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Journal of International Business Policy
https://doi.org/10.1057/s42214-024-00186-3
FDI andhuman capital development: atale oftwo Southeast Asian
economies
J. EduardoIbarra‑Olivo1· ThomasNeise2· MoritzBreul3· JöranWrana4
Received: 17 April 2023 / Revised: 15 March 2024 / Accepted: 25 March 2024
© The Author(s) 2024
Abstract
Middle-income economies must prioritise human capital development to ensure long-term sustainable growth and economic
upgrading. While foreign direct investment (FDI) is believed to aid this endeavour, its impact on technical vocational educa-
tion and training (TVET) remains understudied. This research explores the influence of FDI by multinational enterprises
(MNEs) at various stages of global value chains (GVCs) on TVET graduate numbers in Vietnam and Indonesia from 2006 to
2016. Our findings reveal that greenfield FDI plays a role in shaping TVET supply, with heterogeneous effects across different
GVC segments and subnational regions. Specifically, FDI in logistics, sales and marketing, and support and servicing are
associated with an increase in the supply of TVET graduates in the region, whereas FDI in headquarters and production may
lead to a decline in technical skills. To address these dynamics, public policies should prioritise flexible education systems
capable of adapting to MNEs’ evolving skill demands. By doing so, these economies can elevate local human capital levels
and avoid the stagnation often associated with middle-income traps. This research underscores the importance of aligning
policy with the needs of a rapidly changing global economy to foster sustainable development.
Keywords Foreign direct investment· Multinational enterprises· Human capital· Global value chains· Emerging markets·
Theory of FDI· Panel data analysis
Introduction
Many countries that are undergoing an economic take-off
phase, largely due to inflows of foreign direct investment
(FDI), eventually experience a slow-down of economic
growth rates and often become stuck in the middle-income
trap (Altenburg & Lütkenhorst, 2015; Lee, 2012). These
middle-income economies face the challenge of improving
their human capital base to ensure long-term competitive-
ness and growth (Sala-i-Martin etal., 2007). The process
by which a territory’s initial endowment of human capital
is converted into a source of competitive advantage for its
firms and industries is known as human capital develop-
ment, and it can occur via multiple channels, including for-
mal education or training (Gereffi etal., 2011). Multina-
tional enterprises (MNEs), through their FDI activities, have
acquired a prominent role in contributing to human capital
development in the host economies (Blomström & Kokko,
2002; Miyamoto, 2003). By providing attractive employ-
ment opportunities, MNEs may have an effect on educational
choices (Blomström & Kokko, 2002; Checchi etal., 2007;
Miyamoto, 2003). Increasing FDI may change relative wages
Accepted by Axele Giroud, Area Editor, 25 March 2024. This
article has been with the authors for two revisions.
* J. Eduardo Ibarra-Olivo
eduardo.ibarra-olivo@henley.ac.uk
Thomas Neise
thomas.neise@uni-heidelberg.de
Moritz Breul
moritz.breul@uni-koeln.de
Jöran Wrana
joeran.wrana@region-hannover.de
1 Henley Business School, University ofReading, Reading,
UK
2 Institute ofGeography, Heidelberg University, Heidelberg,
Germany
3 Institute ofGeography, University ofCologne & Global
South Studies Center, Cologne, Germany
4 Region Hannover, Hannover, Germany
Journal of International Business Policy
in the host location, thus modifying people’s incentives to
acquire education or training (Slaughter, 2004). For the host
economy, increasing foreign firms’ presence could poten-
tially either contribute to increases in the human capital base
(Iammarino & McCann, 2013) or hamper the skill formation
process (Atkin, 2016; Ibarra-Olivo, 2021). The question of
which FDI-induced labour market effect dominates over the
other is the issue that we address in this paper.
Most of the evidence regarding the host labour market
effects of FDI focuses on changes arising from MNEs’ skill-
biased demand for labour in terms of wages (e.g., Aitken
etal., 1996; Doms & Jensen, 1998; Feenstra & Hanson,
1997; Taylor & Driffield, 2005; Te Velde & Morrissey,
2004). Moreover, the empirical work assessing the effects
of FDI on direct measures of human capital—e.g., educa-
tional attainment or enrolment—is relatively scarcer and
shows mixed results, while mainly focusing on the effects
on formal general education (Asali etal., 2016; Atkin, 2016;
Checchi etal., 2007; Egger etal., 2010; Ibarra-Olivo, 2021;
Mughal & Vechiu, 2010). In the context of middle-income
economies, however, formal vocational training has been
perceived as a crucial component of human capital develop-
ment, as this type of education helps young and adult work-
ers develop the skills they need for employment (UNESCO-
UNEVOC, 2021),1 as well as preparing them to deal with
technological change (Albizu etal., 2017). Despite consid-
erable efforts to strengthen technical vocational education
and training (TVET) institutional frameworks in many
middle-income economies (Pavlova, 2014), TVET is still
a neglected or insufficiently developed educational subsys-
tem (Wiemann & Fuchs, 2018; Wrana etal., 2019). With
the exception of a few qualitative studies showing evidence
of MNE collaboration with local actors and institutions to
upgrade host-location TVET subsystems (Fuchs etal., 2016;
Kleibert, 2015; Manning etal., 2012; Wrana & Revilla Diez,
2016; Wrana etal., 2019), little is known about how inward
FDI affects human capital development in the vocational and
technical orientation of education.
In a globalising world economy, it has been recognised
that the only sustained sources of competitive advantage are
localised resources and capabilities (Humphrey & Schmitz,
2002; Maskell & Malmberg, 1999). Middle-income econ-
omies typically concentrate in the lowtomedium value-
adding activities of global value chains (GVCs) (Mudambi,
2007), and they often tend to have an imbalance between
vocational training and general education, which renders
their educational systems less effective while hampering
graduates’ ability to develop the skills required for employ-
ment in global industries (Fernandez-Stark etal., 2012). At
the same time, MNEs strive to continually leverage and cre-
ate highly specialised capabilities based on resources exist-
ing in specific locations (Mudambi etal., 2018). A deeper
understanding of the mechanisms through which FDI may
affect the human capital development process requires going
beyond FDI data by extending the analysis to the functional
position of FDI projects across GVC activities, as well as
the local and national educational settings in which they are
embedded (Ramirez & Rainbird, 2010). This paper aims
to explore whether inward FDI in different GVC functions
generates broader impacts and contributes to human capital
development by raising the supply of TVET graduates in
host regions.
Southeast Asia has become an important destination
for FDI in the global economy. Indonesia and Vietnam are
among the largest economies in the region. They consti-
tute middle-income countries whose economic progress
has advanced, to a great extent, hand in hand with FDI
inflows. According to fDi Markets, adatabase developed
by fDi Intelligence (a division of Financial Times Ltd.),
the two countries have a very similar distribution of FDI
across value chain functions, as the vast majority of green-
field FDI jobs are created in production activities—around
86% of accumulated FDI jobs in each countryduring the
2006–2016 period. Notwithstanding, new FDI jobs in other
service-based activities—such as sales and marketing, logis-
tics, and business services—are increasing both in absolute
and relative terms. Moreover, these foreign investments are
unevenly spread across subnational regions and have intro-
duced new economic activities along with a shifting demand
for labour with different skills, thereby posing challenges
for the existing regional human capital base. Against this
background, Indonesia and Vietnam provide appropriate
case studies to assess the extent to which greenfield FDI
inflows in different GVC functions have broader effects on
human capital development.
