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An Anatomy of China’s Trade Growth∗
Mary AmitiFederal Reserve Bank of New York and CEPR
Caroline FreundInternational Monetary Fund
Preliminary and Incomplete
Abstract
China’s exports grew 450 percent between 1992 and 2006. This paper decomposesChina’s export growth into the extensive margin (new varieties of exports) and theintensive margin (more exports of the same varieties). At the HS 6-digit level, we findthat nearly all of China’s export growth was in the intensive margin. Using highlydisaggregated U.S. trade data at the HS 10-digit level, we find that 5-15 percent ofexport growth was in new varieties; almost all of China’s export growth to the U.S.was in the intensive margin. Within these margins, for both exports to the U.S. and tothe world, we find that a small share of goods accounts for most of the trade growth.We also find that the skill content of China’s exports has increased.
Key Words:JEL Classifications:
∗Amiti, International Research, Federal Reserve Bank of New York, email: [email protected]; Fre-und, Research Department, Trade and Investment Division, International Monetary Fund, 700 19th Street,Washington DC 20431, Ph 1-202-623-7767, Fax 1-202-589-7767 , email [email protected]. We would like tothank Jin Hongman of Customs, China for providing us with the data. Prepared for NBER conference. Theviews expressed in this Working Paper are those of the authors and do not necessarily represent those of theIMF or those of the Federal Reserve Bank of New York or the Federal Reserve System.
1. Introduction
China’s exports have increased by a factor 5 over the last 15 years. Was this growth ac-
complished by expanding trade of existing goods or by developing new export varieties?
Traditional theory highlights the expansion of existing products (the intensive margin) as
the only source of export growth. New trade theory gives a dominant role to an expansion
of the number of export varieties (the extensive margin), providing an additional channel
for welfare gains from trade. China’s dramatic export rise offers a unique opportunity to
evaluate these predictions.
This paper decomposes China’s export growth from 1992 to 2005 along various dimen-
sions. Using highly disaggregated trade data, we find that increased exports of existing
goods account for at least 85 percent of trade growth. We begin with HS 6-digit data on
exports from China to the world and find that the extensive margin is responsible for less
that one percent of trade growth. However, at the 6-digit level, many new export products
may go unobserved because the level of aggregation is not fine enough. To ensure that our
results are not driven by aggregation, we also use U.S. 10-digit import data. These data will
show a larger extensive margin both because the level of disaggregation is finer and because
bilateral data define all products that are new in the U.S. market as new exports. Despite
a forty percent increase in the number of varieties imported from China since 1992, we find
that the extensive margin accounts at most 15 percent of China’s export growth to the U.S.
In sum, most of China’s export growth between 1992 and 2005 occurred in products that
were already traded in 1992.
Still, there is evidence of a restructuring of China’s exports. Ranking (6-digit) exports
from smallest to largest in 1992, the export share of the top 10 percent of products declined
substantially, with an offsetting export share increase in the next 40 percent of products.
The change in the export share of the bottom 50 percent of products was negligible. We
also show that China’s export rise was associated with an increase in specialization. The
2
top 100 exports in 2005 account for 50 percent of trade, as compared with just 45 percent
of exports for the top 100 in the early 1990s. Finally, we find that China’s exports have
become more skill intensive. High-skilled products increased from just over 10 percent of
exports in the early 1990s to one-third of exports today.
Our results are supportive of traditional theory, which places endowments, productivity,
and terms of trade effects at the center of trade growth. This has important welfare impli-
cations: it limits the potential gains to importers from China’s expansion, while expanding
the potential losses to China’s competitors.
The importance of the intensive margin stands in sharp contrast to other recent empir-
ical papers. In seminal work, Feenstra (1994) provides insights into how to measure the
intensive and extensive margin. Using the Feenstra index, Hummels and Klenow (2005)
find that variation in the size of exports across countries are largely due to variation in the
extensive margin, suggesting that countries expand along the extensive margin as exports
grow. However, their results are not directly comparable with ours since the forces that
generate product distribution across countries may be quite different from the forces at work
within countries.
