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How Do Patents Shape Global Value Chains?
International and Domestic Patenting and Value-Added Trade
October 2014
Preliminary Draft – Please do not cite
Nikolas J. Zolas, Center for Economic Studies, United States Census Bureau
Travis J. Lybbert, Agricultural & Resource Economics, University of California, Davis;
Abstract
Intellectual property plays an important role in the global economy through its impact on technology
diffusion, knowledge transfer and competition. There is, however, dramatic heterogeneity across both
industries and countries in these effects and their implications for economic growth. In this paper, we
exploit a newly developed algorithmic concordance that links patents to industry and trade
classifications to characterize how patents affect the structure of global value chains. Using recent
techniques to decompose gross exports and construct bilateral measures of value-added trade, we test
how domestic and international (bilateral) patenting specifically related to different industries affects
production fragmentation as measured by decreases in the value-added export (VAX) ratio. Over the
period 1999-2009 for 18 industries and 35 countries, we find that increased international patenting is
associated with greater production fragmentation. This effect is particularly strong for “imported” or
inbound international patent applications. While there is some heterogeneity among industries in this
relationship, there is much greater heterogeneity among countries: For several countries, “exported”
patents reduce production fragmentation and “imported” patents increase production fragmentation.
As empirical research into the structure of global value chains expands, the role of patents and other
forms of intellectual property merit careful consideration. The exploratory results we present are
intended to provide a point of departure for continued characterization of these interrelationships.
2
1 Introduction Potential linkages between patents and international trade motivated the negotiation of the Trade-
Related Aspects of Intellectual Property Rights (TRIPS) agreement as part of the Uruguay Round of the
GATT in 1994. Since the agreement, numerous studies have analyzed the role of patenting institutions
on the composition and quantity of trade flows. These findings suggest that increased use of patents
and other forms of intellectual property (IP) have stimulated imports in knowledge-intensive and high-
tech goods for several countries (Braga and Fink 1999; Vichyanond 2009; Awokuse and Yin 2010; Ivus
2010). The close relationship between patenting and trade partly reflects similarities of costs and
benefits of each activity and how they critically shape technology transfer.
Coe and Helpman (1995) demonstrate the important effects of foreign R&D on domestic productivity,
highlighting the importance of international knowledge spillovers, in which both patents and trade
figure prominently. However, the precise nature of the relationship between patents and trade is not
fully understood. In modeling international patent flows, economists have traditionally relied on
“gravity”-type models, where flows are determined by the relative size (GDP) of countries, as well as
distance and other country-specific variables (see Slama 1981; Bosworth 1983; Harhoff et al. 2007;
Eaton et al. 2004). Little is known about the timing, spatial and industry patterns, and economic
outcomes of patenting and trade. This gap in our understanding stems principally from our inability to
connect patenting and trade at a high enough resolution to account for substantial industry-level
heterogeneity.
This inability to jointly analyze trade and patent data by industry is addressed by algorithmic matching
techniques proposed by Lybbert and Zolas (2014). This algorithmic approach constructs concordances
between the International Patent Classification (IPC) system that organizes patents by technical features
and industry classification systems that organize economic data, such as the Standard International
Trade Classification (SITC) and the International Standard Industrial Classification (ISIC). This ‘Algorithmic
Links with Probabilities’ (ALP) approach applies text analysis software and keyword extraction programs
to a global patent database and enables a rich empirical analysis of the patents and trade by high
resolution trade classifications. Using these ALP concordances, we can link bilateral patent flows from
the PATSTAT database organized by 4-digit IPC and bilateral trade flows from the World Input-Output
Database (WIOD) organized by 2, 3 and 4-digit ISIC.
3
Using this empirical platform, we seek to shed light on how countries interact in the global innovation
system and how these interactions shape the way specific industries are able to upgrade their position
in GVCs. Conceptually, this empirical analysis, which is essentially exploratory, relates to broader efforts
to better understand international innovation systems (e.g., Fromhold-Eisebith, 2007). Our methodology
uses detailed trade and patent data as indicators of the technological trajectories of different industries.
These trajectories can be used to measure the rate of technological absorption and thus provide a basis
for comparison between similar industries in different countries. We specifically compare domestic
patenting and patents sent and received from abroad in terms of their impact on bilateral value-added
trade. We decompose these effects along three dimensions. First, we decompose value-added trade to
its constituent parts in order to trace the pathways by which patents shape GVCs. Second, we
disaggregate these patent effects by industry to characterize heterogeneity by industry. Finally, we
disaggregate these effects by countries and discuss how these results potentially reflect structural
differences in production, value chains and innovation from one country to the next. We find that there
is greater heterogenetiy among countries that among industries in terms of how and how strongly
patenting shapes GVCs.
2 Constructing the Value-Added Trade & Patenting Database In this section, we describe in detail the methods we use to construct value-added trade measures,
which are fundamental to understanding the functioning and evolution of GVCs. Our methodology for
constructing these data follows the multi-country, multi-product methodology first established in
Johnson & Noguera (2012a) and used in their most recent working paper (Johnson & Noguera 2012b).
After giving a detailed overview of these methods, we describe the other data we add to the dataset –
including patent and TM data – in order to conduct analyze the role IP play in GVCs.
2.1 Value-Added Trade Methodology following Johnson & Noguera (2012a)
To construct bilateral value-added trade, we require a global input-output (I/O) table. Assuming N
countries and S industries, the I/O table will be an NS x NS matrix of bilateral country-industry pairs. For
our study, we use the World Input-Output Database (WIOD) highlighted in Timmer (2012), which
contains 41 countries and 35 industries for a 1435 × 1435 matrix for each year in our sample.
4
We define i as the source country, j the destination country, s the source industry, s’ as the destination
industry and t as the year. Using the I/O framework, we define the market clearing condition in value
terms as:
𝑦𝑖𝑡(𝑠) = �𝑓𝑖𝑗𝑡(𝑠) + 𝑗
� �𝑚𝑖𝑗𝑡(𝑠, 𝑠′)𝑠′𝑗
where 𝑦𝑖𝑡(𝑠) is the value of total output in industry s of country i, 𝑓𝑖𝑗𝑡(𝑠) is the value of final goods
shipped from country i to country j in industry s, and 𝑚𝑖𝑗𝑡(𝑠, 𝑠′) is the value of intermediate goods from
industry s used in industry s’. As Johnson and Noguera note, if we define exports 𝑥𝑖𝑗𝑡(𝑠) as the total
number of final goods and intermediate goods exported to country j1, , then the market clearing
condition states that total output is divided between gross exports (sum of 𝑥𝑖𝑗𝑡(𝑠)), domestic final use
𝑓𝑖𝑖𝑡(𝑠) and domestic intermediate use (sum of 𝑚𝑖𝑖𝑡(𝑠, 𝑠′)).
