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DISCUSSION PAPER SERIES
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No. 5737
DOES WHERE YOU GO MATTER?
THE IMPACT OF OUTWARD
FOREIGN DIRECT INVESTMENT ON
MULTINATIONALS' EMPLOYMENT
AT HOME
Peter Debaere, Hongshik Lee and
Joonhyung Lee
INTERNATIONAL TRADE
ISSN 0265-8003
DOES WHERE YOU GO MATTER?
THE IMPACT OF OUTWARD
FOREIGN DIRECT INVESTMENT ON
MULTINATIONALS' EMPLOYMENT
AT HOME
Peter Debaere, University of Texas, Austin and CEPR
Hongshik Lee, Korea Institute for International Economic Policy (KIEP)
Joonhyung Lee, University of Texas, Austin
Discussion Paper No. 5737
July 2006
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Copyright: Peter Debaere, Hongshik Lee and Joonhyung Lee
CEPR Discussion Paper No. 5737
July 2006
ABSTRACT
Does Where You Go Matter? The Impact of Outward Foreign Direct
Investment on Multinationals' Employment at Home
How does outward foreign direct investment (FDI) affect employment growth
of the multinationals (MNCs) in the home country? Does the impact of outward
investment differ by the level of development of the destination country of the
FDI? Using a difference-in-difference approach, we assess the impact of
starting to invest in less advanced countries, of investing in more advanced
countries, and of changing the direction of one’s investment from more to less
advanced nations. To obtain suitable control groups in each case, we use the
propensity score method to select national firms that ex post did not make
investment decisions even though ex ante they would have been equally likely
to each multinational. We find that moving to less advanced countries (as an
initial foreign investment or as a redirection of previous investment) decreases
a company’s employment growth rate. On the other hand, moving to more
advanced countries does not affect employment growth in any significant way.
JEL Classification: F1
Keywords: multinationals
Peter Debaere
Department of Economics
University of Texas at Austin
1 University Station
Austin, TX 78712
USA
Tel: (1 512) 249 9861
Email: debaere@eco.utexas.edu
For further Discussion Papers by this author see:
www.cepr.org/pubs/new-dps/dplist.asp?authorid=157944
Hongshik Lee
Korea Institute of International
Economic Policy
300-4 Yomgok-Dong
Seocho-Gu
Seoul 137-747
South Korea
Email: hslee@kiep.go.kr
For further Discussion Papers by this author see:
www.cepr.org/pubs/new-dps/dplist.asp?authorid=164938
Joonhyung Lee
Department of Economics
University of Texas at Austin
1 University Station
Austin, TX 78712
USA
Email: joonlee@eco.utexas.edu
For further Discussion Papers by this author see:
www.cepr.org/pubs/new-dps/dplist.asp?authorid=164939
Submitted 23 May 2006
1. Introduction
In this paper we study a question that has been at the center of heated public
debates for some time. We investigate the causal link between a multinational’s (MNC)
employment growth rate at home and the multinational’s decision to send its foreign
direct investment (FDI) to either more or less advanced countries. With a unique data set
of South Korean firms that links, at the firm level, the MNC’s parent and its affiliates
abroad, we can explicitly differentiate the impact of FDI by destination. To address
issues of self-selection and endogeneity, we compare the employment growth rate of
firms that change status with that of a carefully chosen control group. For example, we
compare employment growth of established MNCs that, for the first time, invest in less
developed countries with those that continue solely to be active in advanced countries. Ex
ante, however, the latter MNCs are equally likely to shift destination and move to less
advanced countries. Similarly, we also match firms that, for the first time, invest
respectively in more or in less advanced countries with comparable firms that do not
invest abroad at all.
Since the mid 1980s increasingly larger flows of foreign direct investment have
found their way to China.
1
China now tops the list of FDI recipients worldwide and in
recent years it has even occasionally surpassed the US in this respect. China is the
predominant destination of FDI in East Asia. The growing FDI flows into China and their
effects on domestic production have become one of the premier policy concerns in South
Korea, Taiwan, Singapore, and Japan that have increasingly allowed their own firms to
invest abroad. Reminiscent of the debates surrounding NAFTA in the US, the concern for
countries such as South Korea is, as the South Korean investment promotion agency
KOTRA puts it, that there will be a “hollowing out of Korea’s production base as a result
1
See UN (2002)
2
of the rush into China”.
2
As if it underscores the similarity with the NAFTA debate, Ross
Perot’s notorious 1993 phrase -”A giant sucking sound” - has popped up again.
3
South Korea, like other countries in the region, used to predominantly invest in
more advanced countries before China opened its borders to foreign investment. This
changed dramatically around 1992 when South Korea established diplomatic relations
with China. Since then, China has absorbed the lion share of South Korea’s outward FDI
to less advanced nations. From this perspective, the question that we study, to a large
extent, amounts to investigating whether investing in China has in any way different
implications for MNC’s parent firm’s employment in South Korea than investing in the
other region such as the US or in Europe.
Whether the particular destination country of FDI matters for employment at
home is primarily an empirical question. The theory of the multinational has emphasized
two types of FDI: horizontal and vertical FDI.
4
While there is likely to be a horizontal
and vertical dimension to any FDI activity, the public debate suggests and the empirical
literature confirms that multinational activities with the North are more likely of the
horizontal type and those with the South of the vertical kind.
5
Still, the theory does not
conclusively differentiate the impact of FDI on employment by type.
6
For both, one could
argue that short-term losses are likely. For the horizontal firms, these can be related to
how multinational activity is defined, i.e. as a substitute for exports. To save
transportation costs and with moderate plant-specific increasing returns to scale, MNCs
2
See, Economist, August 25, 2001. “Is Taiwan Hollowing Out?”, Asia Times, 2002. “Taiwan hollowing
out to Mainland”, Friedlnet.com, 2003. “Is FDI in China Hollowing out Japan’s Industry?” ,RIETI, 2002.
In the words of the Prime Minister from Singapore, “Our biggest challenge is to secure a niche for
ourselves as China swamps the world with her high quality but cheaper products…We must accelerate the
upgrading of our manufacturing sector, or we will be hollowed out.”
