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ECONOMIC ANALYSIS OF LAW, POLITICS, AND REGIONS
Regional total factor productivity and local
employment growth: evidence from Korea
Jihye Choi
1
•Iltae Kim
2
Received: 16 May 2017 / Accepted: 3 October 2017 / Published online: 16 October 2017
ÓThe Japan Section of the Regional Science Association International 2017
Abstract This paper examines the effect of regional total factor productivity (TFP)
on local employment growth using regional panel data from 2000 to 2014 in Korea.
The employment equation derived from the constant elasticity of substitution pro-
duction function is a function of wage rate, capital stock, and regional TFP. The
demand for labor accounts for dynamics since there is a cost to adjusting demand for
labor in the long-run. This paper introduces a dynamic panel regression model that
considers the effect of lagged employment. TFP is a more appropriate measure of
technology than Research and Development (R&D) expenditure or the number of
patent applications. This paper measures regional TFP using a growth accounting
method as a proxy variable of technology. This paper shows that an increase in
regional TFP has a positive effect on local employment growth that is greater in the
long-run than in the short-run. This suggests that employment policy such as
vocational training adapting to the technological progress for product and process
innovations increases labor force productivity in the long-run.
Keywords Regional total factor productivity Local employment
growth Dynamic panel regression model
JEL Classification O33 R58 J23 C33
&Iltae Kim
kit2603@jnu.ac.kr
Jihye Choi
iamwise_7@naver.com
1
BK21?Graduate Program for Economics, Chonnam National University, 77 Youngbong-ro,
Bukgu, Gwangju 61186, Republic of Korea
2
Department of Economics, Chonnam National University, 77 Youngbong-ro, Bukgu,
Gwangju 61186, Republic of Korea
123
Asia-Pac J Reg Sci (2017) 1:511–518
DOI 10.1007/s41685-017-0053-1
1 Introduction
Technological progress, which is considered as an increase in productivity, not only
increases consumer surplus and firms’ profits, but also improves quality of life in
term of employment growth, accelerates the industrial structure, and promotes
economic growth. Both the neoclassical growth theory since Solow (1956) and the
endogenous growth theory proposed by Romer (1990) and Lucas (1988) emphasize
the importance of technological progress in economic growth. Moreover, the
relationship between technological progress and employment growth is also a key
issue in the recent jobless growth. If technological progress improves productivity, a
producer will have less demand for labor and workers will lose jobs and income,
resulting in slower economic growth. On the other hand, technological progress
lowers production costs and commodity prices can increase the demand for labor as
the production factor.
Recently, job creation and economic growth are globally sensitive and serious
issues. Specifically, Korea has experienced low economic growth and stagnant
employment growth. Recent employment policies in Korea are based on short-term
fiscal support focusing on job-support programs such as short-term or non-regular job
creation. In the long-run, employment policy requires for qualitative job creation in
private and public sectors. The purpose of this paper is to examine the effect of regional
total factor productivity on local employment growth in Korea using dynamic panel
regression model and regional panel data from 2000 to 2014 in Korea.
Previous studies account for variables such as Research and Development
(R&D), agglomeration of industry or industrial complexes, and size of employment
growth as a component of employment growth. For example, Van Reenen (1997)
showed that the technological innovation has a positive effect on the demand for
labor using panel data from 1976 to 1982 in 598 UK firms.
1
Similarly, Lachenmaier
and Rottmann (2011) used data from German manufacturing firms from 1982 to
2002 to show that technological innovation has a positive impact on employment.
2
Bogliacino et al. (2012) also examined the effect of technological innovation with
R&D expenditure using European data, finding that R&D expenditure has a positive
effect on employment and service sector has a greater effect than the manufacturing
sector. Furthermore, they demonstrated that the effect of high-tech manufacturing
sector is greater than that of non-high-tech manufacturing sector.
3
Most previous
studies confirm the job creation effect of technological innovations using R&D
investment amount or the number of patents as a proxy variable of technological
progress. In an agglomeration economy, Blien et al. (2006) investigated the effects
of diversity and specialization for different industries at the local level. They set up
1
Van Reenen (1997) matched innovations with the construction of a count of the number of innovations
that a firm commercialized.
2
Lachenmaier and Rottmann (2011) classified the innovation input and output, and measured them using
innovation expenditure (R&D expenditure) and patents, respectively.
