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 cross-

national 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

host countries among total available options (i.e. incidence of IPSs); 2) how many host countries 1 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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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

portfolio of options to shift enables the MNC to cope better with abrupt environmental changes

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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.

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

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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).

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

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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

export. Thus, we argue that the breadth of an MNC’s subsidiary network will mitigate the

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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 intra-

MNC 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

relatively cheaper input costs, the MNC can exploit this favorable economic condition by

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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 cross-

national 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

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

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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 &

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

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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

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

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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

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

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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

variable, which is measured as the total number of countries in an MNC’s subsidiary network

(Allen & Pantzalis, 1996; Tang & Tikoo, 1999).

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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.

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

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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

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.

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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 cross-

national 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

constraints a firm faces” (Feldman, Amit, & Villalonga, 2016: 435). This is measured as an

MNC’s current assets divided by current liabilities.

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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 fixed-

effects 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:

T he incidenceo f IPSs=β0+β1Crisis+ β2 Total Breadth+β3Crisis∗Total Breadth

+δ Controls+ε (1)

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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 I ncidence o f IPSs+β2 Breadth∈IPSs+β3 Crisis

+β4 I ncidence o f IPSs∗Crisis+β5 Breadth∈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

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

20

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

production shifts between developing and developed countries, because they exploit larger cross-

national differences in economic conditions.

21

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 re-

ran 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).

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.

22

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

economic conditions and that the value of IPSs depends mainly on how well an MNC exploits

23

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.

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

24

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

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

25

findings of this study may have significant implications for research on operational flexibility as

well as for research on MNCs.

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Table 1. Descriptive Statistics and Correlation Matrixa

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 191 Incidence

of IPSs0.38 0.62 1.00

2 ROA 0.03 0.13 0.01 1.003 Crisis 0.09 0.29 -0.08 -0.06 1.004 Total

Breadth6.94 10.08 -0.21* 0.07 0.00 1.00

5 Breadth in IPSs

3.16 2.86 0.08 0.06 0.03 0.48* 1.00

6 Developing Country

0.28 0.25 0.20* -0.00 0.06 0.03 0.29* 1.00

7 DevelopingProportion

0.44 0.32 -0.02 0.03 0.04 -0.15* -0.16* 0.78* 1.00

8 Total size of IPSs

4.55 7.74 0.10* -0.02 0.09* 0.03 0.32* 0.31* -0.20* 1.00

9 Lagged Tobin’s q

19.85 1.63 -0.08 0.07* 0.05 0.09* 0.02 -0.03 0.01 0.02 1.00

10

The MMC size

1.01 0.92 -0.09 0.05 -0.03 0.62* 0.53* -0.10* -0.26* 0.11* 0.02 1.00

11

Lagged Indebtedness

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

12

Change in Assets

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

13

3 year average of R&D intensity

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

14

Advertising intensity

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

15

Export intensity

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* 1.00

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 0.03 1.00

17

Net-profit growth

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 0.04 0.2* 1.00

18

Firm capital intensity

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* 0.08* -0.01 0.03 1.00

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* -0.05 -0.00 -0.01 -0.00 1.00

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).

31

Table 2. Firm fixed effect panel estimation: Hypothesis 1 and 2 a

Crisis= 1998, 2008 Crisis= 1997, 1998, 2008, 2009Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 1-1 Model 2-1 Model 3-1 Model 4-1 Model 5-1Crisis -0.146*** -0.121** -0.209*** -0.134** -0.125** -0.181***

(0.035) (0.035) (0.051) (0.039) (0.038) (0.050)Total Breadth -0.027** -0.026** -0.029** -0.027** -0.026** -0.029**

(0.010) (0.010) (0.010) (0.010) (0.010) (0.010)Crisis x Total Breadth 0.012** 0.010**

(0.004) (0.003)Developing Country 0.688*** 0.684*** 0.709*** 0.756*** 0.700*** 0.688*** 0.675*** 0.709*** 0.697*** 0.689***

(0.131) (0.131) (0.130) (0.130) (0.130) (0.131) (0.130) (0.130) (0.129) (0.127)Total size of IPSs 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.003

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)Lagged Tobin’s q -0.035† -0.030 -0.016 -0.012 -0.013 -0.035† -0.031 -0.016 -0.012 -0.013

(0.020) (0.019) (0.021) (0.021) (0.021) (0.020) (0.019) (0.021) (0.021) (0.021)The MMC size -0.075 -0.078 0.004 -0.001 0.004 -0.075 -0.081 0.004 -0.003 0.001

