working 480 F.pmdOUTWARD FDI AND CROSS- BORDER M&As BY INDIAN FIRMS:
A HOST COUNTRY-LEVEL ANALYSIS
CENTRE FOR DEVELOPMENT STUDIES
(Under the aegis of Govt. of Kerala & Indian Council of Social Science Research)
Thiruvananthapuram, Kerala, India
external refereeing process before being published.
OUTWARD FDI AND CROSS-BORDER M&As
BY INDIAN FIRMS:
October 2018
This is a revised version of the paper presented at the Centre for
Development Studies (CDS), Thiruvananthapuram. I wish to thank an
anonymous referee for very helpful comments. I have greatly benefitted
from interactions with N. Vijayamohanan Pillai and Srikanta Kundu
while working on the econometric model. Sandip Kujur, George Paily
and Kiran Kumar Kakarlapudi were also of help while working on this
paper. I thank S Gauri for helping me to process the data. I have benefitted
from critical comments given by the seminar participants at CDS and
Centre for Economic Studies & Planning, JNU, New Delhi, discussants
and chairs of various national and international conferences held by
Knowledge Forum at Chennai, 2016, GLOBELICS in Athens, during
2017 and Annual Labour Conference at Thiruvananthapuram during
2017. I thank CDS for financial support through ‘Seed Grant’ to carry
out this research.
4
ABSTRACT
This paper tries to understand the trends and the pattern of Outward
Foreign Direct Investment (OFDI) by Indian firms and the factors that
determine OFDI from India through Cross-Border Mergers and
Acquisitions (CBM&As). As economic activities of those firms in the
respective host countries are not accessible, the present study is restricted
to analyse the push and pull factors, based on the macroeconomic
indicators of the leading nineteen countries that hosted such investments
between 2004 and 2015. The study employs Negative binomial and
Ordinary Least Square (OLS) panel data regression. It is based on a
theoretical framework, i) by drawing insights mainly from the Ownership
Location Internationalisation (OLI) theory on determinants of FDI,
namely, market-seeking, resource seeking and strategic asset seeking,
and ii)by controlling other variables such as India’s Real Effective
Exchange Rate and Institutional factors.
OFDI from India largely took place in the form of CBM&As
particularly in US and UK, and especially in the manufacturing and
service sectors. The foreign exchange spending of the sample of 450
firms shows that they spend much more than their foreign exchange
earnings, resulting in an adverse effect on India’s balance of payment.
Firms going for overseas-acquisitions prefer to spend more on in-house
R&D and personnel to strengthen technological capability and skill
formation. The study finds empirical evidence to validate the hypothesis
developed by the theories on MNEs related to market seeking, strategic
assets seeking and resource seeking motivations of OFDI. The study
argues that Indian firms have invested abroad through CBM&As to
support their export activities, with such exports doing a complementary
role rather than as a substitute for exports. Increasing external transaction
cost due to rupee depreciation also has motivated Indian firms to engage
in overseas acquisitions. Institutional set up of India has played a
significant role in facilitating such deals.
Keywords: India; OFDI; CBM&As; OLI theory; Push-Pull Factors.
JEL Classification: F6; F2; O3 and L5
5
Introduction
The new policy packages initiated by Government of India
including ‘Make in India’, removal of entry barriers of foreign investment
in order to tap more foreign savings and better technology are expected
to transform Indian economy as a manufacturing hub. However, the
most successful firms are trying to invest abroad through CBM&As for
various reasons such as access to technology, markets, brands, patents
and design, copyrights and trademarks.1 According to the available
Indian literature, such OFDIs are undertaken in order to increase
corporate global competitiveness by pursuing related diversification
and by integrating affiliates into global production networks and
technology, to move up in their production value chain and secure
international brand names (Sauvant and Pradhan et.al, 2010; Athreye
and Godley, 2009; Athukorala, 2009; Gill and Singh, 2012; Beena,
2014). Competition from MNEs can drive Indian firms to invest abroad
as a survival strategy. There have been various studies on the trends and
determinants of outward FDI through Greenfield2 (Lall,,1983; Lall,
RB,1986; Morris,1991; Nagaraj 2006; Pradhan, 2008; Kumar & Chadha,
2008; Ramamurthy and Singh 2009; Pradhan & Sauvant et.al, 2010;
1 Earlier study has observed that firms investing abroad are mobilizing resources through external borrowings which have serious implications (Beena, 2011; 2014).
2 Greenfield FDI involves capital movement that helps establishing new enterprise by buying fixed assets, goods and services and hiring workers in the host country (WIR, 2009).
6
Khan, 2012; Mani, 2013; Kallummal et al, 2016), and Outward Foreign
Direct Investment (OFDI) through Brownfield/CBM& As (Nayyar, 2008;
Beena, 2011; Beena, 2014). But none of those studies had looked at the
pull factors of the host countries that attracted such Brownfield
investments3 from India and the developmental implication of home
country. This paper makes an attempt to fill this research gap. However,
as economic activities of such firms in those respective host countries
were not accessible to the researcher, the study restricts its analysis
based on the macroeconomic indicators of host countries. The
econometric exercise is employed to determine the push and pull factors
of such investments based on the theoretical framework developed by
drawing insights from Ownership Location Internationalisation (OLI)
theory. An attempt has also been made to analyse the growth pattern of
Outward Foreign Direct Investment from India through Greenfield and
Brownfield/Cross-Border Mergers & Acquisitions (CBM&As) since
1990s; country-wise and sector-wise distribution of such investments
from Indian Firms since mid-2000s and their economic characteristics.