3
A few examples: “The Sucking Sound of FDI flowing into China”, Asia Pacific Review, 2001. “A New
Giant Sucking Sound”, The Nation, 2001. “Giant Sucking Sound Rises in the East”, Utne Magazine, 2003.
4
For a good discussion of the literature, see Markusen and Maskus (2001) and Markusen (2002). Brainard
(1997) and Markusen (1984) have emphasized the horizontal dimension of FDI. Helpman (1984) was the
first to formalize the vertical dimension of multinational activity as firms relocate to take advantage of
factor price differences. Helplman, Melitz and Yeaple (2003) and Antras and Helpman (2004) present the
theoretical counterparts for respectively horizontal and vertical MNCs and explicitly take firm
heterogeneity into account.
5
Hanson, Mattaloni and Slaughter (2004) and Debaere (2004) provide evidence that there is a vertical
dimension to MNC activity and that MNCs take advantage of factor price differences in the way they
organize themselves.
3
decide to produce in the foreign market the goods they used to produce domestically and
export. FDI activity in this way may depress domestic production. As for the vertical
dimension of multinational activity, multinationals can break up the production process
(“fragment production”) and relocate entire stages of production to low-wage countries to
produce more cost effectively. In this way multinational activity may reduce domestic
employment at the parent plant. However, and this is true for both types of FDI, in a
growing market, foreign affiliates may increase demand for the parts/services produced
in the country of origin. In sum, in both cases there is the possibility of negative short-
term effects in which foreign activities may substitute for domestic employment and the
possibility for potentially positive long-term effects that may prove complementary to
domestic production. Therefore, any empirical study will have to be careful about the
timing of investment and its effect.
The question how the particular destination of FDI affects employment in the
MNC at home has so far not been answered satisfactorily. In part, this is due to the lack
of data that match a firm in a home country with the destinations of its affiliates.
7
In spite
of this, the tenor of the existing research has been that concerns about the negative impact
of multinational activity have been overstated.
8
Amiti and Wei (2004), Barba Navaretti
and Castellani (2004), Braconier and Ekholm (2002) and Brainard and Riker (1997a,
1997b) are a few examples of studies that all investigate multinational employment in the
home country. However, these studies all have a somewhat different focus from ours for
one or two reasons. First, most firm-level FDI studies that assess the impact of FDI on
employment or output focus only on the binary decision of whether a firm opens up an
affiliate abroad or not, irrespective of its destination.
9
Moreover, note that outward FDI
studies tend to be for developed countries whose affiliates are overwhelmingly in other
developed countries. Therefore, these studies may not give a sense of whether there is
any ground for the fear of “hollowing out” from outward activities in the “South”.
6
One could argue that because of prevalent use of the Dixit Stiglitz model (and the high degree of
symmetry implied) the existing models are perhaps not particularly well fit to analyze the implications of
FDI on firm size.
7
See Lipsey (2001)
8
For a survey of the relevant literature, see Barba Navaretti and Venables (2004)
4
Secondly, when studies do have data on the destination of multinationals, they mostly
either work with aggregate data on regions or sectors or they only focus on the
substitution of employment between affiliates. This poses a problem if one wants to
gauge the exact impact of FDI on multinational activity, rather than assess whether
multinationals are different from national firms or whether sectoral FDI correlates with
firm employment. Like Barba Navaretti and Castellani (2004), we will instead assess the
impact of FDI by comparing MNC employment growth with what would have happened
if the MNC in question had not made the particular investment decision that they did;
different from them, however, we will differentiate investment decisions by the
destination of the FDI flows.
What is thus needed are proper counterfactuals to compare with the employment
growth of multinationals before and after they invest in more or in less advanced
countries. In other words, one needs to construct hypothetical performance trajectories
for comparable firms that ultimately do not make the same investment decisions. Once
these control groups exist, a difference-in-difference analysis can then show whether
indeed investing in a more or less advanced country affects employment growth or not.
As Meyer (1995) emphasizes, a judicious choice of control groups is key. We address
this concern in two ways.
We construct different comparison groups and investigate whether the results are
consistent across control groups. In particular, to assess the impact of investing in less
advanced countries, we, on the one hand, focus on the performance of established
multinationals that had been investing in more advanced countries and that shift their
destination to China or other less advanced nations. We take the multinationals that did
not “go South” as control group for this case, yet remained investing in more advanced
nations. On the other hand, we isolate firms that become multinationals by investing
abroad for the first time. We distinguish these new multinationals by their destination
and, for each case, compare them with regular firms that did not invest abroad. Finally, to
contrast and compare our findings with previous studies that only consider the binary
9
Note that the same is true for the extensive literature that relates a firm’s performance to its (changing)
export status. See Clerides, Lach and Tybout (1998) and Bernard and Jensen (1999) and references in
Tybout (2001).
5
decision to invest/or not, we also match multinationals as they start investing,
irrespective of destination, with regular domestic firms.
A second way in which we are careful about the control groups is by following
Meyer (1995)’s suggestion that one should try to make sure that the “untreated”
comparison group is very similar to the “treatment group”, in our case, the investing
firms that change status. To achieve this goal, we apply the propensity score method that
has been used in labor market studies such as Heckman et al. (1997). This procedure is
warranted since there is striking firm heterogeneity that is most pronounced for firms
moving in opposite directions. The idea is to match the firms that change status (i.e. the
new MNC to more or less developed nations, the MNCs that change direction) with firms
that ex ante were equally likely to make these decisions, yet in the end did not. To our
knowledge, Barba Navaretti and Castellani (2004) is the only study so far that has
applied this matching methodology combined with a difference-in-difference approach to
multinationals.
Our results indicate that where a firm invests matters for the employment growth
of the multinational at home. We find consistently that a Southward move depresses the
growth of a firm’s employment. This finding is most pronounced for multinationals that
had been investing abroad and that start investing in China and other less advanced
countries; they grow less than they would have, had they kept investing only in the
advanced countries. Similarly, by matching firms that become multinationals by
investing in less advanced countries for the first time with comparable national firms that
do not invest, we find that multinationals tend to grow less than if they had not invested.
On the other hand, we find that the employment growth of firms that, for the first time,
invest in more advanced countries is not significantly different from peer firms that do
not.
