Intra-firm Integration through Knowledge and Product Flows*

HEATHER BERRYDepartment of Management

The Wharton School 2022 Steinberg Hall-Dietrich Hall

University of PennsylvaniaPhiladelphia, PA 19104-6370

Tel: (215) 898-0990Fax: (215) 898-0401

E-mail: berryh@wharton.upenn.edu

May, 2008 DraftComments Welcome

* I would like to thank Mauro Guillen, Vit Hensiz, Steve Kobrin, Evan Rawley and Jordan Siegel for their comments on an earlier draft. The statistical analysis of this data was conducted at the International Investment Division, Bureau of Economic Analysis (BEA), US Department of Commerce under arrangements that maintain legal confidentiality requirements. Views expressed in this paper do not reflect those of the BEA or the Department of Commerce. I sincerely thank Ray Mataloni and Bill Zeile, both at the BEA, for their numerous responses to my questions about the BEA data. Finally, I would like to thank the Mack Center at the Wharton School for funding this research.

1

Intra-firm Integration through Knowledge and Product Flows

Abstract

An understudied issue on the integration of large and complex organizations concerns how firms use knowledge throughout their operations and how knowledge impacts integration across firm operations. In this paper, I focus on several mechanisms that firms use to exploit and create their knowledge assets and examine how these mechanisms impact production integration across firm operations. Results from a comprehensive panel dataset containing the worldwide operations of US manufacturing firms show different influences on production linkages depending on how firms exploit and develop their knowledge, and highlight an important role for people in encouraging intra-firm integration across all firm operations.

Keywords: Knowledge flows, Integration, Intra-firm trade, Expats, MNCs

2

Introduction:

Over four decades ago, Lawrence and Lorsch (1967) argued that all large and

complex organizations must integrate their operations to minimize overlap and conflict

among subunits and to bind them into an operational whole. Subsequent studies on

integration have focused on both the formal and informal mechanisms through which

firms can build, control and link the various subunits of their organization. One of the

more complex organizations that has been studied is the multinational corporation

(Bartlett and Ghoshal, 1989, 1990, Hedlund, 1986, Doz and Prahalad, 1993, and Hansen,

1999). Numerous studies on multinational corporations (MNCs) show that these types of

firms can benefit from the linkages they establish across their subsidiaries (Bartlett and

Ghoshal, 1989, 1990, Ghoshal and Westney, 1993, Doz and Prahalad, 1993, Malnight,

2001) by achieving cross-regional integration, exploiting firm knowledge and tapping

into clusters of expertise across geographically dispersed operations (Hedlund, 1986,

Birkinshaw, 1997, Frost et al. 2002, Feinburg and Gupta, 2003).

An understudied issue on the integration of large and complex organizations

concerns how firms use their knowledge throughout their operations and how knowledge

and information flows impact production integration across firm operations. A primary

reason why MNCs exist is because of their ability to transfer and exploit knowledge more

efficiently through internal expansion than through external market mechanisms (Hymer,

1960, Buckley and Casson, 1974, Teece, 1977, Gupta and Govindarajan, 2000). In

addition, firms create competitive advantages when they integrate their knowledge in

their production processes for goods and services (Ghoshal and Moran, 1996, Grant,

1996). However, while the importance of integrating knowledge in large-scale

enterprises is fairly well established theoretically (Kogut and Zander, 1992, Hansen, 1999

and McEvily et al., 2004), empirical evidence of how firms exploit and develop their

knowledge assets throughout their operations is scarce. To date, there has been little

empirical investigation of the determinants of intra-firm knowledge transfers (Gupta and

Govindarajan, 2000, Singh, 2008), or linkages across firm operations more generally

(Malnight, 2001).

3

In this paper, I examine several knowledge flow mechanisms that MNCs can use

to exploit and create their knowledge assets throughout their organization, including

transferring technology to subsidiaries, sending parent firm products to subsidiaries,

sending home country personnel to subsidiaries and creating knowledge through R&D

employees in subsidiaries. I consider how these knowledge flows through people,

products and technology licensing impact the production integration of firm operations.

Unlike recent research that focuses on knowledge flows through patent citations (Zhou,

2006, Singh, 2008), I intentionally focus on a larger set of firm operations and examine

how knowledge flows throughout a firm’s operations can impact the integration of

production activities. Given the importance of both knowledge and production for

manufacturing firms (and the linkages between these activities), I seek to analyze how

large and complex manufacturing firms use both knowledge and production flows in all

markets in which they operate. Importantly, I control for multiple host country, parent

firm, industry and subsidiary characteristics when examining how knowledge and

information flows impact the integration of firm production activities. Further, I make use

of propensity score matching techniques to control for other unobservable characteristics.

The results from a panel dataset based on survey data from the Bureau of

Economic Analysis (BEA) on the operations of US manufacturing firms that have foreign

investments from 1989-2005 offer several interesting findings about both knowledge and

production linkages across firm operations. First, the results show how a comprehensive

set of US manufacturing firms use people, products and licensing mechanisms throughout

their wholly-owned operations and further, how these firms link their production

operations. These knowledge flow and production linkages are discussed below in both

the results and conclusion sections. Second, the results show how people, product and

licensing knowledge flow mechanisms impact production linkages across firm

operations. People play an important role in all types of subsidiary linkages – whether a

subsidiary is exporting its products back to parent operations or other subsidiary

operations. However, there are interesting differences based on whether a subsidiary’s

production operations are linked with parent firm operations or with other subsidiary

operations. Regarding subsidiary to parent firm production linkages, subsidiaries that

have higher levels of R&D employees, higher levels of expat employees and that receive

4

more imports from their parents are significantly likely to have higher production

integration with parent operations. For subsidiary to other subsidiary production

linkages, subsidiaries that license technology from parent firms and have higher levels of

R&D employees are significantly likely to have higher production integration with other

subsidiary operations. Taken together, these results show how different knowledge flow

mechanisms impact production integration across MNC internal operations. Overall, the

results highlight the important role of people in encouraging production linkages across

all types of intra-firm manufacturing operations.

Integration and Firm Knowledge:

Organizations must address how they will divide and subsequently coordinate

activities across their subunits. To be successful, they must balance differentiation and

integration across their operations (Lawrence and Lorsch, 1967, 1986). Differentiation

refers to segmentation of organizational systems into subsystems while integration

involves combining these separate parts so that they work together or form a whole. For

complex organizations like multinational corporations, extant literature depicts these firm

structures as containing internally differentiated operations that leverage

interdependencies across subunits (Bartlett and Ghoshal, 1989, Ghoshal and Westney,

1993 and Prahalad and Doz, 1987). To respond effectively to environmental

heterogeneity across its subunits, an MNC must differentiate the activities of its

subsidiaries to take advantage of diverse local environments while simultaneously

integrating these activities to leverage interdependencies across these subunits (Ghoshal

and Nohria, 1989). These studies suggest that benefits arise not only from the strength of

dispersed units, but also from the linkages that firms create across their subunits

(Malnight, 2001).

Much of the early literature in organization theory on multinational corporations

focuses on how firms deal with pressures to reduce costs or be locally responsive across

the markets in which they operate. The integration-responsiveness framework (Prahalad

and Doz, 1987) considers the structures that firms choose as they expand their worldwide

operations (including international, multi-domestic, global and transnational strategies

and structures of MNCs). While early studies argue for distinct roles for subsidiaries to

5

either respond to cost or local adaptation pressures, Bartlett and Ghoshal (1987)

emphasized the need for firms to be simultaneously responsive to different environments

while also integrating operations to gain efficiency advantages. Bartlett (1986) criticized

the notion that all subsidiaries are likely to play the same role within an MNC. He argued

that subsidiaries can differ in terms of competencies that reside in them and the strategic

importance of the local environment. More recent research on the organizational

structures of MNCs has increasingly moved away from hierarchical models that focus on

one dimension (ie, responsiveness) at the expense of the other (integration). Instead, an

MNC is often conceptualized as an “inter-organizational network” with multiple vertical

and horizontal relations, and with diverse resources that can be found throughout the

subunits of the organization to allow firms to respond to both integration and

responsiveness dimensions (Ghoshal and Bartlett, 1990, Bartlett and Ghoshal, 2002,

Gupta and Govindarajan, 1991, 2000, and Hedlund, 1986). For example, Jarillo and

Martinez (1990) classified a firm’s subsidiaries according to configuration and

coordination. Further, several scholars have advanced the notion that MNCs can create

centers of excellence across locations to create knowledge that can be used throughout a

firm’s network of operations (see Cantwell, 1989, Birkinshaw and Hood, 1998, Taggart,

1998 and Frost et al., 2002, for example). The “combinative capacity” (Kogut and

Zander, 1992) of a firm’s network of operations can allow multinational corporations to

both exploit and combine distributed knowledge, resources and capabilities throughout

their operations. Rather than respond to either local responsiveness or integration

pressures, the network view of multinational firms provides a much richer view of both

the multiple roles that subsidiaries can play and the importance of coordination,

integration and knowledge flows throughout firm operations.

The idea that subsidiaries can have multiple relationships and play multiple roles

across a firm’s network of operations fits well with research that focuses on the role of

firm capabilities and knowledge in explaining firm growth (Penrose, 1959, Wernerfelt,

1984). Firm knowledge assets play an important role in firm growth because firms can

transfer, augment and build their technological capabilities as they grow (Penrose, 1959,

Hymer, 1976, Dunning, 1980, Buckley and Casson, 1974, Caves, 1996). A knowledge

based view of the firm emphasizes how a firm’s accumulated knowledge provides firms

6

with key competitive advantages, including resources and capabilities to compete and

continually innovate to respond to dynamic markets (Buckley and Casson, 1976, Caves,

1982, Ghoshal, 1987, Teece, 1981, Grant, 1996, Dosi et al., 2000). For manufacturing

firms in particular, deploying knowledge assets across firm operations can provide firms

with competitive advantages in products and production processes across their operations

(Ghoshal and Moran, 1996, Grant, 1996). Several studies confirm that firms will respond

to imperfect markets for knowledge and establish wholly owned subsidiaries as they

internalize cross-border flows of technology (Buckley and Casson, 1974, Teece, 1977,

Doz, 1987). However, while these studies focus on the importance of establishing

wholly-owned subsidiaries to protect and extend knowledge assets, there has been much

less focus on the linkages that firms with knowledge assets create after they have

expanded and established wholly-owned subsidiaries.

To better understand the linkages that firms establish throughout their internal

operations, I examine how different types of knowledge and information flows impact the

production linkages that firms establish across their operations. Below, I consider why

firms with knowledge assets are likely to benefit from integrated production activities.

The mere existence of parent firm knowledge, however, does not tell us much about how

firms use and develop their knowledge throughout their operations. I then consider how

different types of knowledge flows through technology transfer, parent products and

subsidiary knowledge creation can impact integration across subsidiary production

activities. Finally, given several arguments about the difficulty of transferring knowledge

across organizations (intra- and inter-firm), I consider how R&D and home country

personnel can also impact linkages and integration across firm production operations. I

start by considering the aggregate influence of parent firm knowledge on integration

across firm operations.

Parent Firm Knowledge and Integration

There are several benefits that firms with knowledge assets can achieve from

integrating their operations. First, firms can benefit from specialization and scale. Firms

may be able to achieve both higher quality and more efficient production by separating

their production processes of individual components or parts of products and benefit from

7

specialization and scale (Feenstra, Hanson and Swenson, 2000). Through product

integration, firms with proprietary assets can more successfully transfer and exploit their

knowledge in an efficient way throughout their operations. Increases in the technological

intensity of the production processes across firms (Kobrin, 1991) also suggest that there

is an important role for product integration. Further, Kobrin suggests that in many

knowledge-intensive industries, companies need to integrate their production

transnationally to support the level of R&D expenditures needed to develop and produce

products. Second, firms can benefit from interchange across their production operations.

