Embed Size (px)
Citation preview
FDI and indigenous innovation: Foreign R&D subsidiaries as a
medium for the geographic diffusion of knowledge
Joel Blit*
Department of Economics
University of Waterloo
200 University Ave. West
Waterloo, ON, Canada N2L 3G1
ABSTRACT: Governments promote inward foreign direct investment in
the belief that it will generate positive productivity spillovers. This paper
examines a mechanism through which such spillovers may be generated:
the subsidiaries of multinational enterprises (MNEs) may act as a
medium for the geographic diffusion of remote knowledge. The results
suggest that the presence of a foreign MNE subsidiary increases the flow
of knowledge from the MNE's headquarters to firms in the subsidiary’s
host country. This subsidiary effect on knowledge diffusion is largest in
host countries and sectors with strong but not world-class capabilities
that have both the motivation and absorptive capacity to learn from
foreign parties. The findings also suggest that knowledge diffusion is
greatest when subsidiaries are staffed with local staff and not expatriates.
Keywords: knowledge flows, diffusion, innovation, foreign direct investment, patents
JEL Classification: O3; F2; L2
* I am grateful to Ajay Agrawal, Ignatius Horstmann, Joanne Oxley, and Daniel Trefler
for their indispensable guidance, helpful comments, and discussions. Gilles Duranton
offered valuable suggestions as a discussant for an early version of this paper. I
gratefully acknowledge funding from the Social Sciences and Humanities Research
Council of Canada. I acknowledge the National University of Singapore and the
Melbourne School of Business for giving me access to their patent data. Any errors and
omissions are my own.
1
1. Introduction Multinational enterprises (MNEs) are increasingly conducting their R&D in
emerging markets. As of 2010, Fortune 500 companies had 98 R&D facilities in China
and 63 in India.1 Cisco is investing more than US$1 billion to build a second global
headquarters in Bangalore, while Microsoft's Beijing R&D Centre is now the firm's
largest outside of its headquarters.1 Developed-world MNEs are establishing themselves
in emerging countries not only to access their already large and growing markets but also
to tap into the multitude of qualified science, engineering, and computer science
graduates that these countries are producing. And by going into these countries, they
may be promoting development in their host country.
Indeed, many countries have implemented policies to attract foreign MNEs
operating in knowledge-intensive sectors in the belief that the activities of their
subsidiaries will generate spillover benefits to local firms. Given this context, it is not
surprising that numerous scholars have conducted empirical studies to determine whether
FDI indeed confers spillover benefits. Particularly salient is the work of Haddad and
Harrison (1993) and Aitken and Harrison (1999), who use micro-level panel data from
Morocco and Venezuela to determine whether FDI affected the productivity of domestic
firms. More recently, Haskel et al. (2002), Keller and Yeaple (2003), Javorcik (2004),
and Kee (2015) have examined the productivity spillover effects of FDI in the U.K., the
U.S., Lithuania, and Bangladesh, respectively. Overall, the evidence is mixed, in part
because productivity gains as a result of FDI are difficult to measure in the face of
numerous possible confounding factors (Gorg and Strobl 2001, Gorg and Greenaway
2004).
Perhaps, then, the way forward is to more closely examine specific mechanisms
through which FDI may increase productivity in the host country. For example, it might
be the case that productivity increases are achieved through the fostering of indigenous
innovation by the presence of multinational subsidiaries. Such a relationship has yet to
be methodically examined in the literature, and one contribution of this paper is to
present qualitative evidence that this is the case. As shown in Figure 1, it seems that, at
least in China, innovative activity by foreign multinationals is associated with increased
1 The Economist, "Special report on innovation in emerging markets," April 17, 2010. p. 4.
2
indigenous innovation in the same sector some years later. The figure presents Chinese
indigenous patent counts (patents developed in China by Chinese firms) and MNE
offshore patent counts (patents developed in China by foreign multinationals) over time
for the four technological subcategories with the most patenting activity: electrical
devices, power systems, drugs, and computer hardware and software. In each of these
sectors, there is initially no innovation, and we observe the rise of indigenous innovation
some years after multinationals enter China and start innovating there. This relationship
is examined more formally and for more countries in Section 5.
If subsidiaries are encouraging indigenous innovation, it may be because they are
generating knowledge spillovers that provide indigenous firms with inputs for the
innovation process. In particular, subsidiaries may be the knowledge link between
indigenous firms and the outside world. Examining this role of subsidiaries as a medium
for the geographic diffusion of knowledge is the primary focus of this paper.
Knowledge spillovers, in general, have received much attention given their central
role in economic growth (Romer 1990). Numerous scholars have examined the extent to
which these knowledge spillovers are localized; while the results are mixed, the
consensus is that there is some degree of localization (Jaffe, Trajtenberg, and Henderson
1993, Audretsch and Feldman 1996, Keller 2002, Thompson and Fox-Kean 2005,
Thompson 2006). How then, do firms in emerging countries get access to knowledge
generated in the developed world? Coe and Helpman (1995), Keller (1998), and
MacGarvie (2006) examine whether the import of manufactured goods can serve as a
channel for knowledge flow. More recently, van Biesebroeck (2005), De Loecker (2007),
and Lileeva and Trefler (2010) have also examined the possibility of learning by
exporting.
A different way that firms in emerging countries may access remotely generated
knowledge is through MNE subsidiaries that locate near them. This paper presents
evidence that subsidiaries increase the flow of knowledge (as measured by patent
citations) from the MNE's headquarters to firms located in the subsidiary's host country.
Due to the localized nature of knowledge, such a medium for the geographic diffusion of
knowledge might be particularly crucial for firms in emerging countries that do not yet
have the foreign presence or networks to tap into remote sources of know-how.
3
The first to study the relationship between FDI and knowledge flows was
Branstetter (2006), who examines a group of Japanese firms and finds a positive
relationship between the firm's level of outward FDI to the U.S. and the number of
citations that the firm’s patents receive from U.S. patents. As expected, he finds the
relationship to be particularly strong for Japanese FDI investments in American R&D and
product development facilities. In a more recent working paper, Fons-Rosen (2012)
analyzes changes in the degree to which firms in Central and Eastern Europe cite the
patents of foreign firms entering the market. He ingeniously employs a difference in
difference analysis where the treated group is the foreign firms who won privatization
tenders (and hence entered the country) and the control group consists of the losing
bidders. He finds that, on average, the winning bidders experienced a 20% increase in
citations received relative to the losers.
However, such results do not necessarily imply that subsidiaries facilitate the flow
of geographically remote knowledge (from the headquarters) into the host country. It
could simply be that the increased citation rates stem from citations to patents (and
knowledge) developed by the subsidiary in the host country since knowledge diffuses
locally. Therefore, to determine whether subsidiaries act as channels for the geographic
diffusion of remote knowledge, this paper examines whether there is an increase in the
number of domestic firm citations to the stock of patents generated in the headquarters of
the firm doing FDI.
This paper also differs from previous work on FDI and knowledge diffusion in
that, contrary to the large majority of the literature that focuses on one country, or at most
one region, the analysis conducted here examines the effect over 121 host countries. This
allows not only a more general treatment of the topic but also a cross-country comparison
of the country characteristics that facilitate or hinder the flow of remote knowledge
through the medium of the subsidiary. One important finding in this respect is that a host
country’s level of technological development is a critical factor, with knowledge
diffusion through the subsidiary being greatest in countries and sectors with strong, but
not world-class, capabilities. In addition, this analysis spans a much longer timeframe
than other studies (1976-2004).
4
Finally, this paper also considers whether subsidiary staffing policy impacts
knowledge diffusion though the subsidiary. While geographic knowledge diffusion
through the subsidiaries is beneficial from the perspective of international growth and
development, the MNE may be rightly concerned that it weakens its competitive position.
The MNE may thus take measures to better protect its knowledge when venturing abroad.
One such measure may be to staff the subsidiary with expatriates since they have
shallower links to the local environment. Indeed, the results suggest that knowledge
leakage is smaller in subsidiaries that are staffed with higher ratios of expatriates,
although the analysis does not establish a causal relationship.
The paper is organized as follows. Section 2 briefly discusses why we might
expect the subsidiaries of multinational firms to promote the geographic diffusion of
knowledge into their host country and why this flow of knowledge might depend on the
subsidiary’s staffing policy and the country’s existing technical capabilities. Section 3
discusses the data and methodology used to address this question and section 4 presents
the results and discusses the findings. Section 5 offers a first look into the larger question
of whether subsidiaries promote indigenous innovation. Finally, section 6 concludes.
2. Background
The principal question addressed in this paper is whether the subsidiaries of
multinational firms act as a medium for the geographic diffusion of knowledge. In
particular, the paper asks whether subsidiaries give indigenous firms in the subsidiary’s
host country increased access to knowledge generated in the subsidiary’s headquarters.