This paper contributes to the study of FDI’s broader
societal impacts in circumscribed subnational geographies
(Wiessner etal., 2023) by adding to the scant quantitative
evidence on the relationship between FDI and human capital
development through the labour market. Whilst the focus
on vocational training sheds light on a relatively understud-
ied dimension of human capital development, we improve
existing studies by considering one type of FDI, namely,
1 We use the UNESCO worldwide classification of educational sys-
tems (UNESCO, 2011). Formal education (provided by public or
private institutions) has two orientations. General education (also
referred to as academic education) is intended to develop general
knowledge, skills and competencies, as well as literacy and numeracy
skills. Vocational education and training (or professional education)
is designed to impart the knowledge, skills and competencies specific
to a particular occupation, trade, or class of occupations or trades. In
this paper we will use the terms general education and vocational
training to refer to the two orientations. When referring to the formal
educational system as a whole, we also refer to its components as the
general education subsystem and the vocational training subsystem
(TVET).
Journal of International Business Policy
greenfield investments introduced into the relevant subna-
tional regional labour markets. Moreover, we explore the
heterogeneous effects of FDI on the regional human capital
development process by assessing the effects of new FDI
jobs in different segments of the value chain in the context
of GVCs.
Background literature
Human capital development andglobal value
chains
Human capital development may be understood as the pro-
cess by which a territory’s initial endowment of human capi-
tal is converted via multiple channels—general education,
technical and vocational training, and relevant services such
as labour market intermediation, and information—into a
source of competitive advantage for firms and industries
in a given territory (Faggian etal., 2019; Galor & Tsid-
don, 1997; Gennaioli etal., 2013). To the extent that firms’
performance depends on localised region-specific intangi-
ble assets embodied in a knowledge and competence base
rooted in particular institutional settings, subnational regions
have been increasingly recognised as sources of competitive
advantages (Boschma, 2004) and most importantly, as the
main source of sustained competitive advantages in an inter-
dependent world economy (Humphrey & Schmitz, 2002;
Maskell & Malmberg, 1999). In a context of increasing
within-country inequalities, the human capital development
process is likely to take distinct forms across subnational
regions, since socioeconomic characteristics differ markedly
at the subnational scale (Faggian etal., 2019). Therefore,
human capital development is critical for competitiveness
and sustained economic development at the local or regional
level.
In recent decades, major shifts in international trade and
investment flows—driven by transformations in institutional
frameworks, technological changes, and changing geopoli-
tics—have resulted in a particular configuration of the global
economy. This global organisational structure of economic
activity has been encapsulated by different academic stand-
points in the concept of global value chains (GVC) or in
the closely related construct of global production networks
(GPN). A discussion on the nuances of these associated but
distinct frameworks is beyond the scope of this paper (see
Kano etal., 2020 for a multidisciplinary literature review).
However, in its broadest sense, the GVC notion captures
the contemporary architecture of the world economy, as it
reflects the disaggregation and geographic dispersion across
various parts of the value-creating processes (Kano etal.,
2020). It offers a conceptual framework for the present paper
as it links to the particular mechanisms through which sub-
national spaces and their institutions are integrated into, and
shaped by global production networks or GVCs through
MNEs and their FDI activities (Coe etal., 2004).
MNEs have taken a prominent role as the primary shap-
ers and movers of the international business environment
through their trade and investment activities (Dicken, 2015).
Typically, MNEs are the orchestrators of GVCs, as they are
able to disaggregate their value chains while selecting the
activities and locations over which to maintain control or
ownership (Buckley & Casson, 1976; Dunning, 1993; Kano
etal., 2020). MNEs’ strategic decisions can be driven by a
quest to leverage and create highly specialised capabilities
based on resources existing in specific locations (Mudambi
etal., 2018). Hence, MNEs continuously define the firm’s
organisational boundary by constructing and reshaping
complex intra- and inter-firm relationships (Dicken, 2015).
Building on different theories, the literature suggests that
different motivations for MNEs result in three broad modes
of GVC governance: market, hierarchy, and network (Ger-
effi etal., 2005). The scope of the present paper falls into
the second category, in which MNEs retain full control of
wholly owned subsidiaries, namely through greenfield FDI
(Collinson etal., 2020), and the implicit assumption here is
that these GVC functions are coordinated through hierar-
chies within a vertically integrated parent firm.
The GVC framework allows us to better understand the
development and dependency outcomes for a constellation
of subnational regional economies that are connected into
global production systems through FDI activities by MNEs
(Li & Bathelt, 2018; Turkina & Van Assche, 2018). This
approach enables consideration of the multiple possible out-
comes—both positive and negative—of such intersections
(Coe & Yeung, 2019). Therefore, a given place’s involve-
ment in different economic stages along GVCs, through
greenfield FDI by MNEs, may have distinct effects on local
labour markets by increasing demand for workers with skill
sets that match the demand and standards of MNEs (Taglioni
& Winkler, 2014).
The link between the different stages of the value chain
and their reliance on human capital is rooted in the inten-
sity of the application of knowledge and creativity in their
business activities (Jona-Lasinio etal., 2019). Increasingly,
value has migrated towards the upstream and downstream
ends of the value chain, where there is a higher reliance on
applied knowledge, creativity and skills (Mudambi, 2007,
2008). Whilst high-income economies typically specialise
in higher value-adding activities—upstream and downstream
ends of GVCs—middle-income countries are moreconcen-
trated in the midstream stages of the value chain (Palpacuer
Journal of International Business Policy
& Parisotto, 2003). Ultimately, moving up the development
ladder requires continuous human capital development by
acquiring or upgrading skills and increasing dexterity and
productivity on the part of workers in the productive process
(Barrientos etal., 2011). Therefore, regions and countries
must continuously develop their human capital base to ena-
ble them to move into higher value-adding nodes of GVCs
and compete successfully in a globalising economy (Coe
etal., 2008; Ernst & Kim, 2002).
The role ofinward FDI inregional human capital
development
There is little doubt that MNEs are carriers of important
productive knowledge (Blomström & Kokko, 1998; Caves,
1974; Markusen, 2002). Hence, MNEs have acquired a
prominent role in contributing to human capital develop-
ment in the host economies (Blomström & Kokko, 2002;
Miyamoto, 2003). FDI’s role in human capital development
can take several forms; for example, interactions with local
suppliers, direct collaboration with local educational insti-
tutions, or in-house training (Blomström & Kokko, 2002;
Miyamoto, 2003; Slaughter, 2004). In this paper, however,
we centre the analysis on one labour market mechanism
through which FDI may indirectly influence human capi-
tal development in the host region—namely, the relative
labour demand. MNE subsidiaries tend to pay, on average,
higher wages for workers (Lipsey, 2004; Lipsey & Sjöholm,
2004) by reason of enjoying competitive advantages over
domestically owned firms in the host region. These attrac-
tive employment opportunities are likely to modify relative
wages, thereby influencing incentives for individuals in the
host economies to acquire certain skills either through gen-
eral education or vocational training. If individuals in host
economies have access to formal education systems, they
should be able to respond to wage signals emerging from
the labour market (Slaughter, 2004), raising the uptake of
specific educational programmes and thereby increasing the
supply of skills.
Overall, the evidence points to a positive association
between MNE presence and demand for high-skilled work-
ers,2 which may arguably contribute to human capital devel-
opment in the host economy (Iammarino & McCann, 2013).
However, where MNEs increase the demand for low-skilled
labour in the host economy (Braconier etal., 2005), a dis-
incentive for higher educational attainment is introduced
instead, which may hold back skills formation (Atkin, 2016;
Coniglio etal., 2015; Federman & Levine, 2005; Ibarra-
Olivo, 2021) or lead to job polarisation (Amoroso & Mon-
cada-Paternò-Castello, 2018; Davies & Desbordes, 2015).3
Either positively or negatively, MNEs are likely to shape
human capital development through their direct investment
decisions. Moreover, this relationship is not homogeneous
across space. The role of FDI in shaping human capital
development is likely to take different forms across subna-
tional regions since socioeconomic characteristics—impor-
tantly, human capital levels and access to education (Faggian
etal., 2019)—are markedly different across national terri-
tories. The question of which FDI-induced labour market
effect dominates over the other in host subnational regions
is the one we address in this paper.
