WPS6960
Policy Research Working Paper 6960
The Domestic Segment of Global Supply
Chains in China under State Capitalism
Heiwai Tang
Fei Wang
Zhi Wang
The World Bank
Development Research Group
Trade and International Integration Team
June 2014
Policy Research Working Paper 6960
Abstract
This paper proposes methods to incorporate firm higher shares of indirect exports and ratios of value-
heterogeneity in the standard input-output table–based added exports to gross exports compared with foreign-
approach to portray the domestic segment of global value invested and large domestic private firms. Based on
chains in a country. The analysis uses Chinese firm census input-output tables for 2007 and 2010, the paper finds
data for the manufacturing and service sectors, along with increasing value-added export ratios for all firm types,
constrained optimization techniques. The conventional particularly for state-owned enterprises. It also finds that
input-output table is split into sub-accounts, which state-owned enterprises are consistently more upstream
are used to estimate direct and indirect domestic value while small and medium domestic private enterprises are
added in exports of different types of firms. The analysis consistently more downstream within industries. These
finds that in China, state-owned enterprises and small findings suggest that state-owned enterprises still play an
and medium domestic private enterprises have much important role in shaping China’s exports.
This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a
larger effort by the World Bank to provide open access to its research and make a contribution to development policy
discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
The authors may be contacted at hwtang@jhu.edu and Zhi.Wang@usitc.gov.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
The Domestic Segment of Global Supply Chains in China under State Capitalism
Heiwai Tang1, Fei Wang2, and Zhi Wang3
Key words: value-added trade; global supply chain; intra-national trade; state capitalism
JEL Classification Numbers: F1, C67, C82
1
School of Advanced International Studies, Johns Hopkins University, 1717 Massachusetts Ave NW, Suite 709,
Washington, DC 20036, U.S.A.
2
School of International Trade and Economics, University of International Business and Economics, P.O. Box 119, No.
10 Huixin Dongjie, Beijing 100029, China.
3
United States International Trade Commission, 500 E Street SW, Washington, DC 20436, U.S.A.
1. Introduction
The stellar export growth of China is often attributed to its low labor costs, trade liberalization, and
policies that promote processing trade and foreign direct investment (FDI) (Branstetter and Lardy, 2006).
The way that China has integrated itself with the rest of the world resembles a typical catch-up story in
East Asia – by first participating in the downstream of global value chains (GVCs) and gradually moving
upstream. Concurrently, when China was globalizing, many state-owned enterprises (SOEs), especially
those that are small in downstream sectors, were privatized or let go.4 Years of privatization provided room
for the entry of the more productive private firms, which have been shown to be an important driver of the
drastic productivity growth in China (Brandt, et al., 2012; Zhu, 2012). While the shares of SOEs in China’s
total value added, employment, and gross exports have been declining substantially, recent evidence shows
that SOEs still monopolize the key upstream and non-tradable sectors. SOEs also appeared to gain
increasing prevalence and profits in the Chinese economy in recent years, especially after the global
financial crises in 2008-2009. 5
Against this backdrop, this paper aims to answer the following questions: In which sectors did SOEs still
have a prominent presence? How did the sectoral distribution of the prevalence of SOEs and its evolution
in recent years shape the trade patterns of other firms, as well as their own? How did this sectoral
distribution affect the intra-national trade and income distribution in China when the country was
globalizing? To answer these questions, we first propose methods to split a conventional input-output (IO)
table into sub-accounts that feature input-output linkages between different firm types. Specifically, we
use firm-level data to group firms based on their key characteristics, which include export intensity,
value-added to sales ratio, and ownership type. We then estimate the coefficients of the split tables using
constrained optimization techniques, based on known statistics from firm census data for both
manufacturing and service sectors, as well as detailed trade statistics. We can then estimate the volume of
inter-industry trade flows between different types of firms within China and quantify the importance of
different channels of indirect (value added) exports. While the paper focuses on SOEs, our methods are
general enough to portray the domestic input-output linkages of Chinese exports, and can be applied to
assess value-added exports by firm type in other countries. Our results add to the “value added trade”
literature, which has focused mainly on the relative contribution of different countries to GVC, by
formally portraying the composition and dynamics of the domestic segment of GVC in a large
developing country.
4
The 15th Congress of the Chinese Communist Party in 1997 marked the watershed of China’s economic reforms. The
Congress formally sanctioned ownership reforms of the state-owned firms and also legalized the development of private
enterprises.
5
See Zhu (2012) for a comprehensive review of China’s growth experience and the decline role of SOEs. See He, et al.
(2012) for a study showing the continuing importance of SOEs in shaping the Chinese economy. Wang et al. (2012)
develop a theoretical model to rationalize the rising profits of surviving SOEs.
2
Specifically, we split the conventional IO tables of China for 2007 and 2010 into transactions between six
groups of firms, defined by ownership type and firm size, namely large SOEs (LSOE), small and medium
SOEs (SSOE), large foreign invested enterprises (LFIE), small and medium FIEs (SFIE), large private
(LP), and small and medium private enterprises (SME). Based on the six-group split of the IO tables, we
report our results for four types: SOEs, FIEs, LPs, and SMEs. We find that SOEs’ value added (VA)
exports are significantly larger than their gross exports, contrasting with the common finding of low
value added in Chinese exports (Chen et al., 2012; Koopman, et al. 2012). Specifically, the value added
to gross export (VAX) ratio of SOEs is estimated to be 1.2 in 2007 and 1.8 in 2010, compared to around
0.35 for FIEs in both years. These results contrast with the findings in developed countries, such as the
United States, where large firms tend to have lower VAX. Among private firms, large firms’ VAX is
around 0.7 for both years, while SMEs’ VAX exceeded 1 for both years, and increased from slightly
above 1 in 2007 to 1.3 in 2010.
Another advantage of splitting the conventional IO table into sub-accounts based on available micro data
is that we can analyze trade between different firm types in the domestic segment of GVC in great detail.
About 80% of SOEs’ VA exports are indirect (exporting through other firms) in 2007, which increased
further in 2010. Of these indirect exports, about 40% is through small firms, both domestic and foreign.
These findings suggest that although SOEs’ direct participation in exporting has been low, its actual
participation and impact on China’s exports have remained high and have been overlooked. Similar to
SOEs, LPs and SMEs both have a large share of indirect VA exports, though LPs have a much lower
VAX. On the other hand, FIEs tend to export more directly.
We also investigate the reasons behind the high indirect export participation for both SOEs and SMEs.
Turning to the industry distribution of indirect exports by firm type, we find that SOEs’ indirect exports
are due to their prevalence in upstream or non-tradable industries, such as energy and mining; metal and
non-metallic mineral extraction; electricity; gas and water supply; and the financial sector. This may not be
surprising, since we also observe high indirect export shares in similar industries for large domestic private
firms. One can argue that this could also be true in other countries, almost by definition. However, what we
intend to show is that SOEs, not only large firms, have been dominating the upstream of the domestic
segment of GVC in China, possibly due to the sequential pattern of privatization. While the political
economy factors behind this pattern are beyond the scope of this paper, we believe that a systematic
documentation can already provide important insights for understanding China’s past and future economic
growth. The conventional view is that China’s export growth is largely driven by the dynamic
labor-intensive private sector, especially the foreign-dominated processing trade sector. Our findings add
to this conventional view by showing that SOEs, through their protected position in the upstream, have
3
been playing an important role in shaping Chinese export patterns and performance. Based on information
from the IO tables for only two years (2007 and 2010), we find evidence of significant increases in SOEs’
VAX ratio, indirect to direct VA export ratio, and share of VA in aggregate exports. These findings have
important policy implications. For instance, to the extent that SOEs are less productive than non-state firms
(e.g., Zhu, 2012), a deeper privatization of SOEs or lower entry barriers in upstream industries may
increase the efficiency of direct exporters in the downstream, which in turn increases the speed of
upgrading of Chinese exporters’ along GVC.
We find that SOEs’ dominance in upstream industries is observed not only between industries but also
within industries. This fact is established by measuring an industry’s upstreamness by firm ownership
type, based on the methods proposed by Antras et al. (2012) and Fally (2012). Using the estimated
coefficients of our extended IO table, we measure upstreamness by industry and firm type. Based on the
IO table for 2007, Fig. 5 shows that SOEs tend to be more upstream than non-state firms within an
industry (see Fig. 8). Figs. 4 and 5 further confirm that SOEs have larger output and export shares in
upstream industries, while SMEs exhibit the opposite pattern (see Figs. 6-7). These findings suggest that
SOE’s prevalence in upstream industries can be a potential explanation for their high VAX, compared to
other firms. Furthermore, we find that the upstreamness measure increases for more than two-third of the
40 sectors from 2007 to 2010 (see Fig. 9). The increase was across the board for all ownership types,
suggesting that Chinese firms are “moving up” in GVC, a pattern that is opposite to what is observed for
the U.S. (Fally, 2012).
Although SMEs are similar to SOEs in the sense that they also have high value added and indirect export
ratios, the sources of the similarities appear to be quite different. In addition to the fact that SMEs are
more likely to export through other private firms, their upstreamness measures are generally lower than
those of other types of firms within an industry (see Fig. 8). These findings suggest that the high VAX
and indirect export share of SMEs are probably due to their higher propensity to sell intermediate inputs
and services to other large firms that eventually export, not due to their relative upstream position in the
domestic input-output network like SOEs. The findings also highlight a subtle distinction between high
upstreamness and high indirect export shares of an industry.
Did the increase in SOEs’ VAX lead to rising profits for the upstream SOEs, as some recent studies claim?
Using our split IO table, we can examine how much profit in the Chinese economy could be attributed to
exports, both directly and indirectly, and through which type of firms. We find that while total
export-related profits declined from 2007 to 2010, the decline fell largely on SMEs. On the other hand,
SOEs, FIEs, and LPs all experienced an increase in export-related profits between 2007 and 2010.
4
However, unlike the sharp increase in VAX for SOEs, we find no evidence that SOEs’ export-related
profits increased the most. In other words, rising SOEs’ value added exports in recent years did not
automatically translate into higher SOEs’ profits.
Our paper makes several contributions to the literature. First, it adds to the growing literature on production
fragmentation across national borders (e.g., Hummels, Ishii, and Yi, 2001, Johnson and Noguera, 2012a,
2012b; Koopman, Wang, and Wei, 2012; Koopman, Wang, and Wei, 2014). The focus of that literature
has been on the relative shares of domestic versus foreign value added in international trade. While
establishing these facts and providing accurate measures of trade flows is urgently needed in the
increasingly globalized world, the composition and dynamics of the domestic segment of GVC have not
been subject to the same level of scrutiny. In particular, understanding how trade liberalization affects
intra-national trade between industries and in turn shapes the reallocation of resources and across
industries and firms is important for designing development policies. Our paper takes a first step by
analyzing intra-national trade between different firm types, focusing on the roles of SOEs and SMEs in
China.
Related to the value-added trade literature, our approach extends the IO-table based approach to
incorporate the “new new” trade literature that emphasizes firm heterogeneity. In reality, firms differ
substantially in their export intensity, import intensity, and position of participation along GVC. Other
characteristics such as ownership structure (domestic/foreign, private/public), location, size can also
directly affect the way firms respond to trade liberalization and other economic shocks. The usual method
that relies on the aggregate IO tables ignores most of the underlying firm heterogeneity. The lack of
information on between-firm transactions in the micro data also restricts the construction of IO tables by
firm type. Moreover, a widely recognized drawback of using IO tables to measure VAX is the
assumption that firms within an industry use the same technology for production. Proportionality
assumptions are often made in order to distribute imports into different final uses and different source
countries, as information on bilateral trade between suppliers and users is generally not available at the
country-industry level. 6 Our paper provides a method to reduce the measurement bias due to
heterogeneity in export and import intensities across firm sizes and ownership types.
Our paper also contributes to the literature on the determinants of firm export participation and other
indirect export channels. Research in international trade shows that only a small fraction of enterprises,
6
These assumptions have been shown to lead to substantial biases in the estimation of countries’ value added, factor
content of trade, and our general inference of the impact of trade on countries’ macro-economy (e.g., Puzzello, 2012).
For instance, De La Cruz et al. (2011) and Koopman, Wang and Wei (2012) show that by allowing different imported
material intensities for processing and non-processing exporters, the estimated foreign value added ratio in aggregate
exports from both China and Mexico increases significantly.
5
usually large, directly participate in international trade (e.g., Bernard, et al., 2007).7 The standard
argument is that exporting is usually associated with high fixed costs and only large (productive) firms
can make sufficiently high export revenue to amortize them. However, many non-exporters may engage
in international trade indirectly, through wholesalers and other intermediaries, as well as by providing
intermediate inputs and services to exporters of all sizes, particularly large multinationals. While the first
channel has received a lot of attention in the recent literature (e.g., Bernard et al., 2010 and Ahn et al.,
2012), the second channel has not received the deserved attention, partly due to the lack of data on
inter-firm transactions within a country.8 Our paper provides a methodology that combine firm-level and
industry-level data to quantify the volume of indirect exports, and through which channel “non-exporters”
export indirectly.
Finally, our paper relates to the large literature on the role of SOEs in shaping the Chinese economy (e.g.,
Brandt et al, 2012; Zhu, 2012). As discussed before, the conventional view is that the Chinese
government has been reducing the share of SOEs in the economy. Privatization of SOEs is often
attributed to China’s sharp productivity growth and industrial transformation. Little has been done about
the effects of the sequential privatization observed in China. Notable exceptions include the recent
theoretical work by Song et al. (2011) and Wang et al. (2012), who both highlight and rationalize the
high profitability of SOEs.9 Our papers focus on quantifying the export patterns of SOEs themselves and
how they affect other types of exporters. Our estimation can be used to examine some of the specific
predictions in these theoretical models.
The rest of this paper is organized as follows. Section 2 develops our conceptual model and estimation
methods. Section 3 explains our data. Section 4 analyzes our estimation results. Section 5 concludes, with
discussions on potential policy implications and future research.
2. Conceptual Model and Estimation Method
This section first develops a model to split a conventional IO table into sub-accounts that record domestic
transactions between different firm types across sectors. It then describes how we use constrained
optimization techniques along with various adding-up conditions to estimate those transactions. Readers
7
As Bernard et al. (2007) described “engaging in international trade is an exceedingly rare activity: of the 5.5 million
firms operating in the United States in 2000, just 4 percent were exporters. Among these exporting firms, the top 10
percent accounted for 96 percent of total U.S. exports.”
8
A notable exception is the report by the USITC (2010), who also uses the constrained optimization methodology to
estimate the contribution of small and medium enterprise (SMEs) to US exports. The report finds that SMEs’ total
contribution to U.S. exports increased from less than 28% to 41% in 2007, when the value of intermediates supplied by
SMEs to exporting firms is taken into account.
9
Song et al. (2011) further uses the unique feature of SOEs in China to explain several macro outcomes, such as huge
saving and current account surplus.
6
who are primarily interested in the estimation outcomes can skip this section and go to Section 3 directly.
2.1 Conceptual Model
Our conceptual model is built on the conventional IO table, which includes information on sales of
intermediate goods and services by one industry to another in the domestic economy. By construction,
summing up entries horizontally across each row and vertically across each column will both give the
total gross output of an industry. The vertical summation is analogous to the cost approach of measuring
a country’s gross output, which decomposes gross output into different types of intermediate and primary
factor inputs. The horizontal summation is analogous to the sales approach of measuring a country’s
gross output, which decomposes an industry’s gross output into its various domestic usages and exports.
To study the intra-national trade between different types of firms based on their ownership and size, we
first split the non-competitive IO table with 42 industries from China’s National Statistics Bureau (NBS
hereafter) into 6 sub-accounts.10 The 6 sub-accounts are constructed based on 3 ownership types – SOEs,
FIEs, and Others (i.e., non-FIE private), and 2 sizes – large and small-and-medium. Thus, there are
altogether 252 groups (42 industries x 3 ownership types x 2 sizes). To estimate the volume of domestic
transactions between each pair of firm groups, there will be 252 x 252 (including the within-group
transactions between different firms) unknowns to estimate. See Fig. 1 for an illustration of the extended
IO table.
In the IO table, Z, Y, E, X, and M represent, respectively, intermediate inputs, domestic final demand,
exports, total output, and imports. We use a two-alphabet superscript to denote one of the 6 firm groups.
The first alphabet denotes ownership type (S, F, or O) while the second subscript denotes size (L or S). A
combination of a size and an ownership type gives us a firm group, g. Specifically, g can be SL, SS, FL,
FS, OL, or OS, which represent Large SOE, Small SOE, Large FIE, Small FIE, Large Others, and Small
Others, respectively. Subscripts i and j are for supplying and buying product categories (42 of them),
which we will mostly refer to as sectors from now on.
Fig. 1 shows our extended IO table with 6 firm types. The last two rows report value added and the
column sum of gross output, respectively. The last three columns are respectively domestic final use,
exports, and total gross output, which is equal to the row sum by construction (i.e., the IO balance
10
The non-competitive IO table assumes that imported and domestic products are not substitutable, in
contrast to the standard IO table that assumes perfect substitutability between imported and domestic products.
When competitive IO tables are used, only one set IO coefficients are needed. The underlying Leontief or
linear production functions assumed in either approach have their obvious drawbacks, but we consider our
approach, which permits different IO coefficients on imported and domestic inputs across sector-pairs, to be
more suitable for the purpose of our study.
7
condition). The remaining part of the matrix is a 6x6 block of square matrices, each of which is 42x42 in
dimension. For example, Z ,
in the first row (SL) and first column (SL) is a 42x42 matrix, with an
element in row i and column j, ,
, representing output produced by LSOEs in sector i used as
intermediate inputs by other LSOEs in sector j. Moving horizontally across the first row, each matrix,
Z
, ,
, is a 42x42 matrix with an element in row i and column j representing output that is still
produced by LSOEs in sector i but is used as intermediate inputs by group-g firms (e.g., SS) in sector j.
