academy of international business best paper proceedings 2008 · 2010. 7. 23. · academy of...
TRANSCRIPT
Academy of International Business Best Paper Proceedings 2008
# AIB2008-0127
Firm Heterogeneity and Technology Seeking Strategies
Roger Smeets Nijmegen School of Management
Maarten Bosker Utrecht School of Economics
Paper presented on July 2, 2008
at the AIB Annual Conference, Milan, Italy http://aib.msu.edu/events/2008/
© 2008 Roger Smeets and Maarten Bosker. Paper may be downloaded for personal use only and cannot be distributed without the explicit permission of the authors.
Firm Heterogeneity and Technology Seeking Strategies
Roger Smeets and E. M. Bosker Abstract: In this paper we build a model to derive the relationship between firm heterogeneity and the strategies that firms might employ to seek foreign technology. In accordance with some recent empirical inisights, we find that low productivity (laggard) firms are inclined to choose exports as a way of technology seeking, whereas high productivity (leader) firms do so mainly through FDI. We demonstrate that this result is mainly due to the fact that laggard firms posses less intra-firm technology transfer skills and have less absorptive capacity relative to leader firms. We also empirically illustrate the implications of our model using patent-citations of non-US firms to their US counterparts. Key Words: Technology seeking FDI, learning by exporting, firm heterogeneity Acknowledgments: We would like to thank participants at the 2007 EEA meeting in Budapest, the 2008 AIB meeting in Milan, and seminars at the University of Nijmegen, the Catholic University of Leuven, and the Fox School of Business and Management. We also thank Heather Berry, Sjoerd Beugelsdijk, Harry Garretsen, Ram Mudambi and Marc Schramm for useful comments and suggestions. Any remaining errors are our own. Roger Smeets Nijmegen School of Management P.O. Box 9108 6500 HK Nijmegen The Netherlands T: +31 24 361 3002 E: [email protected] E. M. Bosker Utrecht School of Economics Janskerkhof 12 3512 BL Utrecht The Netherlands T: +31 30 253 7259 E: [email protected]
INTRODUCTION
A lot of recent studies on the determinants of Foreign Direct Investment (FDI) have considered
the technology or knowledge seeking motive as an incentive for firms to expand their activities
abroad (Dunning, 1993; Dunning and Narula, 1995; Kuemmerle, 1999; Le Bas and Sierra, 2002).
In this literature, it is argued that firms can benefit from knowledge that they pick up from their
host-country competitors. By distributing this knowledge across firm units, not only does the
foreign subsidiary benefit, but so does the entire firm. In this light, many scholars have argued
and demonstrated that low-productivity (laggard) firms have clear incentives to engage in
technology seeking FDI.
However, more recently some authors have started to take issue with these findings.
Berry (2006) convincingly argues that laggard firms do not stand to benefit from technology
sourcing at all: Due to low absorptive capacity, they will not strongly benefit from knowledge
spillovers in the host country, and even if they did, they would have a hard time distributing this
knowledge across firm units because of their low intra-firm technology transfer skills. She also
stresses that much of the empirical evidence relies on sector-level heterogeneity rather than firm-
level heterogeneity, which masks the fact that there could still be a good deal of firm
heterogeneity within a single sector. Using firm-level data of Japanese investors in the US she
finds quite the opposite: High-productivity (leader) firms are the ones with a technology seeking
motive for FDI.
In another recent study, Salomon and Jin (2007) look at knowledge spillovers that firms
pick up through exporting their products abroad. Specifically, using a panel of Spanish firms,
they investigate whether firms from leading or lagging industries stand to gain the most from
exports. Their results demonstrate that firms from lagging industries benefit most from
2
knowledge spillovers through this activity.
Moreover, in the recent International Economics literature, there has been increased
attention for the relationship between firm heterogeneity and internationalization strategies in
general, as witnessed by the work of Melitz (2003) and Helpman, Melitz and Yeaple (2004).
These studies demonstrate, both formally as well as empirically, that a positive relationship
exists between firm productivity and the resource commitment of internationalization strategies.
More specifically, they show that only high-productivity firms engage in FDI, whereas low-
productivity firms only serve the domestic market and those in the middle export.
The aim of this paper is to extend these latter insights to the literature on technology
seeking internationalization motives. That is, to derive a relationship between firm heterogeneity
and international technology seeking strategies. We do so by formalizing the arguments put
forward by Berry (2006) and by building on her empirical results and those provided by Salomon
and Jin (2007). Eventually, we hope to offer scholars active in this field a framework for
studying international technology seeking behavior, and the possible relevant factors that should
be considered when doing so.
The rest of this paper is structured as follows: In the next section, we review the
empirical literature on firm heterogeneity and technology seeking strategies. Section 3 develops
a simple two-period game-theoretic model for analyzing firm heterogeneity and technology
seeking strategies. Since our model is not analytically solvable, we provide some simulation
results in Section 4, which we then try to generalize using comparative statics. Section 5
provides a preliminary empirical illustration regarding the predictions of our model. Section 6
concludes.
LITERATURE REVIEW
3
The traditional FDI literature (Dunning, 1977; Hymer, 1960; Markusen, 2001) has mainly
focused on technology exploitation as a motive for firms to set up a foreign subsidiary. In this
view, only firms that have a certain competitive advantage are able to overcome their liability of
foreignness when engaging in FDI. However, more recently the literature has identified another
motive for FDI, which argues that firms will set up foreign subsidiaries not to exploit their own
assets, but to gain access to foreign (technology) assets (Dunning, 1993; Dunning and Narula,
1995; Kuemmerle, 1999; Le Bas and Sierra, 2002). In this approach, multinational firms can
capture foreign knowledge through (reverse) knowledge spillovers, flowing from host country
firms to the MNE's subsidiary.
Much of the empirical work on this so-called technology seeking FDI has linked the FDI
motive to firm heterogeneity in terms of productivity. Some of these studies find that low-
productivity (hereafter: laggard) firms are the ones that engage in technology seeking FDI. Using
a sample of 825 Japanese investment projects in the US between 1976-1987, Kogut and Chang
(1991) demonstrate that lagging Japanese firms indeed seek US technology through International
Joint Ventures (IJVs). Hennart and Park (1993) arrive at a similar conclusion for a sample of 270
Japanese investors in the US. Almeida (1996) uses patent citations, made by 22 large foreign
firms in the US semiconductor industry between 1980-1990, and finds that mainly European and
Korean firms engage in FDI that is directed at offsetting their home country disadvantages.
Neven and Siotis (1996) use sector-level data for eight industrial sectors in the UK, Germany,
France and Italy, in order to find the determinants of inward FDI into these sectors between
1984-1989. They find that an increase in the industrial R&D stock of the host country relative to
the home country induces more inward FDI from that home country. More implicit evidence is
4
provided by Teece (1992) and Shan and Song (1998).
This empirical evidence notwithstanding, Berry (2006) takes issue with many of these
studies by arguing that many of them find evidence of technology seeking FDI at the sectoral
level rather than the firm level. This veils the fact that within sectors, firm heterogeneity may be
very large, and sectors that are lagging on average may still accommodate leader firms.
Moreover, she argues that theorizing in this field often assumes that leaders and laggards only
differ from each other in terms of technological capabilities, whereas it is very plausible that
laggard firms will generally also be less skilled in transferring technology across firm units, and
have limited absorptive capacity to capture reverse knowledge spillovers. Using a panel of 631
Japanese manufacturing firms with R&D investments abroad over the period 1974-1994, she
finds that not laggard but leader firms source technology through FDI.
Other empirical studies corroborate this result as well. Cantwell and Janne (1999) find
that European leader firms often geographically differentiate their foreign innovative activities in
order to augment their existing (knowledge) assets, whereas laggard firms mainly replicate their
already existing home country specialization. Investigating a sample of R&D labs of the 32
largest MNEs from the US, Japan, Germany, France and the Netherlands in 1994/1995,
Kuemmerle (1999) also finds that investments in foreign R&D labs mainly take place in order to
augment existing knowledge bases. Le Bas and Sierra (2002) also present explicit evidence of
technology seeking FDI by leader firms in a sample of 350 MNEs from the US, Japan and
Europe. Cantwell and Mumdambi (2005) present some evidence of UK subsidiaries of non-UK
leading MNEs engaging in acquisition strategies in order to augment their competencies. More
implicit evidence is presented in Mutinelli and Piscitello (1998), Branstetter (2006) and Griffith
et al. (2006).