The bulk of studies pertaining to the relationship between
FDI and skills focus mainly on relative wages and do not
consider a direct measure of human capital or educational
outcomes—for example, attainment, enrolments or gradu-
ates—therefore falling short of assessing the full extent
of this association. Empirical studies devoted to directly
exploring the effect of FDI on human capital accumulation
are scarcer, and the evidence on educational outcomes is
rather mixed (Arbache, 2004; Asali etal., 2016; Checchi
etal., 2007; Egger etal., 2010; Ibarra-Olivo, 2021; Kar,
2013; Kheng etal., 2017; Mughal & Vechiu, 2010; Wang &
Wong, 2011; Zhuang, 2008, 2017). Moreover, the focus is
largely on general education, and up to now little is known
about how inward FDI affects human capital development
through vocational training. Aside from some qualitative
studies indicating that MNEs actively collaborate to upgrade
local TVET subsystems in order to ensure a rising supply of
well-skilled future graduates (Fuchs etal., 2016; Kleibert,
2015; Manning etal., 2012; Wrana & Revilla Diez, 2016;
Wrana etal., 2019), there is a considerable gap, particularly
in the context of middle-income economies, for which voca-
tional training has been considered a crucial component of
human capital development.
2 Evidence is vast for both developed (e.g. Aitken etal., 1996; Doms
& Jensen, 1998; Figini & Görg, 1999; Girma & Görg, 2007; Taylor
& Driffield, 2005) and developing host economies (e.g. Aitken etal.,
1996; Arbache, 2004; Feenstra & Hanson, 1997; Ibarra-Olivo, 2019;
Lipsey & Sjöholm, 2004; Noria, 2015; Te Velde & Morrissey, 2004).
3 MNEs through their FDI activities may have broader social impacts
in the host location. Whilst FDI has been associated with positive
effects such as technology transfer, job creation, productivity gains
and economic spillovers in the host economy (Barba Navaretti &
Venables, 2004), FDI may also be associated with negative effects,
for example, child labour exploitation (e.g., Doytch et al., 2014),
environmental degradation (e.g., Duan & Jiang, 2021), tax avoid-
ance (e.g., Windsor, 2017), human rights violations (Wettstein etal.,
2019), and social and income inequalities (e.g., Ibarra-Olivo & Rod-
ríguez-Pose, 2022).
Journal of International Business Policy
Human capital development andvocational training
Given their position in the value chain, middle-income econ-
omies tend to have an imbalance between vocational and
general education, which typically leads to ineffectiveness
of the TVET subsystem, making it difficult for graduates to
develop the skills required for employment in these global
industries (Fernandez-Stark etal., 2012), whilst employers
complain of skill shortages and mismatches in the labour
force (Tan etal., 2010). The level of human capital stock
depends to a great extent on countries’ investment in edu-
cation for their populations (O’Mahony, 2012), yet efforts
to proactively improve the effectiveness of skills upgrading
at the national level are not widespread (Fernandez-Stark
etal., 2010). Notable exceptions are the late-comer indus-
trialising Asian economies that have successfully caught
up by attracting FDI as well as developing technical skills
and well-functioning TVET subsystems to cater to the skill
demands of their current and future economy (Ashton etal.,
2002; Gee & Hou, 1993; Wong, 2001).
Notwithstanding, in many middle-income economies,
TVET is still a neglected or insufficiently developed edu-
cational subsystem (Wiemann & Fuchs, 2018; Wrana etal.,
2019). Despite considerable efforts to strengthen TVET
regulatory frameworks (Pavlova, 2014), some challenges
remain for these institutions, including a lack of quality,
difficulties in teaching the skills demanded by the private
sector, and lack of prestige compared to universities, as tech-
nical and vocational education is perceived to have lower
status and income potential (de Moura Castro & García,
2003; Song Seng, 2008). Consequently, MNEs operating in
these host countries often face a skill mismatch; Indonesia
(Di Gropello etal., 2011) and Vietnam (World Bank, 2014)
are no exception.
Bridging thegap: hypothesis andcontributions
In particular, our aim is to understand the impact of MNEs’
greenfield FDI activities on the local processes of skills for-
mation and human capital development in the context of
GVCs, by going beyond the focus on aggregate FDI and
extending the analysis to the functional position of FDI pro-
jects along GVCs as well as the local TVET educational
systems in which they are embedded (Ramirez & Rainbird,
2010).
Surprisingly, up to now, empirical studies devoted to
exploring the effect of FDI on human capital development
have omitted the vocational training dimension of this process,
seldom distinguishing among FDI in different segments along
the value chain or considering the regional dimension of the
host countries. By adopting a GVC perspective, we improve
on existing studies in three main ways. First, the empirical
exercise is centred on FDI projects by MNEs that exert direct
control and ownership over the subsidiaries, i.e. greenfield FDI
(Collinson etal., 2020). Second, we account for heterogeneous
effects of FDI on TVET across GVC stages; this framework
allows us to overcome the high-low-skilled labour demand
dichotomy by relying instead on the implicit knowledge
intensity of each GVC stage. Third, the regional perspective
addresses the spatial variation of both FDI and TVET across
subnational territories; in other words, how regional character-
istics may affect the localised process of human capital devel-
opment through FDI.
As different GVC functions require different skill levels
from the host region’s labour force (Amoroso & Moncada-
Paternò-Castello, 2018; Davies & Desbordes, 2015; Fer-
nandez-Stark etal., 2012), it is likely that the effects of FDI
on human capital development depend on the type and skill
intensity of the GVC function in which investments by MNEs
are made. If FDI activities by MNEs in a given value chain
function increase the demand for certain skill sets that could
be attained by participating in the vocational training sub-
system, individuals in the host region may respond to FDI-
induced labour market incentives and acquire the necessary
TVET certification to be eligible to work for the MNE, then
our hypothesis is:
H: There is a significant relationship between the inflows
of greenfield FDI and the supply of vocational training in
a host region, and this relationship varies according to the
segment of the GVC in which it occurs.
Empirical strategy
The model
Our interest lies in assessing the extent to which multination-
als—through their greenfield FDI activities—affect the local
human capital development process by incentivising skill
acquisition by the labour force. In particular, we investigate
the effects of regional FDI on the proportion of the labour
force with a TVET certification. The main mechanism through
which this relationship operates is the labour market. The
provision of attractive employment opportunities by MNEs
may incentivise individuals to undertake specific TVET pro-
grammes that will make them eligible to work for an MNE.
For this reason, the basis of our independent variable of inter-
est is the estimated number of jobs created by greenfield FDI
in each region. As mentioned before, the relationship between
FDI and TVET is heterogenous across GVC stages since the
skill requirements vary across nodes along the value chain. To
capture these potential differences, we include the breakdown
of FDI jobs by GVC function of destination. The classifica-
tion includes
s
= 7 stages of the value chain (see Table4 in the
Appendix). Furthermore, the effect of an additional FDI job
Journal of International Business Policy
will depend on its relative contribution to the size of the local
labour market. Therefore, we scale the absolute number of FDI
jobs by the labour force in the region. This allows a relative
effect of FDI jobs to be captured by value chain stage. Thus,
our specification takes the following functional form:
where the outcome variable TVETrt on the left-hand side is
the percentage of the labour force that holds a TVET cer-
tificate in region
r
and year
t
. The independent variable of
interest is the sum of accumulated stocks of FDI jobs
es
rt−1
in stages 1 through 7 in region
r
relative to the size of the
labour force
LFrt−1
, both in the previous year
t−1
. Each of
these shares is multiplied by 1000; hence, the variable is
measured in FDI jobs per 1000 workers. The coefficients
𝛽s
capture the effect of new FDI jobs in each value chain stage
s
on the relative number of workers with TVET. A positive
estimated association means that increases in FDI jobs will
lead to a rise in the percentage of workers with TVET, and
a negative association means that increases in FDI jobs lead
to decreases in the supply of TVET graduates.