For subsidiaries to effectively increase this flow of knowledge, two things must
happen. First, MNE headquarters must share its technological expertise with its
subsidiaries. Second, this knowledge must spill out from the subsidiary to local firms.
The fact that, in general, a headquarters shares knowledge with its subsidiaries should not
come as a surprise. As Dunning (1977) and Kogut and Zander (1993) argue, the very
existence of MNEs may be due to the fact that they are an efficient organizational vehicle
through which to transfer and share knowledge across borders. MNEs that fail to share
knowledge among their different locations lose many of the advantages of being an MNE.
5
We would therefore expect MNEs to actively share headquarters knowledge with its
subsidiaries.
Once a subsidiary has received knowledge from the parent, this knowledge can
spill over to local firms through the usual diffusion mechanisms of local input-output
linkages, social networks, and labor mobility (Von Hippel 1988, Rogers and Larsen 1984,
Almeida and Kogut 1999, Agrawal, Cockburn, and McHale 2006). Whether or not this
overall flow of knowledge - from headquarters to subsidiary to other firms in the
subsidiary’s location - occurs, is an empirical question and the primary focus of this
paper.
We would, of course, expect the magnitude of this overall knowledge flow to be
mediated both by characteristics of the MNE and of the local environment in which the
subsidiary resides. MNEs have an incentive to institute policies and mechanisms that
curtail outward knowledge flow to potential competitors. One way they might achieve
this is for subsidiaries to focus on technologies that are more internally-oriented and
hence useless to third parties [Zhao (2006)].
Another way that firms may be able to protect their knowledge is by staffing key
R&D positions in their foreign units with expatriates instead of locals. While this may
seem counterproductive, given that expatriates are likely to possess more critical
technical capabilities and institutional knowledge of the firm, expatriates are also less
likely to allow knowledge to diffuse to their local environment. By virtue of being
foreign, expatriates are devoid of the local social networks that facilitate localized
knowledge spillovers. They are also less likely to move to another local firm, thereby
passing on their knowledge. Overall, the effect of staffing subsidiaries with expatriates
on total knowledge diffusion is ambiguous: it increases the stock of subsidiary knowledge
that potentially can diffuse, but the likelihood of a given piece of knowledge being passed
on is lower. It is therefore an empirical question: the findings suggest that subsidiaries
staffed with high ratios of expatriates experience less leakage of their headquarters’
knowledge, although the analysis does not establish causality.
A second important factor that mediates the overall flow of knowledge from
headquarters to firms in the subsidiary’s location is the characteristics of local firms. As
Cohen and Levinthal (1990) argue, firms with little expertise in a particular sector may
6
fail to recognize the value of new information or may not be able to assimilate it. In an
international context marked by great disparities in technological capabilities across
countries and sectors, the absorptive capacity (or lack thereof) of local firms is likely to
be a primary driver of whether local firms benefit from the presence of more advanced
MNEs. Hence, we would expect knowledge inflows from MNE headquarters to be
greater in more technologically advanced countries and sectors.
The caveat to this argument is that country/sectors that are already world-class
have little to gain from MNEs that are based in less-advanced countries. Instead, they are
likely to focus their energy on learning from other firms in their home country.
Consistent with this, Singh (2007) finds that knowledge inflows from foreign MNE
subsidiaries to local firms are smaller than knowledge outflows only in technologically
advanced countries.2 The combination of these two arguments leads to the hypothesis that
knowledge diffusion from the MNE headquarters to local firms may be an inverted U
function of the host country's capability in the given technological sector.3 This is indeed
confirmed by the empirical findings.
3. Data and Methodology
To determine whether the subsidiaries facilitate the flow of headquarters
knowledge to third-party firms in their host country I use data on patents granted by the
United States Patents and Trademarks Office (USPTO) from two separate sources. The
NBER Patent Data Project provides a dataset with the application year, technology class
(type), technology subclass (subtype), assignee (owner), and assignee headquarters
country of all patents granted between 1976 and 2006, inclusive. This is supplemented
with inventor country and citation data from the NUS-MBS Patent Database.4 Since the
latter is restricted to patents granted up to 2004, the final sample consists of all patents
granted between 1976 and 2004.
2 Singh’s focus is on local knowledge flows between subsidiaries and host country firms. He does not
consider remote knowledge flows between the headquarters and firms in the host country. 3 More formally, this hypothesis would follow by claiming that both the costs and benefits of knowledge
acquisition from foreign subsidiaries are decreasing in their own technological capabilities (C'<0, B'<0) but
that benefits decrease faster than costs (C''>0, B''<0). 4 The National University of Singapore-Melbourne Business School(NUS-MBS) Patent Database is
available at: http://kwanghui.com/patents/.
7
Patents are assigned to an innovating country based on the residence of the
inventors. Implicit in this definition is the belief that the inventor’s country of residence
most closely proxies for the location where the process of innovating occurred.
Knowledge flows are measured using patent citations. Although citations are a noisy
measure of knowledge flows, studies comparing citation data with inventor surveys have
shown that the correlation between patent citations and actual knowledge flows is high
enough to justify their use in large samples [Jaffe and Trajtenberg, Chapter 12 (2002),
Duguet and MacGarvie (2002)].
The first step in the construction of the dataset consists of identifying firms with
patenting activity in more than one country. While the country of the headquarters is
listed directly in the data,5 the other locations (henceforth referred to as the "subsidiaries")
are determined by looking at the other locations where the firm has innovative activities
(using the country of residence of the firm's patents' inventors). In total, 7264 firms with
foreign innovative activities are spread over 12,953 subsidiaries. These subsidiaries have
generated on average seven U.S. patents, with 6987 generating only one patent, 2090 two
patents, 1007 three patents, and 2869 four or more patents.6
There are two types of cited patents in the analysis: treated and control. The set
of treated patents is composed of all patents generated in the headquarters of these MNEs.
These patents are “treated” in the sense that the knowledge they embody may have
diffused to the host country through the medium of the subsidiary. In order to control for
patterns of geographic agglomeration of technological activity that could be related to the
choice of location for the subsidiary, I generate a matched control group.7 In particular,
each patent in the treated group is matched to a control patent from the same country,
application year, technology class, and if possible technology subclass. Moreover, the
assignee of the control patent cannot have patented in the same country as the treated
firm's subsidiary (i.e., the owner of the patent cannot have a subsidiary in the same host
country). If several potential control patents match the criteria, I choose the patent
belonging to the MNE whose number of locations most closely matches the number of
5 In more than 95% of firms, the "headquarters" country is also the country with the most patenting activity. 6 Focusing on subsidiaries with larger patent outputs yields larger estimates of the subsidiary effect on
knowledge diffusion. 7 The methodology builds on that of Jaffe et al. (1993) and Agrawal et al. (2006).
8
locations of the treated firm. Patents belonging to single-location firms are chosen as a
last resort. If two or more patents are equally suitable controls according to these six
criteria, one is picked randomly. Any treated patent with no matching control is dropped
from the sample. I find controls for 97% of the 10,453,898 potential treated
patent/subsidiary combinations, of which 5,517,325 matches are at the subclass level and
4,620,901 at the class level.8
Figure 2 provides an illustration of the methodology for Microsoft Corporation's
Chinese subsidiary.9 The question of interest is whether headquarter patents are
disproportionately cited by third parties in the locations where the headquarters have a
subsidiary. In particular, the analysis compares the proportion of citations received by
patents in the treated group that are from third-party patents in the subsidiary's location
(�̂�𝑇) to the proportion of citations received by patents in the control group that are from
third-party patents in the subsidiary location (�̂�𝐶). We can interpret the ratio of these as
the MNE subsidiary's effect on knowledge diffusion.
Since knowledge diffuses locally, using countries as the geographic level of
analysis could significantly bias the results downward.10 For example, it is not clear
whether a subsidiary in Beijing would help headquarters knowledge diffuse to firms
located in Shanghai. The results presented in Section 4.3 indeed suggest the presence of
this bias since countries with a more concentrated urban population experience higher
measured knowledge flows. Therefore, we may want to place less emphasis on the
estimated magnitude of this MNE effect on knowledge diffusion and instead view the
estimate as a lower bound on the actual effect. On the other hand, how this effect differs
across host countries can provide relevant insights for management and policy. In
particular, is it the case that this effect is larger in technology-poor countries where
MNEs presumably play a more pivotal role in the inward diffusion of knowledge?
8 There are a total of 428 different three-digit U.S. patent technology classes (with the most active being
"drugs" and "semiconductors") and more than 100,000 technology subclasses. 9 Microsoft Corporation is the U.S. firm with the most patenting activity in China. Research at the Beijing
lab focuses on natural user interface, next generation multimedia, data-intensive computing, search and
online ads, and computer science fundamentals. 10 The geographic unit of analysis is at the country level due to data limitations. Reliable city-level
inventor locations are only available for the U.S. and Canada.