While our approach does not tell whether indeed there is more of a vertical
dimension in the multinational activity in China and other developing countries, our
estimates give some credibility to the public sentiment that there may be some negative
impact on employment growth when firms (irrespective of whether they were
multinationals before or not) move into the South. Moreover, our findings may cast a
light on the existing results in the empirical literature that have emphasized the non-
6
neutral or even positive impact on employment of investing abroad. These studies are
mostly for developed countries that tend to predominantly direct their investment towards
other developed countries. Our findings suggest that these firms, in the absence of any
differentiation by destination country, may be driving the conclusion.
The rest of the article is structured as follows. First, we motivate and describe the
estimation strategy that we follow. We then characterize the data that we use, turn to the
construction of counterfactuals, and finally discuss the estimation results before we
conclude.
2. Estimation Strategy
As mentioned above, a central concern when studying the impact of outward FDI
on the evolution of South Korea’s parent’s employment relates to simultaneity and self-
selection. Does observing differences in firm performance imply that firms have made
different decisions in the past or do firms with unequal performance simply make
different decisions? In other words, in our case, does firm employment growth slow
down because of the investments in a more or a less advanced country, or do firms whose
employment grows faster or slower simply tend to invest in different locations? Another
equally important issue relates to whether changes in firm performance that one observes
are specific to multinationals or whether they are rather due to unobservable shocks that
affect national and multinational firms alike. To address both concerns and to answer the
question how investing in either a more or a less advanced country differs from not
having done so, we take a difference-in-difference approach. We focus, on the one hand,
on employment growth before and after firms change their status. At the same time, we
want to find proper counterfactuals to compare with the employment of these firms that
switch status.
We denote as follows the first difference between the growth rate of employment
of firms that change their status (the c-firms) before (at time t-1) and after (at time t+1)
the change of status,
c
tt
c
tt
EE
1,1,
lnln
−+
∆−∆
. This difference can mean four things
corresponding to our four cases. First, it can stand for the employment growth rate of
multinationals before and after they change the direction of their investment and move to
7
less developed countries. Second and third, the difference can also refer to firms’
employment growth rate before and after they become multinationals and direct their first
investment respectively to a more or a less advanced country. And, finally, as indicated
in the introduction, in order to compare our results with those in the previous literature,
we will also consider firms’ employment growth before and after they become
multinationals (irrespective of the destination of the investment).
To properly assess the changing growth rates of the first difference, we need to
compare them with the growth performance of a control group of firms that do not
change status (the n-firms) and whose employment growth is therefore not affected by
the decision to invest in a particular location, i.e.
n
tt
n
tt
EE
1,1,
lnln
−+
∆−∆
. Once such
proper controls are found, we can determine whether the double-difference estimator
DID
α
ˆ
of equation (1) is consistent with the expectations of the public. Is it negative for
the multinationals that move to China and for the firms that invest in less developed
countries for the first time? Or, is the estimated coefficient positive as suggested by those
who minimize the impact of outward FDI.
)lnln()lnln(
ˆ
1,1,1,1,
n
tt
n
tt
c
tt
c
tt
DID
EEEE
−+−+
∆−∆−∆−∆=
α
(1)
The key issue is, of course, to find proper control groups of firms that do not
change their status. The A and B panels of Figure I should help determine these. The
figures are based, on the one hand, on the hypotheses of the differential impact on
employment for alternative FDI destinations. On the other hand, they relate to stylized
facts about multinationals. Panel A is the familiar graph from Clerides, Lach, Tybout
(1998) and Barba Navaretti and Castellani (2004) who respectively focus on the binary
decision to export or not and to invest or not, irrespective of destination considerations.
Panel A presents the hypothetical average performance trajectories of three groups of
firms. Two of them do not change status and consistently differ in their average
performance. The trajectory of the firms that do change status, the new exporters or the
new investors (referred to as switchers, SW in panel A), then moves from the initially
inferior non-exporters or non-investors to the superior performance of the exporters or
established MNCs. Note that the performance of this third group is allowed to differ
somewhat from that of both other groups. In order to measure the impact of exporting or
8
investing in light of what would have happened if firms did not start exporting or
investing, it is key to compare the performance of firms that change status with the dotted
line, which is the performance of a firm from a control group. The control group consists
of the set of firms that did not export or invest abroad (the nationals), yet these firms (ex
ante) are so similar to the firms that change status that they could have started to export
or invest themselves.
As we want to also relate our results to the existing literature, we use panel A for
the analysis of the impact of multinationals’ investment decisions on their employment
growth rates, irrespective of destination. We will, however, also tailor panel A to the
destination question and consider firms that start investing in the more advanced
countries. We, in addition, consider an alternative scenario in panel B that is of interest
for the question whether moving to less advanced countries adversely affects
employment. Panel B is almost the mirror image of A: The firms that change status see
their superior performance worsen as they invest in the South. In our case, these firms are
either the MNCs that previously invested in the North and that now turn to the South (as
marked in Panel B), or regular firms that target less advanced countries for their first
destination. Here again, note that the firms that change status can differ from the average
of the group of firms that they leave. In our case, the control groups will thus either be
MNCs that were equally likely to move their investment to less advanced countries
(China) but that did not (as marked in panel B), and national firms that end up not
moving to China, even though ex ante they were equally likely to. As the firms move
South, we expect the employment growth rate to drop.
Note that the four scenarios that we choose are informed not just by the public
debate. They also relate to stylized facts that suggest differences in performance across
groups. It has been well-documented (primarily for developed countries that tend to
invest mostly in other developed countries) that multinationals tend to be larger in terms
of employment and output, and that they are also more productive, more profitable and
more capital-intensive than regular firms. The first two columns of Table 3 A that
presents the sample averages of the variables of interest for multinationals vs. national
firms confirm this. Tables 3B, 3C and 3D reveal other facts about multinationals, once
they are differentiated by destination. We find that multinationals that move to more
9
advanced countries (3C) tend to be far superior to national firms in every respect: higher
profits, more productive, more capital intensive and larger in size. On the other hand,
first-time investors in less advanced countries are virtually indistinguishable from
national firms (3B) on average. Interestingly enough, multinationals that redirect their
investment to less advanced countries tend to have stronger indicators than the ones that
stay in the advanced countries.
