The Impact of Foreign Ownership on Research - Am MIT spezialisierte KI-Forschung

The Impact of Foreign Ownership on Research
and Development Intensity and Technology
Acquisition in Indian Industries: Pre and
Post Global Financial Crisis
Aradhna Aggarwal∗

This study examines how interﬁrm heterogeneities in modes of technology
acquisition and technology intensities are linked to ﬁrm ownership in India
using a panel data set of about 2,000 ﬁrms listed on the Bombay Stock Exchange
for the period 2003–2014 drawn from the Prowess database of the Center for
Monitoring Indian Economy. Foreign ownership is categorized according to the
level of control exercised by foreign ﬁrms as deﬁned under the Companies
Act of India. A comparative analysis of domestic and different categories of
foreign ﬁrms was conducted for two time periods: (ich) the global boom period of
2004–2008, Und (ii) the post global ﬁnancial crisis period of 2008–2014. A
horizontal cluster analysis of 3-digit, industry-level data shows that foreign
ﬁrms cluster in high-technology industries. The propensity score matching
Analyse, Jedoch, reveals that in a matched sample of foreign and domestic
ﬁrms, majority-owned foreign ﬁrms spend less on research and development
and more on technology transfers than their local counterparts, demonstrating
that the level of equity holdings by a foreign ﬁrm matters. There is little
evidence of the global ﬁnancial crisis affecting the relocation of research and
development activities to India. An alternative assessment based on panel data
regression analysis conﬁrms these ﬁndings and validates the propensity score
matching results.

Schlüsselwörter: domestic ﬁrms, foreign ﬁrms, global ﬁnancial crisis, local R&D,
majority-owned foreign subsidiaries, minority-owned subsidiaries, Technologie
Erwerb
JEL-Codes: G21, G32, K22, L25

ICH. Einführung

Rapid advances in technology, which have been reinforced by the process of
globalization, have exposed ﬁrms in developing economies to intense technological
competition both in domestic and export markets. Efforts toward building

∗Aradhna Aggarwal: Professor, Asia Research Centre, Department of International Economics and Management,
Copenhagen Business School, Denmark. Email: aa.int@cbs.dk. I would like to thank the managing editor and
anonymous referees for helpful comments and suggestions. Es gilt der übliche Haftungsausschluss. The Asian Development
Bank recognizes “Bombay” as Mumbai.

Asiatischer Entwicklungsbericht, Bd. 35, NEIN. 1, S. 1–26
https://doi.org/10.1162/adev_a_00103

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

2 Asiatischer Entwicklungsbericht

technological capabilities are increasingly becoming vital for them to compete.
Jedoch, building these capabilities is costly, cumulative, and evolutionary; it takes
time and progress is uncertain (Lall 1992). Das erkennen, the governments of
developing economies have encouraged multinational enterprises (MNEs) to set up
local production facilities in the hope of importing new technologies and building
the technological capabilities of domestic ﬁrms. It is expected that the presence
of MNEs will entail technology transfers to domestic ﬁrms through spillover
mechanisms such as labor turnover, Nachahmung, competition, and demonstration.
Jedoch, there is evidence that technology spillover effects are neither robust nor
consistent across economies (sehen, Zum Beispiel, Mebratie and van Bergeijk 2013,
Demena and van Bergeijk 2017). There is growing recognition that foreign direct
investment (FDI) can ensure more profound knowledge spillovers to domestic ﬁrms
if MNEs perform a larger share of their research and development (R&D) Aktivitäten
in host economies (UNCTAD 2005). daher, attracting R&D-intensive FDI is a
critical concern for national policy makers in developing economies.

The present study analyzes the technological behavior of MNEs in India
and investigates whether MNE subsidiaries are signiﬁcantly different from their
domestic counterparts in terms of technology intensity and modes of technology
sourcing. Konkret, it examines whether MNEs spend more on R&D than their
domestic counterparts or whether they are more likely to acquire new technologies
from their global networks through licensing and imported capital goods.

It is assumed that R&D-intensive MNE subsidiaries (with signiﬁcantly more
R&D spending than domestic ﬁrms) are likely to have more robust effects on the
technological capabilities of host economies than those subsidiaries that depend
on technology imports from their parent ﬁrms (d.h., spending more on technology
imports than their domestic counterparts). The former are better embedded into the
local innovation systems and have greater potential for technological spillovers. Der
possibility that MNEs are not signiﬁcantly different from domestic ﬁrms in either
R&D spending or technology imports cannot be ruled out. Such MNEs would be
considered technological laggards. The opposite is true if MNEs spend signiﬁcantly
more on both R&D and technology imports than their local counterparts. Diese
MNEs may have the greatest potential for knowledge transfers to host economies.
Allgemein, the distribution of corporate R&D spending is highly skewed
across industries. A few high-technology sectors account for the overwhelming
share of R&D activity (Hirschey, Skiba, and Wintoki 2012). Given that MNEs
undertake the bulk of global R&D expenditures and tend to have a strong presence
in high-technology industries, differential
foreign
afﬁliates may reﬂect the fact that MNEs are attracted to such industries (Globerman,
Ries, and Vertinsky 1994; Girma, Greenaway, and Wakelin 2001; Bellak 2004).
Jedoch, the possibility that they predominate in resource- or labor-intensive
industries cannot be ruled out either. Selection bias can thus be a major problem
in such studies (Damijan et al. 2003, Javorcik and Spatareanu 2008, Hake 2009).

technological behavior of

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 3

daher, this analysis begins by identifying the sectoral distribution of MNEs
in India by technological intensity using cluster analysis. This is followed by the
use of propensity score matching methods to match each foreign ﬁrm with a
domestic counterpart within broad industry groups to estimate the impact of foreign
ownership on different forms of technological spending. To check the validity of
my results, I also conduct panel data regression analysis on matched samples. Der
data are partitioned into two periods: (ich) the global boom period of ﬁscal year
(FY)2003–2004 to FY2007–2008, Und (ii) the global ﬁnancial crisis and postcrisis
period of FY2008–2009 to FY2013–2014.1 I conduct a separate analysis for each
period to investigate the impact of global conditions on the technological behavior
of foreign and domestic ﬁrms. There is evidence that the global relocation of
R&D activities suffered following the global ﬁnancial crisis (Kinkel and Som 2012;
Dachs, Stehrer, and Zahradnik 2014). This paper explores how the crisis impacted
on the technology sourcing and technology spending of MNEs and domestic ﬁrms
in India in a comparative analytic framework.

The study contributes to the existing literature in the following ways. Erste,
it offers a systematic analysis of the differential technological behavior of MNEs
and domestic ﬁrms. In der Tat, there are studies that indicate the impact of foreign
ownership on the R&D intensity of ﬁrms (Becker 2013; Tomiura 2003; Kumar and
Saqib 1996; Kumar and Aggarwal 2005; Sasidharan and Kathuria 2011; Balsari,
Özkan, and Varan 2015). Noch, few have analyzed the technology strategies of foreign
ﬁrms by considering alternative modes of technology sourcing. Zweite, most
existing studies are concerned with foreign ownership; the strategic importance of
the share of foreign ownership holding is largely ignored. This study identiﬁes three
levels of foreign ownership holding (10%–25%, 25%–50%, Und 50% und darüber)
and analyzes how impacts vary with the level of foreign ownership. Endlich, Die
ﬁrm ownership data available from secondary sources, which form the basis of most
Studien (particularly for India), are subject to several limitations including a lack of
transparency in the identiﬁcation of foreign ﬁrms. Ownership data for the latest
year are used for identifying foreign ﬁrms for all previous years. Given that a ﬁrm’s
ownership structure (particularly of publicly traded ﬁrms) is subject to continuous
ändern, this practice is likely to yield spurious results. The present study addresses
this gap through scrutiny of changes in ownership patterns for each ﬁrm over the
relevant time periods.

Since the economic liberalization of the early 1990s, India has increasingly
is expected that FDI strengthens the
lowered barriers to entry for FDI. Es
competitiveness of Indian industries through technology transfers and by upgrading
the technological capabilities of domestic ﬁrms through spillover effects, thereby
contributing to restructuring and growth in the Indian economy. This study is

1In India, a ﬁscal year is the period between 1 April and 31 Marsch.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

4 Asiatischer Entwicklungsbericht

expected to have important implications for policy makers in India and other
developing economies that have adopted a similar growth path.

The rest of the study is organized as follows. Section II discusses the
changing role of FDI in the Indian economy and establishes the relevance of India
as the reference economy. Section III describes the theoretical underpinnings of
the analysis. Sections IV and V provide methodological and data-related detail,
jeweils. Section VI presents the empirical results and section VII concludes.