9
GDP per capita is too aggregate a measure of technological capability since
countries are heterogeneous in their technological capabilities across sectors. A more
appropriate measure is the patent stock per capita in any given country/sector/year.
Patent stocks are constructed by counting patents in a given country, technology class,
and up to a given year. A discount rate of 15% per year is applied to patents from
previous years to account for technological obsolescence.11 These patent stocks are
normalized by the host country's population (as obtained from the UN Statistics Division)
in the given year to obtain patent stocks per capita. The baseline estimating equation is:
(�̂�𝑇 − �̂�𝐶)𝑝𝑖
= 𝛼 + 𝛽1𝑃𝑎𝑡𝑆𝑡𝑜𝑐𝑘𝑝𝑐𝑖𝑐(𝑝)𝑡(𝑝) + 𝛽2𝑃𝑎𝑡𝑆𝑡𝑜𝑐𝑘𝑝𝑐𝑖𝑐(𝑝)𝑡(𝑝)2 + 𝛽3𝑿𝑖𝑡(𝑝) + 𝛽4𝑿𝑖
+ 𝛽5𝑿𝑖𝑗(𝑝) + 𝛼𝑡(𝑝) + 𝛼𝑐(𝑝) + 𝜀𝑝𝑖
The dependent variable is the proportion of citations to the treated patent that is from the
host country minus the proportion of citations to the associated control patent that is from
the host country.12 The unit of observation is the headquarters (treated) patent p and
subsidiary country i. The independent variable PatStockpcic(p)t(p) is the patent stock per
capita for the subsidiary country i, in the technology class of the treated patent c(p), in the
application year of the treated patent t(p). Xit(p) , Xi , and Xij(p) are vectors of control
variables at the subsidiary country/year, subsidiary country, and subsidiary
country/headquarters country levels. respectively. 𝛼𝑡(𝑝) and 𝛼𝑐(𝑝) are year and
technology class fixed effects.13 Table 1 presents the description and source of these
control variables, and summary statistics are presented in Table 2.
4. Results
11 In addition, this is multiplied by the correction term
1 .85200419761
1 .85t19761 (where t is the stock year) to
adjust for the fact that the dataset has no observations prior to 1976.
12 Using
ˆ P T / ˆ P C as the dependent variable results in a loss of 90% of the observations due to values of
ˆ P C 0 . As Table 7 shows, this alternative dependent variable does not change the principal results (and
in particular the confirmation of the inverted U hypothesis). 13 Due to computational limitations, technology class fixed effects cannot be utilized, and instead fixed
effects at the more aggregate subcategory level are used. The data contains 443 technology classes
aggregated into 37 technology subcategories. Demeaning the dependent variable by technology class
before running the regression does not change the results presented in Section 3.3.2.
10
4.1 Subsidiary Effect on Knowledge Diffusion
A primary hypothesis of this paper is that the subsidiary is a medium for the
geographic diffusion of knowledge, allowing firms in the host country to receive a
disproportionately large amount of knowledge generated in the headquarters of the firm
that established the subsidiary. The findings presented in Table 3 (Column 1) are
consistent with this. While 3.52% of the citations received by the MNEs' headquarters
(treated) patents are from third parties in the location of their subsidiary, only 3.06% of
the citations received by the control group are from third parties in the location of the
corresponding treated patent's subsidiary (�̂�𝑇=.0352 and �̂�𝐶=.0306). Performing a
difference of proportions t-test with unequal variances as in Almeida (1996) yields a t-
statistic of 144.1.14 I compute the effect of the subsidiary on knowledge diffusion to the
host country as �̂�𝑇
�̂�𝐶= 1.15, suggesting that host country firms receive on average 15%
more knowledge from the headquarters.
Columns 2-4 present the results when the same analysis is performed separately
for emerging, middle-income, and developed countries. Countries are classified into
these groups based on 1990 per capita GDP measured at current U.S. dollars as obtained
from the United Nations Statistics Division. Countries with per capita GDP of less than
$5000 are classified as emerging, between $5000 and $15000 as middle-income, and
above $15000 as developed. Table 4 shows each group's countries with the most
patenting activity (though all 121 countries with a subsidiary are included in the analysis).
As expected, the diffusion effect is biggest for emerging host countries.15
4.2 Difference in Differences
It could be the case that the finding of a subsidiary effect on knowledge diffusion
is spurious and results from firms choosing to set up subsidiaries in locations that are
14 Letting H0: PT = PC and H1: PT > PC , I calculate the t-statistic as:
t = P̂T - P̂C( ) / P̂T 1- P̂T( ) / nT + P̂C 1- P̂C( ) / nC , where nT and nC are the total number of citations
received by the treated and control group, respectively. 15 The comparison is qualitative in nature because there is no clear statistic to test the hypothesis that the
diffusion effect is bigger for developing countries than for developed ones.
11
disproportionately sourcing their headquarters' knowledge. For example, firms may
establish subsidiaries in locations that specialize in the same technological areas as
themselves. Then, if control patents match imperfectly, the fact that patents are more
likely to cite within their narrow technology class could lead to finding a subsidiary effect
when none exists. This section addresses this concern, finding that the subsidiary effect
is robust to this critique.
The endogeneity of the location decision is addressed using the time structure of
the data. The principal challenge with deploying this approach is that the date of
establishment of a subsidiary is not directly observed in the patent data. What is
observed is a proxy for the date of establishment: the application date of the first patent
from the subsidiary location. Since patents are generally the result of many years of
development (and many subsidiaries may initially perform non-innovative tasks), the
year of the first application will invariably lag behind the actual year of establishment.
Since the years right before the first application date of a subsidiary patent are the most
problematic to define as pre- or post-subsidiary, I define a "grey window" as the period
starting w years before the first subsidiary patent application and ending the year before
the first patent application.16 This window is dropped from the analysis, and the pre-
subsidiary period is defined as ending w+1 years prior to the first subsidiary patent. The
post-subsidiary period begins the year the first subsidiary patent application is observed.
Clearly, it will still be the case that many subsidiaries are operating and diffusing
knowledge in what has been defined in this way as the “pre-subsidiary” period
(especially for smaller windows). However, this will bias against finding a difference in
knowledge diffusion after the date of the first application.
To ensure that the pre- and post-subsidiary patents are similar, I restrict the
sample to patents from firms that have headquarters patents in each of the pre- and post-
subsidiary periods. I then employ a difference in differences methodology. Observations
are at the patent level, with a treated patent and its associated control patent being
separate observations. The dependent variable (match_ratioi) is the proportion of patent
i's citations that are from third-party patents in the subsidiary location. The explanatory
variables include a dummy variable (treated) that is 1 when the patent is from the treated
16 As will be shown, the results are consistent across different values of w.
12
group and 0 if it is from the control group, a dummy variable (postsub) that is 1 when the
patent's application year is in the post-subsidiary period and 0 if it is in the pre-subsidiary
period, and the interaction (treated_postsub) of these two variables. Table 5 presents the
results of an OLS regression with clustering by firm and robust standard errors for
different window sizes.
The interaction term treated_postsub is highly significant in all cases, suggesting
that the establishment of the subsidiary facilitates knowledge diffusion to third-party
firms in their host country. Given the previously discussed bias associated with not
knowing the actual date of establishment, it is not surprising that the coefficient on
treated is positive and significant. The large decrease in the size of this coefficient and
increase in the coefficient of treated_postsub as larger windows are chosen offers
evidence that this bias is indeed present. Overall, Table 5 suggests that at the very least,
the large majority of the subsidiary effect computed in Section 4.1 is due to actual
knowledge flows through the subsidiary and not to firms establishing subsidiaries in
locations where third-party firms already disproportionately source headquarters'
knowledge.
4.3 Technological Capability
As previously discussed, we expect an inverse U relationship between the amount
of knowledge diffusion received through subsidiaries and the technological stock of the
host country. This hypothesis is tested by regressing (�̂�𝑇-�̂�𝐶) on patent stocks per capita
The results are presented in Tables 6 and 7. While this is the preferred specification
because it allows for values of �̂�𝐶 = 0, the results for the alternative dependent variable
(�̂�𝑇/�̂�𝐶) are presented in Table 8.
Table 6 presents the results of an OLS regression with no country fixed effects.
The coefficients on patent stock per capita and its square are highly significant and
consistent with an inverted U relationship.17 The apex of the estimated inverted U is at a
patent stock per capita value of 4.96e-5, which is well within the 0 to 8.38e-4 range of
patent stock per capita but in the 99.8th value percentile of the variable. This result
17 The coefficients are almost identical when running the four regressions on the original patent stock
variables without taking the natural logarithm.
13
suggests that the most prominent issue for most countries/sectors is a lack of absorptive
capacity that limits their ability to learn from the subsidiary. However, the most
technologically sophisticated countries/sectors also gain less from foreign subsidiaries
since these have little to teach them, though this only applies to truly world-class
clusters.18
The estimated coefficients on the control variables also offer interesting insights.