To what extent such OFDI has helped Indian manufacturing sector to
strengthen technological capability has not been analysed in this paper.
This paper could not look into the overall investment and financing
pattern of such firms as it is beyond the scope of this study.
The paper i) discusses the analytical framework based on the
theoretical and empirical studies carried out in the context of developed
and developing countries including India; The definitions and sources
of data of all the variables considered in the study are also discussed. ii)
then proceeds to deals with the pattern, country-wise and sector-wise
distributions of OFDI through Greenfield and Brownfield investments
3 FDI can be in the form of Brownfield if the entry of capital is in the form of merging or acquiring the assets of existing firm which would not create any further capital formation, additional capacity or employment. When a Brownfield/CBM&A takes place, it registers as both a sale in the country of a target firm and purchase in the home country of the acquiring firm (WIR, 2006).
7
and the economic characteristics of such firms located in India. iii) This
is followed by estimation and result of econometric exercise and iv)
concludes with the major findings and suggestions for further research.
Section I: Theoretical and Analytical Framework
According to the eclectic theory or Ownership Location
Internationalisation (OLI) theory developed by Dunning (1976), a firm
would engage in foreign expansion if it has ownership and
internationalisation advantages. Moreover, the host country must have
locational advantages as compared to the firm’s home country. It should
be kept in mind that this OLI theory was developed (Dunning, 1977;
1979 and 1988) by integrating three approaches to FDI 4, namely the
Industrial Organization Theory, The Internationalisation Theory and
the Location Theory.
establishes a subsidiary in another country must have some firm-
specific advantages(e.g. brand name, patent-protected superior
technology, marketing and managerial skills, etc.) with respect
to the domestic firm in the host country;
2) the Internationalisation theory explains that FDI is the result of
firms replacing external market transactions with internal
transactions, as a way of avoiding imperfections in the markets
for intermediate inputs (Buckley and Casson, 1976). It asserts
that MNEs internalise transactions as long as the benefits
outweigh the costs (Buckley et al., 2009);
3) The Location theory argues that MNE locates its value-adding
activities in a specific host country only if such activities in the
4 Eclectic theory could be used as a framework for analysing the determinants of international production or sale of a firm rather than considering this theory for predicting the activities of MNEs (Dunning 2001).
8
host country function better than in the home country. Location
advantage consists of input-side (abundant natural resources,
appropriate and superior technologies and output-side (large
market size)(Pedersen, 2003). However, it was postulated that
these advantages are not likely to be uniformly spread among
countries, industries, and firms and likely to change over time.
Vertical FDI can be made by firms that geographically fragment
their production into stages, typically on the basis of factor intensities,
exploiting lower factor prices abroad or reducing transaction costs by
internalising upstream or downstream activities, i.e., suppliers, marketing
channels (Ekholm and Markusen, 2002; Kokko, 2006). Accordingly,
firms prefer to allocate stages with high labour intensity to countries
with low levels of labour costs and the stages requiring much skill or
capital to high-income countries. Substitution between employment
levels at home and abroad may take place if an activity, previously
conducted at home, is relocated abroad (Braconier and Ekholm, 1999).
Similarly, horizontal multinational enterprises (MNEs) are
multiplant firms that seek to exploit their existing advantages and
replicate the same activities in many locations. A substitution between
home and foreign employment is expected if the produced goods are
tradable. Several studies argue that substitution effect occurs between
countries with comparable factor endowments. Konings and Murphy
(2003) argued that labour substitution is more likely to take place when
factor proportions are different in various locations, and vertical FDI
prevails.
from that of high-income economies and therefore short-run job losses
due to OFDI from low-cost home country could be unlikely (Masso,
Urmas and Priit, 2008). It is also argued that firms investing abroad from
emerging economies will have only a few firm-specific advantages,
based on technologies, intellectual property and brand names that could
9
be exploited profitably in developed markets (Kokko, 2006; Varblane
et al.2003). The rationale of outward FDI through CBM&As are to get
access to natural resources and seeking strategic–assets investments
especially R&D and Know-how, new technologies; to diversify markets
and risks, to facilitate faster entry into foreign market and synergies
(Wang and Boateng, 2007; Makino et al, 2002;Deng, 2009; Hopkins,
2008) Saturation in the home market would compel firms to invest
abroad through CBM&As. Such investment can take place in order to
promote exports to circumventing trade barriers by foreign countries
(Wong and Chan, 2003). A firm could transform the transaction cost
from an external disadvantage into internal advantage by acquiring a
foreign firm. And such investments are largely influenced by institutional
and political constraints, organisational capabilities and
entrepreneurship (Nolan and Yeung, 2001; Hong and Sun, 2006).
Section II: Analysis / Discussion
II.1 Defining Variables / Leading Push-Pull Factors and Data sources
Based on the available theoretical and empirical literature, the
following variables are found to be the leading push and pull factors
that determine the OFDI through CBM&As. The study considers
variables such as Market size, Endowments of natural resources,
Knowledge based-asset/strategic assets, Political risk, Institutional
factors of host countries, Corporate tax rate and trade-related indicators
as pull factors. Similarly, FDI inflows to home country, Capital market
of the home country, Currency depreciation which is reflected in terms
of Real Effective Exchange Rate and Institutional quality of home
country have been considered as push factors that determine OFDI
through CBM&As.
