POWER RELATIONSHIPS ALONG THE SUPPLYCHAIN: MULTINATIONAL FIRMS, GLOBAL BUYERS,
AND LOCAL SUPPLIERS’ PERFORMANCE
Carlo Pietrobelli
Federica Saliola
CREI, University of Rome 3
CREI, University of Rome 3
c.pietrobelli@uniroma3.it
and
The World Bank
Development Research Group1
saliola@uniroma3.it
Introduction
The increasing globalization of the world economy has changed the economic setting faced
by industries and individual firms in developing countries, as they have in the industrialized
world. Two important features have been the increasing fragmentation of production
processes and the evolution of internationally-dispersed but functionally-integrated economic
activities. These transformations have also led to the spread across countries of Global Value
Chains, with a variety of network forms of governance situated between arm’s length
markets and large vertically integrated corporation.
Global Buyers have been key agents in this transformation by creating international
production and distribution networks between firms and subsidiaries operating in and among
different locations, and undertaking the functional integration and co-ordination of these
1
The authors would like to thank Giuseppe Iarossi and Giovanni Tanzillo for making the data available.
We also wish to thank Jens Matthias Arnold, Davide Castellani, Beata Smarzynska Javorcik and Antonello
Zanfei for helpful comments.
Financial contributions from the PRIN project on “Capabilities dinamiche tra organizzazione di impresa e
sistemi locali di produzione” are gratefully acknowledged.
The findings, interpretations and conclusions expressed in this paper are entirely those of the authors.
Page 1 of 21
activities. Through these networks, Global Buyers might play a significant role in influencing
the worldwide generation and exploitation of knowledge and technology, and offering
learning opportunities to their suppliers along a “supply chain”, often in developing
countries.
Since multinational companies (MNCs) have mediated a large fraction of world trade in
recent years, in this paper we focus on the role of multinationals acting as Global Buyers.
The purpose of this study is threefold. First, we investigate the relationship between MNCs
and their local supplier’ performance. The role of MNCs is not confined to production but
increasingly extends to planning and management of global networks of suppliers and firms.
Therefore, instead of concentrating merely on the case of the relationships between MNCs
and their majority-owned suppliers (i.e. MNCs’ subsidiaries) we consider MNCs sourcing
their inputs from developing countries’ firms – both independent and equity-controlled by
the same MNC. Second, we include in the analysis the issue of the “governance” of the value
chain, looking at the way the buyer-supplier relationship is coordinated and at the strategic
role played by the buyers along the supply chain. The concept of governance is especially
important in view of the impact it may have on suppliers’ efficiency in developing countries.
We particularly focus on the differences between firms which are part of a multinational's
production and distribution network and firms which participate in supply chains led by
national buyers. Finally, we attempt to study which firms’ characteristics are more correlated
to different types of supply chains’ governance.
The analysis has been carried out with new microeconomic evidence from Thailand on a
sample of 1,385 manufacturing firms from 2001 to 2003.
Related Research
The importance of Global Buyers has been emphasized by several schools of thought.
Among these, in the 1990s Gereffi and others developed a framework called “global
commodity chains” that tied the concept of the value-added chain directly to the global
organization of industries (see Gereffi and Korzeniewicz 1994). This work not only
highlighted the importance of coordination across and beyond firm boundaries, but also the
growing importance of new global buyers (mainly retailers and brand marketers) as key
drivers in the formation of globally dispersed and organizationally fragmented production
and distribution networks.
The recent “global value chain approach” (GVC) (Gereffi, 1999; Gereffi and Kaplinsky,
2001; Schmitz 2004) focuses on the role of global buyers in defining upgrading opportunities
for their suppliers. They pay particular attention to the position of developing country firms
selling to large, global buyers. This literature stresses the role played by the GVC leaders, and
particularly by the buyers, in transferring knowledge along the chains, and thereby affecting
firms’ learning and performance..
Page 2 of 21
The concept of internal governance of the value chain is central in the analysis. We use the
term governance to denote co-ordination of economic activities through non market
relationships. The internal governance of the value chain importantly affects the generation,
transfer and diffusion of knowledge and the scope of local firms’ upgrading (Humphrey and
Schmitz, 2000).
The literature highlights two critical parameters of the value chain governance: what is to be
produced, and how it is to be produced. “In each case, the level of detail at which the
parameters are specified can vary. In the case of product definition, the buyer can provide
different levels of specification. It can set a design problem for the producer, which the
producer then solves by providing its technology and design. The buyer might provide a
particular design for the producer to work on, or the buyer might even provide detailed
drawings for the producer. Buyers can also specify process parameters. Once again, these can
be specified at different levels of detail. In some cases, the buyer may merely refer to the
process standards to be attained. In other cases, the buyer will specify precisely how
particular standards should be attained by requiring and perhaps helping to introduce
particular production processes, monitoring procedures, etc. When the buyer plays this role,
we refer to it as the "lead firm" in the chain” (Sturgeon 2001).