10
Unfortunately, the hypothetical trajectories that should yield
n
tt
n
tt
EE
1,1,
lnln
−+
∆−∆
are not directly observable. Moreover, simply comparing the
firms that change status with either MNCs in the North or national firms would not be
appropriate since, as our stylized facts indicate, both types of firms may be very different
from the potential switchers. If we want to isolate the effect of investing in a more or less
advanced country, we need to construct, as Meyer (1995) suggests, counterfactuals that
are as similar as possible to the firms that change status. It is for this purpose that we use
the propensity score matching procedure.
We want to match each firm that changes status with a firm that is similar. This
firm is ex ante equally likely to change the direction of its investment or to invest
respectively in a more advanced or a less advanced country, yet it eventually ends up not
changing its status. We therefore estimate a probit model of the decision to change status
for the four different cases that we investigate, based on observable firm characteristics in
the period before the investment decision is made.
Based on the probit estimates, it is then possible to compute the probability that
each firm changes its status (propensity score) and then to pair each firm that does
change its status with its nearest neighbor (with the closest propensity score) that does
not. This group of ‘nearest neighbors’ will constitute the counterfactuals. They are the
closest approximation to the dotted line in Figure 1 with very similar ex-ante
probabilities of changing their status.
11
10
These findings are consistent with some of the stylized facts reported for Japanese MNCs by Head and
Ries (2004). They provide some evidence that (1) MNCs are larger/more productive than non-MNCs (2)
MNCs that go to countries with a higher per capita GDP have a tendency to be larger and more productive
than those that invest in countries with a lower per capita GDP.
11
The key assumption needed to perform matching based on the propensity score is that, conditional on a
vector of observables, the choice of investing abroad does not depend on future performance (conditional
independence assumption).
10
Once we have the counterfactuals, we can calculate the difference-in-difference
estimator
DID
α
ˆ
. The estimator is obtained from the following regression (2) that has the
added benefit of controlling for unobserved heterogeneity that might not have been
eliminated by matching and that might affect the firm’s performance after its investment,
see Meyer (1995). An important identifying assumption is that
DID
α
ˆ
is zero if a firm
does not change its status, or E[ | ] = 0.
s
it
ε
s
t
d
s
it
s
tDID
s
t
s
it
xdddE
ελαγγγ
+++++=∆ 'ln
210
, (2)
where the covariates x control for other sources of heterogeneity and d refers to different
sets of dummies. The superscripts s = n, c refer to the status of the firms, with n denoting
those firms that do not change status and c the ones that do; the subscripts t = 0, 1 refer to
the period before and after the change of status. To summarize, the dummies take on the
following values:
11
0
t
if t
d
Otherwise
==
⎧
⎨
=
⎩
1
0
s
if s c
d
Otherwise
==
⎧
⎨
=
⎩
11
0
s
t
if t and s c
d
Otherwise
== =
⎧
⎨
=
⎩
The coefficient of interest is the third one,
DID
α
. If it is positive (negative), it
implies that changing status has a positive (negative) effect on the employment growth
rate. The first and second dummy variables will respectively control for any difference
between the pre- and post-change period and between firms that change status and the
ones that do not. For completeness, we will also report the standard matching estimator
(
SM
α
) that is obtained by setting t = 1 in regression (3). In other words, in this case we
focus on the differences in employment growth in the period after the investment
decision.
(3)
s
it
s
SM
s
it
xdE
υδαδ
+++=∆ 'ln
0
3. Data Description
11
The firm-level data used in this paper are taken from the KIS Financial Analysis
System 2000 and KIS Stock Market Analysis Tool 2000 database of the Korea Investors
Services Co., Ltd. The data contains the balance sheets and the profit and loss statements
of all South Korean firms that are listed on the Korea Stock Exchange.
12
The data is
available in annual series from 1980 to 1999. We select the firms in manufacturing
between 1980 and 1996, before the South Korean financial crisis in 1997. In all years
manufacturing is the largest industry on the Korea Stock Exchange. In 1996, 71.8 percent
of all firms listed on Korea Stock Exchange are manufacturing firms. The dataset
includes 235 firms in 1980 and 604 firms in 1996.
The dataset provides information on a firm’s outputs (sales and exports) and
inputs (i.e. total number of workers, capital stock and intermediate inputs). The firms are
classified by the 2-digit Korean Standard Industrial Classification (KSIC) codes that are
closely related to 2-digit Standard Industrial Classification (SIC) codes used in the US.
13
To deflate the value of total output (defined as total firm sales), industry-specific
domestic producer price indices were obtained from the Bank of Korea’s Price Statistics
Summary for various years at the two-digit industry level. The measure of capital input is
the book value of fixed assets. The dataset provides assets in four categories: buildings
and structures, machinery and equipment, vehicles, and other assets. The Bank of
Korea’s Economic Statistics Yearbook provides the implicit price deflator for three asset
categories, buildings and structures, machinery and equipment and vehicles. We weight
these price indexes by the average reported value shares of these categories in the Bank
of Korea survey to obtain an annual capital deflator. To deflate material expenditures, we
use the raw materials price index for the manufacturing sector from the Bank of Korea’s
Price Statistics Summary also for various years.
The KIS database itself does not contain information on firm FDI flows. We
therefore merge the KIS data with data from the Export-Import Bank of Korea. The
Export-Import Bank of Korea publishes sectoral (3-digit KSIC) data on outward FDI in
the Overseas Direct Investment Statistics Yearbook. These data are publicly available
12
To list on the Korea Stock Exchange, firms have to satisfy several criteria from the Korea Stock
Exchange’s Rules and Regulations. The advantage of these criteria is that they make the pool of firms more
comparable which, for the purpose of finding proper matches, is an advantage.
13
See appendix I
12
from 1980 to 2000. We, however, have obtained the unpublished firm-level data from the
Export-Import Bank of Korea. These data not only specify per firm its level of outward
investment, but, critical for our analysis, the host country of a multinational’s subsidiaries
is also listed. As time goes by, the fraction of firms that consists of multinational
corporations increases in the dataset. In 1980 there are only 28 firms that invest abroad,
by 1990, however, we have 237 firms setting up subsidiaries abroad and at the end of the
period we have some 391 multinationals. Note that we call a firm a multinational from
the moment it sends its first investment abroad.
