Labor productivity by worker in 2018 reached 102.0 million VND per person.

Labor productivity by hour is 46.0 thousand VND per hour.

The average growth rate of labor productivity in the period 2011 - 2018 in terms of

the number of employees achieved is 4.87%, the average growth rate of labor

productivity calculated by the working hour is 5.29%.

Vietnam’s labor productivity is still low in comparing with some developing

countries in Asia, and needs to be considered seriously to offer solutions to improve

productiv

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is expected to clarify the theoretical framework of technological progress
impacting labor productivity growth, using the DEA and SFA to evaluate the
contribution of technological progress to increasing labor productivity in Vietnam.
(3) On the basis of the selected model, the study will deeply consider the current
Vietnam’s statistical data situation, study how statistical processing to best exploit
source of current secondary statistical data.
(4) On the basis of research on the relationship of the impact of technological
progress on increasing labor productivity, the thesis is expected to highlight the
significance and role of technological progress in Vietnam’s labor productivity
increase and propose solutions for promoting S&T to increase labor productivity,
thereby promoting economic development and improving the quality of life. At the
same time, the thesis is also expected to propose the supplement of necessary statistical
data for the purpose of studying the impact of technological progress on increasing
labor productivity.
CHAPTER 2: RESEARCH METHODOLOGY FOR STUDYING LABOR
PRODUCTIVITY AND THE IMPACT OF TECHNOLOGICAL PROGRESS
ON LABOR PRODUCTIVITY INCREASE
2.1 Definitions
2.1.1 Productivity definition
Productivity is understood as the output created in relation to the inputs of the
production and service delivery process, it demonstrates the efficiency of using input
resources in creating outputs.
2.1.2 Labor productivity and labor productivity increase
OECD (2001) defines labor productivity as: “quantity index of gross output divided
by quantity index of labor input”.
7
In economics, output can be measured by gross value of production or value added.
Labor input can be measured by the number of employees, the number of employees
converted to full-time employees, working hours, the labor input adjusted by quality.
Increasing labor productivity is to increase the quantity or value produced from a
unit of labor used. Therefore, the increase in labor productivity is of great significance,
is an important factor to increase more products for the society, is the basis for reducing
costs, contributing to improving people's lives and increasing accumulation to develop
production and increasing the competitiveness of the economy.
2.1.3 Technological progress
Technical progress or technological progress are new and better ways to create
products and services, new techniques to use scarce resources in a more productive
way.
2.2 Evaluate the impact of technological progress on labor productivity increase
The thesis presents an approach to assess the contribution of technological progress
in labor productivity increase, using two groups of methods: non-parametric method
and parametric method.
2.2.1 Non-parametric approach to estimate the technology progress
Data Envelopment Analysis (DEA) based non-parametric approach. Malmquist
productivity index based on DEA (DEA - based MPI) is divided TFP change into two
components: "efficiency change" and "technology change".
Suppose there are n DMUs with m inputs and s outputs. The symbol tijx , tijy and 1+tijx
,
1+t
ijy , are the inputs and outputs of DMUj at times t and t + 1 respectively, where i =
1,., M; r = 1,, s; and j = 1,, n.
MPIo =
2/1
1
11111
),(
),(
*),(
),(
+
+++++
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
yxD
yxD
yxD
yxD
Where, ),( tototo yxD and ),( 111 +++ tototo yxD measure the efficiency of DMUo ( { }no ,...,2,1∈
) in times t and t + 1, respectively, ),( 11 ++ tototo yxD measure efficiency in time t + 1 using
the production technology of time t, it is called the DMUo's growth index, ),(1 tototo yxD +
measuring the efficiency of the DMUo in period t using the production technology of
time t + 1.
According to Făre et al., MPIo> 1 indicates an increase in productivity, MPIo = 1
indicates an unchanged productivity, and MPIo<1 indicates a decrease in productivity.
