HIAS-E-71

Hollowing Out or Filling In?
Impacts of Multinational Enterprises on Domestic Plant
Turnover and Job Growth in Factory Asia
Hyunbae Chun(a) , Jung Hur(a),(b) , and Nyeong Seon Son(a)(a) Department of Ecnomics, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea(b) Hitotsubashi Institute for Advanced Study, Hitotsubashi University, 2-1, Naka, Kunitachi, Tokyo 186-8601,
Japan

June, 2018

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
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Hollowing Out or Filling In?
Impacts of Multinational Enterprises on Domestic Plant
Turnover and Job Growth in Factory Asia
Hyunbae Chun*, Jung Hur*§, and Nyeong Seon Son*

Abstract
Recently multinational enterprises (MNEs) originating from Asian countries such as China and Korea
have rapidly expanded their global operations, but the employment effect of these MNEs in their homecountries has rarely been studied. Using Korean firm–plant matched data over 2008–2013, we examine
the effects of MNEs on domestic plant turnover and job growth. We find that Korean MNEs are more
likely than non–MNEs to not only close down their domestic manufacturing plants but also open newplants. Along with active plant turnover, Korean MNEs exhibit greater active job reallocation across theirdomestic manufacturing plants within firms; however, this does not result in net job loss. This suggeststhat Korean MNEs participating in Factory Asia restructured their domestic manufacturing bases rather
than hollowing them out.
JEL Classification: F23, F66, L23
Keywords: Employment, Job Reallocation, Multinational Enterprise, Plant Birth, Plant Death* Department of Economics, Sogang University, 35 Baekbeom–ro, Mapo–gu, Seoul 04107, Korea.
§
Hitotsubashi Institute for Advanced Study, Hitotsubashi University, 2–1 Naka, Kunitachi, Tokyo, 186–8601, Japan.
Corresponding author.
We thank Eric Bartelsman, Teresa Fort, Eiichi Tomiura, Jay Pil Choi, Taiji Furusawa, Tomohiko Inui, and otherparticipants at the 2017 Comparative Analysis of Enterprise Data (CAED) Conference, the 2017 Asia-Pacific Trade
Workshop, the 2017 Hitotsubashi-Sogang Trade Workshop, the 2016 Japan-Korea Microdata Workshop on Firm
Dynamics, Innovation, and Globalization, and the 2017 Western Economic Association Annual Conference for theirhelpful comments. We thank Statistics Korea and the Korea Statistics Promotion Institute for providing access to thedata used in this study. All results have been reviewed to ensure that no confidential information is disclosed.

11. Introduction
Multinational enterprises (MNEs)1 organize their production at home and abroad, creating and destroying
jobs across locations. Most advanced countries have experienced hollowing–out of domestic
manufacturing jobs due to MNEs’ plant shutdown at home and relocation to foreign countries (Disney etal., 2003; Görg and Strobl, 2003; Bernard and Jensen, 2007; Van Beveren, 2007). In contrast,
restructuring global production networks between domestic and foreign plants may fill in manufacturingjobs at home through complementing rather than substituting domestic production. There is abundantevidence in the literature that supports the hollowing–out effect, whereas evidence for the filling–in effect,
particularly job creation at the extensive margin (plant entry), is rare (Brainard and Riker, 1997; Becker etal., 2005; Konings and Murphy, 2006; Muendler and Becker, 2010).
To investigate the filling–in outcome, we focus on the massive rise in the outward foreign direct
investment (FDI) of MNEs originating from Asian countries (specifically Korea in this study) during thelast decade (Figure 1).2 Asian MNEs have built production–supply networks, the so–called Factory Asia,
within the continent.3 MNEs in most advanced countries relocate their plants to foreign countries to takeadvantage of cheap labor, and instead, specialize headquarter services at home, destroying domestic
manufacturing jobs.4 In contrast, Asian MNEs shut down high–cost assembly lines and establish newplants producing intermediate goods at home, and then re–link the domestic plants to their low–costassembly lines in foreign countries. In this case, manufacturing jobs at home can be created through1

We narrowly define the term of MNEs in this paper as manufacturing firms with plants both in domestic andAsia’s share of global outward FDI increased from 10% in 2000 to 36% in 2013.
3
Destinations of Asian MNEs are also heavily concentrated in Asian countries. For instance, Korea and China
invested 75% and 70% of total FDI in Asian countries, respectively, according to UNCTAD’s FDI database. Thus,
Asia countries became not only host countries of inward FDI but also home countries of outward FDI. To
distinguish the home-country effect of outward FDI on domestic manufacturing from the inward FDI effect, it isnecessary to choose an Asian country with massive outward FDI and relatively little inward FDI. In this respect,
Korea is a better case study than China.
4
Hansson (2005) and Head and Rise (2002) showed that MNEs that have foreign affiliates in low-wage countries
raise the skill intensity of employment. In addition, Bernard et al. (2017) showed that offshoring makes firms
reallocate labor away from production toward technology-related occupations.
22

building new domestic plants (e.g., Samsung Electronics).5
In this study, we focus on Korean MNEs that showcase the Factory Asia concept. Since the mid–2000s, massive increases in outward FDI of Korean MNEs have made substantial contributions toestablishing the Asian production–supply network. Consequently, Korean manufacturing has experienced
massive restructuring, but the share of employment in manufacturing has changed little over the last
decade. Using a unique Korean firm–plant matched database for the period 2008–2013, we examine theimpact of MNEs’ foreign operations on both the death and birth of their domestic manufacturing plants
and the resulting employment dynamics in terms of job creation, destruction, and reallocation.
Our main findings are as follows. First, Korean MNEs are more likely to not only shut down domestic
manufacturing plants but also open up new plants, compared to non–MNEs. This simultaneous plant
death and birth at home is evident for the MNEs that have invested in Asian countries, particularly China,
but not in advanced countries. Asian countries are often interpreted as destinations of vertical FDI in theliterature (Hanson et al., 2005; Debaere et al., 2010). Production can be more efficient if MNEs cut off
domestic value chains and relocate plants to low–wage countries in Asia. Our findings imply that vertical
FDI does not necessarily preclude the possibility of creating new plants at home. Active plant turnover(death and birth) can be viewed as a strategy of Korean MNEs’ for growth in Factory Asia. Second, alongwith plant turnover, Korean MNEs have also reallocated domestic manufacturing jobs through not only
destroying but also creating jobs. Despite no statistically significant net employment growth effect, MNEs
have filled in domestic jobs at the extensive margin by establishing new manufacturing plants at home.
These findings are reasonably robust to a wide range of considerations such as alternative definitionsof MNEs and emerging countries, alternative periods with a three–year interval from 2008 to 2011, andexclusion of foreign–owned firms. We also check for the presence of endogeneity using the propensity
score matching method and possible joint decisions vis–a–vis plant shutdown and startup of MNEs.