3
High-tech manufacturing sectors include pharamceuticals, office, accounting and computing
machinery, electrical machinery and apparatus, aircraft and spacecraft, measuring, analyzing, controlling,
... instruments. Non-high-tech manufacturing sectors include food and similar products, fabricated metal
products, chemicals and allied products, and so on.
512 Asia-Pac J Reg Sci (2017) 1:511–518
123
a dynamic panel model and defined explanatory variables such as sector specific
effects, total regional size, specialization, and diversity.
This paper differs from the previous studies by introducing the regional total
factor productivity (TFP) measured by growth accounting as a proxy variable of
technology to investigate the effect of technological progress on local employment
growth. This paper is organized as follows. Section 2introduces the model and data.
Section 3estimates the model and describes the results. Finally, Sect. 4provides the
concluding remarks.
2 The model and data
This paper proposes employment equation model of Van Reenen (1997) derived
from the constant elasticity of substitution (CES) production function to analyze the
effect of technological progress on local employment growth. This analysis
introduces the behavior of profit maximizing firms in a perfectly competitive
market. The CES production function is specified as follows,
Y¼T½ðALÞqþðBKÞq1
q;ð1Þ
where Yis the output, and Land Kare the employment and capital stock, respec-
tively. Technological progress is divided into three types based on neutral techno-
logical progress, which regards the combination of production factors and output as
constant. Hicks-neutral technological progress is factor-augmenting technological
progress occurring when the ratio of marginal products remains unchanged at a
constant capital–labor ratio. Harrod-neutral technological progress is labor-aug-
menting, evidenced by an increase in labor productivity with a constant capital–
output ratio. Solow-neutral technological progress augments the capital stock and
increases capital productivity. This means that even if labor input remains the same
and capital input decreases, the firm can obtain the same amount of production as in
the past.
The first-order condition for labor is equal to the real wage, and the first-order
condition for capital is equal to the real interest rate. These can be written
ln L¼ln Yþðr1Þln Tþðr1Þln ArlnðW=PÞð2Þ
ln K¼ln Yþðr1Þln Tþðr1Þln BrlnðR=PÞ;ð3Þ
where r¼1=ð1þqÞis the elasticity of substitution between labor and capital.
Now, by combining Eqs. (2) and (3), the optimal demand for labor can be derived as
follows,
ln L¼ðr1Þln A
B
rln W
P
þln KþrlnðR=PÞ:ð4Þ
For empirical analysis, Eq. (4) can be expressed in the following stochastic form:
ln Li;t¼b0lnðAi;t=Bi;tÞþb1ln wi;tþb2ln Ki;tþmtþuiþli;t;ð5Þ
Asia-Pac J Reg Sci (2017) 1:511–518 513
123
where ln Lis employment (number of employees), wis the real wage (average
monthly wage), and ln Kis the capital stock. The cost of capital, ðR=PÞis the same
for each panel entity but varies over time and can be expressed as time dummy mt.ui
is the unobserved region-specific time-invariant effect that might be correlated with
the explanatory variables but not with the usual error term li;t.
Previous studies interpreted ðA=BÞas an unobservable technological progress
using R&D investments or patent applications that indirectly indicate technological
progress as a proxy variable. However, it is not easy to assess or measure the
economic value of technological progress. TFP is more appropriate than R&D stock
or patent applications as a measure of technology.
4
This paper uses regional TFP
measured using the growth accounting method for analysis as a proxy variable of
technology. This paper defines ðA=BÞas the unobservable relative factor-augment-
ing technological progress.
To remove the unobserved regional specification ui, Eq. (5) is changed to a first-
difference. The demand for labor reflects dynamics because there is a cost for labor
adjustment in the long-run. Thus, two lags of employment variables are added as the
explanatory variable in employment Eq. (5). The panel analysis model, in which the
lag values of the dependent variable are the explanatory variable as in Eq. (6), is
called a dynamic panel regression model.