II. Foreign Direct Investment in India

There has been a tremendous increase in inward FDI in India since economic
reforms were adopted in 1991, particularly since 2005. Prior to 1991, FDI was
only allowed in core technology-intensive industries in which little technological
progress had been made domestically. The Foreign Exchange Regulation Act
imposed numerous restrictions on MNEs’ ownership control, entry into markets,
und Wachstum, including the setting up of joint ventures with domestic partners, local
content clauses, export obligations, and promotion of local R&D. In the post-1991
Zeitraum, FDI has provided access to international networks and become a critical
source of scarce capital, Technologie, and managerial skills. There has been a
complete shift in government policy in favor of FDI since 1991, einschließlich der
amendment of investment laws and guidelines to facilitate and promote inﬂows
of FDI. In 2005, the Government of India began to accelerate its FDI reforms,
lowering caps on foreign ownership across all sectors, particularly in construction,
development of townships, defense, insurance and pensions, and single brand and
e-commerce retail sectors. Currently, 100% foreign ownership is allowed in most
sectors with a few exceptions.2 In addition, attempts have been made to ease the
norms, streamline rules and regulations, and improve the business climate.

These reforms have led to annual FDI inﬂows in India growing from about
$129 Million (₹3.2 billion) in FY1991–1992 to over $46 Milliarde (₹2.2 trillion) In
FY2011–2012 (Figur 1). FDI inﬂows as a percent of gross domestic product also
grew steadily during this period, with the ratio of FDI to gross domestic product
improving from less than 2% in FY1991–1992 to over 4.5% in FY2008–2009,
before declining to 3.8% in FY2013–2014.

The stock of FDI has increased astronomically since FY1991–1992.
According to the RBI (2015), total foreign liabilities were only $1.23 billion in 1992. This ﬁgure rose sharply to $265 billion as of 31 Marsch 2015 (RBI 2015).
Nearly half of the total FDI stock at market prices was in the manufacturing sector in

2These exceptions include defense (49%), broadcasting content services (49%), print media (26%), insurance
(49%), infrastructure in securities markets (49%), and private security agencies (49%). Zusätzlich, a small negative
list includes lottery, gambling, chit funds, manufacturing of cigars and cigarettes, real estate business, and sectors not
open for the private sector (z.B., atomic energy and railway operations).

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 5

Figur 1. Inward Foreign Direct Investment Flows and Gross Domestic Product in India,
FY1991–1992 to FY2013–2014

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

FDI = foreign direct investment, FY = ﬁscal year, BIP = Bruttoinlandsprodukt.
Quelle: Reserve Bank of India. Monthly Bulletins (various issues). Mumbai.

2015. Information and communication services (15.5%) and ﬁnancial and insurance
(13.6%) were the other major activities attracting FDI. Globally, India has become
one of the most attractive destinations for FDI by improving its position vis-à-vis
other economies. During FY2005–2006, India was the fourth-largest recipient of
FDI in the world. After the global ﬁnancial crisis, it temporarily fell from among
the top 10 recipients of FDI before rejoining this grouping in 2014. On a regional
basis, India accounts for more than 90% of all FDI in South Asia.

There has also been a proliferation of wholly and majority-owned foreign
companies in India in the postreform period. The foreign share of total equity in
foreign companies was about 72% at the end of 2014, while in the manufacturing
sector it was about 85% (RBI 2015). This is signiﬁcant because prior to
FY1991–1992 the foreign equity share was restricted to 40% in most sectors.

Endlich, there is anecdotal evidence that India is receiving R&D-intensive
FDI. According to the National Science Foundation, ﬁrms from the United States
spent $73 billion on R&D in host economies in 2013; of this total, around $6 Milliarde
(8%) was spent in India.3 Many prominent United States ﬁrms have set up their
R&D centers in India, including GE, Intel, Microsoft, and IBM, which has two labs

3Nationale Wissenschaftsstiftung. Statistics. http://www.nsf.gov/statistics/.

6 Asiatischer Entwicklungsbericht

in India employing over 500 Wissenschaftler (Patra and Krishna 2015). In diesem Kontext,
the present study is important because it provides systematic evidence on whether
foreign ownership is important for stimulating domestic R&D activities in India.

III. Theoretical Discussion

Technological Activities: Multinational Enterprises versus Domestic Firms

The theoretical literature on the technological behavior of MNEs comprises
four major strands and is largely ambiguous with regard to predictions. Der
traditional international business literature, comprising the industrial organization
(Caves 1996, Hymer 1976) and transaction cost
theories (Dunning 1993,
Williamson 1975), argues that the existence of MNEs hinges on the relative
monopolistic advantages that they enjoy against rival domestic ﬁrms. They derive
their competitive advantages from the assets they have generated in their home
economies. Proprietary technology is the key ﬁrm-speciﬁc asset and it is guarded
closely through internalization. daher, R&D activities are mostly being carried
out at ﬁrms’ headquarters and the subsidiaries depend upon imported technologies.
Their own R&D activities are at best limited and mainly to adapt products and
services in line with local tastes and requirements. daher, R&D expenditures
among MNE subsidiaries are likely to be smaller than those of their local
counterparts, while the opposite might be true for imports of embodied and
disembodied technologies.

The resource-based view turns the focus from the ﬁrm (MNE) to the
subsidiary (Peng 2001; Rugman, Verbeke, and Nguyen 2011). It conceptualizes an
MNE subsidiary as a semiautonomous entity with its growth driven by its own
distinctive capabilities developed through entrepreneurial efforts, einschließlich der
creation and development of local technological competencies complementary to
the rest of the MNE.

The newly emerged literature on R&D relocation (Cantwell and Janne 1999,
Kuemmerle 1999, Dunning and Lundan 2009) focuses on the internationalization
of R&D by MNEs and views it as part of their strategic business decisions,
which are driven by the motivations of accessing talent at lower costs, tapping
into local centers of excellence, commercializing products in foreign markets with
the speed required to remain competitive, and contributing to their headquarters’
stock of knowledge. But the decision to internationalize R&D is contingent upon
speciﬁc MNE, home country, and host country advantages (z.B., market size,
scientiﬁc and engineering capabilities, lower costs, university research, and level
of industrialization) that shape the decisions of MNEs (sehen, Zum Beispiel, OECD
2008A; Dachs, Stehrer, and Zahradnik 2014).

The social network theory focuses on parent–subsidiary relationships,
subsidiary roles and strategies, and subsidiary resources and capabilities. Es ist

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 7

proponents (Ghoshal and Bartlett 1990; Gupta and Govindarajan 1991; Birkinshaw,
Hood, and Jonsson 1998; Birkinshaw, Hood, und Jung 2005; Cantwell and
Mudambi 2005) argue that an MNE is not a compact, rationally conceived
organization with a uniform goal, but a differentiated network of a variety of
subsidiaries that face heterogeneous national contexts. There are three levels of
Netzwerke:

(ich)

(ii)

intraorganizational networks that encompass headquarters and subsidiaries,
and their interrelationships;

interorganizational networks that are formed between the MNE and other
organizations in joint ventures, strategic alliances, and licensing agreements;
Und

(iii) MNE local networks with customers, Lieferanten, and authorities.

The extent to which an MNE subsidiary is embedded in these networks
determines its technological behavior. The greater
is embedded within
intraorganizational networks the greater will be its dependence on the headquarters
for technological knowledge and information. Andererseits, a greater
embeddedness of MNEs in local networks is associated with greater technology
creation in host economies. But the network embeddedness of subsidiaries is
essentially a matter of the strategic choices of the parent ﬁrm, which in turn are
inﬂuenced by subsidiaries’ own initiatives, resources, and capabilities, sowie
the locational advantages of host economies.

The arguments related to technology spending by MNEs in host economies

are ambiguous. Daher, we set up competing hypotheses for quantitative testing:

(ich) Hypothesis 1: MNE subsidiaries have an R&D intensity signiﬁcantly higher

than that of domestic ﬁrms.

(ii) Hypothesis 2: MNE subsidiaries exhibit a higher intensity of spending on
royalty payments for technology imports from international networks than
their local counterparts.