A recent study by Gereffi, Humphrey and Sturgeon (2003) proposes a more complex
typology of the governance structure of the chain, highlighting three central factors: “the
complexity of information and knowledge transfer required to sustain a particular
transaction; the extent to which this information and knowledge can be codified; the
capabilities of actual and potential suppliers in relation to the requirements of the
transaction”.
The second body of literature to which our paper relates is the empirical work on
multinational firms. The literature has traditionally considered MNCs as possessing some
technological lead and exploiting this proprietary advantage in international markets
(Dunning, 1993), and thereby potentially creating opportunities for knowledge diffusion and
learning for their local suppliers.
Moreover, in response to the rising importance of Foreign Direct Investment (FDI), a recent
theoretical literature has focused on the relationship among multinationals, foreign direct
investments and spillovers, and on the impact these may have on local industry.2 Within this
framework, recent papers have provided evidence of positive FDI spillovers through vertical
linkages along supply chains (backward linkages).3 In particular, they emphasize two
arguments to suggest that supply chains may be a conduit for technology transfer. First,
insofar as multinationals seek to minimize technology leakage to competitors, they have
2
This vast literature has been effectively surveyed by Blomstrom and Kokko (1996), Saggi (2002), and
others. Yet this literature has not been reached consensus. In one of the most cited papers on FDI
spillovers, Aitken and Harrison (1999) show absence of spillovers in Venezuelan manufacturing; Djankov
and Hoekman (2000) found similar results for Czech Republic, and Jozef Konings (2001) for Bulgaria,
Romania, and Poland. Others instead suggest the existence of spillovers in industrialized countries, as the
study by Jonathan E. Haskel et al. (2002) and Wolfgang Keller and Stephen R. Yeaple (2003).
3 See Schoors and van der Tol (2001), Lopez-Cordova (2002), Javorcik (2004) and Blalock and Gertler
(2005)
Page 3 of 21
incentives to improve the productivity of their suppliers through training, quality control, and
inventory management. To reduce dependency on a single supplier, the multinational may
establish such relationships with multiple vendors, and benefits spill over all firms which
purchase these vendors’ output. Second, while the technology gap between foreign and
domestic producers may limit within-sector technology transfer, multinationals are likely to
procure inputs requiring less sophisticated production techniques (for which the gap is
narrower and technology transfer easier).
A third body of literature to which our work relates includes the studies on “knowledge
transfer and co-ordination through supply-chain relationships” (Dunning 1993; Turok, 1993;
Albio et al., 1999; Beecham and Cordey-Hayes, 1998; Best, 1991; Lincoln et al., 1998; HewittDundas et al. 2002) which emphasize the importance of inter-firm linkages “as mechanisms
of technology transfer … collaboration as part of normal trading relationships between firms
may prove the most important means of technology transfer” (Phelps, 1996).
This literature highlights that the realization of these potential knowledge transfers depends
on ‘the extent to which the technology of multinationals is made available to potential users
and that ‘beneficial effects will occur only if the foreign affiliates of TNCs do become linked
to local firms. Furthermore, Dunning (1993) suggests that deliberate knowledge transfers will
only occur where MNC plants perceive there to be a direct benefit (e.g. improved input
quality, reduced cost, or improved service) then it may pay the company to invest resources
in upgrading the efficiency of its suppliers’ (Dunning, 1993, p. 456).
Research Issues
In light of the issues briefly surveyed above, and on the basis of the available empirical
evidence, we try to address the following theoretical hypotheses.
1) According to the existing literature, we expect firms involved in multinational's production
and distribution networks to perform better than firms selling their products only to
domestic buyers.
The underlying assumption is that, due to buyer-supplier relationships, firms face remarkable
opportunities for acquisition of knowledge and best practices, and for technical and product
design assistance. In this context, the role of multinationals acting as buyers can be
(positively) significant, whether or not direct ownership links multinationals to their
suppliers.
Such better performance, however, will also depend upon the fact that foreign investors
choose domestic plants with an above average performance, a pattern sometimes called
“cherry picking” in the literature (Arnold and Javorcik 2005). These two aspects might spark
a process of circular (cumulative) causation between choices made by the multinational and
firms’ performance.