At the end of the sample period, in 1996, South Korea’s FDI flows to 93 host
countries. We list these countries in Appendix II. We group them into more developed
countries, DCs, and less developed countries, LCDs, based on their per capita GDP. For
each year, a country is classified into either category if its per capita GDP is higher or
lower than that of South Korea. As Figure 2 shows, there is steady increase in firms that
start investing abroad - South Korea officially started to gradually liberalize its outward
FDI from 1980 onwards. Initially, most of the new multinationals seek as destination a
more advanced country - the lion share of these multinationals set up affiliates in the US.
However, from the late 1980s onwards there is a dramatic increase in firms that invest in
less advanced countries as they become multinationals. An important factor in this regard
is the normalization of the relations between China and South Korea - In 1992 South
Korea and China establish diplomatic relations. As Figure 3 illustrates, around that same
period, multinationals that were already investing in more advanced countries change the
destination of their investments abroad and also open affiliates into less advanced
countries. Note that the movement from multinationals active in less advanced countries
into more advanced ones is less pronounced, and too limited for a formal analysis.
Table 1 summarizes the movements of the South Korean firms. The first row
reports for each year the total number of firms, which is the sum of the multinationals (in
the second row) and the national firms that do not invest abroad (in the eighth row). For
each year we report the number of new multinationals in row three and break it down
according to whether their initial destination is a more or a less advanced country in the
next two rows. We denote the firms that change status SW, from switchers. For example,
of the total of 35 switchers in 1990, 18 have a more advanced country as their first
13
destination and 17 a less advanced country. The reported numbers here are the ones used
for Figure 2. The next two rows focus on the multinationals that change direction, i.e.
those that were investing in a more advanced country and move to a less advanced
country and vice versa. Figure 3 visualizes these multinationals that switch direction.
For the econometric analysis we end up using 452 firms. There are two reasons
for the attrition of the sample. One, we can only include firms that have a complete list of
variables, i.e. number of workers, capital, output, intermediates, and profits – a fair
number of multinationals do not report all. Second, we drop firms with abnormal values
(excessively low/high variables compared to the other variables in some years).
Accordingly, the number of firms that change status and the number of national firms
decreases as well. The first two columns of the Tables 3 A through D report the sample
averages for the different subsets of our sample that are relevant for our analysis:
multinationals vs. non-multinationals, firms that move into less advanced countries and
those that go to more advanced nations, and multinationals that change direction and
move to less advanced countries vs. those that stay in more advanced countries.
4. Constructing Counterfactuals
Our analysis centers around firms that change status – be it firms that become a MNC
irrespective of destination, that become a MNC respectively in the more or in the less
advanced country, or MNCs that were active in more advanced countries and start
opening affiliates in less advanced countries. We want to match each of these firms with
a comparable firm that could have, yet in fact has not, made an investment decision, in
order to determine whether the investment decisions do matter for employment growth.
For our before-and-after exercise, we need for each firm that changes status a
four-year window. If, say, a firm starts investing in a less advanced country in 1990, we
use its employment figures for 1988 and 1989 to construct the growth rate before, and the
1990 and 1991 numbers for the growth rate after. We move this four-year window
through our data set.
14
Each time, we consider the firm that switches status together with
respectively national firms (that do not invest abroad in a four-year window or before), or
14
Obviously, firms that change status in the first or last two years of the data set cannot be included in the
analysis.
14
with a multinational active in a more advanced country (that does not invest in a less
developed country in a four-year window or before).
To find counterfactuals, we run four probit regressions to derive the probability of
investing per se/investing in a less advanced country/investing in a more advanced
country and switching the destination of one’s investment. Each time, the sample
includes the firms that change status and national firms (for the first three cases) or
multinationals that are active in advanced countries (for the last). The probit is a function
of observable firm-specific characteristics of the year before the switch as well as
industry dummies and year dummies. The indicator variable SW is 1 if the firm switches
or changes status and zero otherwise.
Prob (SW
it
= 1 | X
it-1
, industry dummies, year dummies),
Our firm-specific X
it-1
- variables include labor, output, profit per labor, capital
per labor, a dummy for export experience, and a dummy for whether the firm belongs to
one of South Korea’s big business conglomerates that are called Chaebol or not.
The first column of Table 2 reports the estimation results. In panel A we first
consider the decision to become a multinational, irrespective of the particular destination.
Consistent with the average firm characteristics referred to in the previous section, firms
that become multinationals tend to be larger in size. We also find that they are more
profitable. Also, a good predictor for future investments abroad is whether the firm
exported in the past and whether it belongs to a Chaebol – Chaebol tend to be larger in
size, more capital intensive and more profitable. In Panel B and C, we compare regular
firms with respectively firms that become multinationals in a more and a less advanced
country. Telling is that in comparison with multinationals that move into more advanced
countries, profitability and size seem to be less of a factor for firms that move into less
advanced countries. This is at least consistent with the view that if moving into
developing countries is about relocating a production line rather than breaking into a
competitive market, it probably does not require higher profitability and bigger size,
which seem to be a factor when moving to more advanced countries. The last panel then
considers the decision to move into less advanced countries among multinationals that
invest in advanced countries. Only prior export performance seems to matter, which
suggests that matching comparable firms will be more of a concern for more
15
heterogeneous groups (say multinationals and nationals) rather than comparing among
multinationals. Based on the probit regressions, we match each firm that changes its
status with a firm with a similar propensity score that does not change its status.
15
To check the matched data we run a probit regression only on the pairs of
matched data and find as we expected that none of the variables are any longer
significant. We report these probit regressions in the second column of Table 2. In Table
3, we show how the sample averages for the firms that change their status and those for
the counterfactual matches have become more similar.
The next section presents the estimation results for the various subsamples that
we study.