Market Size
The market size of the host country is a conventional determinant
of FDI flows either through greenfield or brownfield/CBM&As. GDP
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and or GDP per capita of the host country are generally considered as
relevant proxies for market size, and a positive association is expected
between FDI and size of the host country (Buckley, P.J.et al. 2007).
For the purpose of the study, GDP and GDP per capita at the
constant price of 2005 have been considered as a proxy variable to
measure market size. Data has been compiled from the World
Development Indicators, for the years 2004 to 2015.
Strategic Assets
superior technologies and skills which are available in advanced host
countries (Makino et al., 2002). Innovation in the host country could be
the proxy for strategic asset seeking CBM&As.
Innovation in the host country could be considered as a proxy to
measure strategic assets such as R&D and technological know-how.
Innovation index prepared by the Global Competitiveness report has
been considered for this study. According to this report, Innovation
Index is measured by considering the capacity for innovation, quality
of scientific research institutions, R&D spending on company,
university-industry collaboration in R&D, Government procurement of
advanced tech products, availability of scientist and engineers, and
PCT patents, applications per million populations.
Natural Resources
A positive association between the number or value of deals with
the endowment of natural resources of the host country is expected as
internationalisation theory asserts the importance of equity-based control
in the exploitation of scarce natural resources (Buckley and Casson,
1991).
Variables such as the share of exports of Fuels to the total
merchandise exports and the share of ores and steel to the total
11
natural resources. This data is collected from the World Development
Indicators.
frameworks might adversely affect the economic outcome of a given
investment (Lizondo, 1991). Therefore one can assume a positive
correlation between the number and value of CBM&A deals by Indian
firms with the political stability of the host country.
Rule of law index prepared by the Global Competitiveness report
has been considered here to measure the political stability. This index
reflects perceptions of the extent to which stakeholders/agents have
confidence in and abide by the rules of society, and in particular the
quality of contract enforcement, property rights, the police, and the
courts, as well as the likelihood of crime and violence.
Corporate Tax Rate
MNE prefers to locate its business activities in countries with a
low corporate tax rate (Grubert and Slemrod, 1998). And therefore a
negative association is expected between OFDI from India with the
corporate tax rate of host countries. This data has been collected from
the Corporate Tax Rate table prepared by KPMG.
REER of Home Country
expand exports and hinder OFDI (Kohlhagen,1977; Logue and
Willet,1977; Stevens,1993 as cited by Buckley Peter J et al. 2007,
p.506). At the same time, the external transaction cost arriving from
huge import bill which is denominated by the Indian currency would
go up when rupee depreciates. The increased external transaction cost
provides incentives for MNEs to acquire firms in other countries in
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order to internalise their strategic flows (Buckley Peter J et al. 2007,
p.507).
Thus the depreciation of home currency could probably increase
the pace of CBM&As by Indian MNEs. Thus one can expect a positive
or negative association between exchange rate of Indian currency with
the number and value of CBM&A deals. Data on REER has been
collected from the RBI website.
Ownership Advantage
The investment development path theory suggests that inward
FDI boosts economic growth and its own location advantage which in
turn increases outward FDI by domestic firms (Buckley et al., 2007).
The strengthened location advantage helps the indigenous firms to
upgrade their own competitive advantages by acquiring firms in the
advanced countries. Moreover, the investments by indigenous firms
would affect both supply and demand for the products supplied by
foreign firms and their desire to internalise their markets for the
competitive advantages (Dunning, 2001). Thus there could be a positive
association between the number and the value of foreign acquisitions
with the FDI inflows in India.
A positive association is expected between FDI inflows to India
and OFDI through CBM&As. The FDI inflow is compiled from World
Development Indicators.
Liquidity of the Capital
The liquidity of a stock market is expected to rise if foreign
investors invest through purchasing existing equity which would, in
turn, increase the value traded. Therefore it is argued that FDI is positively
correlated with stock market capitalisation and value trading (Classens
et al., 2001). Another study by Giovanni (2005) based on a large panel
data set of CBM&As for the period of 1990-1999 found a strong positive
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association between the size of the stock market (ratio of stock market
capitalisation over GDP) and Outward FDI.
This study also expected a positive association between liquidity
of capital market and OFDI through CBM&As. Market capitalisation to
GDP has been considered to measure liquidity of the capital market.
The data is collected from World Development Indicators.
Institutional Factors
Many studies on international business have shown that
countries with high institutional quality can attract FDI inflows as it
could shape the form of outward investment (Dunning and Lunden,
2008). It is argued that developing and undeveloped countries would
prefer to invest in developed countries where the institutional
framework is well developed. It is also argued in the literature that
Institutional factor can either act as a barrier or a facilitator for FDI and
or CBM&As. Government subsidies or certain policy initiatives such
as simplifying norms of raising funds can promote outward investment
by offsetting ownership and locational disadvantages abroad
(Aggarwal and Agmon,1990).
Institutional framework could be characterised by the respect for
rule of law, an efficiently working judicial system, high levels of
transparency and accountability within public institutions (Global
Competitiveness Report, 2005-06). Institutional index of the host and
home countries has been collected from Global Competitiveness Report.