As for similar research (e.g. “learning by exporting” literature), however, we are conscious of
the difficulties to define the direction of causality between MNC buyer-supplier relationships
and suppliers’ performance. More precisely, do such relationships cause suppliers’
Page 4 of 21
performance improvements or rather MNCs select more efficient firms as their suppliers?
Unluckily, the limited number of years for which data are available cannot help us to
establish the direction of causality with sufficient confidence in this paper.
2) One important contribution of our paper is to define a measure of Value Chains’
governance. According to the present framework, we develop a measure which takes into
account different levels of buyers’ involvement in the suppliers’ specification of product and
process standards and on technology. In addition, we include the nature of the activities in
which buyers’ involvement occurs, and we distinguish among Design and Quality, R&D and
Technology activities, paying particular attention to the last one (for further details see the
section above).
The reasons behind our definition are various. First, we believe these dimensions represent
important conditioning factors for knowledge transfer; second, as the choice of a “wrong”
supplier is onerous for the buyer, it is likely to observe an ex ante selection from MNCs of
more efficient firms as their suppliers (i.e. “cherry picking”); and third, they provide possible
suggestions about suppliers’ capabilities that in turn are expected to affect the governance
structure.
We expect different forms of governance, arising in supply chains led by multinationals
compared to those led by national buyers.
There are several motives to support our belief. First, local suppliers are expected to meet
requirements which may vary according to whether they sell their products to a multinational
company or not. According to these differences, the coordination of the supply chain, in
terms of products specification and the enforcement of standards related to product design
and production processes, will be more or less severe.
We assume that parameters set by multinational will be more complex, and, for this reason,
requiring greater product and process assistance. This assumption does not contrast to the
hypothesis of “cherry picking”. MNCs, in fact, carefully select their suppliers if they need to
invest to improve their competences; it means that they must posses some specific superior
characteristics compared to other firms in the domestic market.
3) Firms’ capabilities
We expect the intensity and the extent of Buyers’ influence on suppliers’ performance to vary
between firms which are part of multinational’ s network and firms in supply chains led by
national buyers. Specifically, we expect that the governance of the supply chain especially
matters for domestic value chains.
Since, as highlighted above, we assume multinationals’ suppliers to be more productive than
domestic firms, we suppose domestic firms heavily depend on the way the buyer assists them
in improving products’ features and production processes. In other words, efficiency
improvements are expected to be powerfully linked to the governance of the supply chain.
Page 5 of 21
Thailand Economy 4
Thailand’s economy has achieved very high rates of growth over the past quarter century, an
average of 6 percent per year between 1977 and 2003, and an even higher 8 percent rate in
the years prior to the 1997-98 crisis. However growth has been mainly dominated by high
rates of capital accumulation. Improvements in total factor productivity (TFP) have been
relatively modest and largely due to shifts of labor out of agriculture into higher productivity
industry and services.
The country is very much in the middle of the pack with respect to growth in both labor
productivity and TFP. While the gains in education have been comparable to other
countries, the overall level of educational attainment in Thailand is still quite modest relative
to countries such as Korea and Taiwan. Empirical evidence presented in the ICA 2005
suggests that poor technological performance in Thailand is related to poor production
technological capabilities and weak linkages, both which lead to less innovation.
Finally, from the World Bank’s study emerge that firms in Thailand identify the lack of
appropriate skills as one the most binding constraints to doing business. More than a third of
Thai firms surveyed report inadequate worker skills as a major obstacle to their activities.
Concerns about skills of the labor force are pervasive and are felt equally by exporters and
non-exporters, by domestic as well as foreign-owned firms. These concerns affect firms in all
regions of Thailand, including the North East, which may suggest a changing nature of the
labor demand as the manufacturing sector transitions to high-technology products.
Estimation Strategy and Main Results
The data come from the “Productivity and the Investment Climate Private Enterprise
Survey” (PICS), conducted by the World Bank on a representative (stratified) sample of
1,385 Thai firms from 2001 to 2003. The database contains comparable qualitative and
quantitative information on firm characteristics.
i) Descriptive statistics of Thai multinationals’ suppliers
We start the empirical analysis by comparing firms which are suppliers of multinational
companies (MNS) to firms which are not (no-MNS) using measures of firm size, wages,
sales, innovation and upgrading.
The data reveal some major differences between the two groups (Table 1). On average,
multinationals’ suppliers are larger, pay higher wages and show a greater value of total sales.
The share of firms exporting more than 5% of sales is considerably higher for MNS. Finally,
data show that MNS are more likely to introduced new technologies.
4 This paragraph is drawn from the Investment Climate Assessment elaborated by the World Bank in
2005. The analysis is based mainly on the results of the Thailand PICS 2003 surveys, although additional
sources are also used, such as the World Bank Doing Business indicators and Transparency International.