5
These findings correspond to the recent literature on firm heterogeneity and
internationalization strategies in general. The starting point of this literature is commonly
identified as Melitz (2003), who demonstrates that in a world with heterogenous firms, the least
productive ones will not internationalize at all, whereas the more productive ones engage in
exports to sell their products abroad. Helpman et al. (2004) extend this insight by showing that
even more productive firms will substitute exports for FDI as a means of serving the foreign
market. Accordingly, there appears to be a positive relationship between firm productivity and
the existence and resource commitment of internationalization strategies, which implies that
laggard firms will not engage in FDI, regardless of the underlying motive.
Nonetheless, laggard firms stand to gain more from technology seeking than leader firms.
How then can productivity laggards benefit from spillovers generated by foreign firms? Salomon
and Jin (2007) provide a possible answer to this question, by demonstrating that Spanish firms in
(relatively) lagging industries obtain knowledge spillovers through exports to other OECD
countries. This corresponds well with the view that productivity laggards do not engage in FDI
but in exports as a means of serving the foreign market. Accordingly, they could potentially also
pick up knowledge spillovers in this way, e.g. by receiving technical assistance from foreign
suppliers or by having to cope with increased foreign competition (Blalock and Gertler, 2004;
Crespi et al., 2006).1
In the next section, we will try to synthesize the findings of this empirical literature in
one model. Specifically, we want to apply the positive relationship between firm productivity
and resource commitment of internationalization strategies to the context of technology seeking
strategies. In order to do so, we build on earlier models developed by Siotis (1999) and Fosfuri
and Motta (1999). We extend these models with the conceptual insights regarding absorptive
6
capacity and technology transfer provided by Berry (2006), as well as the empirical results of
Berry (2006) and Salomon and Jin (2007).
THE MODEL
Consider the following model: There are two countries, North (N) and South (S), and two firms n
(the leader) and s (the laggard). Firm n's home-country is N and firm s's home-country is S.
Leadership in the model is determined with respect to technology parameters as in Siotis (1999)
and Fosfuri and Motta (1999). Specifically, suppose that unit marginal costs functions are given
by:
(1) ii acf −=
where c is a (country-specific) per-unit fixed cost and ai is the technology parameter of firm
i=n,s. Leadership is determined by the technological distance sniaaz ni ,];1,0[)/( =∈≡ .
We consider a two-period model. In period 1, both firms have to choose an
internationalization strategy, which also allows them to seek technology abroad. Each firm can
choose between exports (e) or FDI (f ). Hence, a firm's strategy set σ in period 1 is given by
},{ fe∈σ .
If a firm decides to export its products abroad, it incurs an iceberg transport cost of .
In this case, it obtains a knowledge spillover of magnitude
1≥t
ρ ( 10 ≤≤ ρ ), contingent on its
technology gap z. Knowledge spillovers enter the marginal cost function of firm n (s) as in
d'Aspremont and Jacquemin (1988):
(2) jii azacf ρ−−=
such that jisnji ≠= ;,, . Hence, by engaging in exports, a firm obtains a share of 10 ≤≤ ρ of
7
its competitor's technology stock, regardless of the strategy chosen by the competitor. However,
the effective spillover also depends on the technological distance z - or alternatively - on the
absorptive capacity of the firm (Berry, 2006; Minbaeva, Pedersen, Björkman, Fey and Park,
2003).2 The higher the technological distance (the lower z), the lower are effective spillovers (cf.
Blomström, Globerman and Kokko, 2000).
If a firm decides to engage FDI in period 1, it incurs a fixed setup cost C which is country
specific. In this case, it will capture a share of 10 ≤≤φ of the other firm's knowledge stock,
again contingent on its absorptive capacity z. Doing justice to the large literature on the spatiality
of knowledge spillovers (e.g. Almeida and Kogut, 1999; Audretsch and Feldman, 1996; Jaffe,
Trajtenberg and Henderson, 1993; Keller, 2002), as well as the fact that knowledge spillovers
obtained from a distance will generally have a smaller impact on productivity (Branstetter,
Fisman and Foley, 2006), we assume that knowledge spillovers obtained through exports will be
lower than those obtained through FDI , i.e. φρ < .
In accordance with Berry (2006), we also introduce imperfect intra-firm technology
transfer skills in the case of FDI. Hence, any spillovers that are captured by the foreign
subsidiary can be transferred back to the parent firm at some cost. Specifically, we assume that
only a share 1≤λ of total spillovers captured by the foreign subsidiary is successfully transferred
back home. Also, technology transfer from the parent firm to its subsidiary is costly: Only a
share 1≤μ of the original knowledge stock is successfully transferred abroad. Note that we thus
allow the costs of technology transfer to depend on the direction of transfer (Fors, 1997).
Moreover, we also subscript these parameters by n and s in order to analyze firm heterogeneity
in terms of technology transfer skills.
The implications of all of the above for the different marginal cost functions are
8
summarized in Table 1 below.
<< INSERT TABLE 1 ABOUT HERE >>
From the formulation of marginal costs it follows that firms do not receive knowledge spillovers
automatically. For example, if firm n engages in FDI and firm s in exports, this implies that firm
s's marginal cost in South are given by instead of .
Hence, even though firm n is active in country S through FDI, firm j only receives spillovers
through learning by exporting. This assumption is based on the fact that firms have to undertake
explicit technology seeking efforts to benefit from knowledge spillovers. Consequently, if they
export, they will only benefit from the related spillovers
nss azacf ρ−−= ∗∗ )( μφρ +−−= ∗∗nss zaacf
ρ . Alternatively, if they undertake FDI,
they will only benefit from knowledge spillovers in the host country.3
In period 2, the two firms play Cournot and earn profits. The precise formulation of the
inverse demand functions that firms face depends on the period 1-strategies chosen by them. If
firm s (n) decides to engage in FDI, inverse demand in N (S) is given by:4
(3)
S
n
S
s
N
s
N
n
Sq
Sqp
Sq
Sqp
∗∗∗ −−=
−−=
α
α
where p denotes price, α is a demand parameter, ( ) denotes demand for products by firm
n (s) and ( ) measures market size in N (S). Asterisks denote values in S. In case firm s (n)
decides to export, inverse demand functions become:
nq sq
NS SS
9
(4)
/
/
tSq
Sqp
tSq
Sqp
S
n
S
s
N
s
N
n
∗∗∗ −−=
−−=
α
α
Profits for firm n, contingent on firm s's strategy sσ and its own strategy nσ are denoted
by (and similarly for firm s). They are derived from the fact that firms' strategies are best
responses to each other, and that firms maximize profits in equilibrium. This yields eight explicit
profit functions, which are relegated to the Appendix. Table 2 below presents the game in normal
form.
snnσσΠ
<< INSERT TABLE 2 ABOUT HERE >>
EQUILIBRIUM STRATEGIES
Simulation results
In order to analyze the Subgame Perfect Nash Equilibria (SPNE) of this game we have to
simulate the model, as it is not analytically solvable (there exist no closed form solutions). Since
we are mainly interested in the interplay between technology seeking (via spilloversφ and ρ )
and firm heterogeneity (via technology gap z ) and their consequences for equilibrium strategies,
we will let these parameters vary and fix the others.5 We will consider two scenarios: First, we
limit leader-laggard heterogeneity to their technological capabilities (i.e. productivity). Next, we
will add imperfect intra-firm technology transfer costs and absorptive capacity to the model to
see how extending firm heterogeneity along these lines alters the model outcomes.
10
Scenario 1: Perfect knowledge transfer & absorptive capacity.
Figure 1 below sets 1== ii λμ ( ) and sni ,= 1=z in the MC functions in Table 1. In the figure
),( sn σσ denotes the SPNE in which firm n (s) plays strategy nσ ( sσ ) in period 1, where
},{ fe∈σ .
<< INSERT FIGURE 1 ABOUT HERE >>
If knowledge spillovers are relatively low, both firms prefer to export their products abroad,
regardless of the technology gap. In this way they do not incur fixed setup costs, while still
benefiting from spillovers, although these are smaller than those obtained through FDI. Yet, as
knowledge spillovers increase, the laggard firm soon finds it more profitable to seek technology
abroad via FDI, despite the fixed costs: The increase in spillovers increases the opportunity costs
of exporting, since local spillovers ρ are by assumption smaller (or have a smaller impact on
marginal costs) than spillovers obtained through FDI ( )φ . Since intra-firm technology transfer
skills and absorptive capacity are perfect, the technology gap between the leader and the laggard
is only to the laggard's advantage: The larger the gap, the larger the relative gain, both for the
subsidiary as well as the parent.