Stacking the market clearing conditions by country, we have both total output, 𝑦𝑖𝑡(𝑠) and final goods
𝑓𝑖𝑗𝑡(𝑠) as S × 1 vectors, while the intermediate goods, 𝑚𝑖𝑗𝑡(𝑠, 𝑠′) are an S × S matrix. We next define
𝐴𝑖𝑗𝑡(𝑠, 𝑠′) as the proportion of intermediate inputs used in total output where 𝐴𝑖𝑗𝑡(𝑠, 𝑠′) = 𝑚𝑖𝑗𝑡�𝑠,𝑠′�𝑦𝑗𝑡(𝑠′)
.
This allows us to rewrite the market clearing conditions as an S × N matrix where:
𝑦𝑡 = 𝐴𝑡 𝑦𝑡 + 𝑓𝑡
where 𝐴𝑡 = �𝐴11𝑡 ⋯ 𝐴1𝑁𝑡⋮ ⋱ ⋮
𝐴𝑁1𝑡 ⋯ 𝐴𝑁𝑁𝑡� , 𝑦𝑡 = �
𝑦1𝑡𝑦2𝑡⋮𝑦𝑁𝑡
� , and 𝑓𝑡 =
⎝
⎜⎛∑ 𝑓1𝑗𝑡𝑗∑ 𝑓2𝑗𝑡𝑗⋮
∑ 𝑓𝑁𝑗𝑡𝑗 ⎠
⎟⎞
.
Next, we solve for the total output and rewrite the total output vector as:
𝑦𝑡 = (𝐼 − 𝐴𝑡)−1𝑓𝑡
1 So that total exports are so that 𝑥𝑖𝑗𝑡(𝑠) = 𝑓𝑖𝑗𝑡(𝑠) + ∑ 𝑚𝑖𝑗𝑡(𝑠, 𝑠′)𝑠′
(1)
(2)
(3)
5
where (𝐼 − 𝐴𝑡)−1 is the “Leontief inverse” of 𝐴𝑡. We can further decompose the total output vector
into destination specific vectors so that:
�
𝑦1𝑗𝑡𝑦2𝑗𝑡⋮
𝑦𝑁𝑗𝑡
� = ∑ (𝐼 − 𝐴𝑡)−1 𝑓𝑗𝑡𝑗 where 𝑓𝑗𝑡 =
⎝
⎛
𝑓1𝑗𝑡𝑓2𝑗𝑡⋮
𝑓𝑁𝑗𝑡⎠
⎞
Using this equation and our definition of the proportion of intermediate inputs used to produce final
goods, 𝐴𝑖𝑗𝑡(𝑠, 𝑠′), we are now able to estimate the total value added from the origin country to the
destination. To do this, we first define the ratio of total intermediate inputs in country i as the total
amount of inputs collected from all other industries and countries divided by the total output in country
i so that the ratio 𝑟𝑖𝑡 (𝑠) is defined as
𝑟𝑖𝑡 (𝑠) = 1 − ��𝐴𝑗𝑖𝑡 (𝑠, 𝑠′)𝑠′𝑗
Then we multiply this ratio by the individual elements of the total output vector to obtain our measure
of value-added trade from country i to country j.
𝑣𝑎𝑖𝑗𝑡 (𝑠) = 𝑟𝑖𝑡(𝑠) 𝑦𝑖𝑗𝑡 (𝑠)
This completes the framework for constructing our value-added export measures. The next section
describes the construction and pieces of value-added trade to explain how the value-added export ratio
(VAX) ratio can be used to measure production fragmentation.
2.2 Interpretation and Approximation of Value-Added Trade
As Johnson and Noguera note, the framework above provides details of a circular process of production
where inputs and outputs are continuously transferred from one country-industry to another, which
implies an infinite number of production stages. To simplify the process, we assume a sequential two-
stage production process where intermediate goods are only used to produce final goods (as opposed to
other intermediate goods). The benefit of this approach is that we only need to estimate the first-order
(4)
(5)
(6)
6
influences of bilateral inputs2. This sequential feature of the production process is consistent with
numerous production models such as Yi (2003, 2010), Baldwin and Venables (2010) and Costinot et al.
(2011).
Given this approximation of value-added trade, we can compute the value-added and gross exports
using only the final goods and the intermediates directly used to produce the final goods. Hence, our
measures of gross exports and value-added exports are constructed as:
�̅�𝑖𝑗 = 𝑓𝑖𝑗 + ∑ 𝐴𝑖𝑗𝑓𝑗𝑘𝑘
𝑣𝑎����𝑖𝑗 = 𝑓𝑖𝑗 + �𝐴𝑖𝑘𝑓𝑘𝑗𝑘
− �𝜄 ��𝐴𝑘𝑖𝑘
� 𝑑𝑖𝑎𝑔�𝑓𝑖𝑗��′
where [𝜄 [∑ 𝐴𝑘𝑖𝑘 ]𝑑𝑖𝑎𝑔�𝑓𝑖𝑗�]′ is a measure of the total intermediate input-use matrix for country i.
To build intuition for the components of each figure, Johnson and Noguera further break down the
figures starting with the gross exports �̅�𝑖𝑗. First, they decompose the pieces of the total sum of
intermediate goods exported to a third party k. They note that the piece, 𝑓𝑖𝑗 + 𝐴𝑖𝑗𝑓𝑗𝑗 can be
interpreted as an “absorption” measure that captures the value of the goods from country i that are
absorbed into country j as either a final good (𝑓𝑖𝑗) or intermediate good used to produce a final good
consumed domestically (𝐴𝑖𝑗𝑓𝑗𝑗). Next, they note that the piece 𝐴𝑖𝑗𝑓𝑗𝑖 can be interpreted as “reflected”
trade, since these are intermediate goods from country i to country j that end up eventually as final
goods consumed in country i. This would be an example of outsourced production to country j taken by
country i. The remainder, ∑ 𝐴𝑖𝑗𝑓𝑗𝑘𝑘≠𝑖,𝑗 , is then interpreted as “redirected” trade, since these are
intermediate goods shipped from country i to country j that end as final goods consumed in country k.
In addition, Johnson and Noguera break down the approximate value added exports 𝑣𝑎����𝑖𝑗. As we can
see, once we break down the summation term, the value-added exports also include the “absorption”
expression found in gross exports. However, for the value-added component, we need to subtract all of
2 Johnson and Noguera say that higher order approximations may provide additional information to fit the data better, however, these higher orders add little to the overall measure of fragmentation.