Trade-related Factors
A positive association is expected between exports and OFDI
through CBM&As if the firm prefers to follow their customers. Similarly,
a firm might prefer to buy firms abroad in order to cut down the transaction
cost and therefore a positive association is expected from imports and
OFDI through CBM&As (Buckley Peter J et al. 2007, p.507).
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II.2 Trends and Patterns of OFDI through Greenfield and Brownfield/CBM&As
II.2.1. OFDI from India
A preliminary analysis on India’s OFDI is discussed here with
reference to the FDI Inflows for the period 1990 to 2017. This is based
on the data compiled from World Investment Report (WIR) and many
studies have established that major chunk of FDI has come in the form
of portfolio equity (PE) (Chandrasekhar and Pal, 2006; Rao and Dhar,
2011; Nagaraj, 2017).
Fig. 1: Trends of India’s Outward FDI and Inward FDI
Source: World Investment Report, Various Issues, UNCTAD.
From Fig.1, it is quite clear that FDI inflows to India grew quite
significantly though there was a dip in their growth since 2008 due to
the financial crisis. And that trend is reversed since 2013. Surprisingly,
FDI outflows have also grown significantly, especially after 2000 which
showed a similar dip during 2009 to 2013. And therefore it is important
to understand the nature and economic activities of those firms involved
in such investment flows.
II.2.2 Country-wise and Sector-wise Distribution of OFDI from Indian firms
This sub-section tries to analyse the nature of projects and
destination countries of such OFDI from India at the firm level for the
period 2007 to 2017 based on the data available from RBI website.
From exploratory analysis, it is observed that 8000 firms from India
were investing abroad in 80,000 projects during 2007-2017. However
top 250 firms account for almost 80% of the total / actual OFDI from
India during 2007-2017 which is estimated at US$ 108.4 billion. The
stock of such outflows till 2017 based on those top 250 firms has been
estimated as Rs. 4424.8 billion (PROWESS Data Base, CMIE)5.
Fig. 2: Top ten Country-wise Distribution of India’s OFDI Projects
Although Indian firms were investing in many countries during
2007 to 2017, top 5 countries account for almost 80% of the total OFDI
(Fig.2). Mauritius, Singapore, Netherland, USA and UAE are the leading
host countries of India’s OFDI (Fig.3). 76% of such outward investments
were in the form of wholly-owned subsidiaries during 2007 to 2017,
5 It is further noticed that firms that are located in India and engaged in main economic activities such as Steel, Telecommunications, Computer software, Drugs and Pharmaceuticals, Banking, Crude oil and natural gases account for almost 60% of such outward investments (Beena, 2018).
16
while this share was only 30% in the 80s (Pradhan and Sauvant et al.,
2010).
Fig. 3: Sectoral Distribution of Number of Deals and Share of India’s OFDI
Source: Reserve Bank of India Website
33% of such outward investment was in the manufacturing sector
while financial, insurance and business services account for 25%. Only
14% of the total outward investment that flowed from India during
2007 to 2017 was in the Agriculture sector (Fig.3).
II.2.3. Pattern of OFDI through Brownfield/CBM&As
The pattern of value of purchases/acquisitions and OFDI during
1990 to 2015 is analysed here based on the data collected from WIR.
The present study is to understand country-wise and sector-wise
distribution of such outward investment through CBM&As. From the
analysis, it is observed that overseas expansion by Indian firms has
occurred mainly in the form of CBM&As, especially after 2000 (Fig.4).
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Fig. 4: Pattern of India’s OFDI and Value of Acquisitions
Fig.5: Trends of value of CBM&As and OFDI, 2004-2015
Data on CBM&A deals made by Indian firms has been compiled
from Venture Intelligence Capital database. According to this, there
were 1704 CBM&As deals completed by Indian firms during 2004 to
2015 across 46 countries. 66 out of 1704 deals do not report their deal
value of CBM&As by Indian firms.
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The total number of deals during 2004 was only 37 which
increased to the level of 106 within a year in 2005. This number has
shown a significant growth to the level of 241 during 2007. And that
trend was reversed since 2009 and declined to the level of 127 during
2015 (Fig.5). A similar pattern is also observed in the case of FDI outflows
as well. Further, it is observed that the repatriation/disinvestment by
India has tremendously increased since 2009 and this could be attributed
to the financial crisis and the crash in global demand (Fig.6).
Fig.6: Repatriation/disinvestment inflow to India (US$Million)
Source: Compiled from various issues of RBI Monthly Bulletin.
II.2.4. Country-wise and Sector-wise Distribution of OFDI through CBM&As
Table 1: Country-wise Distribution of Number and Value of CBM&As by Indian firms
Total No. of deals % Share Amount
(US$M) % Share
19
South Africa 36 2.11 946.95 0.86
Netherlands 26 1.52 1848.15 1.68
China 20 1.17 635.2 0.58
Belgium 18 1.06 2355.8 2.14
Sri Lanka 14 0.82 13.8 0.01
Brazil 21 1.23 3393.6 3.08
Indonesia 17 1.00 1211 1.10
Malaysia 14 0.82 926.4 0.84
Mauritius 8 0.47 15.19 0.01
Israel 8 0.47 266 0.24
Japan 10 0.59 150.9 0.14
Russia 6 0.35 1372 1.24
Switzerland 17 1.00 974.98 0.88
Vietnam 9 0.53 136.66 0.12
Spain 20 1.17 671.49 0.61
Bangladesh 4 0.23 375 0.34
Kenya 8 0.47 10805 9.80
Sweden 14 0.82 653 0.59
Others 254 14.89 9409.3 8.53
Total 1706 100 110290.8 100
Source: Compiled from Venture Intelligence Capital Database
US and UK together attracted almost 50 per cent of India’s
investments abroad through CBM&As (Table 1).