Given many arguments about the difficulty of transferring complex knowledge (Teece,

1977, Szulanski, 1996 and Hansen, 1999), integrating operations may be the only way to

exploit and apply knowledge in new products and market settings (Kogut and Zander,

1992). At a minimum, by integrating operations and increasing linkages across subunits,

it is more likely that firm knowledge will successfully flow across these operations from

increased linkages on multiple levels across operations. In addition, integration across

operations could encourage new sources of improvements about proprietary knowledge

of the firm due to additional employees being involved in the production process.

Finally, Kogut (1984) and Ghemawat (2003) have proposed that firms can benefit by

arbitraging differences across countries, including national resource endowments or

capital market conditions. These arguments suggest that firms can combine inputs from

different operations to achieve competitive advantages across all markets. This arbitrage

and leverage approach to international markets suggests that firms can benefit from

integration across their operations on several levels, including production. While there

have not been many studies that consider how firms link and integrate their production

activities across their multinational operations, Kobrin (1991) used industry level data to

show that firm knowledge is a primary determinant of the integration of firm activities

across borders. Taken together, these arguments and findings suggest that firms with

proprietary assets will exploit, replicate and integrate their organizational knowledge

(Kogut and Zander, 1992) and production activities as they grow.

In addition to benefits from integrated activities, it is also important to identify the

additional costs that are likely to be associated with higher levels of integration across

firm operations. Information sharing will give rise to higher communication costs for a

8

firm. To share complex knowledge across an organization involves planning, meetings,

follow-up communications, oversight, and potential adaptation of processes to fit

different environments. Splitting production across firm operations involves additional

communication between subunits, extra planning to transport (and possibly finish) the

product and additional information sharing about production processes. In addition,

integration across activities is not easy. Integration of knowledge and cross border

transfers of complex and tacit knowledge can be quite hard even within firm boundaries

(Teece, 1977, Gupta and Govindarajan. 2000, Hansen, 1999).

The first hypothesis focuses on the benefits from integration that can accrue to

firms with knowledge assets. Firms with knowledge assets are more likely to be in a

position where the benefits they receive from intra-firm integration are likely to offset

some of the extra costs associated with this coordination. This is not to say that there are

no potential cost benefits for firms that do not possess strong intangible assets, but these

firms could likely coordinate outside their firm boundaries to benefit from more efficient

production arrangements. Arguments about the importance of control and ownership

over firm proprietary assets suggest that firms with such assets are more likely to want to

integrate their operations to successfully exploit, develop and deploy their assets

throughout their operations. This reasoning leads to the first hypothesis, which focuses

on the benefits firms with knowledge assets can achieve from linking their operations:

H1: Parent firms with strong knowledge assets will have higher integration across their operations.

While this general argument suggests that parent firm knowledge will influence

aggregate levels of product integration across firm operations, I now consider the

different ways that firms can exploit and develop their knowledge across their operations.

As I argue below, different mechanisms to exploit or develop firm knowledge across a

firm’s operations can impact how knowledge flows are likely to influence production

integration across subsidiary operations. I start by considering knowledge flows that

exploit a parent firm’s knowledge assets through technology transfer.

9

Knowledge Flows through Technology Transfer

As argued above, successful firms must be able to replicate their organizational

knowledge to grow (Kogut and Zander, 1992). The internalization theory argues that

firms will expand into new geographic markets to exploit the resources they have created

in their home market (Buckley and Casson, 1974). Technological know-how provides

firms with advantages that can be exploited by applying this firm knowledge in other

contexts or markets. One way firms can exploit their knowledge assets internally is

through technology transfers to their subsidiaries. Subsidiaries that receive technology

flows from their parent firm are receiving knowledge that was created elsewhere. Firms

that develop new technologies in their home country which are then used in foreign

locations are required to report these transfers and subsidiaries are required to pay fair

value via royalty payments from the foreign location subsidiaries to the parent firm that

created the knowledge. Therefore, in multinational corporations, royalty payments offer

one approach to capturing technology transfer from parents to subsidiaries.1

When thinking about how technology transfers from parent firms can impact

production integration, there are two opposing impacts that need to be considered. First,

the internalization theory argues that firms will expand into new geographic markets to

exploit the resources they have created in their home market (Buckley and Casson, 1974)

and technology transfers to subsidiaries provide one mechanism through which firm

knowledge can be exploited. This suggests a hierarchical flow of knowledge in one

direction: from parents to subsidiaries. Taken to the extreme, these arguments suggest

that there would be no production linkages from subsidiaries to parent firms after

technology transfer has taken place, rather, technology transfers allow parent firms to

exploit their knowledge across their operations. In this view, knowledge flows from the

parent to the subsidiary result in one-way linkages across firm activities. Alternately,

subsidiaries that receive knowledge from their parent firm in the form of technology

transfers could also become more integrated into an MNC’s operations. Because

knowledge flows will have been established to transfer technology to these subsidiaries,

“transmission channels” (Ghoshal and Bartlett, 1988) may exist between these

1 As will be discussed in the methods section below, differences in tax rates can impact the price that is reported. Hines (1995) and Grubert (1998) found that host country income tax rates can be used to control for the effects of tax incentives on reported intra-firm royalties and will also be included in the analysis.

10

subsidiaries and their parent firms. Given the existence of these linkages, this may

enhance the possibility that these subsidiaries will also have higher flows of goods back

to parent firm operations. While knowledge exploitation clearly involves a hierarchical

relationship between a parent and subsidiary (given that knowledge is flowing from the

parent to the subsidiary), it is not clear how this type of knowledge exploitation

mechanism will impact linkages of firm operations.

When examining knowledge transfer, it is quite common to refer to two types of

firm knowledge: tacit and explicit (Nelson and Winter, 1982). Tacit knowledge cannot

be codified and it is revealed through its application. Explicit knowledge refers to

knowledge that is transmittable in formal systemic language (ie, blueprints, manuals or

documents). However, when considering transfers to operations that are internal to a

firm, it is not obvious what type of knowledge intra-firm technology transfers might

reflect. The tacit and explicit distinction is often made regarding the type of knowledge

that firms need to keep internal when considering a joint venture partner, for example

(Hennart, 1991). While licensing technology to third parties may provide more clear

boundaries for the types of knowledge that other firms are allowed to access, intra-firm

technology transfers to wholly owned subsidiaries do not need to have such limitations.

In fact, intra-firm technology transfers could involve both tact and explicit knowledge

where the tacit component is supplemented through continued relationships across

operations, or when the production operations were initially established.

Given competing arguments, I offer alternate hypotheses about the impact of

technology transfer on subsidiary integration:

H2a: Subsidiaries that receive more inward technology transfers from their parent firm will have higher product integration with the parent firm.

H2b: Subsidiaries that receive more inward technology transfers from their parent firm will have lower production integration with the parent firm.

Knowledge flows through Products

Firms can also send products to their subsidiaries. Firm technological know-how

and other resources are often embodied in firm products (Kobrin, 1991). By sending

intermediate products to subsidiaries, firms can benefit from the specialization and scale

economies (Feenstra, Hanson and Swenson, 2000) discussed above. Firms may be able

11

to achieve both higher quality and more efficient production by separating their

production processes for component parts and integrating their operations to product final

goods. In this way, production integration can allow firms with proprietary assets to

more successfully transfer and exploit their knowledge in efficient ways throughout their

operations.

Markusen (2002) has created a knowledge-capital model of multinational

enterprises and discusses a model of vertical integration where skill-intensive aspects of

goods are produced only in a firm’s parent country and labor-intensive assembly of final

goods is possible in either the home or host countries. Markusen illustrates that the

volume of intra-firm exports increases with the parent country’s capital-labor ratios. This

model suggests that fragmented production can thus lead to greater integration across

firm activities. Though data is not consistently available on this issue, Borga and Zeile

(2004) have shown that US firms tend to export intermediate goods to their subsidiaries.

This provides support to the idea that parent firms may be producing the more

technological aspects of their products and using production in other locations to finish

less knowledge-intensive aspects of the production process for local and other markets.

These arguments suggest that parent firm products are likely to be finished or altered

before being sent back to parent firm operations or third country subsidiary locations.

When fragmenting production of products, firms will need to integrate their production

operations to serve multiple markets with these finished products. While final goods

could also be sent from parents to subsidiaries for sale in those host markets, the third

hypothesis focuses on efficient production arguments which suggest that specialization is

likely to occur in the production processes of manufacturing firms and that integration of

operations is a necessary piece for final assembly and sales. The third hypothesis argues

that subsidiaries that receive parent products are more likely to have integrated

production with both parent and other subsidiary operations:

H3: Subsidiaries that receive more imported goods from their parent firm will have higher production integration with their parent firm and other firm subsidiaries.

Knowledge Development through Subsidiary R&D

12

In addition to exploiting parent firm knowledge, subsidiaries can also create

knowledge that can be used throughout a firm’s operations (see Cantwell, 1989,

Birkinshaw and Hood, 1998, Taggart, 1998 and Frost et al., 2002, for example). Similar

to the arguments about parent firm proprietary knowledge, knowledge that is created in

subsidiaries can also be exploited, augmented, and efficiently produced throughout a

firm’s worldwide operations. Firms that create knowledge in their subsidiaries may be

able to increase product quality and innovations across all locations because diverse

locations provide much richer potential combinations of existing or new knowledge

(Reagans and Zuckerman, 2001, Singh, 2008). Whether the knowledge that is generated

in a subsidiary involves new knowledge or adaptations of parent knowledge, this

knowledge could be applied elsewhere in a firm’s operations. Mirroring hypothesis one,

integration of production activities can allow subsidiaries with proprietary assets to more

successfully transfer and exploit their knowledge in more efficient ways. The impact of

knowledge development in subsidiaries leads to the fourth hypothesis, which proposes

that:

H4: Subsidiaries with strong proprietary assets will have higher production integration with their parent firm and other firm subsidiaries.

Knowledge and Information Flows and People

Finally, several studies that have examined knowledge and information flows

within firms have revealed how difficult knowledge transfer is (Mansfield and Romeo,

1980, Teece, 1977, Szluzanski, 1996, Gupta and Govindaragan, 2000, Rosenkopf and

Almeida, 2003, Almeida and Wu, 2003). Szulanski (1996) argues that intra-firm

transfers of knowledge are often laborious and time consuming. Szulanski’s work echoes

some of the earlier findings by Teece (1977) on the difficulty of transferring knowledge

to other related, internal firm operations. A growing number of studies have found that

knowledge transfer does not always take place either efficiently or effectively (Szulanski,

1996, Hansen, 1999 and Gupta and Govindarajan, 2000).

In studies that have examined successful transfers of knowledge and information

across firms, there is an important role for individuals in transporting this knowledge and

information. Rosenkopf and Almeida (2003) show that firms can reach across their

13

existing technological contexts in search of knowledge by hiring other firms’ employees.

When inventors move from one firm to another, they carry knowledge from the prior to

the new employer. Several studies provide evidence consistent with the importance of

employees in transferring knowledge stocks (see Almeida and Kogut (1999) and Song,

Almeida and Wu (2003) for example). For intra-firm transfer of knowledge, this

suggests that one way to transfer knowledge and information across boundaries is to send

employees to manage or oversee affiliates and to explain how to implement the practices

and knowledge of the firm. Because most of the important elements of knowledge tend

to be tacit in nature (Nelson and Winter, 1982), the ability to interact with employees

from the parent firm is likely to improve the effectiveness of knowledge and information

transfer. When foreign subsidiaries have home country personnel, they are increasing the

potential for knowledge and information transfer to occur from the home to the host

country offices. It is important to note that this knowledge does not have to be

technological in nature. While this knowledge could involve actual technological know-

how, it could also involve information that is related to firm practices and beliefs. Expats

could also provide a subsidiary with important contacts to others parts of a firm’s

network of operations. Even more basic, the language and cultural distance that may

separate the operations of many foreign subsidiaries from parent activities is reduced with

expat employees in subsidiaries.