A similar mechanism is at work for the leader, but the technology gap has to decrease
first before the leader will find it profitable to engage in FDI. The reason is that the technology
gap initially works to the leader's disadvantage: For large technology gap, there is relatively little
to gain by absorbing spillovers from the laggard, but relatively a lot to loose by spilling over
knowledge to the laggard. Consequently, the benefits from higher spillovers through FDI are not
sufficient to compensate fixed costs of FDI if the technology gap is too large.
11
From Figure 1 it follows that there still is a large range of combinations for z and φ in
which both the laggard firm and the leader firm engage in technology seeking FDI. This would
imply that the two strands of empirical literature on firm heterogeneity and technology seeking
FDI, reviewed in Section 2, both have it right. However, thus far we have assumed that leader-
laggard heterogeneity is limited to productivity (or technological capabilities). That is, we have
assumed that intra-firm technology transfer of the laggard is perfect, and that absorptive capacity
of the laggard is unlimited. In the next scenario, we will depart from these assumptions.
Scenario 2: Imperfect knowledge transfer & absorptive capacity.
We retain the assumption that 1== nn μλ so that sλ and sμ can be interpreted as the intra-firm
technology transfer skill gap of the laggard relative to the leader. The question is at what values
we should calibrate sλ and sμ . Unfortunately, we are not aware of any study that might give us
some clues on this issue.6 At this point, we choose the rather arbitrary values of 5.0== ss μλ ,
i.e. the laggard possesses 50% of the intra-firm knowledge transfer capabilities of the leader. We
will further investigate the sensitiveness of the results with respect to sλ and sμ in the next
subsection. Additionally, we also introduce imperfect absorptive capacity (i.e. ) of the
laggard (cf. Table 1). Figure 2 gives the resulting equilibrium configuration.
1≤z
<< INSERT FIGURE 2 ABOUT HERE >>
The results change drastically in this case. For the laggard firm, we see that it chooses to
engage in exports for all parameter combinations. For high technology gap (low z), the laggard
has too small absorptive capacity to benefit from the spillovers generated by the leader.
12
Moreover, due to the high intra-firm technology transfer costs, the opportunity costs of engaging
in FDI are simply too high for the laggard firm: On the one hand, technology exploitation
through FDI is difficult due to 5.0=sμ . On the other hand, technology seeking through FDI is
of smaller benefit to the parent since 5.0=sλ . These effects work regardless of the technology
gap, so that it is not beneficial for the laggard to engage in FDI.
Starting from the left-axis of Figure 2, the leader firm initially also chooses exports as a
response to a similar strategy of the laggard. The reason is that knowledge spillovers are very
low and consequently, the fixed setup costs in case of FDI outweigh the higher spillovers
obtained through technology seeking FDI. However, if the extent of knowledge spillovers
increases, the leader will remain an exporter only for a larger and larger technology gap, for only
in case of large technological distance do the benefits of increased spillovers from the laggard
not compensate sufficiently for the fixed setup costs of FDI. Indeed, as soon as the technology
gap reaches a (relatively low) threshold level, the leader finds it optimal to engage in FDI and
capture larger spillovers.
This result perfectly accords with Berry's (2006) line of reasoning when she argues
‘Absorptive capability problems, coordination and technology transfer issues, and questions of
capability all suggest that it may be firms that are more technologically advanced that are going
to be able to benefit from foreign R&D labs’ (2006: 154). Indeed, the theoretical results point out
that technology seeking FDI on behalf of the leader is more likely to occur than technology
seeking FDI by the laggard.
Analytical results
In order to get a more general flavor regarding the relationship between firm heterogeneity and
13
technology seeking strategies and the influence of intra-firm technology transfer skills, we now
turn to some comparative static exercises.7 First consider the situation in which the leader
exports ( en =σ ). We want to determine under which condition the laggard exports as well, i.e.
under which condition it holds that (see Appendix). We normalize to 1 so that
. Furthermore, to keep the analysis tractable, we assume , and
normalize C to 0. Comparing the relevant profit conditions and rearranging terms yields the
following condition under which :
efs
ees Π>Π
efs
ees Π>
na
∗ Ssn aa =saz ≡ / = cc SN S=
Π
(5) Θ−Θ+−++
Θ+−−=<
2)2()21(2)2)(1(
1 ρλφμα
ss
czz
where tt /)1( +≡Θ .8 thus describes the locus which divides the regions (e,e) and (e,f) in
Figure 1. This condition states that once the technological gap falls below a certain threshold
level , the laggard firm chooses to export instead of FDI, conditional on the leader exporting as
well. Note that increases with a decrease in laggard technology transfer skills
1z
1z
1z sμ and sλ , i.e.
0/1 <dz sdμ and 0/1 ddz <sλ .9 The reason for this is twofold: First of all, a decrease in sμ
decreases the extent to which firm j's subsidiary can exploit its technological advantage, so that a
decrease in sμ has to be compensated by a sufficient increase in relative technological ability z
for FDI to still be profitable. Second, a decrease in sλ depresses the benefits of FDI since it
allows less of the captured spillovers to be transferred back to the parent. Hence, a sufficient
increase in absorptive capacity z is required to offset this effect and still make technology
seeking through FDI profitable. Also note that this latter effect is stronger the larger are
spillovers φ , as we would expect. In general, a decrease in either sμ or sλ increases the
threshold above which the laggard finds it profitable to engage in FDI, and as such increases 1z
14
the range of equilibria in which the laggard chooses exports to seek technology.
We can also derive the effects of the other parameters on . An increase in the extent of
knowledge spillovers captured through exports
1z
ρ serves to increase (1z 0/1 >ρddz ) since it
decreases the opportunity costs of exports, given φ . The opposite holds for an increase in φ
( 0/1 <φddz ), since this increases the opportunity costs of exports, given ρ . Finally, an increase
in transport costs t serves to decrease the threshold-level (see the Appendix for a derivation).
The reason is that an increase in t makes exports less attractive than FDI. This captures the well
known tariff-jumping motive for FDI.
1z
Second, we investigate under which condition the laggard will export, given that the
leader engages in FDI. That is, we are now interested under what condition holds.
Again comparing the relevant profit functions in the Appendix and rearranging gives:
ffs
fes Π>Π
(6) )1)(1()1(4)1(2)1(2
)1)(122(2 Θ++−Θ+−+++
Θ+−−−=<
nss
nczzλφρμλφ
μα
As before, decreases in sλ and sμ serve to increase as well (2z 0/2 <sddz λ ; 0/2 <sddz μ ),
and the reasons are of course similar. Also, an increase in ρ again decreases the opportunity
costs of exporting, thus increasing (2z 0/2 >ρddz ).
However, the effect of an increase in the knowledge spillovers captured through FDI (φ )
is not unambiguous in this case. Specifically, sign =φddz /2 sign [ ])1(2)1)(1( sn λλ +−Θ++ .
Consider the case in which transport costs are very high (i.e. 1→Θ ). In this case, 0/2 >φddz
iff sn λλ > . I.e. if and only if the technology transfer skill of the leader is higher than that of the
laggard, an increase in knowledge spillovers φ will increase the range of technology gaps for
which a laggard still engages in exports. The reason is that in this case, not only are knowledge
15
spillovers increasing, but the leader's subsidiary is also better able than the laggard to transfer the
captured external knowledge back to its parent and hence increase competition with firm s in
country N as well. In order for FDI to still be beneficial in this case, the laggard needs sufficient
absorptive capacity z to make up for the technology transfer skill-gap. A decrease in transport
costs (an increase in Θ ) will obviously make this condition less restrictive, because the
opportunity costs of exports decrease with a decrease in t.
Since we are assuming that firm n engages in FDI, we can also infer the direct effect of
changing the leader's intra-firm technology transfer skills. Note that an increase in nμ decreases
( 02z /2 <nddz μ ): The reason is that due to the increase in nμ , the level of the leader's
technology stock exploited in its subsidiary increases, hence making firm n more competitive in
country S. As a response, firm s wants to capture larger knowledge spillovers through FDI and
become more competitive, for a larger range of technology gaps z. The opposite holds for an
increase in nλ as this serves to increase the threshold-level ( 02z /2 >nddz λ ): In this case, the
leader is better able to transfer knowledge spillovers captured by its subsidiary back to the
parent, thus making firm n more competitive in country N. This in turn requires a smaller
technology gap (higher z) for the laggard in order to still be competitive in country N as well,
given that intra-firm technology transfer is imperfect. Note that the strength of this effect
positively depends on the extent of knowledge spillovers from foreign technology sourcing φ , as
we would expect. Finally, as before, an increase in transport costs t unambiguously decreases
(see the Appendix for a derivation).
