Labor Market Rigidity at Home and Multinational
Corporations’ Flexible Production Reallocation
Abroad*JaeBin Ahn† Jaerim Choi‡ Sunghoon Chung§
November 20, 2023
Abstract
An unprecedented regime change following the 2017 presidential impeachment
led to a dramatic shift to more rigid labor market policies in the Republic of Korea, represented by consecutive double-digit hikes in the minimum wage in the next
two years. Using a firm-level data set with detailed information about foreign affiliates over the period of 2013~19, this paper assesses the employment consequences ofstricter labor market regulations. The empirical evidence uncovers an underexploredmechanism through which domestic labor market rigidity can reduce domestic employment as multinational firms with flexible internal networks reallocate production
tasks across borders.
Keywords: Labor Market Rigidity, Production Reallocation, MNCs, Foreign Affiliates, Employment Adjustment
JEL Code: F23, J23, J80* We are grateful to Sangmin Aum, Paulo Bastos, Samuel Bazzi, Mary Ann Bronson, Cheng Chen, Juy-oung Cheong, Hyunbae Chun, Jeffrey Clemens, Julie Berry Cullen, Gordon Dahl, Tyler Daun, David Hummels, Jung Hur, Jay Hyun, Hiau Looi Kee, Daisoon Kim, Gueyon Kim, Ryan Kim, Taehoon Kim, Ohyun
Kwon, Joonhyung Lee, Munseob Lee, Gaelan MacKenzie, Keith Maskus, Teresa Molina, Marc Muendler,
Jakob Munch, Ziho Park, Haruka Takayama, Nori Tarui, Ariel Weinberger, Unjung Whang, Minchul Yum,
Roman David Zarate and participants in UC San Diego, ASSA–KAEA, WALES Conference, Midwest International Trade Conference, University of Hawaii, University of Memphis, Yonsei University, Korea University, Kyung Hee University, KATIS, KEA Conference, USITC and the World Bank for invaluable comments
and suggestions.
†
Research Department, International Monetary Fund. E-mail: jahn@imf.org.
‡
School of Economics, Yonsei University. E-mail: jaerimchoi@yonsei.ac.kr.
§
Research Department, Korea Development Institute. E-mail: sunghoon.chung@kdi.re.kr.

1Introduction

Over the past three decades, the world has witnessed a fundamental transformation of
global production and trade (Elms and Low, 2013). Across-the-board liberalization of
trade accompanied by advances in information and communications technology (ICT)
has greatly facilitated production fragmentation and international production sharing,
giving rise to rapid growth in global trade and the proliferation of multinational corporations (MNCs) over the period. Hence it has been called the age of global value chains(GVCs) (Antràs, 2020).
Beyond their direct impact on the pattern of international trade, GVCs have shaken
up traditional trade policy beliefs and practices: A growing share of imported inputs, notleast via intrafirm trade, calls for recalibrating optimal tariffs to prevent adverse effects on
domestic economies (Blanchard et al., 2017). Moreover, footloose MNCs can make bilateral trade remedies such as anti-dumping duties impotent by easily relocating production
to foreign affiliates in third countries (Chung et al., 2016; Flaaen et al., 2020).
A relatively unexplored aspect of the policy implications of GVCs is the extent towhich the presence of MNCs affects the effectiveness of domestic policy. A case in point islabor market regulation, such as raising minimum wages. In the literature, the standard
way of identifying the employment effects of regulatory changes is to use the variation in
the initial fraction of workers subject to regulatory changes across firms within a country.1
From this perspective and the findings in the literature, large firms, including MNCs,
should be less affected than smaller firms by stricter labor market regulations because
they tend to have a smaller share of workers subject to labor market regulations. In theage of GVCs, however, MNCs may respond more sensitively to regulatory changes: they
can reallocate production tasks to foreign affiliates via their internal networks in a country
with cheaper labor costs, possibly amplifying the adverse effects of labor market rigidity
on domestic employment.2
Although the idea is intuitive from existing theories of MNCs (e.g., Helpman et al.,2004), there is scant empirical evidence on firm-level responses to more stringent labormarket regulation in the context of international production sharing within MNCs. This
paper contributes to the literature by providing detailed firm-level evidence on MNCs’
production task reallocation abroad after an abrupt introduction of stricter labor market
1

Researchers have compared the effects of the minimum wage on workers who earned less than theminimum wage with other workers (Cengiz et al., 2019; Dustmann et al., 2022). Similarly, in the absenceof individual-level data, researchers have utilized the minimum wage "bite" approach, dating back to Card
(1992), which relies on the treatment intensity based on the variation in the fraction of workers initiallyearning less than the new minimum wage across firms, regions, or industries.
2
We will use the terms foreign affiliates and foreign subsidiaries interchangeably throughout the paper.

1

regulations in the Republic of Korea.
Korea offers a unique setting for the purpose of this study for two reasons.3 First, an
unprecedented regime change following the country’s impeachment of President Park
Geun-hye in 2017 led to a dramatic shift toward more rigid labor market policies, including the consecutive double-digit yearly minimum wage hikes and a reduction in the
maximum allowed number of working hours per week by 16 hours (i.e., from 68 to 52
hours a week). The series of events around 2017 and subsequent changes in labor market
regulations were unanticipated in the eyes of firms, yielding the consequent adverse labor
cost shock as an effective source of exogenous variation for our empirical study. Second,firm-level survey data from the Korea national statistical office provides a comprehensive
set of operational information, including locations and industries of each firm’s foreign
affiliates (if any), which enables us to identify MNCs and their responses to the labor costshock through foreign internal networks.
Using the data set over the period of 2013~2019, we investigate the differential impacts of the stricter labor market regulations across firms. We begin by conducting an
event study analysis to track employment trends separately for Korean firms with foreign
affiliates (i.e., MNCs) and without foreign affiliates (i.e., non-MNCs), finding that only theformer group of firms experienced a decline in employment after 2017. To control for any
potential time-varying confounding effects, we then estimate difference-in-differences aswell as dynamic treatment effects by including industry-region-year fixed effects. To alleviate concerns about sample selection bias due to observable differences between MNCs
and non-MNCs, we also employ a propensity score matching (PSM) method. In addition,
we confirm that our main findings indeed reflect MNCs’ response to adverse labor costshocks, by exploring any differential effect within MNCs with varying degrees of exposure to the labor market policy shock. Overall, our empirical results suggest that, as aresult of stricter labor market regulations, domestic employment declined by 3% in firms
with foreign affiliates.4
Subsequent examination indicates that the reduction in domestic employment within
MNCs is linked to the reallocation of production activities between home and abroad via
MNCs’ internal networks. To elaborate, MNCs curtailed their domestic production operations in tandem with workforce reductions, while increasing the share of intra-firm
imports. Moreover, the decline in employment and the accompanying escalation in intrafirm imports are more pronounced when MNCs’ foreign affiliates are located in countries
3

Throughout the paper, “Korea” is used to refer to the Republic of Korea.
We further confirm that the estimated effects are not driven by pre-period differences in employment
trends, while they are robust to controlling for any time-varying firm attributes or to alternative strategies
of categorization.
42

with lower wage levels, exemplified by Asian economies, and engage in analogous tasks.
These results are consistent with the intuition that a large wage differential coupled withthe similarity of production tasks between the headquarters and foreign affiliates disproportionately stimulates MNCs to undertake the reallocation of production. In contrast,
our findings cannot be explained by possible alternative channels such as the substitution of domestic labor for domestic capital or the replacement of low skilled workers withhigh skilled counterparts. Nor do they appear to be driven by foreign demand shocks.
Collectively, all the evidence supports the interpretation that MNCs reallocated production tasks across borders through flexible internal networks in response to a steep rise inlabor costs induced by more stringent labor market regulations.
Our study contributes to a vast literature on firms’ employment responses to minimum wage increases, a topic that has long been central in labor economics (See Neumark
et al., 2007, for a survey). Prior studies have focused largely on two economic types ofadaptations by firms. One is to increase their use of high-skilled workers because thewages for those high-skilled workers become relatively cheaper than the ones for lowskilled workers. This possibility has been supported by some empirical studies (Fairris
and Bujanda, 2008; Giuliano, 2013; Clemens and Wither, 2019; Clemens et al., 2021), but
no definitive conclusion has yet been reached (e.g., Cengiz et al., 2019). Another way
for firms to adapt in a new minimum wage environment is to replace labor with capital, or more narrowly, automation technologies. A burgeoning literature has emphasized this second factor as an important channel through which firms adjust employment
in response to minimum wage increases (Aaronson and Phelan, 2019; Lordan and Neumark, 2018; Harasztosi and Lindner, 2019; Hau et al., 2020, among others).5 We checkedout these two substitution channels as confounding employment adjustment mechanisms
and found that they were not apparent in our context.
Instead, we take a distinctive approach by focusing on another factor that has received
relatively little attention in the minimum wage literature: the substitution between domestic workers and foreign factors of production, particularly within multinationals. To
our knowledge, this is the first study to investigate explicitly, and provide supportive evidence for, the role of MNCs in cross-border production reallocation following domestic
minimum wage increases. A few studies have assessed this channel in a broader offshoring context. Aaronson and Phelan (2019), for example, find no evidence of minimum wage-induced foreign outsourcing of cognitively routine tasks that pay low wages.
5

Firms can also adapt to a minimum wage hike by adjusting non-employment margins such as noncash compensation, output prices, and firm profits. See Clemens (2021) for a comprehensive review of theliterature. Although investigating non-employment margins would be an interesting topic, our interest is
the employment margin in response to minimum wage hikes.

3

By contrast, we propose a distinct substitution mechanism that works flexibly through
MNCs’ internal networks across countries.6
Turning to the international trade literature, this paper is related to existing studies on
the nexus between stringent domestic labor market policy and offshoring activities. In theinternational trade literature, high labor costs due to a high minimum wage and frictionsin the domestic labor market are commonly perceived as drivers of task offshoring andoutward foreign direct investment (FDI). Although the literature has actively investigatedthe economic consequences of those international activities (offshoring and outward FDI),
which and how domestic labor market policies or shocks at home could trigger them has
been underexplored empirically. The few exceptions include studies by Gan et al. (2016)
and Fan et al. (2018). Gan et al. (2016) found that export market participation by Chinese
firms weakened after a continuing rise in the minimum wage, likely reflecting China’s
loss of external competitiveness due to an increase in labor costs. Fan et al. (2018) foundthat a minimum wage increase promotes outward FDI by Chinese firms. The focus of ourpaper is quite close to, but distinct from, that of Fan et al. (2018) in that they investigatedthe extensive margin channel by which firms establish new foreign affiliates in responseto minimum wage increases, whereas we focus on the production reallocation channel
at the intensive margin through existing foreign affiliates. Another notable difference isthat Fan et al. (2018) consider medium-run responses of domestic firms to the minimum
wage increase over a 12-year sample period, while our primary focus is on the immediate,short-run responses (i.e., about three years after a minimum wage increase) of MNCs thathave already set up foreign affiliates.
Last but not least, our study complements previous research on multinational labor
substitution in the international trade literature. For example, Muendler and Becker(2010) assess how the wage differential between home and abroad affects German MNCs’
demand for labor and find significant labor substitution between headquarters and foreign affiliates at both the intensive and extensive margins;7 Harrison and McMillan (2011)6

Our mechanism is quite similar to the substitution effect of Grossman and Rossi-Hansberg (2008) in atask-based offshoring literature, wherein they predicted that (broadly defined) offshoring generates boththe substitution effect and the productivity effect in the source country. Using US industry-level data,
Ottaviano et al. (2013) found that offshoring reduces the share of natives among less-skilled workers but
pushes natives toward jobs that are more skill-intensive. We also add to this task-based offshoring literature
but go beyond the existing studies by providing firm-level, instead of industry-level, evidence on the task
substitution mechanism through MNCs’ internal networks across borders. Furthermore, unlike Ottaviano
et al. (2013), we exploit an exogenous change in labor market policy such as the minimum wage hikes.
7
Muendler and Becker (2010) categorized two critical margins of multinational labor substitution
whereby an MNC’s labor demand responds to international wage differentials (1) at the extensive margin, when the MNC enters a foreign market; and (2) at the intensive margin, when the MNC operates
through existing foreign affiliates.