Also using the Feenstra index, Broda and Weinstein (2006) examine U.S. import growth
from 1972-2001. They find that 30 percent of growth was the result of an expansion in the
extensive margin, and that China was the largest contributor to growth in U.S. varieties.
While their results are suggestive of a larger role for the extensive margin, they also find that
the bulk of growth in new varieties was in the period before 1988. Overall, the extensive
margin was responsible for just 5 percent of U.S. import growth from 1988-2001.
In related work, Kehoe and Ruhl (2002) explore the responses of the intensive and ex-
tensive margin to trade liberalization. They find that trade liberalization leads to a large
increase in the share of trade accounted for by goods that were little traded before liber-
alization, and interpret this as evidence that the extensive margin has increased. We also
analyze how trade shares adjust; however, we argue that it is not clear that this can be
3
interpreted as growth of the extensive margin because the bottom 10 percent of goods cover
more than half of the product categories that are exported in both periods.
The rest of the paper is organized as follows. Section 2 sets out the methodology; and
section 3 describes the data. The results are presented in section 4, and section 5 concludes.
2. Methodology
This section describes the methodology employed to calculate the intensive and extensive
margin.
Let Vti be the value of trade at time t in product i (Vti = ptiqti ).
IEt0 is an indicator variable that is one if the product was exported in both period t and
period 0 (existing products).
IDt0 is an indicator variable that is one if the product was exported in period 0 and not
in period t (disappearing products).
INt0 is an indicator variable that is one if the product was exported in period t but not in
period 0.
Pi Vti −
Pi V0iP
i V0i=
PVti(I
Et0)−
PV0i(I
Et0)P
V0i−P
V0i(IDt0)P
V0i+
PVti(I
Nt0 )P
V0i(2.1)
This is an identity where total growth in trade relative to the base period is decomposed
into three parts: (i) the growth in products that were exported in both periods, the intensive
margin; (ii) the reduction in export growth due to products no longer exported, disappearing
goods; and (iii) the increase in export growth due to the export of new products, the extensive
margin. The disappearing category can either be counted separately or incorporated into
the intensive margin, as they are part of old categories.
4
3. Data
We use bilateral trade data at HS 8-digit level in current U.S. dollars from 1992-2006, source:
China Customs, Beijing. We aggregate the data into a panel at the 6-digit level, as there
are reclassifications of 8-digit codes every year. After adjusting the data by the U.S. CPI to
generate a constant dollar series, we create a 7-period panel of 2-year averages in order to
reduce idiosyncrasies that may occur in any given year.
There was a change in HS classifications in 1996 and 2002. We use the appropriate
concordances to convert all data to HS-1992 6-digit data. We also use mirror import data
from the world (at HS 6-digit 1992 codes) and from the United States (HS 8-digit and HS
10-digit) to check the robustness of the results.
4. Results
Overall, China’s real export growth was just over 450 percent from the first period (1992-
1993) to the last period (2004-2005). In this section, first, we decompose the growth into
the intensive and extensive margin. Second, we examine the extent to which specialization
was important in trade growth. Third, we explore whether growth was broad based or fueled
by a few products. Finally, we examine how the skill intensity of exports has changed.
4.1. Intensive vs. Extensive Margin
We calculate the extensive and intensive margin, using equation 2.1. To calculate the share
of trade growth attributed to each margin, we divide each component by the total growth in
trade. Table 1 records the intensive and extensive share of trade growth. From 1992-2006,
the extensive margin accounts for less than one percent of total trade growth, using 6-digit
HS 1992 data from China or the mirror import data.
To examine whether there is more variety growth to a single market and more disaggre-
gated data we recalculate the margins with U.S. data. The extensive margin is more likely
to be present in bilateral data, as exports can expand by entering new markets with old
5
varieties. Additionally, part of the reason the extensive margin estimated above may be
small is because 6-digit data may not be detailed enough to observe exports of new varieties.