Ownership and Technological Activity

The classical

international business theories postulate that a strategic
(controlling) ownership stake ensures greater embeddedness of subsidiaries within
internal networks to minimize leakages of their proprietary technology. Im Gegensatz,
the network approach argues that the subsidiaries that are subject to a controlling
or majority ownership stake are more likely to compete for excellence within

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

8 Asiatischer Entwicklungsbericht

the organization and commit larger resources to R&D spending because such
subsidiaries are vital to the success of the parent ﬁrms and are therefore more
likely to be assessed with regard to their long-term objectives. This argument also
underpins the institutional approach, which posits that a ﬁrm’s strategic behavior
is inﬂuenced by the surrounding institutional environment (Dunning and Lundan
2008). When the regulatory environment is weak in the host economy and/or the
social and cultural distance between the home and host economies is large, Die
company lowers its ownership stake and commits lower resources. The lower the
ownership stake, the lower the level of support that subsidiaries receive from their
parents for local initiatives. This also implies that their dependence on internal
networks is higher. There are thus conﬂicting arguments regarding the impact of
ownership stakes also. We therefore test two competing hypotheses:

(ich) Hypothesis 3: Majority-owned subsidiaries exhibit a greater tendency to
embed in local networks and incur larger R&D expenditures than their local
counterparts.

(ii) Hypothesis 4: Majority-owned subsidiaries are more likely to depend on
imported technologies from their parent ﬁrms and other internal network
actors.

Global Crisis, Ownership, and Technological Activity

In contrast to the above, there is no clear theoretical prediction regarding
the effects of global economic and ﬁnancial crises on the globalization of R&D
activities by MNEs. One argument is that negative market growth expectations
during a crisis can drive MNEs to lower the coordination costs of dispersed R&D.
The opposing argument is that amid economically challenging conditions, ﬁrms
tend to minimize costs by relocating more of their activities to cheaper locations
(Kinkel and Som 2012). Empirically, Dachs, Stehrer, and Zahradnik (2014) ﬁnd
that in most economies, the R&D spending of MNEs is more severely affected by
global crisis than that of domestic ﬁrms. They observed a reversal in the trend of
R&D internationalization in the period following the recent global ﬁnancial crisis.
Kinkel and Som (2012), andererseits, ﬁnd that small ﬁrms were hurt by the
crisis even as large ﬁrms continued to relocate their R&D amid the crisis. Daher,
once again I set up opposing hypotheses:

(ich) Hypothesis 5: Foreign ﬁrms exhibit higher R&D intensity than their local

counterparts during periods of economic crisis.

(ii) Hypothesis 6: Foreign ﬁrms exhibit lower R&D intensity than their local

counterparts during periods of economic crisis.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 9

IV. Methodik

For empirical analysis, I used a multilevel methodology, which is discussed

below.

Identifying Foreign Firms

Following International Monetary Fund guidelines, a direct

investment
enterprise in India is deﬁned as an incorporated or unincorporated enterprise in
which a foreign direct investor owns 10% or more of the ordinary shares or voting
power (for an incorporated enterprise) or the equivalent (for an unincorporated
enterprise). There is, Jedoch, recognition that a numerical guideline of 10% does
not capture the essence of FDI for economic analysis. This deﬁnition is adopted
for the sake of consistency and cross-country comparability of FDI statistics and is
based on the premise that a share as low as 10% of voting rights or equity capital
allows the investor to “inﬂuence the management,” providing the basis for an FDI
relationship. The System of National Accounts Framework of the United Nations
uses “controlling stakes” as the basis for economic analysis of FDI for which
mehr als 50% ownership is necessary. OECD (2008B, 21–23) deﬁnes “companies
with a 50% or more stake as FDI subsidiaries (controlled enterprises), while those
with a 10%–50% stake are FDI associates (inﬂuenced enterprises).” Under the
Companies Act of India, there are three threshold levels of shareholding from the
perspective of deﬁning “inﬂuence” and “control” (10%, 25%, Und 50%). Based on
this classiﬁcation and the available data, I have identiﬁed three types of foreign
ﬁrms:

(ich) minority holding (10%–25%) with minor inﬂuence,

(ii)

dominant minority holding (25%–50%) with dominant inﬂuence, Und

(iii) majority holding (über 50%) with controlling stake.

Zusätzlich, I have created a category for experiential foreign ﬁrms. Diese
ﬁrms are not predominantly foreign ﬁrms; eher, they have foreign ownership for
only a short time during the review period.

Investigating the Sectoral Distribution of Multinational Enterprises
by Technology Intensity Using Cluster Analysis

To identify the sectors targeted by MNEs, I clustered industries at the
3-digit level by technological orientation and brand value using the “wards linkage”

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

10 Asiatischer Entwicklungsbericht

method of hierarchical clustering (Everitt, Landauer, and Leese 2001). Based on the
dendograms and appropriate stopping rules, I determined the number of clusters in
the sample and then examined the presence of foreign ﬁrms by ownership stake in
each group. The analysis was conducted at the 3-digit level of aggregation using the
STATA statistical package and the following variables:

(ich) R&D spending-to-sales ratio = 1 if it is > 0 for the industry and 0 ansonsten,

(ii)

(iii)

(iv)

(v)

ratio of royalty payments abroad to sales = 1 if it is > 0 for the industry and
0 ansonsten,

ratio of capital goods imports to sales = 1 if it is > 0 for the industry and 0
ansonsten,

ratio of advertisement expenditures to sales = 1 if it is > 0 for the industry
Und 0 ansonsten, und ein

dummy variable = 1 if the ﬁrm is in the manufacturing sector and 0 if it is in
the service sector.

Assessing the Difference in Technology Intensity between Local
and Foreign-Owned Firms

Propensity Score Matching

Propensity score matching is a nonparametric estimation method that creates
a comparison group (domestic ﬁrms) with identical distributions of observable
characteristics to those in the treatment group (foreign ﬁrms) to address the issue
of endogeneity. The basic idea is to ﬁnd for every foreign ﬁrm a matching domestic
ﬁrm in terms of all relevant observable characteristics X. The mean effect of
foreign ownership (or the average treatment effect) is then calculated as the average
difference in outcomes between the foreign and matched domestic ﬁrms.

For matching, I constructed four propensity score models corresponding to
four categories of the foreign ﬁrms using ﬁrm- and industry-speciﬁc attributes.4 In
each case, it was ensured that the balancing property was satisﬁed. A kernel method
was used to identify the domestic ﬁrms that match the foreign ﬁrms. The condition
of common support resulted in discarding some ﬁrms. The level of rejection of
unmatched domestic ﬁrms varied between 14% Und 23%. Considering that there
are a large number of domestic ﬁrms, this does not amount to a signiﬁcant loss of

4Further details are available upon request.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 11

data and is therefore unlikely to compromise the representativeness of the results.
To assess the quality of matching, appropriate tests were conducted. Jedoch, while
matching removes any bias caused by selection on observable variables, it leaves
the possibility of bias due to selection on unobservable variables. Daher, perfekt
matching is not possible, which affects the quality of estimates. The propensity
score matching analysis is therefore complemented by a generalized least squares
(GLS) regression analysis based on the panel database to check the consistency and
robustness of the results.

Generalized Least Squares Regression on the Matched Sample

The variables representing technological activity are regressed on foreign
ownership variables after controlling for ﬁrm- and industry-speciﬁc characteristics
using the matched sample. The ﬁrms that are off the common support are dropped
to include only those in the common support region. The following model is used
for the analysis:

Yit = β0 + β1Xit + vit

where Yit
is the dependent variable representing two alternative modes of
technology sourcing: (ich) R&D, Und (ii) international transfer of disembodied
technologies. These estimations are performed only for local R&D (RD_INT)
and disembodied technology imports (ROY_INT). The control variables are drawn
on the existing literature (sehen, Zum Beispiel, Becker 2013, Cohen 1995) and are
described in Table 5.

The two modes of technology activity, R&D and acquisition, are not
independent of each other. Technology imports by ﬁrms are likely to inﬂuence
their R&D efforts, while the intensity of technology imports may itself depend
on R&D efforts. Daher, there is possible simultaneity between the two. Weiter,
with respect to most explanatory variables in the model, there could be a problem
of endogeneity. To address these issues, I assume that both technology choice
and intensity are strategic decisions with a long-term orientation. Firms do not
spontaneously determine them based on current performance. Eher, they take
account of past, current, and planned behavior and performances in making such
decisions. daher, I converted the behavioral explanatory variables into a moving
Durchschnitt von 3 years comprising the lagged year, current year, and lead year. Jedoch,
tax rate (TAX) and proﬁt margin (PCM) are lagged by 1 year only. The inclusion
of lagged and lead variables addresses the issues of causality and simultaneity, Und
allows us to estimate the two models separately to explore the impact of foreign
ownership on technology intensities.