5. Results
The estimation results for the regressions (2) and (3) are reported in Table 4,
panels A through D. Each time, we report in the first column the standard matching
estimator that only compares the post investment employment growth rate of the firm that
invests with that of the counterfactual. In the second column, we then list the difference-
in-difference estimator that evaluates the post investment employment growth rates in
light of those from the pre-investment period. Also, we include as an additional control
the difference in logarithm of output levels to control for those instances where there still
was a difference in the means of the matched and unmatched series. (We refer to this case
as the conditional difference.)
As a benchmark, we first focus on the results that are familiar from the literature
and that do not differentiate by destination. As one can see, for both the DID and the SM
estimates, the coefficient of interest is negative but insignificant. This result implies that
investing abroad per se does not affect the parent firms’ growth rate of employment. This
conclusion is in line with the findings of Barba Navaretti and Castellini (2003) who
investigate Italian multinationals and also with the results of other previous studies that
mostly could not find any significant negative effect. As indicated in the introduction,
15
Some switching firms do not have any sufficient close neighbor and get dropped from the matched
sample.
16
this result seems to underscore the conclusion that there are no evident worries about
hollowing out. Note that the dummy variable that control for common characteristics of
multinationals (vs. nationals) are insignificant. This suggests that the matching was fairly
effective.
The following panels of Table 4, however, seem to tell a different story. In these,
we explicitly differentiate by investment decisions, i.e. by whether the destination
country is more or less advanced than South Korea. Table B reports the effect of firms
becoming multinationals in less advanced countries - the control group consists of
national firms. With the standard matching estimator, investing in LDCs has a negative
effect on the home employment growth rate. However, the coefficient is not significant.
When one controls for differences in growth rates before the investment decision, as with
the DID estimator, we do find a significant coefficient on the dummy of interest. In other
words, this suggests that the growth rate of employment of a MNC’s parent firm is less
than what it would have been had it not become a MNC that invests in less developed
countries. This result gives some support to concerns about negative employment effects
of investments in less advanced countries, which in most cases is China.
Table D then reinforces the results obtained in Table B. Here we look at
multinationals only. We compare the firms that shift their investment from more to less
advanced countries with the matched multinationals that do not. In this instance the
coefficient of interest for both the standard matching and the difference-in-difference
estimator is significant and negative. This implies again that the employment growth rate
for multinationals that decide to go to China is less than what it would have been had
they decided to keep their investments in more advanced countries.
Table C then shows the estimates for firms that invest abroad for the first time and
that do so in more advanced countries. The control group here again consists of national
firms that do not invest during the four-year window or before. The estimates obtained
are consistent with those of Table A for investment per se. The coefficients for both
specifications are insignificant. They imply that investing in more advanced countries
does not make any significant difference. The results from Table A and C are interesting
in light of the existing literature that mostly does not differentiate by destination at the
firm level. Since the destination countries of FDI flows from most developed countries
17
are overwhelmingly other developed countries, our results confirm that these do not
affect in a significant way employment growth of the parent firms. This, however, does
not preclude the possibility that the multinationals from more advanced countries that do
move into less advanced countries pay a price in term of domestic employment.
16
6. Conclusion
We investigate the effect of outward FDI on home employment with a unique
data set of Korean firm-level data. In line with the literature that investigates the impact
of exporting irrespective of the particular destination, the existing literature on
multinationals has focused on the effect of FDI per se. In most instances no significant
negative impact of outward investment on employment was found, suggesting that
concerns about hollowing out were probably overdrawn. We take this analysis one step
further and we bring the particular destination country of outward investment into the
analysis. A particular feature of our data is that we, at the firm level, can link each South
Korean firm with the particular countries where it has its subsidiaries. We categorize the
destination countries into two groups, those that in terms of per capita GDP are more
advanced than South Korea (mainly the US) and those that choose as destination less
advanced nations (mainly China). In doing so, we take advantage of South Korea’s
position as a middle-income country that in addition has divided its investment across
more and less advanced nations almost evenly.
Our difference-in-difference estimates together with our standard matching
estimates suggest that there is a price to be paid in terms of employment growth when
firms decide to invest in countries that are less advanced. We find this to be the case for
two groups of firms that differ quite significantly from one another. Both the employment
growth rate for established multinationals that start investing in less developed countries
even though they used to concentrate their investment in more advanced nations and the
employment growth rate for firms that invest for the first time in these countries slows
down after they have moved to the South. On the other hand, our findings for firms that
16
We also do the same work on the employment level change, not growth rate change. The results are
qualitatively the same.
18
for the first time invest in more advanced countries show no significant impact on
employment at all.
Our estimates thus indicate that the destination of firms’ foreign direct investment
does matter. Or more specifically, if firms invest in less advanced countries, there may be
a negative impact compared to when they had not invested there. Even though our
findings do not specify the specific channel through which employment is affected, they
do give some credibility to concerns that have surfaced in the public debate about
outsourcing to less developed countries and its impact on employment at home. At the
same time, our findings cast a light on the existing literature that has found no negative
(and sometimes even a positive) impact of investing abroad. This conclusion that is
obtained mostly for studies with firms from developed countries may well be driven by
the destination of the FDI that for developed countries is overwhelmingly the developed
world.
19
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20
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21
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22
Figure 1. Performance Trajectories
Panel A
from Navaretti and Castellini (2003)
Panel B
MNCs in advanced
countries
MNCs switching to less
advanced countries
counterfactual
Avg.
Performance
MNCs in less advanced
countries
Time
23
Figure 2. New Multinationals (SW) from South Korea investing in more (DCs) or
less developed countries (LDCs)
0 10 20 30 40
# of firms
1980 1985 1990 1995
year
SWs to either region SWs to LDCs
SWs to DCs
Figure 3. Multinationals from South Korea switching investment destination (SW)
0 5 10 15 20 25
# of firms
1980 1985 1990 1995
year
SWs to LDCs SWs to DCs
24
Table 1. The number of firms in the data set
Year 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
Total Firms 235 241 411 481 512 540 560 577 585 588 594 600 601 603 604 604
MNCs 28 55 69 85 97 109 124 143 170 202 237 262 287 313 349 368
New MNCs by
investing in either
region*
N.A 27 14 16 12 12 15 19 27 32 35 25 25 26 36 19
New MNCs by
investing in LDCs** N.A 4 1 2 1 1 3 3 4 9 18 17 12 14 25 12
New MNCs by
investing in DCs*** N.A 23 13 14 11 11 10 16 22 21 17 7 12 9 8 5
Destination change
from DCs to LDCs#
N.A 1 1 4 2 3 2 4 14 19 17 9 13 20 23 19
Destination change
from LDCs to DCs## N.A 1 3 1 0 0 3 0 1 3 6 4 6 5 6 8
Nationals 207 186 342 396 415 431 436 434 415 386 357 338 314 290 255 236
* Difference between the number of MNCs in two years, E.g in 1990l 35 firms (237-202) become MNCs.