5

While Samsung Electronics relocated domestic assembly lines for televisions, cellphones, and refrigerators toforeign countries, they constructed huge production sites for high-end semiconductors and displays in Korea.

3

The findings herein provide important insights into the role of MNEs in domestic plant turnover andemployment dynamics. First, Korean MNEs’ foreign activities in Asian countries such as China haveinduced active reallocation in domestic manufacturing industries, establishing new plants along withshutting down existing plants. This restructuring process accompanied by domestic plant entry represents
novel evidence in the MNE literature, contrasting with most previous findings which suggest thatdomestic plant closures occur without new entry caused by the expansion of foreign operations in (oroffshoring toward) low–wage countries (Gibson and Harris, 1996; Bernard and Jensen, 2007; Bandick,2010). Second, the job reallocation process in Korean MNEs also contrasts with that observed inadvanced countries’ MNEs. Korean MNEs actively reallocate domestic manufacturing jobs throughfilling in jobs at new plants, whereas other countries’ MNEs mainly shut down plants and hollow–out
jobs.6 There have been some studies suggesting that foreign and domestic production or employment arecomplementary.7 For instance, Muendler and Becker (2010) offered the first evidence to suggest that thedomestic employment of (German) MNEs can expand at the intensive margin but not at the extensive
margin.8
Furthermore, evidence on Korean MNEs sheds light on understandings of Asian MNEs in the context
of Factory Asia. Starting in the early 1990s, outward FDI from Korean MNEs was characterized byrelocation of manufacturing plants to low–cost countries such as China, resulting in hollowing–out ofdomestic jobs (Debaere et al., 2010). However, after the mid–2000s, Korean MNEs established global
value chains (GVCs) in Asia so that trade in intermediate inputs between foreign and domestic plants

6

The MNE–driven reallocation process may also affect overall manufacturing productivity at home. In this respect,
Alfaro and Chen (2016) showed that most productivity gains from MNEs are attributable to the selection andreallocation mechanism. However, analyses of reallocation–driven productivity impacts of Korean MNEs arebeyond the scope of this paper and can be explored in future work.
7
Desai et al. (2009) also revealed a positive association between foreign and domestic activities (investments andemployment growths) of U.S. MNEs, but did not distinguish extensive margins from complementary effects.
8
They defined the extensive margin where MNEs enter foreign locations and the intensive margin where MNEs
operate existing affiliates.

4

dramatically increased (Ramondo, 2016). 9 This incurs domestic manufacturing restructuringaccompanied by shutting down domestic plants substitutable for foreign inputs, and simultaneously
establishing domestic plants complementary to foreign plants. This is consistent with the findings ofHarrison and McMillan (2011), who noted that domestic and foreign employment can be complements if
MNEs undertake significantly different tasks at home and abroad. The findings of our study contribute toimproving on the existing knowledge of Asian MNEs’ filling–in processes driven by plant entry at home.
However, the international division of labor within Asian MNEs remains an issue for future work.10
The remainder of this paper is organized as follows. Section 2 reviews patterns of outward FDI and
employment in the Korean manufacturing sector over time. Section 3 describes the firm–plant matcheddata applied herein and the construction of plant turnover, employment growth, and job reallocation
variables. Sections 4 and 5 present empirical results and robustness checks, respectively. Finally, Section
6 offers conclusions.

2. Outward FDI and Manufacturing of Korea
Since the mid–2000s, most outward FDI of Korean MNEs has been concentrated in Asian countries. In
the literature (Hanson et al., 2005; Debaere et al., 2010), MNEs’ investments in Asia are often referred toas vertical FDI since Asian countries have cost advantages in manufacturing compared to developed
countries. 11 Through establishing vertical manufacturing plants in low–cost Asian countries, Korean
MNEs can build their manufacturing networks efficiently. Yet Korean MNEs have maintained theirtraditional status as manufacturers within Korea without downsizing jobs. In this section, we briefly
address the regional distribution of Korea’s outward FDI and the current status of domestic manufacturing.
9

Ramondo (2016) showed that MNEs in Asia have strong input–output linkages between domestic and foreign
plants.
10
To identify the nature of international division in Asian MNEs, it is necessary to investigate the characteristics of,and intrafirm trade between, domestic and foreign plants. Such work also requires more detailed datasets.
11
According to the World Integrated Trade Solution (WITS) trade database, almost 80% of the products imported
to Korea from China are intermediate goods as of 2013. Similarly, most exported products from Korea to China arealso intermediate goods.

5Outward FDI from Korea
Figure 2 shows the annual amounts of outward FDI of MNEs in the Korean manufacturing sector for theperiod 1990–2013. One noteworthy observation is that outward FDI rapidly increased after 2005 andbecame more concentrated in Asia. This period coincides with the following two events. First, in 2004
and 2005, the Korean government removed the overseas investment limits imposed on corporate andprivate investors.12 Second, in the mid–2000s the Chinese government began implementing the WTO’s
requirement to remove restrictions on investment that create trade distortions, following China’s
accession to the WTO in 2002. 13 That is, internal and external conditions in the 2000s were quitefavorable to outward FDI from Korean firms.

[Figure 2 about here]

According to the FDI database of the Export–Import Bank of Korea in 2010, China was the top
destination country for Korea’s outward FDI, followed by Vietnam, India, and Indonesia for the period
1996–2009. Since the early 2000s, China has absorbed considerable global FDI from not only advanced
countries but also emerging economies. According to the OECD report by Davies (2013), inward FDI
into China surged from 40 billion USD in 2000 to around 120 billion USD in 2011. Two thirds of thisinward FDI was from 10 Asian countries in 2010. Among them, Korea is the third largest contributor,
followed by Singapore and Japan, and subsequently, Taiwan.14 According to Zhang (2005), compared toother Asian economies, Korea has only recently become a major foreign investor and establishedrelatively new production–supply networks since the mid–2000s. In this sense, Korea is probably the key

12

See Nicolas et al. (2013) for more detail on the history of investment liberalization policy reform in Korea.
See Bransetter and Lardy (2006) for details concerning Chinese investment liberalization and the WTO after
2002. These authors also summarized developments vis–a–vis Chinese globalization in the 1990s.
14Although Hong Kong is the largest investing country into Mainland China, it is now a part of China.
13

6

country to understand home–country impacts of the formation of global production–supply networks,
particularly Factory Asia.

Manufacturing in Korea
Note that the increased outward FDI from Korea is not associated with a contraction of domestic
manufacturing since 2007. Figure 3 shows macro–level stylized facts about the Korean manufacturingsector. First, the manufacturing sector’s share of value–added has been reasonably stable and not fallen
below 28% since 2000; according to the Mining and Manufacturing Survey of Statistics Korea, it was 30%
as of 2013. Second, the manufacturing sector’s share of employment has remained at a high level; theSurvey of Economically Active Population of Statistics Korea shows that this proportion decreasedslightly from 20% in 2000 to 17% in 2007, but has since remained stable at 17%. Third, the total annual
number of manufacturing plants exhibits an upward trend over time. According to the Mining andManufacturing Survey, the number of plants with more than 10 employees was 51,418 in 2000, 61,785 in
2007, and 65,389 in 2013. In summary, even during the period of rising outward FDI, the domestic
manufacturing sector continued to maintain relatively high shares in employment and value–added, andto increase the numbers of production plants.