Similarly, when moving down vertically within a column, each entry is a 42x42 matrix, Z
,
, with
,
elements, , being the output produced by firms in group g1 and sector i, and used as intermediate
inputs by firms in group g2 and sector j.
Moving to the last three rows of the split IO table, the first 6 entries in row 7 (F) are 42x42 matrices,
,
, , ,
. The element in row i and column j of , represents product i imports that are used as
intermediate inputs by group-g2 (e.g., SL) firms in sector j. The 7th entry, Y , is a 42x1 vector, with
element, , being the total amount of product i imports for final consumption. The last entry in row
7, , is a 42x1 vector, with element representing total imports of product i. By definition, is the
sum of the first 7 entries in the same row.
Rows 8 and 9 in Fig. 1 show sectoral value added and gross output of the 6 different firm groups,
respectively. For example, in the first column in Row 8, is a 1x42 row vector that has element i
equal to the direct value added of LSOE in sector i (cost of production factors). In the last row, (X )T is
a 1x42 row vector with element i being the gross output of LSOE in sector i. Superscript T represents the
transpose operation. Other X and V matrices are defined similarly for different firm groups.
The direct IO coefficients in the expanded IO table can be expressed in matrix algebra as:
,
A = = !
, ,
,
and A = = !
, ,
where i is the row subscript and j is the column subscript. A ,
is a 42x42 block matrix, with each
element being an IO coefficient representing the amount of output produced by firms in group g1 used as
intermediate inputs in the production of one unit of output by group-g2 firms. More specifically,
represents output by group-g2 firms in sector j, where g2 can be either LS, SS, LF, SF, OL, or OS,
8
respectively. It is also the jth element in (X )T in the last row of Fig. 1.
,
is the amount of sector i
output produced by group-g1 firms that are used by group-g2 firms in sector j. It is the element in row i
and column j of Z% . Similarly, A
, ,
is a 42x42 matrix, with each element being an IO coefficient
measuring the amount of imported goods used as intermediate inputs by group-g2 firms to produce one
unit of gross output. In other words, the element in row i and column j of Z%
,
in the 3rd row from the
,
bottom of Fig. 1, , is the amount of sector-i imports used by group-g2 firms in sector j.
We then obtain matrix A, with 294 (7x42) rows and 252 (6x42) columns, to represent all IO coefficients
in the economy as follows:
&'
A = − − −!
&)
where
A ,
A ,
A ,
A ,
A ,.
A ,.
- 1
,A ,
A ,
A ,
A ,
A ,.
A ,. 0
, 0
,A ,
A ,
A ,
A ,
A ,.
A ,.
0
A* = , 0，
,A A A A A A
, , , , ,. ,.
0
,A. ,
A. ,
A. ,
A. ,
A. ,.
A. ,. 0
, 0
+A. ,
A. ,
A. ,
A. ,
A. ,.
A. ,. /
and &) = 2A ,
A ,
A ,
A ,
A ,.
A ,. 3.
Thus, final demand for domestically produced goods can be expressed as
X = A* X + Y* + E (1)
X Y E
- 1 - 1 - 1
,X 0 ,Y 0 ,E 0
, 0 , 0 , 0
,X 0 * ,Y 0 ,E 0
where X = , 0, Y = , 0, and E = , 0
,X 0 ,Y 0 ,E 0
,X . 0 ,Y . 0 ,E . 0
, 0 , 0 , 0
+X . / +Y . / +E . /
(i.e., the gross output, domestic final use, and export vectors). Rearranging eq. (1) gives
9
X = (I − A* )7 Y* + (I − A* )7 E = BY * + BE (2)
where B is the well-known Leontief matrix:
B ,
B ,
B ,
B ,
B ,.
B ,.
- 1
,B ,
B ,
B ,
B ,
B ,.
B ,. 0
, 0
,B ,
B ,
B ,
B ,
B ,.
B ,.
0
9 = (I − A* )7 =, 0
,B B B B B B
, , , , ,. ,.
0
,B . ,
B. ,
B. ,
B. ,
B. ,.
B. ,. 0
, 0
+B . ,
B. ,
B. ,
B. ,
B. ,.
B. ,. /
where B ,
is a 42x42 block matrix, each element in which is the total requirement coefficient that
gives the amount of required gross output by firm group g1 for one additional unit of domestic final
demand or exports. The intuition behind the Leontief matrix is as follows: for each dollar of exports, the
first round of value added is generated by the direct exporters. This is the direct domestic value added.
To produce that value added, intermediate inputs have to be used, which in turn generate additional value
added, and so on. Such a process of value-added generation continues iteratively and can be traced
throughout the domestic input-output linkage across firm types and sectors in the economy. The total
domestic value added induced by one dollar of exports is thus equal to the sum of direct and all rounds of
indirect domestic value added generated.
Before getting to the domestic input-output linkage, let us briefly discuss the import identity, which
we will use to trace the indirect linkage across industries (from final sales back to the value-added
embodied in all upstream intermediate inputs) to distribute export value back to different sources of
supply, including foreign suppliers. As imports can be absorbed as final goods and used as
intermediate inputs, the import matrix, M, can be expressed as
M = A; X + Y ; (3)
Substituting (2) into (3) yields
M = A; BY * + A; BE + Y ; (4)
The first term on the right hand side of eq. (4), A; BY * , represents imports used (both directly and
indirectly) to produce final products for domestic use, A; BE stands for imports used (both directly
and indirectly) through the domestic input-output network to produce exports. It will be used below
to estimate foreign value-added in exports. Y ; represents the amount of imports that are consumed
10
as final goods.
@A
>?
Let us define A< = = @A C as the value added vector (1 by 42) for firm group g1 where D%
B?
is the jth
element of in the second last row in Fig. 1; and &< = 2A < , A< , A< , A < , A.
< , A < 3 as the 1x252
.
row vector of value added, covering all sectors and firm groups.
Because total gross output (X) in any sector has to be equal to the sum of direct value-added V, plus the
cost of domestic intermediate inputs (Zg1,g2) from all firm types and imported inputs, (ZF,g), the following
accounting identity always holds :
u = &< + uA* + ϑA; , (5)
which means that each unit of output can be attributed to direct value added, domestic intermediate inputs,
and imported intermediate inputs. u is a 1x252 row vector and ϑ is a 1x42 row vector, respectively.
Taking uA* to the left hand side of eq. (5) and rearranging it yields
u = &< (I − A* )7 + ϑA; (I − A* )7 = &< B + ϑA; B (6)
G, yields
Post-multiplying both sides of eq. (6) by the diagonal matrix of exports, E
G + ϑA; BE
G = AH BE
uE G, (7)
I < is the diagonal matrix of &< with the dimension of 252x252. Thus,
I < , where &
Notice that &< = u&
eq. (7) can be further be rewritten as
G H BE
G = uA
uE G,
G + ϑA; BE (8)
G, a 1x252 row vector, can be decomposed
Eq. (8) states that the country's total gross export value, uE
I < BE
into domestic value added in exports u& G (either used directly for production of exported goods and
services, or indirectly by firms that supply domestic inputs that are used eventually by exporters) and the
G, which includes imported intermediates used directly by
value of imports embedded in exports ϑA; BE
exporters or embodied in other domestic intermediates finally used by them.
G H BE
In eq. (8), the first term on the right hand side, uA G , is the key to our quantification of domestic value
G H BE
added (DVA) in Chinese exports. Specifically, A G is a 252x252 square matrix, with each element
representing the source (from which product category and firm type) and the channel (indirectly used in
11
which product category and firm type) of domestic value added in exports. Depending on the research
I H BE
question, one can aggregate & G horizontally or vertically to estimate DVA in exports. If the goal is to
decompose DVA in exports of the direct exporting sectors by firm type into its various sources of value
added, regardless of the sector or firm-type in which the value added is originally created, we should sum
I H BE
up the elements of & G vertically down a column (the backward-linkage approach). If the goal is to
measure DVA based on their source of contribution by industry-firm-type, we should sum up the
I H BE
elements of & G horizontally along each row (the forward-linkage approach)11. In other words, we
will first use the forward-linkage approach to examine how primary factors employed in a particular
upstream sector-firm-type pair contributes value-added to every downstream sector-firm-type pair’s
exports. Then we will discuss the backward-linkage approach to examine how each downstream
firm-type and sector’s exports can be sourced back to each upstream sector-firm-type pair’s value-added.
Since we need to deal with not only intermediate inputs supplied directly to the exporters, but also those
through the domestic input-output network iteratively before reaching the direct exporting sectors and
firm groups, we further decompose the Leontief matrix B to compute direct and indirect domestic
value-added exports separately. Let us rewrite B as follows
B ,
B ,
B ,
B ,
B ,.
B ,.
- 1
,B ,
B ,
B ,
B ,
B ,.
B ,. 0
, 0
,B ,
B ,
B ,
B ,
B ,.
B ,.
0
9=, 0
,B B B B B B
, , , , ,. ,.
0
,B . ,
B. ,
B. ,
B. ,
B. ,.
B. ,. 0
, 0
+B . ,
B. ,
B. ,
B. ,
B. ,.
B. ,. /
B , −I B ,
B ,
B ,
B ,.
B ,.
- 1
,B , B ,
−I B ,
B ,
B ,.
B ,. 0
, , 0
,B B ,
B ,
−I B ,
B ,.
B ,.
0
=, 0
,B B B B −I B B
, , , , ,. ,.
0
,B . , B. ,
B. ,
B. ,
B. ,.
−I B. ,. 0
, 0
+B . ,
B. ,
B. ,
B. ,
B. ,.
B. ,.
− I/
11
See Wang, Wei and Zhu (2013) for a more detailed discussion on forward- and backward-linkage approaches to
measure value-added exports.
12
I
- 1
, I 0
, 0
, I 0
+ , 0
, I 0
, I 0
, 0
+ I /
Then DVA in exports at the most disaggregated level can be decomposed as
G H BE
DVAX = A GHE
G=A G H (B − I)E
G+A G (9)
B , −I B ,
B ,
B ,
B ,.
B ,.
- 1
,B , B ,
−I B ,
B ,
B ,.
B ,. 0
, 0
,B , B ,
B ,
−I B ,
B ,.
B ,.
0
where B − I = , 0
,B B B B −I B B
, , , , ,. ,.
0
,B . , B. ,
B. ,
B. ,
B. ,.
−I B. ,. 0
, 0
+B . , B. ,
B. ,
B. ,
B. ,.
B. ,.
− I/
Notice that DVAX is a 252x252 square matrix with two separate terms: the first term on the right hand
GHE
side of eq. (9), A G H (B − I)E
G, is direct DVA in exports, while the second term, A G, is indirect DVA in
G H (B − I)E
exports. We can further decompose A G into indirect exports via other firms within the same
firm group (e.g. SOEs exporting via SOEs) or via other firm groups (e.g., SOEs exporting via FIEs). The
same-group indirect exports can be derived from the multiples involving only the diagonal of the block
matrix inside the square brackets. The between-group indirect exports can be derived from the multiples
involving only the off-diagonal part of the block matrix inside the square brackets.
To implement the forward-linkage (supply) approach so that we can trace the final use of VA created by
primary factors employed in a particular sector-firm-type, we post-multiply both sides of eq. (9) by a
252x1 unit column vector, Z. This operation essentially sums up each sector-firm-type’s VA horizontally
to obtain a measure of DVA in exports at the sector-firm-type level, regardless of which downstream
sector-firm-type the VA are embedded. Formally, the forward-linkage based DVA in exports is
GHG
DVAXfw = DVAXμ = A G H (B − I)E
Eμ + A Gμ , (10)
IHE
where DVAXfw is a 252x1 column vector. & I < (9 − \)]
Gμ and & ^ Z on the right hand side are direct and
indirect value-added exports for each firm type at the sector level, respectively. Direct DVAX represents
DVA that comes from the same sector-firm-group of the exporters. Indirect DVAX is the same
13
sector-firm-group’s DVA embodied in intermediate inputs supplied to other sectors and firms groups that
eventually export.
Let us abstract from the sector dimension and focus on different firm groups for the moment. Eq. (10)
I< ]
can be further decomposed along the firm-type dimension. The first row in & ^ Z represents the direct
I < (9 − \)]
VAX from large SOEs (SL). The first row of the second term, & ^ Z,, is the sum of 6 multiples
as follows:
I < (B
& , G μ
− I)E I< B
_+& , G μ
E I< B
_ +& , G μ
E _ (11)
I< B
+& , G μ
E I< B
_ +& ,. G. μ
E I< B
_ + & ,. G. μ
E _,
where μ I < (B
_ is a 42x1 column vector. & , G μ
− I)E _ is indirect DVAX via large SOE firms,
I< B
& , G μ
E I< B
_, & , G μ
E I< B
_, & , G μ
E I< B
_, & ,. G. μ
E I< B
_ , and & ,. G. μ
E _ represent LSOEs’
indirect VAX via SSOEs, LFIE, SFIE, LP, and SME’s exports, respectively. Other rows in eq. (10) can
be interpreted similarly for other firm types. Eq. (10) thus provides detailed information about the volume
of direct and indirect DVAX, as well as through what types of firms that indirect exporting takes place. If
we consider the 42 sectors within each firm-group-sector-pair, we can analyze these different components
of VAX by sector. The estimates of direct and indirect VAX by 6 firm groups and 42 sectors are reported
in Tables A4.1-4.6 in the appendix.
To implement the backward-linkage (user) approach that decomposes each firm type’s exports into their
original value-added source by sector and firm-type, we pre-multiply both sides of eq. (9) by the 1x252
unit row vector u. This operation essentially sums up each sector-firm-type’s VA vertically to obtain a
measure of DVA at the sector-firm-type level. Formally, the backward-linkage based DVA in exports is
GHE
DVAXbw = uDVAX = uA G H (B − I)E
G + uA G (12)
I < BE
By replacing u& G in eq. (8) by eq. (12), we can completely decompose China’s gross exports
according to its various value-added sources as follows:
uE GHE
G = uA G H (B − I)E
G + uA G
G + ϑA; BE (13)
Notice that all terms in eq. (13) are 1x252 row vectors.
Similar to our analysis of the forward-linkage based approach, let us abstract from the sector dimension
G term) for the moment, so that we can
and ignore value added from foreign sources (i.e., the ϑA; BE
14
GHE
focus on different firm groups. The first column of the first term, uA G, represents the direct value added
exports by large SOEs (SL) in all 42 sectors. Notice the direct value-added exports based on the
I< ]
forward-linkage and backward-linkage approaches are identical (i.e. `a& I< ]
^ bT in eq. (13) = & ^ Z in eq.
(11)).
However, the indirect value-added exports measures can be very different for each firm group-sector pair.
The two measures are only equal to each other at the country level (see WWZ, 2013 for details). In the
I < (9 − \)]
second term, a& ^ , the first column is the sum of 6 multiples as follows:
u
_AG H (B , G
− I)E +u
_AGH B , G
E +u
_AGH B , G
E
+u
_AGH B , G
E +u
_AGH B. , G
E + u
_ B. , G
E (14)
Where u _A
_ is a 1x42 row vector. u G H (B , G
− I)E is LSOEs’ indirect VAX via large LSOEs;
_A
u GH B , G , u
E _AGH B , G , u
E _AGH B , G , u
E _AG H B. , G , and u
E _AG H B. , G
E represent SSOEs,
LFIE, SFIE, LP, and SME’s value-added embodied in LSOE’s gross exports, or these firm groups’
I < (9 − \)]
indirect value-added exports via LSOE, respectively. Other columns of a& ^ in eq. (13) can be
interpreted similarly for other firm groups. Therefore, eq. (14) thus provides detailed information about
the value-added sources in exports produced by each firm group. If we consider the 42 sectors within
each firm-group-pair, we can analyze the value-added composition for each firm group by sector. The
full decomposition of each firm type’s exports by value-added sourced from the 6 firm groups and 42
sectors are reported in Table A7 in the appendix.
2.2 Estimation Method
Eqs. (9)-(14) allow us to study the indirect value added by firm type at the aggregate and sector levels,
decompose each firm group’s sectoral exports into its various value-added sources, as well as shed light
on the effects of exports on the distribution of operating surplus (an empirical measure of firm profit)
across sectors and firm types. However, since statistical agencies in most countries normally provide only
a conventional IO matrix, A, and not the disaggregated block matrices by firm groups, such as A ,
or
A ,
, we need to develop a method to construct those subaccounts from the original IO tables using
information available from official statistics. IO tables already include data on industry-level total output,
value added, imports, and exports as well as aggregate inter-industry transactions. To estimate our
extended model with 6 sub-accounts, we need to complement these aggregate data with firm-level data,
which are from the 2008 National Bureau of Statistics of China (NBS hereafter) economic census. See
15
Section 3 for details.12
The following data are observable from a conventional IO table at the broad sector level (42 groups of
products) for 2007 and 2010:
: gross output of sector i;
c
: domestic goods i used as intermediate inputs in sector j;
: imported goods i used as intermediate inputs in sector j;
D : value added in sector j;
d : total exports of sector i goods;
: total imports of sector i goods;
c
: total domestic final demand for sector i goods (excluding exports);
: total final demand for imported goods i.
Using these data from the IO table as controls (constants) in the quadratic programming model, we make
sure that the balance conditions in an official IO table are always satisfied. In other words, we can always
aggregate values from our extended IO table with separate sub-accounts for firm groups back to the
values in the original IO table.
We need to estimate the values of ezg% h for each g1 and g2, where g1 and g2 belong to one of the six
,
,
firm types, namely, SL, SS, FL, FS, OL, and OS. Similarly, we estimate ezg% h for one of the six firm
types, indexed by g at the sector-pair level, indexed by (i, j). We also need to estimate sector-level
domestic final demand by firm group, ey% h, which are not available from the official IO table but can be
constructed using firm-level census data from the NBS and detailed trade statistics from China Custom
Administration. We cast the estimation as a constrained optimization problem. Initial values are selected
relying on proportionality assumptions (e.g., share of market demand in total output in each sector and
firm group, which will be discussed next) and micro data from Chinese official sources. These initial
values do not necessarily satisfy all economic and statistical restrictions on the split IO table.
