumption of CRS holds (RTS=1), SCE equals zero, then the
formula will be the same as the one in Nishimizu and Page (1982).
The estimation of changes in technical efficiency is more complicated, as
mentioned in previous parts. Various specifications of 𝑢𝑖𝑡 will produce different
estimates of TEC. For example, if 𝑢𝑖𝑡 is defined as in (14) and (16), TEC can be
estimated using:
𝑇𝐸𝐶 = −
𝑑𝑢
𝑑𝑡
= 𝜂 × 𝑢, (26)

24
If 𝑢𝑖𝑡 is specified as in (20), then TEC will be obtained from:
𝑇𝐸𝐶 = −
𝑑𝑢
𝑑𝑡
=
𝑑𝑙𝑛𝑇𝐸(𝑥, 𝑧, 𝑡)
𝑑𝑡
=
𝜕𝑙𝑛𝑇𝐸(𝑥, 𝑧, 𝑡)
𝜕𝑡
+ ∑
𝜕𝑥𝑗
𝑑𝑥𝑗
𝑑𝑡
𝑗
+ ∑
𝜕𝑧𝑚
𝑑𝑧𝑚
𝑑𝑡
𝑚
,
(27)
where 𝜕𝑙𝑛𝑇𝐸(. )/𝜕𝑥𝑗 and 𝜕𝑙𝑛𝑇𝐸(. )/𝜕𝑧𝑚 are marginal effects of input
variables 𝑥𝑗 and firm-specific characteristics 𝑧𝑚 on TEC.
The right-hand side of (27) includes three parts: the first part of is the passage
of time, the second part is the effects due to changes in input use, and the last part is
the effects due to changes in the firm-specific characteristics. If Battese and Coelli’s
(1995) model is applied, then the second part is omitted. If the firm-specific
characteristics do not change over time, the last part equals zero. If both second and
third parts equal zero, it means that the technical efficiency change is neutral.
According to Wang (2002), marginal effects of exogenous factors can be non-
monotonic, which implies that exogenous determinants can impact positively and
negatively on the technical efficiency of the firm. For instance, when the firm is
young, its performance may be inefficient due to lacking of experience. Thus, it can
improve the TI over time through learning by doing. Afterward, the increase rate of
efficiency will gradually reduce, especially when the firm exists for a long time that
it becomes less flexible to unexpected shocks. The models of Caudill et al. (1995),
Hadri et al. (2003), or the combined model in Wang (2002) allow for the non-
monotonic efficiency effects. Nevertheless, the non-monotonicity does not exist in
Huang and Liu’s (1994) and Battese and Coelli’s (1995) models because the
specification of 𝑢𝑖𝑡 restricts that all of the sample’s observations have either
efficiency-improving or efficiency-reducing (Wang, 2002).

25
CHAPTER 3. OVERVIEW OF VIETNAMESE HIGH-TECHNOLOGY
MANUFACTURING SECTOR
This chapters attempts to define and classify HT manufacturing industries in
Vietnam. Afterwards, the current status of Vietnamese HT manufacturing sector,
including contribution, pattern, and trends, will be described briefly.
3.1. High-technology (HT)
Organization for Economic Co-operation and Development [OECD] (2005)
identifies that “technology is a stock of (physical or managerial) knowledge which
makes it possible to make new products or new processes”. Then, it implied that high
technology is the most state-of-the-art knowledge available, which make more rapid
progresses than other and requires continuous huge efforts in R&D with a strong
technological base. Law on High technologies, approved by the National Assembly
of Vietnam in 2008, also mentions high technology as “high scientific research and
technological development content” and this kind of technology can make
“environmentally friendly products of superior quality and utilities and high added
value”.
Defining a HT firm, Hecker (1999) used the definition of the U.S.
Congressional Office of Technology Assessment in his paper: Firms that design,
develop, and introduce new products and innovate new production processes by
applying scientific and technical knowledge as well as advanced techniques are
considered HT firms. He also indicated that R&D budgets of HT firms are usually
large, and many experts in science, technology, or engineering are hired in those
firms. Meanwhile, Vietnamese Law on High technologies (2008) provides a simpler
definition: an enterprise is classified as HT when it produces HT products, provides
HT services and conducts R&D activities relating to HT.
Regarding the definition of HT industries, according to Vietnamese Law on
High technologies (2008), which industries manufacturing HT products and
providing HT services will be considered as HT. OECD (2005) makes it clearer when
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26
stating that both intensively producing and utilizing technology belong to high-tech
sectors. Using graphics to demonstrate, the current state of technology in the industry
can be represented by the production frontier, which is a set of maximum outputs
attainable from each input level (Coelli et al., 2005) (see Figure 3).
Despite the fact that “high-technology” or “high-tech” has been agreed in
general definition and used popularly, it is difficult to reach a common agreement on
the classification of industries, products, or even employment into low-tech, medium-
tech, and high-tech sectors. There is still a controversy in this field regarding the
approaches and criteria. Some classifications are based on input-based criteria such
as the embodied R&D expenditures relative to the goods value (Davis, cited in Mani,
2000) or R&D intensity, i.e. R&D spending in relation to value added (OECD, cited
in Kask & Sieber, 2002). Other researchers used the products’ natures (output-based
criteria) for their HT classification (Kask & Sieber, 2002). Each approach has its own
strengths and limitations. For instance, Mani (2000) criticized that using R&D
expenditure as a criteria to consider some products as HT may be reasonable in 1960s
but not in 1980s because the kind of technology adopted at that time would be not
advanced anymore. However, OECD (2005) also stated that R&D intensity is the
only criterion to conduct quantitative research due to the issues of data availability
when one attempts to apply other criteria to classify industries into high-tech sector.
Thus, this study adopts the classification of OECD (2005) to determine the HT
industries. Specifically, based on OECD (2001) and GSO (2007), HT industries
includes (i) Pharmaceuticals; (ii) Computers and peripherals; (iii) Radios, TVs, and
communication equipment; (iv) Precision instruments; and (v) Aircrafts (see
Appendix 1 for more details).
3.2. Overview of Vietnamese HT manufacturing sector
HT manufacturing sector in Vietnam has a bright prospect due to high demand
in the world market. Not only the demand comes from consumption of individual
customers, products of this sector also benefit other manufacturing and services
industries such as online commerce and internet banking. Indeed, world exports of
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27
HT products, which can be considered as a measure of global demand for HT
products, keeps increasing considerably over the recent decade (see Figure 5).
Following that trend of the world, Vietnamese HT manufacturing sector also
contributes to the enlarging proportion in the industrial structure over years (see
Table 1).
Figure 5: World exports & value-added of HT manufacturing sector
(2001-2012)
Source: Appendix tables 6-7 and 6-21 of Science and Engineering Indicators
2014 (National Science Board, 2014)
Table 1: Contribution of Vietnamese HT in value added of manufacturing
sector during 2000–2012 (in percentage)
Industry 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
HT manufacturing 4.22 3.68 3.99 4.38 5.04 4.92 4.77 4.91 5.10 5.37 6.49 7.44 6.03
Source: Appendix tables 6-7 and 6-14 of Science and Engineering Indicators
2014 (National Science Board, 2014)
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
2001 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Millions
of
current
dollars
World value-added World exports
6671374

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