[Figure 3 about here]

Related to the entire manufacturing sector in Korea, one notable observation about Korean MNEs isthat they account for 48% of sales and value–added and 25% of employment in the manufacturing sector
as of 2013, according to the Survey of Business Activities by Statistics Korea. Although themacroeconomic data do not provide information on exactly what has happened inside the manufacturingsector, we can conjecture that the stable employment share of manufacturing sectors on the aggregate has
resulted from Korean MNEs reallocating their plants by simultaneously closing down some while7

opening up others. Although these two opposing dynamics cancel each other out, plant turnover of
Korean MNEs can result in active job reallocation (both job creation and destruction) rather than job
growth. This is what we attempt to test in this study using a unique firm–plant matched data.

3. Data
Firm–Plant Matched Data
To construct a firm–plant matched dataset, we combine two datasets of the Survey of Business Activities(SBA) and the Mining and Manufacturing Survey (MMS), conducted annually by Statistics Korea. SBA isa firm–level survey that covers all firms located in Korea with 50 or more employees and with 300 million
KRW or more capital in all business sectors. SBA includes not only firm characteristics but also various
business strategies and activities. In particular, regarding firms’ foreign activities, SBA collects country
location and two–digit industry information about each foreign plant, of which a Korean parent firm holds
at least 20% equity. MMS is a plant–level survey that covers all mining and manufacturing plants with 10
or more employees located in Korea. MMS collects detailed information on plant characteristics such as
employment, age, and tangible assets.
We use the firm identifier of a plant in MMS to match a firm in SBA. The sample of matchedmanufacturing firms with their domestic manufacturing plants covered 5,399 manufacturing firms with
7,367 manufacturing plants in 2008. Because SBA covers firms with 50 or more employees, some small
plants in MMS are omitted in the matched sample. However, the matched dataset accounts forapproximately 75% of sales and 50% of employment of all plants in the 2008 MMS. In addition, given
our interest in MNE behavior, the exclusion of small plants might not generate a bias in the constructionof a control group of non–MNEs.15 This implies that our matched dataset is representative for analyzing
the effect of MNEs on the domestic manufacturing sector.

15

When we used the Census on Establishments to include these small plants that are omitted in MMS, our main
results were qualitatively identical.

8Following the massive expansion of manufacturing outward FDI across Asian countries after the mid–2000s, Korea’s domestic manufacturing industries experienced massive reallocation and restructuring. To
examine the consequence of MNE expansion on domestic manufacturing after the mid–2000s, we chose
2008–2013 as the sample period. In this five–year period, domestic manufacturing expanded with active
reallocation along with not only 1,628 manufacturing plant deaths but also 1,815 plant births.

MNEs and Firm Characteristics
Using data from SBA, we construct variables related to MNEs and firm characteristics. First, we construct
a measure of MNE status using information about the ownership of foreign plants.16 A dummy variable of
MNE takes the value 1 if a Korean manufacturing firm own at least one foreign manufacturing plant in2008, and 0 otherwise. As noted in previous studies (Braconier and Ekholm, 2000; Debaere et al., 2010;
Harrison and McMillan, 2011), the effect of MNEs on the domestic economy may differ according to thelocation of foreign activity. To address this, we classify MNEs according to country location of their
manufacturing plants. The destinations are divided into two groups: advanced and emerging countries.
Advanced countries include countries in North America, Europe, Oceania, and Japan, while emerging
countries include all Asian and developing countries except for Japan.
Next, we construct measures of firm size, firm age, firm capital intensity, firm productivity, and multi–
plant and multi–product dummies to control for firm heterogeneity. Firm size is the natural log of the sumof employees in all domestic manufacturing plants owned by a firm. Firm age is the natural log of yearsof operation of the firm. Firm–level capital intensity is the natural log of the ratio of the sum of plant–
level tangible assets to total manufacturing employment. Firm productivity is defined as the log of total
manufacturing sales over total manufacturing employment. Multi–plant is a dummy variable that takes thevalue 1 if a firm has at least two domestic manufacturing plants, while multi–product is a dummy variable16

We follow SBA’s definition on the ownership, which is at least 20% capital equity of the foreign plants. However,
our results are qualitatively identical when we consider majority–owned (over 50% of equity) foreign plants because
most foreign plants are majority-owned.

9

that takes the value 1 if a firm has a record of sales that belong to at least two 3–digit KSIC
manufacturing industries.

Plant Deaths and Births
We define a plant death if a plant in MMS 2008 is absent from MMS 2013. A plant birth is defined wherea plant is absent in MMS 2008 but present in MMS 2013. Both plant birth and death are defined over the
five–year span of the sample period. We construct a dummy variable for plant death at the firm level thattakes the value 1 if a firm has at least one plant death in 2008–2013 and 0 otherwise. A dummy for a plantbirth at the firm level is similarly defined. If a firm consists of a single plant (a single–plant firm) in MMS,
plant death also indicates firm death. Plant death does not necessarily imply firm death if a firm owns
multiple plants (a multi–plant firm). Because MMS includes plants with 10 or more employees, a plant
death is recorded if the number of people employed at the plant fall below 10 in the sample period. Using
data on the Census on Establishments (CE) that covers all domestic establishments with at least one
employed worker, we identified whether plant death represents either plant closure or omission due tobeing below the minimum 10–worker requirement. However, our main results remained unchanged if thesample was adjusted for true deaths and births of plants.17

Employment Growth and Reallocation
Following Davis et al. (1998), we construct two measures of employment growth and reallocation at thefirm level. First, we define the net employment growth rate of firm i as the weighted sum of plant–level
employment growth rates:

17

Although CE is comprehensive to include all plants in Korea, it has information on employment of plants only.
So, we still need the MMS for other information such as capital and sales for this extra exercise.

