7. IIAS working paper (Dr Kien)

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Employment Effects of Trade Expansion and Foreign Direct
Investment: The Case of Korean Manufacturing Industry1
Tran Nhuan Kien2
Abstract
Trade and foreign direct investment outflows in Korea have increased significantly
during the last two decades. In this paper, we study whether trade expansion and foreign
direct investment outflows played any role in shaping the Korean manufacturing
employment structure during 1991-2006 period. We find evidence that foreign direct
investment outflows corresponds positively to home country’s employment. In terms of
trade expansion, the role of exports and imports in employment generation has been
changed in that exports have been no longer a source a job creation while import intensity
displaced domestic jobs in recent years.
Key words: Trade, Employment, FDI, Cobb-Douglas production function, Korea
JEL Classification: F14, F15, F16
I. Introduction
Globalization is considered one of the most prominent features of the 21st-century. As
barriers to trade and investment continue to fade away, there have been an increasing
number of firms investing abroad and deepening trade relations with foreign partners.
The proliferation of globalization has sparked debates among economists and
policymakers on the effects of globalization on domestic factors such as economic
1
This paper was written while the author was Korea Foundation Fellow. The author would like to thank the
Korea Foundation for its financial support. Any errors that remain are the author’s sole responsibility.
2
Vice Dean, Faculty of Graduate Studies, Thai Nguyen University of Economics and Business
Administration, Vietnam; Email: [email protected]; Tel. +84 976 626 611. Senior Researcher, IIAS,
Sogang University, Korea.
1
growth, poverty, inequality, and employment. With respect to labor market, evidence on
the effect of openness to trade and foreign direct investment (FDI) on employment is
mixed across countries (Hoekman and Winters, 2005; Masso etc., 2007).
Previous studies presented so far illustrates that there are no unified conclusion on the
effect of trade on employment. In a survey study, Hoekman and Winters (2005) conclude
that there are mixed evidences on the impacts of trade on sectoral employment in
developed countries, but overall the net employment effects of trade are negligible. Using
a dynamic panel data model, both Kien and Heo (2009) and Fu and Balasubramanyam
(2005) find a positive impact of export intensity on employment in Vietnam and China,
respectively. However, imports did not affect negatively Vietnam’s employment. In the
case of Australia, Gaston (1998) shows empirically a strong effect of exports on
employment while a negative impact of imports on employment is found. Greenaway,
Hine and Wright (1999) investigate the effects of trade on employment in the United
Kingdom using a dynamic panel data and conclude that trade expansion, both in terms of
imports and exports, have negative impacts on the country’s labor demand.
Regarding FDI, evidences from literatures show that no solid conclusion can be drawn
regarding the linkages between FDI and employment at both home and host countries. Fu
and Balasubramanyam (2005) find that FDI inflows bring about increased employment in
the China’s case. Onaran (2009) concludes that insignificant effects of trade and FDI
dominate in the case of central and eastern European countries with some evidence of
negative effects appears as well. In Vietnam, Jenkins (2006) however finds that that the
employment effects of FDI inflows have been minimal, or even negative. By making use
of highly disaggregated dataset, Waldkirch etc. (2009) shows a significantly positive,
though quantitatively modest impact on manufacturing employment in Mexico.
FDI outflows may have either positive or negative impacts on domestic employment.
When FDI outflows are regarded as capital flight, thus reducing domestic capital
formation, it may generate a negative impact on employment. FDI outflows may also
stimulate demand by foreign subsidiaries for domestically-produced intermediate
products (Kokko, 2006). Mariotti etc. (2003) investigate the impact of outward FDI on
the labor intensity of domestic production at firm level in the Italian case during 1985-
2
1995. They conclude that the impact is negative in the case of vertical investment in less
developed countries, and positive for horizontal and market-seeking investment in
advanced countries. Masso etc. (2007) also shows that outward FDI positively affects
home-country employment growth in Estonia. Debaere et al. (2010) investigated the
employment effect by using South Korea firm-level data. They conclude that that moving
to less-advanced countries decreases a company's employment growth rate especially in
the short run. On the other hand, moving to more-advanced countries does not
consistently affect employment growth in any significant way. Yamashita and Fukao
(2010) also find the positive employment effect of FDI outflows associated with the
Japanese MNEs overseas expansion.
This study focuses on Korea, which has embraced to globalization for a substantial period.
