Extensive and Intensive Margins of India’s Manufactured Exports: Comparison with
China1
C. Veeramani2, Lakshmi. A3 and Prachi Gupta4
Abstract
Should India‘s export promotion policies target growth along the extensive margin (new
export relationships) or along the intensive margin (increase in exports of existing
relationships)? To help answer this question, we undertake a comparative study of
manufactured exports from India and China by analysing the role of extensive and intensive
margins in export market penetration of both countries during 2000-2012. We find that along
the extensive margin, the gap between the two countries is getting narrower, as India is
clearly catching up with China. However, along the intensive margin, China surpasses India
significantly. Further, we analyse the factors responsible for the differential export
performance between India and China, particularly, the role of exchange rate and
development status of partner countries, using a gravity model of trade. Our regression results
show that China‘s export growth along the intensive margin is mainly on account of markets
in developed countries. We also find that the impact of exchange rate is higher on China‘s
exports as compared to that of India. Also, for both countries, exchange rate generates more
response at the intensive margin, than at the extensive margin. In sum, the differential
performance between India and China, predominantly along the intensive margin is due to
exchange rate effects and China‘s export-orientation in rich countries. Our analysis suggests
that India can reap rich dividends by adopting policies aimed at accelerating export growth
along its intensive margin. Also, there exists great potential for India to expand and intensify
its export relationships with developed countries by focusing on labour-intensive products.
1
This is a revised version of IGIDR Working Paper No: 2014-011.
Associate Professor at Indira Gandhi Institute of Development Research (IGIDR), Mumbai, India. Email:
[email protected]
3
PhD Research Scholar at Indira Gandhi Institute of Development Research (IGIDR), Mumbai, India. Email:
[email protected]
4
PhD Research Scholar at Indira Gandhi Institute of Development Research (IGIDR), Mumbai, India. Email:
[email protected]
2
1
I. Introduction
Rapid economic growth attained by India and China in recent years reinstates the importance
of trade and exports in achieving strong growth prospects. Up until 1980, owing to a set of
inward oriented trade and investment policies, both India and China‘s share in world exports5
was marginal. Adoption of a comprehensive set of reform measures following market
principles led to a structural transformation of both economies. Along with progressive
dismantling of trade barriers and state controls, measures were undertaken to address the
problem of overvalued currency and eliminate the anti-export bias. Towards the end of
1970s, China initiated exchange rate reforms which were aimed at achieving a more realistic
exchange rate through successive devaluation of the renminbi (Zhang, 2000). By 1994, the
dual track exchange rate system was terminated and China adopted a unified market-based
managed floating regime. In India, as part of economic reforms, the rupee underwent a twostep downward adjustment by 9% and 11% in July 1991. India‘s exchange rate regime shifted
from a basket peg to a dual exchange rate system, which allowed exporters to sell 60% of
their foreign exchange earnings at the market rate and 40% to the government at the lower
official rate. In March 1993, the dual exchange rate system was replaced by a unified market
determined regime. Further, in 1994, India adopted full convertibility of rupee in current
account.
As a result of an export oriented trade regime, China‘s share in world merchandise exports6
increased from 1% in 1980 to 3.4% in 1999, thereby recording a growth rate of 14.8%.
Following its accession to the WTO in 2001, China‘s share further increased from 4.3% to
7.2% in 2005 and 11.1% in 2012. In response to the reform measures, between 1991 and
1999, India‘s exports grew at 9.9%7 per annum. The second decade of reforms saw India
catching up with China, with exports from both countries growing at around 20% per annum
during 2000-2012. In spite of attaining high growth rates8 in the post reform period, India‘s
share in world merchandise exports increased very little, from 0.6% in 1995 to 0.9% in 2005
and 1.6% in 2012. That is, even though India‘s export performance is comparable to that of
China9 in terms of growth rates, its competitiveness as measured by share in world exports is
very low despite significant trade reforms. In other words, though both countries adopted
5
In 1979, China‘s share in world merchandise exports was 0.8% and that of India was 0.5%. Author‘s
calculation based on WDI data.
6
Merchandise exports valued in current USD.
7
China‘s export growth rate from 1991 to 1999 was 14.1% per annum.
8
India‘s export growth rate during 1991-2012 was 14.8% per annum.
9
China‘s export growth rate during 1991-2012 was 18.3% per annum.
2
similar export promoting policies, the magnitude of its impact on China‘s trade and exports is
far greater than for India. Therefore, it remains to be seen as to what factors fuelled China‘s
exports, and why India‘s exports did not grow as much.
Also, throughout the post-reform period, India‘s trade deficit has been increasing. During
2002-03 to 2012-13, India‘s merchandise exports recorded a growth rate of 20% per annum,
while merchandise imports grew faster at the rate of 23%. The surpluses in services trade and
private transfers have helped to partially offset the growing trade deficit. In 2012-13, for
instance, the merchandise trade account showed a huge deficit of $195 billion, of which about
$107 billion was offset by invisibles earnings, thereby resulting in a current account deficit of
$88 billion, or 4.8% of GDP. In order to deal with the problem of unsustainably high current
account deficits, the government resorted to ad hoc policy measures such as restricting gold
imports.