(0.057) (0.057) (0.070) (0.070) (0.070) (0.057) (0.057) (0.070) (0.070) (0.070)Lagged Indebtedness -0.228† -0.212† -0.322* -0.306* -0.301* -0.228† -0.194 -0.322* -0.289* -0.280*

(0.122) (0.120) (0.145) (0.142) (0.140) (0.122) (0.118) (0.145) (0.140) (0.137)3 year average of R&D intensity 0.171 0.166 0.197 0.192 0.190 0.171 0.155 0.197 0.181 0.175

(0.132) (0.132) (0.125) (0.125) (0.124) (0.132) (0.131) (0.125) (0.124) (0.126)Export intensity 0.148* 0.120* 0.058 0.038 0.042 0.148* 0.120† 0.058 0.033 0.038

(0.061) (0.060) (0.073) (0.073) (0.017) (0.061) (0.061) (0.073) (0.074) (0.072)Sales growth -0.005 -0.007 -0.016 -0.017 -0.015 -0.005 0.001 -0.016 -0.010 -0.006

(0.040) (0.040) (0.040) (0.039) (0.039) (0.040) (0.041) (0.040) (0.040) (0.041)Firm capital intensity 0.055 0.061 0.077 0.081 0.089 0.055 0.013 0.077 0.037 0.023

(0.199) (0.201) (0.194) (0.195) (0.195) (0.199) (0.200) (0.194) (0.193) (0.193)Current ratio -0.001 -0.001 -0.002 -0.001 -0.001 -0.001 -0.001 -0.002 -0.001 -0.001

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)Constant 1.642 1.731 0.270 0.380 0.298 1.642 1.828† 0.270 0.460 0.407

(1.102) (1.086) (1.300) (1.295) (1.294) (1.102) (1.090) (1.298) (1.292) (1.288)No. of observations 1252 1252 1252 1252 1252 1252 1252 1252 1252 1252N 148 148 148 148 148 148 148 148 148 148R-squared 5.86 6.59 9.89 10.39 10.87 5.86 6.63 9.89 10.57 10.98F-statistics 9.74*** 8.97*** 9.24*** 8.45*** 8.18*** 9.74*** 9.08*** 9.24*** 8.40*** 8.11***a Robust Standard errors appear in parentheses. All tests are two-tailed† p < .10; * p < .05; ** p < .01; *** p < .001.

32

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 -0.037** -0.038** -0.079** -0.038* -0.080**

(-0.011) (0.012) (0.023) (0.017) (0.027)Breadth in IPSs

0.003 0.005* 0.004† 0.005* 0.004†(0.002) (0.002) (0.002) (0.002) (0.002)

Incidence of IPSs

0.005 0.002 0.001 0.002 0.001(0.004) (0.004) (0.004) (0.004) (0.004)

Crisis x Breadth in IPSs

0.011** 0.011**(0.004) (0.004)

Crisis x Incidence of IPSs

-0.000 0.003(0.037) (0.035)

Developing Proportion

0.044* 0.043* 0.043* 0.050† 0.046† 0.046† 0.046† 0.046†(0.021) (0.021) (0.021) (0.027) (0.026) (0.026) (0.026) (0.025)

Total size of IPSs

0.0002 0.0002 0.0002 0.0002 0.0003 0.0003 0.0003 0.0003(0.0004) (0.0004) (0.0003) (0.0005) (0.0005) (0.0005) (0.0005) (0.0005)

Lagged Tobin’s q

0.015* 0.017* 0.014† 0.014† 0.015* 0.016* 0.015* 0.015*(0.007) (0.008) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

The MMC size

0.013 0.013 0.009 0.003 -0.004 -0.007 -0.004 -0.006(0.017) (0.017) (0.017) (0.020) (0.019) (0.019) (0.019) (0.019)

Change in Assets

9.25e-12 7.37e-12 9.34e-12 9.00e-12 7.25e-12 1.22e-12* 7.25e-12 1.23e-12*

(6.65e-12) (6.73e-12) (6.57e-12) (6.27e-12) (6.12e-12) (6.17e-12) (6.10e-12) (6.16e-12)

Lagged Indebtedness

0.029 0.032 0.034 0.036 0.049 0.048 0.049 0.048(0.078) (0.077) (0.078) (0.088) (0.086) (0.085) (0.086) (0.086)

3 year average of R&D intensity

0.040† 0.039† 0.038† 0.035 0.032 0.030 0.032 0.030(0.020) (0.020) (0.020) (0.023) (0.022) (0.021) (0.022) (0.021)