20 T
ab le
Fig.7: Sector-wise distribution of value of CBM&A deals
As for the sectors, Table 2 and Fig.6 present a clear picture of the
share of each sector. The single most attractive sector is the IT sector
which accounts for almost 24% of the total number of deals (Fig.6). The
other three major sectors are Pharma, Business services and auto
components accounting for 9 per cent, 8 per cent and 5 per cent of the
total deals respectively. Coal, Oil, Natural Gas; Metal extraction;
Telecom; Chemicals; Food products and Garments account almost 2
per cent each of the total 1704 CBM&A deals. BFSI, Media and
entertainment, Hotels, IT products, FMCG, Steel, Industrial machinery
and Building & construction accounted only 1 to 1.8 per cent share
each (Table 2). The similar pattern is observed in terms of value of deals
as well though IT sector accounts much smaller share in terms of value.
From this trend, one can infer that Indian firms are targeting mainly
small-sized IT firms. Almost 48% of such deals are in the manufacturing
sector while 47% of them are in the service-oriented sectors. The rest of
them are with the resource-intensive sectors (Fig. 7).
II.3 Economic Characteristics of Indian Overseas Firms
For the purpose of analysing the characteristics of the firms which
are investing abroad, the analysis was done based on the data extracted
22
from PROWESS database. Only a sample of 450 firms was considered,
although Indian corporate sector had completed 1704 deals during 2004
to 2015. This is partly because i) many of the deals were made by the
same firms and ii) only those 450 firms were listed in the PROWESS
database, hence only their financial details could be identified6. The
study also analysed Foreign exchange assets and liabilities of these
sample firms by measuring investment made abroad, Foreign Exchange
spending, Foreign exchange earnings, and Imports and exports intensity.
The production-related performance have been analysed by estimating
the growth of sales and employment. R&D expenditure intensity,
intensity of Foreign technology purchases and Compensation to net
sales have been calculated in order to understand their innovation and
skill formation strategy.
Table 3: Economic Performance of Indian Overseas Firms Year Invest- EE to FS/ GR of GR of R&DI Foreign Compen-
ment OFD FE Sales Employ- Technology sation abroad ment Import to net
(Billion) sales
1991 153.00 4.84 10.5 4.78 13.90 0.00037 0.00000 0.08
1992 158.28 6.60 19.2 36.72 9.08 0.00106 0.00000 0.06
1993 191.02 7.76 128 4.65 -0.62 0.00187 0.00130 0.06
1994 182.72 10.49 43.1 1.39 -24.97 0.00182 0.00046 0.05
1995 404.29 4.82 45.7 19.22 18.28 0.00307 0.00140 0.06
1996 370.09 5.87 26.2 13.56 28.54 0.00484 0.00065 0.08
1997 281.06 5.56 69.4 8.01 -7.87 0.00474 0.00024 0.03
1998 423.66 4.15 34.8 1.35 3.47 0.00411 0.00178 0.04
1999 579.94 3.67 11.6 6.00 7.32 0.00493 0.00138 0.07
2000 325.36 2.75 12.1 22.79 -11.25 0.00349 0.00053 0.10
6 84 firms out of 450 listed firms are engaged in computer software services, 14 firms are with IT enabled services and another 34 firms are with Drugs and Pharma etc.
23
2001 154.70 2.75 10.7 -7.12 16.84 0.00455 0.00103 0.11
2002 153.07 2.55 34.1 15.58 12.11 0.00531 0.00003 0.10
2003 155.56 2.70 5.74 12.82 7.75 0.00494 0.00138 0.10
2004 126.30 3.94 53.5 28.87 4.44 0.00488 0.00048 0.10
2005 140.98 5.30 13.5 22.82 18.98 0.00575 0.00025 0.10
2006 162.50 4.56 13.5 15.28 0.59 0.00593 0.00011 0.11
2007 304.83 3.01 3.43 19.42 12.97 0.00652 0.00000 0.10
2008 374.10 2.65 23.8 7.53 6.42 0.00700 0.00017 0.10
2009 681.33 1.59 31 9.65 0.22 0.00827 0.00021 0.10
2010 745.77 1.44 19.2 0.68 8.40 0.00749 0.00014 0.10
2011 728.69 2.28 21.7 16.19 24.31 0.00752 0.00006 0.10
2012 864.30 2.19 7.14 13.34 7.71 0.00774 0.00003 0.09
2013 834.33 2.48 12.2 1.00 4.92 0.00855 0.00015 0.10
2014 931.19 2.65 69.4 12.88 5.78 0.00926 0.00016 0.11
2015 1069.42 2.27 21.7 -12.87 0.81 0.00918 0.00032 0.12
Avg of 1990- 2000 297.07 5.76 41.58 10.63 5.24 0.00274 0.00072 0.06
Avg of 2000- 2015 473.50 2.99 22.04 11.15 7.43 0.00642 0.00034 0.10
EE = Export Earnings; II=Import Intensity; EI=Export Intensity; FS=
Foreign Exchange Spending; FE= Foreign Exchange Spending
(Source: PROWESS Database)
II. 3.1. Investment Made Abroad
The total investment that flowed from Indian firms as on 1990
was 226.63 billion, and that amount accelerated significantly to the
level of 1069.42 billion during 2015 (Table 3). It is clear that these
sample firms have invested abroad much more during 2000 to 2015 as
24
compared to the previous period i.e., 1990 to 2000. Further, it is observed
that the stock of foreign investment made by these overseas acquiring
firms grew significantly especially after 2000 (Fig. 8).