Similar to the arguments about technology transfer above, the important role that

firm employees can play in transferring knowledge and/or information, and in providing

channels of communication suggest alternate hypotheses for the integration of production

activities for subsidiaries that have such personnel. First, these subsidiaries may be more

likely to have integrated operations with the rest of the MNC. Subsidiaries with US

employees may be more likely to receive transfers of complex knowledge and have an

easier time communicating and understanding the operations of the rest of the firm.

Expats can play an important role in the knowledge integration process and can help to

ensure effective transfer and integration of knowledge resources (Zahra et al., 2000).

Highly effective communication patterns may impact the ability of a subsidiary to both

diffuse and adopt innovations of other subunits. On the other hand, expats could also be

used to control subsidiaries and/or knowledge flows from a parent firm. Because MNCs

14

may want to successfully exploit knowledge across their operations, expats could be used

to transfer knowledge primarily in one direction: from parent firms to subsidiaries. In

fact, subsidiaries with more US employees could have lower levels of linkages back to a

parent firm if US employees are used to control and/or exploit parent firm knowledge,

norms and practices (Edstron and Galbraith, 1977, Black et al., 1992, Tung, 1993).

Though expats would have knowledge of home country operations and norms, this

knowledge does not mean that subsidiaries will be integrated with home country

operations. Expats can provide control mechanisms because they can act as agents of

headquarters – agents who manage the subsidiary in accordance to the stipulations of

headquarters. These arguments lead to the fifth set of alternate hypotheses that focuses

on the role that US employees can play in encouraging or discouraging integration of a

subsidiary with the rest of the activities of the firm:

H5a: Subsidiaries with more home country employees will have higher production integration with their parent firm and other firm subsidiaries.

H5b: Subsidiaries with more home country employees will have lower production integration with their parent firm and other firm subsidiaries.

Overall, these hypotheses focus on influences that impact not only the integration

of firm activities, but also the direction of knowledge and production linkages across a

firm’s network of operations. In the empirical analysis below, I will examine both

aggregate levels of integration within firms and the direction of the flow of goods to test

the hypotheses and to examine how different knowledge exploitation and development

mechanisms impact the integration of firm activities

Data and Estimation:

I use the Bureau of Economic Analysis (BEA) annual survey of US Direct

Investment Abroad from 1989 to 2005 to construct a panel of US firms with foreign

operations. These data provide the most comprehensive information on the operations of

US MNCs because the International Investment and Trade in Services Survey Act

requires that US firms file reports that detail the financial and operating activities of

affiliates and the value of transactions between US parents and their foreign affiliates

15

(See Mataloni, R and D. Yorgason, 2006, for a thorough description of definitions and

survey methodology used by the BEA). The starting point for this sample is determined

by the availability of R&D expenditures at the foreign affiliate level (which is collected

consistently over the 1989-2005 time period) and the ending point is imposed because

2005 is the last year of data that are currently available. I restricted my observations to

US firms in manufacturing industries (SIC codes 200-399). I initially restricted my

sample to include majority-owned affiliates only (because minority owned affiliates (50%

or less ownership) provide much less comprehensive data to the BEA and restrict what I

am able to test). However, given my interest in examining linkages across internal firm

operations, I decided to focus on fully owned (100%) subsidiaries. In practice, this

distinction between majority and fully owned subsidiaries has no impact on the results

reported below – this is driven by the fact that the vast majority of majority owned

subsidiaries are fully owned (with the average ownership percent for all majority owned

subsidiaries of manufacturing firms that report to the BEA over this time period being

95%). Given that I am interested in examining linkages and integration across firm

activities, I focus on fully owned subsidiaries in this paper to avoid any complications

from potential subsidiary-partner issues. In the results reported below, I include all

subsidiaries with sales (as subsidiaries with no manufacturing activities can and do add

manufacturing over the time period under consideration). For robustness (not reported

below), I excluded subsidiaries that only sell (distribute) products (but never manufacture

anything during the 1989-2005 time period) and the signs and significance of all

variables of interest are the same (given that almost two-thirds of the wholly owned

subsidiary observations manufacture goods during the time period).

The BEA administers either benchmark or annual surveys, depending on the year

of the survey. In the years 1989, 1994, 1999 and 2004, the longer benchmark surveys are

administered. In all other years in my sample, the shorter annual surveys are

administered. Many of my variables are available for all survey years, however, some of

my variables of interest (US and R&D employees in foreign subsidiaries, for example)

are only available in the benchmark surveys. Below, I run my models on different sets of

variables to achieve the highest number of observations. For my parent specifications,

after matching up the parent, affiliate and country data (and lagging all independent

16

variables), I end up with an unbalanced panel of 1376 parent firms (with 7032 firm-year

observations) who have a total of 13,561 subsidiary investments (resulting in 50,004

subsidiary-year observations) across an average of 148 countries over the 1989-2004 time

period. Both firms and subsidiaries can and do enter and leave the sample over the time

period. In the more limited samples that consider US and R&D employees of

subsidiaries, I end up with a sample of 10,768 subsidiary-year observations.

Dependent Variables:

For my dependent variable, I am interested in capturing the integration of

production activities (at the aggregate MNC, the parent firm and the subsidiary levels). I

consider a total of four dependent variables in this study. First, I consider a firm’s total

intra-firm trade in goods, measured as the ratio of all affiliate plus parent sales of goods

to affiliated subsidiaries (or parents for subsidiaries) to the total worldwide sales of the

MNC. As subsidiary total sales include both exports and imports (in addition to local

sales), I subtracted all export amounts from each affiliate and parent firm’s sales before

creating this measure (to avoid double counting sales). Second, I consider several sub-

measures to capture parent and subsidiary levels of intra-firm integration. For parent

integration, I created integration ratios for each parent firm (considering parent exports as

a percent of total US sales) to examine the characteristics of parent firms that coordinate

their activities with their subsidiaries. For subsidiary integration, I created integration

ratios for each foreign subsidiary (using subsidiary exports to total subsidiary sales) to

examine the characteristics of subsidiaries that are integrated into a firm’s network of

operations. I consider two subsidiary level dependent variables: one that captures

subsidiary exports to parents and one that captures subsidiary exports to third country

affiliated subsidiaries. By examining all four of these dependent variables, I can analyze

multiple directions and linkages across firm activities.

Independent Variables:

My independent variables include firm, subsidiary, industry and host country

variables. Tables One, Two and Three provide sources and summary statistics for these

17

variables. All independent variables are lagged by one year and are discussed below.

Table Four shows the correlations for the variables used in the specifications.

Parent Firm:

To test the impact of a parent firm’s knowledge intensity (the focus of Hypothesis

1), I measure parent firm R&D expenditures as a percent of the parent firm’s total US

sales. In addition to this key variable, I also control for several other parent firm

characteristics. First, I consider how the extent of foreign operations can influence the

abilities of firms to coordinate their activities. Firms that have more extensive operations

have gained knowledge about doing business in these environments and that may

contribute to the ability of these firms to coordinate and integrate their worldwide

operations. Studies suggest that once this type of knowledge is internalized within the

firm, diverse country experiences lead to a generalizable knowledge asset (Erramilli,

1991 and Barkema et al., 1996). To capture this experience, I include the ratio of a firm’s

foreign assets to its worldwide assets. I include a firm’s total number of manufacturing

subsidiaries in foreign countries to capture the spread of a firm’s production facilities.

Several studies in international business suggest that there are important differences in

firm strategies regarding centralized and decentralized approaches to serving markets.

By including the total number of manufacturing subsidiaries, I control for different

approaches and experiences in production by my parent firms. I also include measures of

parent firm debt and net income in the US to control for parent firm characteristics that

can impact the availability of firm resources to devote to integration efforts.

Industry:

Kobrin (1991) has shown that there are differences in international integration

levels across industries. I follow Kobrin’s operationalization of global industry

integration and calculated the ratio of total affiliate and parent exports to total foreign

sales for each 3 digit SIC code. This measure captures the level of intrafirm to total

cross-border trade within an industry. I created this measure at the 3-digit SIC code level

as this is the most detailed level of industry classification that is available in the BEA

data over my sixteen year time period. I created my industry integration measures

18

separately for each year in my sample. (This measure also includes some consideration

for competitor actions as it reflects industry levels of integration of activities.) I also

include 2-digit industry dummies in all of my equations below to control for other

differences across manufacturing industries.

Subsidiaries:

Hypotheses two to five focus on the impact of different types of knowledge flows across

firm operations. To examine these subsidiary level impacts, I created several variables.

First, to analyze how parent firm technological transfers may impact the integration of

subsidiary operations (hypotheses 2a and 2b), I examine royalty payments by subsidiaries

to parent firms. While a better measure would be specific to technology licensing fees,

such a measure is not available for every year but one in my time period. From the one

year in which the BEA survey asked respondents to provide technology licensing fee

payments information (1989 only) the data reveal that technology licensing fees account

for close to 90 percent of all royalty payments.2 I continue under the assumption that

royalty payments provide a good proxy for technology transfer payments. To examine

hypothesis 3, I include a measure of the parent firm’s imports of products to the

subsidiary to analyze how these parent product linkages may impact integration by

subsidiaries. To examine Hypothesis 4, I created two variables. My main variable is the

ratio of R&D employees to total subsidiary employees and my second measure considers

subsidiary R&D intensity (subsidiary R&D expenditures as a percent of total subsidiary

sales). I originally included the second measure because it is available over the entire

time period (while R&D employees are only available in the benchmark surveys (the four

years including 1989, 1994, 1999 and 2004)). However, I decided to keep both of these

variables given the very different results for each variable shown below. Somewhat

surprisingly, these two variables are not highly correlated. (Though it should be noted

that the raw number of R&D employees and the raw subsidiary expenditures on R&D are

highly correlated – at .85.) By scaling these variables to control for firm size, I am able

to capture two different types of knowledge development at the subsidiary level. More

specifically, by including both of these variables, I am able to examine subsidiaries that 2 See work by Branstetter et al., (2006) that has also used royalty payments in this data to represent technology transfer from parent to affiliate.

19

have higher ratios of R&D expenditures and subsidiaries that have higher ratios of

technical employees. Finally, to examine Hypotheses 5a and 5b, I created a variable that

captures the ratio of US employees to total subsidiary employees for each subsidiary.

I also include several other subsidiary variables to control for other influences on

subsidiary production integration. I include the log of the local sales of an affiliate to

capture firm size. I include subsidiary net income. I also control for whether the

subsidiary reports the same business line as the parent firm (core industry) because it is

likely to be easier to integrate that subsidiary’s operations into the firm’s activities. I

have measured whether the subsidiary is in the same core industry as the parent firm by

comparing the main line of businesses of both. If the subsidiary and parent report the

same 3 digit SIC code as the main line of business, this dummy variable receives a value

of one. Finally, I include a dummy variable for subsidiary age to control for established

versus new operations. The BEA does not report the date of incorporation or

establishment of the subsidiary, but I do have individual subsidiary information (without

R&D information) back to 1982. I tracked when a subsidiary was first reported in the

BEA surveys and created a dummy variable that takes a value of one if a subsidiary has

been in existence for five or fewer years. All affiliate information is reported in current

US dollars (thousands of) in the BEA surveys, so no currency exchange rates were

needed.

Country:

There are several country characteristics that can impact whether a subsidiary will

be integrated into a firm’s network of activities. Market conditions in the local market

will influence how much a subsidiary’s production operations are integrated into a firm’s

network of operations. Large markets are most likely to be served by local production

(Swedenborg, 1979 and Veugelers, 1991). I include the log of GDP to proxy for host

country size. Geographic distance and government barriers are likely to have a negative

influence on the integration of a subsidiary into a firm’s network of operations. I include

a measure of the geographic distance of the host country to the US to proxy for

transportation costs that might discourage integration of subsidiaries from that host

country. It may be harder to integrate the operations of subsidiaries in economies that

have more restrictions on importing and exporting into the country. To proxy for the

20

openness of a country, I include the percent of total foreign direct investment in a country

to its total GDP. I include a country’s lagged GPD growth rate to capture increased local

demand for a firm’s products. All of these time-varying variables come from the World

Bank’s World Development Indicators.

