Download An Economic Crisis and Intra-MNC Production Shifts

ECONOMIC CRISIS, INTRA-MNC PRODUCTION SHIFTS AND
MNC PERFORMANCE FROM A NETWORK PERSPECTIVE
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ECONOMIC CRISIS, INTRA-MNC PRODUCTION SHIFTS AND
MNC PERFORMANCE FROM A NETWORK PERSPECTIVE
ABSTRACT
This study examines how economic crises affect a multinational corporation’s (MNC)
production shifts within its international subsidiary network and how the intra-MNC production
shifts affect MNC performance. Unlike past studies, we use network density to capture the
overall MNC operational flexibility at the subsidiary level. Because economic crises magnify
variation in cross-national economic conditions, MNCs tend to shift production to the
subsidiaries where benefits are magnified, resulting in a reduced number of subsidiaries
engaging in intra MNC production shifts. However, this tendency will be weaker if an MNC has
a wider subsidiary network that enables them to exploit more cross-national differences in
economic conditions. In the same vein, the number of countries involved in intra-MNC
production shifts is positively associated with MNC performance. Analysis of the panel data for
Korean manufacturing MNCs that experienced the 1998 and 2008 economic crises provides
empirical support for our arguments.
Keywords: Operational flexibility, MNC, incidence of production shifts, economic crisis,
breadth
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INTRODUCTION
Multinational corporations’ (MNC) operational flexibility has been a subject of keen
interest among scholars interested in the advantages of MNCs over non-MNCs (Kogut &
Kulatilaka, 1994; Lee & Makhija, 2009a; Tang & Tikoo, 1999). Previous research argues that an
MNC acquires operational flexibility by establishing subsidiaries across multiple host countries,
which in turn gives the MNC the option to shift production from one host country to another in
response to environmental changes (Chung, Lee, Beamish, & Isobe, 2010; Lee & Makhija,
2009a). In this sense, the breadth of an MNC’s subsidiary network (i.e. total number of host
countries) contributes to the MNC’s operational flexibility by offering a portfolio of options to
shift production (Belderbos, Tong, & Wu, 2014; Li & Rugman, 2007). This argument has found
empirical support (e.g. Lee & Makhija, 2009a; Lee & Song, 2012).
However, although studies have investigated whether or not adverse environmental
changes such as an economic crisis spur MNCs to engage in intra-MNC production shifts (IPS),
how the MNCs specifically utilize those shifts to deal with the adverse changes has been rarely
examined (Lee & Makhija, 2009a; Lee & Song, 2012). Thus, to advance our knowledge about
MNC operational flexibility, this study closely examines the way MNCs actually utilize
production shifts in response to economic crises.
In this study, we argue that an economic crisis will actually reduce the incidence of IPSs.
The primary purpose of the IPSs is to exploit cross-national variation in economic conditions
(e.g. labor costs) (Fisch & Zschoche, 2012; Kogut & Kulatilaka, 1994). By generating abrupt and
significant downward environmental changes in crisis-afflicted countries, an economic crisis
magnifies cross-national differences in economic conditions among host countries (Chung et al.,
2010; Lee & Chung, 2007; Lee & Makhija, 2009a). These magnified cross-national differences
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in turn offer MNCs clear directions for production shifts. Thus, an economic crisis would
encourage MNCs to conduct more concentrated, and thereby fewer, IPSs. However, if the
MNC’s international network has a wider breadth, giving it more diverse and exploitable crossnational differences in economic conditions, the negative impact of an economic crisis on the
incidence of IPSs will decrease.
Furthermore, this study also examines how IPSs affect MNC performance, which is one
of the primary research questions in operational flexibility research. We argue that MNCs that
better exploit cross-national differences in economic conditions will perform better. Specifically,
MNCs will show better performance not only when they engage more actively in IPSs but also
when more host countries are involved in those shifts. By taking more host countries into IPSs,
the focal MNC will be able to exploit more effectively the diverse cross-national differences in
economic conditions.
Furthermore, the benefits of exploiting cross-national differences in economic conditions
will be more valuable during an economic crisis than under stable economic conditions. This is
because the focal MNCs have to effectively grapple with the abrupt downward environmental
changes in crisis-afflicted host countries (Chung et al., 2013; Lee & Makhija, 2009a). MNCs
may move production from crisis-afflicted host countries to other countries to escape from
stagnation of demand in the former countries or move production from other host countries to
crisis-afflicted ones to exploit reduced factor costs (Chung et al., 2013; Lee & Chung, 2007). In
this sense, when there is a higher incidence of IPSs and more host countries involved in those
shifts, the MNC will effectively manage the negative influence of an economic crisis on its
performance.
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To examine our arguments, we adopt a network approach. The operational flexibility
literature recognizes an MNC as a network in which a headquarters and subsidiaries are
connected (or interacted) by production (Kogut & Kulatilaka, 1994). However, previous research
has devoted very little attention to interactions between components of an MNC’s multinational
network. For this reason, previous research has utilized indirect measures of production shifts.
The most frequently adopted measures in the literature have been the total number of host
countries (Allen & Pantzalis, 1996; Tang & Tikoo, 1999), the aggregated amount of intra-MNC
trades (Lee & Makhija, 2009a) or the growth of the proportion of a subsidiary’s sales among the
MNC’s total sales (Lee & Song, 2012).1 It is no wonder that prior research is mixed at best in its
support for MNC operational flexibility (Lee & Song, 2012).
The network literature provides researchers with excellent ways to directly capture
interactions among components of a certain network (Brass, 1984; Freeman, 1979). We
specifically focus on density, which refers to the number of interactions that actually occur
among network components over the total possible interactions in the focal network (Sparrowe,
Liden, Wayne, & Kraimer, 2001). For example, if an MNC has a headquarters and two
subsidiaries and the MNC shifts production out to those two subsidiaries, the density of
production shifts in this MNC’s subsidiary network is two (the number of production shifts that
actually occur) over six (the total possible number of production shifts).
By utilizing the construct of network density, we can directly observe how an MNC
utilizes production shifts when adverse environmental changes occur. Specifically, we can
examine: 1) how many options to shift productions an MNC exercises to shift production across
The measure proposed by Lee and Song (2012) is calculated in two steps: 1) subtracting a subsidiary’s sales
volume from the MNC’s total sales volume; 2) then calculating the growth rate of the value calculated from the first
step. This measure intends to capture the change in each subsidiary’s contribution to the total intra-MNC shifts of
production, not capture an MNC’s entire network-level operational flexibility.
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host countries among total available options (i.e. incidence of IPSs); 2) how many host countries
are involved in production shifts; and 3) how the breadth of an MNC’s subsidiary network
affects the incidence of those shifts when adverse environmental changes occur. Getting a more
detailed picture of IPSs through these examinations also helps us better investigate the
performance implications of those shifts.
We exploit a panel of 148 Korean manufacturing MNCs listed on the Korean Stock
Exchange (KSE) from 1993 to 2011, a period that included significant environmental changes,
most notably the 1998 Korean economic crisis and 2008 global economic crisis. Our empirical
findings largely support our arguments.