8
To remove the hypothesis of ),( tototo yxD and ),( 111 +++ tototo yxD are constant (by Caves et
al.) and accept technical inefficiencies, Făre et al. decompose MPI into two parts:
MPIo =
2/1
1
11111
),(
),(
*),(
),(
+
+++++
t
oo
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
yxD
yxD
yxD
yxD
=
2/1
111
11
1
000
111
),(
),(
*),(
),(
),(
),(
+++
++
+
+++
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
ttt
t
o
t
o
t
o
t
o
t
o
t
o
yxD
yxD
yxD
yxD
yxD
yxD
The first component measures the efficiency change (EC) of the DMU0:
ECo = ),(
),(
000
1
0
1
0
1
0
ttt
ttt
yxD
yxD +++
The second component measures the technological change (TC) of the DMUo from
time t to t + 1
TCo =
2/1
111
11
1 ),(
),(
*),(
),(
+++
++
+ t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
t
o
yxD
yxD
yxD
yxD
Measuring efficiency change can be decomposed into pure technical efficiency
change and scale efficiency change.
Pure technical efficiency change = ),(
),( 1011
t
o
t
o
t
ov
tt
o
t
ov
xqD
xqD +++
Scale efficiency change =
2/11111111111
),(/),(
),(/),(
*),(/),(
),(/),(
++++++++++
t
o
t
o
t
oc
t
o
t
o
t
ov
t
o
t
o
t
oc
t
o
t
o
t
ov
t
o
t
o
t
oc
t
o
t
o
t
ov
t
o
t
o
t
oc
t
o
t
o
t
ov
xqDxqD
xqDxqD
xqDxqD
xqDxqD
In which Dov is an assumed function of distance with a variable return on scale and
Doc is an assumed function of distance with constant return on scale.
In the above model, to solve the requirement of assessing technological progress
by decomposing the Malmquist productivity index, the suitable variable to represent y
(the dependent variable) are the added value. The input variable x includes 2 variables:
labor and capital. Then, we have a DEA model with 2 inputs and 1 output.
2.2.2 Parametric approach
Coelli, O’Donnell and Battese (2005) explain the innovative technologies that bring
about economic changes over time. If observed over time, technological changes can
be calculated by an econometric model. For example, the following models
are used:
Linear: n
N
n
no xty ∑
=
++=
1
βθβ
9
Cobb-Douglas: n
N
n
no tAy lnln
1
∑
=
+= βθ
Translog: mn
M
m
nm
N
n
N
n
nno xxxtty lnln2
1lnln
111
2
21 ∑∑∑
===
++++= ββθθβ
Where t is a time trend; and θ, θ1, θ2 are unknown parameters to be estimated.
When economist include time trends in their models, they are making implicit
assumptions about the nature of technological change. To see this, take the above
specifications and consider the percentage change in y in each period due to
technological change. This is given by the derivative of ln y with respect to t:
Linear:
yt
y θ
=
∂
∂ ln
Cobb-Douglas θ=
∂
∂
t
yln
Translog t
t
y
21 2
ln θθ +=
∂
∂
Neoclassical theory assumes that enterprises make full use of technology and full
efficiency. But in reality, production does not achieve this, Farrell (1957) suggests
determining the output of the most efficient firms as the production frontier, firms that
have not reached the frontier are inefficient firms.
Based on Farrell's studies, Aigner and Chu (1968) developed a production function
and then Aigner, Lovell and Schmidt (1977) and Meeusen and van den Broeck (1977)
proposed the stochastic frontier production function model of the form:
ii
t
ii uvxq −+= βln
In which, qi represents the output of the i-th firm; xi is logarithm of inputs; β is a
vector of unknown parameters, vi is a symmetric random error to account for statistical
noise and ui is a non-negative random variable associated with technical inefficiency.
Once the equation has been determined, the unknown parameters will be estimated
by econometric techniques.