2z
This illustrates the sensitivity of the simulation results presented in Figure 2: A further
decrease in sμ or sλ would leave the results unaltered, as the laggard firm is already exporting
for all poss ble parameter combinations. An increase in i sμ or sλ however would serve to
16
decrease the (f,e) region, as the (f,f) region would slowly enter from the North-East. In orde
illustrate this latter point, as well as to shed light on the differences of increasing either s
r to
μ or
sλ , Figure 3 below presents two variations on Figure 2. In panel a, sλ is still fixed at 0.5 but sμ
is increased to 1 whereas in panel b sμ is fixed at 0.5 an sλ increased to 1.
<< INSERT FIGURE 3 ABOUT HE
Comparison of the two panels demonstrates that increasing
RE >>
s
μ to 1 gives a mu
ge o
ch larger
ran f equilibria in which the laggard engages in FDI than when increasing sλ to 1. The
implication of this result is that technology exploitation is a much more dominant motive in a
laggard's internationalization process than technology seeking. This can be seen by noting that
increasing s
λ serves to increase the extent to which the parent-firm can benefit from the
spillovers pi ed up by the subsidiary, whereas increasing sck μ allows the subsidiary to exp
parent’s technology. From Figure 3 it follows that even if technology transfer skills from the
subsidiary back to the parent are on par with those of the leader, the laggard will still opt for
exports as a technology seeking strategy for a large combination of parameter values, given th
its technology exploitation skills of FDI are less developed. This clearly demonstrates the
persistence of exports as a technology seeking strategy on behalf of laggard firms.
Given all of the above, we conjecture that there exists a positive relationship
l
twee
oi
be n
-le
t the
at
firm vel productivity and the resource commitment of foreign technology seeking strategies.
Specifically, we expect that high-productivity leader firms will mainly seek foreign technology
through FDI, whereas low-productivity laggard firms will do so through exports. The former
17
result is in accordance with Berry (2006), whereas the latter result corresponds to those of
Salomon and Jin (2007), albeit at the firm level. As we have demonstrated, the exact thresh
level for this dichotomy crucially depends on the intra-firm technology transfer skills of the
laggard (relative to the leader) and on its absorptive capacity.
old
lso
EMPIRICAL ILLUSTRATION
Data & methodology
reliminary) illustration of our conjecture that leader firms seek technology
r
dge
studies using country of first inventor information (Almeida, 1996;
10 If these are sufficiently high,
laggard firms may also prefer FDI over exports to seek foreign technology. But as argued by
Berry (2006), laggard firms are expected to be lagging not only in terms of productivity, but a
in terms of intra-firm technology transfer skills and absorptive capacity. As such, we believe that
our conjecture is valid.
In order to provide a (p
through FDI and laggard firms through exports, we employ the NBER patent citations database
(Jaffe and Trajtenberg, 2002), containing information on +/- 3 million patents and 16.5 million
patent citations made by these patents. Previous studies have demonstrated that patent citations
are able to reveal the sources from which specific patented innovations derive their knowledge o
technology (Audretsch and Feldman, 1996, Branstetter, 2001,2006; Jaffe et al., 1993). As such,
we may use the citations to patents by US firms, incorporated in patents applied for by foreign
firms, as a measure of the degree to which these foreign firms utilize US knowledge in their
innovations. I.e. we may use those citations to say something about the degree of US knowle
seeking by foreign firms.
In line with earlier
18
Almeid
s not
in
a and Kogut, 1999; Branstetter, 2001; 2006; Griffith, Harrison and Van Reenen, 2006;
Singh, 2007), we assume that if the country of first inventor is the US, the innovation or
innovative process also took place in the US.11 Similarly, if the country of first inventor i
the US, we assume that the firm applying for the US patent conducted the innovation outside the
US. Consequently, we are able to assign each patent a value of zero (outside US) or one (within
US). This binary variable - in combination with the extent of citing US patents, see below - is our
dependent variable FS, and measures applying for a US patent through a subsidiary from with
the US ( 1=FS ) or from the firm's home country ( 0=FS ). Applying the above-mentioned
definition ther with time-period restrictions (1987-1999) leaves us with about 460,000
patents applied for by (and granted to) non-US firms over the period 1987-1999, where 9,602
those patents were applied for from within the US (i.e. for which the country of first inventor
was the US).
s toge
of
<< INSERT TABLE 3 ABOUT HERE >>
Tables 3-5 show some descriptives of these 460,000 patents: The increase in patents
granted s
s
over the period 1987-1999 (Table 3), the different nationalities of inventors and firm
applying for a patent and those having a subsidiary in the US (Table 4) and the number of time
patents cite patents granted to US firms (Table 5). They show some interesting things. First, the
number of US patents granted to foreign firms has steadily risen over the years. Second, by far
the most patents applied for by foreign firms were granted to inventors located in Japan (about
half of all patents), followed by Germany, France and Great Britain. Note also that the patents
applied for from within the US constitute about 2.1% of all patents. This may seem a small
19
number, yet it still ranks the US at place 8 (out of 28) as the location of foreign firms' innova
activity.
tive
<< INSERT TABLE 4 ABOUT HERE >>
When we turn to the nationalities of the f s that are responsible for the 9,602 patents
invente
unt
<< INSERT TABLE 5 ABOUT HERE >>
Ideally, we would aggregate all the patents to the firm level in order to perform our
econom
ent
irm
d in the US in our sample, we observe that most of them are Japanese owned, again
followed by Germany, but also Canada and Switzerland.12 Finally, when looking at the amo
of US citations (our measure of knowledge seeking) made by these 460,000 patents we can see
that about 33% of the sample never cites a US patent, another 25% only cites a US patent once,
about 16% twice and about 30% cites a US patent at least 3 times.
etric analysis. Unfortunately, the NBER database only allows us to assign patents to
firms that are incorporated in the Compustat database, i.e. to firms that are traded on the US
stock market. First of all, most of these firms are US firms and thus of no use to us in the pres
analysis. Second, the foreign firms that are traded on the US stock market are generally large,
multinational firms, and all have innovative activities in the US. In other words, we will be only
left with MNEs in such an exercise. We therefore opt for the second-best strategy and aggregate
the patents into different sectors. One clear disadvantage of this strategy is that it masks the
potentially large firm-level heterogeneity within each sector (Berry, 2006). Therefore, this
20
empirical exercise should mainly be viewed as a preliminary empirical illustration of our m
and not as a full-fledged empirical test.
Using the ANBERD and STAN
odel,
databases from the OECD, we are able to derive detailed
sectora
ed in
l information for 27 OECD countries on R&D stocks, total value added and labor
productivity (per hour worked) over the period 1987-1999. All these variables are comput
millions of constant (1995) PPP US dollars. We will use R&D stocks and labor productivity in
our analysis in order to determine whether a sector can be considered as a leading or lagging
sector relative to the US. For example, our variable R&D is computed as:
(7) ( )( ) USjt
HomejtaddedValuestockDRDR , / &
& = jt addedValuestockDR , / &
where j and t index sector and time respectively. An increase in thus implies that the
is beco
e
arket
ll
(8)
jtDR &
home country (one of the 27 OECD countries other than the US) ming relatively more
R&D intensive in sector j relative to the US. By definition, R&D is bound from below by 0. Th
productivity and value added variables are defined in similar ways, and should thus all be
interpreted as values relative to the US. We include (sectoral) value added as a proxy for m
size in order to capture the technology exploiting motive (Carr, Markusen and Maskus, 2001),
since we argued above that firms will pursue double (investment) motives simultaneously. A fu
list of sectors that are incorporated in the analysis is provided in the Appendix (Table A.1).
Using the variables derived above, we will run probit models of the following kind:
0 ; ]1,0[ ;
)&(),|1(
,
,
321,
≥∈≡
+++++Φ=≥≥=
nxcitcit
seeking
DDVAPRODDRncitxseekingFSP
ijttotal
ijtUSijt
jttjjtjtjtijtUSijtijt β β β ε
21
where i indexes patent. R&D measures relative R&D stocks as defined in (7), similarly PROD
e measures relative labor productivity, VA relative value added, and jD and tD are sector and tim
dummies respectively. The s′β are parameters to be estimated. W ssum at k e a e th ε is normally
distributed with mean 0 and has a variance-covariance structure that allows for sectoral
autocorrelation (given our panel data setup) and heteroscedasticity. The dependent variab
the binary variable described above, which takes a value of 1 if a patent is applied for by a
foreign firm within the US, and 0 if it is applied for by a foreign firm from outside the US.