There is, however, an intrinsic problem using 8-digit or 10-digit data, as there are numerous
reclassifications over time. We follow Debaere and Mostashari (2006) and consider only cat-
egories that are permanently present in the U.S. trade data. This will reduce the problem
of product reclassifications, assuming that reclassifications do not happen among incumbent
product codes. The permanent categories at the HS10-digit account for 65 percent of the
total HS codes and 47 percent of total exports between 1992 and 2005; for the HS 8-digit
codes, the permanent categories at the HS 8-digit account for 75 percent of the total HS
codes and 63 percent of total exports. The last four rows of Table 1 report the extensive
margin using U.S. data. Using HS 6-digit data, we still observe a small extensive margin,
with less than 2 percent of trade growth attributable to new products. The next two rows
show the results using the permanent codes at the HS 8-digit and HS 10-digit level. As would
be expected, the extensive margin is larger when we use more disaggregated data; however,
it is still quite small, with the intensive margin accounting for more than 95 percent of trade
growth.
An important concern is that concorded data will understate the extensive margin if new
exports are also primarily in new classifications. The last row of Table 1 reports growth in ex-
tensive margin using a traditional Feenstra index of variety on unconcorded HS 10-digit data
from the United States. Specifically, we calculate the expenditure share in the last period
on goods available in both the first and last period relative to the expenditure share in the
first period on goods available in both periods (Feenstra index of variety = Vti(IEt0)/ Vti
V0i(IEt0)/ V0i).
The index will be equal to one if there is no growth in varieties relative to the base period
and less (greater) than one if the number of varieties has grown (declined). Variety growth
is 1-Feenstra index. This measure has the nice feature that if classifications are simply split,
and their share of total trade remains unchanged, the index remains unchanged. However,
if growth classifications tend to be split to a greater extent than shrinking classifications are
6
merged, the index will tend to overstate the extensive margin. Using this index we find an
extensive margin of 15 percent. We think of this as an upperbound on the extensive margin.
Our estimates of the extensive margin in trade are considerably smaller than has been
found in previous literature. Exports of new varieties (at the HS-10 digit) from China to the
U.S. accounted for 40 percent of the total number of categories in 2004/2005, however these
new varieties account for at most 15 percent of the total value of China’s exports to the U.S.
4.2. Diversification versus Specialization
Next, we examine whether exports became more or less specialized over the period. Figure
1a shows inverse cumulative trade shares for all products. We graph it as an inverse function,
with products ranked from largest to smallest, in order to focus on the top 1000 and 500
products. Figure 1b and 1c magnify the image, showing the cumulative trade shares when
we keep only the top 1000 and 500 categories, respectively. The figures show that overall
trade diversification is roughly unchanged; however, if we look at the top 500 products,
which account for nearly 80 percent of trade in each period, concentration has increased.
To examine specialization in more detail, we estimate a Gini coefficient of export equality
in each period. It is defined as
Gini = 1− 1n
Xi
(csharei−1 + csharei),
where there are n products, i is a product’s rank (1 is smallest and n is largest), and csharei
is the cumulative share of exports of the ith product. The Gini coefficient uses the trapezoid
approximation to calculate the area between a 45 degree line and the cumulative distribution,
weighting each industry as an equal share of the population of industries (1/n). A Gini
coefficient of one reflects complete inequality and a Gini coefficient of zero reflects complete
equality.
Table 2 reports the Gini coefficient for the first and last period for various different
samples. When we include all products, the Gini coefficient remained constant over the
period at 0.85. Ranking the goods and including only the largest 70 percent, the Gini
7
coefficient increased from 0.46 to 0.55. Alternatively, examining the top 100 products, which
account for nearly 50 percent of trade in the second period and 45 percent in the first period,
the Gini coefficient increased from 0.35 to 0.50. Thus, over the period we see enhanced
specialization, especially for the products that accounted for the largest export shares.
An alternative way to examine specialization is to graph ln trade versus ln rank (the
highest trade share receiving a rank of 1), a kind of Zipf’s law for trade. We use the top 70
percent of trade in each period to eliminate the very small values of highly ranked products.
Figure 2 shows the plot for the first, fourth, and final periods. As shown in the figure, the
plot gets somewhat steeper over time (statistical testing confirms this), implying that moving
to a lower rank is associated with a greater percentage increase in trade in the final period
than in the first period.