Because I include a measure for royalty payments, I also include a variable to

capture differences in tax rates across countries. While firms are supposed to apply fair

market values for all within-firm payments, differences in tax rates can impact the price

that is reported. Hines (1995) and Grubert (1998) found that host country income tax

rates can be used to control for the effects of tax incentives on reported intra-firm

royalties. I consulted the World Tax Database at the University of Michigan to gather

time-varying tax rates for host countries. I used the reported corporate tax rate and

generated the difference from the US corporate tax rate for each country for each year.

Further, because I am interested in examining whether increased communications across

the subsidiary and the parent firm impact integration, I also include a dummy variable to

capture whether Engligh is an official language of a country OR whether at least 9

percent of the population speaks English. This information comes from the CEPII

databases and reports up to three official languages for each country. (The CEPII

information is based on the CIA World Factbook and the ethnologue website

(www.ethnologue.org ).

Finally, because I am examining knowledge flows, I am interested in controlling

for differences in how intellectual property is protected across countries over time. This

is challenging as there are no good sources that focus on both a broad range of countries

over time and specific details about the intellectual property protection that exists within

these countries.3 To focus on intellectual property protection differences across countries

for the US firms in my sample, I consulted the United States Trade Representative’s

Special 301 Priority Watch List, which lists countries that are of concern for American

businesses. I consulted various years that I could access over the time period 1996 to

2004 and included all countries that appear on this list.4 5

3 I started by focusing on the more general measures that consider the rule of law across countries. I downloaded the Kaufman et al. (2002) Rule of Law index (which is a composite of other indices). This more general index is very comprehensive in its inclusion of countries and goes back to 1996. However, this index is also highly correlated with my GDP variables.

21

Estimation:

I consider two equations to examine MNC and subsidiary levels and directions of

product integration. First, I examine the MNC level (which does not include subsidiary

level information, except in aggregation):

MNCIntegration = β0 + β1(ParRDIntt-1) + β2(ForAssets% t-1) +

β3(TotManSubst-1) + β4(ParNetInct-1) + β5(ParDebtt-1) +

β6(IndIntegrationt-1) + ε

This equation models aggregate levels of MNC integration, using two separate

dependent variables: total MNC exports as a percent of total worldwide sales and US

parent exports to subsidiaries (as a percent of parent US sales). The key variable of

interest for H1 is ParRDInt – the R&D intensity of the parent firm. Also included are

measures for the foreign assets ratio, a count of the total number of manufacturing

subsidiaries, the parent’s net income in the US, parent debt and the proportion of industry

international sales that are intrafirm in the parent firm’s main 3-digit SIC code. Finally,

this equation includes an error term.

Next, I examine how knowledge flows can impact product integration and add

subsidiary-specific variables to the model (and consider subsidiary level of product

integration):

SubIntegration = β0 + β1(Royaltiest-1) + β2(USEmp%t-1) +

Β3(RDEmp%t-1 ) + β4(RDIntt-1)+ β5(ImportsPart-1) +

Β6(Coret-1) + β7(NetIncomet-1) + β8(LocalSalest-1) + β9(ParOwn t-1) +

β10(SubAge t-1) + β11(ParRDInt t-1) + β12(ForAssets% t-1) +

4 Including countries like Argentina, Bahamas, Belize, Brazil, China, Colombia, Dominican Republic, Egypt, Greece, Hungary, India, Indonesia, Israel, Korea, Kuwait, Lebanan, Poland, Pakistan, Russia, Turkey, Ukraine, Uruguay, Venezuela.5 As reported in the robustness section below, I also created a dummy variable that takes a value of one if a subsidiary is located in a lower or lower-middle income country, according to World Bank income categories. I do not include this in the main analysis because this variable is highly correlated with 301Priority Watch list variable. When I ran the equations including the developing country dummy variable (but without the 301 Priority Watch list variable), the results for the developing country variable were positive but not significant in Models 3-6 reported below.

22

β13(TotManSubs t-1) + β14(IndIntegrationt-1) + β15(logGDP t-1) +

β16(GDPcapgrowt-1) + β17(Distance t-1) + β18(Debt t-1) +

β19(USTaxDiff t-1) + β20(FDIInflow t-1) + β21(English t-1) +

β22(301List t-1) + ε

This equation models intra-firm integration as a function of subsidiary, firm,

industry and country characteristics. The dependent variable (SubIntegration), subsidiary

integration, is examined considering two separate dependent variables: subsidiary

exports of goods (as a percent of total subsidiary sales) back to the parent and subsidiary

exports of goods (as a percent of total subsidiary sales) to third country affiliated

subsidiaries. The key variables of interest include: royalties, which is the ratio of royalty

payments made to parent firms by total subsidiary sales; USEmp% is a measure of the

percent of US Employees to total subsidiary employees; RDEmp%, a measure that

captures the percent of R&D employees to total subsidiary employees; SubRDint, a

measure of the subsidiary’s investments in R&D, divided by subsidiary sales; and

ImportsPar, a measure of the imports that come from the parent firm (scaled by

subsidiaries total sales). The other variables that are included in this model are subsidiary

size, whether the subsidiary is in the same core business as the parent firm, the net

income of the subsidiary, the debt of the subsidiary, the R&D intensity of the parent firm,

the foreign assets as a percent of total assets of the firm, the number of manufacturing

subsidiaries of the firm, the proportion of industry international sales that are intra-firm in

the parent firm’s main 3-digit SIC code, the GDP of the country, the distance of the

country from the US, the percent of FDI inflows to GDP of the country, the tax rate

difference between the host country and the US, whether English is an official language

or spoken by 9 percent of the population, whether the country has been on the USTR’s

priority list of countries that are of concern for American businesses and finally, an error

term. All of the models include year and industry dummies.

I run several different models to test my hypotheses. First, because my dependent

variable is truncated at zero, I follow a common approach in the trade literature on intra-

firm trade in goods and services and use a Tobit model (see Kumar and Siddkarthan,

1994, de Backer and Sleuwaegen, 2003, Shatz, 2004, Banga, 2006, for example).

23

Because I have panel data, I used the random effects Tobit command in STATA to

estimate my coefficients. (As Tobit specifications do not allow for robust standard errors,

I also ran all equations using the INTREG command in STATA with the robust cluster

option and the results are very similar to the Tobit results reported below). In addition to

including random effects, year effects and 2-digit industry dummies, all Tobit

specification were run with lagged independent variables. Second, I ran all of my models

using OLS regression with firm fixed effects. I also included year dummies and lagged

independent variables in these OLS fixed effect models. Finally, I use a propensity score

matching approach (following Rosenbaum and Rubin, 1983), and use all of my

independent variables to predict intra-firm exporting. Following Imbens (2004), I use the

resulting matched sample with the propensity scores as inverse probability weights in an

OLS regression using differences of both the dependent and independent variables

(differences on differences method). This approach outperforms standard regression

control methods because it allows me to control for unobservable characteristics that

could be correlated with both the assignment to experimental groups and outcomes. In

the current setting, this could relate to differences in firm international expansion

strategies. By using a matched sample, the propensity score matching approach allows

me to compare how my variables of interest differ for subsidiary (and firms) that are

similar in all other characteristics included in the probit model (which includes all of my

lagged independent variables, year dummies and industry dummies). The probit model

used to estimate the propensity scores are shown in the Appendix (in addition to the t-test

for mean differences across all variables for the treatment and control groups).

Results:

Before discussing the empirical results, it is interesting to consider the

information in Table Two, which gives summary statistics, and Table Three, which

provides the means for key variables of interest over the benchmark survey years (due to

the availability of certain variables in these years only). From the summary statistics, we

can see that both intra-firm linkages through flows of goods (all four dependent

variables) and knowledge flows across firm operations (royalties, R&D intensity of

subsidiaries) are not large percentages of total firm sales. Total MNC flows of good as a

24

percent of total MNC sales averages 13 percent over this time period. Flows of parent

goods to subsidiaries average 8 percent of total parent sales. Flows of subsidiary goods

back to parents average 5 percent of total subsidiary sales. Finally, flows of subsidiary

goods to other subsidiaries average 9 percent of total subsidiary sales over this time

period. Technology transfer, measured by royalty payments percents of subsidiary sales,

average 1 percent of subsidiary sales. Interestingly, technology transfer payments are

similar in magnitude to subsidiary R&D intensity (which also averages 1 percent over the

time period). It should be noted, however, that the standard deviation for R&D intensity

is much higher than royalty payments as a percent of affiliate sales. By way of

comparison, parent firm R&D intensity averages 4 percent of total sales during this time

period. While I do not know the purpose for the R&D activities of subsidiaries, the

similarity in statistics across royalty payments and subsidiary R&D intensity could

suggest that there are equal expenditures on knowledge exploitation through technology

transfer and on knowledge creation outside of a US manufacturing firm’s home market.

If subsidiary R&D is devoted to adapting parent knowledge for the local market, this

balance could certainly shift in favor of higher exploitation of parent firm knowledge

across firm worldwide operations. The summary statistics also reveal that subsidiary

R&D employees as a percent of total subsidiary employees averages 1 percent while US

employees as a percent of total subsidiary employees average .5 percent. Overall, these

numbers suggests for an average subsidiary, the personnel that are the focus of

hypotheses three and five amount to a very small percent of total subsidiary employees.

Local sales, non-expat and non-technical employees clearly dominate subsidiary

operations. Table Three shows that most of the key variables have fairly stable means

over time, though there is evidence of an increase in almost all variables from the starting

to the ending years. Though not dramatic, both the flows of goods across firm operations

and firm investments in knowledge (R&D intensities) have increased over time. Industry

global integration levels have also increased over the time period of this study, but

mirroring the firm-level data, the proportion of international sales that are intra-firm in an

industry has not increased dramatically over the time period.

Moving on to the statistical analysis, Tables Five and Six reports the results for

each of the specifications across all four dependent variables. The coefficient and t-

25

statistics are reported for each variable in each model (with the OLS, fe model

coefficients reporting marginal effects). Table Five shows the results for two of the

dependent variables, including the total MNC integration as the dependent variable

(columns 1 and 2) and the parent to subsidiary integration dependent variable (columns 3

and 4). In Table Five, columns 1 and 3 show the results from the tobit specification

while columns 2 and 4 report the results for the OLS with firm fixed effects. Both of

these models only include parent and industry variables as they are at too aggregated of a

level to include subsidiary and country variables. Table Six reports the results for the two

subsidiary level dependent variables. Columns 1-4 show the results considering the

integration of subsidiary production with parent firm operations. Column 1 shows the

results considering the largest sample possible but not including the percent of US

employees (USEmp%), the percent of R&D employees (RDEmp%) and royalty

payments (Royalties) which are only available in the benchmark year surveys, and uses a

Tobit specification. Column 2 shows the Tobit results with these main variables of

interest and a reduced sample size. Column 3 shows the results from the OLS firm fixed

effects specification and Column 4 shows the results from the matched sample using the

inverse propensity scores to weight the sample. It should be noted that propensity score

OLS weighted results use the difference of both the dependent and independent variables

(and therefore, dummy variables at the subsidiary level that do not change over time drop

out of this model). Columns 4-8 in Table Six repeat all of these models for the

dependent variable that measures subsidiary production integration with other third-

country subsidiary operations. The results for the variables of interest from the

hypotheses are fairly consistent across these specifications. I will highlight this

consistency in my discussion of the results.