4use US firm-level data to identify the labor substitution channel through which offshoring
to low-wage countries substitutes for domestic employment.8 Similarly, we investigate
the labor substitution channel via MNCs’ internal networks (i.e., the intensive margin),but break new ground on this important channel by exploiting unanticipated labor market policy changes to identify the mechanism.9
The paper is organized as follows. Section 2 provide some background, in which wehighlight the unique setting of a quasi-experimental design in Korea. Section 3 introduces the sample data for our main analysis. Section 4 introduces the estimation models
and presents the main empirical findings. Section 5 presents evidence on the production reallocation mechanism. Section 6 examines potential alternative drivers of the main
empirical findings. Section 7 concludes.

2

Background2.1

An Unforeseen Change in Labor Market Policy

Stringent labor market regulations had long been pointed to as a major factor hindering
the competitiveness of the Korean economy. According to the Global Competitiveness
Report (Schwab and Sala-i Martin, 2015), Korea was once ranked 115 out of 140 countries
for the efficiency of its hiring and firing practices. Against that background, President
Park Geun-hye’s administration pursued increasing labor market flexibility after taking
office in 2013.
Unexpectedly, however, the impeachment of President Park from December 2016 to
March 2017 due to a series of scandals, followed by the regime change in two months,
led to more rigid labor market policies in the coming years.10 Elected as Park’s successor on May 9, 2017, and inaugurated just one day later, President Moon Jae-in took a
distinctly different labor policy stance from that of the previous government. The newadministration quickly proposed guidelines for converting irregular workers to regular
workers in July 2017 and an annual conversion plan three months later.11 The govern8

Harrison and McMillan (2011) also explored a complementary effect whereby foreign and domestic
employment are complements for firms with significantly different tasks at home and abroad. They foundthat the overall effect is a small decline in manufacturing employment.
9
Several other earlier studies failed to find any impact on home employment through MNC operations
in low-wage locations: Slaughter (2000) in US industries and Konings and Murphy (2006) using European
multinationals. Although these earlier studies explored the intensive margin channel of MNCs, the unit ofanalysis is broad (i.e., industry) and the estimation strategy does not fully address endogeneity issues.
10
The Encyclopedia Britannica (www.britannica.com/biography/Park-Geun-Hye) provides a detailed
description about the impeachment and related events.
11
Although the plan was supposed to apply only to workers in the public sector, moral suasion encour-5

ment also pushed for a revision of the Labor Standards Act, which includes reducing
maximum working hours from 68 hours to 52 hours a week. The amendment was passed
in February 2018 and implemented for firms with 300 or more employees in July of thesame year.12
Yet another, and arguably much more impactful, labor policy came into force in 2017,
namely, a sharp increase in the minimum wage. Specifically, the government raised theminimum wage by nearly 30% in the two years after President Moon’s inauguration, amove that firms did not expect before December 2016. Given that a significant numberof Korean firms had foreign affiliates in 2016, we hypothesize that firms with foreign
affiliates could have mitigated the adverse labor market policy shock by adjusting theirproduction allocation between home and foreign countries, thereby amplifying negative
domestic employment effects.

2.2

Minimum Wage Increases in Korea

The minimum wage increase was one of the most prominent pledges that Moon hademphasized during his campaign for the presidency. He promised to raise the hourly
minimum wage to 10,000 won by 2020, which could only be achieved if it rose by 15.7%
per year over the next three years. In support of the president’s pledge, the Minimum
Wage Commission decided to increase the 2018 minimum wage by 16.4% on July 15,
2017.13 This announcement was well captured by Google Trends in Appendix Figure C.1,
where the search interest for the term "minimum wages" in Korea soared in July, 2017
relative to the previous period.14 Another steep increase of 10.9% in the following year,
aged the private sector to follow the same practice voluntarily.
12
Since its first implementation for large firms, the 52 working hours restriction has been sequentially
activated for firms that hire 50~299 workers in January 2020 and for the rest of small firms in July 2021.
Although this particular policy design offers a potentially useful strategy to identify the effect of the maximum working hour reduction, we note that the policy was widely perceived to be not as binding as theminimum wage policy. Moreover, one important distinction between an increase in minimum wage anda reduction in the maximum working hours is that the former aimed at boosting the income of low-wage
workers with an unambiguous consequence on the cost of employing workers while the latter targeted at
an increase in the number of jobs as firms would have no choice but to hire more workers (i.e., extensive
margins) if they needed to keep the total hours worked same.
13
The minimum wage law applies to entire establishments in Korea with a single rate. That is, the minimum wage rate in Korea does not have region, industry, or occupation variations. The minimum wage
must be determined by August 5th for the next year according to Article 8, Paragraph 1 of the Minimum
Wage Act and is effective from January 1st in the next year.
14
The search interest for the term "minimum wages" over time in Google Trends catch reasonabaly wellregarding actual minimum wage interests in Korea. When it comes to minimum wages, there are two
important months in a given year in Korea—(i) the Minimum Wage Comissions’ decision on the next year’s
minimum wage level in July; (ii) minimum wages are effective from January. In Appendix Figure C.1, we
identify such two peaks in a given year. Furthermore, the minimum wage increases were quite high in 20186

a cumulative increase of 29.1% in just two years, raised strong concern about negative
effects on employment and hence on the economy. In response, the government later
moderated its initial plan and reduced the minimum wage increase to 2.9% and 1.5% in2019 and 2020, respectively.
Figure 1 shows the minimum wage increases in 2018 and 2019. In panel (a), the yearly
increases in the nominal minimum wage are illustrated with annual inflation rates. Eachbar denotes the difference between the two – the yearly increase in the real minimum
wage. The growth rates in the nominal minimum wage in 2018 and 2019 are noticeable given the relatively stable growth rates in previous years (i.e., 2013 - 2017). In addition, low inflation rates throughout the sample period confirm that the minimum wage
growths in real terms in 2018 and 2019 were also sizable, both of which exceeded 10 percent. As a consequence, the number of directly affected employees—who received below
the new minimum wage—reached five million employees or 25% of the total employees
in 2019 (see panel (b)). Note also that the impact of the minimum wage increase was
not just confined to the directly affected workers. Higher-wage workers can be indirectly
affected by a series of minimum wage hikes to prevent wage reversals or labor unions’demand for wage increases proportional to minimum wages (Neumark and Wascher,2010).15
Panel (c) displays a comparison between the changes in Korea’s monthly minimum
wage from 2016 to 2020 and those of its top 40 FDI destination countries where minimum
wage data is available. The comparison is made using nominal exchange rates. The
figure emphasizes the significant increase in Korea’s minimum wage level over the given
period, resulting in even larger relative wage differences in 2020. All of these statistics
suggest that the minimum wage increases indeed exerted strong cost push shocks on
Korean firms.

2.3

Korean MNCs’ Foreign Affiliates

Korean firms began to actively expand foreign affiliates in the mid-1990s, and their activities abroad increased significantly in the 2000s. In particular, investment in China, whichis geographically close to Korea and has both a huge market and low-wage workforce,
had soared up until the global financial crisis. In the 2010s, however, the geographic
pattern of Korean outward FDI began to shift from China to the ASEAN countries ofand 2019, we identify four relatively high spikes from July 2017, including two announcement and two
effective events.
15
This is especially relevant in Korea where seniority-based pay systems remain the most widely used
pay schemes.

7Nominal Min. Wage
Real Min. Wage

Inflation

23
21
13

20
20

19
20

18
20

17
20

16
20

15
20

14

# of Directly Affected Employees (Million, left)% of Affected Employees (%, right)(a) Minimum Wages and Inflation Trend(b) Affected Employees by Minimum Wages

2016
2020

Georgia
Bangladesh
India
BhutanSri Lanka
Uzbekistan
Myanmar
Pakistan
Nepal
Lao PDR
Philippines
Mongolia
Viet Nam
Russian Federation
Indonesia
UkraineCambodiaBrazilColombia
Argentina
China
Peru
Thailand
Malaysia
Hungary
Czech Republic
Slovakia
PolandPortugal
United States
Spain
Japan
Korea, Republic of
CanadaFranceGermany
United Kingdom
Netherlands
New ZealandAustralia

Monthly Minimum Wage (Current US$)0500 1,000 1,500 2,000 2,500

20

13
20

20
20

19
20

18
20

17
20

16
20

15
20

14
20

20

13

2

0

2

15

2.5

4

3

17

19

3.5

4

Growth Rate (%)
6
8 10 12

4.5

14

16

5

25

Figure 1: Minimum Wages in Korea(c) Monthly Minimum Wage Change by Country
Notes: Panel (a) shows the annual growth rates of nominal minimum wages (blue) and consumer priceindex (CPI, red), respectively, of which the difference is real minimum wages (orange bar). Panel (b)reports the number of directly affected employees by the minimum wage increase along with theirpercentage share out of the total employees. The data for panels (a) and (b) are drawn from the Minimum
Wage Commission of Korea. Panel (c) compares monthly minimum wage changes (in current US dollars)
between 2016 and 2020 across Korea’s top 40 outward FDI destination countries. The data for panels (c)come from ILOSTAT.

Southeast Asia partly owing to the rapid rise in low-skilled wages in China. As of 2020,
about half of Korea’s new foreign affiliates were set up in China and Southeast Asia.
8

2014

2015

2016

China
USA & EU15

2017

2018

0

0

# of Total Employees (in thousand)
500
1,000
1,500
2,000

Share of Korean Employees (percent)
2
4
6
8
10

Figure 2: Employment Trends by Korean MNCs’ Foreign Affiliates

2019

ASEAN
Other Countries(a) Total Employees in Foreign Affiliates

China

ASEAN USA & EU15
2014
2017

2015
2018

Others20162019(b) Share of Korean Employees in Foreign Affiliates

Notes: Statistics in panels (a) and (b) are based on foreign affiliates of Korean MNCs that satisfy all of thefollowing criteria: (i) total investment by the Korean headquarters firm is at least 2 million US dollars, (ii)classified as a firm in the non-financial sector, and (iii) reliability of the survey response is verified by the
Korea Export-Import Bank, the administrator of the survey. About 60-70% of all foreign affiliates subject todata collection are included in the sample each year because of the third criterion, which requires caution
in interpretation. All data come from the Korea Export-Import Bank Foreign Direct Investment Database.

The recent trend in employment by foreign affiliates of Korean MNCs is depicted inpanels (a) and (b) in Figure 2.16 Several things stand out. First, panel (a) shows that total employment was concentrated in ASEAN countries from 2014 through 2019, the best
available period in our data at hand, and their employment share continued to rise, witha notable leap in 2018-2019. More specifically, the number of employees in the ASEAN
countries increased by about 40%, from 0.9 million in 2016 to 1.26 million in 2019.17 Panel(b) also confirms that Korea’s production bases have been moving from China to ASEAN
countries. Panel (b) further indicates that the tasks are performed primarily by local workers; Koreans employed by foreign affiliates and receiving wages similar to those paid by
their parent firm account for at best 1% of the total employees in the ASEAN countries
during this period. The two panels thus provide a strong indication of Korean MNCs’
16

Caution is needed in interpreting the statistics in panels (a) and (b) as the data cover only relatively
large foreign affiliates of Korean MNCs. See notes to Figure 2 for the details.
17
We expect, however, that the actual employment growth of foreign affiliates in the ASEAN countries
may be greater than 40% over the period because most of the small affiliates omitted from the data are likely
located in developing countries.