Page 6 of 21
ii.) Total Factor Productivity and Multinationals' supplier status
We study the empirical correlation between firms’ efficiency and multinationals’ supplier
status. As the performance measure, we employ total factor productivity, defined as the
residual of a Cobb-Douglas production function. In order to take into account the problem
of potential correlation between input levels and the unobserved firm-specific productivity
shocks in the estimation of production coefficients,5 we carry out a panel data analysis using
both parametric and semi parametric techniques to estimate TFP. The semi-parametric
estimator is that proposed by Levinsohn and Petrin (2003) with intermediate input use
serving as a proxy for productivity shocks. More specifically, we utilize the information on
the amount of electricity consumed by each plant. As electricity cannot be stored, its
consumption is likely to follow changes in production activity more closely than the use of
materials (appendix A).
Once we estimate our TFP measure, we investigate the existing relationship between firms’
performance and multinational supplier status:
TFPi   0   1 Multinational  X  e
(1.1)
We perform the analysis separately for the two measure of TFP. In the 1.1, Multinational
denotes if firms are suppliers of Multinationals or not; X captures a variety of observed, firmspecific characteristics including size ownership, age, industry, region and year of
observation; e is the error term.
Table 2 shows the results of estimating equation (1.1). Consistent with previous studies, we
find that MNCs’ supplier status is positively correlated with firms’ productivity. Column (1)
shows the results obtained through the OLS, while column (2) displays results for the non
parametric one.
iii.) Firms’ characteristics and multinationals' supplier status.
As a third step, we investigate which firms’ characteristics enhance the probability of
becoming MNCs’ suppliers. This further step might be helpful in shedding some light about
the “cherry picking” phenomenon. As a theoretical model we consider an empirical probit
model in which the MNCs’ suppliers status depends on a variety of observed, firm-specific
characteristics:
MNS  (TFP, Skills , Training , R & D, Pr ofessional , X )
(1.2)
where TFP is our estimated total factor productivity (parametric and semi parametric
respectively), Skills are proxied by the log of average wages, Training indicates whether the
firm run training programs, R&D is the log of R&D expenditure, Professional captures the
5
Explain the problem..
Page 7 of 21
share of professional workers, and X is a vector which captures industry, region, size, age and
year of observation. We compute the marginal effect of each explanatory variable (dy/dx).
Since a multicollinearity problem may arise when the independent variables are significantly
correlated among themselves, we compute both parametric and non parametric tests of
correlation (i.e. Pearson and Kendall test).
Table 3 shows the results of estimating equation (1.2). All the variables considered are
significant and positively correlated to the MNCs’ supplier status; only the TFP in its semi
parametric specification does not seem very significant. We would be tempted to interpret
this result as a sign that firms which become multinational suppliers have better
characteristics a priori, and that a self selection mechanism is taking place. However, due to
the lack of information, we cannot test this hypothesis properly.
iv.) Total Factor Productivity and supply chains' governance
Next, we include the governance of the supply chain into the analysis. We define five types
of governance of the supply chain by using different combinations of the following key
variables (Table A):
- Percentage of sales made exclusively to (suit) buyer’s unique specification
- Whether the buyer provided information on design/quality (product characteristics) and
imposed product quality standards (DQ)
- Whether the buyer engaged the firm in process or product R&D type of activities (RD)
- Whether the buyer developed technology in cooperation with supplier firms and if it sent
employees (personnel exchanges) to disseminate and diffuse new technologies into firms’
production facility (Tech).
It should be mentioned that our index does not intend to reflect a growing involvement of
buyers with their suppliers in all aspects of production, but rather focuses on specific
(crucial) elements of the buyer-supplier relationship as setting product standards, quality
requirements, and technology support. Using Type A as baseline category, we investigate the
relationship between governance and firms’ efficiency through the following specification:
TFPi   0  1TypeB   2TypeC   3TypeD   4TypeE  X  ei
(1.3)
where X captures again firms’ specific characteristics. We estimate two different
specifications of the above equation: first, we take our entire sample, and then we look at the
sub samples of firms which are suppliers of multinational and firms which are not. Again, we
compute both parametric and non parametric tests for correlation among independent
variables.
Page 8 of 21
Table 4 documents some descriptive evidence about the differences between MNS and nonMNS in terms of their relationships with buyers. We do not observe significant differences
between the two groups in terms of percentage of sales made exclusively to buyers' unique
specification. In a different way, for almost half of MNS surveyed, buyers get engaged in
their suppliers’ process or product R&D (30% for no-MNS); finally buyers send their experts
to work to disseminate and diffuse new technologies to the supplier’s more often in MNS
(45% versus 25% of no-MNS).