The long term solution to the problem of unsustainable current account deficit lies in ensuring
that growth of exports keep pace with that of imports. The fundamental question is what type
of policy interventions would help achieve faster export growth. To answer this question, it is
important to distinguish between two varied sources of export growth, the extensive margin
and intensive margin. By extensive margin, we mean export growth attributable to new
products and/or new markets, while intensive margin refers to export growth characterised by
growth of existing products in existing markets. Growth along the intensive margin can arise
as a result of increase in price or quantity or both. Given the presence of these two channels
of export growth, we proceed to examine whether policy makers should target export growth
at the extensive margin (new export relationships) or at the intensive margin (existing export
relationships). In addition to the usual focus on aggregate trade flows, a proper understanding
of export growth along the two margins would better inform policies.
In order to decide whether export promotion policies be targeted at accelerating exports at the
intensive margin or at the extensive margin, it is important to know the relative role that the
two margins have played in India‘s export growth in the recent past. Also, to provide a
comparative perspective, it would be instructive to know the relative importance of the two
margins in China‘s export growth 10 . Hence, we decompose India‘s export performance
during 2000-2012 into changes at the intensive and extensive margins. Intensive margin is
10
Given the spectacular export performance in recent decades and the similarities with India in terms of size,
stage of development and relative resource endowments, China becomes a natural choice for comparison.
3
further decomposed into price and quantity margins 11. To put the analysis in perspective,
India‘s export performance along the margins is compared and contrasted with that of China
for the same period. The decomposition analyses are carried out for manufactured exports12
only, both at the aggregate as well as disaggregate levels.
Further, we proceed to analyse what factors explain the differential export performance of
India and China. It has often been argued that China‘s spectacular export success is largely
due to of its exchange rate policy. China has been accused of setting its currency at
artificially low levels, thereby making foreign currencies stronger and thus providing an
unfair advantage to Chinese exporters. Several studies (Goldstein and Lardy, 2009; Bivens
and Scott, 2006; Scott, 2012) have identified exchange rate as the main reason for China‘s
export growth. In this context, it would be interesting to see if exchange rate explains China‘s
superior export performance vis-à-vis India.
Another important feature of China‘s export pattern is its specialisation in labour-intensive
processes and products and higher trade-orientation with developed countries (Amiti and
Freund, 2010). In contrast, India‘s exports show an undue bias in favour of capital and skillintensive products and towards emerging markets in Asia and Africa (Veeramani and Saini,
2011; Veeramani, 2012). In this paper, we try to address this issue by analysing India and
China‘s export performance in rich country markets.
In view of existing studies, the paper seeks to address the following set of questions. Does
China export significantly more than India? If so, what are the determinants of differential
export performance? Is exchange rate a significant reason for China‘s export success? Is there
any significant difference in the effect of exchange rate across export margins? If so, on
which margin is the impact higher? Can the differential impact of exchange rate on the two
margins explain the gap between India and China‘s exports? Does China show higher tradeorientation with developed countries as compared to India? Does the development status of
partner countries explain the differential performance of India and China?
11
Separating the price and quantity components of intensive margin is important from a policy perspective. For,
if export growth comes from quantity expansion, it implies that the country must use increasing amounts of its
resources- capital, labour and natural resources, so as to sustain growth. Countries in their early stages of
development with large endowments of labour and resources may experience quantity rather than price driven
export growth. Export growth as a result of increasing price plays a critical role in sustaining export growth of
advanced countries. If export growth is mainly driven by price increase, and if price reflects product quality,
then the country must invest more in human capital and technological innovation in order to sustain growth.
12
See Veeramani and Gupta (2014) for the decomposition results of India and China in aggregate merchandise
exports and in disaggregate commodity categories such as mineral fuels (SITC 3) and other products (SITC 0 to
2 plus 4, 68 and 667) during 1995-2011.
4
Using a panel of India and China‘s manufactured exports to various partner countries during
2000-2012, we first examine the differential performance of India and China in aggregate and
along the margins by employing a gravity model. Then we analyse the effect of bilateral
exchange rates and per capita income of partner countries on India and China‘s exports.
Further, we evaluate whether the impact is significantly different across export margins.
The rest of the paper is structured as follows. Section II illustrates the theoretical foundation
for intensive and extensive margins. Section III details the method of decomposition of
exports into extensive, intensive, price and quantity margins and provides an overview of
India and China‘s export performance across these margins. Section IV provides the
regression methodology and variables used in the model. Results are discussed in Section V.
Section VI concludes.
II. Intensive and Extensive Margins- Theory and Evidence
First, we explore how trade theory has evolved over the years to incorporate the concept of
both intensive and extensive margins. Traditional trade theory based on perfect competition
considers industry as its unit of analysis, while firms within an industry are assumed to be
identical. This being the case, growth of exports refers to increase in volume of existing
exports alone; that is, there is only one margin- the intensive margin. In the new trade models
pioneered by Krugman, emphasis shifted to trade in differentiated products. However, the
concept of extensive margin still remained elusive, since these models retained the
assumption that all firms are identical and that the only form of transportation cost is a
variable cost. The concept of extensive margin was formalized with the development of newnew trade models. In a seminal work, Melitz (2003) provided an extension to Krugman‘s
(1980) model of trade under monopolistic competition and increasing returns, by
incorporating two dimensions of firm level heterogeneity- productivity differences and fixed
export costs. In this set up, when transportation costs vary, not only do firms change the
volume of their exports (the intensive margin), but the set of firms that exports varies as well
(the extensive margin).
Productivity differences and fixed entry costs imply the existence of a self-selection
mechanism whereby only those firms which are highly productive become exporters. Given
that the fixed costs of entering the export market are mostly sunk costs, only high productive
firms will be able to make profits over and above that which is necessary to cover these costs.