We condition the probability of this variable on seeking and UScit which are defined
le FS is
as
the cita ma
e
view,
e 1 - which
tions to a US patent as a share of the total number of citations de by a patent, and the
absolute number of citations made to a US patent respectively. We condition on these two
variables to assure a firm included in our sample is actually drawing on US knowledge, as w
would require in the case of technology seeking:13 The reason to condition on the relative
number of US citations is that in order for a firm to be knowledge seeking it should, in our
obtain at least a certain share of its knowledge from the US. The additional conditioning on the
absolute number of patents is added to this to take account of the fact that a lot of patents in our
sample only cite one earlier US patent (cf. Table 4), which introduces a rather large
"coincidence" factor. I.e. given the composition of our sample, seeking taking a valu
should indicate that a firm's patents builds fully on knowledge from the US - may have nothing
to do with knowledge seeking per se. The firm could simply be using knowledge embodied in a
US patent by coincidence, and not at all be engaged in US knowledge seeking. Obviously, the
lower bounds x and n from which point onward we consider a firm (or patent) to be seeking US
knowledge are arbitrary, and we will apply different values of x and n to determine how robust
our results are.
22
In line with our conjecture that leader firms seek technology through FDI (i.e. from
within t that the US) and laggard firms through exports (i.e. from their home countries), we expec
1β and 2β will take positive values, indicating that increased R&D or productivity of home
ntry tors relative to the US also increases the probability that firms in these sectors see
US knowledge from within the US, rather than from their home countries. As we mentioned
before, 3
cou sec k
β captures a technology exploiting motive in that it measures the effect of relative ho
market size. We therefore expect firms to be less inclined to invest in the US when their relative
home market size increases. Consequently, we expect that 3
me
β carries a negative sign. Also note
that this effect should hold, regardless of whether or not we condition on seeking and/or UScit ,
since the exploiting motive is distinct from the seeking motive. The Appendix shows som
summary statistics and correlations between the different variables for different requirement
UScit (Table A.2).
One final co
e
s on
mment is in order: Of course, the fact that a firm applies for a US patent in
which i t
ct
y is
t builds on prior US knowledge while not being in the US does not necessarily mean tha
it is thus seeking technology through exports. It could for example also be benefitting from US
knowledge through its interaction with US firms' subsidiaries present in its home country, or by
using patent information which is available through the internet (cf. Branstetter et al., 2006).
Although these latter strategies are not part of our model, what is of main importance is the fa
that laggards do not set up subsidiaries in the US to seek US technology, but remain in their
home countries to do so. The specific channels that they then employ to access this technolog
of secondary concern. Nonetheless, given our inability to specifically identify exports as the
channel of technology seeking, we would once again like to stress that this is an empirical
illustration of our model, rather than a full-fledged test.
23
Empirical Results
ults, we will follow an approach of increasing the restrictiveness of our
that
he
ative R&D variable (R&D). From the table it follows that for a wide
range o
PROD, results are rather different. This
variabl ).
l
e
In presenting our res
technology seeking definition. First consider Table 6 below. Our general requirement here is
n in (8) is at least one, i.e. a patent should have at least one citation to a patent by a US firm.
Going through the table from left to right, we then increase seeking in (8), i.e. we increase the
share of citations a patent should have to US patents in order to be considered as technology
sourcing. Throughout, our dependent variable is having sourced US knowledge from within t
US (1) or domestically (0).
First consider the rel
f seeking requirements, this variable has no significant effect on the decision whether to
seek domestically or in the US. Only if we require that at least 25% of all citations should be to
US patents, this variable becomes significant. Moreover, it carries the expected positive sign,
indicating that an increase in relative home country R&D will induce a firm to seek US
technology in the US, rather than domestically.
Regarding the labor productivity variable
e is significant in all models, even if we impose no seeking requirements (column 1
Consequently, although its positive effect is in accordance with our expectations, it is doubtfu
whether this variable is capturing the proposed leader-laggard distinction (indeed, looking at the
correlations in Table A.2, it appears that this variable is capturing something different than the
R&D variable). Perhaps a more valid explanation is that this variable is capturing a (skilled
labor) wage premium effect (cf. Braconier, Norback and Urban, 2005), indicating that relativ
increases in the home country wage premium are driving firms' innovative activities to the US.
24
That is, this variable could be capturing an efficiency seeking motive for FDI.
Finally, the relative value added VA variable is significant in all models and has a
negativ US,
n
icant,
<< INSERT TABLE 6 ABOUT HERE >>
Table 7 below presents results similar to now we require that
e effect. This indicates that as the home market size increases relative to that of the
firms become less inclined to venture into the US. Note that it is significant in all models, even i
those where no seeking requirement is imposed (column 1), which indicates that it indeed
captures the distinct technology exploiting motive. Moreover, the fact that it remains signif
together with R&D in columns 5 and 6 illustrates that technology exploiting and seeking motives
exist simultaneously.
n 2, those of Table 6, but
i.e. a pa uded
ent.
d VA
<<INSERT TABLE 7 ABOUT HERE>>
In the Appendix we present an additional table w ere we require that
tent is required to have at least two citations to US firms’ patents in order to be incl
in our sample. A notable difference with the results in Table 6 is that the R&D variable now
becomes (marginally) significant already as soon as we impose the absolute seeking requirem
Yet again, from relative seeking shares of 25% or higher this variable becomes strongly
significant. In all the models it carries the expected positive sign. The variables PROD an
behave in similar ways as before.
n 3h (Table A.3). As can
be seen there, results are very similar to those of Table 7.
25
The industry dummies (not reported) show a rather consistent pattern. Specifically, the
sectors
- which is capturing leader-laggard characteristics - is
perform
y
CONCLUSION
In this paper we have argued that leader and laggard firms will differ from each other in terms of
ty
lls
basic metals (ISIC 27), office machinery (ISIC 30), radio- television and communication
equipment (ISIC 32) and manufacture of medical, precision and optical instruments (ISIC 33)
have a strong positive effect on our dependent variable. This indicates that in high-tech sectors,
firms are particularly likely to go abroad to seek US knowledge, which appears in line with the
intuition that we have developed above.
In sum, our relative R&D variable
ing in line with the expectations derived from our model, and is increasing in robustness
as we increase the restrictiveness of our technology seeking definition. Especially this latter
finding is encouraging: As we indicated before, for very unrestrictive definitions of technolog
seeking, having a foreign patent cite a US patent may be driven more by coincidence rather than
a technology seeking motive. Indeed, for such weak definitions we find no or little evidence for
our hypotheses (Table 6, columns 1-4). Additionally, if we impose no seeking requirements
whatsoever (column 1 in all tables), the R&D variable has no effect either.
the internationalization strategies that they choose to seek foreign technology. Building on recent
insights provided by inter alia Berry (2006) and Salomon and Jin (2007), we have developed a
model in which firms can choose between a strategy of technology seeking FDI versus
technology seeking exports, while simultaneously increasing leader-laggard heterogenei
beyond firm-level productivity by also accounting for differences in technology transfer ski
26
and absorptive capacity. Our key result is that - in a wide range of equilibria - leader firms will
seek technology through FDI while laggard firms will do so through exports. This result seems t
be largely invoked by relatively low absorptive capacity and “backward” intra-firm technology
transfer skills on part of the laggard firm.
Using the NBER patent citations d
o
atabase, we have then used patent citations in a sample
of +/- 2
se
ology
applied in this paper is to conduct
the ana
ed
weak
or
40,000 US patents, applied for by firms in 27 OECD countries over the period 1987-
1999, in order to illustrate our model at the sectoral level. Our empirical results indicate that
increases in the R&D stock of a firm's home country industry relative to that in the US increa
the probability that such a firm will seek technology in the US, rather than from its home
country. This result provides a first and preliminary illustration of the differences in techn
seeking strategies between leader and laggard firms. Moreover, we have also illustrated the
existence of a simultaneous technology exploiting motive.
An obvious extension of the empirical methodology
lysis at the firm level, as only an empirical test using firm-level data can provide a full-
fledged test of our model. Not only could such an empirical test provide a direct test for the
differences in technology seeking strategies between leaders and laggards, it could also be us
to test some of the comparative static implications of our model, as discussed in Section 4.
Some policy implications can be probed as well. Policy makers may do well by identifying
and strong domestic industries, clusters or geographic areas, i.e. those that are dominated by
either leading or laggard firms. This will enable them to construct more finely-tuned policy
measures. For example, this paper shows that stimulating FDI is not in the best interest of
laggard firms. These firms would benefit more from policies aimed at increasing absorptive
capacity with respect to local spillovers from foreign firms, or increasing their export position
27
their access to technological content through the internet. Attracting high quality inward FDI
would be another way to serve the interests of laggard firms. Subsidies for FDI on the other ha
would be more suited for leading firms or sectors, since this is their primary instrument for
seeking foreign technology.
nd
28
REFERENCES
Almeida, P. 1996. Knowledge Sourcing by Foreign Multinationals: Patent Citation Analysis in
Alm rs in
Aud e Geography of Innovation and
Ber rmance: cause, effect, or both?