The coefficient on largest city population % is, as expected, positive and significant at
the .1% level. To reiterate, the motivation for including this variable is that we would
expect a subsidiary in Beijing, for example, to generate spillovers to indigenous firms in
Beijing but not necessarily to indigenous firms in Shanghai. Our finding is consistent
with subsidiaries transferring knowledge to firms in their local (city) environment.
Highly decentralized countries tend to experience a smaller subsidiary effect because the
subsidiary’s presence benefits a smaller fraction of the country’s firms (primarily those
that are located in the same city).
The host country's GDP per capita and its square are also significant but only
when using their logged values. Countries with higher Internet penetration and telephone
installations benefit less from the presence of subsidiaries. While this result seems
counterintuitive, the Internet (and the telephone) facilitates the global flow of knowledge
and may hence be a substitute for the acquisition of knowledge through subsidiaries.
More knowledge is exchanged between countries that have a shared primary language
since this facilitates communication. Countries with a shared colonial past (legal origin)
benefit less from the presence of a satellite, perhaps because of alternative shared
institutions through which knowledge is exchanged.
In Columns 1 and 2, the coefficients on infrastructure and rule of law are also
significant. However, these are host country level variables; when the standard errors are
clustered by host country (Column 3), they lose their significance.
Table 7 incorporates host country fixed effects. The results suggest that the
relationship between patent stock per capita and knowledge acquisition through foreign
18 It may be the case that this applies more broadly but the highly aggregated technological sectors are
masking the effect. A country may be at the technological frontier (and not learning) in half of the
subsectors that make up a sector but some distance behind the frontier (and learning) in the other subsectors.
14
subsidiaries also applies across technological sectors within countries.19 Column 3
presents the results for a Tobit regression with left censoring of the dependent variable at
-1 and right censoring at 1. The estimated coefficients are unchanged. Table 8 shows
that the results are robust to using (�̂�𝑇-�̂�𝐶) as an alternative dependent variable.
4.4 Expatriates and Knowledge Diffusion
While we do not directly observe the number of expatriates in a particular
subsidiary, we can construct a proxy using inventor mobility between the headquarters
and the subsidiary. In particular, I count the number of inventors who generated a patent
in the headquarters location and subsequently in the subsidiary location. This count is
normalized by the total number of different inventors having generated a patent in the
subsidiary location. The variable expat then measures the fraction of subsidiary inventors
who were originally from the headquarters location. The estimating equation is:
(�̂�𝑇/�̂�𝐶)𝑓𝑖
= 𝛼 + 𝛽1𝑒𝑥𝑝𝑎𝑡𝑓𝑖 + 𝑠𝑢𝑏𝑠_𝑝𝑎𝑡𝑠𝑓𝑖 + 𝐻𝑄_𝑝𝑎𝑡𝑠𝑓 + 𝑙𝑜𝑐𝑠𝑓 + 𝛼𝑓 + 𝛼𝑖 + 𝜀𝑓𝑖.
The unit of observation is the subsidiary (firm f and subsidiary country i).20 This
higher level of aggregation permits the use of (�̂�𝑇/�̂�𝐶) as the dependent variable. The
independent variable expatfi is our explanatory variable of interest. The variables
subs_patsfi (number of patents generated by the subsidiary), HQ_patsf (number of patents
generated by the headquarters), and locsf (number of countries where the firm has
patented) are controls for firm size, and 𝛼𝑓 and 𝛼𝑖 are firm and host country fixed effects,
respectively.
The first two columns of Table 9 present the results with no firm fixed effects.
Subsidiaries with high ratios of expatriates experience significantly lower rates of
knowledge leakage. The coefficient on the ratio of expatriates is significant at the .1%
level (Columns 1 and 2). Among the controls, only the size of the headquarters is
significant. One explanation is that third parties can more readily monitor from a
19 The results are similar when absolute patent stocks are used instead of per capita patent stocks. 20 Multiple firm subsidiaries in the same country are lumped as a single subsidiary due to data limitations.
15
distance the innovative activities of larger firms while smaller firms largely fall under the
radar unless they have a presence in the country.
The coefficient on expatfi is larger when firm fixed effects are added to the
regression, though it is only significant at the 6% level. The weaker significance is not
surprising given the limited number of within-firm observations. Of the 7264 firms in the
sample, more than 70% have only a single foreign subsidiary and only 15% have three
subsidiaries or more.
5. Indigenous and MNE Subsidiary Innovation
The principal focus of this paper is to present evidence that subsidiaries facilitate
the flow of foreign knowledge to firms in their host country. However, arguably a more
important objective is to determine the effect of subsidiaries, not on knowledge flow, but
rather on more economically significant variables like innovation and productivity.
There should be a relationship between knowledge flows and innovation since knowledge
is an important input into the innovation process. Thus, given our findings from section 4,
we would expect that the presence of MNE subsidiaries would ultimately result in
increased indigenous innovation and that this would be particularly true in emerging
countries where firms have access to few other sources of knowledge.
This section examines whether the country-level evidence is consistent with this
hypothesis. If so, we should observe that increases in multinational innovative activity in
an emerging country should be associated with increased indigenous innovation in the
same sector some years later. Figure 1 presents Chinese indigenous patent counts
(patents developed in China by indigenous firms) and offshore patent counts (patents
developed in China by foreign multinationals) over time for the four technological
subcategories with the most patenting activity: electrical devices, power systems, drugs,
and computer hardware and software. We observe little indigenous innovation until
multinationals enter the country and start innovating domestically. As this section will
show, increases in offshored innovation are associated with future increases in indigenous
innovation in emerging countries, but not in developed countries where technological
capabilities already largely exist and do not need to be imported through MNEs.
16
5.1 Data
Innovation is measured using patents granted by the United States Patents and
Trademarks Office (USPTO). As before, the dataset provided by the NBER Patent Data
Project provides the application year, patent technology class, and assignee home country
of all patents granted between 1976 and 2006, inclusive. Inventor data is obtained from
the NUS-MBS Patent Database; since it only contains patents granted up to 2004, this
restricts the sample to the grant years 1976-2004. As before, patents are assigned to an
innovating country based on the residence of the inventors.
The analysis focuses on a patent's application year because it captures the timing
of an innovation better than the patent's year of granting. In addition, since the delay
between applying for a patent and obtaining it varies greatly across patents, technology
classes, and over time, using the grant year would introduce additional noise (see Figure
3). Unfortunately, using the application year could result in a selection bias due to
truncation. For example, since our dataset includes patents granted up to the end of 2004,
the only patents with a 2004 application year are the ones that were granted within 12
months of application. As Figure 3 shows, the truncation problem is most pronounced in
the years 2001-2004; therefore, these years are dropped from the sample.21 The sections
that follow restrict the analyses to the application years 1986-2000.22
For the purpose of the analysis, countries are classified into three groups as before,
based on 1990 per capita GDP measured at current U.S. dollars as obtained from the
United Nations Statistics Division. Countries with per capita GDP of less than $5000 are
classified as emerging, between $5000 and $15000 as middle-income, and above $15000
as developed. The final sample consists of each group's countries with the most patenting
activity.23 The countries in the sample and their group assignations are listed in Table
4.24
21 98% of patents are granted within four years 22 Since innovation by emerging countries is a relatively recent phenomenon, the analysis uses the most
recent possible sample. Nonetheless, using an older sample to better address truncation yields the same
qualitative results. 23 As will soon become apparent, countries with little innovative activity will be at best uninformative and
in all likelihood will significantly increase the noise in the results. 24 The sample includes all emerging countries with 175 or more patents with the exception of Russia and
the Czech Republic, which are omitted because of difficulties in allocating patents prior the breakup of the
USSR (1991) and Czechoslovakia(1993), respectively; all middle-income countries with more than 1300
17
The principal variables of interest are total offshore patent counts and total
indigenous patent counts by country, technology class, and application year. A patent is
counted as indigenous to country i if all inventors are from country i and the assignee
headquarters is also in country i. A patent is counted as offshore to country i if at least
one inventor is from country i and the assignee headquarters are in a country other than
i.25
5.2 Methodology
To test whether offshored innovation promotes indigenous innovation, I compute
Kendall’s τ on the first difference of indigenous patent counts and lagged offshored
patent counts (see Appendix A for a discussion of measures of association and Kendall's τ
in particular). Kendall's τ measures the relationship between two variables by considering
every pair of observations and counting the number of such pairs that are concordant
versus discordant. A pair of observations (Xi, Yi) and (Xj, Yj) is said to be concordant if
either Xi < Xj and Yi < Yj or Xi > Xj and Yi > Yj. In this case, τ takes on the value 1.
Similarly, a pair is discordant if either Xi < Xj and Yi > Yj or Xi > Xj and Yi < Yj, in which
case τ=-1. Observation pairs for which either Xi = Xj or Yi = Yj are said to be tied and
τ=0. Because τ focuses on the ordering and not the exact magnitude of a pair of
observations, it is a robust statistic (though the cost is a loss of information).