Melitz (2003) assumes that firms face homogenous fixed export costs, which implies that if a
5
firm exports, it exports to all market destinations. Chaney (2008) relaxes this assumption and
introduces destination-specific export costs. In both these models, a fall in trade barriers or
trade costs leads to adjustment along both intensive and extensive margins. With a fall in
trade costs, high productive firms expand their export sales and less productive firms now
find it profitable to enter export markets.
Although a large body of literature has examined the relative importance of extensive and
intensive margins in explaining export growth, empirically, there is no consensus as to which
margin dominates. While some studies (Evenett and Venables, 2002; Hummels and Klenow,
2005) highlight the importance of extensive margin, others (Felbermayr and Kohler, 2006;
Helpman et al., 2008; Eaton et al., 2008; Besedes and Prusa, 2011) note that intensive margin
is the dominant factor.
In the case of China, some studies have provided evidence for differential export performance
across the two margins. Dimaranan et al. (2007) and Schott (2008) identify increase in
product variety and market penetration as factors driving China‘s export growth. On the other
hand, Amiti and Freund (2010) show that intensive margin is the major factor contributing to
China‘s growth. They analyse exports between 1997 and 2005 and show that China‘s export
performance is a result of a specialisation pattern in line with its intrinsic comparative
advantage in labour-intensive industries.
III. Decomposition of Exports into Extensive and Intensive Margins
a. Decomposition Methodology
Based on Hummels and Klenow (2005) we decompose exports into extensive and intensive
margins. We suppose that a given country i (India and China, in our case) exports product p
to a set of partner countries j in year t. We also suppose that country i competes with the ‗rest
of the world‘ (r) in country j’s market.
First, we define export penetration of country i relative to r as Sit. It is the ratio of total
exports from i and r, or the share of country i in rest of the world‘s exports.
𝑋𝑖𝑡
𝑆𝑖𝑡 =
=
𝑋𝑟𝑡
𝑗
𝑗 ≠𝑖
𝑝
𝑝∈𝑁𝑖𝑗𝑡
𝑝
𝑝
𝑥𝑖𝑗𝑡
𝑝∈𝑁𝑟𝑗𝑡
𝑝
𝑥𝑟𝑗𝑡
where Xit = value of total exports from country i to all partner countries j in year t
6
Xrt = value of total exports from r to all partner countries j in year t
xijtp = value of exports from country i to partner j in product p and year t
xrjtp = value of exports from r to partner j in product p and year t
N ijtp = the set of partner-product pairs where country i records ‗export relationships‘ (i.e., the
set where xijtp  0 )
N rjtp = the set of partner-product pairs where r records ‗export relationships‘ (i.e., the set
where xrjtp  0 )
Sit can be expressed as the multiplicative product of intensive and extensive margins. The
intuition behind this decomposition is that Sit depends on (i) the relative number of export
relationships by i and r, and (ii) the relative value of exports within the common set of
partner-product pairs where both i and r record export relationships. For example, i’s export
share could be lower than r’s because the former records fewer number of export
relationships than r (that is, Npijt<Nprjt) and /or because the value of exports from i is lower
than that from r within the common set of partner-product pairs.
Intensive margin measures the value of country i’s exports relative to total exports from r
within the common set of partner-product pairs.
IM it 
X it

j i
p
xrjt
p
pNijt
The denominator of IMit measures total exports from r in those partner-product pairs in which
country i records positive exports. The value of IMit is always positive and can be above or
below unity.
The extensive margin is defined as the fraction of r’s exports in those partner-product pairs in
which country i records export relationships. The denominator of EMit represents total
exports from r while the numerator is the sum of r’s exports in those partner-product pairs in
which country i records export relationships13. The ratio lies between 0 and 1.

EM it 
13
p
j  i pNijt
p
xrjt
X rt
We assume that Npijt is a subset of Nprjt. This holds true in our data.
7
Now, we see that the numerator of extensive margin is equal to the denominator of intensive
margin. Therefore, multiplying the two ratios we get Sit. Higher extensive margin and lower
intensive margin imply that the country spreads its exports thinly over many products and
markets. That is to say, while the extensive margin captures the breadth of a country‘s export
profile, the intensive margin measures its depth.
Given the fact that intensive margin captures changes in value of exports due to changes in
quantity and/or price, we decompose intensive margin into price margin and quantity margin.
IMit = Pit × Qit
The price index measures the weighted ratio of i’s prices to r’s prices for each product p,
where the weights are the logarithmic mean of share of p in exports of i and r.
Pit  
j

pN ijtp
 uv

 uv

p
ijt
p
rjt




wijtp
where, uvpijt and uvprjt are unit values (proxy for prices) of product p exported by i and r
respectively to j and wijtp is the logarithmic mean of sijtp (share of product p in i’s exports to j)
and s rjtp (share of product p in r‘s exports to j, where p  N ijtp ):
p
ijt
s

p
xijt
,
s
p
rjt
 xijt
p
p
pN ijt

p
x rjt
 p
pN ijt
x
p
rjt
p
p

sijt  s rjt 

p
p
 ln
 ln s rjt 
s
ijt
p


w

p
p
ijt

sijt  s rjt 

p 
p
p 
p N ijt  ln s ijt  ln s rjt 
The quantity index is implicitly derived by dividing intensive margin by the price margin.