Ber Foreign Knowledge. Strategic
Blo . 2000. The determinants of host country spillovers
Bra rises and wage costs:
Bra e-off between
Bra cope?
nal
Branstetter, L. 2006. Is Foreign Direct Investment a channel of knowledge spillovers? Evidence
the U.S. Semiconductor Industry. Strategic Management Journal, 17(2): 155-165.
eida, P. & Kogut, B. 1999. Localization of Knowledge and the Mobility of Enginee
Regional Networks. Management Science, 45(7): 905-917.
retsch, D.B. & Feldman, M.P. 1996. R&D Spillovers and th
Production. American Economic Review, 86(3): 630-640.
nard, A.B. & Jensen, J.B. 1999. Exceptional exporter perfo
Journal of International Economics, 47(1): 1-25
ry, H. 2006. Leaders, Laggards, and the Pursuit of
Management Journal, 27(2): 151-168.
mström, M., Globerman, S. & Kokko, A
from Foreign Direct Investment, CEPR Discussion Paper 2350.
conier, H., Norback, P-J. & Urban, D. 2005. Multinational enterp
Vertical FDI revisited. Journal of International Economics, 67(2): 446-470.
inard, S.L. 1997. An empirical assessment of the proximity-concentration trad
multinational sales and trade. American Economic Review, 87(4): 520-544.
nstetter, L. 2001. Are knowledge spillovers international or intranational in s
Microeconometric evidence from the United States and Japan. Journal of Internatio
Economics, 53(1): 53-79.
from Japan's FDI in the United States. Journal of International Economics, 68(2): 325-344.
29
Bra
Can tion and Innovative Centres: The Role of
Cantwell, J. & Mudambi, R. 2005. MNE Competence-Creating Subsidiary Mandates. Strategic
Carr, D.L., Markusen, J.R. & Maskus, K.E. 2001. Estimating the Knowledge-Capital model of
nstetter, L., Fisman, R. & Foley, C.F. 2006. Do stronger intellectual property rights increase
international technology transfer? Empirical evidence from U.S. firm-level panel data.
Quarterly Journal of Economics, 121(1): 321-349.
twell, J. & Janne, O. 1999. Technological Globalisa
Corporate Technological Leadership and Locational Hierarchy. Research Policy, 28(2-3):
119-144.
Management Journal, 26(11):1109-1128.
the multinational enterprise. American Economic Review, 91(3): 693-708.
Cle icro-dynamic rides, S., Lach, S. & Tybout, J. 1998. Is learning by exporting important? M
evidence from Columbia, Mexico and Morocco. Quarterly Journal of Economics, 113(3):
903 - 948.
Crespi, G., Criscuolo, C. & Haskel, J. 2006. Productivity, exporting and the learning-by-
ndon
D'A emin, A. 1988. Cooperative and Noncooperative R&D in Duopoly with
Dun NE: A Search for an Eclectic
Dun my, Reading, MA:
exporting hypothesis: Direct evidence from UK firms. Queen Mary, University of Lo
Working Paper 559.
spremont, C. & Jacqu
Spillovers. American Economic Review, 78(5): 1133-1137.
ning, J.H. 1977. Trade, Location of Economy Activity and M
Approach. In B. Ohlin, P.O. Hesselborn, & P.S. Wijkman (Eds.), The International
Allocation of Economic Activity: 395-418. London: MacMillan.
ning, J.H. 1993. Multinational Enterprises and the Global Econo
Addison-Wesley.
30
Dunning, J.H. & Narula, R. 1995. The R&D Activities of Foreign Firms in the United States.
Fei ent of R&D
845.
For
Fos Advantages. Scandinavian Journal of
Griffith, R., Harrison, R. & Van Reenen, J. 2006. "How special is the special relationship? Using
Hel . 2004. Export versus FDI with heterogeneous firms.
Hen cquisition: The Strategy of Japanese Investors
Hym s: A Study of Direct Foreign
Jaffe, A.B. & Trajtenberg, M. 2002. Patents, citations and innovations: A window on the
Jaff ographic Localization of Knowledge
-
International Studies of Management and Organization, 25(1-2): 39-73.
nberg, S.E. & Gupta, A.K. 2004. Knowledge Spillovers and the Assignem
Responsibilities to Foreign Subsidiaries. Strategic Management Journal, 25(8-9): 823-
s, G. 1997. Utilization of R&D Results in the Home and Foreign Plants of Multinationals.
Journal of Industrial Economics, 45(3): 341-358.
furi, A. & Motta, M. 1999. Multinationals without
Economics, 101(4): 617-630.
the impact of US R&D spillovers on UK firms as a test of technology sourcing. American
Economic Review, 96(5): 1859-1875.
pman, E., Metlitz, M.J. & Yeaple, S.R
American Economic Review, 94(1): 300-316.
nart, J.F. & Park, Y.R. 1993. Greenfield vs. A
in the United States. Management Science, 39(9): 1054-1070.
er, S.H. 1960. The International Operations of National Firm
Investment, Ph.D. dissertation, Sloan School of Management, Massachusetts Institute of
Technology, Cambridge, MA.
knowledge economy. Cambridge, MA: MIT Press.
e, A.B., Trajtenberg, M. & Henderson, R. 1993. Ge
Spillovers as evidenced by Patent Citations. Quarterly Journal of Economics, 108(3): 577
598.
31
Keller, W. 2002. Geographic Localization of International Technology Diffusion. American
Kogut, B. & Chang, S.J. 1991. Technological Capabilities and Japanese Foreign Direct
Kue tical
Le Bas, C. & Sierra, C. 2002. Location versus Home Country Advantages in R&D Activities:
9.
Ma
Ma ational Firms and the Theory of International Trade. Cambridge,
Minbaeva, D., Pedersen, T., Björkman, I., Fey, C.F. & Park, H.J. 2003. MNC Knowledge
Motta, M. 1996. Multinationals without Advantages. IUI Working Paper 464.
San spillovers with
Studies,
Sha J. 1997. Foreign Direct Investment and the Sourcing of Technological
Economic Review, 92(1): 120-142.
Investment in the United States. Review of Economics and Statistics, 73(3): 401-413.
mmerle, W. 1999. Foreign DIrect Investment in Industrial Research in the Pharmaceu
and Electronics Industries - Results from a Survey of Multinational Firms. Research Policy,
28(2-3): 179-193.
Some further Results on Mulnationals' Location Strategies. Research Policy, 31(4): 589-60
cGarvie, M. 2006. Do firms learn from international trade? The Review of Economics and
Statistics, 88(1): 46-60.
rkusen, J.R. 2002. Multin
MA: MIT Press.
Transfer, Subsidiary Absorptive Capacity, and HRM. Journal of International Business
Studies, 34(6): 586-599.
Neven, D. & Siotis, G. 1996. Technology Sourcing and FDI in the EC: An Empirical Evaluation.
International Journal of Industrial Organization, 14(5): 543-560.
na-Randaccio, F. & Veugelers, R. 2007. Multinational knowledge
decentralised R&D: A game-theoretic approach. Journal of International Business
38(1): 47-63.
n, W. & Song,
32
Advantage: Evidence from the Biotechnology Industry. Journal of International Busin
Studies, 28(2): 267-284
gh, J. 2007. Assymetry o
ess
Sin f knowledge spillovers between MNCs and host country firms.
Sio ' Capabilities. Journal of
Tee evelopment in Silicon Valley.
Journal of International Business Studies, 38(5): 764-786.
tis, G. 1999. Foreign Direct Investment Strategies and Firms
Economics & Management Strategy, 8(2): 251-270.
ce, D.J. 1992. Foreign investment and technological d
California Management Review, 34(2): 88-106.