Advertising intensity

0.153 0.132 0.154 0.215 0.200 0.180 0.200 0.180(0.243) (0.241) (0.245) (0.299) (0.303) (0.303) (0.303) (0.303)

Export intensity

-0.017 -0.023 -0.016 -0.013 -0.017 -0.016 -0.017 -0.016(0.015) (0.015) (0.015) (0.017) (0.018) (0.017) (0.018) (0.017)

Sales growth 0.027 0.027 0.027 0.075** 0.080** 0.077** 0.080** 0.077**(0.017) (0.018) (0.018) (0.026) (0.027) (0.026) (0.027) (0.026)

Net-profit growth

0.002† 0.002* 0.002† 0.003† 0.003† 0.003* 0.003† 0.003*(0.001) (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002)

Firm capital intensity

-0.055 -0.057 -0.057 -0.046 -0.046 -0.047 -0.046 -0.047(0.036) (0.037) (0.036) (0.044) (0.045) (0.045) (0.045) (0.045)

Current ratio 0.0004 0.0004 0.0004 0.001 0.001 0.001 0.001 0.001(0.0007) (0.0007) (0.0007) (0.001) (0.001) (0.001) (0.001) (0.001)

Constant -0.263 -0.261 0.204 -0.089 0.039 0.094 0.039 0.093(0.370) (0.365) (0.364) (0.420) (0.403) (0.403) (0.401) (0.401)

No. of observations

1259 1259 1259 1055 1055 1055 1055 1055

N 144 144 144 142 142 142 142 142 R-squared 5.70 6.72 5.82 7.04 8.35 8.85 8.35 8.85

F-statistics 5.12*** 5.66*** 4.86*** 4.86*** 5.11*** 5.12*** 4.80*** 4.83***a Robust Standard errors appear in parentheses. All tests are two-tailed† p < .10; * p < .05; ** p < .01; *** p < .001.

33

Table 3. Firm level fixed effects panel estimation: Hypothesis 3 and 4a, b (Continued)

34

Variable Model 6-1

Model 7-1

Model 8-1

Model 9-1

Model 10-1

Model 11-1

Model 12-1

Model 13-1

Crisis -0.034*** -0.037*** -0.054*** -0.036** -0.051***(0.008) (0.009) (0.012) (0.011) (0.012)

Breadth in IPSs

0.003 0.005* 0.004† 0.005* 0.004†(0.002) (0.002) (0.002) (0.002) (0.002)

Incidence of IPSs

0.005 0.001 0.001 0.002 0.001(0.004) (0.004) (0.004) (0.004) (0.004)

Crisis x Breadth in IPSs

0.006** 0.006*(0.002) (0.002)

Crisis x Incidence of IPSs

-0.006 -0.019(0.031) (0.033)

Developing Proportion

0.044* 0.043* 0.043* 0.050† 0.047† 0.047† 0.047† 0.047†(0.021) (0.021) (0.021) (0.027) (0.026) (0.026) (0.026) (0.026)

Total size of IPSs

0.0002 0.0001 0.0002 0.0002 0.00002 0.0001 0.0002 0.0001(0.0004) (0.0004) (0.0004) (0.0005) (0.0004) (0.0005) (0.0005) (0.0005)

Lagged Tobin’s q

0.015* 0.017* 0.014† 0.014† 0.014* 0.014* 0.014† 0.0140†(0.007) (0.008) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

The MMC size 0.013 0.012 0.009 0.003 -0.005 -0.006 -0.005 -0.006(0.017) (0.017) (0.017) (0.020) (0.019) (0.018) (0.019) (0.018)

Change in Assets

9.25e-12 7.96e-12 9.34e-12 9.00e-12 7.77e-12 1.09e-11† 7.69e-12 1.09e-11†(6.65e-12) (6.76e-12) (6.57e-12) (6.27e-12) (6.16e-12) (6.31e-12) (6.09e-12) (6.23e-12)

Lagged Indebtedness

0.029 0.037 0.034 0.036 0.054 0.055 0.053 0.054(0.078) (0.076) (0.078) (0.088) (0.085) (0.085) (0.085) (0.085)

3 year average of R&D intensity

0.040† 0.036† 0.038† 0.035 0.029 0.027 0.029 0.027(0.020) (0.020) (0.020) (0.023) (0.021) (0.021) (0.021) (0.021)

Advertising intensity

0.153 0.127 0.154 0.215 0.192 0.181 0.192 0.179(0.243) (0.241) (0.245) (0.299) (0.302) (0.303) (0.303) (0.303)