8
b. Data
We use HS 6-digit product level data on exports provided by UN-COMTRADE 14 . This
database provides extensive information on merchandise export value 15 and quantity, at
detailed commodity and country levels. Unit values (export value divided by quantity),
required to estimate price and quantity margins are also computed at the 6-digit level16.
We restrict our analysis to India and China‘s exports of manufacturing goods17 for the period
2000-201218. In our dataset, we find that India exports 4031 product varieties (i.e., at HS 6digit level) to 222 countries and China exports 4032 products to 214 countries. Exports by the
‗rest of the world‘ (r) is measured as the sum of exports reported by all countries (excluding
i). in a given year. It may be noted that the number of countries that report data to the UN
vary from year to year. For our estimates to be strictly comparable over time, it is important
that our reference group (rest of the world) comprises a homogenous set of countries that
have reported data consistently during the entire period. We note that 109 countries 19
(including India and China) have reported export data for all years from 2000 to 2012.
It must be noted that our calculation of the extensive margin is sensitive to the level of data
aggregation. In general, calculation based on relatively aggregate product categories would
underestimate the extensive margin by incorrectly relegating variety differences within
categories into intensive margin. In this respect, given the availability of comparable data, we
seek to do the best feasible job by using the most disaggregated (6-digit HS level) data for
India and China on a bilateral basis.
14
This database was accessed using WITS software.
Trade value is reported in 1000 USD at current prices.
16
A small number of 6-digit product lines, for which data on quantity are not available, have been excluded
from the analysis.
17
Manufacturing goods correspond to SITC codes 5 to 8 less 68 and 667. They include chemicals, manufactured
materials, machinery and transport equipment, and miscellaneous manufactured articles.
18
This time period was found appropriate for our analysis, given the fact that both India and China‘s exports
grew at the same rate of 20% per annum during this period.
19
This number is subject to data availability as on 19th March, 2014.
15
9
c. Extensive and Intensive Margins: Results for India and China
In order to bring out the contrast between India and China‘s performance in manufactured
exports, we decompose export penetration rates into extensive and intensive margins, and
further into price and quantity margins. Results are provided in Table1.
We see that India‘s export penetration rate increased from 0.6% in 2000 to 1.5% in 2012 at a
rate of 7.4% per annum. The decomposition results clearly indicate that a large part of this
growth is contributed by the intensive margin which grew at 6.3% per year. IMit increased
from 1% in 2000 to 2% in 2012. This implies that as of 2012, the value of India‘s
manufactured exports amounts to 2% that of rest of the world‘s within the common set of
products and partners. At the extensive margin, India‘s exports grew at a rate of 1.1% per
annum, increasing from 60% in 2000 to 74% in 2012. That is, in 2000, 60% of r’s exports
faced direct competition from India, and by 2012, it has increased to 74%.
We note that India‘s export penetration rate has increased at a stable rate except for a drop in
year 2010. This is entirely due to a sharp fall in EMit from 74% in 2009 to 44% in 2010.
Interestingly, the intensive margin does not show any such drastic decline during the entire
period. Further, decomposition of intensive margin into its price and quantity components
indicate that quantity margin grew at 7% per annum, while price margin recorded a negative
growth rate of 0.5% per year. The value of Pit is always observed to be below unity, except in
year 2010, which implies that Indian products are generally cheaper than those from rest of
the world.
The decomposition results for China bring out the contrast between the two countries in terms
of their export performance. We see that China‘s export penetration rate has increased from
5.4% in 2000 to 23% in 2012, thereby recording an annual average growth of 13%. This is
largely due to the intensive margin, which increased from 7.2% in 2000 to 26% in 2012, thus
maintaining a growth rate of 11.5% per year. High growth along the intensive margin is
10
primarily due to the quantity margin which grew at 16% per year 20. The price index marked a
negative growth rate of 4% per annum21. Along the extensive margin, China‘s manufactured
exports grew at a modest rate of 1.4% per year increasing from 75% in 2000 to 87% in 2012.
Based on these results, it is evident that China‘s exports have grown at a higher rate than
India‘s and this is true across both margins, although the contrast is particularly significant
along the intensive margin. In terms of levels of export penetration, China was at a higher
level even in 2000 and the gap between the two countries has only increased with time.
Similarly, China‘s IMit was 7.2% in 2000 compared to India‘s 1%, and as of 2012 the value
of China‘s intensive margin stands at 26%, while that of India is only 2%. Along the
extensive margin, the difference is less stark, and India and China‘s exports show comparable
growth rates. Comparing the quantity margins of both countries, we find that China has
outpaced India tremendously. In 2000, China‘s Qit was 14% while that of India was only 1%,
and as of 2012, the index increased to 75% for China whereas India‘s Qit still remains at a
low level of 3%. Earlier, we mentioned that the values of India‘s Pit remained below unity in
all years (except 2010). Though we observe the same pattern for China‘s Pit, it is striking to
note that the values of price margin for China are much lower than that for India22. That is to
say, though both Indian and Chinese products are priced at a cheaper rate vis-à-vis the ‗rest of
the world‘, Chinese exports are even cheaper than Indian products.
What factors might explain higher Pit values for India as compared to China? A detailed and
careful comparison of manufacturing industries in India and China by Van Ark et al. (2008)
show that the unit labour costs (nominal cost of labour required to produce one unit of output)
in China was slightly higher than in India for the year 2002 23 . Therefore, relative costs
20
This result is similar to Bingzhan (2011), who, using a different decomposition method shows that China‘s
export growth between 2000 and 2007 was mainly driven by quantity growth, which accounted for about 70%
of its overall export growth.