A panel data approach is employed to control for unobserved ﬁrm- Und
time-speciﬁc characteristics. A ﬁxed-effect speciﬁcation of the model is normally
considered ideal but has been ruled out here because it does not return estimates

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

12 Asiatischer Entwicklungsbericht

of the time-invariant variables, which are the main variables. Daher, I have used
the random-effect speciﬁcation. Allgemein, ﬁxed-effect estimates are preferred over
random effects because the latter produce biased estimates if the regressors and the
residuals (ﬁrm effects) are correlated. Recent studies have found this justiﬁcation
insufﬁcient to prefer ﬁxed over random effects based on the argument that if the
units are relatively similar on average, then the appropriate model should be guided
by the researcher’s goals (Clarke et al. 2010, Clark and Linzer 2015). Since I
am using matched samples, the ﬁrms are rather similar. daher, the use of
random-effect GLS estimates is not expected to be inferior. For ensuring the
robustness of the GLS estimates, I have also controlled for the time effects by
incorporating year dummies to capture ﬁxed effects of intertemporal shifts and
corrected the estimates for heteroscedasticity. For yet another validity check, ICH
have obtained Mundlak estimates (sehen, Zum Beispiel, Bell and Jones 2015). Diese
estimates relax the assumption in the random-effects estimator that the observed
variables are uncorrelated with the unobserved variables. But these estimates
cannot control for time-speciﬁc variations and heteroscedasticity. For a comparative
analysis of the main variables, I have presented Mundlak’s estimates only for the key
variables in the text.

V. Data

Empirical analysis is based on ﬁrm-level data from Prowess, a database
of the ﬁnancial performance of over 27,000 listed and unlisted Indian ﬁrms
from a wide section of the manufacturing, utilities, mining, and service sectors.
The data are collected by the Centre for Monitoring Indian Economy from the
balance sheets of ﬁrms and are updated continuously. Along with ﬁnancials, Die
database also provides detailed information on the shareholding patterns of these
Firmen. Most studies use the ownership data of the latest year, assuming that
the shareholding patterns of ﬁrms remain the same over the years prior to the latest
Jahr. Jedoch, this assumption is not reasonable for two reasons. Erste, the shares
of most of these companies are actively traded in the market and the acquisition
of shares of the existing ﬁrms through the market has become an important mode
of entry for foreign ﬁrms in India. Zweite, the data on shareholding patterns for
a given period is available only for those ﬁrms that are actively traded in the
market during that period. Clearly, the studies that use the ownership data from
the latest year are subject to selectivity bias. For different periods, the results
may vary depending upon the availability of ownership data and ﬁrms’ ownership
stakes in the latest year. There is evidence that the distributional properties of
samples drawn from Prowess are not consistent for different periods (Choudhury
2002). To address this limitation, we procured the ownership data of 5,109 listed
ﬁrms as of 31 March of each year from FY2000–2001 to FY2013–2014. The data
were matched with the Stock Exchange Board of India and Bloomberg ownership

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 13

databases available online for validation purposes. For each ﬁrm, the data for the
available years were cross-checked and gaps were ﬁlled wherever possible. Seit
the data pertain only to actively traded ﬁrms, it was cleaned for the purpose of
making consistent comparisons. Erste, only those ﬁrms for which the information
was available for each of the 11 years were included, leaving 2,004 ﬁrms. Zweite,
those ﬁrms reporting zero or negative net sales were dropped. After the cleaning
Verfahren, the ﬁnal data set consisted of a balanced panel of 1,781 ﬁrms spanning 11
Jahre (FY2003–2004 to FY2013–2014).

VI. Empirical Results

Cluster Analysis of the Sectoral Distribution of Multinational
Enterprises by Technology Intensity

Based on the standard rules mentioned above, I identiﬁed ﬁve clusters of
industries. Each of the clusters is well populated, conﬁrming that each cluster is
substantive. Tisch 1 gives the mean values of the variables in the ﬁve principal
clusters. The main dividing line runs between the manufacturing and service
industries on one hand, and between industries that score high and low on the
technology and product differentiation variables on the other hand.

Tisch 2 reports the clustering results by ownership mode, which is of
primary interest here. Between FY2003–2004 and FY2007–2008, the distribution
of foreign companies was highly skewed in favor of high-technology manufacturing
industries. As stated above, the technological or brand superiority of MNEs is the
primary reason they venture into investing abroad in the ﬁrst place. In India, Das
pattern can also be attributed to the legal framework prior to 1991, which sought
to channel FDI into high-technology production by setting higher FDI caps in
these sectors. By the period from FY2008–2009 to FY2013–2014, services had
become more promising and the sectoral distribution of FDI became somewhat
diffused (as shown by the reduced levels of standard deviations). There was a
substantial restructuring in the distribution of experiential ﬁrms from services to
manufacturing during this decade, reﬂecting a shift of FDI from manufacturing
to services. But within each broad sector, changes have been marginal rather than
substantive. Within manufacturing, there is a visible shift of foreign ﬁrms in favor
of medium-technology consumer goods. Jedoch, über 62% of majority-owned
companies still belonged to the high-technology manufacturing cluster and almost
one-fourth of these were concentrated in the high-technology services cluster.
Only about 15% could be classiﬁed as low technology, either in manufacturing or
services.

A critical question is whether foreign ﬁrms are also more active in R&D
than their local counterparts or if they continue to embed in internal knowledge
Netzwerke. In the propensity score matching and regression analyses, I shall address

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

14 Asiatischer Entwicklungsbericht

S
e
C
ich
v
R
e
S

M
u
ich
D
e
M

j
G
Ö
l
Ö
N
H
C
e
T

H
G
ich
H
D
N
A

T
C
u
D
Ö
R
P

H
G
ich
H

j
G
Ö
l
Ö
N
H
C
e
T

H
G
ich
H
D
N
A

T
C
u
D
Ö
R
P

w
Ö
L

j
G
Ö
l
Ö
N
H
C
e
T

w
Ö
L
D
N
A

T
C
u
D
Ö
R
P

G
N
ich
R
u
T
C
A
F
u
N
A
M

M
u
ich
D
e
M

j
G
Ö
l
Ö
N
H
C
e
T

H
G
ich
H
D
N
A

T
C
u
D
Ö
R
P

H
G
ich
H

j
G
Ö
l
Ö
N
H
C
e
T

H
G
ich
H
D
N
A

T
C
u
D
Ö
R
P

N
Ö
ich
T
A
ich
T
N
e
R
e
F
F
ich

S
R
e
T
S
u
l
C
j
B
S
e
u
l
A
V
N
A
e
M

.
1

e
l
B
A
T

0
0
0

5
0
0

0
5
3

2
0
3

2
5

9
3
.
0

0
1
.
0

2
3
.
2

8
9
.
0

5
2

0
0
.
0

9
1
.
1

1
9
.
0

3
5

0
3
.
0

4
0
.
0

4
5
.
2

7
1
.
2

7
6

5
6
.
0

5
3
.
0

9
7
.
1

0
5
.
1

3
8

)

(
Ö
ich
T
A
R

S
e
l
A
S
–
Ö
T
–
D
A
Ö
R
B
A

S
T
N
e
M
j
A
P
j
T
l
A
j
Ö
R

)

(
Ö
ich
T
A
R

S
e
l
A
S
–
Ö
T
–
e
R
u
T
ich
D
N
e
P
X
e
D
&
R

S
e
ich
R
T
S
u
D
N
ich

F
Ö

R
e
B
M
u
N

e
l
B
A
ich
R
A
V

)

(
Ö
ich
T
A
R

S
e
l
A
S
–
Ö
T
–
S
T
R
Ö
P
M

ich

S
D
Ö
Ö
G
l
A
T
ich
P
A
C

)

(
Ö
ich
T
A
R

S
e
l
A
S
–
Ö
T
–
e
R
u
T
ich
D
N
e
P
X
e

T
N
e
M
e
S
ich
T
R
e
v
D
A

.
T
N
e
M
P
Ö
l
e
v
e
D

D
N
A
H
C
R
A
e
S
e
R

=
D
&
R

.
S
N
Ö
ich
T
A
l
u
C
l
A
C

S
'
R
Ö
H
T
u
A

:
e
C
R
u
Ö
S

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 15

Tisch 2. Classiﬁcation of Firms by Technological Orientation of Industries (%)

FY2003–2004 to FY2007–2008

Majority Dominant_ Minority
Owned
Owned Minority

Experiential_
Foreign
Firms

Domestic
Firms

64.6

6.3

1.3

19.0

8.9

62.3

7.5

9.4

18.9

1.9

62.2

2.7

13.5

8.1

36.7

10.0

0.0

46.7

6.7

37.7

13.2

5.5

32.2

11.4

100.0
25.7

100.0
24.4

100.0
24.0

100.0
20.4

100.0
14.1

FY2008–2009 to FY2013–2014

Majority Dominant_ Minority
Owned
Owned Minority

Experiential_
Foreign
Firms

Domestic
Firms

62.4

7.3

0.9

22.9

6.4

56.0

12.0

4.0

22.0

6.0

50.0

9.3

11.1

18.5

11.1

46.2

19.2

7.7

26.9

0.0

37.7

12.9

5.4

32.0

11.9

100.0
25.1

100.0
21.3

100.0
17.1

100.0
17.9

100.0
14.0

High technology and high product
differentiation manufacturing

Medium technology and high
product differentiation
manufacturing

Low technology and low product
differentiation manufacturing
High technology and high product

differentiation services
Medium technology and high

product differentiation services
Total

Standard deviation

High technology and high product
differentiation manufacturing

Medium technology and high
product differentiation
manufacturing

Low technology and low product
differentiation manufacturing
High technology and high product

differentiation services
Medium technology and high

product differentiation services
Total

Standard deviation

Quelle: Author’s calculations.

this question after matching each foreign ﬁrm with a domestic counterpart within
the broad industrial classiﬁcations adopted above.