THEORY AND HYPOTHESES
An Economic Crisis and Intra-MNC Production Shifts (IPSs)
In today’s world, firms confront increasing market volatility and frequent downward
environmental changes (Dreher & Rupprecht, 2007). Therefore, the ability to respond flexibly to
environmental changes is crucial for firms to survive and prosper (Buckley & Casson, 1998;
Tong & Wei, 2011). Operational flexibility is especially crucial for MNCs that operate
simultaneously in heterogeneous national environments with multiple environmental fluctuations
(e.g., abrupt changes in exchange rates) (Allen & Pantzalis, 1996; Belderbos et al, 2014; Kogut
& Kulatilaka, 1994; Lee & Makhija, 2009b).
The operational flexibility literature argues that MNCs can manage an economic crisis
better than pure domestic firms can (Kogut & Kulatilaka, 1994; Lee & Makhija, 2009a). This is
because MNCs acquire operational flexibility by establishing subsidiaries across multiple
countries (Fisch & Zschoche, 2012; Kogut & Kulatilaka, 1994). This international subsidiary
network provides an MNC with options to shift production across host countries. Having a
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portfolio of options to shift enables the MNC to cope better with abrupt environmental changes
that happen in one or several host countries by shifting production from host countries with less
favorable economic environments to those with more favorable ones (Allen & Pantzalis, 1996;
Gomes & Ramaswamy, 1999; Kogut & Kulatilaka, 1994; Tang & Tikoo, 1999). No wonder that
changes in environmental conditions surrounding an MNC, notably an economic crisis, trigger
production shifts within the MNC.
Depending on the literature, the next question this study attempts to answer is how MNCs
specifically utilize intra-MNC production shifts in response to an economic crisis. We argue that
an economic crisis will decrease (rather than increase) the incidence of IPSs by leading the focal
MNCs to make more concentrated production shifts. An economic crisis would significantly
undermine the economic activities in the focal crisis-afflicted country: thereby firms operating in
that country would suffer nontrivial damage (Barlevy, 2002; Pearson & Clair, 1998; Tong &
Wei, 2010).
As a result, differences in economic conditions between crisis-afflicted and not afflicted
countries will be magnified and become salient. These significant differences that MNCs would
not have had under stable economic conditions will in turn give them significant incentives and
clear direction for production shifts. Specifically, an economic crisis will give an MNC
incentives to shift production from crisis-afflicted countries to not afflicted countries to minimize
the negative impact of adverse environmental changes (Tong & Wei, 2010). For example,
plummeting demand in crisis-afflicted countries would be an urgent and serious problem for the
MNC. In this situation, the MNC would concentrate on exercising the best option enabling it to
minimize the negative impact of the decrease of demand instead of trying various expedients.
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In addition, some MNCs may shift production into a crisis-afflicted country from other
countries because economic stagnation resulting from an economic crisis would be likely to
reduce the prices of strategic factors (Chung et al., 2010). Specifically, an economic crisis often
reduces the value of the crisis-afflicted country’s currency. This currency depreciation reduces
production costs in that country and in turn gives MNCs an incentive to move production from
other countries to the crisis-afflicted country. For example, Japanese MNCs shifted their
production from other countries to Indonesia when it experienced a severe economic crisis (Lee
& Chung, 2007). Thus, MNCs that seek cheap production locations have a clear best option for
production shifts and will be likely to concentrate on exercising this option on a large scale.
In this sense, an economic crisis will lead MNCs to focus on the best option to shift
production rather than rearrange their international production configurations by exploiting many
options. Thus:
Hypothesis 1: An economic crisis confronted by an MNC will be negatively associated with the
incidence of production shifts in the MNC’s subsidiary network.
The Breadth of MNCs’ Subsidiary Networks
The breadth of an MNC’s international network (i.e., total number of host countries) has
been identified as an important source of MNC operational flexibility (Allen & Pantzalis, 1996;
Belderbos et al., 2014; Lee & Makhija, 2009b; Tang & Tikoo, 1999). MNCs with a wider
breadth of subsidiary networks have more options to profitably reallocate their production in
response to environmental changes (Allen & Pantzalis, 1996; Tang & Tikoo, 1999). Previous
research finds that the breadth of an MNC’s subsidiary network is positively associated with the
MNC’s market value (Lee & Makhija, 2009b; Tang & Tikoo, 1999) and negatively associated
with firm level downside risk (Belderbos et al., 2014).
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We argue that if the wider the breadth of an MNC’s subsidiary network, the less an
economic crisis will reduce the incidence of production shifts. This is because options to shift
production possessed by an MNC stem from the presence of its multinational subsidiary network
(Chung et al., 2010; Lee & Makhija, 2009b; Rangan, 1998). Suppose that a Korean MNC
operates in China and the United States. In this case, the MNC can technically have six options
to unilaterally shift resources and production (i.e., three bilateral shift options) across its
multinational network. If this MNC incorporates one more host country (e.g., India) in its
network, the focal MNC will have a total of 12 unilateral shift options (i.e., six bilateral shift
options).
However, the breadth of an MNC’s international subsidiary network contributes not only
to the mere number of options to shift productions, but also to the value of those options (Lee &
Makhija, 2009b; Tang & Tikoo, 1999). This is because, as more host countries are included in an
MNC’s subsidiary network, variations in cross-national differences in economic conditions such
as institutional environment, labor cost, domestic demand, or exchange rate will be added to the
network (Belderbos et al, 2014; Chung et al., 2010; Fisch & Zschoche, 2012).
If there are additional valuable cross-national differences that help MNCs maximize their
profits, other than the magnified differences between crisis-afflicted and not afflicted host
countries, MNCs confronting an economic crisis would be willing to exploit them. Suppose that
an MNC built up subsidiaries in two different countries to meet the domestic demands. If one of
those countries experiences an economic crisis, the MNC would be willing to shift production
out of the crisis-afflicted country to the other. However, if it also operates in a host country with
cheap labor costs, the MNC may capitalize on this difference. By increasing production in the
country with cheaper labor costs, the MNC will be able to target a wider customer base through
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export. Thus, we argue that the breadth of an MNC’s subsidiary network will mitigate the
negative association between an economic crisis and the incidence of an MNC’s production
shifts.
Hypothesis 2: The negative association between an economic crisis and the incidence of intraMNC shift of productions will be weaker when an MNC possesses a larger subsidiary network.
Incidence of Intra-MNC Production Shifts (IPSs) and MNC Performance
An economic crisis is likely to have a negative impact on the economic activities of
domestic firms located in the focal country experiencing the crisis (Barlevy, 2002; Pearson &
Clair, 1998; Tong & Wei, 2010). Significant decreases in market demand, stock price, and
investments as well as sudden increase of unemployment rate or chain bankruptcy of firms in a
crisis-afflicted country are expected (Lee & Makhija, 2009a; Tong & Wei, 2010). Thus, firms
confronting an economic crisis will suffer from a decrease in performance. However, by shifting
production in advantageous ways, MNCs may be able to mitigate the negative impact of an
economic crisis on their performance (Kogut & Kulatilaka, 1994; Lee & Chung, 2007).
Since production shifts reflect MNCs’ flexible response to environmental changes,
MNCs that show a higher incidence of production shifts would perform better than MNCs
showing a lower incidence of production shifts. While establishing a subsidiary network offers
MNCs a portfolio of options to shift productions across countries (Belderbos et al., 2014; Kogut
& Kulatilaka, 1994), MNCs cannot benefit from cross-national variations in economic conditions
unless they exercise those options.