As shown in Figure 1, indigenous and offshored patent counts are trending
variables. A correlation between the two may then be spurious and driven by omitted
factors. I address this by looking at the relationship between their first differences.26
Therefore, X will be the lagged increase/decrease in offshored patent counts across two
subsequent periods and Y the increase/decrease in indigenous patent counts across two
subsequent periods. To reduce the volatility in patent counts for a particular country and
patents, with the exception of Spain; and all developed countries with more than 10,000 patents, with the
exception of the U.S. since U.S. patent data is being used for the study. 25 The same patent can be an offshore patent for more than one country. However, the number of such
occurrences is small; this does not cause inconsistencies for the issues addressed in this paper. 26 This addresses first-order trends but does not solve the problem if both variables exhibit exponential
growth (or decay). However, the empirical results are not consistent with double exponential growth since
offshore patents are found to promote indigenous patents but not the converse.
18
technology class over time, I consider non-overlapping three-year periods and sum the
total counts within each period.27
Figure 4 illustrates the methodology in more detail. The first step is to compute
the total patent counts in each period. The first differences of these counts across
subsequent periods are then computed to obtain D_OC2, D_OC3, D_IC4, and D_IC5,
where D_OC2 is the total number of offshored patents (by country and sector) in years
1989, 1990, and 1991 minus the total number of offshored patents in years 1986, 1987,
and 1988 (D_IC is the counterpart for indigenous patent counts). The association
measure τ is then computed on these first differences. In particular:
𝜏 = {
1 𝑖𝑓 (𝐷𝑂𝐶2 − 𝐷𝑂𝐶3)(𝐷𝐼𝐶4 − 𝐷𝐼𝐶5) > 0
0 𝑖𝑓 (𝐷𝑂𝐶2 − 𝐷𝑂𝐶3)(𝐷𝐼𝐶4 − 𝐷𝐼𝐶5) = 0
−1 𝑖𝑓 (𝐷𝑂𝐶2 − 𝐷𝑂𝐶3)(𝐷𝐼𝐶4 − 𝐷𝐼𝐶5) < 0
}
This provides one of three values of τ for each patent technology class/country
combination. As is apparent from Figure 1, τ will most often take values of zero because
emerging countries have zero patents in most sectors in a given year (Figure 1 shows the
most active patent classes). These are relatively uninformative for our purposes and are
therefore thrown out. The analysis consists of comparing whether we observe τ=1 more
often than τ=-1. In particular, the statistic of interest is 𝜏̅, the average τ across all non-
zero τ values within a group of countries (emerging, middle-income, or developed) across
all technology classes. A positive statistic suggests that for that group of countries,
offshored innovation promotes indigenous innovation.
To determine whether the computed association is significantly different from
zero, we take as the null hypothesis that the two variables D_OC and D_IC are
independent. Under the null, we would expect to observe τ = 1 and τ = -1 each about
half the time. Under the null, 𝜏̅ has a binomial distribution with (n, p=.5) where n is the
27 It is not possible to implement a longer period due to lack of observations. Patenting activity in
developing countries is a relatively recent phenomenon generally dating back no earlier than the mid-1980s.
19
total number of country/industry combinations in the group. This allows us to compute
𝜏̅'s level of significance.28
When considering the reverse relationship (whether domestic counts drive
offshore counts), the exact same methodology applies with the variable names reversed in
Figure 4 and the above description.
To ensure that the results are not unduly influenced by technological sectors with
little patenting activity where noise is likely to play a bigger role, it may make sense to
restrict the analysis to the technological classes with the most patenting activity. In
addition to showing results for the full sample, the following section presents results for
the sample of the 25% and 50% of technology classes where emerging countries have the
most patents.
5.3 Results
Applying the above methodology to analyze the relationship between offshored
and indigenous innovation yields the results presented in Tables 10a, 10b, and 10c. Table
10a presents the results for a subsample comprised of the 25% of patent classes with the
most emerging country activity, 10b for the 50% most active classes, and 10c for the full
sample. For each country group, the table presents 𝜏̅, n (the number of country/class
combinations over which 𝜏̅ was computed) and the p-value associated with the two-sided
hypothesis test that the variables are related. The table presents the tests for both
offshored innovation promoting indigenous innovation and the converse.
The results strongly support the hypothesis that offshored innovation promotes
indigenous innovation in emerging countries.29 As expected, this is not the case in more
technologically advanced (richer) countries where domestic sources of innovative know-
how are widely available. In addition, it does not seem that indigenous innovation
attracts offshored innovation to any significant degree.
28 If we believe that τa is correlated across patent classes within a country (or across countries within a
patent class), this approach underestimates the variance of the aggregate τ and will over-reject the null that
the variables are independent. 29 The fact that indigenous patent counts do not promote offshored patent counts in developing countries
suggests that this association is not a result of external factors driving both variables to grow at rates that
would require a second or higher order difference to detrend (exponential growth, for example).
20
6. Conclusion
While there is a large established literature examining the impact of foreign direct
investment on productivity, the mechanisms through which this may occur are not well
understood. This paper examines whether subsidiaries of multinational firms act as a
medium for the geographic diffusion of knowledge. The results suggest that this is
indeed the case, with subsidiaries increasing the flow of knowledge from their
headquarters to third-party firms in their host country by about 25% for emerging
countries, and 15% overall. Further, this learning through subsidiaries is found to be
largest in countries and sectors with strong but not world-class capabilities, presumably
because these have both the motivation and absorptive capacity to learn from foreign
parties.
Since knowledge is a critical input into the innovative process, we would expect
these spillovers to, in turn, impact real economic variables like innovation and
productivity. Section 5 presented an initial analysis of whether multinational subsidiaries
promote innovation in their host country. The strong correlation between innovation by
MNE subsidiaries and follow-on indigenous innovation is a first glance into this bigger
question. The results suggest that for emerging countries, subsidiaries do indeed play a
key role in promoting innovation.
Overall, the findings lend credence to policies designed to attract foreign MNE’s
operating in technology-intensive sectors, since the subsidiaries not only promote
knowledge flow, but also innovation. They further suggest which countries and sectors
are most likely to benefit from policies designed to promote FDI. Lastly, the findings
offer a potentially important prescription for firms that are concerned about protecting
their intellectual property when venturing abroad; staffing key subsidiary technical
positions with expatriates may limit outward knowledge leakage.
In summary, this paper offers a micro-level analysis of the knowledge flows
associated with subsidiaries. As such, it increases our understanding of how FDI may
contribute to development and economic growth and, more importantly, in which
instances it is likely to do so.
21
References
Agrawal, A., Cockburn, I., McHale, J. 2006. Gone but not forgotten: knowledge flows,
labor mobility, and enduring social relationships. Journal of Economic Geography 6:
571-591.
Aitken, B., Harrison, A. 1999. Do Domestic Firms Benefit from Direct Foreign
Investment? Evidence from Venezuela. The American Economic Review 89:605-618.
Almeida, P. 1996. Knowledge sourcing by foreign multinationals: patent citation analysis
in the U.S. semiconductor industry. Strategic Management Journal 17: 155-165.
Almeida, P., B. Kogut. 1999. Localization of knowledge and the mobility of engineers in
regional networks. Management Science 45(7) 905-917.
Audretsch, D. B., Feldman, M. P. 1996. R&D spillovers and the geography of innovation
and production. American Economic Review 86: 630-640.
Aw, B., Chen, X., Roberts, M. 2001. Firm-level evidence on productivity differentials,
turnover, and exports in Taiwanese manufacturing. Journal of Development Economics
66:51-86.
Bernard, A., Jensen, J. 1999. Exceptional exporter performance: cause, effect, or both?
Journal of International Economics 47: 1-25.
Blalock, G., Gertler, P. 2008. Welfare gains from foreign direct investment through
technology transfer to local suppliers. Journal of International Economics 74: 402-421.
Branstetter, L. 2006. Is foreign direct investment a channel of knowledge spillovers?
Evidence from Japan’s FDI in the United States. Journal of International Economics 68:
325-344.
Clerides, S., Lach, S., Tybout, J. 1998. Is learning by exporting important? Micro-
dynamic evidence from Colombia, Mexico, and Morocco. Quarterly Journal of
Economics 113: 903–948.
Cohen W., Levinthal, D. 1990. Absorptive capacity: a new perspective on learning and
innovation. Administrative Science Quarterly 35: 128-152.
De Loecker, J. 2007. Do exports generate higher productivity? Evidence from Slovenia.
Journal of International Economics 73: 69-98.
Duguet, E., MacGarvie, M. 2005. How well do patent citations measure flows of
technology? Evidence from French innovation surveys. Economics of Innovation and
New Technology 14: 375-393.