Developing further studies by Farrell, Aigner et al., Battese and Coelli (1995) put
the model as follows:
iiiii uvxq −++= lnln 0 ββ
or )lnexp( 0 iiiii uvxq −++= ββ
)exp(*)exp(*)lnexp( 0 iiiii uvxq −+= ββ
Deterministic
component
noise ineffciency
10
Assumption that two inputs are capital and labor, the Cobb-Douglas stochastic
frontier model as follows :
ittitkitlitit vtkluy ++++=+ ββββ lnlnln 0
Where, yit is the output, lit is labor, kit is capital and t represents technological
progress.
Translog stochastic frontier model as follow :
ln yit + uit = β0 + βtt + βllnlit + βklnkit + 0,5βttt2 + 0,5βkk (lnkit)2+ 0,5βll (lnlit)2
+ βtl t lnlit + βtk t lnkit + βlk lnkitlnlit +vit,
In which, yit is the output lit is labor, kt is capital, t is the trend of time which is a
variable representing technological progress and βs is the coefficient to be estimated,
random error, exp (v), and ineffective, exp (u).
The production function consists of three main variables k, l and t, (capital, labor
and technology change) that affect other variables and interact with each other. t2
represents technological change in itself that will further push technological change in
the coming years. tlnk: technical change pushing capital increase, and tlnl is technical
change pushing labor increase.
First, output growth is interpreted into three generating factors: input change, scale
efficiency changes and TFP change.
tfpllkky gggg ++= εε
In which, g is the symbol of the growth rate, ɛk and ɛl are output elasticity for capital
and labor, respectively.
P. W. Bauer (1990) has decomposed the increase of TFP into 3 components:
technological progress, technical efficiency change and scale efficiency change.
i. Technological progress:
ittlittkttt lktt
tlkf lnln2),,(ln ββββ +++=
∂
∂
ii. Scale effect:
( ))(1 l
lk
l
k
lk
k
lk gg εε
ε
εε
ε
εε
+
+
+
−+
iii. Change in technical efficiency:
.
t
uu
∂
∂
−=−
•
Thus, increasing TFP can be written as follows:
11
•
−+
+
+
+
−++
∂
∂
= ugg
t
tlkfg l
lk
l
k
lk
k
lkTFP ))(1(
),,(ln
εε
ε
εε
ε
εε
From Solow's model: g (Y/L) = α*g(K/L) + g(TFP), we have technological progress
that affects labor productivity in the following way:
g(Y/L) = α*g(K/L) +
•
−+
+
+
+
−++
∂
∂
ugg
t
tlkf
l
lk
l
k
lk
k
lk ))(1(
),,(ln
εε
ε
εε
ε
εε
By this approach, through the decomposition of factors in the TFP, it is possible to
evaluate the contribution of technological progress to the increase in labor productivity
based on the data on output (added value), input (labor and capital) over time.
CHAPTER 3: LABOR PRODUCTIVITY AND THE IMPACT OF
TECHNOLOGICAL PROGRESS ON INCREASING LABOR
PRODUCTIVITY IN VIETNAM
3.1 Labor productivity assessment
3.1.1 Data processing to measure labor productivity and labor productivity
increase
The thesis calculates the per-worker labor productivity and per-hour labor
productivity at economy level and economic sectors level. Data used include: GDP at
current prices and constant prices, total number of employees and working hours
(classified by industry).
(1) GDP or added value
From the General Statistics Office's data sources in the statistical yearbook, GDP at
current prices and constant prices by economic sector are obtained.
(2) Labor
+ Total number of employees:
Data on the number of employees of the whole economy and for each economic
sector can be obtained from the statistics source in the Statistical Yearbook.