Export intensity

-0.017 -0.023 -0.016 -0.013 -0.018 -0.017 -0.018 -0.017(0.015) (0.015) (0.015) (0.017) (0.017) (0.017) (0.017) (0.017)

Sales growth 0.027 0.028 0.027 0.075** 0.082** 0.081** 0.082** 0.081**(0.017) (0.018) (0.018) (0.026) (0.027) (0.027) (0.027) (0.027)

Net-profit growth

0.002† 0.002* 0.002† 0.003† 0.003† 0.003† 0.003† 0.003†(0.001) (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002)

Firm capital intensity

-0.055 -0.066† -0.057 -0.046 -0.056 -0.061 -0.055 -0.061(0.036) (0.039) (0.036) (0.044) (0.048) (0.049) (0.048) (0.049)

Current ratio 0.0004 0.0004 0.0004 0.001 0.001 0.001 0.001 0.001(0.0007) (0.0007) (0.0007) (0.001) (0.001) (0.001) (0.001) (0.001)

Constant -0.263 -0.245 -0.204 -0.089 0.060 0.095 0.061 0.102(0.370) (0.365) (0.364) (0.420) (0.400) (0.396) (0.399) (0.394)

No. of observations

1259 1259 1259 1055 1055 1055 1055 1055

N 144 144 144 142 142 142 142 142 R-squared 5.70 6.76 5.82 7.04 8.55 8.78 8.55 8.81

F-statistics 5.12*** 5.70*** 4.86*** 4.86*** 5.24*** 5.07*** 4.93*** 4.08***a Robust Standard errors appear in parentheses. All tests are two-tailed† p < .10; * p < .05; ** p < .01; *** p < .001.

35

APPENDIX A. Firm fixed effect panel estimation: concentration of production shifts a

Variable Model 1 Model 2 Model 3 Model 4 Model 5Crisis 0.020 0.025 0.050*

(0.017) (0.020) (0.025)Breadth of an MNC’s subsidiary network -0.002 -0.002† -0.001

(0.001) (0.001) (0.001)Crisis x Breadth of an MNC’s subsidiary network

-0.003*(0.001)

Developing Country 0.516*** 0.517*** 0.474*** 0.474*** 0.476***(0.073) (0.073) (0.080) (0.080) (0.080)

Total size of IPSs 0.011*** 0.011*** 0.010*** 0.010*** 0.010***(0.001) (0.001) (0.002) (0.002) (0.002)

Lagged Tobin’s q -0.013 -0.014 -0.005 -0.006 -0.006(0.021) (0.020) (0.021) (0.021) (0.021)

The MMC size -0.0003 -0.0003 0.010 0.011 0.010(0.025) (0.025) (0.027) (0.027) (0.027)

Lagged Indebtedness -0.002 -0.004 -0.019 -0.022 -0.023(0.051) (0.051) (0.052) (0.052) (0.052)

3 year average of R&D intensity 0.0001 0.001 -0.031 -0.030 -0.030(0.044) (0.044) (0.037) (0.036) (0.037)

Export intensity 0.029 0.033 0.017 0.021 0.020(0.032) (0.032) (0.031) (0.032) (0.031)

Sales growth -0.003 -0.003 -0.030 -0.030 -0.030(0.015) (0.015) (0.034) (0.034) (0.034)

Firm capital intensity -0.113 -0.111 -0.137 -0.138 -0.140(0.099) (0.098) (0.109) (0.109) (0.109)

Current ratio -0.003*** -0.004*** -0.003*** -0.003*** -0.003***(0.001) (0.001) (0.001) (0.001) (0.001)

Constant 0.238 0.234 0.099 0.076 0.100(0.505) (0.505) (0.558) (0.556) (0.555)

No. of observations 1551 1551 1252 1252 1252N 151 151 148 148 148R-squared 21.35 21.40 18.70 18.80 18.98F-statistics 18.96*** 17.37*** 13.65*** 12.95*** 12.42***a Robust Standard errors appear in parentheses. All tests are two-tailed† p < .10; * p < .05; ** p < .01; *** p < .001.

36

Figure 1. The interaction effect between economic crises and total breadth on the incidence of IPSs

-0.3

-0.2

-0.1

5.55111512312578E-17

0.1

0.2

0.3

0.4

0.5

Low Total Breadth

The

Inci

denc

e of

IPSs

No Crisis Crisis

Figure 2. The interaction effect between economic crises and the breadth in IPSs on ROA

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Low Breadth in IPSsHigh Breadth in IPSs

RO

A

No Crisis Crisis

37