Hypothesis one argued that firms with strong proprietary assets will integrate their

activities across their subsidiaries. Columns 1-4 in Table 5 show that parent R&D

intensity significantly influences the level of integration of goods both at the aggregate

MNC level and for goods that flow in the direction of subsidiaries (reflecting hierarchical

parent to subsidiary flows of goods). This is true across both the Tobit and OLS with

firm fixed effects specifications. However, as can be seen in the results in Table Six,

parent R&D intensity is never a significant determinant of subsidiary flows of goods

26

across all of the models. Accordingly, this hypothesis receives support at the aggregate

level and in the direction of outward flows from parent firms to subsidiaries. This lack of

significance for parent firm R&D intensity suggests that it is important to consider both

the mechanism through which firms transfer, exploit and develop their knowledge assets

throughout their operations and how subsidiary level differences might influence the

knowledge and production linkages that firms establish across their operations.

Hypotheses 2a and 2b considered whether subsidiaries that receive more

technology transfers from their parent firm will have higher or lower integration with

parent firm operations. As can be seen in columns 2, 3 and 4, though positive, royalty

payments to parent firms do not significantly impact subsidiary flows of goods to parent

firms. This suggests that technology transfers from parent firms to their subsidiaries do

not significantly impact production linkages back to the parent firm. Columns 7 and 8,

however, reveal that technology transfers do significantly impact the integration of

subsidiary operations with third country subsidiaries. Table Six reveals that technology

transfers have a differential impact on production integration depending on where the

subsidiary products are being sent.

Hypotheses 3 considered whether subsidiaries that receive more products from

their parent firm will have higher integration with parent and other subsidiary operations.

Similar to technology transfers, Table Six reveals that there are differences depending on

where subsidiary products are being sent. In columns 1, 2, 3 and 4, we see support for

Hypothesis 3 as parent firm goods positively and significantly impact subsidiary flows of

goods back to the parent firm. However, columns 5-8 in Table Six reveal inconsistent

support for this hypothesis for subsidiary to subsidiary product integration. While the

Tobit results reveal support for hypothesis 3, the OLS firm fixed effects and propensity

score matching difference on difference results do not. The most difficult test of this

hypothesis through the difference on difference matching method shows different results

depending on the target location of a subsidiary’s goods. This suggests that parent firm

products significantly impact production linkages only when the parent firm is the

recipient of subsidiary goods.

Hypothesis 4 argued that firms will integrate the production activities of foreign

subsidiaries with strong technological assets across their network of operations. As

27

indicated above, I created two variables to test this hypothesis. First, my main variable is

the ratio of R&D employees to total firm employees. All columns in Table Six show that

this variable is highly significant in influencing the integration of production from

subsidiaries to other subsidiaries and back to the parent firm. Firms with high ratios of

R&D employees are more likely to have high levels of production integration. In

contrast, however, the second variable that was examined to test this hypothesis (and that

allows for many more observations because it is included on both the annual and

benchmark surveys) does not positively influence production integration. In fact, the

second variable, which examines the ratio of subsidiary R&D expenditures to total sales

(subsidiary R&D intensity), is always negative. Columns 1, 2, 3 and 4 show that

subsidiary R&D intensity (SubRDInt) is always negative, and further, that it is negative

and significant in Columns 1 and 2, which show the integration of subsidiary operations

with parent firm operations. Taking these results together, hypothesis 4 receives mixed

support. On the one hand, subsidiaries that spend more money on R&D as a percent of

total sales do not coordinate their products back to parent firms or other subsidiaries.

However, subsidiaries that have higher ratios of R&D employees to total employees do

coordinate their products back to both parent firms and other subsidiaries. Interaction

terms between these two main effects were not significant. Interactions with other host

country variables (like subsidiaries located in developing countries and subsidiaries

located in countries that contain intellectual property issues (301List)) were not

significant either. These variables suggest that higher ratios of technical employees need

to be present before subsidiary-created knowledge positively impacts production linkages

across firm activities.

Hypothesis 5a and 5b considered whether subsidiaries with home country

employees have higher or lower product integration. Similar to other results, Table Six

reveals differences depending on where a subsidiary’s products are going. Columns 2, 3

and 4 show that home country employees significantly and positively influence

subsidiary flows of goods to parent operations. This suggests that home country expats

encourage production integration with the home country. However, similar to parent

firm products, columns 5-8 in Table Six reveal inconsistent support for this hypothesis

for subsidiary to subsidiary product integration. While the Tobit results reveal support

28

for hypothesis 5a, the OLS firm fixed effects and propensity score matching difference on

difference results do not. Again, the most difficult test of this hypothesis through the

difference on difference matching method shows different results depending on the target

location of a subsidiary’s goods. This suggests that home country expats significantly

impact production linkages only when the parent firm is the recipient of subsidiary goods.

The control variables behave mostly as expected. Parent firms that have greater

percents of foreign assets to total assets are more likely to have integrated operations,

considering total intra-firm flows of goods. This shows that more experience and a

higher percent of firm activities in foreign markets increases overall firm integration.

Considering subsidiary level integration, parent firm foreign assets negatively impacts

subsidiary integration with the parent firm. This suggests that breadth of parent firm

operations can mean that foreign subsidiaries are more focused on their own host country

markets. The effect of the total number of manufacturing subsidiaries on subsidiary

integration echoes this finding. The more decentralized the manufacturing activities of a

firm, the more likely each subsidiary is to be focused on its host country market. The

industry global integration variable behaves as expected. It is consistently positive and

significant across all specifications. Subsidiary size negatively influences integration at

the subsidiary level. This suggests that large subsidiaries are more focused on the host

country market. Percent ownership and similar core business line to parent both

positively impact the integration of products in subsidiaries. Higher performance at the

subsidiary level also encourages the flow of goods back to parent and other subsidiaries.

Subsidiary age is negative and significant. This suggests that subsidiaries that are older

than five years have less integrated production activities in MNC operations. The

English language country variable is not significant, though it is positive. This suggests

that common country language is not a significant determinant on subsidiary integration

(and helps to reinforce the significance of expats in the results). The negative and

significant signs for the log of GDP and geographic distance suggests that subsidiaries in

larger countries that are further from the US are less likely to have more integrated

activities. The positive sign on FDI inflows suggests subsidiaries that are located in

countries that are more open to trade and investment are more likely to integrate their

activities with their parent firms. While there is a negative coefficient for subsidiaries

29

located in countries that are on the USTR’s Priority Watch List, this country designation

does not significantly impact the integration of firm production integration. I also ran all

of my models excluding the countries on the USTR’s Priority Watch list to ensure that

they were not impacting my results and found very similar signs and significances for all

of my knowledge flow variables to those reported in Table Five.

For parent and total firm integration, parent R&D intensity has a significant

impact on increasing integration across firm activities, however, there are other variables

that have larger marginal effects (including both high levels of foreign assets and high

numbers of manufacturing subsidiaries abroad. Further, when considering the subsidiary

level results, however, the level of parent R&D intensity does not have a large impact on

subsidiary integration. Rather, what firms do with their knowledge assets and how they

exploit or create these assets in their operations have much higher impacts on subsidiary

integration. From the OLS results, we can see that R&D employees, expats and industry

globalization pressures have the highest marginal effects for subsidiary to parent

production integration. R&D employees, royalties and industry globalization pressures

have the highest marginal effects for subsidiary to subsidiary production integration.

Finally, for robustness, I also ran all of these results including a dummy variable

for whether a host country is a developing country according to World Bank

classifications (based on GDP per capita and including all countries not in the high

income category) instead of the USTR Priority Watch list, and included interaction terms

using the main variables of interest in hypotheses two through five with the developing

country dummy. Further, I split the sample into developing country and advanced

country subsidiaries for each of my subsidiary models. The interaction terms were never

significant and the splitting of the sample shows that the royalty payment, US employee,

R&D employee and R&D intensity variables behave similarly for subsidiaries located in

both advanced and developing country locations. As indicated above, I included country

tax rates to help control for potential issues using royalty payments to examine

technology transfers. For robustness, I also ran all of my subsidiary level specifications

dropping all subsidiaries located in tax havens (including 41 countries as identified by in

Hines, 1994).6 Dropping the subsidiaries from these 41 countries did not change any of 6 Given that this publication is from 1994, I also checked the lists from the US Department of Treasury for more current tax havens. In general, the list from 1994 is more inclusive of countries that may not be

30

the results. The results for the subsidiary R&D employees, expats and parent firm import

variables are robust to any specification or subsample I run, and any interactions I include

consistently show that the main effects for these variables are positive and significant

(while the interactions are not).

Discussion:

Knowledge plays an important role in firm expansion (Penrose, 1958, Hymer,

1976, Buckley and Casson (1974), Gupta and Govindarajan, 2000). However, we know

little about how firms use their knowledge assets throughout their organization (Gupta

and Govindarajan, 2000, Singh, 2008), or about how knowledge flows may impact

linkages across firm operations more generally. In this paper, I focus on several different

types of knowledge flow mechanisms to examine how manufacturing firms use, transfer

and develop their knowledge assets throughout their operations. In addition, I consider

how these different types of knowledge flows impact the production linkages that firms

establish across their operations. Given the importance of knowledge and production to

manufacturing firms, I consider both knowledge flows and the impact of different types

of knowledge flows on production linkages. Further, by analyzing a comprehensive

dataset on the worldwide operations of US manufacturing firms, I reveal how these firms

use both knowledge and production linkages across their internal wholly-owned

activities.

Several recent studies have considered the benefits that multinational firms can

achieve from geographically dispersed R&D operations (see Feinburg and Gupta, 2003,

Zhou, 2006 and Singh, 2008). This paper complements these studies by extending the

focus to include not only how firms can organize to create new knowledge, but also how

firms use and create their knowledge assets throughout their internal operations.

Building on the notion that MNCs are inter-organizational networks with multiple

vertical and horizontal relations (Ghoshal and Bartlett, 1990, Bartlett and Ghoshal, 2002,

Gupta and Govindarajan, 1991, 2000, Hedlund, 1986), this paper seeks to understand

how MNCs use their knowledge assets across their operations to create competitive

considered to fall within more recent official “tax haven” lists. For example, Ireland is included in the list by Hines, but does not always appear in more recent lists. I have included firms that appear on any list when dropping potential problem countries and the results remain the same.

31

advantage across the dynamic markets in which they operate. By considering the

exploitation and development of firm knowledge assets throughout a firm’s operations,

this study provides insight into a variety of influences on firm integration. Further, while

there are many studies that focus on how firms can exploit inter-firm relationships

through alliances, joint ventures and supplier relationships, this paper focuses on the less

studied relationships within firm activities to understand when and how firms integrate

their internal operations.

The empirical results reveal that different knowledge flow mechanisms have

different impacts on the integration of internal firm activities. Of the people, products

and technology knowledge flow mechanism examined in this paper, the results show an

important role that R&D employees play in encouraging intra-firm integration.

Subsidiaries that have more R&D employees have higher levels of production integration

no matter the final location for production flows. For the other mechanism, expats,

products, and technology transfers play different roles depending on the target location.

While expats and products are important for subsidiary to parent linkages, technology

transfer is important for subsidiary to subsidiary linkages. For subsidiary to parent firm

product flows, both knowledge flows that increase personnel contacts and exchange, and

knowledge flows through intermediate products that allow firms to take advantage of

specialization and scope encourage integration in production activities. While parent

firm products and people are influential for subsidiary to parent linkages, parent firm

technology that has been licensed to subsidiaries plays a broader role in a firm’s network

of operations. Technology transfers to subsidiaries encourage outward linkages from

subsidiaries to other subsidiaries. This suggests that subsidiaries may develop products

that are based on parent firm knowledge to be sold elsewhere in a firm’s network of

worldwide operations. Overall, these results suggest that people and products have more

of an impact on coordination and integration back to parent firm operations than

subsidiary expenditures on R&D or technology transfer, while people and technology

transfers have more of an impact on subsidiary coordination and integration with other

subsidiary operations. Several robustness checks on host country institutional

deficiencies (including poor intellectual property protection) confirm these results.