Using the notations previously defined, the quadratic programming model is specified by the objective
function in eq. (15) below, subject to the six constraints specified in eqs. (16) through (21) below. The
initial values for the same variables in eq. (15) are denoted with an additional zero. Variables without a
zero (the z’s, and y’s ) are unknowns that are to be solved by minimization. Symbols with a zero in eqs.
(16) through (21) represent parameters in the model and are kept constant throughout the optimization
12
One may prefer to call our optimization exercise a “calibration”, especially since our exercise does not provide
standard errors to gauge the precision of our estimates. We are open to this alternative interpretation, but would like to
emphasize that in research in progress, we are extending our current optimization program with a Monte-Carlo-type first
stage, which will provide standard errors for our estimates.
16
process.
Specifically, the minimization program is
qA,qr qA,qr r
mnop 7nsop t
Min S = ∑y w ∑y w l∑vw ∑ w
v
q,u x
nsop
z,q z,q r q q r
mnop 7nsop t m{p 7{sp t
+ ∑yw l∑vw ∑vw z,q x + ∑yw l∑vw {sp
q x (15)
nsop
s.t.
∑y w ∑vw ` ,
b+ + d0 = 0 (16)
∑y w ∑vw `
,
b + D0 = 0 , (17)
∑y w ∑y w
,
= 0c , (18)
∑yw ,
= 0 , (19)
∑yw = 0c (20)
∑yw ∑vw ,
+ 0 = 0 (21)
And non-negativity constraints
g1, g 2
zij F ,g
, zij , yig ≥ 0. (22)
All constraints need to be satisfied for all i (42 of them) and j (42 of them), g (6 of them), g1 (6 of them),
and g2 (6 of them). These seven sets of constraints have straightforward economic interpretations. Eq.
(16) is a set of supply-and-use balancing (row sum) constraints for the extended IO table. It states that
total gross output by each type of firm in sector i, must equal the sum of their use of intermediate inputs,
their exports, and their delivery to final domestic users in that sector. Eq. (17) is the set of production and
cost balancing (column sum) constraints. It defines the value of gross output by each type of firm in
sector j as the sum of intermediate inputs and primary factors used in the production process. Eqs. (18) to
(21) are a set of adding-up constraints to ensure that the solutions from the model sum to the statistics
(i.e., domestic final demand, imports, and inter-sector transactions) in the official IO table at the sector
and sector-pair levels.
3. Data and Empirical Results
3.1 Data Sources and Model Variable Initialization
The model parameters and initial values of the model variables are derived by combining industry-level
17
data from the 42-sector “non-competitive” IO tables, for 2007 and 2010, respectively, along with firm
census data for 2008. These data sets are obtained from China’s National Bureau of Statistics (NBS).
Notice that all evolutions in value added by firm type reported below arise from the changes in the IO
table coefficients, not from the census data as we only have access to one year of data. The economic
census data cover over 5 million enterprises in China, including all state-owned and private enterprises
spanning all manufacturing and non-manufacturing industries. Balance sheet information, such as
registration ownership type, equity share by ownership, output, value added, four-digit industry code
(about 900 categories), exports, employment, original value of fixed assets, and intermediate inputs. The
ownership type of a firm in our analysis is defined based on the registration type and equity share by
ownership. Specifically, a firm is considered state-owned (foreign-invested) if it is registered as a state
(foreign) company or has more than (and equal to) 50% equity owned by state (foreign) investors.
There are 42 domestic and 42 imported product groups in the original “non-competitive” IO table. Each
product group is further split into six sub-groups by ownership type and size: large SOEs (LSOE), small
and medium SOEs (SSOE), large FIEs (LFIE), small and medium FIEs (SFIE), large private enterprises
(LP), and small and medium private enterprises (SME). Firm size category (large and small-and-medium)
is determined by firm employment and sales, with thresholds specified by the NBS. The classification
criteria vary across industries, and are listed in Table A1 in the appendix.
The decision of putting firms into 6 groups is supported by the underlying firm distribution of export
intensity and value added to sales ratios reported in the NBS micro data. Fig. 2 illustrates that firm
average export intensity differs significantly across ownership types, not so much along the firm size
dimension. In particular, FIEs are a lot more export-oriented than non-FIE firms. Fig. 3 illustrates that
FIEs also appear to have higher value added to output ratios (VAY) than non-FIE firms. Within non-FIE
firms, large firms tend to have higher VAY. Within FIEs, there is little difference in these key variables
between Hong Kong SAR, China, Macau, and Taiwan, China (HKMT) firms and non-Chinese FIEs.
Based on these findings, we separate firms based on 3 ownership types and 2 sizes, and group HKMT
firms with other FIEs.
After assigning firms from the census to different groups, total sales/receipts at the group level are used
to allocate gross output of each sector to each ownership-size type, while groups’ annual payrolls are
used to split labor and non-labor components of the value added within the group. We can also assign
exports (but not imports) into firm types in almost all industries using the firm census data.13 Detailed
import data, obtained from the statistical department of China Customs Administration, are disaggregated
13
Export data are not available for most service sectors.
18
by firm ownership type within each 8-digit HS level. The UN BEC code is used to separate intermediates
from final goods in imports at the 6 digit-HS level, which are then aggregated up to 42 product categories
in the Chinese IO table. These data are used as import-related constraints and to set initial values for our
minimization program.
All initial values 0 and D0 in the model, as well as an industry’s total intermediate inputs were set
based on official statistics. These values constrain the model solutions to a convex set. To initialize all
z0ij’s, we need to allocate each industry’s total intermediate inputs, both domestic and imported, into
different product groups by firm type. To this end, we first use the NBS firm census and the original IO
table to compute for each firm type (6 of them), the sectoral (42 sectors) output 0 and value
added D0 . Then we compute total intermediate inputs ( 0 − D0 ) for each sector and firm type, and
compute the share of intermediate inputs of each firm type in sector j. Using these shares, we distribute
the numbers 0c and 0 from the original IO table into 6 different firm types, e.g., 0
,
. Table
A5-6 in the appendix shows these shares by firm type in all 42 sectors. The specific procedures to set the
initial values for our minimization program are described below.
1. Setting the initial value for 0
,
(the IO coefficients for imports for group g) involves two steps.
For sectors that have zero intermediate imports in the trade statistics, but have positive values in the
IO table, we simply use the shares of each firm type in the sector’s total intermediate inputs and set
the initial value for 0
,
as:
q q
}sp 7>sp
0 = 0 , (g = SL, SS, FL, FS, OL, OS)
,
q q
∑q,p(}sp 7>sp )
(23)
On the other hand, for sectors that have positive imported intermediate inputs in the trade statistics,
we first compute each firm group’s share in the sector’s imported inputs based on customs statistics,
as shown in Table A6.2, to allocate imported inputs into SOEs, FIEs, and others. Using the adjusted
0 and eq. (23), we further allocate the imported inputs belonging to each ownership type to large
and small firms within the same ownership type, respectively.
2. To set the initial value for 0
,
(the volume of domestic intermediates supplied by group g1 in
sector i to group g2 in sector j), we first assume that the share of intermediate inputs produced by g1
in sector i equals the share of g1’s gross output in sector i. Then on the receiving side, we assume that
g2’s share of intermediate input absorption in sector j equals their share of intermediate inputs in total
intermediate inputs demanded by the same sector. All this information is available in the firm census
19
data. Based on these two assumptions, we split the original 0c based on the following formula:
qA qr qr
}so (}sp 7>sp )
0 = 0c , (g1, g2 = SL, SS, FL, FS, OL, OS)
,
}so (}sp 7>sp )
(24)
3. To set the initial value for 0 , total domestic demand for goods and services supplied by firm group
g in sector i (i.e., the sum of private consumption, government spending, fixed capital investment,
and inventory changes), we use the following formula:
q
}so
0 = 0 − ∑ƒw 0c − d0
}so
(25)
Notice that we implicitly assume that the supply of intermediate products/inputs for domestic use from
each firm type in a sector is proportional to their gross output in that sector. To make the model fully
initialized and operational, we also need the relative shares of different firm types in the country’s total
exports and imports for each of the 42 sectors. Such information is readily available in the disaggregated
trade statistics from China’s Customs.
4. Estimating Indirect Contribution to Value-Added Exports by Firm Size and Ownership Type
4.1 Main Results
4.1.1 Relative Importance in the Aggregate Economy
Based on the estimates of the model described in Sections 2 and 3, we portray the domestic segment of
GVC in China. Table 1 shows that SOEs account for 19% and 9% of value added and employment of
China in 2008, respectively. The relatively small shares of SOEs are partly due to years of economic
reforms led by the Chinese authorities to privatize and let go SOEs, especially the small ones in
downstream sectors. SOEs’ contributions to gross exports and value-added exports (VAX) in 2007 are
12% and 21%, respectively. The large difference between SOE’s contributions to value added and gross
exports suggests that SOEs have a higher share of indirect exports through other firms, compared to other
firm ownership types. Notice that while SOEs’ gross export share declined significantly from 12% in
2008 to 9% in 2010, their share in value added exports actually increased. We will focus on analyzing
these opposite trends in greater detail below.
(Insert Table 1 here)
20
Table 1 also shows that SMEs are numerous and employ the majority of workers in China. They account
for 55% and 79% of China’s value added and employment in 2008, respectively. In terms of gross
exports, their contribution is much smaller – only 28%. This low share of exports is consistent with the
conventional view that most small firms do not export because of the potentially high fixed export costs.
In terms of value added exports, they account for 42%. The much larger contribution to VAX implies that
SMEs have a higher share of indirect exports, either through other SMEs or other types of firms. In terms
of the aggregate gross exports and VAX, SOEs and SMEs look similar, but both the share of gross and
value added exports by SMEs decreased from 2007 to 2010. We will reveal key underlying differences in
terms of their distributions across industries and the channels through which they achieve a high value
added to gross export ratio below.
As expected, FIEs are much more export-oriented. They are small in number, similar to SOEs, but
account for close to half of Chinese gross exports. Their share in total value added exports is much
smaller (only 27%), consistent with the literature that finds low domestic value added in Chinese exports,
particularly in processing exports (Koopman, Wang, and Wei, 2012; Kee and Tang, 2013). To the extent
that most of the processing firms are FIEs, which include firms owned by investors from Hong Kong,
Macau, and Taiwan (HKMT), the results are not surprising. Processing firms import a large fraction of
intermediate inputs and are responsible for the final stage of production, by taking advantage of the low
labor costs in China.
4.1.2 The Domestic Segment of GVCs (VAX based on the Forward-linkage Approach)
Next, we use our split IO tables to decompose VAX by firm type into direct and indirect VAX, based on
both the forward- and backward-linkage approaches, as described in Section 2. We will first report results
based on the forward-linkage approach.
For indirect VAX, we further measure the paths through which a firm type export indirectly. Table 2
presents these results, along with the volume of gross exports by firm type. Before turning to the details
of indirect VAX, it is worth highlighting that for the 4 firm groups considered here, both SOE and SME
have the VAXR exceeding 1. Specifically, Panel A shows that the VAXR of SOEs and SMEs are 1.17
and 1.02 in 2007, respectively. As a comparison, the VAXR of FIEs and LPs are 0.36 and 0.70,
respectively. The finding of SOEs’ VAXR larger than unity confirms the results in Table 1 that SOEs’
contribution to Chinese exports is much larger if measured in value added terms than in gross terms.
Moreover, these findings contrast sharply with the evidence for developed countries, such as the United
States, where large firms’ share in gross exports is usually higher than that in value-added exports (i.e., the
21
VAXR is smaller than 1). In summary, the low VAX ratio of Chinese aggregate exports, as reported in
the literature, hides substantial heterogeneity in VAX across firm ownership types and sizes.
Panel B of Table 2 shows the same set of estimates using the 2010 IO table. As reported, all but FIEs
experienced an increase in VAX. The increase was particularly sharp for SOEs and SMEs. SOEs’ VAXR
increased by about 47% while that of SMEs increased by about 27%. The significant increase in the
VAXR of SOEs lends some support to the anecdote that the state sector has advanced their prominence in
the Chinese economy in recent years, especially after the global financial crisis in 2008 when the Chinese
central government implemented policies to stimulate the economy.
(Insert Table 2 here)
The higher-than-unity VAXR of both SOEs and SMEs imply that many non-exporters from these two
groups produce intermediate inputs and services that are embedded in Chinese exports. Table 2 reports
the value of indirect exports. We find the following pecking order – SOEs have the highest share of
indirect exports in VAX, followed by LPs and SMEs, with FIEs having the lowest share. Specifically, in
2007, about 80% of exports from SOEs are indirect (the numbers increased slightly in 2010). In other
words, 80% of SOEs’ exports are values embedded in inputs used by firms that eventually export. For
LPs and SMEs, the indirect export shares are about 72% and 63%, respectively. The indirect export share
of SMEs increased significantly by 10 percentage points from 2007 to 2010, consistent with the
hypothesis that small exporters could be financially constrained after the global finance crisis and less
likely to engage in direct exporting. Once again, FIEs are very different from domestic firms and have a
much lower share of indirect exports (about 46% in 2007, which decreased to 43% in 2010). Given the
prevalence of FIEs in processing trade and the prevalence of intra-firm trade associated with vertical FDI,
the low indirect export ratio is not surprising.
By splitting the IO table along the size and ownership type dimensions, we can also estimate the amount
of indirect exports through different types of firms. As reported in Table 2, most of SOEs’ indirect
exports are through non-SOEs. In particular, in 2007, FIEs account for over 40% (35/80) of SOEs’
indirect exports, which increased to over 55% in 2010. On the other hand, SMEs account for 25% of
SOEs’ indirect exports in 2007, which declined to about 20% in 2010. Both LPs and SMEs also have
high shares of indirect exports, but are both lower than that of SOEs. FIEs also play a more significant
role in helping LPs to export indirectly, compared to SMEs. The role of SMEs in helping other firms
export decreased from 2007 to 2010. For instance, when the SMEs’ indirect export share increased from
2007 to 2010, the role of other SMEs in facilitating their exports declined, with FIEs taking up most of
22
the increase. In summary, both SOEs and LPs have higher than average indirect export shares, with the
former having a much higher VAX ratio. SMEs’ participation in exporting, both direct and indirect,
declined, while SOEs’ indirect exports increased, consistent with an increasing VAX ratio as documented
earlier.
How about the cross-industry pattern of indirect exports? Answering this question can shed light on the
reasons for the similarity in the VAX ratio between SOEs and SMEs. Table 3 exhibits substantial
heterogeneity in indirect export shares (in total value added exports) across 14 broad industries.
“Upstream” industries, such as energy and mining; metal and non-metallic mineral extraction; electricity,
gas and water supply; as well as financial sector all have very high indirect export shares (over 90%).
Tables A4.1-A4.6 in the appendix shows these numbers for 40 disaggregated industries and 6 groups of
firms, revealing similar patterns. One reason for their high indirect export shares is that the sectors with
high indirect export share tend to be non-tradable, either by nature or restricted by the authorities. They
tend to export indirectly by providing essential intermediate inputs and services to downstream exporters.
Thus, focusing only on gross exports in analyzing firms’ export participation can substantially
underestimate their actual participation in GVC and thus the impact of trade liberalization on the
economy.
(Insert Table 3 here)
In addition to the cross-industry variation, within a sector we also see a non-negligible variation in the
indirect export share across firm types. For instance, in the “Light manufacturing” sector, the ratio of
indirect to direct VA exports is 50% in 2007, one of the lowest, but the ratio for SOEs is 75%. A casual
observation shows that SOEs tend to have a higher indirect export share in sectors that are associated
with a lower average indirect export share, such as electronic equipment; while SMEs tend to have a
higher indirect export share in industries that have a higher average indirect export share, such as energy
and mining, and the financial sector. We will use the upstreamness measures proposed by Antras et al.
(2012) to conduct a more systematic analysis below.
4.1.3 The Domestic Segment of GVCs (Export-Related Profits Based on the Forward-Linkage
Approach)
We also apply our framework to answer an important policy-relevant question: how much profit was
generated by exports in China, and how was the export-related profit distributed across different firm
types? Similar to our analysis on value added exports, we can attribute export-related profit (the
23
operating surplus term in an IO table) accruing to a firm type via direct and indirect exports, respectively.
By “direct”, we refer to profits accruing to direct exporters. By “indirect”, we refer to profits accruing to
firms that supply goods and services to downstream exporters, through the domestic input-output
network. Column (1) in Panel A of Table 4 reports a total of 885 billion RMB profits (about 120 billion
USD in 2007 exchange rate) accruing to direct exporters in 2007. Similar to our analysis of value added
exports above, this value of profits for direct exporters may underestimate the actual export-induced
profits in the domestic economy. Therefore, we also estimate profits accruing to firms that sell inputs and
services, directly and indirectly, to exporters in the economy (defined in the same way in Table 2). When
both direct and indirect exporters’ profits are included (column (2)), total export-related profits increased
to 2.3 trillion RMB (about 315 billion USD). As reported in Panel B, direct export-related and total
export-related profits for 2010 were 763 billion and 2.2 trillion RMB, respectively.14 The decline in both
profit measures, despite the fact that value added exports increased between the two years, suggests that
the Chinese economy may have become more competitive over time.
How important are export activities in generating profits in the Chinese economy? According to the IO
tables, total profits (capital income) of the Chinese economy were about 8 trillion RMB in 2007 and 9.7
trillion RMB in 2010. In other words, if we focus on profit accrued to direct exporters only (i.e., 885 and
763 billion RMB), exports generated about 11% and 8% of China’s total profits in 2007 and 2010,
respectively. On the other hand, if we also include profit accrued to firms that also supply intermediate
goods and services to exporters, profits that could be attributed to exports increased to about 29% in 2007
and 23% in 2010.