Additional control variables associated with TVET are
included in vector
Xrt−1
. The first two controls have to do with
the different educational choices that are available to the work-
force. Although each country has nuances in their national
TVET subsystems setup (see Figs.3 and 4 in the Appendix),
in both cases, students have to complete 9 years of primary
and lower secondary education, after which they can choose
between training and general education. To control for such
alternatives, we first include the share of the labour force with
upper secondary and tertiary education as the highest level of
educational attainment. The effect of these two educational
levels on TVET may be either substitutive—if the estimated
association is negative—or complementary if the association is
positive. We test whether TVET qualifications, which have tra-
ditionally been associated with industry and production jobs,
have a significant relationship with the region’s industry share
of GDP. To account for an income effect on TVET, we include
regional GDP per capita (in logarithmic form). Additionally,
to control for the urban scale effect, we include population
density (in logarithmic form), to account for the possibility that
more densely populated regions may have a higher provision
of vocational training services, producing more TVET gradu-
ates.
𝜃r
picks up the region fixed effects, and
𝛿t
the year effects.
Finally,
𝜀st
is the usual error term.The equations are fitted by
ordinary least squares (OLS) with fixed effects (FE) using the
within regression estimator.
(1)
TVET
rt =𝛼+
∑
s𝛽s
(
e
s
rt−1
LFrt
−1)
+𝛾Xrt−1+𝜃r+𝛿t+𝜀rt
,
Biases andendogeneity concerns
Fitting these equations will pose some threats to the internal
validity of the estimated coefficients of the effects of FDI
jobs. We address two of them in turn. Firstly, omitted vari-
able bias is present if we exclude time-varying characteris-
tics of regions correlated with the dependent variable. For
example, the percentage of the labour force with secondary
education might also have an impact on the percentage of
TVET graduates. Therefore, we have included in vector
X
the control variables described above. Moreover, omitting
time-invariant characteristics of regions is likely to intro-
duce further bias if unobserved heterogeneity is correlated
with the independent variables. For instance, some regions
may have a higher concentration of FDI or better formal
educational systems to begin with. Such unobserved hetero-
geneity across units is captured by the region fixed effects.
Furthermore, a set of year dummies will capture shocks to
the share of workers with vocational training; for example,
nation-wide changes in the TVET subsystems in place.
Secondly, it is possible that TVET shares affect the inflows
of FDI jobs. Reverse causality may arise if MNEs are moti-
vated to invest in different stages of the value chain based on
the relative availability of workers with vocational training
across regions. An upward bias may occur if regions with
higher shares of TVET graduates attract more FDI jobs in
certain stages of the value chain. Conversely, there could be a
downward bias if MNEs investing in any GVC stage have less
presence in regions with a higher percentage of workers with
TVET. We tackle this endogeneity problem in two ways. First,
we partially mitigate the simultaneity by using independent
covariates in 1-year lags. Admittedly, there is a certain degree
of simultaneity between TVET and FDI—since the former
may be a locational advantage—however, it is hardly likely
that changes in current TVET shares could explain changes
in FDI in the past. Importantly, these lags also allow the time
that investment decisions take to translate into changes in the
share of TVET graduates in the host region to be considered.
Second, we deploy an instrumental variable (IV) approach to
rule out any remaining endogeneity concerns. We construct
our instrument using the “shift-share” approach (Card, 2007;
Faggio & Overman, 2014; Moretti, 2010). The instrument
uses initial shares of FDI employment by value chain stage
(relative to labour force) and the total number of national FDI
jobs in the same sector to predict changes in regional FDI
jobs. The instrument has two components as follows:
where
(
es
r∕LFr
)
is the initial share of stage
s
FDI jobs rela-
tive to labour force in region
r
at the beginning of the period
(2)
es
r
LFr
×
Es
t−1−es
rt−1
,
Journal of International Business Policy
of study, whilst
(
Es
t−1
−es
rt−1)
captures the overall stage
s
employment in the country, which varies across regions
because we exclude own-region employment when calculat-
ing the total. The rationale behind the IV is the assumption
that in the absence of region-specific employment shocks,
each region would have received a share of the total green-
field FDI stage
s
employment that occurred each year during
our study period in proportion to the initial share.The equa-
tions are fitted with the two-stage least-squares within
estimator.
Data andvariables
The data we use to test the model come from two main
sources. The greenfield FDI raw data comes from the fDi
Markets database, developed by fDi Intelligence (a divi-
sion of Financial Times Ltd.). The outcome variable and
other controls have been collected from Labour Force Sur-
veys (LFS) in each country.4 Our period of study spans
from 2006 to 2016. We estimate the relationship between
FDI and TVET for Indonesia and Vietnam separately. The
administrative unit for each country is defined by the level of
representativeness of the LFS data.5 Summary statistics are
provided in Table1 and the correlation matrix is in Table5
(in theAppendix).
The independent variable of interest is the number of jobs
created by MNEs in the region of destination by GVC stage.
Three points about the variable should be noted. First, by
using fDi Markets, we only consider employment created
in new investment projects; thus, the estimated effects are
restricted to greenfield FDI. A clear advantage of focusing
on greenfield FDI is that unlike brownfield FDI, this kind of
investment is expected to have direct effects on employment
creation in the host economy (Ashraf etal., 2016). Second,
the figures reported by the database are the number of jobs
that a company has announced it will create; this does not
constitute a problem for the empirical exercise, since the
mechanism of interest relies on the signals arising from the
labour market, such as MNEs announcing the creation of
new jobs. Third, the figures reported in the raw data are
for new jobs created yearly, thus representing a flow vari-
able. To obtain more stable results and to match the outcome
variable, which is a stock of human capital, we calculate the
stock of regional FDI jobs by summing the accumulated flow
of new FDI jobs. Finally, the variable is scaled by the size of
the region’s workforce and multiplied by a thousand. This
measures the effect of one additional job per 1000 workers.
The outcome variable is constructed as the proportion
of working-age individuals holding a TVET certification.
To avoid capturing any correlation driven by changes in the
size of the labour force, we normalise the yearly measure of
regional TVET by using the size of the working-age popu-
lation in the initial year as a common denominator. Lastly,
Table 1 Summary statistics:
FDI jobs and TVET Variable Vietnam Indonesia
Mean SD Min Max Mean SD Min Max
TVET 0.083 0.033 0.022 0.199 0.096 0.051 0.027 0.291
FDI jobs (relative)
1. Business services 0.130 0.455 0.000 3.640 0.027 0.105 0.000 0.943
2. Headquarters 0.009 0.047 0.000 0.451 0.003 0.022 0.000 0.245
3. Logistics 0.298 0.846 0.000 5.735 0.051 0.115 0.000 0.879
4. Production 8.349 12.109 0.000 56.493 1.641 3.047 0.000 19.869
5. Research & development 0.050 0.235 0.000 1.797 0.006 0.033 0.000 0.336
6. Sales & marketing 0.273 0.650 0.000 4.075 0.120 0.391 0.000 3.092
7. Support & servicing 0.053 0.186 0.000 1.313 0.012 0.041 0.000 0.252
Secondary education 0.137 0.048 0.046 0.276 0.181 0.047 0.086 0.320
Tertiary education 0.121 0.077 0.030 0.457 0.099 0.046 0.037 0.649
Population density (log) 5.687 0.971 3.695 8.265 4.840 1.569 1.843 9.637
GDP per capita (log) 6.870 0.516 5.974 9.408 7.504 0.833 5.532 9.558
Industry share of GDP 0.390 0.168 0.001 0.965 0.368 0.176 0.074 0.825
Regions = 60; N = 600 Regions = 33; N = 330
4 Labour Force Surveys for Indonesia are published by Statistics
Indonesia (BPS, 2018) and for Vietnam by the General Statistics
Office (GSO, 2018).