+ Working hour:
Labor
productivity
increased by
technology
progress
Labor productivity
increased by scale effect
(SE)
Labor
productivity
increased by
technical
efficiency (TE)
Labor
productivity
increase
Labor
productivity
increased by
capital
intensity
12
Working hour is not available in the Statistical Yearbook, so some additional data
processing steps are required. From the annual employment report of the General
Statistics Office, there are data on the average working hours per person per week of
the economy, thereby calculating the total working hours as follow:
Total working hours per year = Average working hours per person per week x
Number of working weeks per year x Number of employees.
+ Labor by industry:
The data on the working hours by kinds of economic sectors is not included in the
Statistical Yearbook, so it should be processed and calculated by using the same
method apply for the whole economy. From this data source, the total working hours
by economic sector can be calculated by applying the above formula.
3.1.2 Overall labor productivity assessment of Vietnam's economy
Labor productivity by worker in 2018 reached 102.0 million VND per person.
Labor productivity by hour is 46.0 thousand VND per hour.
The average growth rate of labor productivity in the period 2011 - 2018 in terms of
the number of employees achieved is 4.87%, the average growth rate of labor
productivity calculated by the working hour is 5.29%.
Vietnam’s labor productivity is still low in comparing with some developing
countries in Asia, and needs to be considered seriously to offer solutions to improve
productivity.
3.2 Study the impact of technological progress on increasing labor productivity in
Vietnam
3.2.1 Data and additional processing of capital data
The data needed to use the model to evaluate the impact of technological progress
on increasing labor productivity include: added value, labor and capital. The added
value and capital are calculated at constant prices to eliminate price effect.
For assessment of the economy, statistical data can be obtained for kinds of
economic activity (level I - 20 economic sectors). The added value and labor data are
mentioned above. Capital data are not available, because we use capital for production
and business activities (often used as fixed assets), so some additional data processing
is needed.
According to OECD (2001), capital service is an appropriate measure to analyze
productivity. Since capital for production is often not directly observable, it should be
13
estimated by assuming that capital service corresponds to a proportion of the capital
stock that is transferred into production every year.
Capital service ptktk SK ,, σ=
In which, ptkS , is capital stock, σ is a ratio. For a given period σ is a constant.
Calculating the capital stock requires long time series data on past investments and
an initial amount of capital ( S ).
Current capital stock 1
1
0
1 )1()1(
−
−
=
− ∑ −+−= t
t
i
t
t ISS δδ
In which, St 1)1( −−δ is the remaining amount of original equity after deducting
depreciation δ each year, 1)1( −− ti Iδ is the remaining investment after depreciation.
Calculating the capital stock according to the above formula, it is necessary to have
(i) data on investment (or asset accumulation) over time series, (ii) initial capital at the
beginning of time series and (iii) the depreciation rate.
Annual investment (or asset accumulation) data are obtained from GSO’s data,
initial capital amount and depreciation rates are not included in the statistical data, so
it is necessary to estimate. The thesis refers to the Perpetual Inventory Method (PIM)
to calculate the initial capital. The idea of the PIM is to interpret the capital stock of
the economy as an amount of inventory. Inventory is increased with capital
accumulation (or capital investments). The maximum amount of capital is right after
the investment and gradually decreasing over time, the amount decreasing each period
depended on the depreciation rate.
Once the initial capital is available, the capital for the following years is calculated
based on the increase in capital investment and the increase in fixed assets in the year
obtained from the Statistical Yearbook. After calculating the capital stock of the whole
economy, it is possible to allocate capital to economic sectors based on the capital
structure.