For example, an MNC can maximize MNC-level profit by shifting production out of a
host country experiencing high labor costs to a country with cheap labor costs (Fisch &
Zschoche, 2012). Also, when one host country’s currency depreciates and thereby offers
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relatively cheaper input costs, the MNC can exploit this favorable economic condition by
shifting production from other host countries to the focal host country (Lee & Chung, 2007; Lee
& Song, 2012). Since the goods produced in this country will have a competitive price, the MNC
may experience better performance. For this reason, MNCs that more actively exploit crossnational variation in economic conditions are likely to perform better than MNCs that cannot
take advantage of such variations.
Exploiting these cross-national variations in economic conditions will be particularly
critical for MNCs when all or part of their subsidiary networks confront an economic crisis
(Kogut & Kulatilaka, 1994; Lee & Makhija, 2009a). The significant changes caused by an
economic crisis will make ineffective and inflexible responses to the crisis substantially
expensive. The intra-MNC production shift that enables MNCs to actively exploit cross-national
differences in economic conditions is one of the most effective responses uniquely available to
MNCs (Kogut & Kulatilaka, 1994; Lee & Makhija, 2009a; 2009b).
Furthermore, higher incidence of IPSs in the situation where the differences in economic
conditions between crisis-afflicted and non-afflicted countries are magnified may indicate the
more effective utilization of those differences (Chung et al., 2010; Lee & Makhija, 2009a). Thus,
MNCs that actively exploit these differences by engaging in IPSs will be able to mitigate any
negative impact of the crisis on their performance, while MNCs that fail to do that will suffer
from the economic crisis (Lee & Makhija, 2009a; Mello, Parsons, & Triantis, 1995). Thus:
Hypothesis 3: The negative association between an economic crisis and MNC performance will
be weaker when the incidence of intra-MNC shifts of production is higher.
The Breadth in Intra-MNC Production Shifts (IPSs) and MNC Performance
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In this study, we differentiate the breadth of IPSs (i.e. the number of host countries
actually involved in production shifts) from the breadth of an MNC’s international subsidiary
network. This distinction is important in the examination of the real contribution of the breadth
in MNC performance. While past research uses the breadth of MNC subsidiary networks to
capture the potential magnitude of intra-MNC production shift, having the potential candidates
for the shift in production is quite different from actually taking advantage of this potential.
While wider breadth of the entire multinational subsidiary network offers MNCs a larger
portfolio of options for production shifts, the direct benefits of production shifts may be realized
only when the shifts are exercised to maximize the MNCs’ profits. In this sense, the breadth in
production shifts may have significant implications for MNC performance. This is because
production shifts that involve more host countries may indicate that cross-national variations in
input costs or exchange rates are more extensively exploited (Kogut & Kulatilaka, 1994; Tang &
Tikoo, 1999).
Suppose that a US MNC has subsidiaries in three Asian countries: China, India, and
Japan. First, the MNC plans to shift one production from the US and one from Japan to China to
exploit the differences in production costs or market demand between the US and China as well
as between Japan and China. However, if the difference in production costs between Japan and
India is larger than between Japan and China, the MNC may exploit this larger cost difference by
shifting one production from Japan to India instead of to China. While there are two production
shifts in both cases, the number of host countries involved in production shifts (i.e., the breadth
in production shifts) increases from three to four.
If there is no exploitable difference in economic conditions between two countries, a
production shift between those two countries may not occur (Kogut & Kulatilaka, 1994; Lee &
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Song, 2012). Moreover, an MNC is more likely to conduct a production shift expected to
generate a greater outcome because the main rationale behind production shifts is to exploit
cross-national variations in economic conditions (Kogut & Kulatilaka, 1994; Lee & Song, 2012;
Vassolo, Anand, & Folta, 2004).
Exploiting this variation in their favor would be especially crucial for MNCs that face an
economic crisis. An economic crisis magnifies cross-national variations in economic conditions
(e.g., currency exchange rates or labor costs) (Chung et al., 2010; Lee & Makhija, 2009a). For
example, MNCs may obtain lower costs for necessary labor forces in crisis-afflicted countries
than they would in other countries. Thus, MNCs that deal flexibly with an economic crisis by
utilizing cross-national differences in economic conditions will effectively mitigate the negative
impact of the crisis on their performance, while MNCs that do not will have to bear those
negative effects.
The breadth in production shifts may reflect how widely and flexibly an MNC utilizes
cross-national variations in economic conditions during an economic crisis. In this sense, an
MNC would likely be more effective in coping with an economic crisis and maximizing its profit
if more host countries were involved in production shifts. Therefore:
Hypothesis 4: The negative association between an economic crisis and MNC performance will
be weaker when the breadth in IPSs is larger.
METHODS
Data
To test the hypotheses, this study utilizes a panel dataset of 148 Korean manufacturing
MNCs listed on the Korean Stock Exchange (KSE) from 1993 to 2011. This comprehensive
panel dataset provides us with three advantages. First, Korean MNCs in this dataset are widely
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exposed to significantly heterogeneous national environments and economic conditions by being
present in most continents around the world. Second, this dataset includes two years in which
there was an economic crisis (i.e. 1998 Asian economic crisis and 2008 global economic crisis).
Since the purpose of this study is to investigate how an economic crisis affects the extent to
which an MNC engages in production shifts, and how the incidence of production shifts affects
MNCs performance during an economic crisis, this dataset would be appropriate for
accomplishing this purpose.
Third, since this dataset includes comprehensive information about intra-firm trades
among subsidiaries and between Korean headquarters and subsidiaries, utilizing the dataset
enables us to directly observe the interaction (i.e. production shifts) between the components of
an MNC’s international network. Given that intra-firm trades refer to cross-national transactions
among subsidiaries as well as between headquarters and subsidiaries (Irarrazabal, Moxnes, &
Opromolla, 2013), and our data include only intra-firm trades exceeding USD50,000, intra-firm
trades can be a good proxy for production shifts.
This dataset was obtained from the WISEfn database of all Korean public firms listed on
the Korean Stock Exchange (KSE) and the Korea Listed Companies Association (KLCA)
foreign affiliate database.
Dependent variable
This study focuses on two different dependent variables: the incidence of production
shifts and MNC operating performance (i.e. ROA).
Incidence of IPSs: To measure the incidence of each MNC’s production shifts, this study
adopts the construct of network density. Social network literature reports that valuable resources
can be exchanged through transactions between network nodes, and each transaction can be
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regarded as a tie (Brass, 1984; Sparrowe et al., 2001). Given that density indicates the number of
actual interactions over the total possible number of intra-network interactions, network density
enables us to capture directly the incidence of production shifts in an MNC’s multinational
network.
We first establish the matrices to capture each MNC’s annual intra-firm trades from 1993
to 2011. In these network matrices, the network component is each host country and a home
country, rather than each subsidiary and a headquarters, since production shifts aim to exploit
cross-national variation in economic conditions (Kogut & Kulatilaka, 1994; Lee & Song, 2012).
Therefore, multiple subsidiaries in a host country are aggregated into a country level. Then, we
compute the network density of each MNC’s subsidiary network by the year of each MNC. To
compute network density, we use the UCINET 6 software package, which is reliable and
commonly used in social network research (Everton, 2009). By using the density, the
relationship between the incidence of IPSs and MNC performance is also tested.
ROA: To measure the performance of MNCs, we use return on assets (ROA), one of the
most widely used measures of firm performance (Chang, Chung, & Moon, 2013). ROA is
calculated by dividing an MNC’s year-end net income by its year-end total assets. In addition, it
is important to note that ROA refers to the MNC’s level of performance rather than the
performance of each subsidiary.