5 For Indonesia, we use the administrative regions level, of which
there are 34. We drop Kalimantan Utara because the region was cre-
ated in 2013, thus yielding a panel of 33 regions. For Vietnam, we
use the 63-administrative region level (58 provinces and 5 munici-
palities). Before 2004, some of the provinces were part of a larger
province. The analysis is carried out using the original regions; we
thus use a panel of 60 regions (Dien Bien and Lai Chau provinces are
merged into a larger region “Dienlai”; Dak Lak and Dak Nong are
merged into “Daclacmoi”; Can Tho and Hau Giang are merged into
“Canthomoi”).
Journal of International Business Policy
a number of variables that may be associated with the pro-
portion of individuals in the labour force with TVET are
included as controls (BPS, 2018; GSO, 2018): secondary
and tertiary education, population density, GDP per capita,
and industry share of GDP.
Context ofthestudy
National FDI trends andGVC structure
Southeast Asia constitutes an important destination for FDI
in the global economy. In 2019, excluding Singapore, Indo-
nesia was the second largest recipient of inward FDI, having
received 23.5% of these stocks, while Vietnam, in fourth
position, received 16.3% (UNCTAD, 2020). Both countries
have middle-income economies whose economic progress
has advanced, to a great extent, hand in hand with FDI
inflows. Vietnam has, since its renovation policy in 1986,
gradually opened its economy to foreign investors. Conse-
quently, the country has been able to attract large volumes of
FDI. In 2019, its inward FDI stock amounted to 60.9% of the
country’s GDP (UNCTAD, 2020). Indonesia, as a resource-
rich and populous country, provides significant locational
advantages for MNEs. Consequently, it has attracted signifi-
cant volumes of FDI since its recovery following the Asian
financial crisis (Lindblad, 2015), with an inward FDI stock
accounting for 20.5% of its GDP in 2019 (UNCTAD, 2020).
The number of accumulated greenfield FDI jobs during
the 2006–2016 period reached over 800,000 jobs in Vietnam
and more than 350,000 in Indonesia. Table2 shows that the
two countries have a very similar distribution of FDI across
value chain functions, as the vast majority of greenfield FDI
jobs are created in production activities—around 86% of the
accumulated FDI jobs during the sample period. However,
new FDI jobs in other service activities, such as sales and
marketing, logistics, and business services, are increasing
both in absolute and relative terms. For example, jobs in
sales and marketing constitute the second-largest business
function in both countries at 5–7% of total jobs created. In
recent years, these investments have introduced new eco-
nomic activities along with a demand for high and low-
skilled labour, thereby posing challenges for the domestic
human capital base. For all these reasons, Indonesia and
Vietnam provide appropriate case studies to assess the extent
to which FDI inflows have generated spillovers in the form
of changes in the relative supply of TVET graduates in the
regional host economies.
Regional distribution ofgreenfield FDI
Our primary objective goes beyond national trends in try-
ing to explain the relationship between variations in the
proportion of the labour force with vocational training and
greenfield FDI in value chain stages across different sub-
national regions. After all, existing regional differences
in labour market conditions and educational opportuni-
ties are expected to have different effects on individuals’
behaviour and educational choices (Levison etal., 2001).
We explore the effects of greenfield FDI in terms of the
number of jobs created by MNEs relative to the size of the
labour force. The maps in Fig.1 show the spatial distribu-
tion of average relative FDI employment stocks—jobs per
1000 workers—throughout our sample period across sub-
national regions within each country, considering new FDI
jobs in all stages of the value chain. The prevalent trend
in both countries is one of spatial concentration of FDI
jobs. However, most regions had at least some employ-
ment generation through greenfield FDI, with only a few
not experiencing any. In Indonesia, the highest relative
values of new FDI jobs are found in the regions of Ban-
ten, Jakarta, West Java, East Kalimantan, and Central and
South Sulawesi. In Vietnam, the highest concentration of
Table 2 Accumulated greenfield
FDI jobs by GVC stage
The figures in the Jobs columns correspond to the accumulated stock of foreign jobs by GVC stage during
the sample period 2006–2016. The percentage (%) column represents the contribution of each stage to total
foreign jobs created. The Rank column ranks each function at the national level. See Table4 in the Appen-
dix for definitions of value chain stages
Function in the value chain Vietnam Indonesia
Jobs % Rank Jobs % Rank
1. Business services 26,033 3.2 3 6562 1.9 4
2. Headquarters 2423 0.3 7 1452 0.4 7
3. Logistics 21,352 2.6 4 12,249 3.5 3
4. Production 703,029 86.4 1 304,886 86.0 1
5. Research & development 15,371 1.9 5 2872 0.8 5
6. Sales & marketing 40,668 5.0 2 24,798 7.0 2
7. Support & servicing 4899 0.6 6 1841 0.5 6
Total 813,775 100 354,660 100
Journal of International Business Policy
new FDI jobs is in and around Hanoi, Ho Chi Minh City,
Khánh Hòa, and Quảng Nam.
The aggregated numbers of FDI jobs give us an idea of
the distribution of greenfield FDI, but they conceal the het-
erogenous spatial patterns of FDI across GVC functions and
countries (Figs.5 and 6 in the Appendix). Naturally, some
GVC functions will always be more concentrated than others
due to the nature and intensity of the location advantages
they rely on. For instance, greenfield FDI in R&D and head-
quarters are extremely concentrated in fewer regions, while
production activities are widespread across each country’s
subnational space. However, there are also differences in
spatial patterns in the same stage between countries; for
instance, whilst business services are more spatially wide-
spread in Indonesia, in Vietnam they appear to be slightly
more concentrated.
Vocational training acrossregions
As mentioned in previously, TVET systems are crucial in
middle-income economies for human capital development
and GVC participation. Governments have undertaken sig-
nificant efforts to improve their TVET subsystems and match
the skill requirements of GVCs. In particular, Vietnam has
actively worked to encourage the private sector to expand
the provision of TVET offerings (Asian Development Bank,
2018). In contrast, Indonesia has sought to improve the sup-
ply of vocational training in specific sectors through public
and private partnerships (Dong & Manning, 2017). How-
ever, the distribution of TVET graduates differs markedly
across the territory, highlighting differences in the provision
of these programmes. The regional distribution of the pro-
portion of the labour force that holds a vocational training
certification in their region of residence is shown in Fig.2.
These regional differences in the share of TVET graduates
make it possible to analyse the effect of FDI jobs on the
development of vocational training subsystems at a regional
scale.
Results
Against this background, we aim to assess the extent to
which FDI can affect the relative number of TVET grad-
uates. To test our hypothesis, we first introduce the main
regressor based on all FDI jobs per 1000 workers, then
introduce the breakdown for each of the seven value chain
stages. We also test for the exogeneity of our main variable
of interest by using the “shift-share” as an instrumental vari-
able. The relevance of the instruments is confirmed by the
first-stage F statistics. Since our endogenous variables are
exactly identified, we are prevented from directly testing for
the exogeneity of our instruments. However, we run an alter-
native specification with dynamic instruments by including
the first- and second-order lags of our instrumental vari-
able. These account for the path dependency of our main
variable of interest while reducing serial autocorrelation in
the error term. As per the Hansen J test, our instruments
are jointly exogenous and thus valid (see Table6 in Appen-
dix). Estimates for the baseline OLS fixed effects and IV
specifications by country are reported in Table3 for Vietnam
(columns 1–3) and for Indonesia (columns 4–6). The con-
trol variables, region and year fixed effects are included in
all models. Both the outcome variable and the independent
regressors of interest are measured in levels, hence they can
be interpreted as unit changes.
Indonesia Vietnam
Fig. 1 Regional distribution of FDI employment stocks. Note Variable is greenfield FDI jobs per 1000 workers, including all stages of the value
chain. Average values 2006–2016 (quantiles). Source Authors, using data from fDi Markets
Journal of International Business Policy
Vietnam
The overall relative number of new total FDI jobs does not
yield any significant effect on TVET graduates in Vietnam
(Table3, column 1). However, the inclusion of FDI jobs by
GVC stage yields heterogeneous effects of FDI (column 2).