9increasing reliance on cheap labor in low-income countries, especially those close to thehome country, for low-skilled tasks.
The rising outward foreign investment and employment abroad over the last couple ofyears, especially in ASEAN countries, coincides with the sudden labor policy changes in
Korea, but is there a causal relationship? The answer to this question hinges critically on
whether we can find evidence that the domestic employment of Korean MNCs declined
relatively more than that of non-MNCs after the policy changes. A disproportionate drop
in domestic employment in MNCs after the labor market shock can be interpreted as production reallocation between home and abroad within their internal network. Formally,
we test whether Korean firms that already had foreign affiliates before 2017 reduced theirdomestic employment to relocate tasks abroad in response to the exogenous domestic
labor market policies. The production reallocation would take place primarily throughforeign affiliates in nearby, low-wage countries if cost saving was the key motive. A partof subsequent assessments therefore focuses on whether MNCs with foreign affiliates inthose countries experienced a steeper decline in domestic employment. Since our data set
comprises a nontrivial number of Korean firms with affiliates in China and the ASEAN
countries prior to the labor policy changes, the current economic, geographic, and policy
circumstances all provide a unique setup for our empirical test.

3

Data

Our primary data set is the Survey of Business Activities (SBA) from Statistics Korea, anannual firm-level panel survey that was first carried out in 2006. The data set covers
firms that conduct business activities in Korea as of the survey reference date, employing
at least 50 full-time employees and reporting a capital stock value of at least 300 million
Korean won (about 267,869 USD using the 2010 exchange rate). The sample size in eachyear is approximately 13,000 firms in Korea, which represent 80% of the total output of
Korea in the year 2008.
The data set provides typical firm-level variables such as employment, tangible assets,
sales, purchases, value added, wage bill, exports, and imports.18 In addition, it allows us
to identify each firm’s foreign affiliates and their locations (if any), along with equity
shares. For each firm, we can also identify the region, sector, and industry.19 The regions
18

This data set is also used by Choi and Greaney (2022). They found that foreign acquisition increases theshare of women in management positions by exploiting information on foreign ownership in the data set.
In contrast, in this paper, we utilize the information on Korean firms’ foreign affiliates.
19
Admittedly, however, one important limitation of this data set in our context is that foreign affiliatelevel information on operating activities such as employment or sales is not available.

10match 16 first-tier administrative divisions of Korea: one special city, six metropolitan
cities, eight provinces, and one special autonomous province.20 The sectors are identified
by 18 one-digit divisions based on the Korean Standard Industrial Classification.21 At amore disaggregated level, we can identify the industries with 64 two-digit divisions basedon the Korean Standard Industrial Classification.22 In the manufacturing sector, our focus
of interest, there are 24 two-digit industries.
To explore the effect of the adverse labor cost shock, such as abrupt minimum wage
increase announcement in August 2017, following a series of events associated with thepolitical scandal that started in late 2016, we restrict the sample to the years 2013 through2019, a seven-year horizon that includes four pre-period years and three post-period
years.23 Our key conjecture is that, faced with increasing labor market rigidity, including
the minimum wage hikes, Korean firms with foreign affiliates adjusted their workforcesdifferently than firms with no foreign affiliates. We therefore divide Korean firms into
two groups: (i) firms with at least one foreign affiliates in the period 2013–2016 (MNCs);and (ii) firms with no foreign affiliates (non-MNCs) during the same period. Note that asizable portion of Korean firms operated in foreign countries in 2016, which provides agood setup for our empirical analysis.24
Table 1 summarizes the geographic distribution of foreign affiliates included in ourdata set in 2016 by region and country of destination.25 In Korean manufacturing, other
Asian countries were the top destination with 3,237, or 78.9% of the 4,103 in total, followed by North America with 356 (8.68%) and Europe with 315 (7.68%). At the country level, China accounts for 1,851 subsidiaries (45.11%), followed by Vietnam with 488(11.89%), and then the USA with 343 (8.36%). Korean MNCs’ global production sharing
20

Note that, in July 2012, Sejong Special Self-Governing City was created. We classify Sejong as
Chungcheongnam Province (Sejong City’s original province before July 2012) throughout the paper.
21
From 2007 to 2016, the KSIC is based on revision 9; while, from 2017 to 2019, the KSIC is based onrevision 10. Hence, we match the two different revisions based on revision 9 throughout the paper.
22
If a firm produces multiple products across different industries, the survey identifies the industry of a
firm as the one with the highest sales value. If a firm has multiple plants, the survey identifies the firm’s
region based on its headquarters’ location.
23
Note that the baseline year is set to 2016 as the sequence of events in 2017 was totally unexpected in theeyes of firms in 2016. Hence, four pre-event periods are 2013, 2014, 2015, and 2016; three post-event periods
are 2017, 2018, and 2019.
24
In our data set, there is a total of 12,151 firms, 2,786 of which had at least one foreign affiliate in 2016(23% of the total). In the manufacturing sector, there are 5,916 firms, 1,939 of which had at least one foreign
affiliate in 2016 (33% of the total).
25
A summary of industry affiliations for Korean MNCs’ foreign affiliates is provided in Appendix Table B.1. Of the 24 manufacturing industries, "Motor Vehicles, Trailers and Semitrailers" accounts for 761
subsidiaries, or 18.55% of the 4,103 in total, followed by "Electronic Components and Computer" with 738(17.99%) and "Other Machinery and Equipment" with 359 (8.75%).

11

Table 1: Distribution of Foreign Affiliates by Region and Country, 2016
Region/Country
Panel A. Asia
China
VietnamIndonesia
India
Japan
ThailandHong Kong SAR, China
Malaysia
Philippines
Panel B. North America
United States
Panel C. Europe
PolandRussian Federation
Germany
Panel D. Latin America
MexicoBrazilPanel E. Rest of the World
Total# of Subsidiaries
3,237
1,851
488
174
165
81
80
68
60
58
356
343
315
44
40
40
161
91
39
34
4,103

Share
78.89
45.11
11.89
4.24
4.02
1.97
1.95
1.66
1.46
1.41
8.68
8.36
7.68
1.07
0.97
0.97
3.92
2.22
0.95
0.83
100.00Notes: The data comes from the Survey of Business Activities (SBA) in 2016. The sample is restricted to Korean parent firms in manufacturing. There are 1,939 Korean manufacturing firms that have 4,103 foreign affiliates. Regions areclassified into five areas (Asia, North America, Europe, Latin
America, and Rest of the World). The top 15 countries arealso presented.

is most pronounced in emerging Asian countries such as China and Vietnam.26
Although the division of Korean firms into two groups may provide new insights into
the impacts of increasing labor costs, identifying the employment effects is not straightforward. There are observable (and unobservable) differences between MNCs and nonMNCs (see Table 2). MNCs tend to be larger than non-MNCs in many dimensions, such as
the number of employees, capital stocks, sales, and purchases. MNCs also pay more, hireless production task workers, and engage in more international trade than non-MNCs.
Although these differences are well known in the international trade literature (Bernard
and Jensen, 2007; Bernard et al., 2007), it is conceivable that employment trends among
non-MNCs do not represent reasonable counterfactual employment trends for MNCs in
our empirical procedure.
26

Ahn and Park (2021, 2022) document that Korean outward FDI in emerging Asian countries is mostly
vertical, with cost saving as the main motive; FDI in advanced countries is mainly horizontal with market
penetration as the goal.

12

Table 2: MNCs and Non-MNCs Observable Characteristics, 2016
Employees (number)
Share of production workers (percent)
Wage bill (million Korean won)
Capital stocks (million Korean won)
Average wage (million Korean won)
Capital stocks per worker (million Korean won)
Sales (million Korean won)
Purchases (million Korean won)
Exports (million Korean won)
Imports (million Korean won)Observations

MNCs
602
55
46,689212,42458
197
510,629
286,926
194,215
105,913
1,939

Non MNCs
153
63
8,752
28,85953
175
73,19140,96812,653
11,0973,977

Difference449-845,814
183,5655
22437,138
245,958
181,562
94,816

Notes: The data come from the Survey of Business Activities (SBA) in 2016. The sample is restricted to Korean parent firms in manufacturing. There are a total of 5,916 Korean manufacturing firms. The values indicate the average observable characteristics for
MNCs and non MNCs.

We address this concern in four ways. First, throughout our analysis, using the panel
data, we specify two-way fixed effects regressions to adjust for unobserved firm-specificand time-specific confounders. Second, we estimate specifications with leads and lagsthat investigate whether there are any departures in employment trends between MNCs
and non MNCs prior to the minimum wage hike. Third, adopting a propensity score (PS)matching procedure, we estimate specifications that restrict the non-MNCs by matchingobservable characteristics to the MNCs. Fourth, we explore within-MNC heterogeneity
in the degree of exposure to the adverse labor cost shocks.
Table 3 provides the summary statistics used in the empirical analysis, which pools
all years from 2013 through 2019. We focus on the manufacturing sector only. There are
41,793 firm-year observations. In each year, there are about 6,000 unique firms. In PanelA, descriptive statistics of the main dependent variable (log employment) and other auxiliary dependent variables (log value added, log wage bill, log average wage, log capitalstocks and log capital per worker) are presented. In Panel B, descriptive statistics of thetime-varying firm-level control variables are presented, which can control for size (logsales), production technology (log purchase), and trade activities (exporter and importer
dummies). In Panel C, descriptive statistics of two key dimensional variables (firm andtime) are presented. Note that across all years, about 35% of firms in the data set hadforeign affiliates in the period 2013–2016.

13

Table 3: Summary Statistics

Panel A. Main Dependent Variable
Log employment
Panel B. Firm Characteristics
Log value-added
Log wage bill
Log average wage
Log capital stocks
Log capital per worker
Log sales
Log purchase
Exporter dummy
Importer dummy
Panel C. Key Independent Variables
Foreign affiliates dummy
Post dummy

Mean
(1)

SD
(2)

p1
(3)

p99
(4)

Observations
(5)

4.95

0.84

3.91

7.90

41,793

9.32
8.77
3.82
9.58
4.63
10.83
10.06
0.75
0.73

1.16
0.99
0.38
1.47
1.10
1.26
1.58
0.43
0.45

7.08
7.32
2.90
5.47
0.85
8.62
5.93
0
0

13.05
12.24
4.76
13.71
6.90
14.59
14.19
1
1

41,052
41,793
41,793
41,767
41,767
41,78940,632
41,793
41,793

0.35
0.44

0.48
0.50

0
0

1
1

41,793
41,793

Notes: The table presents mean, standard deviation (SD), and the first (p1) andninety-ninth (p99) percentiles of the main variables used in the empirical analysis. The sample is restricted to the manufacturing sector. In Panels A and B, allvariables are in real terms. In Panel C, the foreign affiliates dummy is an indicatorvariable that equals 1 if a firm i has foreign affiliates in the years 2013~2016 and 0
otherwise. Post dummy is an indicator variable that equals 1 if the time t is after
2016 (i.e., 2017, 2018, and 2019) and 0 otherwise.