Fig.8: Stock of Investment abroad by Sample firms
Source: PROWESS Data base
Fig.9: Trends of Foreign Exchange Assets and Liabilities
25
Foreign exchange earnings are not rising in proportion to OFDI
from India (Fig.8)7 . The average ratio of Foreign exchange earnings to
OFDI during 1990 to 2000 was 5.76 which has sharply declined to 2.99
during 2000 to 2015. Further, the foreign exchange spending of these
firms is much higher than its earnings though the average ratio for the
period 1990 to 2010 was 42 which has significantly declined to 22
during 2000 to 2015. These trends indicate that Indian firms that are
investing abroad are accumulating trade deficit which have larger
implications as far as India’s balances of payments are concerned (Fig.9).
II.3.3 Output and Employment:
Fig. 10: Sales & Employment: Trends of Annual Growth
The sales of these sample firms grew much faster which accounts
for 10.63 during 2000 to 2015. However, this growth was relatively
lower than the growth that took place during 1990 to 2010 (Table 3).
From this, it is clear that the new form of a growth strategy that followed
by Indian overseas firms especially after 2000 has not replaced the
domestic production though it has not helped those firms to boost their
overall output/sales growth. GDP deflator has been used here to deflate
the industrial sales data.
7 According to the study by Kallummal et.al (2016), the average ratio of stock of Exchange earnings to the stock of OFDI during 2011 to 2014 was more than one only for 6 out of 100 leading OFDI firms.
26
acquiring firms grew relatively better during 2000 - 2015 which accounts
for 7.4 per cent as compared to the growth (5.2 per cent) observed during
1990 to 2000 (Table 3 and Fig.10). However, a close examination points
out that many of these firms were started during the late 1980s and have
not reported employment and wages for many years during 1990s and
therefore one should be careful while drawing inferences8.
II.3.4 Strategy on Innovation and Skill formation
Fig.11: Trends on R&D Intensity and Foreign Technology Import Intensity
The study noticed that the firms which are investing abroad
through CBM&As is preferring to invest more on in-house R&D and
that the intensity of technology purchases of those firms have reduced
significantly (Fig.11). The average ratio of compensation to net sales of
these firms for the period 1990 to 2000 was .06 which was increased to
.10 during 2010-2015. From this, one could argue that these firms were
spending on their personnel by paying proportionately more as compared
to their sales growth (Table 3). From these indicators, one could infer
8 As on 2015, 50 per cent of our sample firms for which data was available (i.e, 225 firms) engaged around 23 lakhs employees.
27
that these sample firms are making an effort to improve their technological
capability. This is quite true especially in the case of Drugs and
Pharmaceutical industry in India9. Having given a brief overview on the
pattern and characteristics of Indian overseas acquiring firms, the
following section discusses the factors that determine such OFDI through
CBM&As, based on the results arrived at from the models.
Section III: Estimation, Results and Inferences
Although the study observed that Indian firms are investing
through CBM&As in almost 40 countries, top leading 19 countries
account for almost 80 per cent of such investments from India (See
Table 1). Therefore, the econometric exercise has been based on those
19 host countries. The list on a number of deals and value of deals across
host countries for the period 2004 to 2015 has been compiled from the
Venture Intelligence Capital.
Count data model where the variables of interest is a non-negative
integer are commonly used to analyse headcounts. In this context, we
model the count of number of acquisitions as a function of various firm-
specific and country-specific characteristics. Quite often, in order to
study the determinants of count characteristics, two kinds of regression
models have been used by the literature – Poisson regression and
Negative Binomial regression. The study used negative binomial
regression (NBR) for analysing the factors determining the Indian
acquisitions abroad across selected countries where the dependent
variable is a count data (i.e.; number of CBM&A deals by Indian firms
or number of deals) (Bala Ramasamy, Matthew Yeung et al., 2012,p.22).
Following Hausman et al. (1984), the reason for sticking on the negative
binomial model is that it can be used relaxing the assumption of
9 However, it is argued that pharmaceutical market in India is increasingly becoming monopolised and charging very high prices after the re- introduction of product patent protection in line with TRIPS (Chaudhuri Sudip, 2018).
28
asymptotical efficiency which means that the conditional mean (E (Y|X))
is equal to the conditional variance (Var (Y|X)). Given the nature of our
panel data with variations in the number of deals, the negative binomial
model is the most appropriate regression model (see Table.4). In order to
study the determinants of the value of deals, we have adopted OLS
estimation technique. The model specification of the negative binomial
regression is given as follows.