4.3. Stable or Spotty growth
We now know that growth was mainly of goods that were traded in the first period (tiny
extensive margin). We also know that growth led to a distribution of exports that was
more skewed at the top. This picture could be generated by different growth patterns
across products. To understand the growth process we examine the distribution of growth in
more detail. Figure 3 shows the cumulative share of trade growth by the cumulative share
of products, when growth is ranked from smallest to largest. Just 20 percent of product
categories (or 478 products) make up more than 90 percent of trade growth. The top 5
percent of products account for nearly 30 percent of trade growth. Thus, we know that
growth was skewed, with a small share of products accounting for most of the growth.
Next, we determine where these products lie in the initial distribution. Figure 4a shows
a plot of rank in period 1 versus rank in the final period to see whether there is a lot of
churning. It shows that rank is highly correlated, but also that there are numerous outliers.
Figure 4b shows the cumulative export share in the last period when products are ranked
by the cumulative export share in the first period. This picture shows that the bottom part
8
of the distribution does account for a sizeable share of trade growth.
Figure 5 examines the reallocation of trade from 1992-3 to 2004-5 in more detail. Figure
5a divides trade into deciles according to the value of trade in 1992-3. We then examine what
share of trade in 2004-5 is accounted for by the varieties falling into each of these deciles, as in
Kehoe and Ruhl (2002). The figure shows that the categories that accounted for the bottom
two deciles have increased their share of trade, while the categories in the other deciles have
contracted. Figure 5b divides trade into deciles according to the categories of trade in 1992-
3. For example, the tenth decile is the top ten percent of product categories when products
are ranked by value. It shows that the reallocation was largely in the top 50 percent of
categories; products in the top decile shed trade while the next four deciles expanded. How
are these two pictures consistent? The answer is that trade shares are highly skewed and
the bottom 20 percent of trade by value accounts for 88 percent of product categories. In
sum, trade value of the mid-upper rank products expanded the most. These are products
that were in the bottom 20 percent by value and in the mid-range by rank.
4.4. Skill Intensity of Growth Products
Finally, we examine whether the skill content of China’s export growth has increased over
the sample period. Assuming no factor intensity reversals, we use the U.S. data on the
ratio of production workers to total employment from the NBER SIC database for 1992 to
rank industries from low to high skill on the horizontal axis, and then plot the cumulative
export share on the vertical axis, following Chun and Trefler (2005). Figure 5 show that
in 1992/1993, 80 percent of China’s exports were accounted for by 40 percent of the least
skill-intensive industries. The shift of the curve to the right indicates that the skill content
of China’s exports has increased over time. For example, in the first period, 50 percent of
the least skill-intensive industries produced 88 percent of China’s export share. By the last
period, the export share that these industries produced fell to 66 percent.
9
5. Conclusions
China has experienced rapid export growth over the last 15 years. We find that this growth
was largely driven by expanding trade in existing goods. Despite the large growth in number
of varieties, these only account for a small share of China’s trade growth. Even using the
most disaggregated trade data (HS-10 digit) available for the United States, we find that the
new varieties only accounted for at most 15 percent of total export growth over a 15 year
period.
We also find evidence of export specialization and an increase in the skill intensity of
exports.
References
[1] Broda, C. and D.Weinstein (2006). “Globalization and the Gains fromVariety”Quarterly
Journal of Economics, 541-585.
[2] Chun Zhu, S. and D. Trefler (2005). “Trade and Inequality in Developing Countries: A
General Equilibrium Analysis", Journal of International Economics 65, 21-48.
[3] Debaere, P. and S. Mostashari (2006?) “Do Tariffs Matter for the Extensive Margin of
International Trade? An Empirical Analysis” Mimeo University of Texas, Austin.
[4] Feenstra, R. and H.L. Kee (2006) “Trade Liberalization and Export Variety: A Compar-
ison of Mexico and China”. Mimeo World Bank.
[5] Hillberry, R. and C. McDaniel (2002) “A Decomposition of North American Trade
Growth Since NAFTA”, ITC Working Paper 2002-12-A.