The country has enjoyed its high economic growth through its outward-looking policy
initiated in the early 1960s. It is interesting to note that while FDI inflows have not been
encouraged by the Korean government, FDI outflows was strongly encouraged,
especially to transfer knowledge and accumulate technological capabilities domestically
(Sachwald, 2001).
This paper focuses on two major aspects of globalization, international trade and FDI and
their impacts on manufacturing employment in Korea. This paper investigates the
impacts of trade expansion and FDI outflows on the generation of employment. The
focus of this study is on three key questions: (1) What are the impacts of trade expansion
and FDI inflows on employment in Korea? (2) How do these impacts change over time?
(3) What policy implications do these empirical results suggest?
Our contribution to the existing literature is threefold. This study incorporates both trade
and FDI into a single model. International trade and FDI are closely linked with each
other. However, the international trade and FDI have been separated in the analysis of
employment effects in the existing literature. Second, this study uses a system GMM
estimator, which is more appropriate for a short panel dataset than the static or first
differenced GMM estimator. The rest of the paper is organized as follows. Section II
specifies the model of trade and ODI’s impacts on employment in Korea and methods of
estimation of these impacts. Section III discusses the empirical results. The final section
3
brings forward conclusions.
II. Model Specification and Estimation Methods
Based on the Cobb-Douglas production function, this paper investigates the impact of
trade expansion and FDI on employment in the manufacturing sector in Korea using a
system GMM estimator. The Cobb-Douglas production function shows physical output as
a function of labor and capital inputs, that is:
Qit  A Kit Nit
(1)
where:
i denotes industry
t denotes time
Q represents real output
A represents total factor productivity (TFP).
K represents capital stock
N represents units of labor utilized
 and  denote factor share coefficients
 allows for growth in efficiency in the production process
Assuming that firms are profit-maximizing, the marginal productivity of labor equals the
wage (w) and the marginal revenue product of capital equals its real cost (C). Solving this
system simultaneously to eliminate capital from the expression for firms' output yields
the following equation:

  Nit Wi 
Qit  A 
*  Nit
C
 

(2)
Taking logarithms to linearize and rearrange the equation (2) provides the derivation of
the firms', and thus the industry’s, derived demand for labor as:
ln N it  0  1 ln(
Wi
)  2 ln Qit   it
C
(3)

where 0   ( ln A   ln    ln  ) ; 1  
; 2  1
and  it is a disturbance
(   )
(   )
(   )
term.
Regarding the total factor productivity (TFP), A, one may expect that TFP of the
4
production process increases over time and that the rate of technology adoption and the
increases in x-efficiency would be correlated with trade expansion and FDI inflows via
pressures of competition in the international markets and knowledge spillovers from FDIfunded imports and other foreign contacts. In fact, previous empirical studies (Fu and
Balasubramanyam, 2005; Hoekman and Winters, 2005; Greenaway et al., 1999; Lawrence,
2000; Liu and Wang, 2003; Savvides and Zachariadis, 2005) show that exports, imports,
and FDI inflows all have impacts on the TFP. On the one hand, existing studies focusing
on the role of exports and imports as sources of the impacts on TFP conclude that both
exports and imports, by and large, enhance productivity (Hoekman and Winters, 2005;
Greenaway et al., 1999; Lawrence, 2000). Regarding the impacts of FDI on TFP,
empirical evidences indicate the positive effect of FDI on TFP (Fu and Balasubramanyam,
2005; Liu and Wang, 2003; Savvides and Zachariadis, 2005). This can be partly explained
by the fact that the FDI inflows is not only a source of capital, but also a supplier of
technology transfer. Therefore, parameter A is hypothesized in the production function,
which varies with time in the following manner:
Ait  e 0Ti X it1 M it 2 FDI it3 ,
 0 , 1 ,  2 ,  3  0
(4)
Where,
T is time trend
X is export intensity index of industry i in year t (measured by export-output ratio)
M is import penetration index of industry i in year t {measured as a share of apparent
consumption (is measured as domestic production + imports – exports)}.
FDI is the inflows of foreign direct investment of industry i in year t.
Therefore, the labor demand equation can be derived from the combination of (3) and (4)
as follows:
ln N it  0*  0T  1 ln M it  2 ln X it  3 ln FDI it  1 ln(
Where, 0* 
Wi
)  2 ln Qit   it (5)
C
 (ln   ln  )

; 
; 0   0 ; 1  1 ; 2   2 ; and 3   3
(   )
(   )
Many economic relationships are dynamic, and one of the advantages of panel data is that
they allow researchers to understand the dynamics of adjustment (Baltagi, 2001). Thus, a
substantial number of studies have dealt with dynamic effects; for example, Holtz-Eakin
5
(1988) on a dynamic wage equation, and Arellano and Bond (1991) and Greenaway et al.