21
Amiti and Freund (2010) note that between 1997 and 2005, the average prices of goods exported from China
to the United States dropped at the rate of 1.6% per year.
22
Using finely disaggregated (10-digit level) U.S. import data, Veeramani and Saini (2011) show that in a large
majority of cases, the 10-digit level export unit values of India are significantly higher than that of China. This is
consistent with our finding that Pit values are higher for India than for China.
23
The comparison is available for two years, 1995 and 2002. China recorded a higher unit labour cost than India
both in 1995 and 2002, although the difference has declined significantly during 1995-2002. China recorded a
11
differences is not the key factor that explains the difference in values of Pit between India and
China. It is also unlikely that the Pit values reflect differences in quality of products being
exported from the two countries; if superior quality had been the reason for India‘s higher Pit,
it would have resulted in greater quantity penetration by India.
It is more likely that differences in Pit values between India and China closely reflect certain
fundamental differences in the nature of intra-product specialisation undertaken by the two
countries. It is now well recognised that countries engage in production and trade by
specialising in distinct varieties and processes within the same product. The product varieties
sourced from different countries could be differentiated on the basis of quality, factor content,
and other attributes. For example, Schott (2004) notes that the United States increasingly
sources the same product from both high and low-wage countries and that the unit values of
imported products vary widely even within finely detailed product categories24. In general,
capital and skill-abundant countries export varieties that command higher prices while the
varieties exported from labour-abundant countries receive lower prices. Further, a variety‘s
unit value increases with an increase in the capital intensity of the production technique used
to produce it.
A number of studies have noted a general bias in India‘s specialisation pattern in favour of
capital and skill intensive goods as against unskilled labour-intensive goods 25 . This is an
anomaly given the fact that India‘s true comparative advantage lies in unskilled labourintensive activities. Thus, if unit values of varieties are positively correlated with the capital
and skill intensities of production, then India‘s relatively higher Pit values reflect its intraproduct specialisation in capital and skill intensive varieties. A higher price that results from
―distorted‖ 26 specialisation, however fails to translate into higher volume of exports as is
noticeable decline in its unit labour cost as a result of significant growth in labour productivity during this
period. Despite its higher labour productivity, China‘s unit labour cost remained higher due to its relatively
higher level of labour compensation. Van Ark et al. (2008) also note that before the early 2000s, India recorded
higher levels of labour productivity than China reflecting the high capital intensity in India‘s manufacturing
production.
24
For example, Schott (2004, p 647) notes that ―men‘s cotton shirts from Japan are roughly 30 times as
expensive as the identically classified variety originating in the Philippines‖.
25
See for example, Kochhar et al. (2006), Panagariya (2007), Krueger (2010) and Veeramani (2012).
26
This pattern of specialisation is ―distorted‖ as it is inconsistent with India‘s comparative advantage in labourintensive activities. Based on a comparative analysis of relative resource endowments (physical, capital, human
capital and arable land), Veeramani (2012) notes that both India and China are abundantly endowed with
12
evident from low values of India‘s Qit. In contrast, China‘s relatively lower Pit values reflect
its high degree of intra-product specialisation in labour intensive varieties and processes. The
values of China‘s Qit records rapid growth since the pattern of its specialisation is in
accordance with its comparative advantage. Driven by its high level of specialisation, China
has been able to capture a significant market share in labour-intensive varieties (Amiti and
Freund, 2010) 27 . At the same time, high-income OECD countries have responded to
competition from China by specialising in more sophisticated varieties-that is, varieties that
embody higher level of technology, skill and capital (Schott, 2008).
In order to examine whether China outperforms India across different product groups within
manufacturing, we undertake export decomposition at the disaggregated level (See Table 2).
Within the category of India‘s manufactured exports, ‗Machinery and transport equipment‘
(SITC 7) recorded the fastest growth rate of 14.5% per annum. Export penetration rate in
SITC 7 increased from a meagre 0.2% in 2000 to 0.8% in 2012. This is primarily due to the
13.6% growth rate at the intensive margin. Yet, India‘s overall export penetration rate in
SITC 7 is paltry compared to a hefty 23% of China in 2012. India‘s traditional labourintensive
products,
grouped
under
‗Manufactured
materials‘
and
―Miscellaneous
manufactured articles‘ showed poor performance in comparison to ‗Machinery and transport
equipment‘, a capital-intensive category. Export penetration rate under ‗Chemicals‘ (SITC 5),
another capital-intensive category increased from 0.9% in 2000 to 2% in 2012 at a rate of
5.4% per year. In the case of China, both SITC 5 (Chemicals) and SITC 8 (Miscellaneous
manufactured articles) witnessed a growth rate of 9.7% per annum. For China, ‗Machinery
and transport equipment‘ (SITC 7) recorded the highest growth in export penetration,
increasing from 3.5% in 2000 to 23% in 2012 at the rate of 17% per annum. This is followed
by ‗Manufactured materials‘ at 12.2% per year. For all product groups, growth at the
intensive margin is the key to China‘s higher export penetration rates. In the case of
‗Miscellaneous manufactured articles‘, a labour-intensive group, China‘s export penetration
increased remarkably from 13% in 2000 to 41% in 2012. For this product group, the value of
unskilled labour while physical capital, skilled labour and land are relatively scarce in both countries. For the
more recent years, India‘s relative endowment of unskilled labour is significantly higher than that for China. It is
beyond doubt that the true comparative advantage of both countries, more so for India, lies in varieties and
processes that intensively uses unskilled labour rather than physical capital and skilled labour.