(7)
(8)
7
the imported intermediate inputs used prior to absorption. This create a “net absorption” term which
consists of the absorption, 𝑓𝑖𝑗 + 𝐴𝑖𝑗𝑓𝑗𝑗 minus total intermediate use [𝜄 [∑ 𝐴𝑘𝑖𝑘 ]𝑑𝑖𝑎𝑔�𝑓𝑖𝑗�]′ plus the
domestic intermediate use, 𝐴𝑖𝑖𝑓𝑖𝑗. Next, the expression ∑ 𝐴𝑖𝑘𝑓𝑘𝑗𝑘≠𝑖,𝑗 , can be characterized as “indirect
exports” country j since the intermediates from country i comprise a share of the final good shipped
from a third country k to country j. To summarize, using a two-stage sequential production process,
Johnson and Noguera construct values of gross exports and value-added exports using the I/O tables
with the following components:
�̅�𝑖𝑗 = 𝑓𝑖𝑗 + 𝐴𝑖𝑗𝑓𝑗𝑗�������𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛
+ 𝐴𝑖𝑗𝑓𝑗𝑖���𝑟𝑒𝑓𝑙𝑒𝑐𝑡𝑖𝑜𝑛
+ ∑ 𝐴𝑖𝑗𝑓𝑗𝑘𝑘�������𝑟𝑒𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛
𝑣𝑎����𝑖𝑗 = 𝑓𝑖𝑗 + 𝐴𝑖𝑗𝑓𝑗𝑗 + 𝐴𝑖𝑖𝑓𝑖𝑗 − �𝜄 ��𝐴𝑘𝑖𝑘
� 𝑑𝑖𝑎𝑔�𝑓𝑖𝑗��′
�������������������������������𝑛𝑒𝑡 𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛
+ � 𝐴𝑖𝑘𝑓𝑘𝑗𝑘≠𝑖,𝑗�������
𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑥𝑝𝑜𝑟𝑡𝑠
We can then define the approximate VAX ratio as:
𝑉𝐴𝑋������𝑖𝑗 = 𝑣𝑎����𝑖𝑗�̅�𝑖𝑗
= 𝑛𝑒𝑡 𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛𝑖𝑗 + 𝑖𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑥𝑝𝑜𝑟𝑡𝑠𝑖𝑗
𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛𝑖𝑗 + 𝑟𝑒𝑓𝑙𝑒𝑐𝑡𝑖𝑜𝑛𝑖𝑗 + 𝑟𝑒𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑖𝑗
Note that net absorption will always be less than absorption because it subtracts the value of all of the
intermediate inputs used. Therefore, if there is no indirect trade, the approximate VAX ratio will always
be less than one. Also note that if indirect exports are high enough, then the VAX ratio can exceed one.
Since indirect exports are an outcome of production fragmentation (i.e. production processes happening
elsewhere), this implies that a high approximate VAX ratio is indicative of increased production
fragmentation. However, higher fragmentation also implies that “redirected” trade is also high, so that
in the aggregate, these combined measures do not impact the VAX ratios too much. What does drive
movements in the VAX ratio will be the following: (i) the difference between the “absorption” and “net
absorption” measures and (ii) “reflected exports”. An increase in either “reflected” exports or in the
difference between the absorption rates will drive the VAX ratio down – which is why the VAX ratio
serves as a good proxy for production fragmentation since any movement in these two figures is driven
by production happening away from the home country.
(9)
(10)
8
2.3 Construction using World Input-Output Database
Now that the intuition and full methodology for calculating approximate VAX ratios has been
established, we describe in detail the construction of bilateral VAX ratios using the WIOD database. For
details of the WIOD database and its construction, we refer to Timmer (2012), which describes in details
the contents, sources and methods used to construct the database. The database consists of 40
individual countries and 1 “Rest of World” country (“ROW”) and 35 industries encompassing agriculture,
manufacturing, non-manufacturing and service activities from the year 1995 to 2009. For summary
statistics on the countries and industries used, see Tables 1 and Tables 2.
For each year, the WIOD database contains the standard I/O table (1435 × 1435 matrix) which consists
of intermediate goods values 𝑚𝑖𝑗𝑡(𝑠, 𝑠′), as well as 5 columns each of which contain components of the
final goods. These 5 columns were summed together for each country to provide our values for 𝑓𝑖𝑗,
which is now a 1435 × 41 matrix. To compute our measures of 𝐴𝑖𝑗𝑡(𝑠, 𝑠′), we simply divide 𝑚𝑖𝑗𝑡(𝑠, 𝑠′)
by the total output, which we defined in equation (1) as
𝑦𝑖𝑡(𝑠) = �𝑓𝑖𝑗𝑡(𝑠) + 𝑗
� �𝑚𝑖𝑗𝑡(𝑠, 𝑠′)𝑠′𝑗
Once this figure is computed, it is relatively straightforward process to calculate each of the components
of the gross exports and value-added exports (i.e. absorption, net absorption, etc…) using matrix
algebra. Our end result once we perform the algebra are 1435 ×41 matrices for each of the pieces in
equations (9) and (10). These matrices are then transformed into bilateral country-industry pairs
containing 58,835 observations for each year (41 × 35 × 40). These observations consist of the name of
the home country, the industry being shipped and the destination country, along with each of the
components of the VAX ratio. After tabulating for all of the years of the WIOD database, we are left with
882,525 bilateral country-industry observations. We can then collapse this dataset to analyze bilateral
country-only observations, individual country-industry observations, individual country observations and
individual industry observations if necessary. However, as we will see, we limit our analysis to country-
9
industry bilateral pairs and aggregate country-industry measures due to the relative lack in variability in
the aggregate measures across this limited time frame3.
Since we are using the same methodology found in Johnson and Noguera, it makes sense to compare
our initial ratios with theirs. However, it is important to mention that in both papers, Johnson and
Noguera use different databases/values from the WIOD database and there is some discussion in their
working paper about the limitations of the WIOD database. However, for the purposes of our study,
which focus mainly on the bilateral country-industry relationship of value-added trade, we are more
interested in this comparison to ensure that the trends and rankings are consistent rather than actual
values. In Johnson and Noguera (2012a), they use the GTAP 7.1 Database which consists of the global
I/O framework for 57 industries and 94 countries for 2004. We compare the cross-sectional country-
level results of the WIOD for 2004 with these figures, which is found in Table 3. As we can see, there are
some nontrivial discrepancies between the two measures where on average, the Johnson & Noguera
(2012a) data has much higher VAX ratios for agriculture and services than our own measures. However,
the rankings of sectors and countries are relatively similar: Both sector and country rankings appear to
be consistent in the sense that manufacturing typically has the lowest VAX ratios and countries that
have the highest and lowest aggregate VAX ratios are similar.