10

!!"#!,! =!!,! !"!,! !!
!∈!

where !!,! is the number of workers at plant j in year t, !"!,! = (!!,! − !!,!!! )/!!,! is the employment
growth rate of plant j, !!,! = 0.5 !!,! + !!,!!! , and !!,! = !!,! /!∈! !!,! is the employment weight ofplant j in the firm. This growth measure integrates employment growths at continuing (surviving) plants
and death and birth plants. The growth rate lies in the interval [–2, 2] with plant death and birthcorresponding to left and right endpoints, respectively.
We also construct an excess job reallocation measure calculated by the sum of job creation anddestruction minus the absolute value of net employment growth as!!"#!,! = !! !!"!,! + !!"!,! − |!!"#!,! |where !!"!,! =!∈! !!,! !"!,!
!"!,! !!

is the (gross) job creation rate calculated by the weighted sum of

employment growth at continuing plants with positive employment changes and birth plants; and !!"!,! =!∈! !!,! |!"!,! | is the (gross) job destruction rate similarly defined for continuing plants with negative!"!,! !!

employment changes and death plants. The reallocation measure captures gross job flows between
expanding and shrinking plants including birth and death plants that underlie the net employment change.
Note that net employment growth is simply the difference between job creation and destruction. Thus,
even where a firm’s plants exhibit active job flows within the firm through plant expansion, contraction,births, and death, firm–level employment may remain unchanged if the magnitudes of job creation anddestruction are the same. In this respect, the reallocation measure captures the heterogeneity of
employment changes across plants, which enables us to identify whether MNEs reorganize their plants at
home.
11

[Table 1 about here]

Summary Statistics
Table 1 reports summary statistics for Korean manufacturing firms used in our analysis. The first column
shows that 25.6% of 5,399 manufacturing firms closed at least one domestic plant during 2008–2013,
whereas 15.7% of the firms opened new domestic plants during the same period. In the case of
employment–weighted means, the probabilities of both plant closure and opening at the firm level
increased by 29.7% and 27.3%, respectively, which indicates that large firms would have higher
probabilities of plant birth and death. However, the findings do not necessarily indicate a positive
association between firm size and plant death and birth because large firms are more likely to havemultiple plants and multi–plant firms have a high plant turnover rate (Dunne et al., 1989; Bernard andJensen, 2007; Kneller et al., 2012). Table 1 shows that a quarter of firms in the sample are multi–plantfirms, accounting for more than 50% of the total employment in the sample. This suggests that it is
crucial to control for firm characteristics that may affect plant turnover. Overall, the findings confirm that
Korean manufacturing firms actively pursued reallocation and restructuring through closing and opening
domestic plants during 2008–2013.
Employment growth over the five–year period is on average -0.394 (-0.243 for employment–weightedmean) and has a large standard deviation of 0.966. Large negative values of mean employment growthwith a substantial standard derivation are a function of our employment growth measure that integrates
employment changes of not only continuing plants but also plant (and firm) death and birth. For example,
firms that have only exiting plants have an extreme value of -2. To compare employment growth between
MNEs and non–MNEs, however, it is necessary to employ this integrated measure because the exit ratesof MNEs and non–MNEs are substantially different. Consistent with high plant birth and death, mean
excess job reallocation (0.112 and 0.177 for unweighted and weighted means, respectively) indicates12

substantial job creation and destruction (that cancel each other out and do not change the employment
level).

[Table 2 about here]

Table 2 reports the weighted mean of each variable for MNEs (1,382) and non–MNEs (4,017). On
average, MNEs exhibit higher probabilities of both plant death and birth than non–MNEs. In the thirdcolumn, the differences in death and birth probabilities in the two groups are statistically significant at the
1% level. Employment growth and reallocation are also higher for MNEs than non–MNEs. However, it isdifficult to conclude that MNEs have more active reallocation and higher employment growth than non–
MNEs, because MNEs also have different firm attributes that may affect plant turnover. As expected,
Table 2 shows that MNEs are larger, older, more capital–intensive, and more productive than non–MNEs.
MNEs are also more likely to be multi–plant and multi–product firms than non–MNEs. Therefore, it is
crucial to control firm characteristics when identifying the impacts of MNEs on employment growth andreallocation. Accordingly, we carry out multiple regression analyses in the next section.

4. Empirical Results
Empirical Specification
In this section, we examine the effect of MNEs not only on plant death and birth but also on employment
growth and reallocation at the firm level. To relate firm characteristics including MNEs in year t to thefour firm–level outcomes between year !!and ! + 5, we estimate regressions of the form!! = ! + !!"#! + ! ′ !! + !! + !! .

13

The dependent variables are plant death and birth, net employment growth, and excess job reallocationof firm i between years !!(2008) and ! + 5!(2013). Plant death and birth at the firm level are dummy
variables that take the value 1 if firm ! closes or opens a plant during 2008–2013, respectively. The net
employment growth rate at the firm level is measured by the weighted average of plant–level employment
growth rates and the excess job reallocation rate at the firm level is calculated by the sum of job creationand destruction rates minus the net employment growth rate. !!"! is a dummy variable indicating
whether firm !!is an MNE in year !; !! is a vector of firm–level characteristics in year ! that includes
firm size, firm age, firm capital intensity, firm productivity, and multi–plant and multi–product dummies.
The model also includes 61 three–digit industry dummies (!! ) to control for unobserved factors affecting
firm–level outcomes. 18 !! is an error term allowing clustering at the industry level. We employ probit
models where plant death and birth (both dichotomous) are dependent variables, and ordinary leastsquares estimation to model employment growth and job reallocation. To examine the economic
significance of MNE operations, we estimate employment–weighted regressions for all four outcomes inthis section.19[Table 3 about here]

Plant Death and Birth
Table 3 reports the marginal effects of MNEs on the probability of plant death and birth at the firm level.
Column (1) of Table 3 shows that MNEs have an approximately 13% higher probability of plant
shutdown than non–MNEs. This finding is consistent with the effects of MNEs in advanced countries on
their plants at home (Bernard and Jensen, 2007). Outward FDI induces plant closing in the home country,
which hollows out domestic industries. However, column (2) shows that Korean MNEs also have a higher
18

Some three–digit industries in which the dependent variable for all firms in an industry has the same value are
merged into the closest industries, which reduces the number of industries from 82 to 61.
19
Nonetheless, unweighted regressions generate qualitatively similar results (reported in Table A1 in the Appendix).

14

probability of opening a new plant at home than non–MNEs. Together, the results in columns (1) and (2)suggest that Korean MNEs shut down their manufacturing plants at home while opening new plants at
home. 20 The magnitude of MNEs’ effects on the probabilities of domestic plant death and birth also
indicates that the difference in plant birth probability between MNEs and non–MNEs is even larger thanthat for plant death probability. Thus, Korean MNEs are characterized by active plant opening at home,
distinguishing them from MNEs in advanced countries. This suggests that plant closures in Korea driven
by MNEs imply the reorganization of domestic manufacturing industries (and result in net job growth athome) rather than their hollowing out.21
In addition to MNEs, columns (1) and (2) show that some firm characteristics are related to plant
shutdown and opening. Old firms are less likely to close their plants than young firms and multi–plantfirms are more likely to close their plants than single–plant firms (Dunne et al., 1989; Bernard and Jensen,
2007; Kneller et al., 2012). Firm productivity is positively related to the probability of plant birth,
although this is significant only at the 10% level. Firm size and productivity do not affect the probabilityof plant death. Our findings at the firm level are not directly comparable to those in previous plant–levelstudies where the probability of plant survival is positively related to plant size, age, productivity, andcapital intensity. When we related plant size and productivity to plant exit decision, we also found anegative relation.22
Columns (3) and (4) distinguish MNEs according to their destinations: emerging and advanced
countries. A firms’ ownership of manufacturing plants in emerging countries have both positive effects on
the probabilities of plant death and birth at home, while the ownership of plants in advanced countries
have a significant positive effect on plant birth but not plant death. As for the former result on emerging

20

Closing and opening of plants may not be carried out by the same firms. To address this issue, we employ abivariate probit model to estimate the association between plant closing and opening decisions. The estimation
results suggest a positive association between the two decisions. See Table A3 in the Appendix.
21
However, herein, we do not investigate other economic aspects of this reorganization of manufacturing, such aschanges in productivity and production structure.
22
The results are available upon request.