Dln Li;t¼c1Dln Li;t1þc2Dln Li;t2þb0DlnðAi;t=Bi;tÞþb1Dln wi;tþb2Dln Ki;t
þDmtþDli;t:
ð6Þ
The dynamic panel regression model can have endogeneity in the explanatory
variables, that is, a correlation between Dln Li;tsand Dli;t,s¼1;2. This
problem can be solved using Arellano and Bond (1991) method, which uses
instrumental variables (IVs) of the endogenous explanatory variables in a first-
difference model, where the IVs are set to the past values of the endogenous
explanatory variables. If there is no autocorrelation in the error term, it is
reasonable to use the values of the dependent variable as IVs since it satisfies the
constraint of method of moments and the past employment variable is correlated
with current employment. In other words, this methodology can obtain efficient
estimators using a difference equation and solving the problem of endogeneity
using generalized method of moments (GMM). In addition, the GMM estimator
can be efficient when the IVs are over-identified. Dynamic panel GMM has two
types of estimations; a one-step estimation and a two-step estimation. The latter is
a method of substituting estimates obtained from the former into a new weighting
matrix. The two-step estimator is asymptotically more efficient than the one-step
estimator.
In this model, the estimators of Dln Li;t1and Dln Li;t2can be interpreted as the
potential persistence toward equilibrium in the process of adjustment. Moreover,
4
Keller (2010) identified many problems with R&D spending as a variable to estimate R&D stock and
described the limitations of using patent applications as a measurement, in that firms file only a small part
of all technological progresses; most filings are irrelevant to technological progress.
514 Asia-Pac J Reg Sci (2017) 1:511–518
123
they show the speed of employment growth in that region.
5
If 0\c\1, local
employment growth regresses to the mean in the long-run. If c[1, then it implies
that employment increases explosively. We can use these parameters to examine the
long-term effects of the explanatory variables on the dependent variables.
In this paper, we measure the TFP from the Cobb–Douglas production function
by growth accounting, which is a good measure of technology, and derive labor
demand function using CES production function to confirm the technological
progress elasticity of demand for labor. Growth accounting is well known as a
method which measures the TFP. This method is based on the Cobb–Douglas
production function Solow residuals to measure TFP. To obtain the value of ðA=BÞ,
which is a variable in Eq. (6), this paper assumes two cases of Cobb–Douglas
production function where labor-augmented technological progress A, which is
technological progress that make efficient use of labor inputs, and capital-
augmented technological progress B, which is technological progress that make
capital stock more efficient.
6
Although the model should consider the production
factors that reflect the quality level of labor and capital, there is no statistical data on
the average years of schooling, and there is a limit to the data to apply weights
according to the types of capital in each region. Therefore, this paper does not
consider the quality level of labor and capital. For growth accounting, the capital
stock must be estimated first, which is estimated here using the perpetual inventory
method. The permanent inventory method measures the initial capital stock by
discounting the initial investment as the sum of the average investment growth rate
and the depreciation rate, and then continuously accumulating capital stock
according to the capital change formula.
7
Common statistics of regional panel data are described in Table 1. Regional data
in Korea are provided from the Korean Statistical Information Service (KOSIS).
The panel data consist of 16 regions (7 metropolitan councils and 9 provinces) from
2000 to 2014 as annual data. The dependent variable is calculated by multiplying
the monthly average hours worked by number of employees. These data are in the
‘‘Survey Report on the Labor Force at Establishments’’ of Korea. The explanatory
variables are regional TFP, real wage (average monthly wage) and capital stock.
Real wages are expressed as an hourly wage by converting the monthly average
wages from the ‘‘Survey Report on the Labor Force at Establishments’’ into real
variables using the Consumer Price Index (CPI). All data are collected at the local
level and transformed into logarithm form.
5
See Jiwattanakulpaisarn et al.’s (2009) explanation that the parameter creflects the potential persistence
for equilibrium employment.
6
The Cobb–Douglas production functions for measuring TFP are represented by Y¼ðALÞ1aðKÞaand
Y¼ðLÞ1aðBKÞa, respectively. The former assumes the existence of labor-augmented technological
progress, and the latter assumes the capital-augmented technological progress. Here, the capital income
share, ais generally known to a value of 1/3.
7
The initial capital stock of tis Ki;t¼Ii;t=ðgiþdÞand the capital stock of (tþ1) is
Ki;tþ1¼Ii;tþð1dÞKi;t. In this paper, the average investment growth rate of the analysis period is
the variable giand the depreciation rate dis assumed to be 5%.