(1999) on a dynamic employment model. These dynamic relationships are characterized
by the presence of lagged employment among regressors. To take adjustment processes
into account, time lags are also introduced for the independent variables.
t
t
t
j 1
j 0
j o
ln Nit  i  0T   0 j ln Ni ,t  j   1 j ln X i ,t  j   2 j ln M i ,t  j 
t

j o
3j
t
Wi ,t  j
j 0
Ct  j
ln FDI i ,t  j   1 j ln(
t
(6)
)   2 j ln Qi ,t  j  t   it
j 0
where i is unobserved industry-specific effects; t is time-specific effects.
Following Greenaway et al. (1999) and Milner and Wright (1998), variation in users' cost
of capital (c) is captured by time dummies in estimation by assuming perfect capital
markets; thus it varies only over time. Explanatory variables are assumed to have
common impacts across industries.
In order to eliminate the industry specific effects and to ensure that the two-year lag of
level variables is not correlated with error terms, the employment equation (6) is
differenced and a dynamic employment equation is derived as follows.
t
t
t
 ln Nit   0   0 j  ln Ni ,t  j   1 j  ln X i ,t  j   2 j  ln M i ,t  j
j 1
j 0
t
t
Wi ,t  j
j o
j 0
Ct  j
 3 j  ln FDI i ,t  j   1 j  ln(
j o
t
)   2 j  ln Qi ,t  j  t   it
(7)
j 0
 indicates differences in variables’ transformation; for example,
 ln N it  ln N it  ln N i ,t 1 . Unlike the unobserved industry-specific effects, time-specific
where
effects are not eliminated by the difference transformation of variables.
However, the differenced equation (7) creates another problem (namely endogeneity)
because it is clear that ΔlnNi,t-1 and Δεi,t-1 are correlated, thus makes OLS, fixed effects,
random effects, and feasible generalized least squares (FGLS) techniques yield biased
and inconsistent estimates (Baltagi, 2001; Harris & Mátyás, 2004; Nickell, 1981;
Sevestre & Trognon, 1985). It would therefore be inappropriate to estimate equation (7)
by these techniques.
To deal with this problem, the most favorable approaches to date which could give
6
unbiased and consistent results are IV and GMM estimators. However, this study uses a
GMM estimator for two reasons. First, if heteroskedasticity is present, the GMM
estimator is more efficient than the simple IV estimator; whereas if heteroskedasticity is
not present, the GMM estimator is no worse asymptotically than the IV estimator (Baum
et al., 2003). Second, the use of the IV method leads to consistent, but not necessarily
efficient, estimates of the model's parameters because it does not use all available
moment conditions and it does not take into account the differenced structure on the
residual disturbances (Baltagi, 2001, p. 130).
The GMM estimators, which include first-differenced GMM (DIF-GMM) developed by
Arellano and Bond (1991), and system GMM (SYS-GMM) developed by Blundell and
Bond (1998), are increasingly popular for estimating dynamic panel datasets. As
pointed out by Blundell and Bond (1998) and Bond et al. (2001), however, the DIFGMM estimator has been found to have poor finite sample properties, in terms of bias
and imprecision, when lagged levels of the series are only weakly correlated with
subsequent first-differences. They also show that DIF-GMM may be subject to a large
downward finite-sample bias, particular when the number of time periods available is
small. The SYS-GMM estimator thus is more appropriate than DIF-GMM for our
model. Therefore, a SYS-GMM estimator will be employed as the main method to
estimate the employment equation in this study. In this paper, GMM estimated
coefficients are based on the one-step GMM estimator, with standard errors that are not
only asymptotically robust to heteroskedasticity but have also been found to be more
reliable for finite sample inference (see Blundell and Bond, 1998)3.