Next, we compare our VAX ratio results with the latter Johnson and Noguera paper (2012b), which
instead of containing a cross section, contains panel data over a 40-year period of VAX ratios for 42
countries. This data was constructed using trade data from the NBER-UN database and the CEPII BACI
data, along with I/O tables from the OECD (1995 and 2011 versions) and IDE-JETRO. They then aggregate
the industries into 4 sectors to make their calculations over the time period. It is also worth mentioning
that for years where no I/O table was given, values were imputed. Given the difference in datasets and
sectoral make-up, we expect to see some discrepencies between our results and theirs. Since they do
not provide country-level results, we can only compare the World Value Added to Export ratio for the
years 1995 to 2009. These results can be found in Table 4.
3 See Johnson and Noguera (2012b) for a brief discussion in the footnotes discussing how the WIOD database fails to capture some important changes to the VAX ratios over time and how their study relies on the variability accrued over a four-decade long period.
10
We can see that the Johnson & Noguera (2012b) results have very different values for most of the
sectors, with greater variability within each sector. This is most likely a result of the different
methodologies and data used for the construction. However, the ranks of each sector and trends within
each sector are more or less consistent. For instance, both agriculture and non-manufacturing all have
higher VAX ratios than manufacturing and we see that the VAX ratios in both datasets decline over this
time period and even tick upwards in 2009 together. Since the instructions to construct the VAX ratios
are straightforward, we can be confident that no calculation errors occurred in the construction of our
VAX ratios and that any differences are purely input related (in the case of the datasets used, time
period, year of reference value).
Our primary data concern is the relative lack of heterogeneity found in the higher-levels of aggregation.
Over the 15-year period, we can see that the worldwide aggregate VAX ratio moves downward only by
two percentage points. Figure 1 shows the change in average VAX ratios by country from 1999 to 2009:
no country experienced an increase or decrease greater than 10% over this period, but within this
narrow band there is considerable heterogeneity across the 41 countries. When we disaggregate to the
country-level measures, we see that the within sector variability typically is around 10-15 percentage
points, which is quite low. Therefore, for these reasons, we focus much of our analysis to the country-
industry level where we see plenty of movement between country-industries pairs.
2.4 Patent & Other Data
In order to jointly analyze IP and value-added trade, we match bilateral patent and trademark data
taken from the World Intellectual Property Organization (WIPO) with the WIOD database. To ensure
property country-industry matches, we will first need to translate the classification system used for
patents and trademarks into the industry classifications used in the WIOD database. The industries in
the WIOD database are organized by ISIC class4. In order to concord the patent data, which is organized
by the International Patent Classification (IPC) system, we utilized the concordance developed in Lybbert
& Zolas (2012) which uses text analysis software to match technologies to industries.
4 Although it is not clear which revision it is, it does not matter for our analysis, since we can match the industry groups of the WIOD database one-to-one for any ISIC revision. For methodology purposes, we matched the WIOD industries to the ISIC Rev. 4.
11
Once the bilateral patent flows have been matched to the WIOD industry groups, we then incorporate
proxies for trade costs, such as distance, border dummies, language dummies and colonial history taken
from CEPII. This completes our data construction. Unfortunately, not all of the years of the patent and
trademark data coincide with the WIOD data. For instance, the bilateral country-industry patent data
exists from 1990 to 2003, which means that our analysis for patents can only take place in the eight
years between 1995 and 2003. For trademarks, we have bilateral country-industry trademark flows only
for 2004 to 2009.Both of these are limited time frames for any significant changes to the production
process. While we therefore expect the coefficients to be quite small, we believe this provides a useful
point of departure for exploring the role of patents in GVCs.
As a graphical depiction of the structure of our dataset, Figure 2 shows the time series of average
(directional) bilateral VAX ratios and (directional) bilateral international patent application flows for four
trading partners with the US. By far, the most pronounced changes over this period occur for US-China
(top-left panel). In 1999, US firms sent less than 2,000 patent applications to China, but by 2009 these
‘exported’ patents had increased six fold to nearly 12,000. During this same time, but particularly after
2003, the US VAX ratio for China decreased by roughly 15%. The other three panels depict less dramatic
changes and indicate the extreme heterogeneity in annual bilateral patent flows, which ranges from
about 60 patent applications flowing between the US and Mexico to 35,000 between the US and Japan.
To set the stage for the analysis that follows, it is important to note that while the data depicted in
Figure 2 is disaggregated by directional bilateral pair, it aggregates over industries. In our empirical
analysis, we use a host of fixed effects to control for these important dimensions and identify the
relationship of patenting on GVCs.
3 Bilateral Value-Added Trade & Patents: Analysis & Results Our empirical analysis in this paper is exploratory by nature because there is essentially no existing
evidence on the interrelationships between value-added trade and patenting. To begin this empirical
exploration, we adopt an approach that is more inductive than deductive. While we therefore do not
rely on theoretical modeling to provide testable hypotheses, we do expect apriori patents and GVCs to
be related in a few specific ways. In this section, we discuss these priors and relevant considerations as a
way to frame the analysis that follows. For now, this framing is intentionally speculative. We then
present and interpret our results.
12
Much of the emerging GVC literature focuses on measures of international production fragmentation.
This is a useful perspective on potential relationships between GVCs and patenting. We consider three
dimensions of patenting: exported or outbound international patents, imported or inbound
international patents, and domestic patents. Depending on the type of patent (either a process or
product patent), we expect varying results. For process patents (i.e. patents that relate to the
improvement of a particular production process), it is easy to imagine that as firms export more process
patents, then that would be indicative that the firm is planning on conducting more of their production
outside of the country. This would imply that both “reflected” trade and “redirected” trade would
increase in this scenario meaning that the VAX ratio would be expected to decline with increased patent
transfer.
On the other hand, for product patents, which are more likely to relate to the increased transfer of final
goods, we would most likely see a different outcome to the VAX ratio. Higher levels of final goods
exports would have a null affect on the “net absorption/absorption” ratios (the higher 𝑓𝑖𝑗𝑡(𝑠) would
cancel out) and no affect on “redirected” or “reflected” trade. Instead, we would most likely see an
increase of “indirect exports” following these patents, which would imply a higher VAX ratio. While we
cannot differentiate between the two types of patents in patent data, we could potentially use these
results to determine the mix of process and product patents that are being transferred or, alternatively,
which of these patent types have the greatest effect on production and trade in goods and services. If
we see reduced VAX ratios as a result of increased patent transfer, then we can assume that the country
is transferring more process patents. On the other hand, if the VAX ratio increases as a result of
increased patent exports, then it is likely that the country is exporting more product patents.