[6] Hummels, D. and P. Klenow (2005). “The Variety and Quality of a Nation’s Exports”
American Economic Review, 95, 704-723.
10
[7] Kehoe, T. and K. Ruhl (2002) “How Important is the New Goods Margin in International
Trade?” Federal Reserve Bank of Minnesota.
11
12
Table 1: Extensive and Intensive Margin of China's Exports, 1992 to 2005 Extensive Intensive Data
0.2 99.8 China, 6-digit HS 92 0.1 99.9 Mirror, 6-digit HS 92 1.1 98.9 U.S., 6-digit HS 92 2.2 97.8 U.S. permanent, 8-digit HS
4.9 15.0
95.1 85.0
U.S. permanent, 10-digit HS U.S. permanent, all codes (Feenstra Index)
Table 2: Gini Coefficient for China's Exports Period All Top 70% Top 100
1992-1993 0.85 0.46 0.35 2004-2005 0.86 0.55 0.50
13
Figure 1: China’s Export Specialization 1a. All Products
020
4060
8010
0
Cul
ulat
ive
Sha
re o
f Tra
de
0 1000 2000 3000 4000 5000
Rank of Product
Cumulative share of trade, 1992-1993Cumulative share of trade, 2004-2005
Note: Data uses HS 6-digit classifications. Rank is largest to smallest by value.
1b. Top 1000 Products
020
4060
8010
0
Cul
ulat
ive
Sha
re o
f Tra
de
0 200 400 600 800 1000
Rank of Product
Cumulative share of trade, 1992-1993Cumulative share of trade, 2004-2005
Note: Data uses HS 6-digit classifications. Rank is largest to smallest by value.
1c. Top 500 products
2040
6080
100
Cul
ulat
ive
Sha
re o
f Tra
de
0 100 200 300 400 500
Rank of Product
Cumulative share of trade, 1992-1993Cumulative share of trade, 2004-2005
Note: Data uses HS 6-digit classifications. Rank is largest to smallest by value.
14
Figure 2: Zipf’s Law for Trade
1820
2224
ln(tr
ade
valu
e)
0 2 4 6ln(rank)
ln(trade value), 1992-1993ln(trade value), 1998-1999 ln(trade value), 2004-2005
Note: Data uses HS 6-digit classifications.
15
Figure 3: The Distribution of Trade Growth Across Products
0.1
.2.3
.4.5
.6.7
.8.9
1C
umul
ativ
e sh
are
of g
row
th
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Share of product categories
Note: Data uses HS 6-digit classifications.
16
Figure 4: Trade Growth and the Reallocation of Trade
4a. Rank Correlation of Trade, 1992-93 vs. 2004-05
010
0020
0030
0040
0050
00
Ran
k of
pro
duct
, 200
4-20
05
0 1000 2000 3000 4000 5000
Rank of product, 1992-1993Note: Data uses HS 6-digit classifications. Rank is smallest to largest by value.
Figure 4b. Adjustment in Export Shares Over Time
0.1
.2.3
.4.5
.6.7
.8.9
1C
umul
ativ
e E
xpor
t Sha
res
2004
-200
5
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Cumulative Export Shares 1992-1993
Export Share
Note: Data uses HS 6-digit classifications.
17
Figure 5: Trade Shares and the Reallocation of Trade
Figure 5a. By Share of Value
0.0
5.1
.15
.2.2
5S
hare
of t
otal
val
ue in
200
4-20
05
1 2 3 4 5 6 7 8 9 10
Note: Data uses HS 6-digit classifications.Decile by value, 1992-1993
Figure 5b. By Share of Product Categories
0.2
.4.6
.8S
hare
of t
otal
val
ue
1 2 3 4 5 6 7 8 9 10
Note: Data uses HS 6-digit classifications.
Decile by number of products, 1992-1993
Share of total value, 1992-1993
Share of total value, 2004-2005
18
Figure 6. The Skill Intensity of China’s Exports
020
4060
8010
0C
umul
ativ
e ex
port
shar
e
0 .2 .4 .6 .8 1Skill intensity
Cumulative export share, 1992-1993Cumulative export share, 2004-2005
Note: Data uses HS 6-digit classifications.