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The Location Strategies of Emerging Countries
Multinationals in the EU Regions
Riccardo Crescenzi
Department of Geography and Environment
London School of Economics
[email protected]
Carlo Pietrobelli
Inter-American Development Bank
[email protected]
Roberta Rabellotti
Dipartimento di Scienze Politiche e Sociali
Università di Pavia
[email protected]
SPRU – 18th October 2013
Motivations
• FDI from developing economies reached the record level of
$426 billion in 2012, corresponding to 31% of global outflows,
up from 16% in 2007 (UNCTAD, 2013);
• There is a lot of interest in the IB literature about differences
and similarities between Emerging Market Multinationals
(EMNEs) and Advances Countries MNEs (AMNEs) strategies of
internalization (Ramamurti and Singh, 2009);
• Strictly related with the decision of internalization is the choice
of appropriate locations for the subsidiaries;
• The focus of this paper is on the similarities and differences of
the location strategies of EMNEs and AMNEs in the EU-25
regions.
Location is driven by motivations
(Belderbos et al 2011; Dunning 2009)
• Market-seeking investments are directed to
large market size countries;
• Strategic-asset seeking investments go to
technologically advanced countries;
• Efficiency-seeking investments are attracted
by low wage countries;
• Resource-seeking investments are interested
in natural resource rich countries.
Location is also driven by agglomeration
• To reduce the risks attached to uncertainties for
prospective location choice firms tend to match
the location decisions of their competitors
(Knickerbocker, 1973);
• EMNEs to reduce the risks attached to
investments far from home, in locations which
are culturally and geographically distant, are
likely to match the location decision of their
competitors from the same industry;
• MNEs benefit from being part of a geographical
network or cluster of related activities (Dunning,
2009; Beugelsdijk and Mudambi, 2013 ).
MNEs location and Value Chain
• In Crescenzi, Pietrobelli and Rabellotti (2013
forthcoming in JoEG) we empirically show that
different drivers affect the location of investments at
different stages of the value chain in the EU-25
regions;
– For instance R&D investments search for different local
conditions (highly qualified people, innovative regions)
than new manufacturing plants (low paid unskilled labor);
• In this study, we focus on the location determinants
of EMNEs and AMNEs and investigate whether in
their decision to relocate different stages of the Value
Chain they take into account diverse factors.
Research questions
• How do investment strategies by EMNES differ
from those by AMNEs?
• Do EMNEs and AMNEs location strategies vary
according to the VC stage undertaken through
the investment project?
• Are national and regional characteristics of the
destination area valued differently by EMNEs
and AMNEs?
Data sources
• fDi Markets: the dataset includes approximately 72,000
greenfield investments covering all sectors and countries
worldwide from 2003 to 2008;
• Our empirical analysis is based on the 19,444 projects
undertaken by MNEs from the entire world into the EU25
countries (robustness checks with UNCTAD and Euromonitor);
• For each project, the dataset contains detailed information on
the investor (name and state/country of origin), the
destination area (country, state and city), the year of the
investment; the sector and the activity undertaken;
• Each investment is geocoded at the NUTS2 level with the
exception of UK, BE, and DE where NUTS1 is used;
• Two definitions of EMNEs: EME (India, China, Russia, Turkey,
Honk Kong, Brazil, Mexico, South Africa, Thailand and Chile)
and EME2 (also including Argentina, Malaysia and Ukraine).
Investments projects VC classification
Value Chains and EMNEs vs. AMNEs
• The location drivers of the
investments from different
origins are compared across
two sub-samples: productionoriented activities (MAN) and
non-manufacturing activities
(NON MAN) including the
remaining 4 stages
characterised by higher value
added (HQ, INNO, SALES,
LOG&DIST);
• Agglomeration of investments
at the same VC stage is
captured by means of a
specific proxy based on the
cumulative number of
investments at the same VC
stage in the same region.
The Nested Logit Model
• Pij is the probability of choosing a region j in a country i;
• Pj/i is the probability of choosing region j conditioned on the choice
of country i, depending on the characteristics of the ni regions
belonging to country I;
• Pi is the probability of choosing a country i depending on the
characteristics of the country and on those of all its regions.
• The location process involves two simultaneous
decisions: a) choosing a country i and b) selecting
a region j in the chosen i country.
Investment location drivers
The probability of a certain region to be chosen as a destination of a
foreign investment is estimated as a function of:
① Market seeking motivation: Regional GDP pro capite;
② Strategic asset seeking motivation:
a) Patent Intensity to capture the extent to which MNEs expect to
benefit from localised knowledge spillovers from indigenous
firms;
b) Social filter index
③ Efficiency seeking motivation: Regional unemployment as a proxy
of the labour market conditions in terms of the excess of labour
supply over demand;
④ Regional agglomeration of foreign investments:
a) Total pre-existing investments;
b) Investments in the same sector;
c) Investments in the same VC stage.
‘Social Filter’ Index (Crescenzi et al., 2007,
2012; Crescenzi and Rodrıguez-Pose, 2011)
• SF is an indicator based on structural pre-conditions
to establish fully functional regional systems of
innovation and socio-institutional conditions
favorable to the embeddedness of economic
activities;
• SF includes two major domains combined through
principal component analysis:
– educational achievements;
– productive employment of human resources;
• These two domains, when assessed simultaneously,
generate a socio-economic profile that make some
regions prone and others averse to innovation.
Location of MNCs in the EU regions by area of origin
Location of MNCs in the EU regions by area of origin:
Non-manufacturing activities
(HQ, INNO, SALES, LOG&DIST)
Dissimilarity Parameters:
the ‘weight’ the investor ascribes to
regional (1) vs national (0) drivers
EU10 vs EU15
Non-manufacturing activities
(HQ, INNO, SALES, LOG&DIST)
Preliminary conclusions
• Agglomeration at VC stage (and at sectoral level) is a
key location driver both for EMNEs and AMNEs;
• Market seeking investments: intra EU vs extra EU
pattern;
• Strategic asset seeking:
– only NON MAN EMNEs investments are attracted by
patents;
– Soft innovation factors (proxied by the Social Filter) are
relevant only for intra-EU investments;
• The national and the regional drivers play different
roles in different host and home countries.
Thank you
[email protected]
http://sites.google.com/site/robertarabellotti/home
Crescenzi R., Pietrobelli, C.,Rabellotti R. (2013)
Innovation Drivers, Value Chains and the Geography of
Multinational Corporations in Europe
forthcoming in Journal of Economic Geography
The ‘Social Filter’ combines, by means of Principal
Component Analysis
• % employed people with tertiary education
level
• % population with tertiary education level
• Agricultural employment as % of total
employment
• Long term unemployed as % of total
unemployment.
• People aged 15-24 as % of total population
19
Dissimilarity Parameters:
regions vs country factors (NON MAN)
ERSA - Palermo - 27-30 September 2013