3.2.2 Using non-parametric approach
Based on processed statistical data of economic sectors (level I), with added value
data, labor and capital of each economic sector from 2010 - 2018, using DEA software,
calculating the Malmquist index Based on DEA - CRS (input oriented), the estimation
results are summarized as follows:
14
Table 1: The results of the Malmquist index calculated from the added value
data, the number of employees and capital stock of the economic sectors (2011-
2018)
Year
Efficiency
change
(Effch)
Technical
change
(Techch)
Pure
efficiency
change
(Pech)
Scale
efficiency
change
(Sech)
TFP
change
(Tfpch)
2011 1.032 0.920 1.001 1.031 0.950
2012 1.043 0.919 1.031 1.012 0.959
2013 1.016 0.967 1.008 1.008 0.983
2014 0.996 0.984 1.009 0.987 0.981
2015 0.958 1.007 1.007 0.952 0.965
2016 0.986 1.017 1.012 0.974 1.002
2017 0.972 1.053 0.991 0.982 1.024
2018 0.954 1.035 0.997 0.957 0.987
Average
2010-2018
0.994 0.987 1.007 0.988 0.981
The Malmquist TFP index is calculated to be 0.981, less than 1. During the period
from 2010 to 2018, the TFP decreases due to a decrease in technological progress
(0.987) and scale efficiency (0.988). However, in terms of trend, technological
progress has gradually increased, the technological progress index is greater than 1
from 2015 to 2018.
Table 2: The results of the Malmquist index calculated from added value data,
total working hours and capital stock of economic sectors
(2011-2018)
Year
Efficiency
change
(Effch)
Technical
change
(Techch)
Pure
efficiency
change
(Pech)
Scale
efficiency
change
(Sech)
TFP
change
(Tfpch)
2011 1.032 0.922 0.998 1.034 0.952
2012 1.041 0.928 1.027 1.014 0.966
2013 1.010 0.974 1.027 1.014 0.966
2014 0.985 1.001 1.009 0.976 0.986
2015 0.955 1.004 1.001 0.955 0.959
15
2016 0.987 1.015 1.009 0.978 1.002
2017 0.980 1.048 0.993 0.986 1.027
2018 0.955 1.033 0.993 0.962 0.986
Average 2010 –
2018
0.993 0.990 1.005 0.988 0.982
If we replace the data on employees for total working hours, then the Malmquist
productivity index does not differ much. The trend also shows that from 2014 to 2018,
the TFP index has results greater than 1, due to the improvement of technical change,
and pure efficiency change. Scale efficiency change trends to decrease from 2014 to
2018, while technological progress trends to increase. Pure efficiency changes still
trend to get better during this period.
3.2.3 Using the parametric approach - stochastic frontier production function
When applying the stochastic frontier production function, it is necessary to choose
the appropriate type of function. Applying Frontier 4.1 software, the tests were
conducted as follows:
• Test 1: Choose the type of function;
• Test 2: Verifying whether there is a technical inefficiency or not;
• Test 3: Verifying function having technical inefficiency with half-normally
distributed;
• Test 4: Verifying function having invariable technical inefficiency overtime;
• Test 5: Verifying function having technological progress;
• Test 6: Technological progress effect to increase capital and labor.
Test method: Using likelihood-ratio obtained from the estimation of the above
models to test the function form. The statistical test is LR (λ) = -2 [L (H0) - L (H1)],
where L (H0) is maximised log-likelihood value of the model which is considered as
the null hypothesis; and L (H1) is the maximized log-likelihood value of the stochastic
frontier function which considered as alternative hypothesis. This statistical test has an
approximate distribution of χ2 with degrees of freedom equal to the difference between
the corresponding parameters in the null hypothesis and the alternative hypothesis.
A. Application of the "stochastic frontier function" with data of added value, the
number of employees and capital stock in the period 2010 - 2018
The stochastic frontier function is used with the output is added value, the input is
the number of employee and capital stock, with the help of Frontier 4.0. After testing,
the chosen function is a half-normally distributed translog function, with technical
16
inefficiency changes over time, technology progress does not affect labor intensity and
capital intensity:
ln yit + uit = β0 + βllnlit + βklnkit + 0,5βll (lnlit)2 + 0,5βkk (lnkit)2 + βlk lnkitlnlit +
βtt + 0,5βttt2 +vit.