** Firms that become multinationals by investing in LDCs for the first time.
*** Firms that become multinationals by investing in DCs for the first time.
SWs to either region is not necessarily summation of SWs to LDCs and SWs to DCs because there are some firms that start investing in both regions at the same time.
# The firms among MNCs that switches the investment destination from DCs to LDCs.
## The firms among MNCs that switches the investment destination from LDCs to DCs.
25
Table 1-1. The number of firms in the sample we use
Year 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
Total Firms
156 160 286 343 367 392 411 427 434 436 442 448 449 451 452 452
MNCs
9 22 28 39 45 56 63 77 93 110 128 147 161 178 201 213
New MNCs by
investing in either
region
N.A 13 6 11 6 11 7 14 16 17 18 19 14 17 23 12
New MNCs by
investing in LDCs N.A 1 0 1 0 1 0 2 1 4 13 13 6 7 14 7
New MNCs by
investing in DCs N.A 12 5 4 5 8 5 12 13 13 5 5 6 8 6 4
Destination change
from DCs to LDCs
N.A 1 0 1 2 0 2 5 12 8 4 9 10 11 10 5
Nationals
147 138 258 304 322 336 348 350 341 326 314 301 288 273 251 239
26
Table 2. Constructing the Counterfactuals
Prob (SW
it
= 1 | X
it-1
, industry dummies, year dummies)
A. Probability of becoming a MNC (irrespective of destination)
Unmatched Matched
Variable Coefficient Std. Error Coefficient Std. Error
Labor .0002** 0.000101 0.00027 0.00021
Output 3E-10 2E-10 -1.19E-09 9.05E-10
Profit per labor .000003*** 0.000001 -5.08E-08 3.68E-06
Capital per Labor -4.E-08 0.0000001 -1.83E-07 7.29E-07
Export Experience .36*** 0.09 -0.19 0.23
Chaebol .96*** 0.2 -0.16 0.59
Industry dummies Yes Yes
Year dummies Yes Yes
obs. 3391 300
Pseudo R
2
0.12 0.05
*, **, and *** is significant at 10, 5, 1 % level
B. Probability of becoming a MNC that goes to less developed countries,
LDCs
Unmatched Matched
Variable Coefficient Std. Error Coefficient Std. Error
Labor .00017 .00015 .00013 .00037
Output -1.06E-10 5.22E-10 8.52E-10 1.75E-09
Profit per labor .0000030 .0000027 .0000025 .0000092
Capital per Labor -3.94E-07 5.68E-7 -1.35E-07 1.06E-06
Export Experience .32** .13 -.601 .394
Chaebol .85*** .29 -.250 .133
Industry dummies Yes Yes
Year dummies Yes Yes
Obs. 3283 126
Pseudo R
2
.17 .07
27
C. Probability of becoming a MNC that goes to more advanced counties, DCs
Unmatched Matched
Variable Coefficient Std. Error Coefficient Std. Error
Labor .00019* .00012 .00032 .00024
Output 4.52E-10 3.09E-10 -1.76E-09 1.25E-09
Profit per labor .0000026* .0000014 1.70E-06 4.74E-06
Capital per Labor -2.79E-08 1.01E-07 7.54E-07 1.34E-06
Export Experience .398*** .129 .177 .348
Chaebol 1.117*** .240 -.590 .830
Industry dummies Yes Yes
Year dummies Yes Yes
Obs. 3313 156
Pseudo R
2
.15 .10
D. Probability that MNCs change their investment destination from DCs to LDCs
Unmatched Matched
Variable Coefficient Std. Error Coefficient Std. Error
Labor 0.00003 0.00005 0.0001 0.0002
Output 6E-12 1.6E-10 2E-11 7.4E-10
Profit per labor 0.000002 0.000002 -0.000003 5.9E-06
Capital per Labor 8.63E-08 0.0000003 -0.000003 0.000002
Export Experience .61*** 0.23 -0.5 0.5
Industry dummies Yes Yes
Year dummies Yes Yes
Obs. 452 106
Pseudo R
2
0.2 0.05
*, **, and *** is significant at 10, 5, 1 % level
28
Table 3. Sample Means for Unmatched and Matched Data
A. New MNCs (SW, irrespective of destination) vs. Nationals
Unmatched Matched
SWs Nationals SWs Nationals(counterfactuals)
Nr of firms 179 3212 150 150
Labor 602 304 408 419
Output 18,400,000 7,560,000 9,880,000 11,100,000
Capital 14,600,000 2,990,000 3,460,000 4,620,000
Material input 6,600,000 4,000,000 4,610,000 5,640,000
Output per labor 365,860 286,707 322,999 318,621
Profit per labor 24,737 12,493 15,244 15,631
Capital per labor 177,812 95,464 97,654 102,994
B. New MNC in LDCs (SW) vs. Nationals
Unmatched Matched
SWs Nationals SWs Nationals(counterfactuals)
Nr of firms 71 3212 63 63
Labor 455 304 497 463
Output 9,910,000 7,560,000 11,300,000 9,810,000
Capital 3,100,000 2,990,000 3,500,000 3,510,000
Material input 4,350,000 4,000,000 4,670,000 4,690,000
Output per labor 318,193 286,707 343,616 317,280
Profit per labor 13,030 12,493 16,059 13,740
Capital per labor 84,756 95,464 90,397 106,102
C. New MNCs in DCs (SW) vs. Nationals
Unmatched Matched
SWs Nationals SWs Nationals(counterfactuals)
Nr of firms 101 3212 78 78
Labor 732 304 366 415
Output 24,800,000 7,560,000 9,930,000 12,900,000
Capital 23,600,000 2,990,000 3,750,000 5,800,000
Material input 8,400,000 4,000,000 5,090,000 6,810,000