Table A – Classification of Supply Chains’ governance
Types of Supply
Chains’ Governance
% of sales made
according to
buyers’ unique
specification
Design/qua
lity and
product
quality
standards
Technology
and process
and product
R&D
Type A
Less than 20%
No
No
More than 20%
No
No
More than 20%
Yes
No
More than 20%
No
Yes
More than 20%
Yes
Yes
Low requirements
Type B
Higher requirements
Type C
Higher requirements&DQ
Type D
Higher requirements&Tech_RD
Type E
Higher
requirements&DQ&Tech_RD
Table 5 presents the results of estimating equation (1.3). Columns (1-3) uses parametric
techniques, and columns (4-6) non parametric ones. While the results for the whole sample
show a significant relationship between firms’ productivity and both governance Type C and
Type E, the picture emerging from the two sub-samples suggests a different conclusion.
Data reveal that the way the supply chain is organized does not matter for local firms
supplying multinational buyers (MNS), although it is very relevant for non MNCs’ suppliers.
“Domestic firms” (no-MNS) with high customization of products to buyers’ standard, which
also receive assistance about design & quality definition and technology (Type E), seem to be
more productive.
Can we interpret these results to suggest that firms participating in national VCs rely on a
greater involvement of the chain leader to foster their process of learning and efficiency
improvement? Once again we should interpret these results very cautiously, due to the
problem of causality. On the one hand, MNCs may select their suppliers among the most
Page 9 of 21
efficient firms – and indeed firms which are suppliers of multinationals are more efficient
than “domestic” firms. We may explain this for example by observing that firms are often
forced to improve their efficiency before starting the relationship with the MNCs in order to
qualify as MNCs’ suppliers, therefore the form of governance of the VC would not matter
for them.6 On the other hand, if the self-selection hypothesis were not confirmed, the test of
the existence of a learning process would require longer (dynamic) observations.
Another possible explanation of these results might be due to the different nature of the
information and knowledge exchanged along global and domestic chains. In GVC, the gap
of competencies between multinationals and their suppliers is expected to be smaller, and
this makes it easier to have cooperative relationships. In contrast, hierarchy is more likely to
occur in national chains due to the suppliers’ poor level of skills and competencies.
v.) Firms’ characteristics and supply chains' governance
Finally, in order to investigate the correlation between firms’ characteristics and different
levels of governance of the supply chain, we use a multinomial logit model estimated by
maximum likelihood method. Basically, we consider a 1 X K vector of explanatory variables
to examine the correlation between these variables and the y-th alternative in terms of
governance levels. The components of the vector are: Multinational - indicates whether firms
are suppliers of Multinationals or not; TFP – both parametric and semi parametric measures;
the share of professional workers, Train - captures the formal training; Export; Skills; R&D
investments; Brand – possession of a brand name; Owner – indicates whether the firm is
foreign or domestic. We control for industry, age, year and region.
Let x be the 1 X K vector, the multinomial logit model response probability takes the
following form:
j
P( y  j | x)  exp( x j ) /[1   exp( x h )]
h 1
(1.4)
We define a multicategory variable (y) as dependent variable which takes value 0 – 5
according to the five levels of governance. We first estimate the marginal effect (dy/dx) of
the explanatory variables on each of the governance levels. Since the interpretation of
parameters and marginal effects sometimes is not straightforward for logit models, we also
compute the effects of X on the odds of the outcome.
Given two realizations of X, say X1 and X0, we can define the odds ratio as
(Y  1 | X 1 )
 e ( X 1 Xo) 
(Y  1 | X 0 )
(1.5)
A careful test of this hypothesis would require a dynamic analysis of the learning and efficiency improving
process that is not feasible with only three years available.
6
Page 10 of 21
This statistics tells us how the odds of observing Y = 1 change when X changes from X0 to
X1 (see Ichino 2003). More specifically, the odds ratio is the ratio of two odds and is a
summary measure of the relationship (effect size) between two variables.
Then, to measure the strength and direction of relationship between our variables, we use the
odds ratios of the estimated coefficients. The ratio of odds ratios of the independents is the
ratio of relative importance of the independent variables in terms of effect on the dependent
variable's odds. The odds ratio has frequently been used as measures of relationship (effect
size). For example for the variable j:
e j
tells us how the odds of observing Y = 1 change when Xj changes by one unit.
• If e
j
> 1, the variable j increases the odds of observing Y = 1.
j
• If e < 1, the variable j decreases the odds of observing Y = 1.7
We first present the results in terms of marginal effect for each explanatory variable (Table
6). According to the results highlighted above, we focus only on Type C and Type E of
governance.