27
Amiti and Freund (2010, pp 54-55), based on a detailed analysis of China‘s exports during 1992-2005
observes that ―the skill content of China‘s manufacturing exports remained unchanged once processing trade is
excluded. When examining the skill content of China‘s total manufacturing exports, it looks like there has been
an increase over the sample period. However, it turns out that this is mainly due to the increased skill content of
imported inputs that are then assembled for export—a practice known as processing trade.‖
13
China‘s intensive margin in 2012 is 43% as compared to a meagre 3% for India, thus
affirming the increasingly dominant role that China plays in the world market for labourintensive products.
Next, we compute the margins for manufactured exports classified according to their factor
intensities of production28. The results are reported in Table 329. For India, we find that the
average annual growth rate of export penetration has been highest in ‗Human capital
intensive products‘ (12%) followed by ‗Technology intensive products‘ (9.7%) and ‗Natural
resource intensive products‘ (6.7%). In the case of human-capital and technology intensive
products, the growth rates at the intensive margin (9.4% and 9.3% respectively) is noticeably
higher than that for aggregate manufactured exports (6.3%) while the growth rates at the
extensive margin are not significantly different from that of aggregate manufactured exports.
Also, it is evident that high growth along the intensive margin in these two categories is due
to above average growth rates in quantity.
China recorded highest growth rate in technology-intensive products (15.8%) followed by
human-capital intensive products (12.9%) and unskilled labour-intensive products (11.2%).
China‘s high export penetration rates in technology and human capital-intensive products are
driven by growth along the intensive margin. For instance, in the case of technologyintensive products, China‘s intensive margin grew at a remarkable rate of 14.5% while the
extensive margin grew at 1.1% per year.
Perhaps, the most striking aspect of China‘s export performance is the phenomenal increase
of export penetration in unskilled labour-intensive products from 18% in 2000 to 61% in
2012. On the other hand, for the same category, India showed a dismal performance with a
meagre 2.7% growth rate30. It should be noted that the extensive margin growth rate in this
category is less than 1% for both India and China. But, China‘s 10.2% growth rate at the
28
The Heckscher-Ohlin model, the workhorse model of international trade, postulates that comparative
advantage of a country is closely related to its relative factor endowments. According to this model, a country
will specialize in and export products that are intensive in the use of the factor that is abundant in that country.
Thus, India being a labour-abundant country, trade liberalisation is expected to generate faster growth of labourintensive rather than capital-intensive exports.
29
Using the factor-intensity classification of the International Trade Centre (ITC), adapted by Hinloopen and
van Marrewijk (2008), we classify manufactured products into four categories: natural resource intensive,
unskilled labour intensive, human capital intensive and technology intensive. This information is available at:
http://www2.econ.uu.nl/users/marrewijk/eta/intensity.htm (Viewed on 25 May, 2014).
30
India‘s Sit in unskilled labour-intensive products increased from 2.1% in 2000 to 2.9% in 2012.
14
intensive margin resulted in an overall 11.2% growth in export penetration rate 31. It is clear
that India‘s low export penetration in unskilled labour intensive products is due to lack of
intensification. While India and China are more or less equal in establishing new export
relationships over many products and partners, India lags far behind in terms of deepening its
existing market presence.
The bias in India‘s incentive structure against labour-intensive manufacturing has a bearing
on the geographical pattern of exports from the country 32 . Arguably, India‘s product
specialisation patterns provide it with a comparative advantage in relatively poorer markets
(such as Africa) but at the cost of losing market shares in the richer countries. Products from
India with high technology and skill content are unlikely to make inroads into the quality
conscious richer country markets. These products, however, might enjoy a competitive
advantage in the relatively poorer countries. At the same time, rich country markets provide a
huge potential for labour-intensive exports from developing countries such as India. Thus,
specialisation out of traditional labour-intensive products implies a general loss of India‘s
export potential to advanced country markets33.
In the past, traditional developed country markets (Australia and New Zealand, Europe, Japan
and North America) accounted for a major share of India‘s export basket. However, their
dominance has declined considerably over the last two decades. The aggregate share of these
markets in India‘s merchandise exports declined from 63% in 1993 to 35% in 2010
(Veeramani, 2012). As of 2010, other countries in South and Central America, Caribbean,
Asia and Africa accounted for nearly two-third of India‘s merchandise exports. The share of
31
It is important to note that the above analysis underestimates the importance of labour-intensive exports from
China. This bias arises due to China‘s significant presence in global production networks and fragmentation
trade in a range of manufactured products (Athukorala, 2012).
32
It has been argued that India‘s labour laws create severe exit barriers and discourage large firms from
choosing labour-intensive activities and technologies (see Kochhar et al., 2006; Panagariya, 2007; Krueger,
2010). Another group of scholars, however, question this argument (see Nagaraj, 2011 and Bhattacharjea,
2006). Though there is no unanimity of opinion in this regard, a growing number of empirical studies suggest
that the role of labour laws cannot be ignored (see Hasan et al., 2007 and Aghion et al., 2008). Other constraints
that stand in the way of labour-intensive manufacturing include inadequate supply of physical infrastructure
(especially power, road and ports) and a highly inefficient and cumbersome land acquisition procedure. Faced
with power shortages, capital and skill-intensive industries, such as automobiles and pharmaceuticals, might be
in a position to rely on high-cost internal power sources. But this option is unaffordable to firms in labourintensive segments which typically operate with low margin.