4

Evidence on Firm-Level Employment Adjustments

4.1

Estimation Strategy

We are interested in the average employment evolution before and after the 2017 labormarket policy changes, with a particular focus on any differential patterns between MNCs
and non-MNCs. We thus begin by estimating respective employment trends by year forthese two groups in all sectors. Specifically, we estimate the following event study regression, first for the MNC group and then for the non-MNC group:ln Eit =X

βs × 1{s = t} + ψi + εit(1)s̸=2016

where the dependent variable, ln Eit , is the log of employment for firm i in year t. Firm
fixed effects are captured by ψi , and 1{s = t} is an indicator variable corresponding to atime indicator that equals 1 if the time (year) is t and 0 otherwise. The sample period is
2013~2019, with 2016 as the reference year. We cluster standard errors at the sector level.
The production reallocation channel is expected to be more pronounced in the manufacturing sector than the non-manufacturing sector for the following reasons. First, Ko14

rean firms’ outward FDI has been highly concentrated in manufacturing (see section 3 formore details). Second, international production fragmentation and the growth in global
value chains occurred predominantly in the manufacturing sector. Third, by their nature, domestic workers in the service sector cater mostly to domestic demands and are
not easily replaced by workers abroad. As such, estimating equation (1) separately forthe manufacturing and non-manufacturing sectors will be particularly informative in ourcontext.
However, the event study estimates may be biased if time-varying industry-specific,
region-specific, and/or industry-region-specific shocks were confounded and thus affected the MNC and non-MNC groups differently. We address such concerns by employing a difference-in-differences estimator with industry-region-year fixed effects as in
the following specification:
ln Eit = βMNCi × Postt + γi + ψjrt + εit(2)where i is a firm, j is an industry, r is a region, and t is time (i.e., year). The dependent
variable is the log of employment for firm i in year t. MNCi is an indicator variable that
equals 1 if a firm i has foreign affiliates in the years 2013~2016 and 0 otherwise. Postt is
an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017, 2018, and 2019) and
0 otherwise. γi and ψjrt capture firm fixed effects and industry-region-year fixed effects,
respectively. Standard errors are clustered at the sector level.
Although a difference-in-differences estimator can control for time-varying industryregion-specific shocks, there remains a concern about a parallel pre-trend condition—that
is, one might still worry that difference-in-differences estimation results cannot be informative if Korean MNCs with foreign affiliates would have experienced a slowdown in
employment growth even in the absence of the labor market policy changes. This would
be indeed the case if Korean firms with foreign affiliates had already been experiencing a
relative decline in employment growth even before the labor market policy changes. To
confirm that a parallel pre-trend condition holds, we check for differential employment
trends between MNCs and non-MNCs in the three years leading up to the labor market
policy changes by specifying the following regression:ln Eit =X

βs 1{s = t} × MNCi + γi + ψjrt + εit(3)s̸=2016

where 1{s = t} is an indicator variable that equals 1 if the time is t and 0 otherwise. All
the rest is the same as before.

15

Considering that a key element of our estimation approach is to compare MNCs with
non-MNCs where the latter group includes all domestic firms in our data set, the mostserious concern could be that multinational firms are substantially different from most
non-multinational firms in various dimensions as suggested in Table 2. MNCs tend to belarger, more capital- and skill-intensive, and more likely to be engaged in international
trade. Likewise, non-MNCs may hire a higher share of workers who receive less than the
new minimum wage than MNCs, which in turn may nullify our comparison approach.
To alleviate potential endogeneity issues stemming from fundamental difference between
the two groups, we further conduct a propensity score (PS) matching procedure as a robustness check, by restricting the comparison to non-MNC firms whose characteristics
are most similar to MNCs’. To obtain a proper subset of non-multinational firms, the indicator variable of MNCs is regressed on three firm-level factor compositions based on
2016 values: (log of) capital per worker, (log of) wage bill per worker, and the share of
production workers. The employment growth rate over the years 2013–2016 is included
as a control variable to reflect firm-specific employment time trends prior to the policychange. Moreover, we run this logistic regression for each 2-digit manufacturing industry so that the matching occurs only within the same industry (with replacement).27
Lastly, we acknowledge that variation around 2017 cannot be attributed entirely tolabor cost shocks. To confirm that labor cost shocks are indeed major forces behind any
differential employment responses between the two groups, we further explore any differential effect within MNCs by the degree of exposure to the adverse labor cost shocks,
which can be measured based on firm-level average domestic wage levels. The idea isthat the average wages of MNCs tend to reflect the degree of exposure to the labor costshock, albeit not completely accurate, since a firm with a lower level of average wages ismore likely to have a higher share of its workers directly impacted by the minimum wage
increase. Specifically, we divide all MNCs in the sample into four quartiles based on theiraverage wages during the pre-treatment period. Then, we use each quartile group of
MNCs together with non-MNCs and estimate equation (3) separately. If a lower averagewage MNC group shows more employment reduction than a higher average-wage MNCgroup after the event, then this can be highly indicative of a labor cost shock effect thanother potentially confounding factors.28
27

As some industries have only a few active firms, exact matching of industries is not feasible. We incorporated these industries into industry groupings that produce similar goods (e.g., we merged the beverageindustry with the food industry). As a result, the number of industries used for the matching was reduced
from 23 to 15.
28
We further leverage the location information of MNCs’ foregin affiliates to test whether an adverse
labor cost shock, rather than other confouding factors, was main reason behind the employment declineof MNCs in Section 5.2. To do so, we classify Korean MNCs into four quartiles according to the level

16

4.2

Estimation Results

Figure 3 plots the βs coefficients from the event study estimates of equation (1), separately for Korean firms with foreign affiliates (in blue) and Korean firms with no foreign
affiliates (in red). We do not detect differential pretrends between the two groups as thelead coefficients up to the year 2016 are all statistically insignificant for both groups. Following the minimum wage announcement in July 2017, employment levels among thenon-MNC group remained almost the same through the year 2019. On the contrary, employment in the MNC group declined by 1.0% in 2017, 3.4% in 2018, and 4.1% in 2019.
These differences suggest that the MNCs may have reduced domestic labor in Korea andreplaced those domestic workers with foreign factors of production associated with theirsubsidiaries in foreign countries. These results are inconsistent with the traditional employment response channel that would predict a decline in employment for non-MNCsrelative to MNCs.

.02
0-.08 -.06 -.04 -.02

Log Employment

.04

.06

.08

Figure 3: Event Study Analysis: All Sectors

2013

2014

2015

2016

MNCs

2017

2018

2019

Non MNCs

Notes: The figure plots event study analysis results from equation (1) where the dependent variable is thelog of employment. The results are illustrated separately for firms with foreign affiliates (blue circles) andfirms with no foreign affiliates (red circles). Coefficients along with 95% confidence intervals areillustrated. Standard errors are clustered at the sector level.

Figure 4 repeats the event study regression in equation (1) by sector. Panel (a) reports
the results in the manufacturing. As in the previous analysis, we do not find differential
pretrends between the two groups. More importantly, for the MNCs, the employmentof (weighted) average wages in the countries where their foreign affiliates operate. Then, we investigate
heterogeneous employment responses within MNCs.

17

.08
2015
MNCs

2016

2017

2018

2019

.02

.04
2014

0

Log Employment
2013-.08 -.06 -.04 -.02.02
0-.08 -.06 -.04 -.02

Log Employment

.04

.06

.06

.08

Figure 4: Event Study Analysis: Manufacturing and Others

2013

Non MNCs

2014

2015
MNCs

(a) Manufacturing

2016

2017

2018

2019

Non MNCs(b) Non-Manufacturing

Notes: The figure plots event study analysis results from equation (1) where the dependent variable is thelog of employment. In panel (a), the results are illustrated for the manufacturing sector; in panel (b), theresults are illustrated for the non-manufacturing sector. Coefficients along with 95% confidence intervals
are illustrated. Standard errors are clustered at the industry level.

levels dropped by 2.2% in 2017, 4.9% in 2018, and 5.1% in 2019, respectively.29 In contrast,
we do not find any downward trends in employment after 2017 for both groups in the
non-manufacturing sector. Indeed, none of the coefficient estimates in panel (b) is statistically significant. Taken together, the event study results appear to support the presenceof the production reallocation channel in the manufacturing sector. Our analysis thus
focuses on the manufacturing sector henceforth.
Table 4 presents the estimates of equation (2). We include firm fixed effects only incolumn (1) and add year fixed effects, region-year fixed effects, and industry-region-year
fixed effects in columns (2) through (4), respectively. Across all columns, we find negative coefficients with statistical significance at the 10% level. In column (4), the preferred
specification, the coefficient estimate suggests that employment by the MNC group is
3.0% lower than by the non-MNC group.30 In Appendix Table B.2, we repeat the analysis
in Table 4 using the non-manufacturing group only and find that the treatment effect isstatistically insignificant.
29

Note further that when we restrict our sample to the manufacturing sector, the coefficient of the year
indicator 2017 becomes significant, unlike when we use all sectors.
30
We estimate equation (2) with a common linear time trend, excluding time fixed effects, and find thatthe coefficient of the common linear time trend is statistically indistinguishable from zero. We cautiouslyinterpret the absence of the common linear trend as an indication that employment level for the non-MNCs
group remained the same while the one for the MNCs group declined by 3.0%, corroborating the event
study result in panel (a) of Figure 4.

18

Table 4: MNCs and Employment in Manufacturing, 2013~2019

MNCi × Postt
Observations
R-squared
Fixed Effects:
Firm
Year
Region-Year
Industry-Region-Year

Dependent Variable: Log of Employment
(1)
(2)
(3)
(4)
-0.0325* -0.0312** -0.0329*** -0.0295***(0.0158) (0.0114)(0.0117)(0.0101)
41,03641,03641,03541,0300.95020.95020.95070.9541
Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign
affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicatorvariable that equals 1 if the time t is after 2016 (i.e., 2017, 2018, and 2019)
and 0 otherwise. Standard errors are clustered at the industry level. ***p<0.01, ** p<0.05, * p<0.1.

.02
0-.08 -.06 -.04 -.02

Log Employment

.04

.06

.08

Figure 5: Dynamic Treatment Effects: Manufacturing

2013

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects

Notes: Manufacturing only. The figure plots coefficient estimates on βs ’s from the dynamic treatment
effects analysis as specified in equation (3) where the dependent variable is the log of employment.

Figure 5 plots the coefficient estimates from regressions in equation (3) with lead indicators for the three years leading up to the year 2016 and lag indicators for the three
years after 2016. We do not detect any differential pretrends in that employment growth
for the two groups shows a similar trend leading up to 2016. Moreover, the effect of theadverse labor cost shock increases grew over time: following the minimum wage changeannouncement in July 2017, the employment drops by 2 percent immediately. In the year
2018, the first effective year of Moon Jae-in’s regime, employment declines by 4.5%; it

19then drops by 5.3% in 2019.31

2014

2015
MNCs

2016

2017

2018

2019

.02 .04 .06 .08
0

Log Employment
2013-.1 -.08 -.06 -.04 -.02.02
0-.08 -.06 -.04 -.02

Log Employment

.04

.06

.08

Figure 6: Estimation with Propensity-Score Matched Sample

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(a) Event Study Analysis(b) Dynamic Treatment Effects

Notes: Manufacturing only. Each subfigure is analogous to Figure 4-(a) and Figure 5, respectively, but now
with the PS matched sample and frequency-weighted regressions.

For further analyses, regarding the PS matching process, the estimated propensity
scores are used to select the single nearest match for each MNC under the common support restriction.32 Table B.3 reports mean and median values of the matching variables
by group. In all characteristics, the matched non-MNCs are now more similar to MNCs
than the other non-MNCs. Panels (a) and (b) of Figure 6 show the estimation results ofequations (1) and (3), respectively, with the PS matched sample. We use the frequencybased weighted regressions to account for the duplicated non-MNC observations, sincenon-MNCs can be chosen multiple times with replacement. The average employment
response of the matched non-MNCs in (a) does not look different from either the MNCgroup or the entire non-MNC group (see Figure 4-(a)) up to 2016; we do observe slight(statistically insignificant) increases in employment from 2017 through 2019. The gradual
employment growth among the matched non-MNCs is reflected in panel (b), highlightingthat the estimates over the post-policy period are even larger in absolute terms.
Moving on to the differential effects within MNCs, the findings illustrated in Figure 7
31
In Appendix Figure C.2, we estimate equation (3) in non-manufacturing firms only and find no negative
employment effects.
32
We applied various methods to check the sensitivity of PS matching and subsequent estimation results,
including the selection of the five nearest neighbors (instead of one), no replacement of once selected match,without the common support restriction, or using the average values of matching variables over the prepolicy period (2013~2016) instead of 2016 values. Our estimation results do not change qualitatively in any
method.