32

While the results show that firm knowledge flows significantly impact production

linkages across firm operations, it is important to note that the flows of goods across firm

operations constitutes less than fifteen percent of all sales activities of US multinational

firms in manufacturing industries. While this number is clearly above zero, it does not

represent even one-sixth of the total sales of these firms. This mean tendency percentage

suggests either that firms are not undertaking large investments in the integration of

goods across their activities, or that it is difficult to integrate firm operations. While there

are many reasons that firms can benefit from integrating their activities, there are also

costs that are associated with such integration. Further, extant research show how

difficult it is for firms to transfer knowledge both internally and externally (Mansfield

and Romeo, 1980, Teece, 1977, Szluzanski, 1996, Hansen, 1999, Gupta and

Govindaragan, 2000). While this study reveals several interesting influences on intra-

firm linkages and how firms exploit and develop their knowledge assets, these findings

also complement the results from prior studies that show how difficult it is to transfer and

exploit knowledge assets throughout a firm’s operations (Teece, 1977, Kogut and Zander,

1993, Szulanski, 1996).

The results in this study offer several additional findings about both knowledge

and production linkages across firm operations. The average percent of subsidiary sales

that are devoted to technology transfer and (through royalty payments) and subsidiary

R&D expenditures are similar in magnitude. While this could suggest that equal amounts

of knowledge exploitation and creation are occurring, the results that R&D expenditures

negatively impact subsidiary production integration suggests that at least some portion of

the knowledge creations expenditures are being devoted to local markets only and likely

involve more adaptation of existing firm knowledge. The results also show that

knowledge creation at the subsidiary level influences integration in unexpected ways.

While the integration of goods is negatively impacted by the R&D intensity of a

subsidiary, higher ratios of R&D employees to total employees positively impacts

integration. This suggests that subsidiaries need higher percents of technically inclined

employees in their mix of employees before knowledge from this subsidiary will be

integrated back through a firm’s operations. The highly significant negative sign for

subsidiary R&D intensity suggests that larger amounts of money are spent at the

33

subsidiary level for sales that are local. Perhaps higher R&D intensities are being used to

adapt products to local markets rather than generate knowledge that could be used

throughout a firm’s worldwide operations. The surprising and somewhat contrary results

would benefit from further analysis in future research.

The results in this paper support prior findings on the important role that people

play in encouraging knowledge and information flows (see Hansen, 1999). They extend

these prior results by focusing on additional types of mechanisms through which firms

transfer, exploit and develop their knowledge throughout their operations. The results in

this paper also confirm earlier industry-level findings by Kobrin (1991) and show that

knowledge protection strategies are important in explaining the aggregate flows of goods

and parent firm integration of goods across subsidiaries. However, while technological

intensity is an important determinant of parent flows of good to subsidiaries, the context

and characteristics of subsidiaries also play important roles in influencing the direction

and level of integration from subsidiaries to all other firm activities. These results

suggest that integration efforts by firms are likely to depend on many things, including

subsidiary, parent firm, industry and country influences. For example, subsidiaries that

are in the firm’s core US industry and that have high levels of ownership coordinate tend

to integrate their production with parent firms. Subsidiaries that are older, that have high

local sales and are distant from the home country operations of a firm are not likely to

have integrated production with either parent or third country subsidiary operations.

While this study highlights the role of knowledge flows on production linkages, there are

clearly many other influences that impact the role of subsidiaries within a firm’s network

of operations that could be explored in future research.

Finally, there are several limitations to this paper. First, I have examined US

firms only. While the BEA data provides detailed information on the worldwide

operations of US firms, the results may not be generalizable to firms from other

countries. Second, there are several informal mechanisms that firms can use to transfer,

exploit and develop their knowledge assets. Employee rotations, cross-national teams

and employee compensation can provide incentives for firms to coordinate and integrate

their activities. While I have rich time series data on several types of knowledge flows

within multinational corporations, I do not have access to any of the more informal

34

mechanisms that are likely to exist within the firms in my dataset. It would be interesting

to combine some of the more detailed, smaller sample firm information with the large

sample, time series information to better understand how formal and informal

mechanisms influence linkages across firm operations. Finally, data on whether firms are

integrating intermediate or final products is not available in a consistent fashion across

the BEA surveys. Future studies that examine integration of goods could benefit from

considering detailed product level information to further understand how firms with

proprietary assets are exploiting these assets as they expand their operations. While I

have focused on a role for knowledge and employees, additional information about

subsidiaries that produce intermediate products for other markets would provide

additional insight into production linkages across firm operations.

35

References:Almeida, P. and B. Kogut 1999. “Localization of knowledge and the mobility of

engineers in regional networks.” Management Science: 905-917.

Argote, L., and P. Ingram. 2000. “Knowledge transfer: A basis for competitive advantage in firms.” Organizational Behavior and Human Decision Processes 82(1): 150-169.

Banga, R., 2006. “The export diversifying impact of Japanese and US FDI in the Indian manufacturing sector.” Journal of International Business Studies 37: 558-68.

Barkema, H., J. Bell and J. Pennings, 1996. “Foreign entry, cultural barriers and learning.” Strategic Management Journal, 17(2): 151-166.

Bartlett, C., and S. Ghoshal, 1989. Managing Across Borders: The Transnational Solution. Harvard Business School Press (Boston, MA).

Birkinshaw, J., 1997. “Entrepreneurship in multinational corporations: the characteristics of subsidiary initiatives.” Strategic Management Journal 18 (3): 207-229.

Birkenshaw, J., and N. Hood, 1998. “Multinational subsidiary evolution: capability and charter change in foreign owned subsidiary companies.” Academy of Management Review 23: 773-95.

Black, J., Gregersen, H., and M. Mendenhall, 1992. “Toward a theoretical framework of repatriation adjustment,” Journal of International Business Studies, 23(4): 737-61.

Borga, M., and W. Zeile, 2004. “International fragmentation of production and the intrafirm trade of US multinational companies,” BEA Working Paper

Branstetter, L., R. Fisman and C. F. Foley, 2006.“Do stronger intellectual property rights increase technology transfer? Empirical evidence from US firm-level data,” Quarterly Journal of Economics, 121(1): 321-349

Buckley, P and M. Casson, 1976. The Future of the Multinational Enterprise. MacMillan, London

Buckley, P. and R. Pearce, 1979. “Overseas production and exporting by the world’s largest enterprises: A study in sourcing policy.” Journal of International Business Studies 10: 9-20.

Cantwell, J., 1989. Technological Innovation and Multinational Corporations. Blackwell: NY, New York.

Caves, R., 1996. Economic Analysis and the Multinational Corporation (2nd Edition) Cambridge University Press, Cambridge UK.

36

Coff, R., 2003. “Bidding wars over R&D intensive firms: knowledge, opportunism and the

market for corporate control,” Academy of Management Journal 46(1): 74-85.

De Backer, K., and L. Sleuwaegen, 2003. “Does foreign direct investment crowd out domestic entrepreneurs?” Review of Industrial Organization 22(1): 67-84.

Dosi, G., R. Nelson, S. Winter, 2000. The Nature and Dynamics of Organizational Capabilities, Oxford University Press, Oxford.

Doz, Y. and C.K. Prahalad, 1993. “Managing DMNCs: A search for a new paradigm. In Ghoshal and Westney (Eds) Organizational Theory and the Multinational Corporation. St. Martin’s Press (NY, New York), 24-50.

Dunning J., 1980. “Toward an eclectic theory of international production: Some empirical tests.” Journal of International Business Studies 11: 9–31.

Edstrom, A., and J. Galbraith, 1977. “Transfers of managers as a coordination and control strategy in multinational corporations,” Administrative Science Quarterly, 22: 248-63.

Erramilli, M., 1991.“The experience factor in foreign market entry behavior of service firms.”

Journal of International Business Studies 21(3): 479-501.

Ethier, W.J., 1986. “The multinational firm,” Quarterly Journal of Economics 101(4): 806-833.

Feenstra, R., Hanson, G, and D. Swenson, 2000. “Offshore assembly from the United States: product characteristics of the 9802 Program” in Feenstra, ed., The Impact of International Trade on Wages, Chicago, NBER and University of Chicago Press: 85-125.

Feinburg, S., and A. Gupta, 2003. “Knowledge spillovers and the assignment of R&D responsibilities to foreign subsidiaries.” Strategic Management Journal 25:

Frost, T., 2001. “The geographical sources of foreign subsidiaries’ innovations.” Strategic Management Journal 23: 997-1018.

Frost, T., Birkinshaw, J., and P. Ensign, 2002. “Centers of excellence in multinational corporations,” Strategic Management Journal 23: 997-1018.

Ghemewat, P. 2003.“Semiglobalization and international business strategy,” Journal of International Business Studies, 34(2): 138-52.

37

Ghoshal, S. and P. Moran, 1996. “Bad for practice: A critique of transaction cost theory”, Academy of Management Review, 21, 13-47.

Ghoshal, S., and Nohria, N, 1989. “Internal Differentiation within Multinational Corporations.” Strategic Management Journal, 10: 323-37.

Ghoshal, S., and E. Westney 1993. “Introduction and Overview.” In Ghoshal and Westney (ed) Organization Theory and the Multinational Corporation St. Martin’s Press, (NY, New York), 1-23.

Grant, R. 1996. “Toward a knowledge-based theory of the firm”, Strategic Management Journal, 17: 109-122.

Grant, R., 1996. “Prospering in dynamically-competitive environments: organizational capability as knowledge-integration.” Organization Science 7(4): 375-387. Grubert, H., 1988. “Taxes and the division of foreign operating income among royalties,

interest, dividends and retained earnings.” Journal of Public Economics 68: 269-90.

Gupta, A. and V. Govindarajan, 1991. “Knowedge Flows and the Structure of Control Within Multinational Firms.” Academy of Management Review 16: 768-92.

Gupta, A., and V. Govindarajan, 2000. “Knowledge flows within multinational corporations.” Strategic Management Journal, Vol. 21: pp. 473-496.

Hansen, M., 1999. “The search-transfer problem: the role of weak ties in sharing knowledge across organization subunits.” Administrative Science Quarterly 44(1): 82-111.

Head, K. and J. Reis, 2001. “Overseas investment and firm exports,” Review of International Economics 9(1): 108-122.

Hedland, G., 1986. “The hypermodern MNC: A heterarchy?” Human Resource Management 25(1): 9-35

Hennart, J.F., 1991. “The Transaction Cost Theory of Joint Ventures: An Empirical Study of Japanese Subsidiaries in the United States.” Management Science 37(4): 483-497.

Hines, J., 1994. “Fiscal paradise: foreign tax havens and american business.” NBER Working Paper 3477.

Hines, J., 1995. “Taxes, technology transfer and R&D by multinational firms.” In Feldstein, M., J. Hines and R.G. Hubbard (eds), Taxing Multinational Corporations. Chicago, IL., University of Chicago Press.

38

Hymer, S., 1976. The International Operations of National Firms. Cambridge, MA: MIT Press.

Imbens, G. 2004. Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review. The Review of Economics and Statistics 86(1): 4-29.

Jarillo, C., and J., Martinez, 1990. “Different roles for subsidaries: The case of multinational corporations in Spain.” Strategic Management Journal 11: 501-512.

Kogut, B. 1984. “Normative observations on the value added chain and strategic groups,” Journal of International Business Studies, fall: 151-86.

Kogut, B., and U. Zander, 1992. “Knowledge of the firm, combinative capabilities and the replication of technology.” Organization Science 3(3): 383-397.

Kobrin, S. 1991. “An empirical analysis of the determinants of global integration,” Strategic Management Journal 12:17-31.

Kumar, N., and H. Siddharthan1994. “Technology, firm size and export behavior in developing countries: The case of Indian Enterprises.” The Journal of Development Studies 31(2): 289-309.

Lall, S., 1978. “The pattern of intra-firm exports by US multinationals.” Oxford Bulletin of Economics and Statistics 40: 209-222.