3.1 Pooled Bilateral Country-Industry Results
In our first analysis, we estimate how directional, bilateral VAX ratios change with changes in exported,
imported and domestic patenting, and a host of trade cost controls and fixed effects. To begin, we pool
all bilateral country pairs, all industries and all years and estimate overall effects of patenting on value-
added trade. Specifically, we build on a specification used by Johnson and Noguera (2014) by adding
patenting intensity measures as explanatory variables as follows:
13
( ) ( ) ( ) ( )out in dom
1 2 1log log log logd ijzt d ijzt d ijzt d ijzt
ijzt
y p p pα β β β
ε
∆ = + ∆ + ∆ + ∆
′+ + + + +origin-year dest-yearij ijzt ijzt zγ x θ θ θ
(1)
where ijzty is the outcome of interest, VAX ratio in this case, for bilateral country pair i-j where i denotes
the origin country and j denotes the destination country for industry z in year t, outijztp measures outbound
patenting intensity (patents/1,000,000 population) based on the patent applications filed from i to j in
industry z in year t, inijztp and dom
ijztp are analogous measures of inbound (applications filed from j to i) and
domestic patenting intensity, ijx is a vector of pair-specific trade cost variables such as sharing a
common language or being members of a regional trade agreement, origin-yearijztθ are origin-year fixed
effects, dest-yearijztθ are destination-year fixed effects, zθ are industry fixed effects, and ijztε is the error
term, which is clustered by origin-destination pair. d∆ is the differencing operator such that
( ) ( ) ( ), ,log log logd ijzt ijz t ijz t dy y y −∆ = − .5
The results from estimating equation (1) for d={8,4,1} are shown in Table 5. All patenting intensity
coefficients are negative, suggesting that patenting leads to greater production fragmentation. The
largest and most precisely measured effects come from inbound (“imported”) patents, which implies
that receiving patents from a given trade partner in a given industry is associated with greater
production fragmentation vis-à-vis that trade partner. These results may further imply that the strongest
production effects of patents come from process patents rather than product patents.
To further explore these results, we decompose these effects on the VAX ratio (using 4-year
differences). Our hypothesis predicts that we should see increased gross exports as a result of process
patents being transferred. The results of the estimation of equation (1) for each of these VAX
components are shown in Table 6. From the first two columns, it is clear that international patenting
increases both value-added and gross exports but increases the latter more than the former, which
induces decreases in the VAX ratio. The final four columns of Table 6 further breakdown the
components of value-added trade. We expect to see most of the movement occurring in “reflected” and
5 In ongoing work, we are exploring more sophisticated specifications that used lag structures to more carefully identify causal effects.
14
“redirected trade”. As expected, the outcome from increased patent transfer are higher levels of
“reflected” and “redirected” trade, which appear to be driving the movements in the VAX ratio. Taken at
face value, this pattern of results is consistent with the majority of patents (or the most influential
patents) transferred abroad being used for production purposes and offshoring as opposed to
protecting demand for final goods.
3.2 Industry-Specific Results
Next, we estimate separate equations for each industry. We estimate a slightly modified version of the
equation (1) as follows:
( ) ( ) ( ) ( )out in dom
4 1 4 2 4 3 4log log log logz z zijt ijt ijt ijt
ijt
y p p pα β β β
ε
∆ = + ∆ + ∆ + ∆
′+ + + +origin-year dest-yearij ijt ijtγ x θ θ
(2)
where variables are defined above and the patenting coefficients (and all other coefficients) are now
industry specific. This estimation allows us to explore the heterogeneous effects of patenting on value-
added trade across different industries.
To facilitate interpretation, we present the results of these industry-specific estimations graphically in
Figure 3. This graphical depiction scales the point estimate markers according to the average patenting
intensity of each industry. The dominant trend towards a negative relationship between patenting and
VAX ratios is clear. For roughly half of the industries, imported patents are significantly associated with
greater production fragmentation; in no case does the international patenting effect run in the opposite
direction.
3.3 Country-Specific Results
We estimate similar country-specific regressions of the following form:
( ) ( ) ( ) ( )out in dom
4 1 4 2 4 3 4log log log logi i ijzt jzt jzt jzt
jzt
y p p pα β β β
ε
∆ = + ∆ + ∆ + ∆
+ + + +year destjt jt zθ θ θ
(3)
This specification generates origin country-specific coefficients, which we depict graphically in Figures 4a
and 4b. The heterogeneity that is evident in these coefficients is striking. Consider first the results
depicted in Figure 4a. Imported patenting intensity is associated with greater production fragmentation
in 12 of 18 countries, but is associated with less production fragmentation in South Korea. For many
15
countries, exported patents reduce production fragmentation, and domestic patents increase
fragmentation as captured by these movements in the VAX ratio.
4 Discussion The empirical analysis in this paper is distinctly exploratory in nature, but the results are nonetheless
intriguing. We find that receiving patents from bilateral trading partners is significantly associated with
greater production fragmentation vis-à-vis these trading partners. These results vary in strength and
statistical precision by industry and (especially) country of origin. Taken as a whole, the results suggest
that both international and domestic patenting can shape GVCs by changing the composition of bilateral
trade. We conclude with discussion of several limitations of this analysis and potential next steps.
Because the opportunity to empirical test the relationship between patents and GVCs is new and
unexplored, we have opted for an essentially inductive approach in this paper. As we continue to refine
these empirical results, it will be important to develop an analytical framework – perhaps formalized as
a theoretical model – with which to generate testable hypotheses and through which to provide a richer
interpretation of results. This is beyond the scope of the current paper, but we believe that
characterizing these relationships with exploratory empirical analysis, which is the focus of this paper, is
an important first step in this direction.
While the results we present in this paper do help to characterize these patterns, we are in the process
of refining and improving our dataset. As is nearly the always the case in applied research, there is more
we could do with more and better data. Fortunately, empirical research into GVCs is expanding rapidly,
which is likely to bring improvements in data sources and modelling approaches. The algorithmic
concordance we use in this paper is similarly being used to test a variety of research questions, and this
growing body of work related to the effect of patents on economic (and other) outcomes across both
industries and countries is likely to improve our understanding of the role patents play in the
contemporary global economy.
As work in this direction continues, we are interested in identifying more carefully causal relationship
between patenting and GVCs. While our focus in this exploratory analysis has been effects moving from
patenting to value-added trade, the opposite direction – from changes in GVCs and value-added trade to
16
patent-based (or more general) innovation – is at least as interesting and potentially more important.
How might production fragmentation impact domestic and international patenting? In a basic model of
offshoring, increased production fragmentation allows the domestic firm to reallocate more workers
towards higher-end production processes, which include R&D. We would expect that countries that
increase production fragmentation over time by becoming more integrated in GVCs to thereby benefit
with higher levels of innovation. This would suggest that a declining VAX ratio may lead to higher
outputs in patents.