15

countries, the existing literature (Hanson et al., 2005; Debaere et al., 2010; Harrison and McMillan, 2011)
describes that FDI to developing countries is often defined as vertical FDI mainly because of the regional
advantage of labor costs. Such MNEs downsize their domestic production scale and relocate labor–
intensive plants to developing countries. However, our empirical findings suggest us more than this story;
vertical FDI does not simply preclude the possibility of establishing new plants at home. Plant death andbirth can be viewed as MNEs’ strategy of vertically linking domestic plants and foreign plants. As for thelatter result on advanced countries, the traditional theory of proximity–concentration tradeoff (Markusen,
1984) hints us that FDI to developed countries could be regarded as horizontal FDI because of the market
size and purchasing powers. Such MNEs directly producing in developed countries can save trade costs
and be closer to consumer markets with a large purchasing power. This theory implies that horizontal FDI
may not be related to a plant or job creation at home. However, our second finding in column (4) reveals apossibility that horizontal FDI can stimulate domestic investments through establishing new plants. For
instance, the new manufacturing plants at home country can export and serve foreign consumer markets
with new products, together with the manufacturing plants located in developed countries. Although these
two mechanisms about vertical and horizontal FDIs need to be analyzed empirically, micro–level
information on foreign plants and products of MNEs is not available. We leave this to the future research
agenda.

[Table 4 about here]

Employment and Reallocation
Table 4 presents results on net employment growth and job reallocation within firm over the five–year
sample period. Column (1) of Table 4 shows that net employment growth in MNEs relative to non–MNEs
is positive but statistically insignificant. Column (2) reports that MNEs have more active job reallocation
relative to non–MNEs, which is statistically significant at the 1% level. Consistent with the results for16

plant death and birth in Table 3, Table 4 shows that MNEs reorganize their domestic manufacturing
activities through closing and opening plants, inducing active job reallocation but not net job growth athome.
Columns (3) and (4) of Table 4 present the differential effects of MNEs on employment andreallocation according to the destinations of outward FDI. MNEs with plants in emerging countries
exhibit active job reallocation but insignificant net employment growth at home. In contrast, MNEs withplants in advanced countries exhibit both active job reallocation and positive net employment growth.
Again, consistent with the findings for plant death and birth in columns (3) and (4) of Table 3, theseresults suggest that the effects on net employment growth and job reallocation vary according to thedestination of FDI from Korean MNEs.

5. Robustness
To assess the robustness of our findings, we examine various issues related to the main results. First, we
examine whether or not our results are robust to alternative definitions of MNEs (majority–owned foreign
plants) and emerging countries (further disaggregating them to Asian or East Asian countries). Second,
we examine alternative sample periods (sub–periods of 2008–2011 for shorter time and another sub–period of 2009–2013 for excluding the global crisis years) and alternative sample firms (excluding firms
owned by foreign parent firms). Third, we address possible endogeneity concerns by using propensity
score matching methods. Fourth, we will check if plant entry and exit decisions are related within an
MNE by using bivariate probit models. This wide range of robustness tests produces qualitatively similar
results to those shown in Tables 3 and 4.

Alternative Definitions: MNEs and Emerging Countries
We define an MNE as of 2008, i.e., the beginning of the five–year sample period, so that we can link the17expansion of outward FDI starting in the mid–2000s to the plant turnover effect at home in the late–2000s.
However, some firms might have exhibited outward FDI activities before the mid–2000s, and thus,
completed reorganizing their manufacturing plants at home before the start of our sample period.23 If theshare of these MNEs is non–negligible, our definition of MNEs might not correctly estimate the impact
on domestic plant turnover. To address this issue, we define here MNEs as firms that undertook outward
FDI only during 2006–2008. So, we redefine the MNEs as those who increased the amount of FDIs forexisting foreign manufacturing plants or those who set up new plants in foreign countries between 2006
and 2008. This alternative definition covers approximately 86% of MNEs based on the ownership offoreign affiliates as of 2008, showing a strong association between the ownership of foreign
manufacturing plants in 2008 and outward FDI in the mid–2000s.
To examine whether the reallocation effects of MNEs are related to more narrowly defined emerging
countries in Asia, we use two alternative definitions of emerging countries: Asian countries or East Asian
countries. Because emerging countries are more diversely located in several continents (Asia, theAmericas, etc.) compared to advanced countries, it is important to examine whether our findings of MNEs
to emerging countries are related to global production chains built by Korean MNEs in East Asian
countries.

[Table 5 about here]

Panels A1 and B1 in Table 5 show results for plant turnover and employment dynamics when thealternative definition of MNEs is used. In the case of advanced countries, MNEs are also more likely toclose their domestic plants, and thus, the positive net employment growth effect found from Table 4
disappears. Overall, the results are qualitatively identical to those found in Tables 3 and 4. Alternative

23

Some large Korean firms began to transform to MNEs in the 1990s, but undertook massive outward FDI after the
mid-2000s.

18definitions of emerging countries are used in Panels A2 and B2 of Table 5: East Asian countries incolumns (1) and (2) and Asian countries in columns (3) and (4). Results for East Asian countries are
qualitatively identical to those pertaining to all emerging countries, which suggests that active plant
turnover and job reallocation in domestic manufacturing are related to the rapid expansion of outward
FDI toward East Asian countries, particularly China, during the mid–2000s.

Alternative Samples: Period and Coverage
In our main analysis, we use a five–year sample period spanning 2008–2013 to investigate the impacts ofoutward FDI on domestic plant turnover and employment dynamics. To examine whether or our results
are robust to a shorter timespan, we use two alternative sample periods. First, we use a three–year period
spanning 2008–2011, which requires MNEs’ impacts on reallocation to be realized more quickly than thefive–year period. Second, our five–year sample period includes the global financial crisis of 2007–2008,
which might affect our results. To address this issue, we use a sample period spanning 2009–2013. Table
6 indicates that different sample periods do not alter the main results on plant turnover and employment
dynamics reported in Tables 3 and 4.