20

reveal a clear order in the magnitude of the effect: MNCs in the lowest wage quartile,which were supposed to be hit the hardest by the labor cost shock, reduced their workforce the most. On the other hand, the highest average-wage MNC group did not makeany significant adjustment in employment over the post-treatment period. By comparing
average-wage variation within MNC groups, the result further supports that a labor costshock, rather than other potentially confounding factors, was responsible for the employment reduction of MNCs. Overall, the set of our baseline results strongly suggests that
it is the policy-driven adverse labor cost shock that mostly, if not all, caused the relative
employment reduction of MNCs after 2017.

.03
0-.12 -.09 -.06 -.03

Log Employment

.06

.09

.12

Figure 7: Dynamic Treatment Effects by MNCs’ Domestic Wage Quartiles

2013

2014

2015
Q1

2016
Q2

2017
Q3

2018

2019
Q4

Notes: The figure plots the dynamic effects results from equation (3). Q1—Q4 refers to the quartile groupof MNCs according to their domestic average wages (over the pre-shock period). Coefficients along with
95% confidence intervals are illustrated.

5

MNCs’ Production Reallocation Abroad

So far, we have confirmed robust findings that, in response to the abrupt labor market
policy changes, Korean manufacturing firms with foreign affiliates reduced domestic employment more than those without foreign affiliates. We now investigate the specificmechanisms responsible for our findings. In particular, we hypothesize that the negative employment effects are associated with production reallocation between home andabroad within MNCs’ internal networks.
In 2016, about one-third of the Korean manufacturing firms in our sample had foreign affiliates, and the majority of foreign affiliates were located in East Asian countries
21

whose comparative advantage lies in an abundance of cheap labor, as shown earlier inTable 1 and Figure 1-(d). As long as an increase in labor costs associated with changes
in labor market policies in Korea implies a decrease in relative production costs abroad,
it is likely to have induced Korean MNCs to save costs by reducing domestic production
tasks through production reallocation: for example, shifting labor intensive production
tasks toward existing foreign affiliates, while concentrating on knowledge intensive nonproduction tasks such as R&D activities at home.33
In this section, we test this mechanism (MNCs’ production reallocation abroad) indetail as follows. First, if production task reallocation occurred, then we would expect
to see a reduction in domestic tasks in addition to a decline in employment. Second, thenegative employment effects should be most pronounced between parent-foreign affiliate
pairs that perform similar tasks and/or those firms whose subsidiaries were located in
nearby, low-wage countries close to Korea (e.g., China and the ASEAN members). In
both cases, firms would be relatively easier to reallocate production tasks in responseto the increases in labor cost. Lastly, in response to adverse labor cost shocks at home,
Korean MNCs are likely to expand such vertical FDI operations by substituting physicaldomestic in-house intermediates for imported intermediates from their foreign affiliates,
implying an increase in the share of intra-firm imports in total imports. This will be
particularly more pronounced for MNCs hit harder or whose affiliates are located in lowwage countries.

5.1

Reducing Production Tasks in the Domestic Market

To investigate the task substitution channel, we first check whether Korean MNCs’ domestic production contracted more than other firms as a result of the task reallocation toforeign affiliates in response to the labor market policy changes. For this, we replace thedependent variable in equation (2) with real value added in the log, the results of which
are summarized in Table 5. As earlier, we begin by including firm fixed effects only, andadding year fixed effects, region-year fixed effects, and industry-region- year fixed effects,
respectively, from columns (1) through (4). In column (4), the preferred specification, thecoefficient estimate is -0.0459 and statistically significant at the 5% level. Quantitatively,
the real value added of the MNC group contracted by 4.6% more than that of the nonMNC group. This finding corroborates our earlier finding that Korean MNCs reduced
domestic employment more than other Korean firms by reallocating tasks to foreign af33

MNCs with foreign production networks will find it less costly to reallocate production abroad thanfirms without any foreign affiliate because they do not need to incur additional fixed costs to set up new
plants (Markusen, 1984; Helpman, Melitz and Yeaple, 2004).

22

filiates, which would have also led to a disproportionate drop in domestic production
among Korean MNCs.34
Table 5: MNCs and Value Added in Manufacturing, 2013~2019

MNCi × Postt
Observations
R-squared
Fixed Effects:
Firm
Year
Region-Year
Industry-Region-Year

Dependent Variable: Log of Real Value Added
(1)
(2)
(3)
(4)
0.0083-0.0409* -0.0416*-0.0459**(0.0239) (0.0207) (0.0205)(0.0208)
40,27540,27540,27540,2700.90450.90570.90630.9137
Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign
affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017, 2018, and 2019) and 0
otherwise. Standard errors are clustered at the industry level. *** p<0.01, **p<0.05, * p<0.1.

5.2

Production Networks in Low-Wage, Nearby Countries

One notable pattern of Korean manufacturing over the past three decades has been therelocation of manufacturing to a host of emerging Asian countries such as China and Vietnam as Korean firms have sought relatively lower costs in labor inputs. Korean MNCs
have already set up a large number of manufacturing plants in those emerging Asian
countries.35 Indeed, according to our sample data, there were 1,851 manufacturing subsidiaries (45.11% of the total) operating in China and 488 subsidiaries (11.89% of the total)
in Vietnam, respectively (see Table 1). Unlike foreign affiliates in the more developed
countries of Europe and in the US and Japan, the main incentive for foreign direct investment in “Factory Asia” is often to leverage inexpensive labor costs. Faced with the more
stringent labor market policies, we would therefore expect Korean MNCs with foreign
affiliates in emerging Asian countries to reduce domestic employment (and production)
more than those with foreign affiliates in other countries. We thus make the most of thedata set that allows us to identify the geographic location of foreign affiliates.
To investigate the task reallocation channel, we first define MNCi as an indicator variable that equals 1 if a firm i has foreign affiliates in a specific country in the years 2013~2016
34

Appendix Figure C.3 illustrates corresponding event study analyses and dynamic treatment effects,
confirming the robustness of the results summarized in Table 5.
35
Samsung’s smartphone factories in a northern province of Vietnam is a good example.

23

Table 6: MNCs and Employment by Country in Manufacturing, 2013~2019
Panel A.
MNCi × Postt
Observations
R-squared
Panel B.
MNCi × Postt
Observations
R-squared
Panel C.
MNCi × Postt
Observations
R-squared
Fixed Effects:
Firm
Industry-Region-Year

Dependent Variable: Log of Employment
China
VietnamJapan
Other Asia
(1)
(2)
(3)
(4)
-0.0362***-0.0617***-0.0205-0.0363**(0.0120)(0.0215)(0.0284)(0.0147)
36,06029,36527,82931,7550.95380.94730.94930.9566
EuropeNorth America Latin America
ROW
(5)
(6)
(7)
(8)
-0.0127-0.0181-0.0014-0.0552(0.0197)(0.0128)(0.0243)(0.0586)
28,85530,962
27,573
26,7920.9591
0.95880.95380.9488
Q1
Q2
Q3
Q4
(9)
(10)
(11)
(12)
-0.0608***-0.0269**-0.0201-0.0067(0.0173)(0.0116)(0.0132)(0.0191)
29,864
31,14929,66929,3480.93680.9386
0.95370.9472
Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign affiliates inthe years 2013~2016 and 0 otherwise. In each column, foreign affiliates are restricted tothe specific country or wage group. For instance, in column (1), MNCi refers to foreign
affiliates in China. Q1 ~Q4 in columns (9) through (12) refers to the quartile group of
MNCs according to the level of (weighted) average wages in the countries where theirforeign affiliates operate, respectively. Postt is an indicator variable that equals 1 if thetime t is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

and 0 for non-MNCs. Here, we have eight different countries (or regions) as follows:
China, Vietnam, Japan, Other Asia, Europe, North America, Latin America, and Rest of
the World. We then estimate equation (2) separately for each one. Columns (1) through(8) in Table 6 show the estimation results. In each column, we include firm and industryregion-year fixed effects. Throughout the eight columns, we found patterns that are exactly aligned with the employment adjustment channel through GVCs in Factory Asia.
For the emerging Asian countries, such as China, Vietnam, and Other Asia, the coefficient estimates are -0.0362, -0.0617, and -0.0363, respectively, and they are all statisticallysignificant at the 5% level. In stark contrast, for other countries/regions (Japan, Europe,
North America, Latin America, and Rest of the World), all the coefficient estimates are
statistically insignificant.36
36

We provide event study analyses and dynamic treatment effects corresponding to Panels A. and B. of
Table 6 in Appendix Figures C.4, C.5, and C.6.

24

.12
2014

2015
Q1

2016
Q2

2017

2018

Q3

2019

.03
0

Log Employment
2013-.12 -.09 -.06 -.03

0
-.03
-.06
-.12

-.09

Log Employment

.03

.06

.06

.09

.09

Figure 8: Heterogeneous Responses of MNCs by Wage Quartiles of Foreign Affiliates

2013

Q4

2014

2015
Q1

(a) Event Study Coefficients

2016
Q2

2017
Q3

2018

2019
Q4

(b) Dynamic Treatments

Notes: The sample is restricted to the manufacturing sector only. The left figure plots event study analysis
results from equation (1). The right figure plots dynamic treatment effects results from equation (3).
Q1~Q4 in panels (a) and (b) refer to the quartile group of MNCs according to the level of (weighted)
average wages in the countries where their foreign affiliates operate. Coefficients along with 95%
confidence intervals are illustrated.

Alternatively, we look at whether the MNCs reduced their employment when theirforeign affiliates operate in low-wage countries, regardless of the geographic location.
To do so, we classify the Korean MNCs into four quartiles according to the level of(weighted) average wages in the countries where their foreign affiliates operate.37 Eachquartile group of the MNCs is then separately compared to the common non-MNC groupthrough the same equation (2). The estimation results are presented in column (9) through(12) in Table 6. Consistent with our conjecture, the negative employment effect is thelargest at -0.0608 when the foreign affiliates are located in the lowest-wage countries (i.e.,
the first quartile). Both economic and statistic significances reduce gradually to nil as thewages in host countries rise. Figure 8 shows event study analyses and dynamic treatment
effects corresponding to Panel C. of Table 6.
In Table B.4, we also show that the value added, a proxy for domestic production activities, declined the most for the emerging Asian countries (China, Vietnam, and Other
Asia) or the lowest-wage countries. The results further corroborate our key interpretationthat the employment adjustment is mainly driven by already established foreign production networks in low-wage countries near Korea.
37

The wages in each host country of foreign subsidiaries are measured by the average monthly earningsof employees in all industries (converted to 2017 US$ PPP; data source: ilostat.ilo.org/topics/wages). If
an MNC has multiple foreign subsidiaries in different countries, the weighted average of the wages iscalculated where the weight is the equity share value of Korean MNC for its subsidiary.