The study tested the null hypothesis that the coefficient estimated
by the random effects estimator are the same as the ones estimated by
the fixed effects. The results of the Hausman Test in NBR Model 1 and
Model 2 show that “Prob>chi-sq is positive and significant. Thus it is
observed that differences of coefficients are not systematic in the two
regressions which imply that Random Effect (RE) model is more
preferable than the Fixed Effect (FE) model. The study has therefore
reported only the result of random-effect in the case of Model 1 and
Model 2. High values of Wald Chi-square statistics of estimated
regressions suggest that they are statistically significant.
29
Table 4: Random Effect Negative Binomial and OLS Regression Models Dependent Variables No. of deals Value of deals
Independent RE NBR RE OLS Variables Model 1 Model 2
Market size of Ln GDP .306 (2.53)** -.0511 (-.49) Host country Trade Ln Imports -.255 (-0.71) -.106 (-.61)
Ln Exports 1.55 (4.17)*** .45(3.28)*** Natural resources Ores metal .05 (3.92)*** .003(.27) Endowments of export share Host country (Iron ore) to world Natural resources Fuel export .009 (1.22)** .0008(.23) Endowments of share to world Host country (Fuels) Endowments of Innovation score .516(2.18)** .218(.77) strategic assets of Host country Political stability of Rule of Law .228(0.89) .44(1.01) host country Corporate tax of Corp tax rate -.001(.15) .0048(.73) host country Exchange rate of REER .058(3.62)*** .04 (1.70)* India against US$ Inward FDI of Ln FDI 1.71(5.8)*** .247(1.9)** home country Domestic capital Market .001(0.75) .0063 (1.06) market capitalisation
to GDP
Institutional quality Institutional .1588(0.56) -.649(-1.97)** of host country Index Institutional Quality Institutional 2.692(6.93)*** .495(1.94)** of Home country Index Wald 172.24 101.06 Prob>chi-sq .0000 .0000 Haussman test: 13.83 4.87 chi-sq Prob>chi-sq 0.38 0.997 N 209 209
Note: Z statistics in parentheses; *, ** and *** respectively indicate significant at 10%, 5% and 1% levels.
30
Although the study could not find any association of per capita
GDP either with the number of deals or with the value of deals, a strong
statistically significant correlation was observed between the numbers
of overseas acquisitions by Indian firms with the GDP of host countries.
This result does not hold well when we consider value of deals as
dependent variable10. Based on NBR model, one could argue that market
seeking was a key motive for CBM&As by Indian firms. Strategic-asset
endowment in the host countries is found to be statistically significant
with the expected sign in Model 1 in Table 4.
However, the study could not find such association between
strategic assets and value of CBM&As (see Model 2 in Table 4).
Similarly, significant association is observed between the natural resource
endowments of host countries with number of CBM&A deals and such
association is not observed with the value of the deals (Model 1 and
Model 2 in Table 4). Real effective exchange rate is positively correlated
with the number/value of CBM&As by Indian firms. From this, one
could infer that the increasing external transaction cost due to rupee
depreciation would have given enough incentives for Indian firms to
buy firms abroad in order to locate production in other countries and
internalise their strategic flows.
The study has also found a positive correlation between India’s
exports to host countries and the number and value of CBM&A deals.
(NBR Model 1 and Model 2 in Table 4). Such Indian FDI through
CBM&As could be defensive and must have taken the complementary
role of exports rather than a substitute for exports. Therefore one could
argue that ownership-specific advantages could play an important role
for facilitating CBM&A by Indian firms. Institutional quality of the
host country is found to be inversely related to the value of CBM&As of
10 A positive association was observed between size of the market with OFDI from China in the manufacturing sector while the association was negative in the case of service and resource based sectors (Amighini, Rabellotti and Sanfilippo, 2011). However, major share of OFDI from Chinese firms took place in Asian countries.
31
Indian firms while institutional quality of India is found to be positively
associated with the value and number of CBM&As.
The study could further argue that ownership-specific advantages
developed by firms in India through inward FDI have played an important
role for facilitating such deals. From this, one could infer that host
countries could attract India’s investment through CBM&As in spite of
the low-level score of the institutional quality. However, institutional
set up of India did play a complementary role in facilitating such deals.
Section IV: Conclusion & Suggestions for Further Research
Indian economy has experienced outward FDI through CBM&As
especially after the implementation of WTO. Major share of outward
FDI from India is taking place in the form of CBM&As. According to
the RBI database, 50 per cent of OFDI is mainly attracted by Mauritius,
Singapore and Netherlands. But the exploratory analysis found that
almost 50 per cent of OFDI through CBM&As is attracted by US and UK
in contrast with the overall OFDI trends observed based on RBI database.
Majority of such investments are made in the manufacturing such
as Pharma and service sector especially in computer software, and IT-
enabled services. The study does not provide enough empirical evidence
to support the argument that OFDI from India can replace home country
production which is measured in terms of output, exports and
employment. However, these firms were spending relatively more foreign
exchange than their foreign exchange earnings. Exchange earnings are
not rising in proportion to OFDI and such a trend has an adverse effect
on India’s balance of payment. Firms which are investing abroad through
CBM&As prefer to invest more on in-house R&D in order to strengthen
their technology capability. These firms are also spending relatively
more on their employees especially after 2000. This indicates that
successful firms that are operating in India prefer to spend on
technological capability and skill formation in order to compete with
the firms at the global level.