(Insert Table 4 here)
Similar to the decomposition of value added exports conducted in Table 2, we can also distribute
export-related profits to different firm types. As reported in column (3), we find that FIEs have the
highest profit per worker derived from exports (both direct and indirect), while SMEs have the lowest
export-related profit per worker. Specifically, profit per worker due to exports was 6140 RMB for FIEs in
2007, 1250 RMB for SOEs, 1720 RMB for LPs, and only 700 RMB for SMEs. Using 2008 firm census
data, along with 2007 and 2010 IO tables, we find that export-related profit per worker declined from
1150 RMB in 2007 to 999 RMB in 2010 for the aggregate economy. Those for FIEs and LPs, however,
increased to 6440 and 1980 RMB, respectively.
Column (4) reports each firm type’s share in total export-related profits. SMEs are responsible for 47% of
14
Notice that we are still using 2008 firm census to measure aggregate surplus and surplus by firm type.
24
the export-related profits in 2007, followed by FIEs that account for 25%. Given that SMEs hire most of
the workers in China (92% in 2007) and produce over half of the country’s GDP (55%), their low share
of total profit implies an uneven distribution of profits across firm types. Once again, we find a small
increase in SOEs’ share of export-related profit. Consistent with the slight increase in SOEs’ share in
value added exports, their share of profits increased from 18.5% in 2007 to 19.2% in 2010. Is this
supporting evidence for the claim that SOEs have advanced in the Chinese economy at the expense of the
private sector? Notice that both FIEs and LPs also experience an increase in their shares of export-related
profits. The increase of SOEs’ export-related profits was not the sharpest. It went up by 4%, compared to
9% for FIEs and LPs, respectively. In other words, the entire decline in export-related profits falls on
SMEs, as the other three firm types all experienced an increase in profits.
The drastic differences in export-related profits across firm types hide substantial heterogeneity in the
channels through which different firm types derive their profits from downstream exports. Column (9)
shows that domestic firms (SOEs, LPs, and SMEs) derive most of their export-related profits indirectly.
The share of profits that firms derive from indirect export ranges from 61% for SMEs to 79% for SOEs.
Columns (5) to (8) show that FIEs play a dominant role in exporting for other upstream firms (ranging
from 23 to 35% depending on upstream firm types). Perhaps surprisingly, SMEs also serve as an
important channel through which other firms can derive profits from exports (between 11 to 20%). Panel
B shows that from 2007 to 2010, the roles of FIEs in serving as downstream exporters to generate profits
for other firm types increased from 28% in 2007 to 37% in 2010. Despite an increase in profit shares,
SOEs become less important as a channel to pass on export-related profits from downstream exporters to
upstream firms. As reported in column (5), the SOE channel, measured as the share of profits generated
by indirect exporting, dropped from 9.2% (Pane A) to 6.3% (Panel B).
4.1.4 The Domestic Segment of GVCs (VAX Based on the Backward-Linkage Approach)
So far, we have been using the forward-linkage approach, which involves summing up the entries of
G H BE
A G (in eq. (7)) horizontally along each row, to estimate direct and indirect value added exports by
different types of firms. In this section, we use the backward-linkage approach and ask “For each dollar
of Chinese exports (aggregate or by firm type), how much of it is coming from SOEs, FIEs, etc.?”
Different from the forward-linkage approach that focuses on the channels through which each firm type’s
VAX (by sector or at the aggregate) is generated, the backward-linkage approach decomposes each firm
type’s gross exports into direct VA, indirect VA from the same type, and indirect VA from other firm
types. For example, SOEs’ gross exports now include not only VA of the SOE exporters themselves, but
also domestic VA from all other upstream firm types, including other SOEs, as well as other firm types’
25
VA embedded in inputs used to produce those exports.15 This decomposition exercise permits an
analysis on the distribution of VAX across firm types embedded in each firm type’s downstream exports,
complementing the forward-linkage approach that focuses on the “paths” of exporting.
By using this backward-linkage VAX measure, we provide another set of results to examine how the
domestic VA in Chinese exports is distributed across firm types, and how the distribution changed
between 2007 and 2010. As reported in Table 5, of the 10 trillion RMB Chinese gross exports in 2007,
14% can be attributed to SOEs, directly and indirectly; while the contribution by FIEs, LPs, and SMEs
are 18%, 7% and 29%, respectively. The findings of high value added by SOEs and SMEs resonate well
with the finding that both types of firms have high VAX, as reported in Table 2. Foreign VA in Chinese
exports in 2007 is 32%. We also decompose each firm type’s gross exports into contributions by different
firm types’ indirect exports. For instance, we find that for each dollar of SOEs’ gross exports, SOEs
themselves contribute about 39 cents (24 cents directly and 15 cents indirectly), followed by 18 cents
from SMEs and 10 cents from FIEs. Foreign value added from abroad accounts for 26 cents, lower than
its contribution in aggregate export. Notice that the numbers along the diagonal is always the highest
compared to other numbers in the same column, suggesting that each firm type contributes the most VA
to its own gross exports, compared to other firm types.
(Insert Table 5 here)
The lower panel of Table 5 reveals that while Chinese gross exports increased by only 9.7% from 2007 to
2010, the contribution of SOEs in terms of VA increased by 14.8%. Specifically, for each dollar of
Chinese gross exports, 14.2 cents ultimately came from SOEs in 2007, while 16.3 cents came from them
in 2010. SOEs are not the only group that experienced an increase in VA shares between the two years.
All three other groups also experienced an increase, at the expense of foreign VA. However, it is the
SOEs that experienced the sharpest increase in VA contribution, followed by FIEs that had its VA share
increased by 9.2%. Another fact revealed in Table 5 is that SOEs’ VA shares increased for exports by all
firm types. This is not observed for other firm types. For instance, FIEs’ VA shares increased only for
FIEs’ exports but not for other firm types.
The backward-linkage approach can be used to distribute sectoral DVA in exports into different sources
of firm types. Such an exercise provides another perspective to portray the cross-sector pattern of
contributions by different firm types. As reported in Table 6, a few sectors have more than 30% DVA
15
Such a backward-linkage perspective aligns well with case studies of GVC of specific sectors and products, such as
the iPod or iPhone examples frequently cited in the literature.
26
originating from SOEs. In 2007, these sectors include “Mining and Washing of Coal” (SOEs’ share in
total sector’s VAX = 39.98%), “Extraction of Petroleum and Natural Gas” (49.56%), “Mining of
Non-Ferrous Metal Ores” (32.50), “Processing of Petroleum, Coking and Nuclear Fuel” (44.16),
“Smelting and Rolling of Metals” (36.67), “Production and Supply of Electricity and Heat” (52.05).
These are obviously “upstream” sectors that provide essential inputs to downstream exporters. In the next
section, we will conduct a systematic analysis on SOEs’ potential dominance in “upstream” sectors,
using Antras et al.’s (2012) measures.
(Insert Table 6 here)
While SOEs appear to have a dominant position in some sectors, they are not the firm group that has the
highest VA shares for most sectors. It is the SMEs that often contribute more than 30% of VAX in most
sectors. In fact, SOEs’ VA share exceeded 30% for only 13 sectors (out of 40) compared to 24 for SMEs.
For example, SMEs’ shares of VAX in “Foods and Tobacco” and “Manufacture of Textile Products” are
60% and 52%, respectively. These findings suggest that SMEs have been playing an important role
driving Chinese exports. This is consistent with the hypothesis that a lot of SMEs do not export directly,
possibly because of high fixed export costs. Instead, they participate actively by supplying intermediate
inputs and services to larger downstream exporters. In 2010, the number of sectors in which SOEs’ share
in VAX exceeded 30% actually dropped from 13 to 11. However, in those sectors that SOEs had the
highest VAX share in 2007, SOEs’ VAX shares have increased substantially. For example, in the
“Mining and Washing of Coal” sector, SOEs’ VAX share was 40% in 2007, which increased to 56% in
2010.
4.2 Industry Upstreamness by Firm Type
Table 3 shows a vast heterogeneity in indirect export shares across industries, consistent with the
conventional view that non-tradable sectors do not export much and typically participate in exports
indirectly. Table 6 further shows that SOEs seem to prevail in “upstream” sectors. These findings hint
that SOEs and SMEs derive their large indirect exports through different channels. To analyze these
channels more systemically, we use the method proposed by Antras et al. (2012) to measure industry
upstreamness. We make two important extensions to the original method. First, given our split IO table,
we can measure an industry’s upstreamness by firm size and ownership type. With these measures in
hand, we can then examine whether within an industry, some firm types are relatively more upstream on
27
average. We construct the upstreamness measure for 40 industries and 6 firm groups.16 The second
extension is that we relax the proportionality assumptions they make about the allocation of imports and
exports in each industry pair. Specifically, our estimated IO coefficients already have imports taken out
by explicitly including A) in our model. When dealing with exports from sector i to sector j by firm
type, we use data on exported intermediate inputs from China’s customs and assign the bi-sectoral
exports to different firm types based on their shares in each IO link in the domestic economy. See the
appendix for details. Table A3 in the appendix report the 240 upstreamness measures, along with the
industry upstreamness estimated based on the conventional IO table (without any split).
Table 7 reports the top 5 and bottom 5 industry upstreamess measures based on the conventional IO table.
By construction, the upstreamness measure ranges between 1 and the maximum number of the industries
in the country’s IO table. The top 5 most “upstream” industries (out of 40) are “Extraction of Petroleum
and Natural Gas”, “Mining of Ferrous Metal Ores”, “Mining and Washing of Coal”, “Production and
supply of Electricity and heat”, “Processing of Petroleum, Coking and Nuclear Fuel”. The values of
upstreamness for these industries range between 4 and 5, meaning that these industries are on average 4-5
industries away before reaching final consumers. These raw material and energy industries sell
intermediate inputs to many other industries, including other upstream industries. They are expected to
rank high up in the domestic production network. The bottom 5 “upstream” industries are “Real Estate”,
“Health and Social service”, “Education”, “Construction industry”, “Public administration and social
organization”. They tend to sell final goods and services directly to customers.
(Insert Table 7 here)
By using the split IO table, we can estimate the upstreamness measures for different firm groups.
Consistent with the high indirect export ratio, SOEs, particularly the small ones, tend to have the highest
upstreamness measure among all firms types within each industry, while SMEs tend to have the lowest
upstreamness, particularly in the least upstream industries, among all firm types. Fig. 4 plots the SOEs’,
FIEs’, LPs’ and SMEs’ upstreamness measures against the industry overall measures, which are
estimated using the original aggregate IO table. Most measures for the SOEs (blue squares) are above the
45-degree line, suggesting that SOEs are often more upstream than other firm types within the same
industry. SMEs, on the other hand, are often the most “downstream” within industries.
Another way to show that SOEs have a dominant position in the upstream industries is to examine the
correlation between the share of SOEs in different aggregate outcomes and industry upstreamness. Fig. 5
16
The original IO table has 42 industries, but we dropped
28
shows a positive and (marginally) significant correlation between the share of SOEs in total industry
output and industry upstreamness, suggesting that SOEs have a dominant position in upstream industries.
Fig. 6 shows a positive and significant relationship between SOEs’ share in the industry’s gross exports
and industry upstreamness. Figs. 7-8 show no particular relationship between upstreamness, output, and
exports for SMEs. In sum, these findings confirm that the high VAX ratio for SOEs is partly driven by
their dominance in the upstream sectors, while SMEs’ high VAX is due to other reasons. One possibility
is that exporting is associated with high fixed costs and only large (productive) firms can make
sufficiently high export revenue to amortize them. Thus, SMEs tend to export indirectly and have a high
VAX ratio.
We use the split IO table from 2010 and estimate the industry measures of upstreamness for different
firm types again (see Table A3 in the appendix for the estimates). Fig. 9 shows that for 27 of the 40
industries, the upstreamness measure increased. This finding is exactly the opposite of what recent
studies have documented for the U.S., where industries have shown to become more downstream over
time (Fally, 2012). If more upstream activities are being offshored from the U.S. to China, our results can
provide the “mirror-image” support to Fally (2012).
5. Concluding Remarks
This paper proposes methods to incorporate firm heterogeneity in the standard IO-table based approach
to portray the domestic segment of global supply chains in a country. Using conventional IO tables, firm
census data for both manufacturing and service sectors, and constrained optimization techniques, we are
able to estimate direct and indirect value added exports (VAX) for different types of firms in China, and
decompose a firm type’s indirect VAX into different channels through which they are realized.
Based on our split IO table, we find that in China, both state-owned enterprises (SOEs) and small and
medium domestic private enterprises (SMEs) have much higher shares of indirect exports and ratios of
value-added exports (VAX) to gross exports, compared to foreign-invested and large domestic private
firms. Using China’s IO tables for 2007 and 2010 respectively, we find evidence of increasing VAX
ratios for all firm types, particularly for SOEs. By extending the method proposed by Antras et al. (2012),
we find that SOEs are consistently more upstream while SMEs are consistently more downstream within
industries. These findings suggest that SOEs still play an important role in shaping China’s downstream
exports.
Our findings imply that years of privatization have led to the dominance of SOEs, not only large firms, in
29
the upstream sectors. While the political economy factors behind such privatization outcomes are beyond
the scope of this paper, documenting these unique patterns shed light on understanding China’s past and
future economic growth. The conventional view is that China’s export growth is largely driven by the
dynamic labor-intensive private sector, especially the foreign-dominated processing sector. We have
documented coherent evidence that SOEs still play a significant role in shaping China’s aggregate export
patterns and performance.
Whereas SMEs are similar to SOEs in the sense that they also have high value added and indirect export
ratios, the sources and the channels behind these similarities appear to be quite different. In addition to
the fact that non-state SMEs are more likely to export through other non-state firms, their upstreamness is
also lower within industries. This finding suggests that the higher VAX and indirect export share of
SMEs are probably due to their higher propensity to sell intermediate inputs and services to other large
firms who eventually export, rather than having an upstream position in the domestic production network,
as have been enjoyed by SOEs.
30
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32
Figure 1: Input-Output table with separate production account for firms by ownerships and size and abroad
Intermediate use
SOE Large SOE SM FIE Large FIE SM Others Large Others SME Domestic Total Gross
Export
(SL) (SS) (FL) (FS) (OL) (OS) Final Use Output
DIM 1,2,…, N 1,2,…, N 1,2,…, N 1,2,…, N 1,2,…, N 1,2,…, N 1 1 1
1
SOE
, , , , ,.
Z ,.
Y E X
.
Large
.
(SL)
N
1
, , , , ,.
Z ,.
Y E X
SOE SM .
(SS) .
N
1
, , , , ,.
Z ,.
Y E X
FIE Large .
Domestic (FL) .
Intermediate N
Inputs 1
FIE
, , , , ,.
Z ,.
Y E X
.
SME
.
(FS)
N
1
Others
. , . , . , . , . ,.
Z. ,.
Y. E. X.
.
Large
.
(OL)
N
1
Others
. , . , . , . , . ,.
Z. ,.
Y. E. X.
.
SME
.
(OS)
N
Imported 1
, , , , ,.
Z ,.
Y M
Intermediate Abroad( .
Inputs F) .
N
Value-added 1 . .
(X ) (X ) (X ) (X ) (X .
) (X ).
T T T T T T
Total Gross Output 1
33
Appendix A
Extending the method by Antras et al. (2012) to measure industry upstreamness
To measure industry upstream based on our IO table with 6 sub-accounts, we need to modify the method
proposed by Antras et al. (2012). First, we construct a 42x42 matrix for each firm type g1 with the
following elements
qA,qr qr qA
∑‡ …op †p ˆ‰op
„g% = qA
†o
(A1)
Where superscripts Š1, Š2 = (‹Œ, ‹‹, •Œ, •‹, ŽŒ, Ž‹) represent 6 firm types, a
,
is the IO
coefficient between a pair of firm-type-sector discussed in Section 2 in the text. X and X are gross
output by group g1 and g2 in sector j, respectively. ] represents exports from sector i by firm type g1
used in sector j abroad.
When computing industry upstreamness, Antras et al. (2012) assume that the share of imports (and
exports) of sector i that is used by sector j is the same as the share of domestic intermediate inputs of
sector i used by sector j. We improve upon their computation by relaxing both of these assumptions. First,
in eq. (A1), we do not need to subtract imports from total intermediate inputs. It is because when we
estimate our extended IO model, we already make the corresponding adjustment to deal with imported
materials by having a separate A) matrix. In other words, our IO coefficients, a , do not include
,
imported intermediate inputs. Thus, we do not need to make the proportionality assumptions as Antras et
al. (2012) to exclude imports from domestic intermediate inputs in our computation of upstreamness.
Second, when computing ] , we use data of exported intermediate inputs at the sector-pair level (i-j)
from China’s customs. To assign exported intermediate inputs to each firm type, we use the share of each
qA,qr
∑qr •op
qA,qr
∑qA,qr •op
supplier’s firm type in domestic inter-sector transaction volume (i.e., ) as the weight. For
sectors that we do not have exported intermediate inputs from China’s Customs (most of them are service
sectors), we follow Antras et al. (2012) and make the same proportionality assumption to obtain ] 。
We also adjust for the change in inventory at the sector level carefully. First, we obtain inventory by firm
type and sector. Then following the approach proposed by Antras et al., (2012), we subtract inventory
from • in eq. (A1). After obtaining a 42x42 block matrix of „g% , we use eq. (4) in Antras et al. (2012)
to compute upstreamness by sector and firm type.