Propensity Score Matching and Generalized Least Squares Results

Descriptions of the variables are provided in Table 3. Tisch 4 reports the

summary results of matching quality assessment tests.

The results of matching for individual covariates show large differences in
the covariates between the foreign and domestic ﬁrms in the original sample. Diese

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

16 Asiatischer Entwicklungsbericht

Category

Foreign ownership

Modes and intensities of
technological activities

Firm speciﬁc

Tisch 3. List of Variables

Variable

DFOR50
DFOR25

DFOR10

Description

Firms that had been majority holders (über 50%)
Firms that had been dominant minority holders

(25%–50%)

Firms that had predominantly been minority holders
(10%–25%) (Predominantly is deﬁned as more
than two-thirds of the period.)

DFOR_EXP:

The remaining ﬁrms that have been under a 25% oder

more foreign ownership stake in at least one of the
years but less than two-thirds of the period.

RD_INT

Total research and development expenditure of ith

ROY_INT

CAPIMP_INT

ﬁrm as a proportion of its sales (%)

Royalties and technical fees paid abroad by ith ﬁrm
as a proportion of its sales to measure acquisition
of disembodied technologies (%)

Imports of capital goods by ith ﬁrm as a proportion
of its sales to measure acquisition of embodied
technologies (%)

SIZE
SIZE2
AGE
EX_INT
CAPINT
IMPR_INT

Net sales (transformed into logarithms)
Square of SIZE
The current year net of the year of incorporation
Exports of goods and services as % of net sales
Net ﬁxed assets as % of net sales
Imports of raw materials and components as % von

net sales

PCM
Tax

Proﬁts before tax as % of net sales
Proﬁts before tax as % of proﬁts after tax

Industry speciﬁc (Based on

the cluster analysis)

HTECH_MFG High technology and high product differentiation
manufacturing industry = 1

MTECH_MFG Medium technology and high product differentiation
manufacturing industry = 1
LTECH_MFG Low technology and low product differentiation
manufacturing industry = 1

HTECH_SER

High technology and high product differentiation

MTECH_SER Medium technology and high product differentiation

services

Quelle: Author’s description.

differences are considerably reduced after the kernel matching. In all the cases, Die
absolute mean bias turns out to be insigniﬁcant.

The pseudo R-squared, which is obtained by regressing treatment propensity
scores on all covariates used in matching on the matched and unmatched samples,
substantially decreased after matching in all cases. Rosenbaum and Rubin (1983)
suggest that a standardized difference of more than 20 should be considered to be
groß. Our results show that, post matching, none of the standardized differences
have an absolute value larger than 3. Endlich, the likelihood ratio is insigniﬁcant in

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 17

Tisch 4. Kernel Matching Performance: Results of the Mean and
Median Absolute Bias, Pseudo R-Squared, and LR Tests

Pseudo R2

LR chi2

p>chi2 Mean Bias Medium Bias

Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched

0.102
0.002
0.049
0
0.063
0.006
0.057
0.002
0.071
0.001
0.026
0
0.024
0.002
0.015
0

LR = likelihood ratio.
Quelle: Author’s calculations.

64.78
0.49
23.36
0.06
22.71
0.65
17.05
0.14
58.26
0.27
11.94
0.03
11.80
0.35
3.88
0.01

0
1.000
0.005
1.000
0.007
1.000
0.03
1.000
0.000
1.000
0.217
1.000
0.225
1.000
0.867
1.000

33.5
2.6
20.9
1.3
23.2
3.3
19.1
3.2
28.0
2.0
17.0
0.8
13.4
0.9
19.0
0.7

24.2
2.2
15.3
1.1
20.2
1.1
14.1
2.6
24.9
1.1
10.3
0.6
11.0
0.4
9.6
0.6

all models in the matched samples, conﬁrming the results of the previous two tests.
Matching clearly removes a large part of mean and median biases across the board.
The average treatment effects presented in Table 5 show the average
difference in the technology intensities between foreign and domestic ﬁrms. Der
GLS estimates are presented in Table 6.

The results reveal that the majority-owned and dominant-minority MNEs
were technologically more active than their minority-holding and experiential
counterparts during the precrisis period, even though they largely depended on their
internal networks to acquire technologies. The modes of technology acquisition
adopted by them and technology intensities were also found to be different from
those of matched domestic ﬁrms. The ﬁrms with minority ownership were not
signiﬁcantly different from their local counterparts, while experiential ﬁrms appear
to be the technological laggards, possibly because they were clustered in the
service sector where R&D expenditures were relatively small. The gap between
technology expenditures by local and foreign enterprises across all categories
narrowed considerably in the postcrisis period. The GLS estimates indicate that
this was due to MNEs accelerating their technological expenditures. I discuss the
results by mode of technology sourcing below.

Research and Development Activity

It may be seen that the average treatment effect on R&D intensity is negative
across almost all groups in the pre global ﬁnancial crisis period. The average R&D

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

18 Asiatischer Entwicklungsbericht

Tisch 5. Propensity Score Matching Estimates of the Average Effect of Foreign Ownership
and Ownership Stakes

FY2004–2005 to FY2007–2008

Foreign Domestic
Firms

Firms

DFOR50

DFOR25

DFOR10

DFOR_EXP

DFOR50

DFOR25

DFOR10

DFOR_EXP

77
77
77
53
53
53
37
37
37
30
30
30

109
109
109
50
50
50
50
50
50
26
26
26

1,549
1,549
1,549
1,336
1,336
1,336
1,305
1,305
1,305
1,206
1,206
1,206

1,499
1,499
1,499
1,340
1,340
1,340
1,340
1,340
1,340
1,209
1,209
1,209

Bootstrapped
Standard
Deviation

Technologie
Spending
Indicator

RD_INT
ROY_INT
CAPIMP_INT
RD_INT
ROY_INT
CAPIMP_INT
RD_INT
ROY_INT
CAPIMP_INT
RD_INT
ROY_INT
CAPIMP_INT

Average
Treatment
Effect
−.131
.520
−.496
0.147
0.403
−0.305
−0.168
−0.018
0.615
−0.276
−0.087
0.520

FY2009–2010 to FY2013–2014

RD_INT
ROY_INT
CAPIMP_INT
RD_INT
ROY_INT
CAPIMP_INT
RD_INT
ROY_INT
CAPIMP_INT
RD_INT
ROY_INT
CAPIMP_INT

0.091
0.526
0.136
0.604
0.220
0.291
−.005
0.031
−0.135
2.703
0.112
0.034

.079
.118
2.342
0.237
0.181
0.424
0.165
0.061
1.173
0.129
.050
1.054

0.133
0.111
0.320
0.560
0.120
0.464
0.184
0.046
0.351
2.836
0.115
0.569

t-statistics
−1.658*

4.410***
−.212
0.619
2.219**

−0.721
−1.1018
−0.299
0.524
−2.132**
−1.1734
0.493

0.685
4.760***
0.426
1.079
1.830*
0.627
−0.027
0.672
−0.384
0.953
0.974
0.059

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

Notiz: ***, **, Und * denote signiﬁcance at the 1%, 5%, Und 10% Ebene, jeweils.
Quelle: Author’s calculations.