25

5.3

Product Proximity in Parent-Subsidiary Pairs

If the production reallocation channel played a dominant role in generating our baseline results, then the employment effects should also be more noticeable in Korean firms
whose foreign affiliates are in the same sector (or same industry). As an example, supposethat Korean manufacturing firm A has a foreign affiliate in the manufacturing sector andKorean manufacturing firm B has a foreign affiliate in a non-manufacturing sector. We
would expect within-firm production reshuffling to be more likely for firm A than for firm
B because the product produced by firm A and its foreign affiliate are more similar thanthose by firm B and its foreign affiliate (i.e., product proximity in the parent-subsidiarypairs is closer in firm A than in firm B).
Table 7: MNCs and Product Proximity in Manufacturing, 2013~2019

MNCi × Postt
MNC(Same SEC)i × Postt
MNC(Same IND)i × Postt
Observations
R-squared
Fixed Effects:
Firm
Industry-Region-Year

Dependent Variable: Log of Employment
Baseline
Same Sector
Same Industry
(1)
(2)
(3)
(4)
(5)
-0.0295***
0.0254-0.0128(0.0101)(0.0168)(0.0135)-0.0414*** -0.0644***(0.0104)(0.0167)-0.0355*** -0.0250*(0.0101)(0.0132)
41,03041,03041,03041,03041,0300.9541
0.9542
0.9542
0.9541
0.9541
Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign affiliates in theyears 2013~2016 and 0 otherwise. MNC(Same SEC)i is an indicator variable that equals
1 if a firm i’s foreign affiliate is in the same sector (i.e. manufacturing) and 0 otherwise.
MNC(Same IND)i is an indicator variable that equals 1 if a firm i’s foreign affiliate is in
the same industry and 0 otherwise. Postt is an indicator variable that equals 1 if the time
t is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are clustered at
the industry level. *** p<0.01, ** p<0.05, * p<0.1.

To examine the role of product proximity, we define MNC(Same SEC)i as an indicatorvariable that equals 1 if a firm i’s foreign affiliate is in the same sector (i.e., manufacturing) and 0 otherwise. We further define MNC(Same IND)i as an indicator variable that
equals 1 if a firm i’s foreign affiliate is in the same industry (i.e., 2-digit manufacturingindustry) and 0 otherwise. We then modify equation (2) with an alternative interaction
term (MNC(Same SEC)i × Postt ). Next, we include both interaction terms (MNCi × Postt )and (MNC(Same SEC)i × Postt ) together. We repeat the analyses using the same industry
criterion.38
38

Note that the MNC(Same SEC)i and MNC(Same IND)i variables equal 1 only if a firm has a foreign

26

Table 7 shows the estimation results. Column (1) shows the benchmark case as in column (4) of Table 4. In column (2), with an alternative interaction term (MNC(Same SEC)i ×
Postt ), the employment effect becomes larger such that the coefficient estimate is -0.0414
with statistical significance. The result is consistent with the product proximity channel, wherein Korean manufacturing firms with same-sector foreign affiliates experienced
a larger employment reduction than other Korean manufacturing firms. In column (3),
we further separate employment effects into those for Korean manufacturing firms withforeign affiliates in non-manufacturing and those for Korean manufacturing firms withforeign affiliates in manufacturing. We find that the domestic employment effects fromour baseline results are exclusively driven by Korean manufacturing firms with manufacturing foreign affiliates. In columns (4) and (5), we repeat the analysis with a stricter
criterion and find a similar pattern.39 Taken together, we conclude that Korean manufacturing firms responded to domestic labor cost shocks by substituting production tasks
through foreign affiliates producing similar products.

5.4

Intra-firm Imports

Korean MNCs tend to engage in vertical FDI especially at ASEAN locations, importingintermediate inputs and services from foreign affiliates for downstream assembly in Korea (Ahn and Park, 2021, 2022). To the extent that adverse labor cost shocks triggered
cost-saving motives thereby shifting domestic tasks of input productions toward vertically integrated foreign subsidiaries, an expansion of vertical FDI would be consistent
with an increase in the share of intra-firm imports in total imports as it would lead to anincrease in intra-firm imports (sourcing inputs from foreign affiliates instead of own local production), particularly more pronounced for MNCs whose subsidiaries are locatedin low wage countries relative to Korea or those that are more exposed to the adverse
labor cost shocks. The former is measured by the level of (weighted) average wages in
the countries where their foreign affiliates operate, while the latter is measured based on
average domestic wage levels (i.e., the lower the average domestic wage level, the higher
the share of minimum wage workers, and hence more sensitive to minimum wage hikes).
Table 8 summarizes our regression results where the dependent variable is the shareof intra-firm imports in total imports. Columns (1) and (2) consider firm-level heterogeneity in (weighted) average wage levels in host countries, columns (3) and (4) consider
firm-level heterogeneity in domestic average wage levels, and columns (5) and (6) conaffiliate.
39
Appendix Figure C.7 illustrates corresponding event study analyses and dynamic treatment effects,
confirming the robustness of the results summarized in Table 7.

27

Table 8: Intrafirm Imports in Manufacturing, 2013~2019
Dep. Variable: Share of Intrafirm Imports in Total Purchases (%)
(1)
(2)
(3)
(4)
(5)
(6)
log Host Country Wagei × Postt-1.1128***(0.2728)-1.0154***(0.2364)log MNC Average Wagei × Postt
Observations
R-squared
Fixed Effects:
Firm
Region-Year
Indudstry-Region-Year-0.9630*(0.4712)-0.9336*(0.4587)-1.0846***(0.2726)-0.8631*(0.4637)-0.9878***(0.2405)-0.8373*(0.4739)

14,2980.4182

14,1050.4705

14,3170.417814,1240.4702

14,2980.4183

14,1050.4706

Yes
Yes
No

Yes
No
Yes

Yes
Yes
No

Yes
No
Yes

Yes
Yes
No

Yes
No
Yes

Notes: The sample is restricted to the MNCs in manufacturing sector only. Host Country Wagei is(weighted) average wages of the host countries where the MNC’s affiliates operate during the pre-policy
period. MNC Average Wagei is the average wage bill of MNC in South Korea. Postt is an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are
clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

sider both at the same time. Overall, the results confirm that MNCs whose subsidiaries
are located in lower wage countries or who are more exposed to the adverse labor costs
shocks experienced an increase in the share of intra-firm imports relative to other MNCs,
consistent with an expansion of vertical FDI hypothesis.

6

Alternative Mechanisms

In this section, we explore the consistency of our main results with alternative mechanisms: first, we examine whether Korean MNCs replaced domestic labor with either domestic capital or domestic labor with different skill levels within domestic entities; next,
we investigate whether foreign demand shocks can explain the observed patterns of negative domestic employment effects for Korean MNCs.

6.1

Labor-Capital Substitution

It is possible that, instead of reallocating tasks abroad, Korean MNCs replaced domestic
labor with domestic capital in their home operations. Exploiting regional variation inminimum wage changes in China between 2002 and 2008, Hau et al. (2020) found thatminimum wage hikes accelerated the input substitution of capital for labor in low-wage
Chinese firms. If employment adjustment occurs through domestic capital instead offoreign linkages, then we would expect capital trajectories for the MNC group after the28

2017 policy change to diverge from those of the non-MNC group. More precisely, if there
had been input substitution of capital for labor within firms, we would expect the capitaleffects to be positive or zero for Korean firms with foreign affiliates.
Table 9: MNCs and Capital in Manufacturing, 2013~2019
Panel A. Dependent Variable: Log of Real Capital Stocks
(1)
(2)
(3)
(4)
MNCi × Postt
0.0075-0.0598** -0.0587** -0.0525**(0.0279) (0.0267)(0.0272)(0.0241)
Observations
41,00941,00941,00841,003R-squared
0.9456
0.94610.94640.9493
Panel B. Dependent Variable: Log of Real Capital Stocks per Worker
(5)
(6)
(7)
(8)
MNCi × Postt
0.0401**-0.0282-0.0253-0.0226(0.0184) (0.0232)(0.0233)(0.0233)
Observations
41,00941,00941,00841,003R-squared
0.90220.90300.90350.9078Fixed Effects:
Firm
Yes
Yes
Yes
Yes
Year
No
Yes
No
No
Region-Year
No
No
Yes
No
Industry-Region-Year
No
No
No
Yes
Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017, 2018,and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

To investigate the validity of labor-capital substitution within Korean MNCs, we modify equation (2) by replacing the dependent variable with real capital stock or with realcapital stocks per worker. Analogous to the baseline analysis, we begin by including firm
fixed effects only, and adding year fixed effects, region-year fixed effects, and industryregion-year fixed effects, respectively, in columns (1) through (4) in Panel A of Table 9. Incolumn (4), the preferred specification, the coefficient estimate is 0.0525 and statisticallysignificant. Quantitatively, it implies that capital stocks of the Korean firms with foreign
affiliates declined on average by 5.3% more than those of the non-MNC group, invalidating the idea that there was labor-capital substitution within Korean MNCs. Panel B
of Table 9 summarizes estimation results with capital stocks per worker as a dependent
variable. In column (8), the coefficient estimate is -0.0226 and statistically insignificant,suggesting that Korean MNCs do not appear to have replaced labor with capital.40
40

In Appendix Figure C.8, the event study analyses and the dynamic treatment effects are presented,
which corresponds to Table 9.

29

6.2

Skilled-Unskilled Labor Substitution

It has been well documented that minimum wage changes can change the within-firmskill mix of workers (Fairris and Bujanda, 2008; Giuliano, 2013; Clemens and Wither, 2019;
Clemens et al., 2021). According to the literature, firms can respond to minimum wage
increases by switching their personnel policies from low-skilled labor to higher-skilledlabor. To the extent that an increase in domestic high-skilled workers was more thanoffset by a decrease in domestic low-skilled workers among Korean MNCs, our baseline
findings could be potentially consistent with such labor-labor substitution within Korean
MNCs. In other words, since wages should be lower for low-skilled workers than for
high-skilled workers, the observed decline in domestic employment could reflect a workforce reorganization within Korean MNCs. In the absence of worker-level data, we cannot
directly test the validity of this hypothesis. Nevertheless, we are able to compare the average wage—defined as total wage bill divided by the number of total workers at the firmlevel—of the MNC group with that of the non-MNC group, whereby a relative increasein the average wage of the MNCs would be potentially consistent with the labor-labor
substitution within Korean MNCs.
Table 10: MNCs and Wage Bill in Manufacturing, 2013~2019
Panel A. Dependent Variable: Log of Average Wage
(1)
(2)
(3)
(4)
MNCi × Postt
0.0661***-0.0093-0.0094-0.0082(0.0070)(0.0075)(0.0074)(0.0068)
Observations
41,03641,03641,03541,030R-squared
0.72270.73100.73230.7431Panel B. Dependent Variable: Log of Real Wage Bill
(5)
(6)
(7)
(8)
MNCi × Postt
0.0336*-0.0405** -0.0423** -0.0377***(0.0193)(0.0163)(0.0165)(0.0130)
Observations
41,03641,03641,03541,030R-squared
0.95350.9546
0.9551
0.9583Fixed Effects:
Firm
Yes
Yes
Yes
Yes
Year
No
Yes
No
No
Region-Year
No
No
Yes
No
Industry-Region-Year
No
No
No
Yes
Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign
affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicatorvariable that equals 1 if the time t is after 2016 (i.e., 2017, 2018, and 2019)
and 0 otherwise. Standard errors are clustered at the industry level. ***p<0.01, ** p<0.05, * p<0.1.