33
APPE DIX
Equilibrium profit functions
N
If both firms export:
nee ☺ 2c c an 2 z as 2 1 2 SN SS /t
9
see ☺ c 2c an 2z 1 as 2 2 SN/t SS
9
both firms engage in FDI: If
nff ☺ c an 2 z as 2 n s
2SN
9☺ c an 2 n z s as 2 1 2SS
9 C
sff ☺ c an 2z 1 as 2 s n
2SN
9☺ c an 2z s n as 2 2SS
9 C
If n exports and s engages in FDI:
nef ☺ c an 2 z as 2 s
2SN
9☺ 2c c an 2 z s as 2 1 2SS
9t
sef ☺ c an 2z 1 as 2 s
2SN
9☺ c 2c an 2z s 1 as 2 2SS
9 C
34
n engages in FDI and s exports: If
nfe c an 2 z as 2 n 1 2SN
9☺ 2c
☺ c an 2 n z as 2 1 2SS
9 C
sfe ☺ c 2c an 2z 1 as 2 n
2SN
9t☺ c an 2z n as 2 2SS
9
Derivation of dz/dt
t of transport costs t on : 1zFirst consider the effec
[ ]2
1
/)1()D
tcdt
11 2)((Ddz +Θ++=
−Θ αρ − Θ∂ ∂
wher denotes the denominator in (5). Given our assumption of nonnegative profits (i.e. e 1D
1− 0)( >− cα ) and the fact that 0/ <∂Θ∂ t , it follows that sign =dtdz /1
])2)( Θ+Θ+[ (1 + ρD-sign . Subst yields sign signituting 1D =dt dz /1
-sign [ ] 02 < . )21(2 Θ+++ jj λφμ
The effect of t on is given by: 2z
[ ]22
2 /)122())1()1D
tcdt
ii ∂Θ2 (4)(1(Ddz ++Θ++=
φρ λ + −α − μ − ∂
where denotes the denominator in (6). Again, given our assumption of nonnegative profits 2D
(i.e. 0)122( >−−− ic μα ) and the fact that 0/ <∂Θ∂ t , it follows that sign =dtdz /2
-sign [ ]))1+(4)(1(2 −Θ++ iD λφ . Substituting sign 2D =dtdz /2 yields
-sign [ ] 0)1(4)1(2)1(2 <+++++ ρμλφ jj
35
36
<< INSERT TABLE A.1 ABOUT HERE >>
<< INSERT TABLE A.2 ABOUT HERE >>
Figure 1: Perfect intra-firm technology transfer
0.0 0.1 0.2 0.3 0.4 0.50.0
0.2
0.4
0.6
0.8
1.0
phi
z
(f,f)
(e,f)
(e,e)
37
Figure 2: Asymmetric intra-firm technology transfer
0.0 0.1 0.2 0.3 0.4 0.50.0
0.2
0.4
0.6
0.8
1.0
phi
z
(e,e)
(f,e)
38
Figure 3: Asymmetric backward and forward technology transfer skills
0.0 0.1 0.2 0.3 0.4 0.50.0
0.2
0.4
0.6
0.8
1.0
spillovers (phi)
z
(f,f)
(f,e)
(e,e)
a: 1 ;5.0 == ss μλ
0.0 0.1 0.2 0.3 0.4 0.50.0
0.2
0.4
0.6
0.8
1.0
spillovers (phi)
z
(f,f)
(f,e)
(e,e)
b: 5.0 ;1 == ss μλ
39
Table 1: Marginal cost functions n,s export n,s FDI n export, s FDI n FDI, s export snn aacf ρ−−= snnn aacf φλ−−= snn aacf ρ−−= snnn aacf φλ−−= nss azacf ρ−−= ∗ nsss azacf φμ −−= nsss azacf φμ −−= nss azacf ρ−−= ∗
snn aacf ρ−−=∗ snnn aacf φμ −−= ∗∗ snn aacf ρ−−=∗ snnn aacf φμ −−= ∗∗
nss azacf ρ−−= ∗∗ nsss azacf φλ−−= ∗∗ nsss azacf φλ−−= ∗∗ nss azacf ρ−−= ∗∗
Note: Asterisks denote values in the South (S) Table 2: The game in normal form n, s exports FDI exports ee
seen ΠΠ , ef
sefn ΠΠ ,
FDI fes
fen ΠΠ , ff
sffn ΠΠ ,
Table 3: Distribution of patents by application year appl. year Freq Perc Cum Perc 1987 423 0.09 0.09 1988 11,612 2.54 2.63 1989 31,411 6.86 9.49 1990 33,681 7.36 16.85 1991 36,552 7.98 24.83 1992 37,496 8.19 33.02 1993 37,577 8.21 41.23 1994 37,839 8.27 49.49 1995 37,534 8.2 57.69 1996 40,085 8.76 66.45 1997 41,306 9.02 75.47 1998 55,195 12.06 87.53 1999 57,099 12.47 100 Total 457,810
40
Table 4: Distribution of patents by country of first inventor Country Home Patents Perc Cum Perc Country Home Patents Perc Cum PercAustria 3,062 0.67 0.67 Japan 250,234 54.66 89.75 Australia 3,267 0.71 1.38 South Korea 13,283 2.90 92.65 Belgium 3,286 0.72 2,10 Luxemburg 167 0.04 92.69 Canada 12,640 2.76 4.86 Mexico 193 0.04 92.73 Czech Republic 36 0.01 4.87 Netherlands 4,406 0.96 93.69 Denmark 2,309 0.50 5.37 Norway 1,096 0.24 93.93 Finland 3,820 0.83 6.20 New Zealand 370 0.08 94.01 France 26,719 5.84 12.04 Poland 95 0.02 94.03 Germany 72,287 15.79 27.83 Portugal 30 0.01 94.04 Great Britain 20,761 4.53 32.36 Spain 1,164 0.25 94.29 Greece 41 0.01 32.37 Sweden 7,605 1.66 95.95 Hungary 543 0.12 32.49 Switzerland 8,875 1.94 97.89 Ireland 334 0.07 32.56 Slovak Republic 7 0.00 97.90 Israel 16 0.00 32.56 United States 9,602 2.10 100.00 Italy 11,562 2.53 35.09 Total 457,810
41
Table 4 continued: Distribution of US patents by country of assignee (firm) Country US Patents Perc Cum Perc Country US Patents Perc Cum Perc Austria 17 0.22 0.22 Japan 2,443 30.95 71.75 Australia 76 0.97 1.19 South Korea 271 3.45 75.20 Belgium 77 0.98 2.17 Luxemburg 73 0.93 76.13 Canada 939 11.95 14.20 Mexico 12 0.15 76.28 Czech Republic 1 0.01 14.13 Netherlands 572 7.28 83.56 Denmark 71 0.90 15.03 Norway 37 0.47 84.03 Finland 179 2.28 17.31 New Zealand 7 0.09 84.12 France 355 4.52 21.83 Poland - - - Germany 953 12.12 33.95 Portugal - - - Great Britain 377 4.80 38.75 Spain 20 0.25 84.37 Greece 3 0.04 38.79 Sweden 288 3.67 88.13 Hungary 1 0.01 38.80 Switzerland 932 11.86 100.00 Ireland 157 2.00 40.80 Slovak Republic - - - Israel - - - United States - - - Italy - - - Total 7,861 Table 5: Distribution of patents by number of US patent-citations No. US citations Freq Perc Cum Perc No. US citations Freq Perc Cum Perc 0 142,044 31.03 31.03 9 3,408 0.74 98.08 1 105,860 23.12 54.15 10 2,233 0.49 98.57 2 74,012 16.17 70.32 11 1,467 0.32 98.89 3 48,686 10.63 80.95 12 1,118 0.24 99.14 4 30,366 6.63 87.58 13 825 0.18 99.32 5 19,474 4.25 91.84 14 593 0.13 99.45 6 12,252 2.68 94.51 15 484 0.11 99.55 7 7,821 1.71 96.22 > 15 2054 0.45 100.00 8 5,113 1.12 97.34 Total 457,810
42
Table 6: Probit results for technology seeking strategies – n ≥ 1 (1) (2) (3) (4) (5) (6) n requirement none 1 1 1 1 1 seeking requirement
none none 5% 10% 25% 50%
R&D (x 100) 0.00 (0.35)
0.00 (0.21)
0.00 (0.21)
0.00 (0.30)
0.01* (0.04)
0.01* (0.01)
PROD (x 100) 0.05*** (0.00)
0.06*** (0.00)
0.06*** (0.00)
0.06*** (0.00)
0.06*** (0.00)
0.03 (.034)
VA -0.02*** (0.00)
-0.02*** (0.00)
-0.02*** (0.00)
-0.02*** (0.02)
-0.02*** (0.00)
-0.02*** (0.00)
sector dummies yes yes yes yes yes yes year dummies yes yes yes yes yes yes N 240,347 176,576 176,382 173,141 140,361 80,963 Pseudo R 2 0.04 0.04 0.04 0.04 0.04 0.04
Notes: Dependent variable is FS (1/0). P-values within parentheses (standard errors corrected for sectoral autocorrelation) Coefficients are (computed) marginal effects from Probit model † if p<0.10, * if p<0.05, ** if p<0.01, *** if p<0.001
43
Table 7: Probit results for technology seeking strategies – n ≥ 2 (1) (2) (3) (4) (5) (6) n requirement none 2 2 2 2 2 seeking requirement
none none 5% 10% 25% 50%
R&D (x 100) 0.00 (0.33)
0.01†
(0.05) 0.01† (0.05)
0.01† (0.05)
0.01* (0.02)
0.01** (0.00)
PROD (x 100) 0.05*** (0.00)
0.05** (0.00)
0.07** (0.00)
0.07** (0.00)
0.06** (0.00)
0.00 (0.85)
VA -0.02*** (0.00)
-0.02*** (0.00)
-0.02*** (0.00)
-0.02*** (0.00)
-0.02*** (0.00)
-0.02** (0.00)
sector dummies yes yes yes yes yes yes year dummies yes yes yes yes yes yes N 240,347 113,598 113,558 113,379 103,177 60,923 Pseudo R 2 0.04 0.04 0.04 0.04 0.04 0.04
Notes: Dependent variable is FS (1/0). P-values within parentheses (standard errors corrected for sectoral autocorrelation) Coefficients are (computed) marginal effects from Probit model † if p<0.10, * if p<0.05, ** if p<0.01, *** if p<0.001
44