22
Dunning, J. 1977. Trade, location of economic activity and the multinational enterprise:
A search for an eclectic approach. In B. Ohlin, P.O. Hesselborn & P.M. Wijkman, editors,
The international allocation of economic activity. London. Macmillan.
Fons-Rosen, C. 2012. Knowledge flows through FDI: the case of privatisations in central
and eastern Europe. Working Paper.
Frost, T.S. 2001. The geographic sources of foreign subsidiaries’ innovations. Strategic
Management Journal 22:101-23.
Ginarte, J.C., Park, W.G. 1997. Determinants of patent rights: A cross-national study.
Research Policy 26:283-301.
Gorg, H. and Greenaway, D. 2004. Much ado about nothing? Do domestic firms really
benefit from foreign direct investment? The World Bank Research Observer 19: 171-197.
Gorg, H. and Strobl, E. 2001. Multinational companies and productivity spillovers: a
meta-analysis. The Economic Journal 111: F723-F739.
Haddad, M., Harrison, A. 1993. Are there positive spillovers from direct foreign
investment? Evidence from panel data from Morocco. Journal of Development
Economics 42(1):51-74.
Hall, B. H., Jaffe, A. B., Trajtenberg, M. 2001. The NBER Patent Citation Data File:
Lessons, Insights and Methodological Tools. NBER Working Paper 8498.
Haskel, J., Pereira, S., Slaughter, M., 2002. Does inward foreign direct investment boost
the productivity of domestic firms? NBER Working Paper 8724.
Jaffe, A. B., Trajtenberg, M., Henderson, R. 1993. Geographic localization of knowledge
spillovers as evidenced by patent citations. Quarterly Journal of Economics 108: 577-598.
Jaffe, A.B., Trajtenberg, M. 2002. Patents, Citations, and Innovations: A Window on the
Knowledge Economy, MIT Press: Cambridge and London.
Javorcik, B. 2004. Does foreign direct investment increase the productivity of domestic
firms? In search of spillovers through backward linkages. American Economic Review
94 (3):605– 627.
Kaufmann, A.K., Mastruzzi, M. 2009. Governance Matters VIII: Aggregate and
individual governance indicators, 1996-2008. World Bank Policy Research Working
Paper 4978.
Kee, H. L. 2015. Local intermediate inputs and the shared supplier spillovers of foreign
direct investment. Journal of Development Economics 112: 56-71.
23
Keller, W. 2002. Geographic localization of international technology diffusion. American
Economic Review 92: 120-142.
Keller, W., Yeaple, S., 2003. Multinational enterprises, international trade, and
productivity growth: firm-level evidence from the United States. NBER Working Paper
9504.
Kogut, B., Zander, U. 1993. Knowledge of the firm and the evolutionary theory of the
multinational corporation. Journal of International Business Studies 24: 625-645.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R. 1999. The Quality of
Government. Journal of Law, Economics and Organizations 15: 222-279.
Liebetrau, Albert M. 1983. Measures of Association, Beverly Hills, USA: Sage
Publications Inc.
Lileeva, A., Trefler, D. 2010. Improved access to foreign markets raises plant
productivity... for some plants. Quarterly Journal of Economics 125: 1051-1099.
MacGarvie, M. 2006. Do firms learn from international trade? Review of Economics and
Statistics 88: 46-60.
Rogers, E., J. Larsen. 1984. Silicon Valley fever. Basic Books, New York.
Romer, P. 1990. Endogenous technological change. Journal of Political Economy 98:
S71-S102.
Singh, J. 2007. Asymmetry of knowledge spillovers between MNCs and host country
firms. Journal of International Business Studies 38: 764-786
Song, J., Shin, J. 2008. The paradox of technological capabilities: a study of knowledge
sourcing from host countries of overseas R&D operations. Journal of International
Business Studies 39: 291-303.
Thompson, P. 2006. Patent citations and the geography of knowledge spillovers:
evidence from inventor- and examiner-added citations. Review of Economics and
Statistics 88: 383-389.
Thompson, P., Fox-Kean, M. 2005. Patent citations and the geography of knowledge
spillovers: a reassessment. American Economic Review 95: 450-460.
Van Biesebroeck, J. 2005. Exporting raises productivity in sub-Saharan African
manufacturing firms, Journal of International Economics 67: 373-391.
Von Hippel, E. 1988. Sources of innovation. Oxford University Press, New York.
24
Zhao, M. 2006. Conducting R&D in countries with weak intellectual property rights
protection. Management Science 56(7): 1185-99.
25
FIGURES
Figure 1: Chinese Indigenous and Offshored Patent Counts by Application Year
Notes: The figure presents indigenous and offshored patent counts in the four technology
subcategories with the most patenting activity in China. Indigenous patents are patents
with all local (Chinese) inventors and a local assignee, while offshored patents are patents
with at least one local inventor and a foreign assignee. We observe that for each of these
sectors, innovation by multinationals seems to precede indigenous innovation.
020
40
60
80
Nu
mbe
r of P
ate
nts
1985 1990 1995 2000Year
Indigenous Patent Counts
Offshored Patent Counts
Electrical Devices
010
20
30
40
Nu
mbe
r of P
ate
nts
1985 1990 1995 2000Year
Indigenous Patent Counts
Offshored Patent Counts
Power Systems
05
10
15
Nu
mbe
r of P
ate
nts
1985 1990 1995 2000Year
Indigenous Patent Counts
Offshored Patent Counts
Drugs
05
10
15
20
Nu
mbe
r of P
ate
nts
1985 1990 1995 2000Year
Indigenous Patent Counts
Offshored Patent Counts
Computer Hardware and Software
26
Figure 2: Illustration of Methodology Using Microsoft Corporation's U.S.
Headquarters and Chinese Subsidiary
Notes: I examine whether the treated patents’ proportion of citations that are from third
party patents in the host country (�̂�𝑇), is greater than the control patents’ proportion of
citations received from third party patents in the host country (�̂�𝐶). Treated patents
consist of the patents generated in the headquarters of a firm with a subsidiary in the host
country, while control patents are patents that are matched one to one to these treated
patents so as to be from the same country, application year, class, and to the extent
possible, subclass. The ratio �̂�𝑇/�̂�𝐶 is a measure of the MNE subsidiary's effect on
knowledge diffusion.
27
Figure 3: Illustration of Truncation Using Patents Granted Between 1976 and 2004
Notes: The figure shows the average time between patent filing and granting by the
application year of the patents. Because the dataset includes patents granted up to the end
of 2004, the only 2004-filed patents that appear in the dataset are those granted within the
same year of filing. As a result, using patents filed in 2004 could introduce a selection
bias. The analysis is therefore conducted on patents with application years 2000 and
earlier where there is a relatively insignificant selection bias due to truncation.
0.5
11.5
22.5
Avera
ge
Dela
y in
Gra
nting
1975 1980 1985 1990 1995 2000 2005Application Year
Average Delay Between Patent Filing and Granting
28
Figure 4: Methodology for Computing τa Between Lagged Offshore Patent Counts
and Indigenous Patent Counts
Notes: D_OC2, D_OC3, D_IC4, and D_IC5 are the difference between the patent counts in
subsequent periods for offshored and indigenous patents, respectively. The association
measure τ is then computed on these first differences according to
𝜏 = {
1 𝑖𝑓 (𝐷𝑂𝐶2 − 𝐷𝑂𝐶3)(𝐷𝐼𝐶4 − 𝐷𝐼𝐶5) > 0
0 𝑖𝑓 (𝐷𝑂𝐶2 − 𝐷𝑂𝐶3)(𝐷𝐼𝐶4 − 𝐷𝐼𝐶5) = 0
−1 𝑖𝑓 (𝐷𝑂𝐶2 − 𝐷𝑂𝐶3)(𝐷𝐼𝐶4 − 𝐷𝐼𝐶5) < 0
}
The statistic of interest is 𝜏̅, the average τ across all non-zero τ values within a group of
countries (emerging, middle-income, or developed) across all technology classes. A
positive statistic suggests that for that group of countries, offshored innovation promotes
indigenous innovation.