The estimated coefficients using the instrumental variable
(column 3) have the same direction and significance as in the
OLS FE regression, although the magnitudes differ slightly.
While the overall fit of all regressions is not too high, the
economic effects of FDI jobs are non-negligible. We discuss
them in turn.
FDI jobs may have a positive effect on the supply of voca-
tional training graduates; increases in these jobs in logistics
and sales & marketing are associated with an increase in the
share of TVET holders. More specifically, one additional
FDI job per thousand workers in these GVC stages is cor-
related with an increase in the expected relative number of
workers with TVET. For example, in the case of sales &
marketing, the estimated increase is 2.16%. FDI may also
have a negative effect on the supply of TVET. This is the
case for headquarters, where an increase in the relative num-
ber of FDI jobs is associated with a 14.12% decrease in the
expected number of workers with TVET. In some stages,
FDI may also have no impact on TVET; additional FDI jobs
in business services, production, R&D, and support & ser-
vicing have no significant effect on TVET, suggesting that
MNEs in these GVC stages may not necessarily hire TVET-
educated workers.
Regarding the control variables, it appears that ter-
tiary education is complementary to TVET; an increase in
the share of the workforce with this level of educational
attainment is associated with an increase in the share of
TVET graduates. The rest of the controls are not signifi-
cant, but their inclusion improves the goodness of fit of the
regressions.
Indonesia
Similarly to the case of Vietnam, in Indonesia an increase
in the number of total FDI jobs does not yield a significant
effect on TVET graduates (Table3, column 4) and the inclu-
sion of FDI jobs by GVC stage also reveals heterogeneous
effects on TVET (column 5). In column 6, we test the exog-
eneity of FDI jobs by using the “shift-share” as instruments
for the 1-year lags. We discuss each of the estimated effects
in turn.
Increased FDI jobs in sales & marketing and support &
servicing are associated with an increase in the expected
relative supply of TVET graduates. More specifically, one
additional FDI job per 1000 workers in these GVC stages is
correlated with an increase in the expected relative number
of workers with TVET. For example, in the case of sales &
marketing, the increase is estimated to be 7.7%. Conversely,
an increase in the relative number of FDI jobs in produc-
tion is associated with a slight decrease in the percentage
of workers with TVET. Additional FDI jobs in business
services, logistics, and R&D have no significant effects on
TVET, suggesting that MNEs operating in these GVC stages
in Indonesia do not rely heavily on TVET-educated workers.
For business services, the effect is estimated to be nega-
tive and barely significant in the OLS, but loses significance
when endogeneity is partialled out.
With respect to the control variables, it appears that
tertiary education as a form of educational attainment has
a weak complementary effect, as it loses significance in
IndonesiaVietnam
Fig. 2 Regional distribution of TVET graduates. Note Proportion of the labour force holding a TVET certification; Average values 2006–2016
(quantiles). Source Authors, using data from Labour Force Surveys
Journal of International Business Policy
the fuller models. Moreover, increases in GDP per capita
appear to be associated with decreases in the share of TVET
graduates, but the effect turns out to be insignificant in the
full model. The remaining controls are not significant but
increase the goodness-of-fit, suggesting that they have some
explanatory power for TVET.
Discussion
Adopting a GVC approach has allowed us to better under-
stand the human development outcomes for a constellation
of subnational regions that are connected to GVCs through
the FDI activities by MNEs. Results for Indonesia and
Vietnam show that the inflow of FDI jobs per se does not
have any influence on a host region’s number of vocational
training graduates. While MNEs modify local labour mar-
ket conditions by offering more attractive employment
opportunities—relative to domestic firms—our results
suggest that when considering the aggregate relative
number of new greenfield FDI jobs, the estimated effect
on TVET graduates is not significant. However, we find
consistent evidence that favours our hypothesis; increases
in the relative number of FDI jobs have heterogeneous
effects on TVET across GVC stages, thus confirming the
importance of accounting for heterogeneous FDI effects by
considering the different value chain stages and the sub-
national regional context in which investment takes place.
Table 3 Vietnam & Indonesia:
effects of new FDI jobs on
vocational training
Clustered standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1 Note Headquarters stage is
omitted in the case of Indonesia because all FDI jobs are located in Jakarta
Dep. Var. TVET Vietnam Indonesia
(1) (2) (3) (4) (5) (6)
FE FE IV FE FE IV
All FDI jobs 0.0001 0.0004
(0.000) (0.001)
1. Business services – 0.0127 – 0.0054 – 0.1339* – 0.1443
(0.013) (0.017) (0.076) (0.111)
2. Headquarters – 0.0842* – 0.1402* – –
(0.045) (0.076) – –
3. Logistics 0.0045** 0.0040* 0.0058 0.0231
(0.002) (0.002) (0.024) (0.021)
4. Production – 0.0002 – 0.0001 – 0.0004 – 0.0008**
(0.000) (0.000) (0.000) (0.000)
5. Research & development 0.0035 0.0039 – 0.1056 – 0.3119
(0.005) (0.005) (0.123) (0.189)
6. Sales & marketing 0.0166*** 0.0216*** 0.0495 0.0771**
(0.005) (0.007) (0.030) (0.036)
7. Support & servicing 0.0004 – 0.0071 0.4617*** 0.3830***
(0.009) (0.014) (0.066) (0.096)
Secondary education – 0.0449 – 0.0694 – 0.0733 – 0.1208 – 0.0365 – 0.0343
(0.058) (0.053) (0.053) (0.126) (0.131) (0.139)
Tertiary education 0.0946*** 0.0891*** 0.0863*** 0.0199** 0.0119 0.0098
(0.032) (0.030) (0.030) (0.008) (0.010) (0.009)
Industry share of GDP – 0.0180 – 0.0159 – 0.0148 0.0196 – 0.0016 0.0051
(0.014) (0.015) (0.015) (0.032) (0.024) (0.025)
Log GDP per capita 0.0084 0.0163 0.0186 – 0.0192* – 0.0044 – 0.0062
(0.013) (0.014) (0.014) (0.010) (0.008) (0.008)
Log population density – 0.0211 – 0.0143 – 0.0210 0.0054 0.0008 0.0024
(0.018) (0.017) (0.020) (0.008) (0.006) (0.006)
Observations 600 600 600 330 330 330
R-squared 0.427 0.447 0.443 0.646 0.717 0.705
Number of regions 60 60 60 33 33 33
Region FE Yes Ye s Yes Yes Yes Yes
Year FE Ye s Yes Yes Yes Ye s Yes
Journal of International Business Policy
The results indicate that greenfield FDI projects have
the potential to contribute to the host region’s human capi-
tal development via the introduction of incentives in the
local labour markets. We find that FDI jobs in service-
based GVC segments may be associated with an increase
in the supply of TVET graduates in the host region. Con-
versely, FDI jobs in headquarters or production may lead
to declining supply of technical and vocational skills in the
region. And, in other value chain segments, FDI have no
significant effects on TVET. The presence of both positive
and negative effects of FDI on human capital development
in terms of TVET is in line with existing insights holding
that FDI-induced labour market effects either incentiv-
ise or disincentivise human capital development in host
economies depending on the type of skills demanded (Bra-
conier etal., 2005; Iammarino & McCann, 2013). Distin-
guishing between different GVC stages allows these dif-
ferent effects to be captured, since different GVC stages
rely on different skill levels of the host region’s labour
force (Amoroso & Moncada-Paternò-Castello, 2018;
Davies & Desbordes, 2015; Fernandez-Stark etal., 2012).
Importantly, we find that effects are heterogenous not only
across value chain segments but across countries as well.