Lawrence, P. R., and J. W. Lorsch, 1967. “Differentiation and integration in complex organizations.” Administrative Science Quarterly, 12 (1), 1-47.

Lawrence P.R., and J.W. Lorsch, 1986. Organization and Environment: Managing Differentiation and Integration, Harvard Business School Classics ed., Harvard Business School Press, Boston.

Lipsey, R. and M.Y. Weiss, 1981. “Foreign production and exports in manufacturing industries,” Review of Economics and Statistics, 63(4): 488-494.

Malnight, T., 2001. “Emerging structural patterns within multinational corporations: toward process-based structures.” Academy of Management Journal 44(6): 1187-1210.

Mansfield, E. and A. Romeo, 1980. “Technology transfer to overseas subsidiaries by US based firms,” Economica 46: 737-750.

March, J. G., H. A. Simon, 1958. Organizations, Wiley, New York.

Markusen, J. R., 2002, Multinational firms and theory of international trade, The MIT

Press, Cambridge, Massachusetts. London, England.39

Mataloni, R., and D. Yorgason, 2006.“Operations of US multinational companies:

preliminary results from the 2004 benchmark survey,” Survey of Current Business 86: 37-68.

McEvily, S., Eisenhardt, K. and J. Prescott, 2004. “The global acquisition, leverage and protection of technological competencies,” Strategic Management Journal, 17: 713-22

Nelson, D., and S. Winter, 1982. An Evolutionary Theory of Economic Exchange. Belknap Press: Cambridge, MA.

Prahalad, CK, and Y. Doz, 1987. The Multinational Mission, Free Press, NY, New York.

Reagans, R., and E. Zuckerman, 2001. “Networks, diversity and productivity: the social structure of corporate R&D teams.” Organization Science 12: 502-517.

Rosenbaum, P.R., and Rubin, D.B., 1983. “Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score,” The American Statistician, 39(1): 33-38.

Rosenkopf, L. and P. Almeida, 2003. “Overcoming local search through alliances and mobility.” Management Science 49(6): 751-766.

Shatz, H., 2004. US multinational affiliate exports from developing countries,” Journal of Economic Geography 4(3): 323-44.

Singh, J., 2008. Distributed R&D, cross-regional knowledge integration and quality of innovative output,” Research Policy 37: 77-96.

Sleuwaegen, L., 1985. Monopolistic advantages and the international operations of firms: disaggregated evidence from US-based multinationals.” Journal of International Business Studies 16: 125-133.

Song, J, P.Almeida and G. Wu, 2003. “Learning-by-hiring: when is mobility more likely to facilitate interfirm knowledge transfer?” Management Science 49(4): 351-365.

Swedenborg B. 1979. The Multinational Operations of Swedish Firms. Stockholm Industrial Institute for Economic and Social Research.

Szulanski, G., 1996. “Exploring internal stickiness: impediments to the transfer of best practice within the firm.” Strategic Management Journal, 17: 27-43.

Taggart, J., 1998. ”Strategy Shifts in MNC Subsidiaries.” Strategic Management Journal, 19(7): 663-681.

40

Teece, D., 1977. “Technology transfer by multinational firms: the resource costs of transferring technological know-how.” The Economic Journal 87: 242-261.

Teece, D., 1986. “Transaction cost economics and multinational enterprise.” Journal of Economic Behavior and Organization, 7: 21-45.

Tung, R., 1993. “Managing cross-national and intra-national diversity,” Human Resource Management, 32(4): 461-77.

Williamson, O., 1979. Transaction Cost Economics: The Governance of Contractual Relations.

Zahra, S., R., Ireland, and M. Hitt, 2000. “International expansion by new venture firms: international diversity, mode of market entry, technological learning and performance,” Academy of Management Journal, 43(5): 925-50.

Zejan, M., 1989. “Intra-firm trade and Swedish multinationals.” Weltwirtschaftliches Archiv 125: 814-833.

Zeile, W., 1997. “U.S. intrafirm trade in goods.” Survey of Current Business: 23-38

Zhou, M., 2006. “Conducting R&D in countries with weak intellectual property rights protection.” Management Science 52: 1185-1199.

41

Table One: Variables:

Dependent variables:

MNCIntegration Total parent and affiliate exports as a percent of a firm’s worldwide sales

ParSubIntegration Parent exports to subsidiaries as a percent of total parent sales in the US

SubParIntegration Subsidiary exports to parents as a percent of total subsidiary sales

SubSubIntegration Subsidiary exports to affiliated subsidiaries in third countries as a percent of total subsidiary

Independent Variables:

Subsidiary Level Variables:

Size t-1 Lagged log of Local Sales in host country Core Dummy = 1 if main business line of subsidiary is in

the same primary 4 digit SIC code as the parent’s main business line

RDEmpt-1 Lagged R&D employees as a percent of total employees in the subsidiary

USEmp%t-1 Lagged US employees as a percent of total employees in the subsidiary

Royaltiest-1 Lagged Royalties paid to the US parent firm for parent firm assets (available starting in 1994)

SubRDIntt-1 Lagged subsidiary R&D expenditures as a percent of total subsidiary sales

SubNetInc t-1 Lagged subsidiary net income in host countrySubImPar t-1 Lagged percent of Imports from Parent Firm to

Subsidiary Local SalesSubAge Dummy variable that takes a value of one for any

subsidiary that was established within the past five years from time t.

Firm Level Variables

ParRDIntt-1 R&D expenditures as a percent of Parent US salesForAssets% t-1 Lagged ratio of foreign to total assetsTotSubs t-1 Lagged total number of foreign subsidiaries of

parentParNetInc t-1 Lagged parent net income in the USParDebt t-1 Lagged total parent debt in the US

42

Industry Level Variables:

IndIntegration t-1 Lagged proportion of international sales that are Intra-firm in the 3-digit main industry of the parent firm

Country Level Variables

GDDt-1 Lagged log of Gross Domestic Product in host country in current US dollars

GDPcapgrow Lagged GDP per capita growth rate in host countryFDIInflow t-1 Lagged foreign direct investment as a percent of

host country GDP GeoDist Physical Distance from host country to USUSTaxDiff Lagged corporate tax rate difference from host

counry to USDevCnty: Dummy variable that equals one if the subsidiary is

located in a country that is in the low or middle income classifications according to World

Bank tables. English Dummy variable that takes a value of 1 if the host

country has English as its official language OR at least 9% of the population speak English.

301List Dummy variable that takes a value of 1 if the host country has been on the USTR’s Special 301 Priority Watch List from all available USTR reports during the time period 1996 to 2004.

43

Table Two: Summary Statistics:*

Variable Obs Mean Std. Dev. Parent:MNCIntegration 8834 .133 .13 ParSubIntegration 8834 .08 .14 ParRDInt 9999 .05 .24 ForAsset% 10009 .540 .18 TotManSubs 10009 24.83 24.69ParNetIncome 9973 1072889 3019197 ParDebt 10015 101798 500869

Industry:GlobalIntegration 10009 .19 .09

Subsidiary:SubToParIntegration 122690 .05 .18SubToSubIntegration 122690 .09 .21RDEmp% 35225 .01 .05USemp% 35232 .005 .09Royalties 47072 .01 .06SubRDInt 78622 .01 .48SubImPar 122091 .06 .17SubAge 131898 .27 .39Size 77314 10.15 2.23Core 131898 .52 .27SubNetIncome 129450 12814.41 110411

Country:GeoDist 112055 7370.34 3804.29GdpCapGrow 97104 3.19 10.26LogGDP 97079 26.56 1.51FDIInflow 93822 .04 .21USTaxDiff 126594 4.28 9.18DevCnty 107362 .26 .44EngLanguage 129389 .46 .49IPRList 131898 .09 .29

*Min and Max values withheld due to confidentiality concerns.

44

Table Three: Key Variable Means Over Time*

All Years 1989 1994 1999 2004

Dependent Variables:MNCIntegration .13 (.13) .11 .13 .13 .14 PartoSubIntegration .08 (.14) .07 .07 .07 .09 SubToParIntegration .05 (.18) .05 .05 .04 .06SubToSubIntegration .09 (.21) .07 .07 .06 .10

Independent Variables:RDEmp% .01 (.05) .01 .01 .01 .02USemp% .005 (.09) .005 .005 .005 .003Royalties .01 (.06) N/A .01 .01 .01SubRDInt .01 (.48) .01 .01 .02 .02SubImPar .06 (.17) .03 .04 .04 .07ParRDInt .05 (.24) .05 .05 .05 .05 TotManSubs 24.83 (24.69) 23 24 25 27IndIntegration .19 (.09) .17 .17 .18 .23

*Standard Deviations are only reported for the entire sample, though they are similar for each individual year

Table Four: Correlations:

I. Parent Specifications:

1.MNCIntegration(DV)1.02.PartoSubInt (DV) .88 1.03.ParRDInt .12 .09 1.004. ForAsset% .04 -.06 -0.04 1.005. TotManSubs .05 -.03 -0.01 -0.38 1.006. ParNetIncome -.03 -.02 -.04 -0.07 0.17 1.007. ParDebt -.01 -.02 0.01 -0.03 0.43 0.09 1.008. IndIntegration .22 .18 .02 -.05 -.01 .01 .01 1.0

45

Table Four: Correlations (Con’t)

II. Subsidiary Specifications:

1 2 3 4 5 6 7 8 9 10 11 121. SubtoSub(DV) 1.02. SubtoPar(DV) .62 1.03. RDEmp% t-1 0.05 0.14 1.04. USEmp% t-1 0.03 0.06 0.01 1.05. Royalties t-1 0.01 0.01 -0.01 -0.01 1.06. SubRDint t-1 -0.00 -0.04 0.11 -0.00 -0.01 1.07. SubImportPar t-1 0.10 0.05 0.03 -0.01 -0.01 -0.01 1.08. LogLocalSales t-1 -0.09 -0.10 0.02 0.01 -0.01 0.06 -0.04 1.09. Core t-1 0.10 0.13 0.10 -0.03 0.05 -0.05 -0.01 0.05 1.010. SubNetIncome t-1 0.02 0.08 0.02 0.01 0.04 0.05 -0.01 0.13 0.03 -1.011. SubDebt t-1 0.06 0.13 0.04 0.05 -0.01 0.07 -0.01 0.23 0.03 -.35 1.012. SubAge5 t-1 -0.02 -0.04 -0.00 -0.01 -0.04 -0.02 0.01 -0.05 -0.01 .01 .01 1.013. ParR&Dt-1 0.01 0.01 0.15 -0.01 -0.02 -0.04 0.00 0.03 -0.04 .00 -.01 .0114. ParForeignAssets t-10.08 -0.06 -0.06 -0.02 -0.01 0.13 0.03 -0.14 0.04 -0.10 -0.07 -0.07 15. ParManSubs t-1 0.07 -0.05 0.04 0.00 0.01 -0.16 -0.02 0.11 -0.07 0.08 0.07 0.01 16. ParNetIncomet-1 -0.04 -0.01 -0.03 0.05 -0.06 -0.11 -0.01 0.20 -0.17 0.16 0.15 0.12 17. GeoDistance t-1 -0.13 -0.03 0.01 0.02 -0.00 -0.20 -0.02 -0.06 -0.07 -0.02 -0.05 0.04 18. LogGDP t-1 -0.03 -0.01 0.11 -0.04 -0.01 -0.06 0.01 0.13 0.06 0.01 0.06 0.02 19. GDPCapGrow t-1 0.07 0.05 -0.05 0.01 0.02 -0.23 0.00 -0.06 0.01 0.01 -0.02 0.05 20. FDIInflows t-1 0.04 0.05 -0.01 0.01 -0.00 -0.06 -0.00 -0.06 -0.01 0.03 0.01 0.02 21. USTaxDiff t-1 0.05 -0.02 0.01 0.00 -0.04 -0.28 -0.01 -0.01 -0.02 0.08 -0.01 0.06 22.GlobalIntegration t-10.14 0.23 0.11 0.01 -0.07 -0.00 -0.01 0.02 0.04 0.03 0.04 0.14 23. English -0.01 -0.01 -0.01 -0.01 0.01 -0.05 -0.01 -0.01 0.01 -0.05 -0.02 -0.01 24. IPR -.01 -.04 -.02 -.02 -.01 -0.04 0.08 -0.12 -0.01 -0.03 0.04 0.02