Independent variables
Total Breadth: We argue that the breadth of an MNC’s subsidiary network offers a
foundation for production shifts, especially during an economic crisis. To test whether or not the
breadth of an MNC’s subsidiary network is associated with the incidence of IPSs, we include this
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variable, which is measured as the total number of countries in an MNC’s subsidiary network
(Allen & Pantzalis, 1996; Tang & Tikoo, 1999).
Breadth in IPSs: In testing the relationship between operational flexibility and MNC
performance, we focus on breadth in production shifts (i.e., the number of host countries actually
involved in production shifts), rather than breadth in the entire subsidiary network of MNCs.
Thus, we measure this variable as the number of countries that actually participate in production
shifts.
Crisis: In order to examine whether MNCs’ operational flexibility is more valuable in the
context of an economic crisis than under stable economic conditions, this study incorporates a
crisis variable into the analysis in order to compare the impact of intra-firm trade on MNCs’
performance in these two different economic contexts. The year 1998 and 2008 are coded as
“1,” whereas the other years are coded as “0.”
Control variables
The analysis of this study incorporates a number of control variables. First, we include a
Developing Country variable, measured as the number of developing countries involved in IPSs
over the breadth of IPSs in Model (1). If more developing countries, which usually offer cheaper
production costs to an MNC, are involved in the production shifts, there would be more frequent
production shifts. We control for the proportion of production shifts between developing and
developed countries among the total number of IPSs by including a Developing Proportion
variable in Model (2). Production shifts between a developing and a developed country can have
a more positive impact on MNC performance than otherwise. We control for the size of IPSs as
well, measured as the logarithm of the total amount of those shifts in a certain year.
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We also include Lagged Tobin’s q as a control variable. Tobin’s q reflects the expected
future value of the current investment (Lee & Makhija, 2009b). In order to isolate the impact of
the expected value of previous investment on an MNC’s performance and its propensity for
conducting IPSs, we control for Tobin’s q at the end of the previous year. Following Chung and
Pruitt (1994), Tobin’s q is calculated by dividing the sum of the market value of common stock,
the book value of preferred stock, and the book value of debt by the book value of total assets.
Moreover, we control for the MNC’s indebtedness, which reflects not only the financial distress
the focal MNC faces, but also creditors’ (e.g., banks’) confidence in the focal MNC’s
performance during an economic crisis (Barclay, Smith, & Watts, 1997; Makhija, 2003). Thus,
an MNC’s indebtedness may significantly hinder the focal MNC from flexibly shifting resources
and production in response to adverse environmental changes. We measure the MNC’s
indebtedness using total debt over total assets.
In addition, if an MNC depends more on exports than on its domestic operation, it may be
able to respond more flexibly to an economic crisis by accessing multiple markets through
exports (Lee & Makhija, 2009a). Thus, an MNC with higher export intensity may be less likely
to conduct production shifts and more likely to perform better. We include export intensity,
measured as the MNC’s total export sales over total sales. The literature notes that research and
development capabilities are an important source of a firm’s competitive advantage, and thereby
have a positive impact on the focal firm’s performance (Dierickx & Cool, 1989). R&D
investment may also have non-trivial influences on an MNC’s decision to shift out from a certain
host country if the MNC has made significant R&D investments in that country. Thus, we
control for MNCs’ R&D capabilities by including the three-year average of R&D intensity.
R&D intensity indicates a firm’s overall degree of investment in new technologies, and reflects
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its R&D capabilities (Cohen & Levinthal, 1990). We measure this variable as the average of an
MNC’s total research and development expenditures over its total assets for the last three years.
This is because previous research finds that R&D investment usually has a time lag in its impact
on firm performance (Ernst, 2001).
Previous research reports that advertising has a positive influence on firm performance by
increasing customer loyalty and firm reputation as well as by differentiating the focal firm from
competitors (Fombrun & Shanley, 1990; Milgrom & Roberts, 1986; Rumelt, 1987). Thus, in
Model (2), we control for Advertising intensity, measured as the MNC’s total advertising
expenses over its total assets. Furthermore, given that the positive impact of the growth in sales
on firm performance has been well recognized in the literature (Nohria & Ghoshal, 1995), we
include Sales growth as a control variable. In addition, since the change in the incidence of IPSs
may result from a mere increase in sales, the focal MNC may also have less incentive to engage
in production shifts in response to adverse environmental changes. We also control for Net-profit
growth in Model (2).
Since the size of an MNC indicates its overall resources and capabilities, size can influence the
extent to which the focal MNC engages in production shifts and affect MNC performance,
especially during an economic crisis. Thus, we control for the Firm size of an MNC, measured as
the logarithm of total assets. In Model (2), we control for the change in total assets because
acquiring more assets may have a significant impact on MNC performance. We also control for
an MNC’s Firm Capital Intensity, measured using fixed assets over total assets. When the
proportion of fixed assets among total assets of an MNC is higher, the focal MNC’s crossnational production shift can be more restrained. Firm capital intensity may also have an effect
on MNC performance. We control for Current Ratio which reflects the degree of “cash
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constraints a firm faces” (Feldman, Amit, & Villalonga, 2016: 435). This is measured as an
MNC’s current assets divided by current liabilities.
Model specification
Since this study utilizes a panel dataset, we implement the panel data analysis method to
investigate how an economic crisis is associated with a change in the incidence of production
shifts and how the incidence of IPSs is associated with MNCs’ operating performance. The two
most commonly used panel data estimations are: fixed effects estimation and random effects
estimation. On the one hand, panel data estimations with firm level fixed effects enable
researchers to address potential bias stemming from unobserved firm heterogeneity (i.e., omitted
variable bias) (Wooldridge, 2012). On the other hand, panel data estimation with random effects
assumes there is no correlation between omitted variables, if they exist, and independent
variables and controls. As a result, estimations with unit fixed effects have been recognized as a
more convincing approach than estimations with random effects (Wooldridge, 2012). We
therefore choose the firm (MNC) fixed effect panel regression model as our main estimator. Both
the robust Hausman test and the Wald test support our model specification. The firm level fixedeffects model allows us to exclude time-constant controls such as industry dummies or whether a
firm (i.e., an MNC) is a Chaebol firm or not.
We establish two regression models to test our hypotheses. In our Model (1), we set the
incidence of IPSs as the dependent variable to test Hypothesis 1 and Hypothesis 2. Those two
hypotheses examine the association between an economic crisis and the incidence of IPSs. To
test Hypothesis 2, we interact breadth of an MNC’s subsidiary network with the crisis variable.