Table A.1: Patent distribution by industry ISIC Rev.3 Code Industry Freq Perc Cum Perc 01 Agriculture 2,509 0.58 0.58 10-14 Mining and quarrying 3,907 0.90 1.48 15 Food and drinks 2,752 0.63 2.11 16 Tobacco 456 0.10 2.21 17 Textiles 9,128 2.10 4.21 18 Clothing 438 0.10 4.31 19 Leather and footwear 691 0.16 4.47 20 Wood and products of wood and cork 4 0.00 4.48 21 Pulp, paper and paper products 1,600 0.37 4.85 22 Printing and publishing 2,501 0.58 5.43 23 Mineral oil refining, coke and nuclear fusion 204 0.05 5.48 24 Chemicals 102,881 23.66 29.14 25 Rubber and plastics 890 0.20 29.34 26 Non-metallic mineral products 19,546 4.49 33.38 27 Basic metals 14,769 3.40 36.78 28 Fabricated metal products 15,246 3.51 40.29 29 Mechanical engineering 46,840 10.77 51.06 30 Office machinery 44,190 10.16 61.22 31 Manufacture of electrical machinery, n.e.c. 35,621 8.19 69.41 32 Radio, television and communication equipment 55,538 12.50 81.91 33 Manufacture of medical, precision and optical instruments 56,570 13.01 94.92 34 Motor vehicles 7,292 1.68 96.60 35 Railroad and transport equipment 2,515 0.57 97.17 36 Miscellaneous manufacturing 4,868 1.12 98.29 40 Electricity and gas 3,036 0.70 98.99 45 Construction 1,500 0.34 99.33 74 Business activities 269 0.06 99.93 80 Education 259 0.06 100.00
45
Table A.2: Descriptive statistics and correlations mean st. dev. 1 2 3 4 n ≥ 1 1. Technology seeking (FS) 0.02 0.12 1 2. R&D 1.42 7.50 0.001 1 3. Labor productivity 1.21 2.98 0.002 -0.011 1 4. Value added 0.69 0.54 -0.035 -0.025 0.272 1 n ≥ 2 1. Technology seeking (FS) 0.02 0.13 1 2. R&D 1.41 7.16 0.001 1 3. Labor productivity 1.23 3.30 0.002 -0.012 1 4. Value added 0.69 0.55 -0.037 -0.025 0.289 1 n ≥ 3 1. Technology seeking (FS) 0.02 0.15 1 2. R&D 1.39 6.86 0.005 1 3. Labor productivity 1.27 3.60 0.001 -0.014 1 4. Value added 0.68 0.56 -0.034 -0.029 0.305 1
46
47
Table A.3: Probit results for technology seeking strategies - n ≥ 3 (1) (2) (3) (4) (5) (6) n requirement none 3 3 3 3 3 seeking requirement
none none 5% 10% 25% 50%
R&D (x 100) 0.00 (0.33)
0.01† (0.06)
0.01† (0.06)
0.01† (0.07)
0.01* (0.04)
0.01** (0.01)
PROD (x 100) 0.05*** (0.00)
0.05** (0.02)
0.05** (0.02)
0.05** (0.02)
0.06** (0.02)
-0.03 (0.23)
VA -0.02*** (0.00)
-0.02** (0.00)
-0.02** (0.00)
-0.02** (0.00)
-0.02** (0.00)
-0.02** (0.00)
sector dummies yes yes yes yes yes yes year dummies Yes yes yes yes yes yes N 240,347 74,432 74,426 74,378 71,210 46,286 Pseudo R 2 0.04 0.04 0.04 0.04 0.04 0.04
Notes: Dependent variable is FS (1/0). P-values within parentheses (standard errors corrected for sectoral autocorrelation) Coefficients are (computed) marginal effects from Probit model † if p<0.10, * if p<0.05, ** if p<0.01, *** if p<0.001
1
1It should be noted that the micro-economic literature on learing-by-exporting is not conclusive in its results, as some studies demonstrate that productive firms self-select into exporting (Clerides et al., 1998; Bernard and Jensen, 1999; MacGarvie, 2006). 2Defining absorptive capacity in this way - i.e. as technological distance with respect to the leader - of course implies that the leader has perfect absorptive capacity ( =z ). 3This way of modelling technology seeking effort thus assumes that being spatially proximate does not automatically generate spilloversφ . An alternative approach would be to consider explicit R&D decentralization from the parent to the subsidiary as a necessary condition for technology seeking through FDI (Sanna-Randaccio and Veugelers, 2007). However, we would then have to model an additional R&D decentralization stage. For now, we prefer this simpler setup, but modelling explicit R&D decentralization is left for future research. 4For a formal proof on the derivation of these demand curves, see Motta (1996). 5In general, we require that all profits are nonnegative, i.e. that (see
Appendix) and that market size is positive for i)2()12(2 ρφλα −−−−−> ∗
jji azacc
iS SN ,= . In line with this requirement (and Siotis' (1999)
simulated parameter settings) we set 5=α , , 2== ∗cc 1=na 4/ , φρ = , 05.1=t , 100== SN SS20== ∗C sns aaaz
and C . Note that =≡ /
s
in this case. Values for t and C are chosen such that equilibrium results are not solely determined by the usual proximity-concentration trade-off (Brainard, 1997). In this way, we can explicitly focus on the technology seeking motives of both firms. 6There is a study by Fors (1997) who investigates the parent-firm rate of return on R&D generated by its subsidiary and vice versa, in s ample of 121 Swedish MNEs. He finds that of the technology generated by a MNE parent, about one-fifth is employed in its foreign subsidiaries. Conversely, of the technology generated and acquired in the subsidiaries, no significant amount is re-employed in the parent. Accordingly, we could calibrate the parameters as λ =0 and sμ =0.2. However, one problem is that Fors' study does not only pertain to low-productivity firms, so
that these values may not be applicable. Second, with sλ =0, the technology seeking function of FDI is restricted to the subsidiary, as no amount of knowledge can be transferred back to the parents. This would already ex ante induce any kind of firm interested in technology seeking to do so through exports, which obviously rules out any interesting comparisons between exports and FDI. 7The analytical part of this subsection up until the final paragraph of page 17 can be skipped without loss of continuity. The main text can be picked up from there. 8Note that since by definition t , 2≤ 1limΘ =1≥ Θ and that
∞→t
)1(
c− −9This holds under the assumption that the term α in the numerator of (z1) is positive, which always holds under the assumption of nonnegative equilibrium profits (cf. footnote 5). The same applies to ( )122 −− ic μ −α in condition (z2) below.\ 10Additionally, this result also depends on the assumption that knowledge spillovers are spatially bounded, i.e. that ρ φ< .
11Obviously, this is an assumption rather than a necessity, since foreign firms may also (temporarily) employ US inventors outside the US. Although this thus introduces a bias, we know of no previous studies that have tried to quantify this bias. 12It should be noted that the NBER database does not provide home country information for those patents applied for by foreign firms within the US. Hence, for these 9,602 patents we ran search queries on the United States Patent Office website (http://patft1.uspto.gov/netahtml/PTO/search-adv.htm) in order to find the relevant patent numbers. The difference between the 9,602 patents reported in Table 4 and the 7,861 reported in Table 5 is due to the fact that we could not trace all the home countries. 13Griffith et al. (2006) apply a similar argument in estimating the impact of US R&D on the productivity of UK firms that undertake research in the US.
48