intensity of majority-owned subsidiaries is 0.13 percentage points less than that of
matched domestic ﬁrms and was signiﬁcant at 10%. The GLS estimates presented
in Table 6 conﬁrm this result. This gap in R&D intensity increased as foreign equity
holdings declined, with dominant-minority MNEs being an exception. The gap is as
large as –0.276% for experiential MNEs. Allgemein, the R&D intensities of foreign
ﬁrms (leaving aside dominant-minority ﬁrms) across all categories turned out to
be less than that of matched domestic ﬁrms during the global boom period. Diese
ﬁndings are in line with earlier studies on the post-1991 period, which suggest that
MNEs are not signiﬁcantly more R&D intensive than their local counterparts in
Indien. In an earlier study on the R&D behavior of manufacturing ﬁrms in India,
Kumar and Aggarwal (2005) used ﬁrm-level data from FY1992–1993 to FY1998–
1999. Their ﬁndings reveal that MNEs have increased their R&D expenditures

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 19

Tisch 6. GLS Estimates of R&D and Technology Transfers on Matched Samples

FY2004–2005 to
FY2007–2008

FY2009–2010 to
FY2013–2014

Variable

RD_INT

ROY_INT

RD_INT

ROY_INT

SIZE

SIZE2

AGE

3 years average of ROY_INT

3 years average of RD_INT

3 years average of CAP_INT

3 years average of CAP_IMP

3 years average of EXINT

3 years average of IMPR_INT

PCM with 1-year lag

MTECH_SER

HTECH_SER

HTECH_MFG

MTECH_MFG

DFOR50

DFOR25

DFOR10

DFOR_EXP

TAX with 1-year lag

Constant

Year dummies
Beobachtungen
Number of code

Modell 1
−0.0149
(−0.741)

0.00480
(1.512)
−0.00381**
(−2.082)
−0.00330
(−0.770)

−4.93e-06
(−0.672)

0.00831
(0.845)
0.0198***
(2.638)
−0.00911***
(−2.648)
−2.89e-06
(−1.276)
−0.0332
(−0.655)
0.179
(1.290)
0.448***
(5.014)
−0.0311
(−0.754)
−0.296***
(−3.517)
−0.109
(−0.908)
−0.268***
(−3.321)
−0.189*
(−1.761)
−8.71e-07
(−0.265)
0.122
(1.037)
Ja
4,800
1,690

Modell 5
0.0272*
(1.716)
−0.000495
(−0.307)

7.14e-05
(0.0387)

−0.000169
(−0.452)

4.01e-06*
(1.722)
−0.000104
(−0.234)

0.000595
(0.617)
−0.000325
(−0.636)

5.48e-07
(0.419)
−0.0791
(−0.726)
0.110
(0.679)
−0.0469
(−0.342)
−0.125
(−1.047)

0.450***
(3.948)
0.482**
(2.228)
0.00817
(0.180)
−0.0735
(−1.192)
−9.07e-07
(−1.006)
−0.0659
(−0.716)
Ja
4,800
1,690

Modell 9
0.0489
(1.430)
−0.00402
(−0.925)
−0.00428**
(−2.057)
−0.0911
(−1.112)

−5.58e-07
(−0.759)

0.000207
(0.935)
0.0169***
(3.018)
0.00111
(0.422)
1.42e-05
(1.261)
−0.109*
(−1.769)
0.133
(0.947)
0.454***
(4.708)
−0.159***
(−2.805)
0.0565
(0.401)
–0.0490
(−0.420)
−0.0835
(−0.720)
0.583
(0.721)
−1.16e-06
(−0.518)
0.204**
(2.113)
Ja
5,229
1,615

Modell 13
0.0314**
(2.113)
–0.00198
(−1.508)

0.000673
(0.489)

–0.00667
(−1.177)

1.15e-07
(0.340)
2.30e-05
(0.210)
–0.000569
(−0.886)

0.00123*
(1.700)
1.84e-07
(0.298)
0.0292*
(1.688)
0.208***
(2.823)
0.0961***
(4.815)
0.0130
(0.874)
0.520***
(4.883)
0.242*
(1.771)
0.0377
(0.894)
0.186
(1.067)
2.11e-06
(1.168)
–0.175***
(−3.033)
Ja
5,229
1,615

GLS = generalized least squares, R&D = research and development.
Notes: Parentheses represent t-statistics. ***, **, Und * denote signiﬁcance at the 1%, 5%, Und 10% Ebene,
jeweils.
Quelle: Author’s calculations.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

20 Asiatischer Entwicklungsbericht

faster than their local counterparts in response to the process of liberalization.
Jedoch, after controlling for the effects of other ﬁrm-speciﬁc characteristics, their
average R&D intensity still turned out to be less than that of domestic ﬁrms.
The study was revisited by Sasidharan and Kathuria (2011). They showed that
the average R&D intensity of foreign ﬁrms was signiﬁcantly lower than that of
domestic ﬁrms between FY1993–1994 and FY2004–2005. For other economies,
the results are mixed. Rasiah (2007) reviewed the cases of the People’s Republic
of China; Indonesien; Malaysia; die Phillipinen; and Taipei,China; and García, Jin,
and Salomon (2013) reviewed the case of Spain (a developed economy with a lower
level of dynamism). These studies found that ﬁrms with foreign ownership preferred
technological transfer instead of R&D investment as a technological achievement
Politik (sehen, Zum Beispiel, Fors and Svensson 2002). Jedoch, in probit estimates
based on 28,000 ﬁrm observations, Falk (2008) shows that foreign-owned ﬁrms are
more innovative than domestic ﬁrms, particularly in new European Union member
Staaten. Balsari, Özkan, and Varan (2015) and Pamukçu and Utku-˙Ismihan (2009)
found similar results for Turkey. Evidence from the People’s Republic of China is
mixed. While Fu (2008) and Yang and Lin (2012) show positive effects, Chen et al.
(2008) are not so optimistic.

In contrast to the precrisis period, foreign ﬁrms in the post global ﬁnancial
crisis period in India outperformed local ﬁrms in R&D intensity, albeit weakly.
Tisch 6 shows that it was due to acceleration in the R&D intensity of MNEs, welche
may have been partly due to accelerated reforms in the FDI regime in India that
were initiated in the post-2005 period (Figur 1). It could also be that the crisis
in advanced economies shifted the focus to emerging markets where competition
for market share intensiﬁed, forcing MNEs to increase their technological efforts.
But the possibility of the global ﬁnancial crisis affecting companies’ offshoring
strategies for R&D as a result of the credit crunch in the developed world cannot
be ignored. This could have forced ﬁrms to search for highly qualiﬁed personnel at
lower cost. Apparently, India offered an ideal location with its pool of engineers
and technologists growing at breakneck speed. The share of students enrolled
in engineering and technology institutions as a percentage of total enrollments
increased from 13% in FY2006–2007 to 26% in FY2011–2012 on average annual
growth of around 25%; growth in enrollment at education and medical institutions
followed closely at around 16% per year (Government of India 2013). Daher,
contrary to global patterns of contraction in R&D relocation (Kinkel and Som
2012; Dachs, Stehrer, and Zahradnik 2014), India exhibited growing R&D spending
by MNEs in almost all categories except for minority-held companies. India’s
experience mirrored that of France, Poland, and the United Kingdom, which also
showed rising trends in R&D relocation activities during the review period (Dachs,
Stehrer, and Zahradnik 2014). Jedoch, in no case was the R&D intensity of foreign
ﬁrms signiﬁcantly greater than that of matched local ﬁrms.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 21

Royalty Payment Intensity

During the precrisis period of FY2003–2004 to FY2008–2009, majority-
owned and dominant-minority ﬁrms spent signiﬁcantly more on royalty payments
than matched local counterparts. Their average royalty-to-sales ratios were 0.53
Und 0.40 percentage points higher than that of local ﬁrms, jeweils. Im
postcrisis period, the gap did not show any perceptible change. Jedoch, Die
other two categories, minority-holding and experiential ﬁrms, which appeared
to be technologically laggards in the precrisis period, enhanced technology
acquisition from internal networks and managed to outperform domestic ﬁrms,
albeit insigniﬁcantly, in the postcrisis period. These results support the traditional
view of the greater embeddedness of MNEs in internal networks to protect against
the spillover of proprietary technologies. This translates into a slow process of
R&D relocation and is in line with the results related to R&D spending. The GLS
estimates in Table 6 conﬁrm these results. DFOR50 and DFOR25 are signiﬁcant in
all speciﬁcations for ROY_INT during both periods. The results for other categories
of foreign ﬁrms also indicate that foreign ﬁrms are not technologically embedded
in India. They are more likely to depend on their parent labs.