TABLES
Table 1: Control Variable Descriptions
Variable Description Unit of Observation Source
GDP Per Capita GDP per capita at current prices in U.S. Dollars Country/Year
(215 countries, 1970-2004)
UN Statistics Division
Geographic Distance Great Circle distances between capital cities of MNE
headquarters and subsidiary countries in thousands of
kilometers
Country pair
(9045 country pairs)
Jon Haveman†
Shared Language Indicator for whether the MNE headquarters and
subsidiary countries share the same primary language
Country pair
(14028 country pairs)
Jon Haveman†
Shared Legal Origin Indicator for whether the legal origin of the MNE
headquarters and subsidiary countries are the same
(among British, French, German, Scandinavian,
Socialist)
Country pair
(22,366 country pairs)
Laporta et al. (1999)
Largest City Population
Percentage
Population of largest city as a percentage of the
country's urban population
Country/Year
(118 countries, 1960-2004)
World Bank Urban
Development
Telephone Lines Fixed telephone lines per 100 inhabitants Country/Year
(211 countries, 1975-2004)
UN World Telecom/ICT
Indicators Data
Internet Penetration Estimated Internet users per 100 inhabitants Country/Year
(206 countries, 1990-2004*)
UN World Telecom/ICT
Indicators Data
IP Rights Index of intellectual property rights Country/Year
(120 countries, 1960-1995)
Ginarte and Park (1997)
Infrastructure Infrastructure quality index Country
(60 countries)
Laporta et al. (1999)
Rule of Law Rule of law index for 1996 Country
(171 countries)
World Bank Governance
Matters VIII
Corruption Index of corruption Country
(126 countries)
Laporta et al. (1999)
*I extend the Internet Penetration variable to earlier years by assuming zero Internet penetration prior to 1990.
†http://www.macalester.edu/research/economics/page/haveman/trade.resources/tradedata.html
Table 2: Summary Statistics
Variable Mean Std. Dev. Min. Max.
Patent Stock Per Capita 2.33e-06 5.63e-06 0 .000838
GDP Per Capita 15532 10387 72.5 102246
Geographic Distance 7322 3995 174 19007
Shared Language .212 .409 0 1
Shared Legal Origin .303 .460 0 1
Largest City Population % 28.8 18.6 2.6 101.8
Telephone Lines 21.1 20.1 .07 89.20
Internet Penetration 3.35 10.03 0 81.00
IP Rights 2.67 .872 0 4.86
Infrastructure 7.30 1.48 2.5 9.15
Rule of Law 1.40 .809 -1.57 2.17
Corruption 8.29 1.75 1.67 10
Table 3: Magnitude of MNE Subsidiary Effect on Knowledge Diffusion to Host
Country
All
Countries
Emerging
Countries
Middle-Inc.
Countries
Developed
Countries
% Treated Matching 3.52 0.023 0.58 5.37
% Controls Matching 3.06 0.019 0.51 4.67
t-statistic 144.13 7.89 23.10 141.75
Effect 1.15 1.25 1.15 1.15
N - Treated Citations 60,469,336 11,272,109 10,144,120 38,443,828
N - Control Citations 63,959,472 12,158,719 10,547,492 40,618,492
Notes: The first column shows that while 3.52% of the citations received by headquarter
(treated) patents are from third parties in their subsidiary’s host country, only 3.06% of
the citations received by the control patents are from that same country. The ratio of the
two is 1.15, suggesting that host country firms receive on average 15% more knowledge
from the headquarters as a result of having the subsidiary. We note that the effect of the
subsidiary on knowledge diffusion is largest for emerging host countries (column 2).
31
Table 4: Classification of Countries
Emerging Countries Middle-Income
Countries
Developed Countries
Argentina Hong Kong Canada
Brazil Israel France
China South Korea Germany
Hungary Singapore Italy
India Taiwan Japan
Malaysia Netherlands
Mexico Sweden
Poland Switzerland
South Africa U.K.
Venezuela
Notes: Countries are classified according to their 1990 per capita GDP at current U.S.
dollars. Countries with per capita GDP of less than $5000 are classified as emerging,
between $5000 and $15,000 as middle-income, and above $15,000 as developed.
Although all 121 countries are used in the analysis, for illustrative purposes only those
with the most patenting activity are included in the table.
32
Table 5: Difference in Differences Estimation of Subsidiary Effect
Dependent variable: match_ratioi
(1) (2) (3) (4) (5) (6)
0 year
window
1 year
window
2 year
window
5 year
window
10 year
window
15 year
window
Treated .00145*** .00130*** .00128*** .00130*** .00104*** .000765**
(.00029) (.00028) (.00029) (.00031) (.00029) (.00030)
Post Subsidiary .0229*** .0236*** .0241*** .0251*** .0261*** .0279***
(.0032) (.0032) (.0033) (.0034) (.0036) (.0039)
Treated*Post Sub .00241*** .00253*** .00256*** .00241*** .00258*** .00276***
(.00048) (.00049) (.00049) (.00051) (.00056) (.00064)
Clustering by Firm Yes Yes Yes Yes Yes Yes
Robust Std Errors Yes Yes Yes Yes Yes Yes
Observations 15,154,131 14,509,815 13,938,391 12,532,913 10,780,629 9,293,737
R-Squared .0056 .0056 .0055 .0049 .0035 .0023
Standard Errors in Parentheses
*p<0.05, **p<0.01, ***p<0.001
Notes: The regression is at the level of the patent, with treated (headquarters) and their
associated control patents being separate observations. The dependent variable,
match_ratio, is the proportion of the patent’s citations that are from third-party patents in
the subsidiary’s location. Treated is a dummy variable equal to 1 if the patent was
generated by the headquarters and 0 if the patent is a control. Post subsidiary is equal to
1 when the patent’s application year is after the subsidiary’s first patent application, and
equal to 0 otherwise. Since the subsidiary’s first patent application year will invariably
be some time after the subsidiary has been established, I drop from the sample some of
the years directly before the first patent application (since it is not clear whether these
should be considered pre or post). The different columns present the results when
dropping increasingly large windows from the analysis. As expected, the coefficient on
treated decreases with the window size and the coefficient on the interaction term
increases. Overall, the results suggest that the large majority of the observed subsidiary
effect is due to knowledge flows through the subsidiary and not to firms establishing
subsidiaries in locations where third-party firms already disproportionately cite them.
33
Table 6: Host Country Characteristics and Magnitude of MNE Subsidiary Effect on
Knowledge Diffusion (�̂�𝑻-�̂�𝑪) - No Country Fixed Effects
Dependent variable: (�̂�𝑇-�̂�𝐶)
(1) (2) (3)
Ln(1+Patent Stock Per Capita) 1558*** 1558*** 1558*
(401) (278) (691)
Ln(1+Patent Stock Per Capita) ^2 -1.57E7*** -1.57E7*** -1.57E7*
(3.81E6) (2.87E6) (6.58E6)
Ln(GDP Per Capita) -.0104* -.0104* -.0104
(.0045) (.0045) (.0061)
Ln(GDP Per Capita) ^2 -.000838** -.000838* -.000838
(.000312) (.000329) (.000422)
Geographic Distance .000081 .000081 .000081
(.000086) (.000072) (.000169)
Shared Language .00336*** .00336*** .00336
(.00102) (.00079) (.00227)
Shared Legal Origin -.00245** -.00245*** -.00245
(.00085) (.00065) (.00162)
Largest City Population % .000119*** .000119*** .000119
(.000025) (.000025) (.000092)
Telephone Lines -.000247*** -.000247*** -.000247*
(.000055) (.000059) (.000115)
Internet Penetration -.00186 -.00186** -.00186*
(.00098) (.00061) (.00070)
IP Rights .000002 .000002 .000002
(.000525) (.000511) (.001369)
Infrastructure .00220** .00220** .00220
(.00073) (.00070) (.00192)
Rule of Law -.00338*** -.00338** -.00338
(.00099) (.00111) (.00413)
Corruption -.000060 -.00006 -.00006
(.000550) (.00044) (.00117)
Year, Class Fixed Effects Yes Yes Yes
Country Fixed Effects No No No
Clustering Country/Class Country/Year Country
Robust Standard Errors Yes Yes Yes
Observations 4,059,850 4,059,850 4,059,850
R-squared .0021 .0021 .0021
Standard Errors in Parentheses
*p<0.05, **p<0.01, ***p<0.001
34
Table 7: Host Country Characteristics and Magnitude of MNE Subsidiary Effect on
Knowledge Diffusion (�̂�𝑻-�̂�𝑪) - Country Fixed Effects
Dependent variable: (�̂�𝑇-�̂�𝐶)
(1) (2) (3) Tobit
Ln(1+Patent Stock Per Capita) 1719*** 1719** 1736***
(379) (617) (44)
Ln(1+Patent Stock Per Capita) ^2 -1.67E7*** -1.67E7** -1.69E7***
(3.45E6) (5.57E6) (6.47E5)
Ln(GDP Per Capita) .0009 .0009 .0008
(.0075) (.0047) (.0029)
Ln(GDP Per Capita) ^2 -.000112 -.000112 -.000107
(.000454) (.000271) (.000159)
Geographic Distance -.000116 -.000116 -.000117***
(.000114) (.000078) (.000035)
Shared Language .00300* .00300 .00302***
(.00141) (.00187) (.00048)
Shared Legal Origin -.00149 -.00149 -.00150***