34
TABLE 1: Estimated Contribution in Main Economic Activities by Firm Type
Gross Gross
Firm Type Number of Value Employmen Exports Value Added Exports Value Added
Firms (08) Added (08) t (08) (07) Exports (07) (10) Exports (10)
Panel A: Share (%)
SOE 4.73 19.16 9.24 12.07 20.81 9.40 22.02
FIE 3.01 16.34 6.49 49.47 26.50 56.65 26.67
Large Enterprise (LP) 0.22 9.91 4.82 10.08 10.35 10.41 10.10
Small and Medium Private (SME) 92.04 54.58 79.45 28.38 42.34 23.54 41.21
Panel B: Value (Billion for values; million for employment)
SOE 188829 5098.20 71.16 1230.94 1445.75 1051.85 1820.57
FIE 120073 4348.44 49.94 5045.69 1841.05 6340.14 2205.26
Large Enterprise (LP) 8836 2637.43 37.09 1028.06 719.12 1164.47 834.88
Small and Medium Private (SME) 3674676 14520.31 611.71 2894.76 2941.58 2634.63 3407.06
Total 3992414 26604.38 769.91 10199.79 6947.49 11191.10 8268
Note: Data on value added and employment are from China's National Bureau of Statistics (NBS) firm census in 2008. Data on gross
exports and value added exports are computed based on 2007 IO tables.
35
TABLE 2: Indirect Exports via Different Firm Types
VA Exp/
Value Added Exports Gross Exp
Panel A: 2007 (Bil RMB) Share of Indirect VAX (%) (VAXR)
via SOE FIE LGO SMO Total
SOE 1446 13.14 35.27 10.94 20.19 79.54 1.17
FIE 1841 6.56 23.35 5.72 10.68 46.31 0.36
Large Enterprise (LP) 719 11.13 32.02 9.77 19.32 72.24 0.70
Small and Medium Private (SME) 2942 7.66 26.70 7.22 21.65 63.23 1.02
Total 6947 8.87 28.15 7.86 18.20 63.07 0.68
Change
relative to
Panel B: 2010 2007 (%)
SOE 1821 9.18 43.65 10.50 16.78 80.10 1.73 47.37
FIE 2205 3.86 26.40 5.18 7.71 43.15 0.35 -4.67
Large Enterprise (LP) 835 6.98 39.35 9.05 14.92 70.30 0.72 2.50
Small and Medium Private (SME) 3407 5.60 38.77 8.05 20.83 73.25 1.29 27.26
Total 8268 6.06 36.60 7.93 15.84 66.43 0.74 8.46
Note: Authors' estimation based on data from I/O tables for 2007 and 2010. Both from China's NBS.
36
Table 3: Indirect VAX/ Total VAX (4 types; 14 industries) (%)
Panel A: 2007
Industry All SOE FIE LP SME
Energy and mining 94.03 94.57 92.30 93.01 94.58
Metal and non-metallic mineral extraction 90.14 89.15 88.18 92.17 91.17
Light manufacturng 49.61 74.83 36.87 58.18 51.70
Petrochemical 74.89 87.58 62.67 75.69 79.79
Metal and non-metal processing 67.29 68.87 69.00 75.37 60.58
Machinery and equipment 47.02 72.52 36.86 53.35 46.90
Electronic equipment 20.75 45.45 16.71 34.41 36.29
Other manufacturing 76.35 59.75 29.67 36.68 87.02
Electricity, gas and water supply 99.41 99.51 99.56 98.85 98.95
Building industry 33.63 33.18 35.94 33.39 33.38
Transportation and warehousing 52.87 59.78 87.92 90.06 40.50
Wholesale and retail trade 42.72 72.22 82.72 76.36 24.26
Financial sector 98.18 97.94 97.82 97.78 98.51
Other Services 66.02 75.35 79.31 80.82 45.23
Total 63.07 79.54 46.31 72.24 63.23
Panel B: 2010
Industry All SOE FIE LP SME
Energy and mining 0.97 0.96 0.93 0.97 0.99
Metal and non-metallic mineral extraction 0.95 0.98 0.82 1.00 0.94
Light manufacturng 0.54 0.92 0.32 0.61 0.68
Petrochemical 0.74 0.80 0.56 0.78 0.88
Metal and non-metal processing 0.73 0.76 0.56 0.79 0.83
Machinery and equipment 0.48 0.72 0.34 0.48 0.66
Electronic equipment 0.32 0.72 0.25 0.46 0.72
Other manufacturing 0.55 0.93 0.45 0.67 0.65
Electricity, gas and water supply 0.99 0.99 1.00 0.99 1.00
Building industry 0.18 0.28 0.77 0.27 0.10
Transportation and warehousing 0.69 0.71 0.74 0.91 0.66
Wholesale and retail trade 0.46 0.53 0.61 0.56 0.38
Financial sector 0.97 0.97 0.97 0.98 0.96
Other Services 0.70 0.72 0.78 0.87 0.61
Total 0.66 0.80 0.43 0.70 0.73
Note: Authors' estimation based on data from 2007 and 2010 IO tables from China's NBS. Italic fonts indicate
industries that have indirect export share exeeding 90%. Bolded face denotes the highest among the four
ownership types within the industry.
37
TABLE 4: Export-related Profits via Different Firm Types
Profits accrued to Total export- Exp-related % of total
direct exporters related profits profits per worker exp-related
Panel A: 2007 (billion RMB) (billion RMB) ('000) profits Share of profits through indirect exporting (%)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
via SOE FIE LP SME Total
SOE 89 427 1.25 18.51 13.32 35.32 10.95 19.57 79.17
FIE 307 568 6.14 24.60 6.50 23.26 5.66 10.59 46.01
Large Enterprise (LP) 64 232 1.72 10.06 11.19 31.99 9.90 19.40 72.48
Small and Medium Private (SME) 425 1081 0.70 46.82 8.57 26.30 7.50 18.29 60.66
Total 885 2308 1.15 100.00 9.20 27.79 7.93 16.75 61.67
Panel B: 2010
SOE 86 427 1.21 19.23 9.16 44.04 10.53 16.13 79.85
FIE 322 594 6.44 26.77 4.24 27.56 5.73 8.27 45.80
Large Enterprise (LP) 74 244 1.98 10.99 7.02 39.07 9.14 14.60 69.83
Small and Medium Private (SME) 281 954 0.46 43.01 6.14 39.10 7.90 17.36 70.49
Total 763 2218 0.99 100.00 6.31 36.96 7.96 14.39 65.61
Note: Data on aggregate profits and those by firm type (col 2) are based on 2008 firm census. Profits related to indirect exports and its decomposition are estimated using
data from IO tables for 2007 and 2010.
38
TABLE 5: Gross Exports and Distribution of the Source of VAX (Backward-linkage Approach)
2007 Total SOE FIE LP SME
Gross Exports 10199 1231 5046 1028 2895
SOE 14.17 39.46 10.11 15.38 10.08
(24.03, 15.43)
FIE 18.05 9.81 28.11 10.25 6.79
(19.59, 8.52)
VA Contribution
LP 7.05 6.50 4.56 26.25 4.80
(%)
(19.42, 6.83)
SME 28.84 18.30 15.56 20.66 59.37
(37.37, 22.00)
Abroad 31.88 25.92 41.66 27.47 18.95
change relative
2010 Total to 07 SOE FIE LP SME
Gross Exports 11191 9.72 1052 6340 1164 2635
SOE 16.27 14.77 50.32 12.53 16.41 11.60
(34.44, 15.89)
FIE 19.71 9.17 8.09 28.95 9.82 6.46
(19.77, 9.18)
VA Contribution
LP 7.46 5.81 5.54 5.18 27.78 4.73
(%)
(21.29, 6.49)
SME 30.44 5.56 18.14 20.83 23.56 61.53
(34.59, 26.93)
Abroad 26.12 -18.07 17.90 32.50 22.42 15.69
Note: Estimation based 2007 and 2010 IO Table. Numbers in brackets are direct and indirect VA export share, respectively,
39
TABLE 6: Gross Exports via Different Firm Types (Backward-linkage Approach)
2007 2010
Sector DVA share DVA share
# Sector Share in Domestic VA (%) > 30% Share in Domestic VA (%) > 30%
SOE FIE LP SME SOE FIE LP SME
2 Mining and Washing of Coal 39.98 17.57 13.93 28.52 SOE 55.60 7.64 16.67 20.09 SOE
3 Extraction of Petroleum and Natural Gas 49.56 16.52 23.31 10.61 SOE 61.64 10.52 14.37 13.47 SOE
4 Mining of Ferrous Metal Ores 27.17 21.78 7.10 43.95 SME 27.19 12.95 7.15 52.71 SME
5 Mining of Non-Ferrous Metal Ores 32.50 24.00 12.91 30.58 SOE, SME 25.67 17.53 7.59 49.21 SME
6 Foods and Tobacco 15.56 17.32 7.25 59.86 SME 13.34 17.90 6.58 62.18 SME
7 Manufacture of Textile Products 15.34 22.60 10.51 51.55 SME 13.56 23.04 10.20 53.20 SME
Wearing apparel, leather, fur, down and
8 related products 14.53 32.29 8.76 44.41 FIE. SME 13.90 28.71 9.08 48.32 SME
Processing of wood and Manufacture of
9 Furniture 16.01 20.61 9.25 54.13 SME 19.13 26.47 8.76 45.64 SME
Paper Products and Articles for Culture,
10 Education and Sports Activities 16.07 23.26 7.79 52.88 SME 17.12 36.05 7.27 39.56 FIE, SME
Processing of Petroleum, Coking and
11 Nuclear Fuel 44.16 17.63 15.57 22.64 SOE 53.60 13.27 17.64 15.49 SOE
12 Manufacture of Chemical Products 20.80 26.23 10.61 42.36 SME 24.01 26.92 11.36 37.71 SME
13 Manufacture of non-ferrous metal products 22.76 16.48 9.15 51.60 SME 24.41 25.88 11.00 38.71 SME
14 Smelting and Rolling of metals 36.67 14.04 19.08 30.21 SOE, SME 38.12 16.25 15.62 30.01 SOE, SME
15 Manufacture of Metal Products 22.88 19.25 12.02 45.85 SME 25.36 29.05 11.80 33.79 SME
Manufacture of General Purpose and Special
16 Purpose Machinery 20.98 26.46 11.43 41.14 SME 23.46 29.47 12.31 34.77 SME
17 Manufacture of Transport Equipment 25.58 29.49 15.85 29.07 None 24.71 29.44 15.25 30.60 SME
Manufacture of Electrical Machinery and
18 Equipment 23.09 28.44 14.10 34.37 SME 22.80 31.43 12.87 32.90 FIE, SME
Manufacture of Communication Equipment,
19 computers and Other Electronic Equipment 16.17 55.43 8.09 20.31 FIE 17.14 42.92 9.35 30.59 FIE, SME
Manufacture of Measuring Instruments and
20 Machinery for Office Work 25.39 33.29 13.79 27.53 FIE 18.58 44.16 9.59 27.66 FIE
21 Handicrafts and other Manufacturing 17.25 26.26 11.71 44.78 SME 10.84 41.80 6.93 40.44 FIE, SME
22 Scrap and Waste 1.94 1.25 0.78 96.03 SME - - - - None
23 production and supply of Electricity and heat 52.05 13.59 11.56 22.80 SOE 64.01 7.15 6.44 22.40 SOE
40
TABLE 6 (cont')
2007 2010
Sector DVA share DVA share
# Sector Share in Domestic VA (%) > 30% Share in Domestic VA (%) > 30%
24 Production and Supply of Gas - - - - None - - - - None
25 Production and Supply of Water - - - - None - - - - None
26 construction industry 29.70 17.39 13.40 39.51 SME 25.31 9.70 9.75 55.24 SME
27 Transportation and warehousing 32.17 7.58 5.00 55.25 SOE, SME 38.32 9.12 6.02 46.54 SOE, SME
28 Post service 30.21 30.79 12.14 26.86 SOE, FIE 65.71 11.71 5.53 17.05 SOE
29 IT industry 30.41 31.96 13.92 23.70 SOE, FIE 27.80 36.17 9.23 26.80 FIE
30 wholesale and retailing 13.85 7.10 4.96 74.09 SME 23.48 8.82 8.12 59.58 SME
31 Hotels and Catering Services 19.21 16.08 8.29 56.42 SME 19.16 13.18 6.08 61.58 SME
32 Finance 34.88 16.08 10.94 38.10 SOE, SME 27.85 6.97 2.58 62.61 SME
33 Real Estate - - - - None - - - - -
34 Leasing and commerce service 22.12 15.43 8.40 54.05 SME 30.00 15.85 6.87 47.28 SOE, SME
35 Research and test development industry 33.83 22.61 15.96 27.60 SOE 38.35 14.94 10.52 36.19 SOE, SME
36 Polytechnic Services - - - - None - - - - None
37 Water, environment and public facilities - - - - None - - - - None
38 Resident and Other Services 26.16 21.35 12.79 39.70 SME 15.45 10.26 10.22 64.07 SME
39 Education 26.88 20.64 14.81 37.67 SME 18.69 10.46 16.89 53.95 SME
40 Health and Social service 31.85 20.85 14.58 32.72 SOE, SME 31.79 12.40 14.20 41.61 SOE, SME
41 Culture , Sports and entertainment 34.84 21.85 14.51 28.80 SOE 48.37 9.05 4.51 38.08 SOE, SME
41
Table 7: Top and Bottom Industry Upstreamness
All By Type
Code Industry SOE FIE LP SME
2007
Top 5
3 Extraction of Petroleum and 5.09 6.02 5.31 4.99 4.39
4 Mining of Ferrous Metal Ores 5.03 5.80 5.79 5.27 4.30
2 Mining and Washing of Coal 4.90 5.72 5.35 4.91 3.98
23 Production and supply of
4.46 5.09 4.69 4.35 3.75
Electricity and heat
11 Processing of Petroleum, Coking
4.27 5.22 4.77 4.04 3.59
and Nuclear Fuel
Bottom 5
33 Real Estate 1.67 2.65 2.58 1.53 1.22
40 Health and Social service 1.26 1.50 1.48 1.48 1.08
39 Education 1.20 1.43 1.46 1.31 1.05
26 Construction industry 1.06 1.08 1.24 1.08 1.02
42 Public administration and social
1.02 1.05 1.10 1.05 1.01
organization
2010
Top 5
3 Extraction of Petroleum and
5.22 6.31 4.91 5.32 4.22
Natural Gas
2 Mining and Washing of Coal 5.04 5.66 5.84 5.24 4.68
4 Mining of Ferrous Metal Ores 5.13 5.86 5.09 5.04 4.68
23 production and supply of
4.60 5.31 4.30 4.14 3.85
Electricity and heat
11 Processing of Petroleum, Coking
4.38 5.57 5.08 4.19 4.06
and Nuclear Fuel
Bottom 5
33 Real Estate 1.60 3.41 3.00 1.46 1.22
40 Health and Social service 1.20 1.34 3.03 1.37 1.05
39 Education 1.09 1.39 1.77 1.11 1.02
26 Construction industry 1.06 1.10 2.83 1.09 1.02
42 Public administration and social
1.03 1.11 2.50 1.13 1.01
organization
Note: Authors' estimation based on data from 2007 I/O tables. Bolded face denotes the highest in each row for
the top 5, and the lowest in each row for the bottom 5. there are altogether 40 industries.
42
Figure 2: Firm Average Export Intensity
Small SOE
Small others
Large SOE
Large others
Small foreign
Small HKMT
Large HKMT
Large foreign
0 .05 .1 .15 .2 .25
Export Intensity
Source: China's National Bureau of Statistics Firm Census Data (2008)
Figure 3: Firm Average Value Added to Output Ratio
Small others
Small SOE
Large SOE
Large others
Small foreign
Large HKMT
Small HKMT
Large foreign
0 .2 .4 .6 .8
VA/output
Source: China's National Bureau of Statistics Firm Census Data (2008)
43
Figure 4: Upstreamness of by Ownership Type
6
5
4
3
2
1
1 2 3 4 5
overall upstreamness
SOE SME
LGE FIE
overall upstreamness
Figure 5: Share of SOEs in Sector Value Added and
Sector Upstreamness
1
N = 40; R_sq = 0.06; t-stat = 1.61.
.8
SOE Share in Industry VA
Post Sercices Electricity and Heat
.6
Culture , Sports, Entertainment
Water
.4
Mining Coal
Research and Test Development
Water, environment Polytechnic Services
Health and Social Service Transportation
Leasing Warehousing
and commerce Petroleum
.2
Construction IT industry
of Metalsof Petroleum
Processing
Smelting
Gas
Finance
Public Admin and Tobacco
Foods Transport Equip Ferrous Metal Ores
Education Real Estate Resident andGeneral
Other& Special Purpose
Services Non-ferrous
Mach Metal Ores
Mesauring Instrument
Non-ferrous Paper
Metal Chemical Products
Products
Products
Comm, Electrical
Computers, Mach
Textile
Electronic Products
Apparel Wood
Handicrafts
Wholesale
Hotels & Retailing
and Catering
0
1 2 3 4 5
Industry Upstreamness (07)
Figure 6: Share of SOEs in Sector Exports and Sector Upstreamness
.4
Gas
SOE Share in Industry Exports
.3
N=23; R_sq = 0.14; t-stat = 2.13.
Mining Coal
Smelting of Metals
.2
Water
Electricity and Heat
Transport Equip
.1
Non-ferrous Metal Ores Petroleum
General & Special Purpose Mach
Chemical Products
Foods and Tobacco Processing of Petroleum
Electrical
MesauringNon-ferrous
Instrument Metal Products
Textile
Mach
Metal Products
Apparel
Comm, Computers,
Handicrafts
Wood Paper
Electronic Products Ferrous Metal Ores
0
1 2 3 4 5
Industry Upstreamness (07)
44
Figure 7: Share of SMEs in Sector Output versus Sector
Upstreamness
Hotel s a nd
Resident Cate
and rin gSer vices
Other
Public Admin
Education
.8
N=40; R=0.21; t-stat = 0.10.