[Table 6 about here]

Firms owned by foreign parent firms are included in the current analysis. In such a case, because theforeign parent firms are also MNEs, the outward FDI decisions of firms in our sample may be dependent
upon global production strategies of the foreign MNEs. So, here we exclude such firms owned by foreign
parent firms with more than 50% of capital equity. This results in a minor exclusion of 343 firms owned
by foreign parent firms; this does not alter the previously reported results.24

24

Results pertaining to the sample where foreign-owned firms are omitted are reported in Table A2 of the Appendix.

19

Endogeneity
Endogeneity may arise in estimating the effects on outcomes: the probability of plant death or birth,
employment growth, and reallocation. To address this issue, we use the MNE variable in year ! prior tothe outcome variables in years !!and ! + 5. We also control for various firm–level characteristics in the
study. If there are unobserved positive shocks, such as rising labor costs at home, that may induce firms toincrease outward FDI and shut down plants at home, our estimate of MNEs’ effect on the probability ofplant death might be biased. Most previous studies focusing on plant death can be sensitive to theseunobserved shocks, but our results may circumvent this issue because we examine not only plant death
but also plant birth, i.e., reallocation. Nonetheless, to address possible endogeneity, we employ propensity
score matching methods to estimate the impacts of MNEs. Propensity score matching methods have
hitherto been widely used to reduce endogeneity problems. Propensity scores are fitted by the predictedvalues of the probit specification, which includes firm size, firm age, firm capital intensity, firm
productivity, and multi–plant and multi–product variables. After calculating propensity scores, we paireach MNE with a non–MNE that has a similar propensity score. We impose the requirement that thematch must emanate from the same industry and adopt five nearest neighbor non–MNEs.25 These non–
MNEs are assigned equal weight to calculate the treatment effects. We use a bootstrap method forstandard errors with 300 replications.

[Table 7 about here]

Table 7 reports matching results. The coefficients are the average treatment effect of MNEs. The
impacts of MNEs remain unchanged. Overall, the matching results confirm that the main results in Tables
3 and 4 are not biased by endogeneity issues.
25

When we use 10 and 20 nearest-neighbor variants, the results are qualitatively similar.

20

Finally, our results concerning MNE effects on plant turnover do not necessarily imply that both plant
deaths and births occur in the same firm, because we estimate birth and death regressions separately. We
thus estimate bivariate probit models to explore whether plant deaths and births are positively related
within an MNE. Results suggest that the two decisions concerning plant death and birth within an MNE
are positively correlated and the correlation coefficient (Rho) is statistically significant at the 1% level.26
This finding confirms that a Korean MNE not only closes domestic manufacturing plants but also opens
new plants.

6. Conclusion
In this study, we examine the impacts of outward FDI of Korean firms on their choices of domestic plantbirth and death using firm–plant matched data. Our empirical results show that Korean MNEs, especiallythose owning manufacturing plants in Asian countries, are more likely to not only shut down but also
open up domestic manufacturing plants as compared to non–MNEs. Additionally, Korean MNEs exhibit
more active job reallocation than non–MNEs. However, the net job growth effect of MNEs isinsignificant.
Our findings suggest that while building Asian supply chains during the 2000s, Korean MNEs
reorganized domestic manufacturing rather than hollowing out. This may further imply that the impacts ofAsian MNEs on their domestic plant turnover and employment dynamics are different from those ofadvanced countries’ MNEs. The existing literature suggests that the reduction in domestic employment isdue to the extensive margin of plant exit while the MNEs increase employment at existing plants at home.
However, we found a new pattern here whereby Korean MNEs exhibit job losses and gains only at
extensive margins of plant entry and exit, without any influence from intensive margin. We may interpret
this as an Asian case of the filling–in effect at the extensive margin.

26

Results for the bivariate models are reported in Table A3 of the Appendix.

21

More importantly, we emphasize that Korean MNEs’ investment mechanism is distinctive compared tothat of advanced countries’ MNEs because the former restructured their manufacturing plants throughboth closing down existing plants and opening up new plants. This calls for further study into what typesof plants have been closed and opened within MNEs during the period of building Asian supply chainsthrough outward FDI. One potential avenue in this respect is a vertical linkage or technology–driven
hypothesis. While Korean MNEs may remove domestic assembly lines of final products, they constructvertical chains of inputs both domestically and globally. The removal of assembly lines may help mitigate
cost pressures on domestic manufacturing, and at the same time, the vertical linkage of capital–intensiveor high–tech input producing plants in domestic to labor–intensive or low–tech input producing plants inforeign may elevate their position in global value chains. Alternatively, another possible mechanism forthe filling–in phenomenon is a horizontal investment or a market expansion hypothesis. Since the mid–2000s, Korean MNEs have had the opportunity to enter huge Chinese markets. As these markets
increasingly open up to foreign manufacturers, Korean MNEs may invest in both domestic and foreigncountries to meet increased demand from the global market engagement of China. As the production of
Korean MNEs increases, economies of scale would eventually benefit the firms selling products in China.
Understanding these reallocation mechanisms led by Korean MNEs would provide new insights into thehome–county effects of MNEs not only in terms of production structure but also employment andproductivity growth; we leave this for future study.

22

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25

Figure 1: Regional Shares of Outward FDI (1990–2013)
1

0.8

0.6

0.4

0.2

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

0
North america & Europe

Source: UNCTAD FDI Statistics.

26

Asia

Other

Figure 2. Outward FDI of Korean Firms in the Manufacturing Sector: 1990–2013
Average OFDI: 6.4b

12
10
8
6

Average OFDI: 2.1b

4
2

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013

0

Other

North America & Europe

Asia

Source: Export-Import Bank of Korea.
Note: Outward FDI measured in Billion USD

Figure 3. Value-added and Employment Shares of Manufacturing in Korea
0.4

0.3

0.2

0.1

1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012

0

Emp share

VA shareSources: National Accounts (Value-added), Bank of Korea
Economically Active Population Survey (Employment), Statistics Korea

27

Table 1. Summary Statistics: Korean Manufacturing Firms
Mean

Mean
(weighted)

Standard
deviation

Minimum

Maximum

Plant death

0.256

0.297

0.437

0

1

Plant birth

0.157

0.273

0.363

0

1

Employment growth-0.394-0.243

0.966

-2

1.927

Excess job reallocation

0.112

0.177

0.329

0

2

Firm size

4.682

6.790

0.856

0

11.237

Firm age

2.766

3.031

0.683

0

4.532

Firm capital intensity

4.448

4.984

1.092

-5.017

8.676

Firm productivity

5.626

6.077

0.854

-0.068

9.907

Multi-plant

0.236

0.545

0.424

0

1

Multi-product

0.172

0.277

0.377

0

1

Plant turnover at the firm level

Employment dynamics

Firm characteristics

Notes: Figures in the first and second columns are unweighted and weighted means of characteristics for 5,399
Korean manufacturing firms, respectively. Plant death and birth are dummy variables that take the value 1 if firms
closed and opened domestic manufacturing plants during 2008–2013, respectively. Firm characteristics pertain to2008. Firm size (employment), firm age, firm capital intensity, and firm productivity are logarithmic values.