(.00106) (.00168) (.00034)
Largest City Population % -.00009 -.00009 -.0000881
(.00017) (.00016) (.0000861)
Telephone Lines .000060 .000060 .000060
(.000085) (.000058) (.000033)
Internet Penetration -.000355 -.000355 -.000362**
(.000987) (.000744) (.000115)
IP Rights -.000844 -.000844 -.000860
(.000827) (.000593) (.000486)
Year, Class Fixed Effects Yes Yes Yes
Country Fixed Effects Yes Yes Yes
Clustering Country/Class Country Pair No
Robust Standard Errors Yes Yes No
Observations 4,183,122 4,183,122 4,183,122
R-squared .0031 .0031 -.0041
Standard Errors in Parentheses
*p<0.05, **p<0.01, ***p<0.001
35
Table 8: Host Country Characteristics and Magnitude of MNE Subsidiary Effect on
Knowledge Diffusion (�̂�𝑻/�̂�𝑪)
Dependent variable: (�̂�𝑇/�̂�𝐶)
(1) (2) (3)
Ln(1+Patent Stock Per Capita) 35056*** 35056*** 32,693***
(4623) (8674) (4862)
Ln(1+Patent Stock Per Capita) ^2 -4.31E8*** -4.31E8** -3.47E8***
(9.22E7) (1.48E8) (9.8E7)
Ln(GDP Per Capita) .761*** .761 1.43***
(.222) (.560) (.31)
Ln(GDP Per Capita) ^2 -.0372** -.0372 -.0686***
.0140 .0301 (.0168)
Geographic Distance .00701*** .00701 .00155
(.00199) (.00461) (.00246)
Shared Language .269*** .269*** .201***
(.026) (.066) (.028)
Shared Legal Origin -.153*** -.153*** -.118***
(.020) (.031) (.021)
Largest City Population % .00255*** .00255 .0174
(.00067) (.00151) (.0112)
Telephone Lines -.00484*** -.00484 -.00205
(.00120) (.00341) (.00271)
Internet Penetration -.0218** -.0218 -.0103
(.0065) (.0114) (.0071)
IP Rights .203*** .203* .153*
(.021) (.088) (.072)
Infrastructure .295*** .295***
(.011) (.039)
Rule of Law -.201*** -.201
(.045) (.209)
Corruption -.162*** -.162
(.019) (.082)
Year, Class Fixed Effects Yes Yes Yes
Country Fixed Effects No No Yes
Clustering Country/Class Country Country/Class
Robust Standard Errors Yes Yes Yes
Observations 505,775 505,775 506,095
R-squared .0387 .0387 .0408
Standard Errors in Parentheses
*p<0.05, **p<0.01, ***p<0.001
36
Table 9: Expatriates and Magnitude of MNE Subsidiary Effect on Knowledge
Diffusion
Dependent variable: (�̂�𝑇/�̂�𝐶)
(1) (2) (3)
Expatriates -.278*** -.244** -.363
(.074) (.081) (.193)
Subsidiary Patent Count (1000s) -.247 .122 .077
(.159) (.194) (.215)
Headquarters Patent Count (1000s) -.0222*** -.0205**
(.0065) (.0070)
Firm Number of Locations .0114 -.0029
(.0065) (.0078)
Host Country Fixed Effects No Yes Yes
Firm Fixed Effects No No Yes
Clustering by Firm Yes Yes Yes
Robust Standard Errors Yes Yes Yes
Observations 8181 8181 5665
R-squared .0019 .0204 .3464
Standard Errors in Parentheses
*p<0.05, **p<0.01, ***p<0.001
Notes: The regression is at the level of the subsidiary. Subsidiaries with more expatriates,
as measured by the fraction of subsidiary inventors that previously patented while living
in the country of the headquarters, decrease knowledge flow through the subsidiary. The
decrease associated with expatriates remains significant (at the 10% level) even when
controlling for host country and firm fixed effects.
37
Table 10a: Relationship Between Offshore and Indigenous Patent Counts by
Country Group - 25% of Patent Technology Classes with Most Emerging Country
Activity.
Country
Group
Offshore Patents Promoting
Indigenous Patents
Indigenous Patents Promoting
Offshore Patents
𝜏̅ n p 𝜏̅ n p
Emerging 0.16* 159 0.028 0.06 156 0.236
Middle Inc. 0.08 215 0.138 -0.02 231 0.347
Developed 0.02 904 0.286 -0.06* 916 0.035
Table 10b: Relationship Between Offshore and Indigenous Patent Counts by
Country Group - 50% of Patent Technology Classes with Most Emerging Country
Activity.
Country
Group
Offshore Patents Promoting
Indigenous Patents
Indigenous Patents Promoting
Offshore Patents
𝜏̅ n p 𝜏̅ n p
Emerging 0.13* 196 0.037 0.08 206 0.148
Middle Inc. 0.07 340 0.106 0.02 387 0.380
Developed -0.01 1612 0.283 0.00 1656 0.451
Table 10c: Relationship Between Offshore and Indigenous Patent Counts by
Country Group - All Patent Technology Classes with Most Emerging Country
Activity.
Country
Group
Offshore Patents Promoting
Indigenous Patents
Indigenous Patents Promoting
Offshore Patents
𝜏̅ n p 𝜏̅ n P
Emerging 0.14* 208 0.022 0.09 225 0.091
Middle Inc. 0.06 424 0.112 0.01 488 0.410
Developed -0.01 2391 0.270 0.01 2509 0.302
Notes: Tables 10a, 10b, and 10c examine the relationship between offshore patents and
indigenous patents, for the 25% of patent classes with the most emerging country
patenting activity, the 50% with the most activity, and all patent classes, respectively.
The first panel examines whether offshore patents are correlated with future indigenous
patents, and the second panel examines the converse. The table presents 𝜏̅ (the average
measure of association), n (the number of country/class combinations over which 𝜏̅ was
computed) and the p-value associated with the two-sided hypothesis test that 𝜏̅ is
significantly different from zero. The results support the hypothesis that offshored
innovation promotes indigenous innovation in emerging countries, while the converse is
not true. This relationship is furthermore not present in middle income and developed
countries.
38
Appendix A: Measures of Association
Measures of association can be classified according to the requirements they place
on the data. While some measures have been developed to examine the association of
nominal variables, others are designed for ordinal or even continuous data. The table
below presents some of the most commonly used measures classified according to the
lowest data requirement. For example, while Cramer’s V can be applied on nominal,
ordinal, or continuous data, Kendall’s τa is only applicable to cases where both variables
are continuous. Hence, measures requiring only nominal data are more generally
applicable. However, when analyzing data that is not nominal, they have less power than
a measure that is designed specifically for the data type in question since they ignore
information.
Classification of Most Commonly Used Measures of Association
Nominal Variables Ordinal Variables Continuous Variables
Pearson Chi-Square
Likelihood ratio Chi-Square
Cramer’s V
Kendall’s τb
Goodman-Kruskal γ
Somer’s d
Correlation (ρ)
Kendall’s τa
Spearman’s ρs
The most commonly used continuous measure is Correlation (ρ). This measure (ρ)
is the best choice when the variables in question are normally distributed. Otherwise,
Kendall’s τa or Spearman’s ρs may be a better choice (Liebetrau, 1983).
Kendall’s τa is computed by considering every possible pair of observations in the
dataset and determining whether that pair is concordant, discordant, or tied. A pair of
observations (Xi, Yi) and (Xj, Yj) is said to be concordant if either Xi < Xj and Yi < Yj or Xi >
Xj and Yi > Yj. Similarly a pair is discordant if either Xi < Xj and Yi > Yj or Xi > Xj and Yi
< Yj. Observation pairs for which either Xi = Xj or Yi = Yj are said to be tied. Hence,
Kendall’s τa is simply a measure of the total number of concordant pairs minus the total
number of discordant pairs, divided by the total number of pairs in the dataset:
𝜏𝑎 =2(𝐶 − 𝐷)
𝑛(𝑛 − 1)
39
where n is the total number of observations, C is the total number of concordant pairs,
and D is the total number of discordant pairs.
By definition, -1 ≤ τa ≤ 1. If the two variables in question are monotonically
related, then τa=-1 or τa=1, depending on whether the relationship is positive or negative.
These end values can only be reached if no pair of observations in the data is tied. Hence,
for discrete data (such as patent counts), it is best to take ties into consideration. In
particular, we would like a measure where the denominator (total number of pairs)
decreases along with the numerator when there are ties. To account for this, Kendall
developed a related measure with an adjusted denominator called τb:
τ𝑏 =𝐶 − 𝐷
[((𝑛2) − 𝑈) ((𝑛
2) − 𝑉)]
12⁄
where n, C, and D are defined as before. U is the total number of observation pairs not
counted in computing C-D because of tied X values. Similarly, V is the total number of
observation pairs not counted in computing C-D because of tied Y values. Hence,
although the numerators of τb and τa are the same, the denominator of τb is the geometric
mean of the total number of pairs with distinct X values and the total number of pairs
with distinct Y values. Although neither τa nor τb are able to achieve their extreme values
of -1 or 1 when ties are present, the range of τb is the less restricted of the two.