We argue that these differences are contingent on (i) the
economic activities and tasks in which MNEs’ investment
is made along GVCs, (ii) the ability of workers to respond
to labour market incentives, and (iii) the availability and
flexibility of TVET subsystems to adapt their output to
changing skill demands of inward FDI. Other possible
explanations for distinct effects between similar countries
are home-country characteristics of the investing MNE as
well as the motivations for investment.
The policy implications stemming from our results are
paramount for middle-income economies. Developing
human capital to keep pace with changing skill demands
should be a policy priority in preventing a slowdown in
economic growth and the risk of becoming stuck in mid-
value-adding GVC activities. Moving up along GVCs neces-
sitates the strategic coupling of regional assets and MNE
assets, whereby an intentional convergence and articulation
of actors in both regional economies and global value chains
for mutual gains and benefits (Yeung, 2015) may facilitate
the process of creation, enhancement, and capture of value,
upon which regional economic development ultimately
depends (MacKinnon, 2012). Our tale of two Southeast
Asian economies indicates that FDI in service-oriented value
chain segments increases the number of TVET graduates at
the subnational region level, suggesting that greenfield FDI
through hierarchical control of subsidiaries could provide
middle-income countries with the opportunity to reap wider
employment benefits from GVC segments, such as sales &
marketing as well as support & servicing, and contribute to
human capital development. This empirical evidence is in
line with the findings by Kleibert (2014) on offshored ser-
vice industries through FDI in the Philippines. Amoroso and
Moncada-Paternò-Castello (2018) show that FDI in ICT—as
part of support and servicing—leads to skills upgrading.
FDI-assisted human capital development should build on the
principle of aligning the type of FDI that suits the local skill
availability, but emphasising the importance of supporting
further investment in the longer value chain (Becker etal.,
2020), while continuing to develop the human capital base
in order to move into higher value-adding nodes of GVCs
(Coe etal., 2008; Ernst & Kim, 2002).
Our results suggest that in attempting to enhance human
capital development, it seems reasonable to upgrade both
vocational and general education simultaneously, as they
appear to be complementary. In the context of middle-
income economies, locational upgrading along GVCs
requires, among other conditions, a solid human capital
base (Crescenzi & Harman, 2022) drawn from both TVET
and university graduates (Gereffi etal., 2011). An exces-
sive focus on improving the number and quality of general
education graduates risks depleting a well-endowed labour
force with a sufficiently developed technical skill set match-
ing the requirements of MNEs participating in higher-value-
adding GVC stages. Due to a perceived mismatch between
skills supply and demand, MNEs will tend to focus more on
on-the-job-training rather than collaborating directly with
local TVET providers (Vind, 2008). As a result, general
education graduates often work in jobs for which they are
overqualified, a misallocation of resources. Especially in
times of rapid technological change and high investor mobil-
ity, policymakers should develop a diverse regional labour
force that includes graduates of general higher education
and vocational training in order to build a resilient economy
and labour market (Becker etal., 2020). Promotion of MNE
engagement in public or private partnerships in both general
and vocational education programmes could help to align the
supply and demand of skill sets.
However, there are detrimental effects of relying exces-
sively on MNE-led human capital development; in other
words, there exist some dangers of external dominance
(Dicken, 2015). In tailoring the domestic skill supply to the
specific needs of greenfield FDIs, caution should be exer-
cised: Unless the total number of TVET graduates rises in
the local economy, it is plausible that domestic companies
will see little benefit from human capital development,
which may prevent these local companies and subnational
regions from developing the absorptive capacity necessary
to leave the mid-value chain. As GVCs become increas-
ingly task-oriented, rather than product- or industry-specific
(Pietrobelli etal., 2021), favouring task- and transferable
skills-oriented TVET systems can assist middle-income
economies in developing their human capital base in a less
narrow manner, allowing the flexible use of skilled labour
Journal of International Business Policy
in a changing global economy, whilst reducing the dangers
of external dominance and building resilience; for example,
requiring that MNEs guarantee to support TVET beyond
their own labour demand, through public–private partner-
ships, so that local companies can absorb the surplus of
skilled labour, therefore developing the “right” skills and
raising the overall level of local human capital. Finally, FDI
policy on this issues may help reduce territorial inequalities
(Iammarino, 2018), by implementing place-sensitive TVET
provision for lagging regions, enabling a broader regional
spread of human capital development.
Conclusion
In sum, our paper contributes to advancing the existing
understanding of the role of FDI in human capital develop-
ment in middle-income economies in three important ways.
First, we put forward evidence on a less-studied orienta-
tion of formal education, namely vocational training. We
show for two Southeast Asian economies that FDI has the
potential to contribute to an increase in vocational training
graduates and therefore have a positive impact on human
capital development. However, this process is neither guar-
anteed nor automatic, hence public policy should be aimed
at strengthening this educational subsystem. Second, we add
to the GVC literature by highlighting the strengthening of
the link between FDI in serviced-based activities and human
capital development. By focusing on greenfield FDI, we put
forward evidence on one GVC governance mode, namely
hierarchical control of wholly owned subsidiaries, and its
effect on the skill acquisition process. Third, we also con-
tribute to the regional literature by shedding light on the
relationship between FDI and human capital development
on a subnational scale. Territorial inequalities in middle-
income economies are commonplace. The marked differ-
ences in regional economic structure and local provision of
TVET programmes will result in diverse economic develop-
ment paths as regions strive to increase and improve their
participation in GVCs.
Given the limitations of our study and building on the
findings herein, we put forward some open questions for
further research. For example, what are the conditions under
which a closely coupled labour force may lead to GVC
upgrading and regional development in specific locations?
The potential constraints of this link should also be further
explored, such as whether GVC participation could ham-
per human capital development by hindering the ability of
domestic firms to upgrade and retain skilled workers. Along
the same lines, considering the different GVC governance
mechanisms (market, hierarchy, and network) will help cast
additional light on the conditions under which MNEs may
enable or hinder human capital development. Finally, further
research is needed to shed light on the dynamic interactions
between human capital development, GVC participation, and
economic upgrading; for example, whether skill upgrading
necessarily leads to deeper forms of economic upgrading—
such as functional or chain upgrading—and ultimately to
economic growth.
Appendix
See Tables4, 5, 6, Figs.3, 4, 5, 6.