Capital Goods Imports

Imports of capital goods have been a signiﬁcant mode of technology
transfer for both local and foreign-owned ﬁrms in India. In the precrisis period,
minority-holding and experiential foreign ﬁrms were associated with larger
spending on capital goods imports than matched local ﬁrms; while in the postcrisis
Zeitraum, MNEs with higher ownership stakes enhanced their spending on capital
goods imports, along with R&D, over their local counterparts. The minority-holding
companies focused more on disembodied technology acquisition. Jedoch, Die
difference in average spending on this mode of technology acquisition is not
signiﬁcantly different between foreign and domestic ﬁrms in either period for any
category of foreign ﬁrms.

Mundlak’s estimates presented in Table 7 validate the results for the main
Variablen. A comparison of these results with the GLS estimates shows that the
results are robust.

Endlich, it is observed that R&D and royalty intensities are affected differently
by other strategic explanatory variables (Tisch 6). High-technology industries in
both the manufacturing and service sectors attract signiﬁcant technology transfers,
but only those in manufacturing induce signiﬁcantly higher R&D intensities. Daher,
promoting high-technology manufacturing is more likely to accelerate R&D efforts
in Indian industries. Weiter, exporting is signiﬁcantly associated with local R&D
efforts, while its relationship with technology imports is insigniﬁcant. Age turns out
to be negative, indicating that younger ﬁrms are more likely to undertake R&D. Der

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

22 Asiatischer Entwicklungsbericht

Tisch 7. Mundlak Estimates of the Main Variables

ROY_INT

RD_INT

Variable

Coefﬁcient

t-statistics

Variable

Coefﬁcient

t-statistics

FY2004–2005 to FY2007–2008

DFOR50
DFOR25
DFOR10
DFOR_EXP

0.451
0.467
−0.023
−0.042

3.91***
2.15**

−0.40
−0.89

DFOR50
DFOR25
DFOR10
DFOR_EXP

FY2008–2009 to FY2013–2014

DFOR50
DFOR25
DFOR10
DFOR_EXP

0.525
0.240
0.029
0.170

6.61***
2.07**
0.26
1.05

DFOR50
DFOR25
DFOR10
DFOR_EXP

−0.336
−0.054
−0.172
−0.164

0.073
0.004
−0.081
0.595

Notiz: ***, **, Und * denote signiﬁcance at the 1%, 5%, Und 10% Ebene, jeweils.
Quelle: Author’s calculations.

−1.34
−0.18
−0.49
−0.42

0.36
0.02
−0.29
1.47

size variable indicates that relatively larger ﬁrms are more likely to engage in R&D,
while relatively smaller ﬁrms exhibit a greater tendency to import technologies.
Endlich, the relationship between R&D intensity and technology transfers is found to
be negative; the gap appears to have widened over time. Daher, technology transfers
may not positively inﬂuence local R&D efforts. It is important to identify the
triggers for such efforts to augment the technological capabilities of ﬁrms.

VII. Abschluss

Majority-owned and dominant-minority-owned ﬁrms are considered to be
conduits of technology transfers. This study ﬁnds that their local R&D intensities
are less than those of their local counterparts in India. The activities of technology
generation are found to be concentrated in the home economies of MNEs located in
Indien. It is also found that minority-owned ﬁrms are not signiﬁcantly different from
their local counterparts. Endlich, the technological dynamism of MNEs was found
to have increased across all categories in the postcrisis period. Aber, it did not result
in signiﬁcant changes in the modes of technology acquisition or signiﬁcantly larger
technology intensities than that of local ﬁrms. I ﬁnd no evidence of a signiﬁcant
increase in relocation of R&D activity to India by production ﬁrms after the global
ﬁnancial crisis despite India being much less affected by the crisis than many other
economies and having an expanding pool of skilled labor.

The global distribution of R&D is essentially the result of strategic decisions
among ﬁrms to gain global efﬁciency through local responsiveness and worldwide
learning. A ﬁrm’s strategic objective is to leverage its innovative advantages to
exploit a host economy’s knowledge base by tapping into local clusters (z.B.,
well-educated workforce and high-quality research institutions) and by creating

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 23

network relations with external partners (z.B., customers and suppliers) and building
a strong knowledge base and competitiveness advantages (Andersson, Forsgren,
and Holm 2002). Their ability to create, verwalten, and take advantage of internal and
external network-based knowledge ﬂows is a strong source of their competitiveness.
daher, the host-speciﬁc advantages in creating assets are the key attractions
for them; markets and resources alone are not sufﬁcient. There are numerous
studies that have analyzed the factors affecting the internationalization of R&D
by MNEs. According to Hall (2010, 12), „[T]he variables that most strongly
affect location choice are invariably the size of the market, the R&D intensity
of the host country, the availability of technical and educated workers, und das
presence of lead customers.” Their decision to relocate R&D depends in part on
the quality of host economy R&D networks, the sophistication of its markets, Und
the intellectual property rights regime. India is beneﬁting from a growing pool
of engineers and technologists, as well as from the presence of a World Trade
Organization-compliant intellectual property rights regime. Gleichzeitig, Indien
needs to focus on improving the quality of its skilled labor and local networks,
and pursue more effective implementation of the intellectual property rights regime
to establish itself as a hub of R&D-intensive FDI. The results in this paper have
important policy implications for governments in developing economies such as
Indien. They must strengthen their capabilities to attract knowledge-intensive FDI
and exploit the beneﬁts generated through knowledge spillovers (sehen, Zum Beispiel,
Fu, Pietrobelli, and Soete 2011). Building strong local technological capabilities
through a well-designed innovation strategy should be at the core of an FDI-induced
development strategy.

References*

Andersson, Ulf, Mats Forsgren, and Ulf Holm. 2002. “The Strategic Impact of External Networks:
Subsidiary Performance and Competence Development in the Multinational Corporation.”
Strategic Management Journal 23 (11): 979–96.

Balsari, Çagnur Kaytmaz, Serdar Özkan, and Seçil Varan. 2015. “Impact of Foreign Ownership

on Innovation.” Journal of Economics & Management 20 (A): 86–96.

Becker, Bettina. 2013. “The Determinants of R&D Investment: A Survey of the Empirical

Research.” Loughborough University Economics Discussion Paper Series 9.

Glocke, Andrew, and Kelvyn Jones. 2015. “Explaining Fixed Effects: Random Effects Modeling of
Time-Series Cross-Sectional and Panel Data.” Political Science Research and Methods 3
(1): 133–53.

Bellak, Christian. 2004. “How Domestic and Foreign Firms Differ and Why Does It Matter?”

Journal of Economic Surveys 18 (4): 483–514.

Birkinshaw, Julian, Neil Hood, and Stefan Jonsson. 1998. “Building Firm-Speciﬁc Advantages
in Multinational Corporations: The Role of Subsidiary Initiative.” Strategic Management
Zeitschrift 19 (3): 221–42.

*The Asian Development Bank recognizes “China” as the People’s Republic of China.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

24 Asiatischer Entwicklungsbericht

Birkinshaw, Julian, Neil Hood, and Stephen Young. 2005. “Subsidiary Entrepreneurship, Internal
and External Competitive Forces, and Subsidiary Performance.” International Business
Rezension 14 (2): 227–48.

Cantwell, John, and Odile Janne. 1999. “Technological Globalisation and Innovative Centres: Der
Role of Corporate Technological Leadership and Locational Hierarchy.” Research Policy 28
(2): 119–44.

Cantwell, John, and Ram Mudambi. 2005. “MNE Competence-Creating Subsidiary Mandates.”

Strategic Management Journal 26 (12): 1109–28.

Caves, Richard E. 1996. Multinational Enterprise and Economic Analysis. Cambridge, Vereinigtes Königreich:

Cambridge University Press.

Choudhury, Mita. 2002. “Potential Selectivity Bias in Data: Evaluation of a Firm-Level Database

on Indian Industry.” Economic and Political Weekly 37 (8): 758–66.

Chen, Victor Zitian, Jing Li, and Daniel M. Shapiro. 2008. “FDI Knowledge Spillovers and
Product Innovations of Chinese Firms.” Working Paper at Jack Austin Center for Asia
Paciﬁc Business Studies, Simon Fraser University.

Clark, Tom S., and Drew A. Linzer. 2015. “Should I Use Fixed or Random Effects?” Political

Science Research and Methods 3 (2): 399–408.

Clarke, Paul, Claire Crawford, Fiona Steele, and Anna F. Vignoles. 2010. “The Choice between
Fixed and Random Effects Models: Some Considerations for Educational Research.”
CMPO Working Paper, University of Bristol.

Cohen, Wesley M. 1995. “Empirical Studies of Innovative Activity.” In Handbook of the
Economics of Innovation and Technological Change, edited by Paul Stoneman. Oxford:
Blackwell.