Table 3: production function’s parameters estimation results with the data of
added value, number of employees and capital stock of the economic sectors in
the period 2010 – 2018
β Standard error t-ratio
β0 15.979 1.604 9.957
βl -0.463 0.186 -2.478
βk -0.637 0.256 -2.479
βll 0.062 0.021 2.834
βkk 0.037 0.012 3.084
βlk -0.012 0.022 -0.587
βt -0.004 0.010 -0.466
βtt 0.002 0.0006 3.449
From the production function, the ratio of increase in labor productivity sourced
from technological progress is: t
t
tlkf
ttt ββ 2),,(ln +=∂
∂
Table 4: Estimates of technological progress based on data of added value,
capital stock and number of employees of economic sector (2011-2018)
Year
The ratio of increase in labor productivity by
technology progress
2011 0.0047
2012 0.0094
2013 0.0142
2014 0.0189
2015 0.0237
2016 0.0285
2017 0.0334
2018 0.0382
Average 2011- 2018 0.0179
17
The change in labor productivity due to technological progress (average 2011 -
2018) that estimated from the data of economic sector is 1.79%. The trend shows that,
the technological progress increases gradually from 2011 to 2018, this result is quite
consistent with the results of the Malmquist index over the years.
The contribution ratio of technological progress to labor productivity increase can
be calculated by dividing the degree (or ratio) of technological progress change by the
degree (or ratio) of change in labor productivity. With an average growth rate of labor
productivity of 4.87% in this period, technological progress 1.79% is estimated to
contribute 36.7% to increase labor productivity.
The average technical efficiency of this period is 35.1%. The technical efficiency is
still low but trends to increase during this period.
A. Application of "stochastic frontier function" to data of added value, total working
hours and capital stock in the period 2010 - 2018
Using data on added value, total working hours and capital stock, after doing test,
the appropriate production function is chosen. It is the form of half-normally
distributed translog function, with technical inefficiency changes over time.
The change in labor productivity due to technological progress (average 2011 -
2018) is estimated 2.90%. With an average growth rate of labor productivity 5.29% in
this period, technological progress increases by 2.90%, technological progress is
estimated to contribute 54.8% to increase labor productivity.
The average technical efficiency of this period is 41.4%.
Economic sector (level I)’s data are suitable for a general assessment of the
economy, because economic sectors (level I) aggregates different business activities
including enterprises and types of cooperatives, households and individual business.
3.2 Impact of technological progress on productivity in manufacturing
enterprises
Manufacturing has been a fast-growing economic sector in the past 10 years and is
a good performer of the contribution of technology progress to the output growth.
Therefore, the thesis will study manufacturing enterprises to illustrate the impact of
technological progress on increasing labor productivity.
The data used include added value (constant prices) from 2010 to 2018, number of
employees and fixed assets as of December 31 from 2010 to 2018 (converted to
constant prices) of enterprises in the manufacturing sector (24 secondary economic
sector) obtained from the GSO data source.
18
From the data, added value in the period from 2010 to 2018 average increase of
14.0% is a rapid growth. Labor growth in this period was also quite high 6.0% and the
labor productivity increase 6.8% per year.
The results of calculating TFP Malmquist index are as follows:
Table 5: Results of the Malmquist index calculated from data of manufacturing
(2010-2018)
Year
Efficiency
change
(Effch)
Technical
change
(Techch)
Pure
efficiency
change
(Pech)
Scale
efficiency
change
(Sech)
TFP
change
(Tfpch)
2011 1.178 0.940 1.024 1.151 1.107
2012 1.066 0.969 1.046 1.019 1.032
2013 0.808 1.223 0.975 0.829 0.988
2014 0.929 1.099 1.027 0.905 1.021
2015 1.022 1.001 0.915 1.117 1.023
2016 1.012 1.110 0.864 1.172 1.123
2017 1.063 0.926 1.012 1.051 0.984
2018 1.016 0.862 1.082 0.938 0.984
Average 2010 -

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