Table 4 Definition of value chain stages
Adapted from Crescenzi etal. (2014) and Sturgeon (2008)
GVC function Industry activity (fDi Markets classifica-
tion)
1. Business services Business services [legal, finance, public
affairs and government relations,
accounting]
2. Headquarters Headquarters
3. Logistics Logistics, distribution & transportation
4. Production Construction
Electricity
Extraction
Manufacturing
5. Research & development Design, development & testing
Research & development
6. Sales & marketing Retail
Sales, marketing & support
7. Support & servicing Customer contact centre
Education & training
ICT & internet infrastructure
Maintenance & servicing
Technical support centre
Shared services centre
Recycling
Journal of International Business Policy
Table 5 Correlation matrix
Legend:***p < 0.01, **p < 0.05, *p < 0.1
Indonesia (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
(1) TVET 1
(2) Business services 0.49*** 1
(3) Headquarters 0.39*** 0.93*** 1
(4) Logistics 0.47*** 0.68*** 0.70*** 1
(5) Production 0.27*** 0.28*** 0.25*** 0.26*** 1
(6) Research & development 0.41*** 0.94*** 0.98*** 0.70*** 0.26*** 1
(7) Sales & marketing 0.49*** 0.95*** 0.89*** 0.70*** 0.25*** 0.92*** 1
(8) Support & servicing 0.64*** 0.85*** 0.76*** 0.58*** 0.35*** 0.80*** 0.85*** 1
(9) Secondary education 0.47*** 0.24*** 0.18*** 0.25*** 0.39*** 0.18*** 0.22*** 0.37*** 1
(10) Tertiary education 0.47*** 0.40*** 0.35*** 0.39*** 0.24*** 0.35*** 0.40*** 0.40*** 0.52*** 1
(11) Industry share of GDP 0.34*** – 0.062 – 0.066 0.24*** 0.16*** – 0.056 – 0.047 0.081 0.18*** 0.0015 1
(12) Log GDP per capita 0.60*** 0.37*** 0.29*** 0.48*** 0.27*** 0.32*** 0.37*** 0.42*** 0.45*** 0.41*** 0.55*** 1
(13) Log population density 0.49*** 0.50*** 0.43*** 0.32*** 0.078 0.48*** 0.58*** 0.54*** 0.019 0.28*** – 0.12** 0.12** 1
Vietnam (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
(1) TVET 1
(2) Business services – 0.0014 1
(3) Headquarters – 0.024 0.88*** 1
(4) Logistics 0.0091 0.31*** 0.24*** 1
(5) Production 0.059 0.38*** 0.23*** 0.51*** 1
(6) Research & development 0.068* 0.82*** 0.78*** 0.34*** 0.30*** 1
(7) Sales & marketing 0.058 0.86*** 0.70*** 0.46*** 0.46*** 0.71*** 1
(8) Support & servicing – 0.013 0.44*** 0.47*** 0.13*** 0.26*** 0.31*** 0.38*** 1
(9) Secondary education 0.51*** 0.43*** 0.32*** 0.22*** 0.43*** 0.42*** 0.49*** 0.28*** 1
(10) Tertiary education 0.32*** 0.14*** 0.10** 0.061 0.025 0.12*** 0.16*** 0.086** 0.25*** 1
(11) Industry share of GDP 0.24*** 0.11*** 0.018 0.29*** 0.55*** 0.023 0.17*** 0.18*** 0.43*** 0.0025 1
(12) Log GDP per capita – 0.028 0.45*** 0.33*** 0.67*** 0.70*** 0.32*** 0.49*** 0.21*** 0.31*** 0.0086 0.54*** 1
(13) Log population density 0.078* 0.47*** 0.37*** 0.26*** 0.46*** 0.42*** 0.48*** 0.16*** 0.53*** 0.049 0.34*** 0.52*** 1
Journal of International Business Policy
Table 6 Robustness tests:
dynamic vs. static instruments
Clustered standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1
Dep. Var. TVET Vietnam Indonesia
(1) (2) (3) (4) (5) (6)
FE IV IV lags FE IV IV lags
Foreign jobs
1. Business services – 0.0127 – 0.0054 – 0.0220 – 0.1339* – 0.1443 – 0.1500
(0.013) (0.017) (0.022) (0.076) (0.111) (0.093)
2. Headquarters – 0.0842* – 0.1402* – 0.1783*** – – –
(0.045) (0.076) (0.049) – – –
3. Logistics 0.0045** 0.0040* 0.0225** 0.0058 0.0231 0.0373
(0.002) (0.002) (0.011) (0.024) (0.021) (0.025)
4. Production – 0.0002 – 0.0001 – 0.0009** – 0.0004 – 0.0008** – 0.0007
(0.000) (0.000) (0.000) (0.000) (0.000) (0.001)
5. Research & development 0.0035 0.0039 – 0.0128 – 0.1056 – 0.3119 – 0.2370
(0.005) (0.005) (0.020) (0.123) (0.189) (0.150)
6. Sales & marketing 0.0166*** 0.0216*** 0.0372*** 0.0495 0.0771** 0.0798**
(0.005) (0.007) (0.011) (0.030) (0.036) (0.037)
7. Support & servicing 0.0004 – 0.0071 0.0266*** 0.4617*** 0.3830*** 0.4562***
(0.009) (0.014) (0.009) (0.066) (0.096) (0.102)
Secondary education – 0.0694 – 0.0733 0.0379 – 0.0365 – 0.0343 0.4813***
(0.053) (0.053) (0.042) (0.131) (0.139) (0.080)
Tertiary education 0.0891*** 0.0863*** 0.0157 0.0119 0.0098 0.0106
(0.030) (0.030) (0.010) (0.010) (0.009) (0.025)
Industry share of GDP – 0.0159 – 0.0148 – 0.0246 – 0.0016 0.0051 – 0.0085
(0.015) (0.015) (0.015) (0.024) (0.025) (0.028)
Log GDP per capita 0.0163 0.0186 – 0.0013 – 0.0044 – 0.0062 0.0069**
(0.014) (0.014) (0.010) (0.008) (0.008) (0.003)
Log population density – 0.0143 – 0.0210 – 0.0136 0.0008 0.0024 0.0084
(0.017) (0.020) (0.013) (0.006) (0.006) (0.010)
Observations 600 600 600 330 330 330
R-squared 0.447 0.443 0.005 0.717 0.705 0.598
Number of regions 60 60 60 33 33 33
Region FE Ye s Yes Ye s Ye s Yes Yes
Year FE Yes Ye s No Yes Yes No
Hansen J statistic (p value) – – 0.246 – – 0.445
Journal of International Business Policy
Fig. 3 Vietnam: Formal educa-
tion system and subsystems.
Notes In Vietnam, TVET is
provided at three educational
levels: elementary training
(3–12 months), secondary
TVET (up to 2 years) and
college training (2–3 years).
Source World TVET Database,
Vietnam. UNESCO-UNEVOC
International Centre for Techni-
cal and Vocational Education
and Training
Journal of International Business Policy
Fig. 4 Indonesia: formal educa-
tion system and subsystems.
Notes In Indonesia, TVET con-
sists of provision at two levels:
vocational high schools at the
secondary level (2–3 years) and
the polytechnics and community
colleges at the tertiary level.
Source World TVET Database,
Indonesia. UNESCO-UNEVOC
International Centre for Techni-
cal and Vocational Education
and Training
Journal of International Business Policy
Business services Headquarters Logistics
Production Research & Developmen
tS
ales & marketing
Support & servicing
Fig. 5 Indonesia: relative FDI jobs by value chain stage. Source Authors, using data from fDi Markets. Notes Average values 2006–2016 (quan-
tiles)
Journal of International Business Policy
Business servicesHeadquartersLogistics Production
Research & DevelopmentSales & marketingSupport & servicing
Fig. 6 Vietnam: relative FDI jobs by value chain stage. Source Authors, using data from fDi Markets. Notes Average values 2006–2016 (quan-
tiles)
Journal of International Business Policy
Acknowledgements We are grateful to editor-in-chief Ari Van Ass-
che, area editor Axèle Giroud and three anonymous reviewers for their
sharp comments to improve this paper. We also thank participants of
International Business and Strategy seminar series at Henley Business
School, RSAI-BIS Early Career Colloquium 2021, ERSA Congress
2021, RSA Conference Lugano 2018, and YEGN Cologne 2018 for
their valuable suggestions to early manuscripts of this paper. All errors
remain our own.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
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Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
J. Eduardo Ibarra‑Olivo is Assistant Professor in International Business
and Strategy at the Henley Business School, University of Reading in
the UK. His primary research interests lie at the intersection between
economic geography and international business, in particular focus-
ing on the broader social and developmental effects of multinational
enterprises, both in their home and in host countries within the context
of emerging economies.
Thomas Neise is Interim Professor in Economic and Social Geography
at the Heidelberg University in Germany. His research focuses on the
adaptation and resilience strategies of companies to extreme weather
events, climate change, and crises. Another research interest lies in
risks in global production networks. He also studies the role of foreign
direct investment for regional development in Southeast Asia.
Moritz Breul is an economic geographer affiliated to the Institute
of Geography at the University of Cologne, Germany. His research
focuses on the emergence of new regional industrial paths (Germany,
Namibia, Vietnam). A second line of research is dedicated to the
regional economic impact of global production networks/global value
chain integration.
Jöran Wrana works as a technology and innovation consultant for small
and medium-sized enterprises at Region Hannover, focussing on the
further development of firms in the fields of information security, AI
potentials, and sustainability.