Output per labor 393,400 286,707 324,931 323,342
Profit per labor 31,240 12,493 16,037 16,973
Capital per labor 250,603 95,464 106,419 102,470
29
D. Established MNCs switching to LDCs (SW) vs. MNCs staying in DCs
Unmatched Matched
SWs MNCs SWs MNCs(counterfactuals)
Nr of firms 75 377 53 53
Labor 1668 1301 1082 994
Output 54,700,000 39,100,000 25,400,000 25,300,000
Capital 29,900,000 57,800,000 9,600,000 18,500,000
Material input 23,300,000 16,300,000 10,500,000 12,300,000
Output per labor 723,699 296,087 291,674 298,884
Profit per labor 47,086 12,122 11,336 13,139
Capital per labor 415,862 145,254 96,112 125,060
30
Table 4. Difference-in-Difference (DID) and Standard Matching (SM) Estimates
s
it
s
tDID
s
t
s
it
xdddE
ελαγγγ
+++++=∆ 'ln
210
(2)
s
it
s
SM
s
it
xdE
υδαδ
+++=∆ 'ln
0
(3)
A. For firms that become MNCs (irrespective or destination)
SM method DID method
Dependent variable
Unconditional
difference
Conditional
difference
Unconditional
difference
Conditional
difference
Dummy on time (
1
γ
)
.034**
(.017)
.028*
(.016)
Dummy on SWs (
2
γ
)
.019
(.021)
.015
(.019)
Dummy on SWs’ post investment (
α
)
-.011
(.018)
-.004
(.016)
-.026
(.026)
-.017
(.023)
R-squared .09 .29 .08 .23
Year dummy Yes Yes
Industry dummy Yes Yes
Obs. 300 600
*, **, and *** is significance at 10, 5, and 1% level.
Number in parenthesis is bootstrapped standard error with repetition 1000.
B. For firms that become MNCs in less developed countries, LDCs
SM method DID method
Dependent variable
Unconditional
difference
Conditional
difference
Unconditional
difference
Conditional
difference
Dummy on time (
1
γ
)
.014
(.026)
.012
(.026)
Dummy on SWs (
2
γ
)
.036
(.031)
.039
(.028)
Dummy on SWs’ post investment (
α
)
-.032
(.028)
-.020
(.027)
-.069*
(.036)
-.062*
(.036)
R
2
.16 .30 .14 .21
Year dummy Yes Yes
Industry dummy Yes Yes
Obs. 126 252
31
C. For firms that become MNCs in developed countries, DCs
SM method DID method
Dependent variable
Unconditional
difference
Conditional
difference
Unconditional
difference
Conditional
difference
Dummy on time (
1
γ
)
.036
(.025)
.026
(.022)
Dummy on SWs (
2
γ
)
.009
(.031)
.005
(.026)
Dummy on SWs’ post investment (
α
)
-.0005
(.026)
.010
(.023)
-.0008
(.031)
.012
(.029)
R
2
.18 .38 .11 .30
Year dummy Yes Yes
Industry dummy Yes Yes
Obs. 156 312
D. For established MNCs that move from DCs into LDCs
SM method DID method
Dependent variable
Unconditional
difference
Conditional
Difference
Unconditional
Difference
Conditional
difference
Dummy on time (
1
γ
)
-.009
(.033)
.029
(.030)
Dummy on SWs (
2
γ
)
.040
(.033)
.036
(.024)
Dummy on SWs’ post investment (
α
)
-.048*
(.027)
-.050*
(.029)
-.082**
(.040)
-.080**
(.033)
R
2
.19 .30 .19 .46
Year dummy Yes Yes
Industry dummy Yes Yes
Obs. 106 212
*, **, and *** is significance at 10, 5, and 1% level.
Number in parenthesis is bootstrapped standard error with repetition 1000.
32
Appendix 1. Industry Index
1 Food and beverage
2 Textile and apparel
3 Leather and footwear
4 Wood and furniture
5 Paper and printing
6 Petroleum and Chemicals
7 Non-metallic minerals
8 Basic metals
9 Fabricated metals
10 Machinery and equipment
11 Electronics and telecommunication equipment
12 Transportation vehicles
13 other manufacturing
33
2. Host Countries
1 Algeria 36 Hong Kong 71 Slovak Republic
2 Argentina 37 Hungary 72 Solomon Islands
3 Australia 38 India 73 South Africa
4 Austria 39 Indonesia 74 Spain
5 Bahamas 40 Iran, Islamic Rep. 75 Sri Lanka
6 Bahrain 41 Ireland 76 Sudan
7 Bangladesh 42 Italy 77 Sweden
8 Banuat 43 Jamaica 78 Switzerland
9 Belgium 44 Japan 79 Syrian Arab Republic
10 Bermuda 45 Kazakhstan 80 Taiwan
11 Brazil 46 Laos 81 Tajikistan
12 Brunei 47 Macao, China 82 Thailand
13 Bulgaria 48 Malaysia 83 Turkey
14 Cambodia 49 Mexico 84 Ukraine
15 Cameroon 50 Mongolia 85 United Arab Emirates
16 Canada 51 Morissus 86 United Kingdom
17 Cayman Islands 52 Morocco 87 United States
18 Chile 53 Myanmar 88 Uruguay
19 China 54 Netherlands 89 Uzbekistan
20 Colombia 55 New Zealand 90 Venezuela, RB
21 Costa Rica 56 Nicaragua 91 Vietnam
22 Czech Republic 57 Nigeria 92 Virgin Islands
23 Dominican Republic 58 North Mariana Rep 93 Yemen, Rep.
24 Ecuador 59 Pakistan
25 Egypt, Arab Rep. 60 Panama
26 El Salvador 61 Papua New Guinea
27 Fiji 62 Peru
28 Finland 63 Philippines
29 France 64 Poland
30 Gabon 65 Portugal
31 Germany 66 Puerto Rico
32 Guam 67 Romania
33 Guatemala 68 Russian Federation
34 Guinea 69 Saudi Arabia
35 Honduras 70 Singapore
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