Dachs, Bernhard, Robert Stehrer, and Georg Zahradnik. 2014. The Internationalisation of

Business R&D. Cheltenham: Edward Elgar Publishing.

Damijan, Jože P., Mark S. Knell, Boris Majcen, and Matija Rojec. 2003. “Technology Transfer
through FDI in Top-10 Transition Countries: How Important are Direct Effects, Horizontal
and Vertical Spillovers?” William Davidson Institute Working Papers Series 549.

Demena, Binyam A., and Peter AG van Bergeijk. 2017. “A Meta-Analysis of FDI and Productivity
Spillovers in Developing Countries.” Journal of Economic Surveys 31 (2): 546–71.
Dunning, John H. 1993. Multinational Enterprises and the Global Economy. Wokingham:

Addison-Wesley.

Dunning, John H., and Sarianna M. Lundan. 2008. Multinational Enterprises and the Global

Economy. Edward Elgar Publishing.

_____. 2009. “The Internationalization of Corporate R&D: A Review of the Evidence and Some
Policy Implications for Home Countries.” Review of Policy Research 26 (1–2): 13–33.
Everitt, Brian, Sabine Landau, and Morven Leese. 2001. Cluster Analysis, 4th edition. London:

Oxford University Press.

Falk, Martin. 2008. “Effects of Foreign Ownership on Innovation Activities: Empirical Evidence
for Twelve European Countries.” National Institute Economic Review 204 (1): 85–97.
Fors, Gunnar, and Roger Svensson. 2002. “R&D and Foreign Sales in Swedish Multinationals: A

Simultaneous Relationship?” Research Policy 31 (1): 95–107.

Fu, Xiaolan. 2008. “Foreign Direct Investment, Absorptive Capacity and Regional Innovation

Capabilities: Evidence from China.” Oxford Development Studies 36 (1): 89–110.

Fu, Xiaolan, Carlo Pietrobelli, and Luc Soete. 2011. “The Role of Foreign Technology and
Indigenous Innovation in the Emerging Economies: Technological Change and Catching-
Up.” World Development 39 (7): 1204–12.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

Foreign Ownership, R&D Intensity, and Technology Acquisition 25

García, Francisco, Byungchae Jin, and Robert Salomon. 2013. “Does Inward Foreign Direct
Investment Improve the Innovative Performance of Local Firms?” Research Policy 42 (1):
231–44.

Ghoshal, Sumantra, and Christopher A. Bartlett. 1990. “The Multinational Corporation as an

Interorganizational Network.” Academy of Management Review 15 (4): 603–25.

Girma, Sourafel, David Greenaway, and Katharine Wakelin. 2001. “Who Beneﬁts from Foreign
Direct Investment in the UK?” Scottish Journal of Political Economy 48 (2): 119–33.
Globerman, Steven, John C. Ries, and Ilan Vertinsky. 1994. “The Economic Performance of

Foreign Afﬁliates in Canada.” Canadian Journal of Economics 27 (1): 143–55.

Government of India, Planning Commission. 2013. Twelfth Five-Year Plan. Neu-Delhi.
Gupta, Anil K., and Vijay Govindarajan. 1991. “Knowledge Flows and the Structure of Control
within Multinational Corporations.” Academy of Management Review 16 (4): 768–92.
Hake, Mariya. 2009. “Firm Growth and Ownership Change in CEE Countries.” WIFO Discussion

Paper.

Hall, Bronwyn H. 2010. “The Internationalization of R&D.” Maastricht UNU-Merit Working

Paper Series 2010-049.

Hirschey, Markieren, Hilla Skiba, and M. Babajide Wintoki. 2012. “The Size, Concentration and
Evolution of Corporate R&D Spending in US Firms from 1976 Zu 2010: Evidence and
Implications.” Journal of Corporate Finance 18 (3): 496–518.

Hymer, Stephen. 1976. The International Operations of National Firms: A Study of Direct Foreign

Investment. Cambridge, MA: MIT Press.

Javorcik, Beata Smarzynska, and Mariana Spatareanu. 2008. “To Share or Not to Share: Does
Local Participation Matter for Spillovers from Foreign Direct Investment?” Journal of
Development Economics 85 (1): 194–217.

Kinkel, Steffen, and Oliver Som. 2012. “Changing Patterns of R&D Relocation Activities
in the Course of the Global Economic Crisis.” Paper presented at DRUID Conference.
Copenhagen. June 19–21.

Kuemmerle, Walter. 1999. “The Drivers of Foreign Direct Investment

into Research and
Development: An Empirical Investigation.” Journal of International Business Studies 30
(1): 1–24.

Kumar, Nagesh, and Aradhna Aggarwal. 2005. “Liberalization, Outward Orientation and In-
house R&D Activity of Multinational and Local Firms: A Quantitative Exploration for
Indian Manufacturing.” Research Policy 34 (4): 441–60.

Kumar, Nagesh, and Mohammed Saqib. 1996. “Firm Size, Opportunities for Adaptation and
In-house R&D Activity in Developing Countries: The Case of Indian Manufacturing.”
Research Policy 25 (5): 713–22.

Lall, Sanjaya. 1992. “Technological Capabilities and Industrialization.” World Development 20

(2): 165–86.

Mebratie, Anagaw Derseh, and Peter AG van Bergeijk. 2013. “Firm Heterogeneity and
Development: A Meta-analysis of FDI Productivity Spillovers.” The Journal of
International Trade & Economic Development 22 (1): 53–74.

Nationale Wissenschaftsstiftung. Statistics. http://www.nsf.gov/statistics/.
und Entwicklung
Organisation

for Economic Co-operation

(OECD).

2008A. Der

Internationalisation of Business R&D: Beweis, Impacts and Implications. Paris.
_____. 2008B. OECD Benchmark Deﬁnition of Foreign Direct Investment, 4th edition. Paris.
Pamukçu, Teoman, and Fatma Muazzez Utku-˙Ismihan. 2009. “Determinants of R&D Decisions
of Firms in Developing Countries the Case of Turkey.” Paper presented at the Anadolu
International Conference in Economics. Eski¸sehir, Truthahn. June 17–19.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3

26 Asiatischer Entwicklungsbericht

Patra, Swapan Kumar, and Venni V. Krishna. 2015. “Globalization of R&D and Open Innovation:
Linkages of Foreign R&D Centers in India.” Journal of Open Innovation: Technologie,
Market, and Complexity 1 (1). https://jopeninnovation.springeropen.com/articles/10.1186
/s40852-015-0008-6.

Peng, Mike W. 2001. “The Resource-Based View and International Business.” Journal of

Management 27 (6): 803–29.

Rasiah, Rajah. 2007. RIETI Discussion Paper Series 07-E-025. http://www.rieti.go.jp/jp

/publications/dp/07e025.pdf.

Reserve Bank of India (RBI). 2015. “Census on Foreign Liabilities and Assets of Indian

Companies 2013–14.” India.

Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in

Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55.

Rugman, Alan M., Alain Verbeke, and Quyen T. K. Nguyen. 2011. “Fifty Years of International
Business Theory and Beyond.” Management International Review 51 (6): 755–86.
Sasidharan, Subash, and Vinish Kathuria. 2011. “Foreign Direct Investment and R&D:
Substitutes or Complements—A Case of Indian Manufacturing after 1991 Reforms.” World
Development 39 (7): 1226–39.

Tomiura, Eiichi. 2003. “Changing Economic Geography and Vertical Linkages in Japan.” Journal

of the Japanese and International Economies 17 (4): 561–81.

United Nations Conference on Trade and Development (UNCTAD). 2005. World Investment

Bericht. New York and Geneva.

Williamson, Oliver E. 1975. Markets and Hierarchies: Analysis and Antitrust Implications: A

Study in the Economics of Internal Organization. New York: Free Press.

Yang, Chih-Hai, and Hui-Lin Lin. 2012. “Openness, Absorptive Capacity, and Regional

Innovation in China.” Environment and Planning 44 (2): 333–55.

D
Ö
w
N
Ö
A
D
e
D

F
R
Ö
M
H

T
T

:
/
/

D
ich
R
e
C
T
.

ich
T
.

e
D
u
A
D
e
v
/
A
R
T
ich
C
e
–
P
D

F
/

3
5
1
1
1
6
4
3
9
4
0
A
D
e
v
_
A
_
0
0
1
0
3
P
D

B
j
G
u
e
S
T

Ö
N
0
7
S
e
P
e
M
B
e
R
2
0
2
3
PDF Herunterladen