Forms of supply chain’s governance featured by buyers’ involvement in both design and
quality and technology activities (Type E) are enhanced by the relationship with MNCs. The
other variables included in the model also reveal a positive marginal effect. On the contrary,
we do not find significant effects related to governance featured by buyers’ involvement only
in design and quality activities (Type C). Finally, possessing a brand name seems to reduce
the probability to observe both the forms of governance considered.
Table 7 presents the results in terms of log odds ratios (RRR). For each explanatory variable
of the vector we focus on three ratios: the odds of observing Type C as opposed to Type A
(column A); the odds of observing Type E as opposed to Type A (column B); and the odds
of observing Type E as opposed to Type C (column C).
Variables increasing the odds to observe forms of governance featured by buyers’
involvement in design and quality and technology activities (Type C and Type E) vs.
governance with no involvement of buyers and low requirements (Type A) are: MNCs’
supplier status, TFP (both parametric and non parametric) and the export status (Columns A
and B). Differently, possessing a brand name increase the odds in the opposite way.
7
More precisely, when the odds ratio is less than 1, going from the value of the independent variable
associated with the denominator to the value associated with the numerator decreases the odds that the
dependent variable equals a given value. For the case where the odds ratio is greater than 1, then increases
the odds that the dependent variable equals a given value. The larger the odds ratio, the stronger the
relationship, meaning the higher the ratio of success versus failure for those in that category compared to
the reference category.
Page 11 of 21
Table 6 - Multinomial Logit Model - Marginal effects on supply chains' governance
dy/dx on governance Type C
MNCs' supplier status
TFP (OLS)
TFP (Levinsohn and Petrin)
Professional
Skills
Training
R&D
Brand
Owner
Export
dy/dx on governanceType E
-0.023
(-1.47)
0.029
(-1.42)
0.003
(1.28)
-0.1007
( -0.63 )
-.0102
(-1.56 )
-.051
( -2.95 )*
.002
(1.30 )
-.064
( -4.22)**
-.055
(-3.09)**
0.028
(1.58 )
0.152
(9.34)**
0.027
(1.25)
0.005
(2.04)*
0.106
(0.63)
.0398
( 5.72 )**
.0738
(4.15 )**
.001
( 0.74)
-.094
(-5.80)**
0.082
(4.21)**
.029
(1.76)
Robust z statistics in parentheses
* significant at 5%; ** significant at 1%
The variables increasing the odds to observe forms of governance featured by buyers’
involvement in technology activities (Type E) vs. governance featured by buyers’
involvement in design and quality (Type C) are (Column C): MNCs’ supplier status, Skills,
training and foreign ownership.
TABLE 7 - Multinomial Logit Model - Log odds ratios
BASE Type A
Type C/Type A Type E/Type A
MNCs' supplier status
TFP (OLS)
TFP (Levinsohn and Petrin)
Professional
Skills
Training
R&D
Brand
Owner
Export
1.35
(2.45)*
1.50
(2.82)*
1.06
(2.08)*
0.50
(-0.62)
1.03
(-0.63)
0.93
(-0.59)
1.03
(-1.97)
0.49
(-6.01)**
1.29
(-1.62)
1.72
(-4.22)**
Robust z statistics in parentheses
Source: authors' calculation on PICS 2004 data - The World Bank
Page 12 of 21
2.25
(6.76)**
1.47
(2.7)*
1.07
(2.24)*
0.95
(-0.05)
1.19
(3.56)**
1.36
(2.39)*
1.02
(-1.76)
0.47
(-6.46)**
1.94
(-4.45)**
1.69
(-4.12)**
BASE Type C
Type E/Type A
1.67
(5.86)**
0.98
(-0.19)
1.00
(-0.38)
1.88
(-0.73)
1.16
(4.04)**
1.46
(4.05)**
1.00
(-0.43)
0.96
(-0.52)
1.51
(-4.13)**
0.98
(-0.23)
To sum up:
- On average, the involvement of the buyer in the supply chain is more likely to happen with
firms which are suppliers of multinational, have good performance in terms of TFP and do
not possess its own brand name.
- Specifically, Type E of governance (i.e. high requirements’ standards, involvement of the
buyer in design and quality and technology activities) appears to prevail frequently with firms
which are suppliers of multinational, have higher skills, positive TFP, run training programs
for its employees and do not have its own brand name.
- The variables which increase the probability to observe buyers’ involvement in technology
activities (Type E) instead of a simply involvement in design and quality are (Type E vs. Type
C): MNCs’ supplier status, skills, training and foreign ownership. Difference in TFP does not
seem to matter, as well as the brand name.