To examine the validity of labor-labor substitution within Korean MNCs, we estimate
equation (2) with average wages in log as a new dependent variable. Similarly to the30main analysis, we begin by including firm fixed effects only, and adding year fixed effects,
region-year fixed effects, and industry-region-year fixed effects, respectively, in columns(1) through (4) in Panel A of Table 10. In column (4), the preferred specification, the coefficient estimate is -0.0082 and statistically insignificant, invalidating the idea that there
was labor-labor substitution within Korean MNCs. Replacing the dependent variable inequation (2) with real wage bills in log, Panel B of Table 10 summarizes estimation results, whereby column (8), the preferred specification, shows that the coefficient estimate
is -0.0377 and statistically significant at the 1% level. Quantitatively, the real wage billof the Korean firms with foreign affiliates declined by 3.8% more than that of the Korean
firms with no foreign affiliates, which is contrary to what the labor-labor substitution
hypothesis would predict for Korean MNCs.41

6.3

Foreign Demand Shocks

Global trade has been sluggish since the recovery from the global financial crisis (Bemset al., 2013; Boz et al., 2015; Aslam et al., 2018; Shin, 2019; Constantinescu et al., 2020).
If global trade slowdown reduced overall global demand, then Korean MNCs may beaffected by external demand shocks and thus reduced employment. Therefore it is plausible to hypothesize that our baseline findings simply reflect the fact that weak global
demand hit Korean MNCs harder than other Korean firms.
To check for this possibility, we first restrict the sample to firms without any foreign
affiliates in the period 2013~2016 to eliminate the labor reallocation channel via internal foreign network. Then we divide the remainder of the sample into two groups: (i)Korean firms with no foreign affiliates and no exporting during the period 2013~2016,
which should have been less vulnerable to external demand shocks; and (ii) Korean firms
with no foreign affiliates but with exporting during the period 2013~2016, which shouldhave been more vulnerable to external demand shocks.42 The corresponding estimation
equation is as follows:ln Eit = βExporteri × Postt + γi + ψjrt + εit(4)where Exporteri is an indicator variable that equals 1 if a firm i conducts exporting in theyears 2013~2016 and 0 otherwise. All the rest is the same as before. If foreign demand
41

In Appendix Figure C.9, the event study analyses and the dynamic treatment effects are presented,
which corresponds to Table 10.
42Here, we classify firms as exporters if they reported positive amounts of exports at least once in the
period between 2013 and 2016.

31

shocks had driven the negative employment effects for Korean MNCs, then we would
expect to see the coefficient estimate of β in equation (4) to be negative.
Table 11: Exporters and Employment in Manufacturing, 2013~2019

Exporteri × Postt
Observations
R-squared
Fixed Effects:
Firm
Year
Region-Year
Industry-Region-Year

Dependent Variable: Log of Employment
(1)
(2)
(3)
(4)
0.00460.0293** 0.0264*
0.0239(0.0106) (0.0141) (0.0135)(0.0148)
26,36426,36426,364
26,3380.92670.92670.9276
0.9342
Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Notes: The sample is restricted to firms without foreign affiliates in
2013~2016. Exporteri is an indicator variable that equals 1 if a firm iconducts exporting in the years 2013~2016 and 0 otherwise. Postt is
an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017,2018, and 2019) and 0 otherwise. Standard errors are clustered at theindustry level. *** p<0.01, ** p<0.05, * p<0.1.

Table 11 shows the estimation results. Across all columns, the coefficient estimates
are positive. In column (4), our preferred specification, the coefficient estimate is positiveand statistically insignificant, implying that foreign demand shocks are less likely to haveplayed a role.

7

Conclusion

As the role of MNCs in the economy grows, it is increasingly important to reassess theeffects of not only trade policies but also other domestic policies from a transnational
perspective. Still, the focus of trade researchers has tended to be limited to trade policy,
and labor studies rarely have considered the prevalence of MNCs in their evaluation ofdomestic labor policy. This paper addresses one of such underexplored links between
the two research areas by investigating how MNCs adjust their employment differently
from domestic firms in response to an unexpected, radical minimum wage hike in Korea.
Contrary to the conventional wisdom that MNCs would be the least likely to be affectedby minimum wage increase, we find strong evidence of their relative reduction of employment by about 3% after the policy shock compared to non-MNCs. Further analyses
reveal that MNCs reallocated manufacturing tasks to their foreign affiliates, particularly
located in nearby, low-wage countries. Our results thus highlight that the cross-country
network within MNCs may lead to unintended consequences of domestic labor market
32policies in the age of global value chains. Policymakers need to be aware of the possibility
and be better prepared with labor adjustment programs, such as retraining, to mitigate
the negative employment effects.
The results also provide a new insight into the ongoing minimum wage discussion.
Previous minimum wage studies have typically focused on employment effects for young
and unskilled workers; and they have not found consistent results (Card, 1992; Neumark
and Wascher, 1992; Neumark et al., 2014; Allegretto et al., 2017). In this study, we findnegative employment effects for MNCs, arguably the most productive and largest firms(Bernard and Jensen, 2007; Bernard et al., 2007). An important implication of the finding is that a relatively swift decline in employment by MNCs may imply a deteriorationin aggregate labor productivity. Put differently, the jobs specifically vulnerable to being
replaced by foreign factors of production due to minimum wage hikes may be "better"
paying jobs in an MNC-prevalent economy. This idea is somewhat different from thegeneral view in the literature that minimum wage increases have the greatest effect on thelowest-paid workers, causing them to move from less to more productive establishments(Dustmann et al., 2022). Further research is warranted to investigate these seemingly
contradictory views.

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36

Appendix
Appendix A: Additional Robustness Checks
Table A.1: Additional Firm-Level Controls in Manufacturing, 2013~2019

MNCi × Postt
Log sales
Log purchase
Exporter
Importer

Observations
R-squared
Fixed Effects:
Firm
Industry-Region-Year

Dependent Variable: Log of Employment
Full Sample
Matched Sample
(1)
(2)
(3)
(4)
(5)
(6)
-0.0295*** -0.0159** -0.0152** -0.0507*** -0.0251*** -0.0244***(0.0101)(0.0066)(0.0065)(0.0124)(0.0088)(0.0086)
0.3290*** 0.3288***
0.3610***0.3608***(0.0097)(0.0096)(0.0107)(0.0107)-0.0019-0.0022-0.0076-0.0078(0.0050)(0.0050)(0.0056)(0.0057)
0.00880.0178(0.0057)(0.0116)0.00770.0027(0.0051)(0.0059)
41,0300.9541

39,6070.964439,6070.9644

26,6180.959325,7850.969125,7850.9692

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Notes: Columns (1)-(3) use the full sample, while columns (4)-(6) are based on the PS matchedsample and frequency-weighted regressions. MNCi is an indicator variable that equals 1 if a firmi has foreign affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicator variable that
equals 1 if the time t is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Sales and purchases
are in real terms. Exporter (Importer) dummy indicates whether the firm exports (imports) in eachyear. Standard errors are clustered at the industry level. The number of observations differs acrosscolumns because some observations of firm-level control variables are missing or zero. *** p<0.01,** p<0.05, * p<0.1.

We conduct additional robustness checks. First, we check the robustness of our resultsby adding time-varying firm attributes in the specification. Considering that the employment effects could be also affected by within-firm changes that are omitted in the baseline
specification, we add essential firm-level variables—(log of) sales, (log of) purchase, exporter dummy, and importer dummy—into equation (2) to control for time-varying firm
characteristics that may have affected employment.43 Table A.1 presents the estimation
results. Column (1) is from column (4) in Table 4 and shown here as a benchmark. We add
sales and purchase variables in column (2); and exporter and importer dummies in column (3). All the columns yield negative and statistically significant coefficient estimateson the interaction term, confirming the baseline result above. In addition, coefficient estimates on firm-level control variables show expected signs. Sales, export, and import
43

Note that time-invariant firm-level attributes are controlled for by firm fixed effects.

37

are positively associated with employment, while purchase is negatively associated with
employment. Columns (4) through (6) repeat the same estimations as (1) - (3) with thePS matched sample and frequency-weighted regressions. All estimates do not change
qualitatively.
Table A.2: Alternative non-MNC Group in Manufacturing, 2013~2019

MNCi × Postt
Log sales
Log purchase
Exporter
Importer

Observations
R-squared
Fixed Effects:
Firm
Industry-Region-Year

Dependent Variable: Log of Employment
Full Sample
Matched Sample
(1)
(2)
(3)
(4)
(5)
(6)
-0.0242** -0.0125*-0.0118* -0.0373** -0.0187** -0.0180**(0.0103)(0.0067)(0.0066)(0.0136)(0.0086)(0.0084)
0.3571*** 0.3571***
0.3294*** 0.3292***(0.0108)(0.0107)(0.0124)(0.0125)-0.0029-0.0032-0.0018-0.0022(0.0056)(0.0056)(0.0053)(0.0054)0.00820.0026(0.0059)(0.0078)0.00800.0081(0.0049)(0.0055)
38,7300.955337,3580.965337,3580.9653

26,4480.9601

25,5850.969925,5850.9699

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Notes: Columns (1)-(3) use all of the alternative non-MNC firms as the comparison group,
while columns (4)-(6) use a subset with the PS matching procedure described in 4.1 and run
frequency-weighted regressions. MNCi is an indicator variable that equals 1 if a firm i has foreign affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicator variable that equals
1 if the time t is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Sales and purchases are
in real terms. Exporter (Importer) dummy indicates whether the firm exports (imports) in eachyear. Standard errors are clustered at the industry level. The number of observations differs
across columns because some observations of firm-level control variables are missing or zero.
*** p<0.01, ** p<0.05, * p<0.1.

Second, we apply an alternative definition of the non-MNC group. To the extent thatlabor market policy changes (such as an abrupt minimum wage hike) induce firms to
conduct outward foreign direct investment (Fan et al., 2018), Korean firms without any
foreign affiliates before the abrupt minimum wage announcement in 2017 may have decided to establish new foreign affiliates in response to rising labor costs (i.e., in 2017,2018, and 2019). One could argue that classifying them as non-MNCs is problematic. To
address this concern, we drop Korean firms that established new foreign affiliates after
2017 from the sample and redo the analysis. Column (1) of Table A.2 presents the estimation result with the alternative definition of non-MNCs. Reassuringly, the core result isthe same, which is not surprising given that non-MNCs account for much less than 5% of
the sample from a comparison of the number of observations in Table A.1 and Table A.2.
38

Adding time-varying firm-level controls in columns (2) and (3) yields qualitatively identical results. Note that the alternative definition of the non-MNC group may produce a
different PS matched sample. Columns (4) through (6) redo the PS matching with thealternative non-MNC group and show the frequency-weighted regression results. Again,
all estimates are qualitatively the same.

Appendix B: Tables
Table B.1: Industrial Variations of Foreign Affiliates, 2016
Industry
Food Products
Beverages
Tobacco Products
Textiles, Except Apparel
Wearing Apparel, Clothing Accessories and Fur Articles
Leather, Luggage and Footwear
Wood
Pulp, Paper and Paper Products
Printing
Coke, Briquettes and Refined Petroleum Products
Chemicals and Chemical Products
Pharmaceuticals, Medicinal Chemical and Botanical Products
Rubber and Plastics Products
Other Non-Metallic Mineral Products
Basic Metals
Fabricated Metal ProductsElectronic Components and Computer
Medical, Precision and Optical Instruments, Watches and Clocks
Electrical Equipment
Other Machinery and Equipment
Motor Vehicles, Trailers and Semitrailers
Other Transport Equipment
Furniture
Other Manufacturing
Total# of Subsidiaries
149
13
8
115
249
79
14
22
11
13
261
72
212
67
172
201
738
146
287
359
761
55
52
47
4,103

Share
3.63
0.32
0.19
2.80
6.07
1.93
0.34
0.54
0.27
0.32
6.36
1.75
5.17
1.63
4.19
4.90
17.99
3.56
6.99
8.75
18.55
1.34
1.27
1.15
100.00Notes: Industry is defined at the KSIC 2-digit level. There are 24 manufacturing industries. The
data come from the Survey of Business Activities (SBA) in 2016. The sample is restricted to Korean parent firms in manufacturing. There are 1,939 Korean manufacturing firms that have 4,103
foreign affiliates.