32
As suggested by OLI theory, the study did find empirical evidence
to validate the hypothesis developed by the theories on MNEs related
to market seeking, resource seeking and strategic asset seeking
motivations. The study argues that Indian firms have invested abroad
through CBM&As in order to support their export activities rather than
as substitutes for exports. Indian currency depreciation against the dollar
would have given incentives for Indian firms to engage in continuous
investments through CBM&As and engage production. This paper
restricted to analyse the push and pull factors of OFDI through CBM&As
based on the macroeconomic indicators of leading nineteen countries
for the entire sector, and this is the limitation of this study. A similar
exercise could be further carried out for different sectors such as
Manufacturing, Services and Resource-based sectors. Further research
could be done at the firm level in order to understand the motives and
developmental implications of OFDI through CBM&As across countries
and industries if firm-level data from the respective host countries are
made available.
Beena P.L is an Associate Professor at CDS and her
research interest include the "Developmental Implcations
of Industry, trade and investments; Political economy of
corporate mergers and acquisitions; Competition policy.
Email: beena@cds.edu
Amountusm 209 445.785 1566.879 0 14301.9
Ln gdp percapita 209 4.104663 0.606775 2.71077 4.66657
No of deals 209 5.6 11.29 0 99
Ln gdp 209 27.09 1.55 23.59 30.32
Ln Imports to India 209 9.58285 0.602651 7.68701 10.7652
Ln Exports from 209 9.558447 0.464739 8.36554 10.6303 India
Share of fuels to 209 15.93743 16.83142 0.008211 70.5595 the total exports
Share of iron ore 209 6.391129 8.454019 0.391456 38.2226 to the total exports
Innovation score 209 4.399856 0.801715 3.1 5.9
Corporate tax 209 30.65249 7.930589 17 55
REER 209 104.6545 4.150068 99.7 112.7
Inwardfdi 209 10.352 .272 9.761 10.637
Market 209 78.02978 26.50328 52.872 146.856 capitalisation
Rule of law 209 0.779984 1.013257 -1.05064 1.99277
Institutional index 209 4.75756 0.867216 2.97 6.19 of host country
Institutional index 209 4.166699 0.279923 3.84 4.55 of India
34
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39
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W.P. 478 A.V. JOSE, Changing Structure of Employment in Indian States. October 2018.
W.P. 477 P. KAVITHA, Trends and Pattern of Corporate Social Responsibility Expenditure: A Study of Manufacturing
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W.P. 476 MANMOHAN AGARWAL , International Monetary Affairs In the Inter War Years: Limits of Cooperation. June 2018
W.P. 475 R. MOHAN, D. SHYJAN, N. RAMALINGAM Cash Holding and Tax Evaded Incomes in India- A Discussion. January 2018.
W.P. 474 SUNIL MANI, Robot Apocalypse Does it Matter for India’s Manufacturing Industry ? December 2017
W.P. 473 MANMOHAN AGARWAL The Operation of the Gold Standard in the Core and the Periphery Before the First World War. June 2017.
W.P. 472 S.IRUDAYA RAJAN, BERNARD D' SAMI, S.SAMUEL ASIR RAJ Tamil Nadu Migration Survey 2015. February 2017.
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W.P. 468 K. C. ZACHARIAH, Religious Denominations of Kerala, April 2016.
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W.P. 467 UDAYA S. MISHRA, Measuring Progress towards MDGs in Child Health: Should Base Level Sensitivity and Inequity Matter? January 2016
W.P. 466 MANMOHAN AGARWAL, International Monetary System Response of Developing Countries to its shortcomings, December 2015
W.P. 465 MANMOHAN AGARWAL, SUNANDAN GHOSH Structural Change in the Indian Economy, November 2015.
W.P. 464 M. PARAMESWARAN, Determinants of Industrial Disputes: Evidence from Indian Manufacturing Industry, November 2015
W.P. 463 K. C. ZACHARIAH, S. IRUDAYA RAJAN, Dynamics of Emigration and Remittances in Kerala: Results from the Kerala Migration Survey 2014, September 2015.
W.P. 462 UDAYA S MISHRA, VACHASPATI SHUKLA, Welfare Comparisons with Multidimensional Well-being Indicators: An Indian Illustration, May 2015.
W.P. 461 AMIT S RAY, SUNANDAN GHOSH Reflections on India’s
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W.P. 460 KRISHNAKUMAR S Global Imbalances and Bretton
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W.P. 459 SUNANDAN GHOSH Delegation in Customs Union
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W.P. 458 M.A. OOMMEN D. SHYJAN, Local Governments and the
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W.P. 457 R. MOHAN, N. RAMALINGAM, D. SHYJAN, Horizontal Devolution of Resources to States in India- Suggestions before the Fourteenth Finance Commission, May 2014
W.P. 456 PRAVEENA KODOTH, Who Goes ? Failures of Marital Provisioning and Women’s Agency among Less Skilled Emigrant Women Workers from Kerala, March 2014
W.P. 455 J. DEVIKA, Land, Politics, Work and Home-life at Adimalathura: Towards a Local History. January 2014.
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De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.) /NOR <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> /PTB <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> /SUO <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> /SVE <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> /ENU (Use these settings to create Adobe PDF documents for quality printing on desktop printers and proofers. 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