Therefore, the models used for empirical analysis take the following form:
19
𝑇ℎ𝑒 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑜𝑓 𝐼𝑃𝑆𝑠 = 𝛽0 + 𝛽1 𝐶𝑟𝑖𝑠𝑖𝑠 + 𝛽2 𝑇𝑜𝑡𝑎𝑙 𝐵𝑟𝑒𝑎𝑑𝑡ℎ + 𝛽3 𝐶𝑟𝑖𝑠𝑖𝑠 ∗ 𝑇𝑜𝑡𝑎𝑙 𝐵𝑟𝑒𝑎𝑑𝑡ℎ
+ δ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + ε
(1)
In order to test Hypothesis 3 and 4, we set ROA as the dependent variable. We interact
the incidence of intra-MN production shifts with the crisis variable. By the same token, we
interact breadth in production shifts with the crisis variable to test Hypothesis 6. Therefore, the
model used for empirical analysis takes the following form:
𝑅𝑂𝐴 = 𝛽0 + 𝛽1 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑜𝑓 𝐼𝑃𝑆𝑠 + 𝛽2 𝐵𝑟𝑒𝑎𝑑𝑡ℎ 𝑖𝑛 𝐼𝑃𝑆𝑠 + 𝛽3 𝐶𝑟𝑖𝑠𝑖𝑠
+𝛽4 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑜𝑓 𝐼𝑃𝑆𝑠 ∗ 𝐶𝑟𝑖𝑠𝑖𝑠 + 𝛽5 𝐵𝑟𝑒𝑎𝑑𝑡ℎ 𝑖𝑛 𝐼𝑃𝑆𝑠 ∗ 𝐶𝑟𝑖𝑠𝑖𝑠
+ δ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + ε
(2)
RESULTS
Table 1 summarizes descriptive statistics and correlation coefficients of the variables
used in this study. Table 1 shows that the correlation coefficients among the independent
variables in each Model (1) and (2) are not significantly large. We tested the variance inflation
factor to check potential multicollinearity and found that the highest variance inflation factor
(VIF) across the variables was 1.79 (mean=1.26) for Model (1) and 2.08 (mean=1.27) for Model
(2), lower than the commonly used VIF value, 10. This result indicates that multicollinearity is
not an issue for our analysis.
[Insert Table 1 about here]
Table 2 reports the empirical results of the firm fixed effect estimation for testing
Hypotheses 1 and 2. Model 1 includes only the controls. Among the control variables, the
20
Developing Country variable has constantly positive and significant effects on the incidence of
IPSs, indicating that as IPSs involve more developing countries, more frequent production shifts
will occur. However, an MNC’s indebtedness consistently has a negative and significant impact
on the incidence of IPSs. This may imply that MNCs experiencing more severe financial
restrictions will less often conduct production shifts that are not cost free.
Hypothesis 1 predicts that an economic crisis will be negatively associated with the
incidence of IPSs. We found that the coefficients of the Crisis variable are negative and
statistically significant even at the 0.001 level across all models that include this variable. Thus,
Hypothesis 1 is supported. Moreover, in Model 5, the coefficient of the interaction term between
Crisis and Total Breadth is positive and statistically significant at the 0.01 level. This result
shows that when an MNC’s subsidiary network has wider breadth, the negative association will
be weaker between an economic crisis and the incidence of IPSs. Thus, Hypothesis 2 is
supported. Overall, we can conclude that the impact of an economic crisis on the incidence of
IPSs is conditional on the breadth of an MNC’s subsidiary network. Interaction effects are
illustrated in Figure 1.
[Insert Table 2 about here]
[Insert Figure 1 about here]
Table 3 reports the main empirical results of Model (2) based on firm fixed-effects panel
estimations. Model 6 includes only controls. Among the control variables, the lagged Tobin’s q,
Sales growth, and Net-profit growth variables are consistently positively associated with MNC
performance (ROA). Also, the Developing Proportion variable, measured as the number of IPSs
between developing and developed countries over the total number of IPSs, is positively
associated with ROA across all models. This result implies that MNCs benefit more from
21
production shifts between developing and developed countries, because they exploit larger crossnational differences in economic conditions.
Hypothesis 3 argues that in the context of an economic crisis, the incidence of IPSs (i.e.,
the density) will weaken the negative association between an economic crisis and MNC
performance. The direct term of the incidence of production shifts and interaction term with the
Crisis variable do not show any significant result in any model. Thus, Hypothesis 3 is not
supported.
Hypothesis 4 predicts that the breadth in IPSs will weaken the negative association
between an economic crisis and MNC performance. The coefficients of the interaction term
between breadth in IPSs and Crisis are positive and statistically significant at the 0.01 level in
Model 10 and Model 12. Thus, Hypothesis 4 is supported. Moreover, the coefficients of the
breadth in IPSs are positive and significant at the 0.05 level in the models where the incidence of
IPSs is controlled.
[Insert Table 3 about here]
These results are illustrated in Figure 2.
[Insert Figure 2 about here]
Robustness tests
We conducted robustness tests to determine the sensitivity of our findings. First, we reran our regression Model (1) and (2) by setting the year 1997 and 2009 as an economic crisis
period. The Asian foreign exchange crisis started in mid-1997 and Korean economic conditions
were significantly affected by this crisis (Lee & Makhija, 2009a). Also, previous research often
defines the period of recent global economic crisis as 2008 and 2009 (Chodorow-Reich, 2014).
22
Hypothesis 1, 2 and 4 are supported at 0.01 significance level. However, Hypothesis 3 is still not
supported in any model. These results are reported in Tables 2 and 3.
Second, in our Hypothesis 1 and 2, we predict that MNCs confronting an economic crisis
will conduct highly concentrated production shifts. To test this prediction, we perform
supplementary analysis by setting the concentration of IPSs as the dependent variable. We adopt
the equation of Herfindahl–Hirschman Index to calculate the degree of concentration of an
MNC’s production shifts in a certain year. We find that an economic crisis is positively
associated with the degree of concentration of IPSs, while the breadth of the MNC’s network
will weaken this positive association. These findings are significant at the 0.05 level. The
empirical results of these supplementary analyses are summarized in Appendix A.
DISCUSSION
This study makes three major contributions to the literature. First, this study examines
how MNCs actually utilize IPSs in response to adverse environmental changes, notably
economic crises, and how those IPSs associate with performance of the focal MNCs. While
previous research has long argued that MNCs have a portfolio of options to shift resources and
production in response to adverse environmental changes (Kogut & Kulatilaka, 1994; Tang &
Tikoo, 1999), the specific way that MNCs exercise those options to shift rarely has been
examined.
Adopting the construct of network density, we find that when an MNC confronts an
economic crisis, it exercises fewer options to shift production among available options; instead,
it makes more concentrated production shifts. Also, the number of host countries involved in
IPSs, rather than the incidence of IPSs, is positively associated with MNC performance. These
findings may show that the primary purpose of IPSs is to exploit cross-national variation in
23
economic conditions and that the value of IPSs depends mainly on how well an MNC exploits
that variation. These findings may add a detailed picture of MNC operational flexibility to the
literature.
Second, by incorporating network perspective into operational flexibility research, this
study makes a methodological contribution to the literature. Specifically, this study directly
observes each production shift across MNCs’ multinational networks by establishing intra-firm
trade network matrices by year for each MNC. Then, adopting the construct of network density
that measures the rate of interactions that actually occur over total possible interactions inside the
network, we directly measure the incidence of production shifts. We suggest that this empirical
approach may allow researchers to assess MNCs’ operational flexibility more directly than the
approaches of previous research that measure MNCs’ operational flexibility using the total
number of host countries or the change of a subsidiary’s sales volume (Lee & Song, 2012; Tang
& Tikoo, 1999).
Third, this study will be the first empirical research to examine how the breadth of an
MNC’s international subsidiary network contributes to MNC operational flexibility. Although,
the breadth of an MNC’s international network has been regarded as the main source of MNC
operational flexibility (Allen & Pantzalis, 1996; Lee & Makhija, 200b; Tang & Tikoo, 1999),
how the breadth actually affect IPSs has not been investigated. We found that while the breadth
of an MNC’s international network is negatively associated with the incidence of IPSs under
stable economic conditions, it offers a valuable foundation of production shifts to MNCs
confronting economic crises. These findings augment the rare empirical support for the argument
that breadth matters to MNC operational flexibility.