Health and Socia l Service
Non-ferrous Me tal Ores
SME Share in Industry VA
.6
Wood Fina nce
Wholesale & Reta il ing
Non-ferrous Me tal Products
Wate r, environment Ferrous Metal Ores
Textile
Metal Products
Polytechnic Services
Paper Products
Handicr afts Gener al & Specia Purp ose
and lcommerce Mach
.4
Real Esta te Transportation
Leasing Ware housing
Construction Appare l
re , Sports,
CultuFoods Enterta inm ent
Researand
ch andTob acco
Test De velopme ntChemical Produ cts
Mining Coa l
Electr
Mesauring ical Mach
Instrument
.2
Wate r
IT indu str y
Transport Equip
Gas
Smelting of Metal s
Comm, Comp uters, Electron ic rcices
Post Se Processing Petrol
of and eum
Electr icity Hea t
Petroleum
0
1 2 3 4 5
Industry Upstreamness (07)
Figure 8: Share of SMEs in Sector Exports versus Sector
Upstreamness
Non-ferrous Me tal Ores
.5
N = 23; R = 0.03; tstat = -0.78.
SME Share in Industry Exports
Ferrous Metal Ores
.4
Handicr afts
Wood
.3
Textile
Foods and Tob acco
Non-ferrous Me tal Products
Appare l
Metal Products
.2
Gener al & Specia
Paper l Purp ose Mach
Products
Chemical Produ cts
Electr ical Mach
.1
Transport
Mesauring Equip
Instrument
Smelting of Metal s
Electr icity and Hea t
Comm, Comp uters, Electron
Gasic Petrol eum
Processing of Mining Coa l
Wate r Petroleum
0
1 2 3 4 5
Industry Upstreamness (07)
Figure 9: Upstreamness 2007 and 2010
Petr oleum and Natur al Gas
Coal
Fer rous Metal Or es
5
Electricity and heat
Processing of Petr oleum
4
Chemical
Non-FSmelting Rolling
and Or
errous Metal es of metals
Paper Pr oducts
Metal Products
Finance
Post ansportation
Trser vice and warehousing
Textile
Leasing and commer ce ser vice
Handicrafts Polytechnic Ser vices
3
Measur ing
WoodInstr Machinery
Fur nitur
and
uments and e Gas
Machinery f or Of fice Work
Non-f
Hotelswholesale
and rous
er and
Catering metal
r etailing
Ser vices
Electrical Machiner y and Equip
Communication Equipment
Foods and Tobacco Water
ResidentTr ansport
and Other Equipment
Ser vices
Culture IT
, industr
Spor
Research y enter
ts and
and tainment
test development
Apparel, leather, fur
2
Water, environment and public facilities
Real Estate
Health and Social service
Education
Constr uction&industry
1
Public admin social or ganization
1 2 3 4 5
up07
up10 up07
45
Appendix
Table A1: Classification of Large, Medium and small firms
(by NBS of China, 2011)
Industry Indicator Unit Large Medium Small
Employment Persons >=1000 300-1000 <300
Manufacture Total Sales RMB10,000 >=40000 2000-40000 <2000
Total Assets RMB10,000 >=40000 4000-40000 <4000
Total Sales RMB10,000 >=80000 6000-80000 <6000
Construction
Total Assets RMB10,000 >=80000 5000-80000 <5000
Employment Persons >=200 20-200 <20
Wholesales
Total Sales RMB10,000 >=40000 5000-40000 <5000
Employment Persons >=300 50-300 <50
Retails
Total Sales RMB10,000 >=20000 500-20000 <500
Employment Persons >=1000 300-1000 <300
Transportation
Total Sales RMB10,000 >=30000 3000-30000 <3000
Employment Persons >=1000 300-1000 <300
Postal Services
Total Sales RMB10,000 >=30000 2000-30000 <2000
Accommodation & Employment Persons >=300 100-300 <100
Catering Total Sales RMB10,000 >=10000 2000-10000 <2000
Employment Persons >=200 <200
Finance and Banking
Total Sales RMB10,000 >=30000 <30000
Employment Persons >=200 <200
Real Estates
Total Sales RMB10,000 >=30000 <30000
Other Service
Employment Persons >=500 <500
Industries
1. Manufacture above includes three industries: mining, manufacturing and Electricity, Gas, and Utility
production and supply.
2. Total sale in manufacturing industry is expressed by the annual sale/revenue of products calculated according
to the current statistic system; total sale in construction firms is represented by the annual receipt from projects
done according to the current statistic system; total sale of wholesales and retails is shown as the annual sales
calculated according to the current accounting forms; total sale in transportation, postal services, accommodation
and catering firms is the annual operating revenue calculated according to the current statistic system; the total
asset is replaced by accumulated assets according to the current statistic system.
3. The large and medium firms should meet all the criteria defined for the large firm and medium firm,
respectively. Otherwise, it will be classified to the next lower category of firm size.
Other definitions (authors’ definition according to the rule of NBS of China):
Large firms of finance and banking industry are those firms with more than 200 employees and more than 300
million Yuan (RMB) in sales.
Large firms of real estate industry are those firms with more than 200 employees and more than 300 million Yuan
(RMB) in sales.
Large firms of other service industries are those firms with more than 500 employees.
Remarks: the lowest standard of employment and the highest standard of total sale are used to define large firms
considering the properties of finance and banking industry and real estate industry. The firm in other service
industries will use the only criteria of employment to distinguish the large firm and SME.
46
Table A2: Indirect VAEX/ Total VAEX 2007 (6 types, 42 industries)
Industry LSOE SSOE LFIE SFIE LGE SME
Mining and Washing of Coal 0.96 0.93 0.92 0.90 0.93 0.95
Extraction of Petroleum and Natural Gas 0.92 0.84 1.00 0.85 0.88 0.99
Mining of Ferrous Metal Ores 1.00 0.95 0.90 1.00 1.00 0.95
Mining of Non-Ferrous Metal Ores 0.75 0.74 0.82 0.72 0.82 0.65
Foods and Tobacco 0.66 0.65 0.65 0.67 0.66 0.80
Manufacture of Textile Products 0.62 0.59 0.50 0.30 0.44 0.60
Wearing apparel, leather, fur, down and related
products 0.67 0.66 0.40 0.13 0.48 0.23
Processing of wood and Manufacture of Furniture
- 0.69 0.57 0.40 0.58 0.40
Paper Products and Articles for Culture,
Education and Sports Activities 0.87 0.84 0.74 0.46 0.81 0.55
Processing of Petroleum, Coking and Nuclear Fuel
0.88 0.91 0.84 0.85 0.90 0.89
Manufacture of Chemical Products 0.82 0.87 0.72 0.49 0.67 0.78
Manufacture of non-ferrous metal products 0.62 0.60 0.46 0.27 0.40 0.42
Smelting and Rolling of metals 0.66 0.89 0.86 0.81 0.76 0.88
Manufacture of Metal Products 0.80 0.79 0.70 0.41 0.74 0.36
Manufacture of General Purpose and Special
Purpose Machinery 0.76 0.82 0.67 0.33 0.66 0.52
Manufacture of Transport Equipment 0.63 0.70 0.51 0.59 0.55 0.65
Manufacture of Electrical Machinery and
Equipment 0.76 0.75 0.30 0.24 0.44 0.37
Manufacture of Communication Equipment,
computers and Other Electronic Equipment 0.62 0.70 0.17 0.27 0.45 0.61
Manufacture of Measuring Instruments and
Machinery for Office Work 0.18 0.20 0.06 0.09 0.14 0.10
Handicrafts and other Manufacturing 1.00 0.58 0.36 0.26 0.39 0.25
Scrap and Waste 0.98
production and supply of Electricity and heat 1.00 0.99 0.99 0.99 0.99 0.99
Production and Supply of Gas 1.00 1.00 1.00 1.00 1.00 1.00
Production and Supply of Water 1.00 1.00 1.00 1.00 1.00 1.00
construction industry 0.38 0.38 0.31 0.38 0.38 0.38
Transportation and warehousing 0.82 0.54 0.89 0.90 0.91 0.45
Post service 0.75 0.75 0.76 0.78 0.80 0.77
IT industry 0.76 0.76 0.76 0.76 0.76 0.76
wholesale and retailing 0.72 0.74 0.82 0.82 0.76 0.28
Hotels and Catering Services 0.74 0.75 0.75 0.75 0.75 0.75
Finance 0.98 0.98 0.88 0.98 0.98 0.99
Real Estate 1.00 1.00 1.00 1.00 1.00 1.00
Leasing and commerce service 0.56 0.44 0.60 0.57 0.60 0.18
Research and test development industry 0.91 0.91 0.91 0.90 0.91 0.91
Polytechnic Services 1.00 1.00 1.00 1.00 1.00 1.00
Water, environment and public facilities 1.00 1.00 1.00 1.00 1.00 1.00
Resident and Other Services 0.81 0.81 0.80 0.81 0.81 0.82
Education 0.86 0.87 0.86 0.87 0.87 0.87
Health and Social service 0.88 0.88 0.88 0.88 0.88 0.88
Culture , Sports and entertainment 0.57 0.57 0.57 0.57 0.57 0.57
47
Table A3: Industry Upstream Index
2007 2010
All By Type All By Type
Industry SOE LFIE LGE SME SOE LFIE LGE SME
3 Extraction of Petroleum and Natural Gas 5.09 6.02 5.31 4.99 4.39 5.22 6.31 4.91 5.32 4.22
4 Mining of Ferrous Metal Ores 5.03 5.80 5.79 5.27 4.30 5.04 5.66 5.84 5.24 4.68
2 Mining and Washing of Coal 4.90 5.72 5.35 4.91 3.98 5.13 5.86 5.09 5.04 4.68
23 production and supply of Electricity and
4.46 5.09 4.69 4.35 3.75 4.60 5.31 4.30 4.14 3.85
heat
11 Processing of Petroleum, Coking and
4.27 5.22 4.77 4.04 3.59 4.38 5.57 5.08 4.19 4.06
Nuclear Fuel
14 Smelting and Rolling of metals 3.98 4.86 4.73 4.27 3.22 3.95 5.00 4.92 4.31 3.52
12 Manufacture of Chemical Products 3.83 3.70 4.20 3.92 3.89 4.02 3.65 4.54 4.50 4.30
5 Mining of Non-Ferrous Metal Ores 3.77 3.78 4.16 3.70 3.92 3.94 3.84 4.86 3.94 3.98
24 Production and Supply of Gas 3.35 3.70 3.75 3.56 3.01 2.92 4.25 3.10 4.87 2.11
10 Paper Products and Articles for Culture, 3.32 3.89 3.65 3.90 2.97 3.76 3.50 4.08 4.24 4.14
27 Transportation and warehousing 3.31 3.82 4.34 4.08 2.47 3.46 4.13 4.68 4.53 3.08
32 Finance 3.28 4.42 4.54 4.22 2.32 3.49 4.69 5.01 4.84 3.04
15 Manufacture of Metal Products 3.27 3.88 4.14 3.68 2.57 3.60 3.45 4.34 4.20 3.48
25 Production and Supply of Water 3.22 3.48 3.72 3.72 2.75 2.51 2.28 4.20 4.75 2.39
28 Post service 3.21 3.54 3.62 3.45 2.83 3.45 3.84 4.79 4.60 3.56
34 Leasing and commerce service 3.14 3.80 3.93 3.66 2.33 3.38 4.51 4.77 4.62 2.78
36 Polytechnic Services 3.11 3.28 3.54 3.12 2.56 3.15 3.56 4.74 3.87 2.46
7 Manufacture of Textile Products 3.06 2.96 4.01 3.14 2.76 3.40 3.67 3.12 4.38 3.54
16 Manufacture of General Purpose and 2.90 3.98 3.73 3.46 2.04 2.93 4.39 3.67 3.46 2.37
13 Manufacture of non-ferrous metal products 2.73 2.89 3.21 2.80 2.65 2.85 2.69 4.45 3.63 2.67
17 Manufacture of Transport Equipment 2.72 3.36 3.02 3.02 2.14 2.46 3.06 2.87 2.69 2.15
18 Manufacture of Electrical Machinery and
2.71 3.94 3.84 2.92 1.79 2.71 4.85 3.62 3.03 2.12
Equipment
9 Processing of wood and Manufacture of 2.65 3.27 3.18 3.31 2.11 2.90 4.40 3.41 3.74 2.80
30 wholesale and retailing 2.64 3.60 4.01 3.52 1.66 2.84 4.09 4.72 4.22 2.23
29 IT industry 2.46 2.94 2.69 2.93 1.96 2.34 3.11 2.72 3.58 1.84
38 Resident and Other Services 2.44 3.45 3.57 3.29 1.46 2.43 4.65 4.99 4.48 1.83
31 Hotels and Catering Services 2.43 3.59 3.69 3.51 1.46 2.81 4.42 4.83 4.56 2.14
6 Foods and Tobacco 2.42 2.85 2.44 2.52 2.09 2.54 3.15 2.73 2.73 2.34
35 Research and test development industry
2.41 2.90 2.36 2.70 2.32 2.28 2.26 3.65 3.35 1.86
20 Manufacture of Measuring Instruments and 2.36 2.90 3.11 2.28 1.86 2.91 4.59 2.67 3.73 3.44
21 Handicrafts and other Manufacturing 2.29 2.50 2.69 2.72 1.84 3.12 4.88 3.94 4.32 2.77
41 Culture , Sports and entertainment 2.19 2.48 2.26 2.58 2.02 2.33 2.35 4.76 4.42 1.99
19 Manufacture of Communication 2.17 3.38 3.91 2.54 2.10 2.62 4.80 2.56 3.90 3.38
37 Water, environment and public facilities 1.95 1.97 2.09 1.96 1.70 1.86 1.91 3.95 3.12 1.30
8 Wearing apparel, leather, fur, down and
1.85 2.97 1.92 2.37 1.39 2.05 4.89 2.34 3.32 1.66
related products
33 Real Estate 1.67 2.65 2.58 1.53 1.22 1.60 3.41 3.00 1.46 1.22
40 Health and Social service 1.26 1.50 1.48 1.48 1.08 1.20 1.34 3.03 1.37 1.05
39 Education 1.20 1.43 1.46 1.31 1.05 1.09 1.39 1.77 1.11 1.02
26 Construction industry 1.06 1.08 1.24 1.08 1.02 1.06 1.10 2.83 1.09 1.02
42 Public administration and social
1.02 1.05 1.10 1.05 1.01 1.03 1.11 2.50 1.13 1.01
organization
48
Table A4.1: Direct, indirect, and VA Exports by ownership-sector (LSOE)
Indirect VAEX
Value
Added
Exports Direct
(VAEX) VAEX via LSOE SSOE LFIE SFIE L SM Gross Exports
2 Mining and Washing of Coal 69.98 2.68 6.33 4.69 11.33 17.49 9.89 17.57 3.98
3 Extraction of Petroleum and Natural Gas
120.95 9.76 12.14 9.57 17.08 25.83 17.94 28.64 11.99
4 Mining of Ferrous Metal Ores 8.74 0.00 1.29 0.44 1.38 1.92 1.44 2.27 0.00
5 Mining of Non-Ferrous Metal Ores 8.04 2.03 0.45 0.40 1.02 1.75 0.91 1.47 5.90
6 Foods and Tobacco 30.42 10.25 1.81 1.86 3.56 5.35 2.88 4.71 31.00
7 Manufacture of Textile Products 22.09 8.38 0.56 0.71 1.72 5.14 2.09 3.50 51.48
8 Wearing apparel, leather, fur, down and
related products 5.29 1.72 0.21 0.23 0.56 1.22 0.48 0.87 15.26
9 Processing of wood and Manufacture of
Furniture 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
# Paper Products and Articles for Culture,
Education and Sports Activities
10.97 1.40 0.78 0.83 1.84 2.52 1.31 2.29 7.73
# Processing of Petroleum, Coking and
Nuclear Fuel 16.22 1.90 1.19 1.46 2.09 3.29 1.80 4.48 26.28
# Manufacture of Chemical Products 32.06 5.76 1.85 1.79 5.29 7.60 3.79 5.98 40.65
# Manufacture of non-ferrous metal
products 6.38 2.41 0.36 0.30 0.94 1.00 0.59 0.77 20.05
# Smelting and Rolling of metals 151.80 52.00 9.90 4.44 16.02 22.09 14.60 32.74 218.56
# Manufacture of Metal Products 12.33 2.50 0.77 0.70 2.27 2.60 1.37 2.12 15.71
# Manufacture of General Purpose and
Special Purpose Machinery 18.76 4.41 1.32 1.06 2.74 3.88 2.10 3.24 30.34
# Manufacture of Transport Equipment 22.15 8.13 1.46 1.30 3.02 2.96 2.14 3.13 45.22
# Manufacture of Electrical Machinery and
Equipment 9.94 2.38 0.62 0.56 1.84 1.94 1.07 1.53 20.56
49
# Manufacture of Communication
Equipment, computers and Other