Conclusions
In this paper we have examined the relationship between the presence of multinationals and
their local supplier’ performance, using a representative sample of 1,385 Thai firms from
2001 to 2003. Since the role of MNCs is not confined to production but increasingly extends
to planning and management of global networks of suppliers and firms, instead of
concentrating merely on the case of the relationships between MNCs and their majorityowned suppliers (i.e. MNCs’ subsidiaries) we consider MNCs sourcing their inputs from
developing countries’ firms – both independent and equity-controlled by the same MNC. As
the performance measure, we employ total factor productivity, defined as the residual of a
Cobb-Douglas production function. In order to take into account the problem of potential
correlation between input levels and the unobserved firm-specific productivity shocks in the
estimation of production coefficients, we carry out a panel data analysis using both
parametric and semi parametric techniques to estimate TFP. We find that MNCs’ supplier
status is positively correlated with firms’ productivity.
We include in the analysis the issue of the governance of the value chain, looking at the way
the buyer-supplier relationship is coordinated and the strategic role of the buyers along the
supply chain. We define a measure to capture Value Chains’ governance. Our definition
includes the intensity of buyers’ requirements in terms of process and products parameters’
specification, and the nature of activities in which the buyer is involved. Regarding the latter,
we distinguish among Design and Quality, R&D and Technology activities.
We especially consider the differences between firms which are part of a multinational's
production and distribution network and firms which participate in supply chains mediated
by national buyers
Data reveal that the governance of the supply chain does not matter for local firms supplying
multinational buyers, although it is very significant for non MNCs’ suppliers. “Domestic
firms” (no-MNS), with high customization of products to buyer’s standard, which also
Page 13 of 21
receive assistance about design & quality definition and technology, seem to be more
productive.
Finally, we attempt to study which firms’ characteristics are more correlated to different
types of supply chains’ governance. Results reveal that on average, the involvement of the
buyer in the supply chain is more likely to happen with firms which are suppliers of
multinational, have good performance in terms of TFP and do not possess its own brand
name. Specifically, governance with high requirements’ standards, involvement of the buyer
in design and quality and technology activities appears to prevail frequently with firms which
are suppliers of multinational, have higher skills, positive TFP, run training programs for its
employees and do not have its own brand name. The variables which increase the
probability to observe buyers’ involvement in technology activities instead of a simply
involvement in design and quality are: MNCs’ supplier status, skills, training and foreign
ownership. Difference in TFP does not seem to matter, as well as the brand name.
Certainly more research is needed to fully understand the determinants of supply chain
governance. In particular, it would be useful to confirm the findings of this paper using data
that allow addressing the issue of causality. It is to be hoped that improved data availability
will allow us to address these issues in the future. Furthermore, the explicit inclusion of
sectoral peculiarities is currently being explored by the authors.
Page 14 of 21
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Page 16 of 21
TABLE 1 - Descriptive statistics of the sample
No MNCs' suppliers
Number of firms
Age
Export share >5%
638
15.76
31.82
MNCs' suppliers
747
17.04
80.46
Percentiles
10%
25%
Number of employees
20
45
32
94
50%
68
215
75%
90%
160
370
521
1348
Skewness
Kurtosis
5.2
38.5
7.5
97.8
Percentiles
10%
25%
Sales (in US dollars)
112,877
495,926
289,676
1,281,528
50%
855,125
4,518,952
75%
3,360,246
14,808,257
90%
13,000,677
50,056,081
11.9
197.0
7.1
64.4
Skewness
Kurtosis
Percentiles
10%
25%
Average wages (in US dollars)
19,835
52,715
39,560
131,603
50%
101,899
412,499
75%
90%
278,089
794,872
1,123,944
3,406,594
Skewness
Kurtosis
4.858436
32.32842
11.17144
170.0937
% of sales to large customer
43.7
45.8
Share of sales from new products
6.31
11.11
ISO 9002
18.65
54.78
Introduced new technology
34.48
66.12
Source: The World Bank - Private Sector Investment Climate (PICS) survey 2004
Page 17 of 21
TABLE 2 - The relationship between MNCs' suppliers status and firms' Total Factor Productivity
Dependent variable TFP
Column 1
Column 2
OLS
Lev-Pet
MNCs' supplier status
P>|t|
N
Robust z statistics in parentheses
* significant at 5%; ** significant at 1%
Source: authors' calculation on PICS 2004 data - The World Bank
0.031
(2.33)*
4085
0.212
( 2.21)*
4021
TABLE 3 - Firms’ characteristics which enhance the probability of MNCs’ suppliers status.