17
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19
Figure 1 Changes in VAX ratio by country over the period covered by our data, 1999-2009
20
Figure 2 Trends in bilateral (directional) VAX ratios and patent flows aggregated over al industries for each year for four trading partners of the US
21
Figure 3 Estimated patenting intensity coefficients for the 18 industries in the WIOD dataset with 95% confidence intervals. The size of the point estimate markers is proportional to the average patenting intensity of each industry.
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Ag/Forest/Fish
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Mining/Quarrying
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Food/Bev
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Textiles
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Leather/Shoes
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Wood
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Paper/Printing
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Petroleum
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Chemicals
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Rubber/Plastic
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Minerals
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Metals
ExportedImportedDomestic
-.2 -.1 0 .1 .2
MachineryExportedImportedDomestic
-.2 -.1 0 .1 .2
Elect/OpitcExportedImportedDomestic
-.2 -.1 0 .1 .2
Transport
ExportedImportedDomestic
-.2 -.1 0 .1 .2
Oth.ManufExportedImportedDomestic
-.2 -.1 0 .1 .2
UtilitiesExportedImportedDomestic
-.2 -.1 0 .1 .2
Construct
22
Figure 4a Estimated patenting intensity coefficients and 95% confidence intervals for the 18 countries in the WIOD dataset with the highest average patenting intensity x. The size of the point estimate markers is proportional to the average patenting intensity of each country.
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
JPN
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
USA
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
KOR
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
DEU
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
FRA
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
CHN
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
RUS
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
GBR
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
NLD
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
CAN
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
SWE
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
AUS
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
ESPExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
ITAExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
AUT
ExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
IRLExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
FINExportedImportedDomestic
-.1 -.05 0 .05 .1 .15
DNK
23
Figure 4b Estimated patenting intensity coefficients and 95% confidence intervals for the rest of the countries in the WIOD dataset (with lower than median patenting intensity). The size of the point estimate markers is proportional to the average patenting intensity of each country.
ExportedImported
Domestic
-.4 -.2 0 .2 .4
BELExportedImported
Domestic
-.4 -.2 0 .2 .4
LUXExportedImported
Domestic
-.4 -.2 0 .2 .4
POL
ExportedImported
Domestic
-.4 -.2 0 .2 .4
GRCExportedImported
Domestic
-.4 -.2 0 .2 .4
PRTExportedImported
Domestic
-.4 -.2 0 .2 .4
CZE
ExportedImported
Domestic
-.4 -.2 0 .2 .4
BRAExportedImported
Domestic
-.4 -.2 0 .2 .4
HUNExportedImported
Domestic
-.4 -.2 0 .2 .4
SVN
ExportedImported
Domestic
-.4 -.2 0 .2 .4
SVKExportedImported
Domestic
-.4 -.2 0 .2 .4
BGRExportedImported
Domestic
-.4 -.2 0 .2 .4
EST
ExportedImported
Domestic
-.4 -.2 0 .2 .4
MEX
24
Table 1 Summary Statistics by Country for Value Added, Patents
VAX Ratio Export. Patents Import. Patents
Country Mean Diff. Mean Diff. Mean Diff.
RUS 1.03 0.092 768 253 5,107 3,976
AUS 1.00 0.002 5,215 1,333 29,091 19,856
BRA 1.00 (0.001) 510 422 13,853 5,506
JPN 1.00 (0.004) 118,905 67,701 54,315 20,254
IDN 0.97 0.026 27 27 1,286 (290)
CHN 0.96 (0.011) 1,904 3,306 52,117 64,709
IND 0.96 (0.088) 1,108 1,673 321 (1,817)
KOR 0.95 (0.010) 19,180 18,008 33,656 65,650
GRC 0.93 (0.004) 152 150 378 (2,128)
ITA 0.93 (0.001) 9,869 631 991 (1,702)
USA 0.93 0.016 155,650 60,601 111,085 67,515
FIN 0.92 (0.002) 4,710 580 466 (1,714)
LVA 0.92 0.002 18 (6) 52 (51)
TUR 0.92 (0.017) 105 114 1,737 (2,188)
GBR 0.91 0.018 26,438 (1,772) 7,586 396
DEU 0.90 (0.025) 58,427 9,650 44,058 (25,944)
ESP 0.90 (0.003) 2,104 328 12,541 (12,264)
FRA 0.90 (0.016) 23,334 (304) 3,384 (320)
POL 0.90 (0.058) 184 155 4,339 (3,129)
TWN 0.90 0.043 10,491 10,813 11,956 5,836
BGR 0.89 (0.012) 53 52 234 (407)
ROM 0.89 (0.015) - - - -
AUT 0.88 (0.024) 3,514 (32) 18,523 (7,265)
CYP 0.88 0.049 73 99 27 (89)
SWE 0.88 (0.004) 9,473 (787) 622 56
DNK 0.87 (0.009) 3,854 773 4,911 (5,244)
LTU 0.87 0.005 8 2 58 (16)
LUX 0.87 (0.038) 410 58 41 (68)
PRT 0.87 0.025 125 92 2,591 (3,779)
CAN 0.86 0.062 5,373 (1,077) 43,078 10,658
CZE 0.86 (0.080) 274 233 2,922 (4,399)
MLT 0.86 0.026 20 9 - -
NLD 0.85 (0.014) 12,073 8,559 592 88
EST 0.84 0.032 45 (228) 474 (455)
MEX 0.82 (0.003) 182 107 7,546 12,184
SVN 0.82 (0.019) 162 72 873 (654)
BEL 0.81 0.015 3,176 (102) 236 (147)
HUN 0.81 (0.121) 425 (17) 2,899 (4,156)
IRL 0.80 0.037 839 (76) 108 30
SVK 0.80 (0.124) 110 14 1,320 (2,016)
25
Table 2 Summary Statistics by Industry for Value Added and Patents
VAX Ratio Export. Patents
Industry Description Mean Diff. Mean Diff.