Electronic Equipment 13.80 5.24 0.48 0.46 4.37 1.37 0.86 1.01 46.54
# Manufacture of Measuring Instruments
and Machinery for Office Work
8.07 6.62 0.15 0.14 0.28 0.36 0.22 0.32 37.27
# Handicrafts and other Manufacturing 1.17 0.00 0.09 0.09 0.24 0.32 0.18 0.26 0.00
# Scrap and Waste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
# production and supply of Electricity and
heat 148.74 0.37 13.58 9.04 24.44 39.08 21.50 40.73 1.21
# Production and Supply of Gas 0.92 0.00 0.09 0.07 0.17 0.25 0.14 0.21 0.00
# Production and Supply of Water 2.21 0.00 0.20 0.18 0.42 0.59 0.32 0.49 0.00
# construction industry 1.33 0.82 0.05 0.05 0.10 0.12 0.07 0.11 6.70
# Transportation and warehousing 52.56 9.66 3.88 3.74 8.16 10.89 6.11 10.14 25.18
# Post service 1.35 0.33 0.09 0.09 0.20 0.27 0.15 0.22 0.82
# IT industry 14.65 3.59 1.07 0.92 2.43 2.67 1.58 2.40 7.42
# wholesale and retailing 50.51 14.16 3.09 2.80 8.66 9.22 5.14 7.44 31.71
# Hotels and Catering Services 13.79 3.52 0.97 0.99 1.94 2.49 1.45 2.44 11.79
# Finance 41.27 0.75 3.59 3.48 8.58 10.09 5.75 9.05 1.50
# Real Estate 13.05 0.00 1.21 1.23 2.60 3.24 1.86 2.92 0.00
# Leasing and commerce service 13.88 6.14 0.71 0.70 1.62 1.91 1.10 1.70 28.65
# Research and test development industry
2.27 0.20 0.18 0.16 0.55 0.51 0.30 0.38 0.43
# Polytechnic Services 8.50 0.00 0.76 0.67 1.69 2.23 1.23 1.92 0.00
# Water, environment and public facilities
1.76 0.00 0.16 0.16 0.30 0.45 0.25 0.45 0.00
# Resident and Other Services 5.12 0.99 0.41 0.42 0.74 0.98 0.58 1.00 4.74
# education 0.76 0.10 0.06 0.06 0.13 0.16 0.09 0.15 0.55
# Health and Social service 2.29 0.27 0.18 0.16 0.40 0.54 0.30 0.44 0.76
# Culture , Sports and entertainment 6.11 2.61 0.34 0.32 0.69 0.88 0.51 0.76 5.54
50
Table A4.2: Direct, indirect, and VA Exports by ownership-sector (SSOE)
Indirect VAEX
Value Added Direct Gross
Exports (VAEX) VAEX via LSOE SSOE LFIE SFIE L SM Exports
2 Mining and Washing of Coal 18.11 1.26 1.59 1.24 2.90 4.48 2.53 4.11 3.88
3 Extraction of Petroleum and Natural Gas 28.27 4.49 2.29 1.97 4.12 6.18 3.48 5.74 9.34
4 Mining of Ferrous Metal Ores 8.39 0.46 1.17 0.41 1.26 1.75 1.30 2.06 1.87
5 Mining of Non-Ferrous Metal Ores 8.58 2.25 0.48 0.43 1.08 1.83 0.96 1.55 6.41
6 Foods and Tobacco 17.38 6.14 1.00 1.04 2.03 3.01 1.62 2.54 28.67
7 Manufacture of Textile Products 16.69 6.76 0.41 0.51 1.24 3.73 1.51 2.53 58.32
8 Wearing apparel, leather, fur, down and
7.93 2.73 0.30 0.34 0.81 1.81 0.68 1.25 19.33
related products
9 Processing of wood and Manufacture of
6.84 2.11 0.26 0.33 0.75 1.23 0.59 1.56 13.64
Furniture
10 Paper Products and Articles for Culture,
Education and Sports Activities 11.88 1.84 0.80 0.87 1.92 2.64 1.36 2.44 10.29
11 Processing of Petroleum, Coking and
14.94 1.35 1.31 1.20 2.33 3.43 1.95 3.37 8.55
Nuclear Fuel
12 Manufacture of Chemical Products 21.89 2.77 1.38 1.34 3.85 5.49 2.79 4.28 22.46
13 Manufacture of non-ferrous metal
6.95 2.77 0.39 0.32 0.99 1.05 0.62 0.82 22.83
products
14 Smelting and Rolling of metals 10.03 1.10 0.82 0.54 1.82 2.40 1.29 2.05 15.62
15 Manufacture of Metal Products 12.64 2.70 0.78 0.71 2.30 2.64 1.38 2.15 17.36
16 Manufacture of General Purpose and
15.08 2.76 1.13 0.92 2.37 3.33 1.81 2.76 20.87
Special Purpose Machinery
17 Manufacture of Transport Equipment 10.99 3.26 0.82 0.75 1.54 1.70 1.18 1.74 26.47
18 Manufacture of Electrical Machinery
9.39 2.37 0.58 0.52 1.71 1.80 0.99 1.42 21.81
and Equipment
19 Manufacture of Communication
Equipment, computers and Other 11.30 3.34 0.41 0.40 4.33 1.18 0.76 0.90 31.80
Electronic Equipment
51
20 Manufacture of Measuring Instruments
and Machinery for Office Work 7.03 5.64 0.14 0.13 0.27 0.34 0.21 0.30 33.41
21 Handicrafts and other Manufacturing 4.31 1.82 0.20 0.20 0.52 0.67 0.37 0.53 10.87
22 Scrap and Waste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
23 Production and supply of Electricity and
74.03 0.41 6.77 4.95 12.81 19.58 10.78 18.73 1.21
heat
24 Production and Supply of Gas 0.92 0.00 0.09 0.07 0.17 0.24 0.14 0.20 0.00
25 Production and Supply of Water 2.85 0.00 0.26 0.24 0.54 0.76 0.42 0.64 0.00
26 construction industry 2.11 1.30 0.08 0.08 0.15 0.19 0.11 0.18 6.70
27 Transportation and warehousing 133.78 60.99 6.44 6.33 13.37 18.04 10.06 18.55 121.87
28 Post service 1.75 0.43 0.12 0.11 0.26 0.36 0.19 0.29 0.87
29 IT industry 18.81 4.54 1.38 1.19 3.13 3.45 2.03 3.09 7.45
30 wholesale and retailing 44.55 11.66 2.81 2.55 7.70 8.39 4.68 6.77 28.27
31 Hotels and Catering Services 15.78 3.96 1.12 1.14 2.23 2.86 1.67 2.80 12.10
32 Finance 62.17 0.98 5.37 5.23 13.29 15.04 8.57 13.69 1.50
33 Real Estate 13.29 0.00 1.23 1.25 2.65 3.29 1.89 2.97 0.00
34 Leasing and commerce service 22.14 12.31 0.90 0.89 2.06 2.42 1.40 2.16 45.95
35 Research and test development industry 2.27 0.20 0.18 0.16 0.55 0.51 0.30 0.38 0.43
36 Polytechnic Services 7.76 0.00 0.69 0.61 1.55 2.03 1.13 1.75 0.00
37 Water, environment and public facilities 1.84 0.00 0.16 0.16 0.31 0.47 0.26 0.47 0.00
38 Resident and Other Services 5.74 1.09 0.46 0.47 0.83 1.10 0.65 1.13 4.75
39 Education 0.94 0.13 0.08 0.08 0.16 0.20 0.12 0.19 0.55
40 Health and Social service 1.99 0.23 0.16 0.14 0.35 0.47 0.26 0.38 0.76
41 Culture , Sports and entertainment 6.46 2.77 0.35 0.34 0.73 0.93 0.54 0.81 5.54
52
Table A4.3: Direct, indirect, and VA Exports by ownership-sector (LFIE)
Indirect VAEX
Value Added Direct Gross
Exports (VAEX) VAEX via LSOE SSOE LFIE SFIE L SM Exports
2 Mining and Washing of Coal 15.40 1.21 1.33 1.05 2.44 3.80 2.14 3.44 3.92
3 Extraction of Petroleum and Natural Gas 4.86 0.00 0.46 0.38 0.83 1.29 0.72 1.18 0.00
4 Mining of Ferrous Metal Ores 7.70 0.75 1.01 0.36 1.10 1.54 1.14 1.80 3.35
5 Mining of Non-Ferrous Metal Ores 6.08 1.09 0.37 0.33 0.84 1.47 0.76 1.22 3.48
6 Foods and Tobacco 22.21 7.74 1.30 1.34 2.57 3.84 2.07 3.35 31.79
7 Manufacture of Textile Products 36.38 18.33 0.75 0.93 2.17 6.74 2.64 4.82 108.38
8 Wearing apparel, leather, fur, down and related
18.43 11.02 0.46 0.52 1.17 2.61 0.95 1.71 61.84
products
9 Processing of wood and Manufacture of
10.28 4.42 0.33 0.44 0.96 1.52 0.75 1.87 24.80
Furniture
10 Paper Products and Articles for Culture,
16.88 4.45 0.99 1.06 2.34 3.27 1.65 3.11 22.19
Education and Sports Activities
11 Processing of Petroleum, Coking and Nuclear
15.45 2.43 1.26 1.15 2.25 3.30 1.87 3.19 13.33
Fuel
12 Manufacture of Chemical Products 43.37 12.22 2.17 2.10 6.26 9.01 4.47 7.14 76.75
13 Manufacture of non-ferrous metal products 9.24 4.99 0.39 0.32 1.01 1.07 0.63 0.83 40.16
14 Smelting and Rolling of metals 29.45 4.25 2.37 1.48 5.03 6.64 3.62 6.06 29.05
15 Manufacture of Metal Products 15.90 4.79 0.88 0.80 2.59 2.93 1.53 2.38 28.77
16 Manufacture of General Purpose and Special
21.69 7.27 1.33 1.07 2.76 3.90 2.11 3.26 49.70
Purpose Machinery
17 Manufacture of Transport Equipment 40.72 19.81 2.10 1.82 4.96 4.15 3.18 4.69 90.65
18 Manufacture of Electrical Machinery and
39.16 27.58 0.98 0.88 2.79 2.96 1.63 2.33 176.69
Equipment
19 Manufacture of Communication Equipment,
438.05 361.89 1.17 1.07 66.34 3.19 2.02 2.37 1793.26
computers and Other Electronic Equipment
20 Manufacture of Measuring Instruments and
22.03 20.65 0.14 0.13 0.26 0.34 0.21 0.30 125.48
Machinery for Office Work
53
21 Handicrafts and other Manufacturing 8.36 5.32 0.25 0.25 0.63 0.81 0.46 0.64 26.20
22 Scrap and Waste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
23 Production and supply of Electricity and heat
16.19 0.20 1.44 1.20 2.94 4.28 2.38 3.75 1.18
24 Production and Supply of Gas 0.92 0.00 0.09 0.07 0.17 0.25 0.14 0.21 0.00
25 Production and Supply of Water 2.18 0.00 0.19 0.18 0.42 0.58 0.32 0.49 0.00
26 construction industry 0.37 0.25 0.01 0.01 0.02 0.03 0.02 0.03 7.47
27 Transportation and warehousing 29.54 3.31 2.36 2.27 5.01 6.73 3.77 6.10 12.09
28 Post service 1.77 0.43 0.12 0.11 0.26 0.36 0.19 0.29 0.87
29 IT industry 18.57 4.48 1.36 1.17 3.09 3.41 2.01 3.05 7.45
30 wholesale and retailing 30.01 5.51 2.10 1.92 5.53 6.32 3.54 5.10 16.30
31 Hotels and Catering Services 15.13 3.81 1.07 1.09 2.14 2.74 1.60 2.68 12.03
32 Finance 0.77 0.10 0.06 0.05 0.14 0.17 0.10 0.16 1.19
33 Real Estate 13.85 0.00 1.29 1.30 2.76 3.44 1.97 3.09 0.00
34 Leasing and commerce service 10.65 4.24 0.59 0.58 1.34 1.58 0.91 1.41 23.53
35 Research and test development industry 2.21 0.19 0.18 0.15 0.54 0.49 0.29 0.37 0.43
36 Polytechnic Services 7.94 0.00 0.71 0.62 1.59 2.08 1.16 1.78 0.00
37 Water, environment and public facilities 1.58 0.00 0.14 0.14 0.27 0.41 0.22 0.41 0.00
38 Resident and Other Services 4.45 0.88 0.35 0.37 0.64 0.85 0.50 0.87 4.74
39 education 0.71 0.10 0.06 0.06 0.12 0.15 0.09 0.14 0.55
40 Health and Social service 1.23 0.15 0.10 0.09 0.21 0.29 0.16 0.24 0.76
41 Culture , Sports and entertainment 4.77 2.04 0.26 0.25 0.54 0.69 0.40 0.60 5.44
54
Table A4.4: Direct, indirect, and VA Exports by ownership-sector (SFIE)
Indirect VAEX
Value Added
Exports Direct Gross
(VAEX) VAEX via LSOE SSOE LFIE SFIE L SM Exports
2 Mining and Washing of Coal 6.03 0.62 0.50 0.39 0.91 1.46 0.82 1.33 3.87
3 Extraction of Petroleum and Natural Gas 16.90 2.53 1.36 1.16 2.50 3.79 2.11 3.44 6.75
4 Mining of Ferrous Metal Ores 7.21 0.00 1.05 0.37 1.14 1.59 1.18 1.87 0.00
5 Mining of Non-Ferrous Metal Ores 8.80 2.44 0.48 0.43 1.08 1.84 0.96 1.57 7.10
6 Foods and Tobacco 24.53 8.01 1.41 1.45 2.75 4.34 2.28 4.29 37.60
7 Manufacture of Textile Products 113.82 79.71 1.39 1.67 3.74 12.17 4.66 10.48 362.36
8 Wearing apparel, leather, fur, down and related
101.44 88.36 0.78 0.88 1.96 4.82 1.59 3.06 308.57
products
9 Processing of wood and Manufacture of Furniture
19.25 11.62 0.42 0.55 1.20 1.92 0.94 2.60 54.84
10 Paper Products and Articles for Culture, Education
37.66 20.16 1.35 1.45 3.17 4.57 2.24 4.74 81.30
and Sports Activities
11 Processing of Petroleum, Coking and Nuclear Fuel
30.60 4.67 2.51 2.28 4.50 6.57 3.74 6.34 13.64
12 Manufacture of Chemical Products 152.90 78.37 4.11 3.92 14.05 21.29 9.44 21.72 384.56
13 Manufacture of non-ferrous metal products 22.56 16.42 0.57 0.47 1.45 1.54 0.90 1.21 99.58
14 Smelting and Rolling of metals 31.19 5.92 2.38 1.47 5.02 6.64 3.63 6.14 41.74
15 Manufacture of Metal Products 38.31 22.67 1.21 1.09 3.72 4.10 2.11 3.40 109.26
16 Manufacture of General Purpose and Special
86.23 57.36 2.64 2.09 5.40 7.77 4.16 6.81 222.35
Purpose Machinery
17 Manufacture of Transport Equipment 23.02 9.46 1.42 1.26 2.91 2.87 2.07 3.03 53.77
18 Manufacture of Electrical Machinery and
56.17 42.43 1.14 1.02 3.38 3.52 1.91 2.77 245.68
Equipment
19 Manufacture of Communication Equipment,
32.47 23.63 0.33 0.30 6.07 0.90 0.57 0.68 371.83
computers and Other Electronic Equipment
20 Manufacture of Measuring Instruments and
18.26 16.63 0.17 0.15 0.31 0.40 0.24 0.36 84.86
Machinery for Office Work
55
21 Handicrafts and other Manufacturing 14.98 11.03 0.33 0.33 0.82 1.05 0.59 0.83 41.97
22 Scrap and Waste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
23 production and supply of Electricity and heat 23.21 0.12 2.10 1.74 4.25 6.17 3.42 5.41 0.53
24 Production and Supply of Gas 0.91 0.00 0.09 0.07 0.17 0.24 0.14 0.20 0.00
25 Production and Supply of Water 2.27 0.00 0.20 0.19 0.43 0.61 0.33 0.51 0.00
26 construction industry 0.80 0.50 0.03 0.03 0.06 0.07 0.04 0.07 6.70
27 Transportation and warehousing 7.88 0.81 0.61 0.59 1.34 1.83 1.03 1.68 8.10
28 Post service 1.94 0.42 0.13 0.13 0.30 0.41 0.22 0.33 0.80
29 IT industry 19.74 4.75 1.44 1.24 3.29 3.62 2.14 3.25 7.45
30 wholesale and retailing 27.83 4.95 1.95 1.79 5.16 5.91 3.31 4.77 15.45
31 Hotels and Catering Services 14.31 3.62 1.01 1.03 2.02 2.59 1.51 2.53 11.92
32 Finance 31.72 0.67 2.72 2.65 6.51 7.79 4.44 6.93 1.50
33 Real Estate 13.16 0.00 1.22 1.24 2.62 3.26 1.87 2.94 0.00
34 Leasing and commerce service 12.66 5.39 0.66 0.66 1.52 1.80 1.04 1.59 26.65
35 Research and test development industry 1.64 0.16 0.13 0.11 0.40 0.36 0.21 0.27 0.46
36 Polytechnic Services 8.02 0.00 0.72 0.63 1.60 2.10 1.17 1.80 0.00
37 Water, environment and public facilities 1.70 0.00 0.15 0.15 0.28 0.44 0.24 0.44 0.00
38 Resident and Other Services 5.46 1.05 0.43 0.45 0.79 1.05 0.62 1.07 4.75
39 education 0.83 0.11 0.07 0.07 0.14 0.18 0.10 0.17 0.55
40 Health and Social service 1.50 0.18 0.12 0.11 0.26 0.36 0.19 0.29 0.76
41 Culture , Sports and entertainment 3.82 1.64 0.21 0.20 0.43 0.55 0.32 0.48 5.27
56
Table A4.5: Direct, indirect, and VA Exports by ownership-sector (LGO)
Indirect VAEX
Value Added
Exports Direct Gross
(VAEX) VAEX via LSOE SSOE LFIE SFIE L SM Exports
2 Agriculture, forestry, fishing, and husbandry
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
3 Mining and Washing of Coal 18.68 1.26 1.65 1.29 3.01 4.63 2.61 4.23 3.76
4 Extraction of Petroleum and Natural Gas 55.09 6.77 4.91 4.05 8.12 12.11 7.15 11.98 10.29
5 Mining of Ferrous Metal Ores 8.29 0.00 1.22 0.42 1.31 1.82 1.36 2.15 0.00
6 Mining of Non-Ferrous Metal Ores 7.24 1.30 0.45 0.40 1.00 1.72 0.90 1.47 3.89
7 Foods and Tobacco 22.14 7.57 1.30 1.34 2.57 3.86 2.08 3.42 32.25
8 Manufacture of Textile Products 64.73 35.98 1.12 1.34 3.02 10.36 3.79 9.12 180.48