Dependent Variable: MNCs' supplier status
TFP (Levinsohn and Petrin)
Skills
Training
R&D
Professional
Size dummies
Industry dummies
Region dummies
Year dummies
Pseudo R2
N
0.012
-1.82
0.147
(7.23)**
0.358
(7.07)**
0.026
(4.98)**
1.939
(4.50)**
included
included
included
included
0.1625
4018
Robust z statistics in parentheses
* significant at 5%; ** significant at 1%
Source: authors' calculation on PICS 2004 data - The World Bank
Page 18 of 21
TFP (OLS)
Skills
Training
R&D
Professional
Size dummies
Industry dummies
Region dummies
Year dummies
Pseudo R2
N
0.132
(2.12)*
0.253
(16.39)**
0.393
(7.99)**
0.028
(5.36)**
1.565
(3.68)**
included
included
included
included
0.1508
4081
Table 4 - Descriptive statistics on firms' relationships with buyers
BY MNCS' SUPPLIER STATUS
Brand
MNCs' suppliers
No MNCs' suppliers
Cl.spec (mean)*
55.3
46.5
Client enforcement***
MNCs' suppliers
No MNCs' suppliers
84.6
71.6
47.5
47.1
R&D activiities ****
46
30.5
Prod inf. by client **
78.6
68.7
Empl. for tech diff.
44.2
25.4
BY INDUSTRIES
Brand
Textile and Clothing
Food Processing
Machinery
Electonics
Wood
Rubber and Plastics
Automotive Parts
Cl.spec (mean)*
37.1
81.0
70.6
56.0
35.2
43.7
44.1
Client enforcement***
Textile and Clothing
Food Processing
Machinery
Electonics
Wood
Rubber and Plastics
Automotive Parts
84.7
76.0
75.7
83.7
72.0
70.6
83.5
52.6
40.0
37.3
50.8
52.4
46.6
48.5
R&D activiities ****
38.6
43.6
37.3
47.0
28.0
34.9
42.1
Prod inf. by client **
79.1
69.8
71.8
78.3
71.2
68.5
76.6
Empl. for tech diff.
31.3
39.7
32.2
48.2
18.4
39.5
38.6
Source: The World Bank - PICS 2004
* % of sales made exclusively to buyers' unique specification
** Information on design/quality provided by the buyer
*** Product quality standards enforced by the buyer
**** Engagement of the buyer in process or product R&D type of activities
***** Employees from the buyer to work to disseminate and diffuse new technologies into suppliers' production facility
Page 19 of 21
TABLE 5 - The relationship between firms' TFP and supply chains' governance
Dependent Variable: Total Factor Productivity (Levinsohn-Petrin)
Type B
Type C
Type D
Type E
Size dummies
Industry dummies
Region dummies
Year dummies
N
ALL FIRMS NO-MNS
0.206
0.251
-1.51
-1.72
0.34
0.568
(2.56)* (3.09)**
0.088
0.108
-0.66
-0.72
0.386
0.567
(2.68)** (2.72)**
included
included
included
included
included
included
included
included
4015
1851
MNS
0.141
-0.54
0.005
-0.03
-0.027
-0.12
0.137
-0.63
included
included
included
included
2164
Dependent Variable: Total Factor Productivity (OLS)
Type B
Type C
Type D
Type E
Size dummies
Industry dummies
Region dummies
Year dummies
N
ALL FIRMS NO-MNS
0.046
0.039
-1.67
-1.19
0.059
0.089
(2.49)* (3.08)**
0.013
0.003
-0.48
-0.09
0.052
0.065
(2.31)*
(2.36)*
included
included
included
included
included
included
included
included
4079
1873
Robust z statistics in parentheses
Source: authors' calculation on PICS 2004 data - The World Bank
Page 20 of 21
MNS
0.052
-1.07
0.011
-0.27
0.01
-0.22
0.021
-0.55
included
included
included
included
2206
APPENDIX A
Following Olley and Pakes (1996) and Levinsohn and Petrin (2003) we estimate a Total
Factor Productivity measure employing a semi-parametric technique. We use intermediate
inputs to proxy for the unobservable productivity term.
The production function considered is the following:
Yit   1lit   2 k it   3 mit  it   it
(1.2)
where mit is the intermediate input (electricity), wit the transmitted component and εit the
i.i.d. component. Then, we construct our TFP measure as:
wˆ  exp( y  ˆl  ˆk  ˆm)
(1.3)
The Cobb-Douglas production function specification is of the following form:
Yit   1lit   2 k it   3 mit   it
while the TFP measure is defined as
ˆ  exp( y  ˆl  ˆk  ˆm)
Page 21 of 21