Wholesale Trade 1.04 0.034 - -
Inland Transport 1.02 0.013 394 96
Private Households 1.00 (0.035) - -
Mining and Quarrying 0.99 (0.020) 17,278 1,803
Wood and Products of Wood and Cork 0.99 0.006 3,037 613
Retail Trade 0.99 0.003 - -
Other Transport Activities 0.99 (0.011) 467 50
Financial Intermediation 0.99 (0.015) 16,793 9,128
Real Estate Activities 0.99 (0.016) - -
Education 0.99 (0.001) - -
Other Non-Metallic Mineral 0.98 0.003 13,333 2,681
Sale and Repair of Motor Vehicles 0.98 0.009 1,437 371
Water Transport 0.98 (0.017) - -
Post and Telecommunications 0.98 (0.009) 13,354 8,972
Renting and Other Business Activities 0.98 0.010 70,524 39,294
Hotels and Restaurants 0.97 (0.002) 5,064 1,638
Public Admin and Defense 0.97 (0.022) 67 (9)
Other Social and Personal Services 0.97 (0.010) 5,194 1,518
Agriculture, Forestry and Fishing 0.96 (0.004) 20,780 3,370
Basic Metals and Fabricated Metal 0.96 0.013 35,358 10,566
Health and Social Work 0.96 (0.011) 9,082 2,607
Pulp, Paper, Printing and Publishing 0.94 (0.006) 13,020 4,282
Rubber and Plastics 0.94 0.006 4,115 924
Electricity, Gas and Water Supply 0.94 (0.003) 9,849 1,191
Construction 0.94 0.020 13,115 4,250
Air Transport 0.94 (0.018) - -
Food, Beverages and Tobacco 0.91 (0.013) 21,944 6,641
Chemicals and Chemical Products 0.91 (0.052) 74,309 11,234
Leather, Leather and Footwear 0.88 0.013 6,723 1,981
Machinery, Nec 0.88 (0.026) 25,439 10,908
Textiles and Textile Products 0.87 0.016 14,382 5,220
Manufacturing, Nec; Recycling 0.87 (0.034) 18,907 5,285
Electrical and Optical Equipment 0.85 (0.016) 68,008 46,408
Coke and Refined Petroleum 0.83 (0.053) 6,873 111
Transport Equipment 0.83 0.009 12,190 3,323
26
Table 3 Comparison of VAX ratios (aggregate and by sector) with Johnson & Noguera (JN; 2012a) for the year 2004. ZL
denotes our estimates.
Aggregate VAX Agriculture Manufacturing Services Country JN ZL JN ZL JN ZL JN ZL
RUS 0.87 1.03 0.99 0.99 0.41 1.03 2.49 1.06 AUS 0.86 1.00 0.87 1.06 0.50 1.00 1.64 1.00 BRA 0.86 0.99 0.95 1.03 0.51 0.99 3.27 1.01 JPN 0.85 1.01 2.70 0.98 0.53 0.99 3.93 1.07 IND 0.81 0.95 1.80 1.05 0.46 0.93 1.68 1.01 GBR 0.79 0.92 1.05 0.96 0.51 0.89 1.24 0.99 IDN 0.79 0.99 1.47 1.01 0.45 0.99 2.39 0.97 CYP 0.77 0.89 1.18 0.84 0.64 0.83 0.79 0.94 GRC 0.77 0.93 1.44 0.98 0.56 0.89 0.82 0.95 ITA 0.77 0.94 2.18 0.97 0.53 0.93 1.77 1.01 USA 0.77 0.93 0.86 0.99 0.49 0.88 1.58 1.01 TUR 0.76 0.91 1.25 0.98 0.51 0.90 1.46 0.99 ESP 0.75 0.90 1.19 0.96 0.46 0.87 1.32 0.99 DEU 0.74 0.89 1.56 0.94 0.47 0.88 2.52 1.00 DNK 0.73 0.88 1.27 0.98 0.53 0.86 1.01 0.94 FRA 0.73 0.90 1.17 0.95 0.47 0.89 1.79 1.00 FIN 0.72 0.93 3.83 0.99 0.50 0.92 1.52 0.99
SWE 0.72 0.89 1.94 0.94 0.43 0.86 1.84 1.00 CAN 0.70 0.86 1.00 1.01 0.44 0.82 1.97 0.98 CHN 0.70 0.94 4.11 0.99 0.46 0.92 2.75 1.02 POL 0.70 0.87 1.34 0.96 0.52 0.86 1.57 0.96 NLD 0.69 0.85 0.96 0.89 0.43 0.81 1.29 0.96 PRT 0.68 0.87 2.25 0.94 0.46 0.84 1.17 1.01 AUT 0.67 0.87 2.09 0.94 0.49 0.83 1.01 0.99 MEX 0.67 0.81 0.69 0.98 0.65 0.77 0.93 1.02 IRL 0.66 0.81 2.05 0.91 0.46 0.75 1.11 0.96 LVA 0.64 0.90 0.84 0.91 0.51 0.84 0.96 0.99 SVN 0.64 0.82 2.26 0.96 0.44 0.80 1.59 1.00 BGR 0.63 0.88 0.85 0.98 0.38 0.83 1.17 0.96 KOR 0.63 0.95 2.53 0.97 0.46 0.94 2.62 0.99 LTU 0.63 0.87 0.95 0.96 0.46 0.82 1.23 0.98 MLT 0.63 0.86 0.71 0.88 0.62 0.82 0.64 0.92 CZE 0.59 0.84 1.52 0.96 0.43 0.82 1.51 0.96
TWN 0.58 0.92 1.36 0.92 0.39 0.92 3.18 0.95 SVK 0.55 0.76 1.29 0.95 0.39 0.72 1.77 0.96 HUN 0.54 0.80 0.96 0.96 0.38 0.76 1.39 0.98 EST 0.53 0.86 1.07 0.91 0.34 0.83 0.94 0.97 BEL 0.48 0.81 0.54 0.89 0.32 0.77 1.29 0.97 LUX 0.40 0.85 0.83 0.91 0.43 0.81 0.39 0.86
ROM - 0.87 - 0.99 - 0.83 - 0.98 ROW - 0.84 - 0.95 - 0.78 - 0.96
JN refers to Johnson & Noguera (2012b). LSZ refers to this paper’s results.
27
Table 4 World VAX ratio comparison by sector: JN denotes Johnson and Noguera (2012b) and ZL denotes our estimates.