9 Wearing apparel, leather, fur, down and
15.44 7.95 0.46 0.52 1.17 2.65 0.95 1.73 44.72
related products
10 Processing of wood and Manufacture of
10.42 4.35 0.34 0.45 0.98 1.56 0.76 1.98 24.22
Furniture
11 Paper Products and Articles for Culture,
14.12 2.67 0.92 0.98 2.16 3.01 1.53 2.85 14.12
Education and Sports Activities
12 Processing of Petroleum, Coking and
18.19 1.89 1.41 1.61 2.47 3.84 2.11 4.87 19.99
Nuclear Fuel
13 Manufacture of Chemical Products 72.23 23.78 2.96 2.83 9.41 14.00 6.50 12.76 138.87
14 Manufacture of non-ferrous metal products 12.30 7.39 0.46 0.37 1.16 1.24 0.72 0.97 53.14
15 Smelting and Rolling of metals 141.38 33.35 10.91 4.57 16.60 23.35 15.61 36.98 151.21
16 Manufacture of Metal Products 15.19 3.93 0.88 0.80 2.64 2.97 1.55 2.42 23.77
17 Manufacture of General Purpose and Special
25.65 8.83 1.55 1.24 3.19 4.54 2.46 3.84 54.38
Purpose Machinery
18 Manufacture of Transport Equipment 28.07 12.61 1.60 1.41 3.40 3.22 2.36 3.46 67.09
19 Manufacture of Electrical Machinery and
26.36 14.70 0.95 0.85 2.88 3.00 1.61 2.37 97.02
Equipment
20 Manufacture of Communication Equipment,
computers and Other Electronic Equipment 24.89 13.67 0.62 0.58 5.96 1.72 1.07 1.26 104.85
57
21 Manufacture of Measuring Instruments and
11.49 9.92 0.16 0.15 0.30 0.38 0.24 0.34 51.31
Machinery for Office Work
22 Handicrafts and other Manufacturing 7.60 4.65 0.24 0.24 0.61 0.79 0.44 0.62 23.67
23 Scrap and Waste 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
24 production and supply of Electricity and 21.06 0.25 1.89 1.57 3.83 5.57 3.09 4.87 1.20
25 Production and Supply of Gas 0.92 0.00 0.09 0.07 0.17 0.24 0.14 0.20 0.00
26 Production and Supply of Water 1.79 0.00 0.16 0.15 0.34 0.48 0.26 0.40 0.00
27 construction industry 1.86 1.15 0.07 0.07 0.14 0.17 0.10 0.16 6.70
28 Transportation and warehousing 17.26 1.58 1.39 1.34 3.00 4.04 2.26 3.65 8.52
29 Post service 1.36 0.28 0.09 0.09 0.21 0.29 0.15 0.24 0.65
30 IT industry 15.71 3.82 1.15 0.99 2.61 2.88 1.70 2.58 7.44
31 wholesale and retailing 39.34 9.32 2.57 2.33 6.98 7.68 4.28 6.19 24.21
32 Hotels and Catering Services 14.39 3.64 1.01 1.03 2.03 2.61 1.52 2.54 11.93
33 Finance 23.48 0.54 1.98 1.93 4.78 5.77 3.30 5.17 1.50
34 Real Estate 17.43 0.00 1.62 1.64 3.48 4.32 2.48 3.89 0.00
35 Leasing and commerce service 11.21 4.46 0.62 0.61 1.41 1.67 0.96 1.48 23.55
36 Research and test development industry 2.22 0.19 0.18 0.16 0.54 0.49 0.29 0.37 0.43
37 Polytechnic Services 7.87 0.00 0.70 0.62 1.57 2.06 1.14 1.78 0.00
38 Water, environment and public facilities 1.75 0.00 0.15 0.16 0.29 0.45 0.24 0.45 0.00
39 Resident and Other Services 5.91 1.13 0.47 0.49 0.86 1.14 0.67 1.16 4.75
40 education 1.50 0.20 0.12 0.12 0.25 0.32 0.18 0.30 0.55
41 Health and Social service 1.98 0.23 0.16 0.14 0.34 0.47 0.26 0.38 0.76
42 Culture , Sports and entertainment 5.16 2.21 0.28 0.27 0.58 0.74 0.43 0.65 5.49
58
Table A4.6: Direct, indirect, and VA Exports by ownership-sector (SMO)
Indirect VAEX
Exports Direct Gross
(VAEX) VAEX via LSOE SSOE LFIE SFIE L SM Exports
2 Mining and Washing of Coal 34.89 2.59 3.28 2.23 5.54 8.45 4.56 8.23 4.63
3 Extraction of Petroleum and Natural Gas 15.13 0.11 1.63 1.13 2.48 3.92 2.09 3.77 0.27
4 Mining of Ferrous Metal Ores 40.18 2.14 5.91 1.58 5.40 7.31 6.89 10.96 3.64
5 Mining of Non-Ferrous Metal Ores 10.87 2.37 0.67 0.54 1.47 2.30 1.13 2.40 4.85
6 Foods and Tobacco 51.12 16.69 1.98 1.99 4.23 8.30 3.49 14.45 70.79
7 Manufacture of Textile Products 170.80 65.55 1.69 2.01 4.94 34.95 6.64 55.02 323.13
8 Wearing apparel, leather, fur, down and 51.12 40.18 0.57 0.62 1.50 4.48 1.18 2.59 171.03
9 Processing of wood and Manufacture of
60.77 37.06 0.69 1.00 2.30 3.95 1.78 13.99 123.14
Furniture
10 Paper Products and Articles for Culture,
79.14 39.98 2.13 2.24 5.14 8.99 3.38 17.27 135.47
Education and Sports Activities
11 Processing of Petroleum, Coking and 43.97 4.60 3.74 3.55 6.32 9.71 5.14 10.92 13.04
12 Manufacture of Chemical Products 268.70 58.57 8.37 7.66 38.29 69.08 21.69 65.03 236.15
13 Manufacture of non-ferrous metal products
59.95 28.00 2.39 1.56 4.95 4.79 3.03 15.23 69.62
14 Smelting and Rolling of metals 107.47 14.73 9.57 3.70 14.04 19.20 13.50 32.73 74.00
15 Manufacture of Metal Products 91.23 59.24 2.05 1.70 7.58 7.97 3.54 9.15 225.98
16 Manufacture of General Purpose and Special
123.74 63.20 4.41 2.98 8.76 14.37 6.61 23.42 213.20
Purpose Machinery
17 Manufacture of Transport Equipment 29.84 11.07 1.94 1.59 4.35 3.82 2.80 4.27 48.29
18 Manufacture of Electrical Machinery and
54.72 36.33 1.42 1.20 4.48 4.89 2.39 4.02 175.08
Equipment
19 Manufacture of Communication Equipment,
computers and Other Electronic Equipment 17.51 7.12 0.53 0.48 6.02 1.43 0.89 1.05 64.49
20 Manufacture of Measuring Instruments and
15.12 13.67 0.15 0.13 0.28 0.36 0.21 0.33 69.83
Machinery for Office Work
21 Handicrafts and other Manufacturing 21.73 16.45 0.43 0.41 1.11 1.43 0.76 1.15 45.25
59
22 Scrap and Waste 124.99 2.59 13.69 6.26 20.03 30.13 18.10 34.19 3.20
23 production and supply of Electricity and heat 26.78 0.31 2.47 1.81 4.83 7.12 3.70 6.53 1.27
24 Production and Supply of Gas 0.79 0.00 0.07 0.06 0.15 0.21 0.11 0.19 0.00
25 Production and Supply of Water 2.36 0.00 0.21 0.18 0.45 0.64 0.32 0.56 0.00
26 Construction industry 3.55 2.36 0.12 0.12 0.22 0.29 0.16 0.28 7.20
27 Transportation and warehousing 246.41 146.61 8.62 8.41 17.35 23.88 12.39 29.14 225.34
28 Post service 2.27 0.53 0.15 0.14 0.33 0.48 0.24 0.40 0.85
29 IT industry 19.79 5.28 1.41 1.15 3.20 3.52 1.97 3.25 7.83
30 wholesale and retailing 320.81 242.99 6.03 5.03 21.04 19.18 9.74 16.80 294.04
31 Hotels and Catering Services 31.61 15.43 1.51 1.48 3.00 3.91 2.13 4.15 33.39
32 Finance 136.24 2.03 10.94 10.97 32.67 30.60 16.21 32.82 2.31
33 Real Estate 16.49 0.00 1.50 1.47 3.27 4.16 2.21 3.88 0.00
34 Leasing and commerce service 115.73 96.02 1.77 1.67 4.12 4.93 2.65 4.57 166.93
35 Research and test development industry 2.52 0.23 0.20 0.16 0.63 0.56 0.32 0.42 0.46
36 Polytechnic Services 8.24 0.00 0.73 0.59 1.64 2.19 1.13 1.96 0.00
37 Water, environment and public facilities 1.77 0.00 0.15 0.15 0.29 0.47 0.23 0.49 0.00
38 Resident and Other Services 18.74 4.36 1.41 1.42 2.53 3.43 1.90 3.70 5.65
39 education 3.02 0.36 0.25 0.24 0.50 0.66 0.36 0.65 0.43
40 Health and Social service 2.42 0.29 0.19 0.16 0.42 0.58 0.30 0.48 0.72
60
Table A5: Original data from NBS Firm Census (2008) and customs (2007) used to split the IO table
Unit: % Output Value Added Exports
LSOE SSOE LFIE SFIE LGO SMO LSOE SSOE LFIE SFIE LGE SME LSOE SSOE LFIE SFIE LGE SME
2 Mining and Washing of Coal 47 5 2 0 9 36 52 5 2 0 10 30 67 0 3 0 21 8
3 Extraction of Petroleum and
53 12 0 2 30 2 53 11 0 1 33 1 15 63 0 11 12 0
Natural Gas
4 Mining of Ferrous Metal Ores 10 6 2 3 7 72 13 8 3 3 9 64 0 0 24 0 0 76
5 Mining of Non-Ferrous Metal Ores
3 7 1 6 4 78 5 9 1 6 6 73 5 8 1 30 1 56
6 Foods and Tobacco 11 3 9 18 11 48 27 3 9 15 11 35 2 1 8 43 11 34
7 Manufacture of Textile Products 1 1 4 19 18 57 1 1 4 19 17 57 1 1 10 38 15 35
8 Wearing apparel, leather, fur, down
0 1 10 35 11 43 0 1 11 36 10 41 0 0 14 51 8 27
and related products
9 Processing of wood and
0 1 5 20 6 68 0 1 5 20 7 67 0 1 13 45 8 33
Manufacture of Furniture
10 Paper Products and Articles for
Culture, Education and Sports 3 3 11 26 8 50 3 3 10 28 8 48 0 1 24 53 3 19
Activities
11 Processing of Petroleum, Coking
34 4 5 4 34 19 28 5 5 6 32 24 63 0 7 8 19 2
and Nuclear Fuel
12 Manufacture of Chemical Products
7 3 8 22 14 46 7 3 9 22 15 44 6 1 14 45 13 21
13 Manufacture of non-ferrous metal
1 4 3 14 9 69 1 4 4 14 9 67 1 1 9 43 12 34
products
14 Smelting and Rolling of metals 25 2 7 7 29 30 31 2 8 6 29 24 40 1 9 16 26 8
15 Manufacture of Metal Products 2 3 6 25 7 58 2 3 6 24 8 57 2 2 22 41 8 26
16 Manufacture of General Purpose
7 4 7 19 11 52 8 4 8 20 11 48 5 1 16 40 14 23
and Special Purpose Machinery
17 Manufacture of Transport
16 3 25 14 18 24 15 3 29 14 17 21 13 2 29 20 25 11
Equipment
18 Manufacture of Electrical
2 2 15 21 18 42 2 2 16 21 18 41 1 1 34 36 14 15
Machinery and Equipment
19 Manufacture of Communication
Equipment, computers and Other 2 1 63 19 8 8 3 1 51 22 11 12 1 0 76 16 6 2
Electronic Equipment
20 Manufacture of Measuring
Instruments and Machinery for 3 4 30 27 7 30 3 5 17 31 7 36 0 0 56 32 4 7
Office Work
21 Handicrafts and other
0 2 8 30 6 55 0 1 9 31 6 52 0 0 12 43 6 39
Manufacturing
61
22 Scrap and Waste 0 0 0 0 0 100 0 0 0 0 0 100 0 0 0 0 0 100
23 production and supply of Electricity
54 25 2 5 3 10 49 29 2 6 4 10 14 82 0 0 1 2
and heat
24 Production and Supply of Gas 10 15 7 30 5 32 11 14 10 31 4 30 29 9 0 62 0 0
25 Production and Supply of Water 17 45 1 13 1 23 13 45 1 20 0 21 15 1 0 81 0 3
26 construction industry 18 13 0 1 15 53 6 10 0 1 6 77
27 Transportation and warehousing
14 30 6 0 2 47 18 24 5 0 0 52
28 Post service 48 39 6 1 0 5 40 43 6 2 0 9
29 IT industry 5 18 18 21 10 27 7 21 21 23 8 20
30 wholesale and retailing 13 12 4 3 10 58 14 10 5 3 9 59
31 Hotels and Catering Services 3 12 9 5 6 66 5 21 9 8 6 50
32 Finance 13 17 0 3 1 66 7 26 0 4 1 63
33 Real Estate 5 5 9 5 32 44 5 7 11 5 32 40
34 Leasing and commerce service 10 21 6 9 6 49 16 21 2 7 5 51
35 Research and test development
22 28 5 7 11 27 29 15 9 8 8 32
industry
36 Polytechnic Services 27 13 3 4 16 36 26 14 3 4 13 40
37 Water, environment and public
10 26 2 5 7 51 10 23 3 5 10 50
facilities
38 Resident and Other Services 3 6 2 4 8 77 2 5 1 3 8 80
39 Education 1 6 1 2 18 72 2 6 1 2 19 71
40 Health and Social service 15 11 1 1 11 61 12 12 1 1 10 64
41 Culture , Sports and entertainment
29 34 2 3 4 29 24 31 3 3 4 34
42 Public administration and social
0 12 0 0 2 86 0 11 0 0 2 87
organization
Total 14 8 9 10 14 46 15 9 9 11 13 44 5 1 39 30 11 15
Data Source:
(1) 2008 China's NBS Firm Census. Data for Sector 27 (Transportation and warehousing) is inferred from information from 2008 NBS Economic Census and the
railway sector in the 2007 135-sector I/O table. (2) Import data are from 2007 customs. (3) Total is the sum of all data for manufacturing, mining and services
(agriculture is excluded).
62
Table A6: Original data from NBS Firm Census (2008) and customs (2007) used to split the IO table (cont)
Unit: % Employment Imported Materials
LSOE SSOE LFIE SFIE LGE SME SOE FIE Others
2 Mining and Washing of Coal 53 8 1 0 7 32 33 5 61
3 Extraction of Petroleum and Natural Gas 66 2 0 0 29 3 37 0 63
4 Mining of Ferrous Metal Ores 13 6 1 2 7 70 65 2 32
5 Mining of Non-Ferrous Metal Ores 4 15 1 4 4 71 19 47 34
6 Foods and Tobacco 4 4 8 16 11 57 18 30 52
7 Manufacture of Textile Products 1 3 4 22 13 58 9 51 40
8 Wearing apparel, leather, fur, down and related products 0 1 10 44 5 39 7 55 38
9 Processing of wood and Manufacture of Furniture 0 2 5 24 5 65 13 36 51
10 Paper Products and Articles for Culture, Education and Sports
1 4 8 34 4 49 17 33 49
Activities
11 Processing of Petroleum, Coking and Nuclear Fuel 25 5 6 4 27 33 58 7 35
12 Manufacture of Chemical Products 6 4 6 21 11 52 15 40 45
13 Manufacture of non-ferrous metal products 1 6 3 12 7 71 10 51 39
14 Smelting and Rolling of metals 27 2 5 5 27 33 31 38 31
15 Manufacture of Metal Products 1 2 5 25 6 61 16 56 28
16 Manufacture of General Purpose and Special Purpose Machinery
5 6 5 18 7 59 24 42 35
17 Manufacture of Transport Equipment 10 5 11 16 15 42 16 18 66
18 Manufacture of Electrical Machinery and Equipment 1 2 15 27 11 44 13 57 30
19 Manufacture of Communication Equipment, computers and Other
2 2 45 31 6 15 7 68 25
Electronic Equipment
20 Manufacture of Measuring Instruments and Machinery for Office
3 5 17 34 4 37 8 68 25
Work
21 Handicrafts and other Manufacturing 0 1 4 43 4 48 7 53 40
22 Scrap and Waste 0 0 0 0 0 100 14 23 64
23 production and supply of Electricity and heat 40 36 2 3 5 15 97 0 3
24 Production and Supply of Gas 16 27 5 19 7 26
25 Production and Supply of Water 12 66 0 5 0 16
26 Construction industry 7 12 0 1 6 74
27 Transportation and warehousing 1 31 2 0 0 66
28 Post service 39 48 4 1 0 8
63
29 IT industry 3 16 11 16 5 48
30 wholesale and retailing 5 7 3 2 7 76
31 Hotels and Catering Services 1 13 6 5 3 72
32 Finance 8 9 0 2 3 79
33 Real Estate 3 10 2 5 16 64
34 Leasing and commerce service 3 19 1 5 2 71
35 Research and test development industry 19 14 6 8 6 47
36 Polytechnic Services 15 14 2 3 10 56
37 Water, environment and public facilities 13 19 1 2 12 53
38 Resident and Other Services 2 4 2 3 12 77
39 Education 1 5 0 1 11 81
40 Health and Social service 10 11 1 1 7 70
41 Culture , Sports and entertainment 18 17 3 4 5 53 7 86 7
42 Public administration and social organization 1 4 0 0 7 87
Total 7 9 3 8 8 65 19 44 36
Data Source:
(1) 2008 China's NBS Firm Census. Data for Sector 27 (Transportation and warehousing) is inferred from information from 2008 NBS Economic Census
and the railway sector in the 2007 135-sector I/O table. (2) Import data are from 2007 customs. (3) Total is the sum of all data for manufacturing, mining
and services (agriculture is excluded).
64