Asia-Pac J Reg Sci (2017) 1:511–518 515
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3 Analysis
In this section, we estimate Eq. (6) using a dynamic panel GMM. The effects of the
regional TFP on local employment growth are reported in Table 2. For comparison,
this analysis includes both a one-step and two-step estimation results, but there is no
significant difference. The unobservable relative factor-augmenting technological
progress has a positive effect on local employment growth; a one percent increase
leads to an increase in local employment of 0.007%. The increase in the
unobservable relative factor-augmenting technological progress lnðAi;t=Bi;tÞmeans
that productivity improved due to labor-augmenting technological progress. In other
words, productivity improvement due to labor-augmenting technological progress
increases employment (here, hours worked). In general, the labor productivity
improvement due to technological progress can lead to a decrease in employment
because of the increase in output per unit of input. On the other hand, creating new
demand for a commodity may increase employment in the process of increasing
production input.
In a dynamic panel model, the long-run effect can be confirmed by combining the
past values of the explanatory variables with the dependent variables of the current
period. The long-run effect of the unobservable relative factor-augmenting
technological progress on employment is positive because b0
ð1c1c2Þ¼0:014. Since
the short-run effect of the unobservable relative factor-augmenting technological
progress is 0.007, the long-run effect of technological progress on employment is
greater than the short-run effect.
Table 1 Common statistics
Variables Obs. Mean Std. dev. Min. Max.
L(one million hours) 240 1358.085 1447.846 166.240 6458.090
A/B240 0.212 0.271 1.712E-09 1.121
w(KRW) 240 11,324.08 2002.239 7637 17,647
K(one million KRW) 240 319.969 315.281 26.773 1337.438
Table 2 Estimates of dynamic
panel GMM
[] standard error
**p\0.05, ***p\0.01
Variables One-step GMM Two-step GMM
ln Li;t10.762*** [0.108] 0.743*** [0.125]
ln Li;t2-0.230*** [0.042] -0.241*** [0.069]
lnðAi;t=Bi;tÞ0.007*** [0.002] 0.007*** [0.002]
ln wi;t-0.018 [0.060] -0.038 [0.070]
ln Ki;t0.507** [0.231] 0.550** [0.240]
AR(1) test: Zvalue -3.17 -2.92
AR(2) test: Zvalue -1.60 -1.56
Hansen test (df) 15.15 (10) 15.15 (10)
pvalue 0.127 0.127
516 Asia-Pac J Reg Sci (2017) 1:511–518
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From coefficients of ln Li;t1and ln Li;t2, the coefficient of the two-lagged
employment variable shows a negative sign reflecting substitutions between
unskilled and skilled labor, while the coefficient of the one-lagged employment
variable shows a positive sign representing adjustments of labor cost. The
coefficients of lagged employment variables have between 0 and 1 in absolute
value, which means that employment regresses to the mean value in the long-run.
The coefficient of real wage was negative, as expected, but not statistically
significant. The capital stock elasticity of employment shows a statistically
significant result of about 0.55. The autocorrelation of the error term in Table 2
appears in the results for AR(1) and AR(2) test. This result confirms that there is no
autocorrelation in the error term of the regression model. To use GMM, the number
of IVs should be greater than the number of endogenous explanatory variables. It is
reasonable to use GMM because IVs are over-identified in this model.
4 Concluding remarks
This paper examines the effects of the regional TFP on local employment growth
using regional panel data from the period 2000–2014 in the Korean economy and a
dynamic panel GMM, which takes care of the endogeneity problem. This paper
shows the improvement in regional TFP, implying that the unobservable relative
factor-augmenting technological progress has a positive effect on local employment
growth. In this sense, this shows that it is worth estimating regional TFP as an
alternative to the method employed by previous studies that use proxy variables of
technological progress such as R&D expenditure or number of patents applications.
In addition, this paper reveals that two-lagged employment variable has a negative
effect and the one-lagged employment variable has a positive effect on current
employment and implies that the employment variables have the property of
returning to the mean in the long-run, which comes from the fact that the
coefficients of both lagged employment variables have between 0 and 1 in absolute
value.
This paper also finds that the job creation effect of technological progress is more
effective in the long-run than in the short-run. This suggests that employment policy
such as vocational training adapting to the technological progress for product and
process innovations increases labor force productivity in the long-run.
Acknowledgements We would like to thank two anonymous referees for helpful comments.
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