We estimate the model based on a panel dataset on manufacturing sector corresponding to
the two-digit International Standard Industrial Classification level. The dataset were
collected from the following sources. Data on industry exports and imports were
extracted from the United Nations Statistics Division Commodity Trade Statistics
Database (UN COMTRADE). Data on wages and output were extracted from the Korean
Statistical Information Service (KOSIS). The original source of these data was from the
Mining and Manufacturing Survey which is conducted annually covering all firms with
five or more employees in mining and manufacturing industries. The survey adopted the
new classification from 2007. Therefore, the dataset used for regression cover the period
of 1991-2006. However, the year 1998 is considered as an outliner as Korea’s economy
was deeply affected by the Asian financial crisis. Hence, 1998 data were excluded from
3
In finite samples, the asymptotic standard errors in the two-step GMM estimators can be seriously biased
downwards and thus give an unreliable guide for inference (Bond etc., 2001).
7
the regression. To obtain the real wages and real output, these data were deflated by
industrial producer price index which was also from the KOSIS. Finally, ODI data was
obtained from the Overseas Direct Investment Statistics Yearbook (published by The
Export-Import Bank of Korea).
III. Estimation Results and Discussions
Tables 1 to 3 report the results of one-step GMM estimations of Equation (7) for Korea.
The estimations are made first for the full sample dataset, and then for two separate subperiods, that are the period before the Asian financial crisis from 1991-1997 and the
period after the crisis from 1999-2006. The purpose is to capture possible changes in the
effect of trade and ODI on employment in manufacturing sector after the financial crisis.
In our GMM estimation, we treat all the regressors as endogenous variables.
Table 1. Korea’s System one-step GMM Estimation Results: Full Sample
Independent Variables
 ln Nt-1
 ln (W/C)t
 ln (W/C)t-1
 ln Qt
 ln Qt-1
 ln EXTENt
 ln EXTENt-1
 ln IMPENt
 ln IMPENt
 ln ODIt
 ln ODIt-1
Constant
AR (1) p-value
AR (2) p-value
Instrument validity test (Sargan)
No. of groups
Total observations
Specification 1
(Base model)
Coefficient
t-ratio
0.2228
4.11***
-0.2484
-2.80 **
-0.0732
-1.28
0.2934
4.71 ***
0.0752
2.71 **
-0.0116
-3.41***
0.017
0.847
0.19
22
286
Specification 2
(Full model)
Coefficient
t-ratio
0.183
4.13***
-0.274
-2.63**
-0.072
-1.61
0.346
4.34***
0.096
3.26***
0.019
1.53
0.014
1.69
0.003
0.12
-0.005
-0.25
0.006
2.04*
0.005
2.36**
-0.013
-3.59***
0.024
0.355
0.19
22
286
Note: 1. The dependent variable is  ln Nt
2. Coefficients on time dummies are not reported
3. ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively.
Table 1 reports the regression results for full sample data of 1991-2006 period. The
Sargan test of overidentifying restrictions and Arellona-Bond second order
autocorrelation test are presented at the end of the table. The Sargan test of over-
8
identifying restrictions can not reject the validity of the instrumental variables. In
addition, the Arellona-Bond test shows the evidence of first order autocorrelation, which
is expected, but no evidence of second order autocorrelation.
In the first part of Table 1, estimated coefficients of our base specification where both
output and wage have the expected impacts. It shows that growth in current output
positively impacts employment at 1% significant level; whereas growth in current wage
has a negative effect on employment at 5% significant level. While the impact of wage
fades away, the impact of output is still strong and robust. The estimated coefficient of
the lagged dependent variable is positive and statistically significant, indicating the
persistence both the wage and output effects on the level of employment.
In the second part of Table 1, both trade and ODI were introduced into the model. The
expected sign and significant level of lagged dependent variable, wage, and output are
still the same as in the base model, indicating the robustness of the model. The results of
second order autocorrelation and instrumental validity indicate that the model performs
well with no second order autocorrelation and no correlation between the instrument set
and the residuals. According to the results of this specification, we can not find any
statistical significant relationship between exports and employment as well as imports
and employment. However, outward direct investment corresponds positively to home
country’s employment. This result is consistent with the results of Lipsey etc. (2000) for
the case of Japan and Masso etc. (2007) for Estonia. Lipsey etc. (2000) justified that the
supervisory and ancillary employment at home to support foreign operations outweighs
any allocation of labor-intensive production to developing countries. This fact also can be
attributed to the demand stimulation by foreign subsidiaries for domestically-produced
intermediate products.