46

Table Four: Correlations (Con’t)

II. Subsidiary Specifications (Con’t)

13 14 15 16 17 18 19 20 21 22 23 2413. Par R&Dt-1 1.0014. Par ForeignAssets t-1.-0.11 1.015. ParManSubs t-1 0.10 0.44 1.016. ParNetIncomet-1 -0.03 -0.40 0.39 1.017. GeoDistance t-1 0.09 -0.13 0.11 0.06 1.018. LogGDP t-1 0.01 0.16 -0.17 -0.18 -0.13 1.019. GDPGrow t-1 -0.01 -0.03 0.01 0.06 0.08 -0.15 1.020. FDIInflows t-1 -0.01 -0.02 0.01 0.03 -0.01 -0.20 .07 1.021. USTaxDiff t-1 0.05 -0.13 0.06 0.06 0.21 -0.15 -0.04 .05 1.022. GlobalIntegration t-10.17 -0.15 -0.01 0.08 0.04 0.08 0.08 -0.00 0.11 1.023. English -0.02 -0.02 0.01 0.01 0.01 -0.07 -0.00 0.01 -0.01 -0.00 1.024. IPR 0.02 -0.09 0.09 0.04 0.13 0.01 0.14 -0.07 0.19 0.01 -0.03 1.0

47

Table Five: MNC and Parent Results

Dependent Variable: MNC Total Parent Exports Exports to Subsidiaries

Tobit OLS Tobit OLS (1) (2) (3) (4)

Independent Variables:

Parent Level Variables: R&Dt-1 H1(+) .01* .08* .01* .05*

(3.05) (2.67) (3.01) (2.86) ForeignAssets t-1. .05* .07* .06* .06*

(4.25) (3.34) (4.27) (3.21) ManSubs t-1 .02 .09 -.03** -.11

(1.59) (1.09) (-3.3) (-1.61) NetIncome t-1 -.002 -.03 -.001 -.04

(-.51) (-.42) (-.49) (-.31) Debt t-1 -.01 -.02 -.01 -.01

(-1.63) (-1.03) (-1.55) (-1.11)

Industry Level Variables: GlobIntegration t-1 .13* .13* .18* .12*

(6.41) (2.78) (6.67) (2.95)

Intercept: .14* .11* -.24* -.15*Firm Effects (random): Re Fe Re FeYear Dummies: Yes Yes Yes Yes2Digit INDDum: Yes No Yes NoN = 7424 7424 7424 7424Log likelihood or F-test -1564* 35.65* -1324* 44.13*

* p<.01 ** p<.05 Robust standard errors for OLS results

48

Table Six: Subsidiary Results

Dependent Variable: Subsidiary Exports to Parents Subsidiary Exports to Subsidiaries

Tobit Tobit OLS P-score Tobit Tobit OLS P-score (Δ on Δ) (Δ on Δ)

(1) (2) (3) (4) (5) (6) (7) (8)

Independent Variables:Subsidiary Level Variables: Royaltiest-1 H2(+/-) .06 .03 .01 .07 .16** 3.76**

(.38) (.82) (.43) (1.21) (2.04) (3.06) R&DEmpt-1 H4(+) .05* .21* .04** .04* .18* 2.54*

(4.45) (4.78) (2.01) (5.16) (4.23) (4.45 Expatst-1 H5(+/-) .06* .13* .03* .04* .04 .002

(4.21) (4.65) (3.04) (2.45) (1.04) (.48) ImportsPar t-1 H3(+/-) .002* .001** .04* .02* .03* .01* .01 .11

(2.31)(2.09) (4.02) (3.03) (4.91) (4.78) (1.58) (1.04) SubR&Dt-1 -.03* -.02* -.04 -.02 -.04 -.01 -.03 -.002

(-3.85)(-3.99) (-.82) (-.59) (.23) (-.81) (-1.47) (-.07) LocalSales t-1 -.06* -.05* -.09* .01* -.08 -.06 -.05 -.21

(-5.46)(-3.02) (-5.65) (3.52) (-1.5) (-1.41) (-1.54) (-.35) SubNetInc t-1 .03* .01* .03* .02* .03* .02* .02* 1.42*

(3.84) (2.61) (4.85) (2.41) (3.01) (2.31) (2.61) (2.78) CoreIndustry .01* .01* .03* .09* .14* .02*

(6.63) (5.61) (6.67) (6.62) (6.75) (4.21) SubAge -.03* -.03* -.04 -.02** -.02** -.01

(2.44)(2.33) (-2.43) (-2.01) (-2.06) (-1.46)

Parent Level Variables: R&Dt-1 H1(+) .02 .02 .02 .01 .03 .02 .02 .01

(1.11) (1.13) (1.34) (1.57) (1.21) (1.44) (1.84) (1.65) ForeignAssets t-1. -.08* -.11* -.08* -.01 .11* .10* .04 .01

(-6.6) (-5.14) (-3.74) (-.32) (6.9) (5.23) (1.56) (1.23) ManSubs t-1 -.04* -.03** -.18* -.02 .06* .03* .11 1.76

(-5.91) (-1.99) (-2.87) (-.44) (6.11) (2.32) (1.89) (1.34) NetIncome t-1 -.01 -.01 -.04 -.01 -.02 -.01 -.01 -.004

(.61) (.42) (-1.54) (-1.02) (-.57) (-.42) (-.87) (-.56) Debt t-1 -.01 -.01 -.01 -.01 -.01 -.01 -.01 -4.21

(1.48) (1.01) (-.41) (.36) (-1.31) (-1.22) (-1.02) (-1.11)

Industry Level Variables: GlobIntegration t-1 .96* .81* .24* .03 .98* .92* .17* 2.02*

(6.34) (5.81) (6.64) (1.61) (6.73) (6.21) (2.98) (2.54)

Country Level Variables logGDPt-1 -.01* -.01* -.11 -.04* -.01* -.04* -.06 -.15**

(-8.01) (-6.17) (-8.32) (2.54) (-4.12) (-4.13) (-1.55) (2.24) FDIInflows t-1 .02* .08* .05* .01 .03* .02* .04* .01

(2.32) (2.83) (3.74) (.65) (3.31) (2.97) (2.31) (1.42)

49

Table Six: Subsidiary Results (Con’t)

(1) (2) (3) (4) (5) (6) (7) (8)

USTaxDifft-1 -.02 -.02 -.02 -.01 .04* .04* .06 .02 (-.65) (-.92) (-.97) (.21) (2.82) (2.83) (1.69) (1.21)

GDP/Capgrow t-1 .07* .06* .08* .01 .07* .06* .07* .01 (3.98) (3.21) (2.92) (1.52) (3.52) (3.13) (2.19) (1.71)

GeoDistance -.01* -.01* -.09* .02* .02* .07 (6.64) (6.82) (6.89) (5.22) (4.22) (1.65)

English -.01 -.01 -.01 -.01 -.01 -.01 (-.42) (-.51) (-.78) (-.65) (-.42) (-.65)

301List -.01 -.02 -.02 -.02 -.02 -.02 (-.83) (-.61) (-1.45) (-.75) (-.62) (-1.67)

Intercept: -.46* -.09 -.12* 0.01* -.29* -.38* -.21* -.07Firm Effects: Re Re Fe No Re Re Fe NoYear Dummies: Yes Yes Yes Yes Yes Yes Yes Yes2Digit INDDum: Yes Yes No No Yes Yes No NoN = 50004 10768 3447 50004 10768 3729Log likelihood -1942* -2470* 39.95* 21.51* -1445* -2964* 23.07* 19.65*

p<.01 ** p<.05 Robust standard errors for OLS results

50

Appendix: Calculating Propensity Score Weighting

Below are the probit results from the propensity score matching (columns 1 and 5, for exports to parent firms and exports to other subsidiaries, respectively) and t-test for differences in means for treated and control groups (columns 2, 3 and 4 for subsidiary exports to parent firms, and columns 6, 7 and 8 for subsidiary exports to other subsidiaries). I used a kernel based matching method where each treated observation was matched with a weighted sum of observations with similar scores, with the greatest weight given to those with closer scores. The propensity score weights were used in columns 4 and 8 in Table Six.

Appendix Table: Propensity Score Results

Dep Var 0/1 for Subsidiary to Parent Exports: Subsidiary to Subsidiary Exports:

Probit Mean Mean T-test for Probit Mean Mean T-test forCoef Treat Control Difference Coef Treat Control Difference(1) (2) (3) (4) (5) (6) (7) (8)

Subsidiary Level Variables: Royaltiest-1 .01 .01 .01 .43 .07* .01 .01 .46

(1.50) (4.06) R&DEmpt-1 .53* .029 .019 1.98 .59* .025 .019 1.87

(4.79) (3.56) Expatst-1 .16* .01 .01 1.58 .01 .01 .01 1.63

(3.85) (.48) ImportsPar t-1 .27* .13 .12 1.41 .01 .098 .10 -1.36

(5.02) (.11) SubR&Dt-1 -.09 .01 .01 .46 -.04 .01 .01 .24

(-.27) (-1.11) LocalSales t-1 -.19* 11.46 11.21 .97 -.21* 11.52 11.08 .88

(- 4.56) (-6.59) SubAge -.17** .10 .11 -.62 -.06 .11 .12 -.69

(-2.21) (-1.43) CoreIndustry .83* .52 .47 .76 .54* .45 .46 -.51

(5.46) (3.75) SubNetInc t-1 .03 11911 11132 .43 .04 12019 11283 1.51

(.14) (.87)Parent Level Variables: ParR&Dt-1 .06 .05 .05 .94 .07 .06 .05 1.23

(1.57) (1.49) ForeignAssets t-1. -.43* .55 .53 1.05 .38 .55 .51 1.52

(-2.54) (1.42) ManSubs t-1 -.21 33.1 30.6 .83 .42 32.4 29.8 1.45

(-1.56) (.97) NetIncome t-1 -.19 1.1e+06 1.2e+06 -1.61 -.01 1.4e+06 1.5e+6 -1.61

(.17) (-1.01) Debt t-1 -.07 105874 109321 1.40 -.09 103574 109243 1.65

(-.78) (-1.32)

51

Appendix Table: Propensity Score Results (Con’t)

(1) (2) (3) (4) (5) (6) (7) (8)

Industry Level Variables: GlobIntegration t-1 1.27* .13 .12 1.2 1.34* .13 .13 1.04

(6.87) (5.34)

Country Level Variables: logGDPt-1 -.06 26.91 26.84 1.09 -.15* 26.6 26.4 1.53

(-1.03) (-5.34) GDP/Capgrow t-1 .67* 3.59 3.64 -.44 .88* 3.7 3.62 .94

(3.82) (2.76) FDIInflows t-1 .22 .16 .13 .37 .42* .15 .13 1.52

(1.78) (2.04) USTaxDifft-1 -.36 2.54 2.76 -1.22 .24** 2.6 2.5 .28

(-1.49) (1.87) GeoDistance -.09* 7739 7932 -1.48 .06* 8487 8342 1.5

(-4.02) (2.28) English -.24 .41 .43 -.87 -.21 .39 .40 -1.42

(-.71) (1.23) 301List -.32 .05 .06 -1.21 -.27 .06 .08 -1.43

(-1.23) (-1.41)

Intercept: -2.71* -.78Year Dummies: Yes Yes2Digit INDDum: Yes YesN = 10768 638 2809 10768 762 2967Log likelihood -2563* -3101*

* p<.01 ** p<.05

52