33
An illustrative example will make this point clearer. India‘s exports of passenger motor vehicles, a capital and
skill-intensive product group, increased remarkably from $151 million in 2002 to $4511 million in 2010,
registering annual growth rate of 44%. Low and middle income countries are the major destinations for these
exports. In 2010, high-income countries accounted for only 8% of the Indian exports of passenger motor
vehicles while Sub-Saharan Africa accounted for 11%. On the other hand, high-income countries accounted for
58% of India‘s exports of HS 6105 (men‘s or boy‘s shirts, knitted or crocheted), a traditional labour-intensive
group, while Sub-Saharan Africa accounted for just 1%.
15
high-income OECD countries in India‘s total manufacturing exports declined from 58% in
2000 to 41% in 2010.
In order to reflect further on the changing market destination of India‘s manufactured exports,
we carry out the decomposition exercise for two broad groups of markets - high income
OECD countries and other countries and compare the results with that for China (see Table
4). The growth rate of India‘s export penetration to non-OECD countries is only 6.8% while
for China, it is 12.6%. The difference between the two countries is even more pronounced in
exports to high income OECD countries, where the export penetration growth rate is 12.3%
per year for China while for India, it is 6.4%. Along the intensive margin, while China
records a growth of 11%, India shows a growth of only 5.3%. The growth of China‘s quantity
margin in high-income OECD countries is 15% while that of India is only 5.5%. Thus, in
contrast to India, China shows a higher trade orientation with the traditional developed
country markets. This pattern is consistent with China‘s high degree of specialisation in
labour-intensive processes and product lines.
The different panels in Figure1 depict a disaggregated profile, across market groups, for the
four major factor-intensity based product groups. Several interesting patterns can be
observed. First, India shows a low and almost constant intensive margin in both the market
destination groups and across all commodity groups while China‘s intensive margin shows
significant growth. Second, both India and China record relatively higher levels of intensive
margin in non-OECD countries than in high-income OECD countries. This is not surprising
given the greater degree of competition in the OECD markets and a high level of intra-OECD
trade. As noted earlier, China has an extremely high intensive margin in unskilled labourintensive products. For the year 2012, China‘s intensive margin in this category is as high as
95% in other countries and 52% in high-income OECD countries.
Third, as far as the extensive margin is concerned, India is clearly catching up with China
both across different product categories as well as market groups and consequently the gap
between the two countries is narrowing. Yet, there exists further scope to improve India‘s
extensive margin particularly in the case of unskilled labour-intensive products. Though the
growth rates at the extensive margin are fairly equal for both countries, in non-OECD
markets, India‘s extensive margin in unskilled labour-intensive products for 2012 is only
56% while that of China is 90%.
16
In sum, it is beyond doubt that China‘s export success is entirely due to phenomenal growth
along the intensive margin. This has been achieved by decline in prices and quantity
expansion. Lower growth for India is because of lack of intensification of its exports, as
indicated by comparatively low values of intensive margin. The contribution of extensive
margin to overall exports of India and China is more or less the same.
IV. The Model
Our analysis of manufactured exports across different margins reveals that China surpasses
India tremendously, particularly along the intensive margin. To provide more empirical
rigour, we set up a gravity model to examine the factors causing the differential export
performance. Empirical research has used different variants of the gravity model to explain
bilateral trade flows. In our version, we include variables such as GDP and per capita GDP of
partner countries as measure of their economic size and development status respectively,
bilateral exchange rate, distance, and dummy variables denoting trade agreements and
geographical, historical, linguistic and cultural ties between trading partners.
Primarily, our objective is to empirically examine the determinants of differential export
performance of India and China across the different margins. In particular, we capture the
differential impact of bilateral real exchange rate and real per capita income of partner
countries on India and China‘s total exports, extensive, intensive, price and quantity margins.
Our decomposition results indicate that China‘s exports are biased in favour of rich countries
as compared to India. To account for this trade-orientation, we analyse how China‘s total
exports and margins respond to development status of destination countries. In other words,
we examine whether China‘s growth is because of intensification of exports to high-income
countries.
As mentioned earlier, China‘s policy of managing the yuan exchange rate to benefit its
exporters has generated a lot of attention and criticism from across the globe34. As for India35,
34
The Chinese currency was pegged to the US dollar at 8.28 yuan until 2005. Owing to international pressure,
on July 21st 2005, the People‘s Bank of China revalued yuan to 8.11 per dollar. This amounted to 2.1%
17
several studies have examined the link between exchange rate and exports, but with no
conclusive results. Virmani (1991) and Joshi and Little (1994) find a positive relationship
between rupee depreciation and exports, whereas others like Mallik (2005) and Veeramani
(2008) show that India‘s exports have grown even during periods of rupee appreciation.
Given this, it is imperative to analyse the role of exchange rate in explaining both India and
China‘s exports, and to examine whether exchange rate explains the differential performance
between India and China. We also focus on the differential impact of exchange rate changes
across export margins36.