Agriculture Non-Manufact. Manufacturing Services Agg. VAX Ratio
Year JN ZL JN ZL JN ZL JN ZL JN ZL
1995 1.30 0.97 1.16 0.99 0.53 0.90 1.55 0.99 0.80 0.92
1996 1.30 0.97 1.12 0.98 0.52 0.89 1.54 0.99 0.80 0.91
1997 1.30 0.96 1.11 0.98 0.52 0.89 1.55 0.99 0.79 0.91
1998 1.40 0.96 1.25 0.98 0.51 0.88 1.54 0.99 0.79 0.91
1999 1.38 0.96 1.19 0.98 0.50 0.88 1.55 0.99 0.78 0.90
2000 1.40 0.97 1.07 0.98 0.49 0.87 1.54 0.99 0.77 0.90
2001 1.53 0.97 1.13 0.98 0.49 0.87 1.54 0.99 0.77 0.90
2002 1.53 0.97 1.15 0.98 0.49 0.88 1.55 0.99 0.77 0.90
2003 1.53 0.96 1.12 0.98 0.48 0.88 1.55 1.00 0.77 0.90
2004 1.61 0.96 1.11 0.98 0.48 0.87 1.53 0.99 0.76 0.90
2005 1.58 0.96 1.06 0.98 0.47 0.87 1.52 1.00 0.76 0.90
2006 1.56 0.96 1.06 0.98 0.46 0.87 1.52 0.99 0.75 0.90
2007 1.55 0.96 1.07 0.97 0.46 0.87 1.50 0.99 0.75 0.90
2008 1.46 0.96 1.00 0.96 0.45 0.87 1.50 0.99 0.74 0.90
2009 1.43 0.96 1.07 0.97 0.46 0.88 1.49 0.99 0.77 0.91
JN refers to Johnson & Noguera (2012b). LSZ refers to this paper’s results.
28
Table 5: 8-year, 4-year and 1-year changes in bilateral country-industry VAX ratios based on directional, bilateral
industry-specific patenting (including all manufacturing industries)
Δln(VAX Ratio)
8-year 4-year 1-year
Δln(Exported patent intensity) -0.00296 -0.00873** -0.00297
(0.00476) (0.00329) (0.00190)
Δln(Imported patent intensity) -0.0367*** -0.0240*** -0.00400*
(0.00522) (0.00454) (0.00156)
Δln(Domestic patent intensity) -0.0139** -0.00857* -0.00185
(0.00528) (0.00351) (0.00197)
Border Dummy 0.0136 -0.0105 0.00470
(0.0297) (0.0132) (0.00872)
ln(Distance) 0.158*** 0.0463*** 0.00156
(0.0193) (0.00942) (0.00434)
Colonial Relationship Dummy -0.0305 -0.0299* -0.0265
(0.0338) (0.0172) (0.0161)
Common Language Dummy 0.115*** 0.0462*** 0.00873
(0.0300) (0.0122) (0.00581)
Regional Trade Agreement Dummy 0.245*** 0.0612** -0.00909
(0.0460) (0.0191) (0.00891)
Common Legal Origin Dummy -0.0738*** -0.0163* -0.00288
(0.0197) (0.00804) (0.00411)
Common Currency Dummy -0.0472 -0.00759 -0.000757
(0.0321) (0.0125) (0.00679)
Constant -1.572*** -0.454*** -0.00402
(0.177) (0.0941) (0.0386)
Observations 72094 79828 92993
R-squared 0.121 0.090 0.051
All specifications include fixed effects by industry, origin-year, and destination-year.
Robust standard errors clustered by origin-destination pair in parentheses.
* p<0.1, ** p<0.01, *** p<0.001
29
Table 6: Decomposition of VAX ratio effects by component using 4-year differences (including all manufacturing
industries)
Δln( [ component of exports ] )
Value-Added Gross Absorbed Reflected Redirected Indirect
Δln(Exported patent
intensity)
0.148*** 0.161*** 0.000135 0.163*** 0.151*** 0.108***
(0.0188) (0.0220) (0.000402) (0.0201) (0.0182) (0.00825)
Δln(Imported patent
intensity)
0.102*** 0.132*** 0.00256*** 0.182*** 0.215*** 0.0619***
(0.0153) (0.0186) (0.000379) (0.0177) (0.0142) (0.00692)
Δln(Domestic patent
intensity)
-0.0242* -0.0150 0.00105*** -0.0408*** -0.0318*** -0.0476***
(0.00980) (0.0113) (0.000304) (0.00927) (0.00955) (0.00535)
Border Dummy
0.128** 0.143** 0.000423 0.233*** 0.169*** -0.0198
(0.0390) (0.0448) (0.000562) (0.0489) (0.0371) (0.0157)
ln(Distance) -0.234*** -0.282*** 0.000195 -0.110*** -0.208*** -0.0362***
(0.0265) (0.0306) (0.000371) (0.0277) (0.0236) (0.0102)
Colonial
Relationship Dummy
0.0316 0.0429 0.00111 -0.0907* 0.0262 0.0499**
(0.0423) (0.0490) (0.000694) (0.0442) (0.0380) (0.0154)
Common Language
Dummy
-0.103* -0.133* 0.0000718 0.0683 -0.0663 -0.0260*
(0.0452) (0.0521) (0.000688) (0.0552) (0.0419) (0.0157)
Regional Trade
Agreement Dummy
-0.208*** -0.272*** 0.00128 -0.0431 -0.199*** 0.00984
(0.0576) (0.0660) (0.000908) (0.0668) (0.0504) (0.0270)
Common Legal
Origin Dummy
0.0986*** 0.120*** -0.000439 0.0345 0.0537* -0.0228*
(0.0262) (0.0299) (0.000400) (0.0281) (0.0253) (0.0105)
Common Currency
Dummy
0.0761* 0.0847* -0.000664 0.0634* 0.0838** 0.0268*
(0.0307) (0.0360) (0.000642) (0.0353) (0.0294) (0.0136)
Constant 1.982*** 2.399*** -0.00847* 0.874*** 1.823*** 0.349**
(0.248) (0.282) (0.00436) (0.250) (0.214) (0.127)
Observations 89281 89283 88851 89283 89267 89283
R-squared 0.667 0.593 0.154 0.360 0.572 0.822
All specifications include fixed effects by industry, origin-year, and destination-year.
Robust standard errors clustered by origin-destination pair in parentheses.
* p<0.1, ** p<0.01, *** p<0.001
30
Table 7: Concordance of select AIO sectors to HS and SITC classifications
HS4 AIO
Sector Description SITC4 Codes
8501-8506,
8514-8515 48 Heavy Electrical Equip.
716x, 7373, 7413, 771x, 7781,
7788
8517-8531 49
Sound Recorders & Reproducers, TV
sets, Radios, Audios & Comm.
Equip., Parts & accessories thereof
761x, 762x, 7633-7638, 764x,
7788, 898x
8469-8473 50 Electronic Computing Equip. 751x-752x, 759x
8541 51 Semiconductors & Integrated
Circuits 7763
8508-8511,
8516 53 Household electrical equip. 7754, 7757, 7758, 7783, 7784
8532-8540,
8542-8543 52
Other Electronics & Electronic
products
6639, 6996, 7723 - 7728,
776x, 7782, 7786, 7787
8507,8512-
8513,8544-
8548
54 Lighting fixtures, batteries, wiring
and others
7731, 7732, 7781, 7783, 7788,
8131, 8138