39

Table B.2: MNCs and Employment in Non-manufacturing, 2013~2019

MNCi × Postt
Observations
R-squared
Fixed Effects:
Firm
Year
Region-Year
Industry-Region-Year

Dependent Variable: Log of Employment
(1)
(2)
(3)
(4)
-0.0032-0.0130-0.0176-0.0137(0.0137) (0.0147) (0.0150)(0.0146)
43,29543,29543,291
43,2810.95550.95550.95720.9586
Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign affiliates in the years 2013~2016 and 0 otherwise. Postt is an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017, 2018,and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

Table B.3: Propensity Score Matched Sample Statistics

Log capital per worker
Log average wages
Production worker share
Employment growth rate

Obs.
1970
1970
1970
1970

MNCs
Mean
4.738
3.857
0.570
0.017

p50
4.806
3.842
0.629
0.023

Non-MNCs: All
Obs. Mean
p50
3773 4.576 4.688
3774 3.795 3.778
3774 0.629 0.699
3190 0.012 0.015

Non-MNCs: MatchedObs. Mean
p50
1970 4.740
4.781
1970 3.838
3.816
1970 0.562
0.6501970 0.021
0.028

Notes: The table compares mean and median (p50) of the matching variables by group. The sample
only includes Korean manufacturing firms. All values are as of 2016 (Employment growth rate is theemployment growth over the years 2013~2016). The propensity score matching is conducted separately for each of fourteen 2-digit manufacturing industries. Some non-MNCs are selected multiple
times during the matching procedure due to the replacement. Thus, for non-MNCs, the mean andp50 of the variables are weighted by their selection frequency. Unmatched observations—in part by
the common support restriction—receive zero weight.

40

Table B.4: MNCs and Value Added by Country in Manufacturing, 2013~2019

MNCi × Postt
Observations
R-squared

MNCi × Postt
Observations
R-squared

MNCi × Postt
Observations
R-squared
Fixed Effects:
Firm
Industry-Region-Year

Dependent Variable: Log of Real Value-Added
China
VietnamJapan
Other Asia
(1)
(2)
(3)
(4)
-0.0454**-0.1209***-0.0312-0.0464***(0.0209)(0.0424)(0.0339)(0.0135)
35,43328,88327,371
31,2170.91440.90270.90920.9188EuropeNorth America Latin America
ROW
(5)
(6)
(7)
(8)
-0.0426*-0.0487**-0.0337-0.0378(0.0226)(0.0183)(0.0277)(0.0505)
28,38530,405
27,13926,3640.92070.92070.91540.9068
Q1
Q2
Q3
Q4
(9)
(10)
(11)
(12)
-0.1114***-0.0296-0.0220-0.0437(0.0370)(0.0209)(0.0202)(0.0320)
29,38730,641
29,18428,8040.88970.89310.91400.9038
Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Notes: MNCi is an indicator variable that equals 1 if a firm i has foreign affiliates in theyears 2013~2016 and 0 otherwise. In each column, foreign affiliates are restricted to thespecific country. For instance, in column (1), MNCi refers to foreign affiliates in China.
Postt is an indicator variable that equals 1 if the time t is after 2016 (i.e., 2017, 2018, and2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, **p<0.05, * p<0.1.

41Appendix C: Figures0

Search Term: Minimum Wages
20
40
60
80

100

Figure C.1: Google Trends over Time: Minimum Wages in Korea

2013m1

2014m1

2015m1

2016m1

2017m1

2018m1

2019m1

Notes: Numbers represent search interest relative to the highest point on the chart for Korea between
2013M1 and 2019M12 in Google Trends. A value of 100 is the peak popularity for the term. A value of 50
means that the term is half as popular. A score of 0 means there was not enough data for this term. The redvertical line indicates July, 2017, when the Minimum Wage Commission decided to increase the 2018minimum wage by 16.4%.

42

.02
0
-.08 -.06 -.04 -.02

Log Employment

.04

.06

.08

Figure C.2: Dynamic Treatment Effects: Non - manufacturing

2013

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects

Notes: Non - manufacturing only. The figure plots coefficients estimate on βs ’s from the dynamic
treatment effects analysis as specified in equation (3) where the dependent variable is the log of
employment.

.05
0

Log Value-Added

-.05

.05
0
-.05
-.2

-.15

-.15

-.1

-.1

Log Value-Added

.1

.1

.15

.2

.15

Figure C.3: MNCs and Value Added: Event Study and Dynamic Treatment Effects

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

2014

2015

2016

2017

Non MNCs

Dynamic Treatment Effects(a) Event Study Coefficients(b) Dynamic Treatments

2018

2019

Notes: The sample is restricted to the manufacturing sector only. The left figure plots event study analysis
results from equation (1) where the dependent variable is the log of real value added. The right figure
plots dynamic treatment effects results from equation (3) where the dependent variable is the log of realvalue added. Coefficients along with 95% confidence intervals are illustrated. Standard errors are
clustered at the industry level.

43

.15
.1
.05
0

Log Employment

-.15

-.1

-.05

.05
0
-.05
-.15

-.1

Log Employment

.1

.15

Figure C.4: MNCs and GVCs: Event Study and Dynamic Treatment Effects

2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2016

2017

2018

2019

.15
.05
0

Log Employment

0
-.05

-.15

-.15

-.1

-.05

.05

.1

.1

.15

(b) Dynamic Treatments (China)-.1

Log Employment

2015

Dynamic Treatment Effects(a) Event Study Coefficients (China)

2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2014

Non MNCs

2015

2016

2017

2018

2019

Dynamic Treatment Effects

.1
.05
0

Log Employment

0

-.15

-.1

-.05
-.15

-.1

-.05

.05

.1

.15

(d) Dynamic Treatments (Vietnam).15(c) Event Study Coefficients (Vietnam)

Log Employment

2014

Non MNCs

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(e) Event Study Coefficients (Other Asia)(f) Dynamic Treatments (Other Asia)

Notes: The sample is restricted to the manufacturing sector only. The left figures plot event study analysis
results from equation (1). The right figure plots dynamic treatment effects results from equation (3).
Coefficients and 95% confidence intervals are illustrated. Standard errors are clustered at the industry
level.

44

.15
.1
.05
0

Log Employment

-.15

-.1

-.05

.05
0
-.05
-.15

-.1

Log Employment

.1

.15

Figure C.5: MNCs and GVCs: Event Study and Dynamic Treatment Effects

2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2016

2017

2018

2019

.15
.1
.05
0

Log Employment

0

-.15

-.15

-.1

-.05

-.05

.05

.1

.15

(b) Dynamic Treatments (Japan)-.1

Log Employment

2015

Dynamic Treatment Effects(a) Event Study Coefficients (Japan)

2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2014

Non MNCs

2015

2016

2017

2018

2019

Dynamic Treatment Effects

.1
.05
0

Log Employment

0

-.15

-.1

-.05
-.15

-.1

-.05

.05

.1

.15

(d) Dynamic Treatments (Europe).15(c) Event Study Coefficients (Europe)

Log Employment

2014

Non MNCs

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(e) Event Study Coefficients (North America)(f) Dynamic Treatments (North America)

Notes: The sample is restricted to the manufacturing sector only. The left figure plots event study analysis
results from equation (1). The right figure plots dynamic treatment effects results from equation (3).
Coefficients and 95% confidence intervals are illustrated. Standard errors are clustered at the industry
level.

45

.15
.1
.05
0

Log Employment

-.1
-.15
2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2014

Non MNCs

2015

2016

2017

2018

2019

Dynamic Treatment Effects.10

Log Employment

0
-.1

-.3

-.3

-.2

-.2

-.1

.1

.2

.2

.3

(b) Dynamic Treatments (Latin America).3(a) Event Study Coefficients (Latin America)

Log Employment

-.05

.05
0
-.05
-.15

-.1

Log Employment

.1

.15

Figure C.6: MNCs and GVCs: Event Study and Dynamic Treatment Effects

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(c) Event Study Coefficients (RoW)(d) Dynamic Treatments (RoW)

Notes: The sample is restricted to the manufacturing sector only. The left figure plots event study analysis
results from equation (1). The right figure plots dynamic treatment effects results from equation (3).
Coefficients and 95% confidence intervals are illustrated. Standard errors are clustered at the industry
level.

46

.1
.05
2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2014

Non MNCs

2015

2016

2017

2018

2019

Dynamic Treatment Effects

0
-.1

-.1

-.05

-.05

0

Log Employment

.05

.05

.1

(b) Dynamic Treatment Effects (Same Sector).1(a) Event Study Coefficients (Same Sector)

Log Employment

0

Log Employment

-.1

-.05

0
-.1

-.05

Log Employment

.05

.1

Figure C.7: MNCs and Task: Event Study and Dynamic Treatment Effects

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(c) Event Study Coefficients (Same Industry)(d) Dynamic Treatments (Same Industry)

Notes: The sample is restricted to the manufacturing sector only. The left figures plot event study analysis
results from equation (1) where the dependent variables are the log of employment. The right figures plot
dynamic treatment effects results from equation (3). Coefficients and 95% confidence intervals areillustrated. Standard errors are clustered at the industry level.

47

0

Log Capital

.05
0

-.1

-.15

-.1

-.05

-.05

Log Capital Stocks

.05

.1

.1

.15

Figure C.8: MNCs and Capital: Event Study and Dynamic Treatment Effects

2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2015

2016

2017

2018

2019

Dynamic Treatment Effects(b) Dynamic Treatment Effects (Capital Stocks)

.05
0
-.05
-.1

-.15

-.1

-.05

0

.05

Log Capital Stocks Per Worker

.1

.1

.15

(a) Event Study Coefficients (Capital Stocks)

Log Capital Stocks Per Worker

2014

Non MNCs

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(c) Event Study Coefficients (K / L)(d) Dynamic Treatments (K / L)

Notes: The sample is restricted to the manufacturing sector only. The left figures plot event study analyses
results from equation (1) where the dependent variables are the log of real capital stocks in panel (a), andthe log of real capital stocks per worker in panel (c), respectively. The right figures plot dynamic treatment
effects results from equation (3) where the dependent variables are the log of real capital stocks in panel(b), and the log of real capital stocks per worker in panel (d), respectively. Coefficients and 95% confidence
intervals are illustrated. Standard errors are clustered at the industry level.

48

-.1

-.15

-.1

-.05

0

Log Average Wage

.05
0
-.05

Log Average Wage

.05

.1

.1

.15

Figure C.9: MNCs and Wage Bill: Event Study and Dynamic Treatment Effects

2013

2014

2015

2016

MNCs

2017

2018

2019

2013

2014

Non MNCs

2015

2016

2017

2018

2019

Dynamic Treatment Effects(b) Dynamic Treatments (Average Wage)

-.1

-.15

-.1

-.05

0

Log Wagebill

.05
0
-.05

Log Wagebill

.05

.1

.1

.15

(a) Event Study Coefficients (Average Wage)

2013

2014

2015
MNCs

2016

2017

2018

2019

2013

Non MNCs

2014

2015

2016

2017

2018

2019

Dynamic Treatment Effects(c) Event Study Coefficients (Wage Bill)(d) Dynamic Treatments (Wage Bill)

Notes: The sample is restricted to the manufacturing sector only. The left figures plot event study analyses
results from equation (1) where the dependent variables are the log of real average wage in panel (a), andthe log of real wage bill worker in panel (c), respectively. The right figures plot dynamic treatment effects
results from equation (3) where the dependent variables are the log of real average wage in panel (b), andthe log of real wage bill in panel (d), respectively. Coefficients and 95% confidence intervals are illustrated.
Standard errors are clustered at the industry level.

49