24
Despite these contributions, this study has two limitations that provide opportunities for
future research. First, we could not find support for our Hypothesis 3 predicting that the
incidence of IPSs mitigates the negative association between economic crises and MNC
performance. We also could not find any positive association between the incidence of IPSs and
MNC performance, even during the years of stable economic conditions. These findings are
particularly interesting given that the breadth in IPSs has a positive association with the MNC in
any economic conditions. Therefore, we suspect that these results may reveal the costs of
production shifts across host countries. If the cost of IPSs exceeds their benefits, frequent
production shifts may be detrimental rather than beneficial to MNC performance. Thus, future
research may be required on how costs of IPSs affect MNC performance in the different contexts
and what factors generate costs during IPSs.
Second, in order to focus on cross-national production shifts, we aggregate subsidiaries
into a country level. While previous research notes that the depth (i.e., the number of subsidiaries
in one country) of MNCs’ multinational network does not add value to MNCs’ operational
flexibility (Allen & Pantzalis, 1996), the extent to which depth and breadth of MNCs’
multinational network interactively affect MNCs’ behaviors and performance remains unclear.
Therefore, future research may want to consider using detailed network matrices, including all
the MNC’s particular subsidiaries for the empirical analysis.
CONCLUSION
Operational flexibility research makes significant contribution to the international
business literature by revealing that an MNC acquires a portfolio of options to shift production
across its international subsidiary network. By examining how MNCs exercise those options in
response to economic crises, we endeavor to extend the literature. We find that MNCs exercise
25
their options to shift production in different ways in different contexts, and how well they exploit
cross-national variation in economic conditions has a significant effect on their performance. The
findings of this study may have significant implications for research on operational flexibility as
well as for research on MNCs.
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28
Table 1. Descriptive Statistics and Correlation Matrixa
1
2
3
4
5
6
7
8
9
10
Variable
Incidence
of IPSs
ROA
Crisis
Total
Breadth
Breadth
in IPSs
Developing
Country
Developing
Proportion
Total size
of IPSs
Lagged
Tobin’s q
The MMC
size
Mean
0.38
SD
0.62
1
1.00
2
3
4
5
6
7
8
9
0.03
0.09
6.94
0.13
0.29
10.08
0.01
-0.08
-0.21*
1.00
-0.06
0.07
1.00
0.00
1.00
3.16
2.86
0.08
0.06
0.03
0.48*
0.28
0.25
0.20*
-0.00
0.06
0.03
0.29*
1.00
0.44
0.32
-0.02
0.03
0.04
-0.15*
-0.16*
0.78*
1.00
4.55
7.74
0.10*
-0.02
0.09*
0.03
0.32*
0.31*
-0.20*
1.00
19.85
1.63
-0.08
0.07*
0.05
0.09*
0.02
-0.03
0.01
0.02
1.00
1.01
0.92
-0.09
0.05
-0.03
0.62*
0.53*
-0.10*
-0.26*
0.11*
0.02
10
11
12
13
14
15
16
17
18
0.03
0.04
1.00
0.2*
1.00
0.08*
-0.01
0.03
1.00
-0.05
-0.00
-0.01
-0.00
19
1.00
1.00
11
Lagged
0.52
0.24
-0.09
-0.12*
0.04
-0.06
-0.07
-0.12*
-0.11*
0.02
0.09*
0.03
1.00
Indebtedness
Change
3.61e+08
2.63e+09
0.01
0.04
-0.06
0.28*
0.23*
-0.02
-0.07
-0.03
0.03
0.31*
-0.03
1.00
in Assets
13
3 year
0.26
0.34
0.07
0.03
0.01
0.07
0.11*
0.17*
0.01
0.21*
-0.00
-0.06
0.03
-0.01
1.00
average of
R&D
intensity
14
Advertising
0.01
0.03
-0.08
0.08*
0.01
0.14*
0.06
-0.09*
-0.10
0.01
0.06
0.17*
-0.10*
0.04
-0.07*
1.00
intensity
15
Export
0.31
0.34
0.07
-0.00
-0.03
0.01
0.11*
0.09*
0.00
0.20*
0.00
0.02
0.06
0.05
0.57*
-0.12*
intensity
16
Sales growth
0.11
0.63
-0.07
0.08*
-0.01
-0.01
-0.02
-0.09*
-0.04
-0.02
0.00
0.03
0.02
0.06
-0.03
-0.02
17
Net-profit
1.55
77.88
0.01
0.01
-0.00
-0.01
0.01
0.01
0.00
-0.01
-0.01
0.01
0.01
0.00
0.01
-0.01
growth
18
Firm capital
0.57
0.16
0.04
-0.04
0.03
0.13*
0.21*
-0.07
-0.14*
0.08*
-0.06
0.31*
-0.05
0.04
-0.05
0.15*
intensity
19
Current ratio
1.83
4.46
-0.01
0.06
0.03
-0.01
-0.02
0.05
0.08
-0.00
-0.01
-0.03
-0.22*
-0.01
-0.05
0.19*
a
*p<.05: the STATA option, “sidak sig” was used to control for any “multiple comparison fallacy” in the Pearson correlation. It identifies the handful of significant values at the 0.05 level (Hamilton, 2006).
12
29
1.00
1.00
Table 2. Firm fixed effect panel estimation: Hypothesis 1 and 2 a
Variable
Model 1
Crisis
Model 2
Crisis= 1998, 2008
Model 3
Model 4
-0.146***
(0.035)
Total Breadth
-0.027**
(0.010)
-0.121**
(0.035)
-0.026**
(0.010)
0.709***
(0.130)
0.004
(0.003)
-0.016
(0.021)
0.004
(0.070)
-0.322*
(0.145)
0.197
(0.125)
0.058
(0.073)
-0.016
(0.040)
0.077
(0.194)
-0.002
(0.002)
0.270
(1.300)
1252
148
9.89
9.24***
0.756***
(0.130)
0.004
(0.003)
-0.012
(0.021)
-0.001
(0.070)
-0.306*
(0.142)
0.192
(0.125)
0.038
(0.073)
-0.017
(0.039)
0.081
(0.195)
-0.001
(0.002)
0.380
(1.295)
1252
148
10.39
8.45***
Crisis x Total Breadth
Developing Country
Total size of IPSs
Lagged Tobin’s q
The MMC size
Lagged Indebtedness
3 year average of R&D intensity
Export intensity
Sales growth
Firm capital intensity
Current ratio
Constant
No. of observations
N
R-squared
F-statistics
0.688***
(0.131)
0.004
(0.003)
-0.035†
(0.020)
-0.075
(0.057)
-0.228†
(0.122)
0.171
(0.132)
0.148*
(0.061)
-0.005
(0.040)
0.055
(0.199)
-0.001
(0.002)
1.642
(1.102)
1252
148
5.86
9.74***
0.684***
(0.131)
0.004
(0.003)
-0.030
(0.019)
-0.078
(0.057)
-0.212†
(0.120)
0.166
(0.132)
0.120*
(0.060)
-0.007
(0.040)
0.061
(0.201)
-0.001
(0.002)
1.731
(1.086)
1252
148
6.59
8.97***
a
Robust Standard errors appear in parentheses. All tests are two-tailed
† p < .10; * p < .05; ** p < .01; *** p < .001.