Table 2. Korea’s System one-step GMM Estimation Results: 1991-1997
Independent Variables
 ln Nt-1
 ln (W/C)t
 ln (W/C)t-1
 ln Qt
 ln Qt-1
 ln EXTENt
 ln EXTENt-1
 ln IMPENt
 ln IMPENt
 ln ODIt
Specification 1
(Base model)
Coefficient
t-ratio
0.381
0.003
0.077
0.041
0.243
4.18***
0.01
0.47
0.24
3.41***
Specification 2
(Full model)
Coefficient
t-ratio
0.245
-0.071
-0.001
0.141
0.374
0.037
0.034
-0.037
0.023
0.011
2.59**
-0.31
-0.01
0.71
5.78***
1.69
2.22**
-0.68
0.63
1.98*
9
 ln ODIt-1
Constant
AR (1) p-value
AR (2) p-value
Instrument validity test (Sargan)
No. of groups
Total observation
0.008
-0.021
-0.08
0.051
0.850
0.09
22
110
1.03
-0.127
-0.49
0.064
0.785
0.26
22
110
Note: 1. The dependent variable is  ln Nt
2. Coefficients on time dummies are not reported
3. ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively.
Table 2 presents the result of estimations for the sub-period of 1991-1997. However, the
impact of wage on employment is not statistically significant. It is essential to highlight
in this period that exports are positively correlated with employment whereas imports do
not have statistically significant impacts on employment. It is argued that the major bulks
of manufacturing imports were machinery and transport equipments (accounted for
around 35 percent of total imports in the 1990s, Table 3), which were highly intraindustry trade. Thus, imports were a complementary to domestic productions thus it did
not necessarily have negative impacts on employment. Regarding ODI, current
investment outflows are positively correlated with employment at 10 percent significant
level. However, lagged investment outflows are positive but statistically insignificant,
indicating that the positive impact is weak in this period and that the positive impact
fades away.
Table 3. Korea’s System one-step GMM Estimation Results: 1999-2006
Independent Variables
 ln Nt-1
 ln (W/C)t
 ln (W/C)t-1
 ln Qt
 ln Qt-1
 ln EXTENt
 ln EXTENt-1
 ln IMPENt
 ln IMPENt
 ln ODIt
 ln ODIt-1
Constant
AR (1) p-value
Specification 1
Specification 2
(Base model)
(Full model)
Coefficient
t-ratio
Coefficient
t-ratio
0.151
1.28
0.150
1.44
-0.383
-8.33***
-0.265
-5.52***
-0.086
-1.13
-0.146
-1.96*
0.437
10.13***
0.496
10.08***
0.041
0.61
-0.015
-0.21
0.017
0.99
0.005
0.39
0.033
1.26
-0.079
-2.41**
0.014
3.18***
0.004
1.39
-0.159
-3.67***
-0.174
-4.40***
0.002
0.002
10
AR (2) p-value
Instrument validity test (Sargan)
No. of groups
Total observation
Note: 1. The dependent variable is  ln Nt
0.631
0.08
22
132
0.862
0.194
22
132
2. Coefficients on time dummies are not reported
3. ***, **, and * represent statistical significance at the 1%, 5%, and 10% level, respectively.
The estimated coefficients for the post crisis period are reported in Table 3. As compared
to the first period, wage and output behave better in terms of statistical significance. Also,
the magnitude of the impacts is stronger. It is noteworthy to witness the changes in the
effects of exports and imports on employment. Exports are no longer positively correlated
with employment at the conventional level of significance. On the other hand, imports
have negative impacts on employment in this period. This means that the growth of
imports is negatively associated with the employment, indicating that import intensity
will displace domestic job. This result is consistent with the study of Heo and Park (2008),
which shows that import penetration in Korean manufacturing has positively impacted
the job displacement rate. Concerning ODI, we find a positive impact of investment
outflows on employment at a 1% statistical significance. The positive employment effect
of ODI was stronger in this period as compared to the previous period owning to the
deepening of the market-seeking investment.
IV. Conclusion
This study analyzes the impacts of trade expansion and outward direct investment on
employment in the case of Korea. The study yields several notable results. It shows that
growth in current output positively impacts employment; whereas growth in current wage
has a negative effect on employment. The impacts of output have been found to be
stronger in compared to wage on employment. Outward direct investment corresponds
positively to employment which can be explained in a number of ways such as the
supervisory and ancillary employment at home and the demand stimulation by foreign
subsidiaries for domestically-produced intermediate products. The role of exports and
imports in employment generation has been changed in that exports have been no longer
a source a job creation while import intensity displaced domestic jobs in recent years.
11
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*This research project was carried out by the support of the Korea Foundation under the
academic guidance of Professor Yoon HEO at Sogang GSIS.
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