28

Table 2. Means of Characteristics for MNEs and Non-MNEs
MNE

Non-MNE

Difference

Plant death

0.366

0.223

0.143 (0.012)***

Plant birth

0.393

0.144

0.248 (0.011)***

Employment growth-0.167-0.3220.155 (0.020)***

Excess job reallocation

0.245

0.105

0.140 (0.009)***Firm size

7.847

5.667

2.180 (0.053)***Firm age

3.222

2.827

0.395 (0.020)***Firm capital intensity

5.242

4.709

0.533 (0.028)***Firm productivity

6.368

5.767

0.601 (0.023)***

Multi-plant

0.700

0.380

0.320 (0.012)***Multi-product

0.374

0.173

0.201 (0.012)***

Observations

1,382

4,017

Employment weight

0.513

0.487

Plant turnover

Employment dynamics

Firm characteristics

Notes: Figures in the first and second columns are weighted means of characteristics for MNEs and non-MNEs,
respectively. Plant death and birth are dummy variables that take the value 1 if firms closed and opened domestic
manufacturing plants during 2008–2013, respectively. Firm characteristics and employment weights pertain to 2008.
Firm size (employment), firm age, firm capital intensity, and firm productivity are logarithmic values. Numbers inparentheses are standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

29

Table 3. Korean MNEs and Home-Country Effects on Manufacturing Plant Turnover

MNE

(1)

(2)

(3)

(4)

Plant death

Plant birth

Plant death

Plant birth0.130***

0.153***(0.043)(0.034)0.117***

0.103***(0.039)(0.024)0.078

0.152**(0.059)(0.059)

MNE: Emerging countries

MNE: Advanced countries

Firm size

Firm age

Firm capital intensity

Firm productivity

Multi-plant

Multi-productPseudo R

2

Sample size

-0.0440.032-0.052*0.017(0.032)(0.025)(0.029)(0.023)-0.072***0.002-0.072***0.004(0.022)(0.039)(0.021)(0.038)-0.008-0.019-0.007-0.017(0.017)(0.017)(0.017)!!

(0.016)0.003

0.048*

0.005x10

0.043*(0.024)(0.025)(0.025)(0.024)0.366***

0.033

0.369***

0.043(0.031)(0.034)(0.029)(0.031)

0.023

0.085

0.021

0.087

(0.059)(0.074)(0.058)(0.076)

0.313

0.277

0.315

0.281

5,399

5,399

5,399

5,399

Notes: The dependent variables in columns (1) and (3) are dummy variables that take the value 1 if firms closed
domestic manufacturing plants during 2008–2013. Dependent variable in columns (2) and (4) are similarly defined forfirms’ plant births. The table reports marginal effects of probit estimates. The sample includes all Korean
manufacturing firms with 50 or more employees in 2008 (that are linked to their manufacturing plants with 10 or more
employees). All regressions include 61 three-digit industry dummies. All regressions are weighted by firm
employment in 2008. Numbers in parentheses are industry-clustered standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

30

Table 4. Korean MNEs and Home-Country Effects on Employment Dynamics
(1)

MNE

(2)

(3)

(4)

Employment
growthJob reallocation

Employment
growthJob reallocation

0.024

0.064***(0.048)(0.018)-0.0270.044**(0.048)(0.017)0.122**

0.074***(0.060)(0.028)

MNE: Emerging countries

MNE: Advanced countries

Firm size

Firm age

Firm capital intensity

Firm productivity

Multi-plant

Multi-product0.058***

0.002

0.047**-0.005(0.019)(0.017)(0.022)(0.015)0.039*-0.001

0.037-0.004(0.023)(0.016)(0.024)(0.016)

0.059***-0.0120.060***-0.010(0.018)(0.010)(0.018)(0.010)0.074**

0.010

0.071**

0.007(0.028)(0.014)(0.027)(0.014)-0.092**0.119***-0.081**0.123***(0.036)(0.025)(0.035)(0.023)-0.011

0.053-0.0050.055(0.041)(0.041)(0.040)(0.041)

2

0.110

0.285

0.112

0.289

Sample size

5,399

5,399

5,399

5,399

Adjusted R

Notes: Employment growth is the net job creation rate defined as the job creation rate minus the job destruction rate.
The excess job reallocation rate is defined as the sum of job creation and destruction rates minus employment growth(the net job creation rate).The sample includes all Korean manufacturing firms with 50 or more employees in 2008
(that are linked to their manufacturing plants with 10 or more employees). All regressions include 61 three-digit
industry dummies. All regressions are weighted by firm employment in 2008. Numbers in parentheses are industryclustered standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

31

Table 5. Robustness Checks: Alternative Definitions of MNEs and Emerging Countries
A. Plant Turnover
(1)

(2)

(3)

(4)

Plant death

Plant birth

Plant death

Plant birth0.135***

0.154***(0.049)(0.038)0.111***

0.084***(0.043)(0.032)0.119*

0.253***(0.069)(0.087)0.117***

0.103***(0.039)(0.030)A1. Alternative definition of MNEs
MNE
MNE: Emerging countries
MNE: Advanced countries
A2. Alternative definition of emerging countries
MNE: East Asian countries

0.115***

0.098***(0.040)(0.029)

MNE: Asian countries
MNE: Advanced countries

0.080

0.155***

0.076

0.148**(0.060)(0.060)(0.059)(0.058)

B. Employment Dynamics
(1)

(2)

(3)

(4)

Employment growth

Job reallocation

Employment growth

Job reallocation-0.0030.051**

B1. Alternative definition of MNEs
MNE

0.032

0.073***(0.049)(0.020)

MNE: Emerging countries
MNE: Advanced countries(0.052)(0.019)0.067

0.094**(0.065)(0.040)B2. Alternative definition of emerging countries
MNE: East Asian countries-0.0300.042**(0.049)(0.019)

MNE: Asian countries
MNE: Advanced countries-0.0250.040**(0.046)(0.018)0.123**

0.075***

0.122**

0.075***(0.060)(0.028)(0.061)(0.028)Notes: All regressions include firm size, firm age, firm capital intensity, firm productivity, and multi-plant and multiproduct variables as controls. Panel A reports marginal effects of probit estimates. All regressions include 61 threedigit industry dummies. All regressions are weighted by firm employment in 2008. Numbers in parentheses areindustry-clustered standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

32

Table 6. Robustness Checks: Alternative Sample Periods
A. Plant Turnover
(1)

(2)

(3)

(4)

Plant death

Plant birth

Plant death

Plant birth

0.067**

0.112***(0.029)(0.027)0.059**

0.087***(0.026)(0.020)