30
Model 5
-0.209***
(0.051)
-0.029**
(0.010)
0.012**
(0.004)
0.700***
(0.130)
0.004
(0.003)
-0.013
(0.021)
0.004
(0.070)
-0.301*
(0.140)
0.190
(0.124)
0.042
(0.017)
-0.015
(0.039)
0.089
(0.195)
-0.001
(0.002)
0.298
(1.294)
1252
148
10.87
8.18***
Model 1-1
Crisis= 1997, 1998, 2008, 2009
Model 2-1 Model 3-1 Model 4-1
-0.134**
(0.039)
0.688***
(0.131)
0.004
(0.003)
-0.035†
(0.020)
-0.075
(0.057)
-0.228†
(0.122)
0.171
(0.132)
0.148*
(0.061)
-0.005
(0.040)
0.055
(0.199)
-0.001
(0.002)
1.642
(1.102)
1252
148
5.86
9.74***
0.675***
(0.130)
0.004
(0.003)
-0.031
(0.019)
-0.081
(0.057)
-0.194
(0.118)
0.155
(0.131)
0.120†
(0.061)
0.001
(0.041)
0.013
(0.200)
-0.001
(0.002)
1.828†
(1.090)
1252
148
6.63
9.08***
-0.027**
(0.010)
-0.125**
(0.038)
-0.026**
(0.010)
0.709***
(0.130)
0.004
(0.003)
-0.016
(0.021)
0.004
(0.070)
-0.322*
(0.145)
0.197
(0.125)
0.058
(0.073)
-0.016
(0.040)
0.077
(0.194)
-0.002
(0.002)
0.270
(1.298)
1252
148
9.89
9.24***
0.697***
(0.129)
0.004
(0.003)
-0.012
(0.021)
-0.003
(0.070)
-0.289*
(0.140)
0.181
(0.124)
0.033
(0.074)
-0.010
(0.040)
0.037
(0.193)
-0.001
(0.002)
0.460
(1.292)
1252
148
10.57
8.40***
Model 5-1
-0.181***
(0.050)
-0.029**
(0.010)
0.010**
(0.003)
0.689***
(0.127)
0.003
(0.003)
-0.013
(0.021)
0.001
(0.070)
-0.280*
(0.137)
0.175
(0.126)
0.038
(0.072)
-0.006
(0.041)
0.023
(0.193)
-0.001
(0.002)
0.407
(1.288)
1252
148
10.98
8.11***
Table 3. Firm level fixed effects panel estimation: Hypothesis 3 and 4a, b
Variable
Model 6
Model 7
Model 8
Model 9
Model 10
Model 11
Model 12
Model 13
Crisis
-0.038**
-0.079**
-0.038*
-0.080**
(0.012)
0.005*
(0.002)
0.002
(0.004)
(0.023)
0.004†
(0.002)
0.001
(0.004)
(0.017)
0.005*
(0.002)
0.002
(0.004)
(0.027)
0.004†
(0.002)
0.001
(0.004)
-0.037**
(-0.011)
Breadth in
IPSs
Incidence of
IPSs
0.003
(0.002)
0.005
(0.004)
Crisis x
Breadth in
IPSs
Crisis x
Incidence of
IPSs
Developing
Proportion
0.011**
(0.004)
0.044*
(0.021)
0.043*
(0.021)
0.043*
(0.021)
0.050†
(0.027)
0.046†
(0.026)
Total size of
IPSs
Lagged
Tobin’s q
The MMC
size
0.0002
(0.0004)
0.015*
(0.007)
0.013
(0.017)
0.0002
(0.0004)
0.017*
(0.008)
0.013
(0.017)
0.0002
(0.0003)
0.014†
(0.007)
0.009
(0.017)
0.0002
(0.0005)
0.014†
(0.007)
0.003
(0.020)
Change in
Assets
9.25e-12
7.37e-12
9.34e-12
(6.65e-12)
(6.73e-12)
Lagged
Indebtedness
0.029
(0.078)
3 year
average of
R&D
intensity
Advertising
intensity
Export
intensity
Sales growth
0.011**
(0.004)
-0.000
(0.037)
0.003
(0.035)
0.046†
(0.026)
0.046†
(0.026)
0.046†
(0.025)
0.0003
(0.0005)
0.015*
(0.007)
-0.004
(0.019)
0.0003
(0.0005)
0.016*
(0.007)
-0.007
(0.019)
0.0003
(0.0005)
0.015*
(0.007)
-0.004
(0.019)
0.0003
(0.0005)
0.015*
(0.007)
-0.006
(0.019)
9.00e-12
7.25e-12
1.22e-12*
7.25e-12
1.23e-12*
(6.57e-12)
(6.27e-12)
(6.12e-12)
(6.17e-12)
(6.10e-12)
(6.16e-12)
0.032
(0.077)
0.034
(0.078)
0.036
(0.088)
0.049
(0.086)
0.048
(0.085)
0.049
(0.086)
0.048
(0.086)
0.040†
(0.020)
0.039†
(0.020)
0.038†
(0.020)
0.035
(0.023)
0.032
(0.022)
0.030
(0.021)
0.032
(0.022)
0.030
(0.021)
0.153
(0.243)
0.132
(0.241)
0.154
(0.245)
0.215
(0.299)
0.200
(0.303)
0.180
(0.303)
0.200
(0.303)
0.180
(0.303)
Net-profit
growth
-0.017
(0.015)
0.027
(0.017)
0.002†
(0.001)
-0.023
(0.015)
0.027
(0.018)
0.002*
(0.001)
-0.016
(0.015)
0.027
(0.018)
0.002†
(0.001)
-0.013
(0.017)
0.075**
(0.026)
0.003†
(0.002)
-0.017
(0.018)
0.080**
(0.027)
0.003†
(0.002)
-0.016
(0.017)
0.077**
(0.026)
0.003*
(0.002)
-0.017
(0.018)
0.080**
(0.027)
0.003†
(0.002)
-0.016
(0.017)
0.077**
(0.026)
0.003*
(0.002)
Firm capital
intensity
-0.055
(0.036)
-0.057
(0.037)
-0.057
(0.036)
-0.046
(0.044)
-0.046
(0.045)
-0.047
(0.045)
-0.046
(0.045)
-0.047
(0.045)
Current ratio
0.0004
(0.0007)
-0.263
(0.370)
1259
0.0004
(0.0007)
-0.261
(0.365)
1259
0.0004
(0.0007)
0.204
(0.364)
1259
0.001
(0.001)
-0.089
(0.420)
1055
0.001
(0.001)
0.039
(0.403)
1055
0.001
(0.001)
0.094
(0.403)
1055
0.001
(0.001)
0.039
(0.401)
1055
0.001
(0.001)
0.093
(0.401)
1055
144
5.70
144
6.72
144
5.82
142
7.04
142
8.35
142
8.85
142
8.35
142
8.85
5.12***
5.66***
4.86***
4.86***
5.11***
5.12***
4.80***
4.83***
Constant
No. of
observations
N
R-squared
F-statistics
a
Robust Standard errors appear in parentheses. All tests are two-tailed
† p < .10; * p < .05; ** p < .01; *** p < .001.