0.038

0.066

(0.046)(0.051)0.130***

0.090***(0.030)(0.023)

A1. 3-year period: 2008–2011
MNE
MNE: Emerging countries
MNE: Advanced countries
A2. Excluding the global financial crisis: 2009–2013
MNE

0.139***

0.125***(0.034)(0.033)

MNE: Emerging countries
MNE: Advanced countries

0.067

0.095*(0.046)(0.055)

B. Employment Dynamics
(1)

(2)

(3)

(4)

Employment growth

Job reallocation

Employment growth

Job reallocation

0.068

0.039***(0.058)(0.010)0.036

0.024**

B1. 3-year period: 2008–2011
MNE
MNE: Emerging countries
MNE: Advanced countries(0.054)(0.010)0.087*

0.055***(0.051)(0.018)-0.1040.039**(0.074)(0.016)B2. Excluding the global financial crisis: 2009–2013
MNE-0.0530.044***(0.065)(0.015)

MNE: Emerging countries
MNE: Advanced countries

0.119*

0.003(0.064)(0.016)

Notes: All regressions include firm size, firm age, firm capital intensity, firm productivity, and multi-plant and multiproduct variables as controls. Panel A reports marginal effects of probit estimates. All regressions include 61 threedigit industry dummies. All regressions are weighted by firm employment in 2008. Numbers in parentheses areindustry-clustered standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

33

Table 7. Endogeneity: Propensity Score Matching
A. Plant Turnover

MNE

(1)

(2)

(3)

(4)

(5)

(6)

Plant death

Plant birth

Plant death

Plant birth

Plant death

Plant birth

0.238**

0.278***(0.100)(0.100)0.237**

0.277***(0.093)(0.102)0.301*

0.384**(0.168)(0.154)

306

306

MNE:
Emerging countries
MNE:
Advanced countries
Sample size

1,382

1,382

1,279

1,279

B. Employment Dynamics

MNE

(1)

(2)

(3)

(4)

(5)

(6)

Employment
growthJobreallocation

Employment
growthJobreallocation

Employment
growthJobreallocation-0.0320.179**(0.028)(0.077)-0.0460.176**(0.032)(0.080)0.047

0.247*(0.033)(0.135)

306

306

MNE:
Emerging countries
MNE:
Advanced countries
Sample size

1,382

1,382

1,279

1,279

Notes: Dependent variables are plant death and birth dummies at the firm level for Panel A and employment growthand excess job reallocation rates for Panel B. Propensity scores are estimated by the weighted probit model.
Matches are assigned within the same 3-digit industry. Bootstrapped standard errors are in parentheses.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

34

Appendix
Table A1. Plant Turnover Results: Probit Model (Unweighted)

MNE

(1)

(2)

(3)

(4)

Plant death

Plant birth

Plant death

Plant birth0.037**

0.046***(0.016)(0.009)0.044**

0.033***(0.017)(0.009)-0.006

0.061**(0.029)(0.024)

MNE: Emerging countries

MNE: Advanced countries

Firm size

Firm age

Firm capital intensity

Firm productivity

Multi-plant

Multi-productPseudo R

2

Sample size

-0.062***0.034***-0.062***0.032***(0.010)(0.006)(0.010)(0.006)-0.067***-0.012*-0.067***-0.012*(0.012)(0.007)(0.012)(0.007)!!

!!

-0.005-0.001x10(0.008)(0.006)(0.008)(0.006)-0.0150.019**-0.0150.018**(0.010)(0.008)(0.010)(0.008)0.430***

0.059***

0.429***

0.059***(0.016)(0.011)(0.016)(0.011)-0.0080.081***-0.0090.082***(0.016)(0.013)(0.016)(0.013)

0.137

0.065

0.137

0.066

5,399

5,399

5,399

5,399

-0.001x10-0.005

Notes: The dependent variables in columns (1) and (3) are dummy variables that take the value 1 if firms closed
domestic manufacturing plants during 2008–2013. Dependent variables in columns (2) and (4) are similarly definedfor firms’ plant births. The table reports marginal effects of probit estimates. The sample includes all Korean
manufacturing firms with 50 or more employees in 2008 (that are linked to their manufacturing plants with 10 or more
employees). All regressions include 61 three-digit industry dummies. Numbers in parentheses are industry-clustered
standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

35

Table A2. Robustness Checks: Excluding Foreign-owned Firms
A. Plant Turnover

MNE

(1)

(2)

(3)

(4)

Plant death

Plant birth

Plant death

Plant birth

0.118***

0.157***(0.042)(0.038)0.108***

0.107***(0.040)(0.028)0.065

0.165***(0.065)(0.058)

MNE: Emerging countries

MNE: Advanced countries

Pseudo R

2

Sample size

0.324

0.289

0.326

0.296

5,056

5,056

5,056

5,056

(1)

(2)

(3)

(4)

Employment growth

Job reallocation

Employment growth

Job reallocation

0.035

0.062***(0.053)(0.019)-0.0220.042**(0.052)(0.019)0.143*

0.083***(0.074)(0.028)

B. Employment Dynamics

MNE

MNE: Emerging countries

MNE: Advanced countries

2

0.109

0.300

0.112

0.305

Sample size

5,056

5,056

5,056

5,056

Adjusted R

Notes: All regressions include firm size, firm age, firm capital intensity, firm productivity, and multi-plant and multiproduct variables as controls. Panel A reports marginal effects of probit estimates. All regressions include 61 threedigit industry dummies. All regressions are weighted by firm employment in 2008. Numbers in parentheses indicate
industry-clustered standard errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

36

Table A3. Plant Turnover Results: Bivariate Probit Model

MNE

(1A)

(1B)

(2A)

(2B)

Plant death

Plant birth

Plant death

Plant birth

0.100***

0.124***(0.033)(0.027)0.089***

0.084***(0.030)(0.019)0.058

0.114***(0.043)(0.041)

MNE: Emerging countries

MNE: Advanced countries

Rho
Wald test for Rho = 0
Log likelihood
Sample size

5,399

0.375***

0.371***

36.00

39.54

-978,343-973,829
5,399

5,399

5,399

Notes: The dependent variables in columns (1A) and (2A) are dummy variables that take the value 1 if firms closed
domestic manufacturing plants during 2008–2013. Dependent variables in columns (2A) and (2B) are similarly
defined for firms’ plant births. The table reports marginal effects of bivariate probit estimates. The sample includes all
Korean manufacturing firms with 50 or more employees in 2008 (that are linked to their manufacturing plants with 10
or more employees). All regression specifications include firm size, firm age, firm capital intensity, firm productivity,and multi-plant and multi-product variables as controls. All regressions include 61 three-digit industry dummies. All
regressions are weighted by firm employment in 2008. Numbers in parentheses are industry-clustered standard
errors.
* Significant at the 10% level; ** Significant at the 5% level; *** Significant at the 1% level.

37