31
Table 3. Firm level fixed effects panel estimation: Hypothesis 3 and 4a, b (Continued)
Variable
Model
Model
Model
Model
Model
6-1
7-1
8-1
9-1
10-1
Crisis
-0.034***
-0.037***
(0.008)
(0.009)
Breadth in
0.003
0.005*
IPSs
(0.002)
(0.002)
Incidence of
0.005
0.001
IPSs
(0.004)
(0.004)
Crisis x
Breadth in
IPSs
Crisis x
Incidence of
IPSs
Developing
Proportion
Model
11-1
-0.054***
(0.012)
0.004†
(0.002)
0.001
(0.004)
Model
12-1
-0.036**
(0.011)
0.005*
(0.002)
0.002
(0.004)
0.006**
(0.002)
Model
13-1
-0.051***
(0.012)
0.004†
(0.002)
0.001
(0.004)
0.006*
(0.002)
-0.006
(0.031)
-0.019
(0.033)
0.044*
(0.021)
0.043*
(0.021)
0.043*
(0.021)
0.050†
(0.027)
0.047†
(0.026)
0.047†
(0.026)
0.047†
(0.026)
0.047†
(0.026)
Total size of
IPSs
Lagged
Tobin’s q
The MMC size
0.0002
(0.0004)
0.015*
(0.007)
0.013
(0.017)
0.0001
(0.0004)
0.017*
(0.008)
0.012
(0.017)
0.0002
(0.0004)
0.014†
(0.007)
0.009
(0.017)
0.0002
(0.0005)
0.014†
(0.007)
0.003
(0.020)
0.00002
(0.0004)
0.014*
(0.007)
-0.005
(0.019)
0.0001
(0.0005)
0.014*
(0.007)
-0.006
(0.018)
0.0002
(0.0005)
0.014†
(0.007)
-0.005
(0.019)
0.0001
(0.0005)
0.0140†
(0.007)
-0.006
(0.018)
Change in
Assets
Lagged
Indebtedness
9.25e-12
(6.65e-12)
0.029
(0.078)
7.96e-12
(6.76e-12)
0.037
(0.076)
9.34e-12
(6.57e-12)
0.034
(0.078)
9.00e-12
(6.27e-12)
0.036
(0.088)
7.77e-12
(6.16e-12)
0.054
(0.085)
1.09e-11†
(6.31e-12)
0.055
(0.085)
7.69e-12
(6.09e-12)
0.053
(0.085)
1.09e-11†
(6.23e-12)
0.054
(0.085)
3 year average
of R&D
intensity
0.040†
(0.020)
0.036†
(0.020)
0.038†
(0.020)
0.035
(0.023)
0.029
(0.021)
0.027
(0.021)
0.029
(0.021)
0.027
(0.021)
Advertising
intensity
0.153
(0.243)
0.127
(0.241)
0.154
(0.245)
0.215
(0.299)
0.192
(0.302)
0.181
(0.303)
0.192
(0.303)
0.179
(0.303)
Export
intensity
Sales growth
Net-profit
growth
-0.017
(0.015)
0.027
(0.017)
0.002†
(0.001)
-0.023
(0.015)
0.028
(0.018)
0.002*
(0.001)
-0.016
(0.015)
0.027
(0.018)
0.002†
(0.001)
-0.013
(0.017)
0.075**
(0.026)
0.003†
(0.002)
-0.018
(0.017)
0.082**
(0.027)
0.003†
(0.002)
-0.017
(0.017)
0.081**
(0.027)
0.003†
(0.002)
-0.018
(0.017)
0.082**
(0.027)
0.003†
(0.002)
-0.017
(0.017)
0.081**
(0.027)
0.003†
(0.002)
Firm capital
intensity
-0.055
(0.036)
-0.066†
(0.039)
-0.057
(0.036)
-0.046
(0.044)
-0.056
(0.048)
-0.061
(0.049)
-0.055
(0.048)
-0.061
(0.049)
Current ratio
0.0004
(0.0007)
-0.263
(0.370)
1259
0.0004
(0.0007)
-0.245
(0.365)
1259
0.0004
(0.0007)
-0.204
(0.364)
1259
0.001
(0.001)
-0.089
(0.420)
1055
0.001
(0.001)
0.060
(0.400)
1055
0.001
(0.001)
0.095
(0.396)
1055
0.001
(0.001)
0.061
(0.399)
1055
0.001
(0.001)
0.102
(0.394)
1055
N
R-squared
144
5.70
144
6.76
144
5.82
142
7.04
142
8.55
142
8.78
142
8.55
142
8.81
F-statistics
5.12***
5.70***
4.86***
4.86***
5.24***
5.07***
4.93***
4.08***
Constant
No. of
observations
a
Robust Standard errors appear in parentheses. All tests are two-tailed
† p < .10; * p < .05; ** p < .01; *** p < .001.
32
APPENDIX A. Firm fixed effect panel estimation: concentration of production shifts a
Variable
Crisis
Model 1
Model 2
0.020
(0.017)
Breadth of an MNC’s subsidiary network
Crisis x Breadth of an MNC’s subsidiary
network
Developing Country
Total size of IPSs
Lagged Tobin’s q
The MMC size
Lagged Indebtedness
3 year average of R&D intensity
Export intensity
Sales growth
Firm capital intensity
Current ratio
Constant
No. of observations
N
R-squared
F-statistics
a
0.516***
(0.073)
0.011***
(0.001)
-0.013
(0.021)
-0.0003
(0.025)
-0.002
(0.051)
0.0001
(0.044)
0.029
(0.032)
-0.003
(0.015)
-0.113
(0.099)
-0.003***
(0.001)
0.238
(0.505)
1551
151
21.35
18.96***
Robust Standard errors appear in parentheses. All tests are two-tailed
† p < .10; * p < .05; ** p < .01; *** p < .001.
33
0.517***
(0.073)
0.011***
(0.001)
-0.014
(0.020)
-0.0003
(0.025)
-0.004
(0.051)
0.001
(0.044)
0.033
(0.032)
-0.003
(0.015)
-0.111
(0.098)
-0.004***
(0.001)
0.234
(0.505)
1551
151
21.40
17.37***
Model 3
-0.002
(0.001)
Model 4
0.025
(0.020)
-0.002†
(0.001)
0.474***
(0.080)
0.010***
(0.002)
-0.005
(0.021)
0.010
(0.027)
-0.019
(0.052)
-0.031
(0.037)
0.017
(0.031)
-0.030
(0.034)
-0.137
(0.109)
-0.003***
(0.001)
0.099
(0.558)
1252
148
18.70
13.65***
0.474***
(0.080)
0.010***
(0.002)
-0.006
(0.021)
0.011
(0.027)
-0.022
(0.052)
-0.030
(0.036)
0.021
(0.032)
-0.030
(0.034)
-0.138
(0.109)
-0.003***
(0.001)
0.076
(0.556)
1252
148
18.80
12.95***
Model 5
0.050*
(0.025)
-0.001
(0.001)
-0.003*
(0.001)
0.476***
(0.080)
0.010***
(0.002)
-0.006
(0.021)
0.010
(0.027)
-0.023
(0.052)
-0.030
(0.037)
0.020
(0.031)
-0.030
(0.034)
-0.140
(0.109)
-0.003***
(0.001)
0.100
(0.555)
1252
148
18.98
12.42***
Figure 1. The interaction effect between economic crises and total breadth on the incidence
of IPSs
0.5
0.4
The Incidence of IPSs
0.3
0.2
0.1
Low Total
Breadth
0
High Total
Breadth
-0.1
-0.2
-0.3
No Crisis
Crisis
Figure 2. The interaction effect between economic crises and the breadth in IPSs on ROA
0.14
ROA
0.12
0.1
0.08
Low Breadth
in IPSs
0.06
High Breadth
in IPSs
0.04
0.02
0
No Crisis
Crisis
34