Drawing from new-new trade models, the underlying mechanism by which trade margins
respond to exchange rate changes can be outlined as follows. Exchange rate depreciation
leads to growth along the intensive margin when already existing firms increase their export
sales. In addition to this effect, depreciation improves international competitiveness of less
productive firms- that is those firms which could not justify the export costs previously. Entry
of new firms results in export growth along the extensive margin. As noted earlier, we
assume that firms produce differentiated products. Thus, firm-level margins translate into
product-level margins. Finally by adding a country dimension, we propose that exchange rate
depreciation leads to increase in product varieties exported to newer destinations and increase
in exports of existing products to existing countries.
appreciation of the yuan vis-à-vis the dollar. Since then, the Chinese currency has appreciated at a modest rate
of 3.8% per annum reaching 6.31 yuan per dollar in 2012. Note that the renminbi was re-pegged to the dollar
from July 2008 to June 2010, to mitigate the effect of global financial crisis on demand for Chinese exports.
35
After a period of steady depreciation in the nineties, the rupee-dollar exchange rate saw a trend reversal
beginning from 2002. The rupee-dollar rate appreciated from Rs.48.61 in 2002 to Rs.41.35 in 2007 at the rate of
2.6% per annum (Veeramani, 2008). Thereafter, the rupee depreciated from Rs.43.51 in 2008 to Rs.53.44 in
2012 at the rate of 3.8% per annum.
36
Empirically, very few studies have explored the issue of export responses to exchange rate movements by
separating the effect across the two margins. Freund and Pierola (2008) find that in the case of developing
countries, episodes of export surges are preceded by depreciation of real exchange rate and lower exchange rate
volatility. In addition to the positive impact on total manufacturing exports, competitive currency has a
significant impact on extensive margin resulting in exports of new products and in new markets. Colacelli
(2010) uses bilateral trade data of 136 countries during 1981-1997 and finds that extensive margin shows higher
response than intensive margin to bilateral real exchange rate fluctuations. Cimoli et al. (2011) uses a panel of
111 countries from 1965 to 2005 to show that real exchange rate exert a highly significant positive effect on
export diversification. Using firm level monthly data, Tang and Zhang (2012) examine the effect of exchange
rate on China‘s extensive and intensive margins during 2000-2006. They do not find evidence for impact of real
exchange rate on aggregate bilateral exports and on intensive margin. Their results indicate that extensive
margin plays an important role in driving China‘s exports and that depreciation of renminbi leads to increase in
number of firms and products, thus affecting the extensive margin of exports.
18
This empirical exercise is conducted using an unbalanced panel dataset consisting of real
exports of manufacturing goods from India and China to various countries during 2000-2012.
We estimate our gravity model using least squares dummy variable method which is
equivalent to the fixed effects estimator. Inclusion of dummy variables for each partner
country accounts for all possible sources of unobserved heterogeneity that remains constant
for a given country across both the exporting nations. A key advantage of using dummy
variables is that the method provides estimates of time invariant variables which would get
omitted in typical fixed effects estimation. That is, in addition to income and exchange rate
elasticities, we can also estimate the effect of other variables measuring trade costs such as
distance, border, common language, colonial ties etc. Further, we estimate the same model
for extensive margin, intensive margin, and price and quantity margins separately.
To capture the differential export performance between India and China, we include a
dummy variable indicating the exporting country. The differential impact of exchange rate
and per capita income on India and China‘s exports is captured by interacting bilateral real
exchange rate and real per capita GDP of partner country with the exporter dummy.
The model is given as
lnXijt=α+β1lnGDPjt+β2lnPGDPjt+β3lnRERijt+β4RTAijt+β5lnDistij+β6Landlockedj+β7Fij+β8Di
+β9(Di×lnRERijt)+ β10(Di×lnPGDPjt)+γj+λt+εijt
where RTAijt is a dummy variable which takes value 1 if trading partners are members of a
regional trade agreement at time t and 0 otherwise; Distij indicates the simple distance (in
kilometres) between capital cities of trading partners. Landlockedj is a dummy variable which
takes value 1 if the partner country is landlocked. Fij denotes a set of pair dummies, each of
which takes value 1 if country pairs share a common border, official language and colonial
ties. Di is a dummy variable that takes value 1 if China is the exporting country and 0 for
India; γj indicates destination fixed effects and λt denotes time fixed effects.
Data on exports are obtained from UN-COMTRADE in 1000 US dollars at current prices.
This is deflated using US consumer price index37 with 2005 as base year. Data on GDP and
per capita GDP are from World Development Indicators in constant 2005 USD. Data on
37
Source: World Development Indicators.
19
distance, contiguity, common language etc. come from CEPII. We use WTO database 38 for
information on regional trade agreements. The variable, bilateral real exchange rate is defined
as
𝑅𝐸𝑅𝑖𝑗𝑡 =
𝐸𝑅𝑖𝑡 𝐶𝑃𝐼𝑖𝑡
÷
𝐸𝑅𝑗𝑡 𝐶𝑃𝐼𝑗𝑡
where, i stands for the exporting country (India or China) and j stands for the partner country.
ERit and ERjt denote nominal exchange rates39 measured as number of units of local currency
per US dollar. Nominal exchange rates are deflated using consumer price indices to arrive at
real exchange rates. An increase in RERijt indicates a depreciation of exporting nation‘s
currency in real terms vis-à-vis country j.
The point estimates of 𝛽1 and 𝛽2 captures the effect of economic size and average level of
income of the partner country on exports from India and China. Gravity model suggests that
larger and richer partner countries account for higher exports. β4 captures the impact of
regional trading agreements on exports, a positive coefficient of which would indicate that
such agreements promote exports between the two trading partners. Distance is supposed to
have a negative impact on exports. A positive coefficient for the dummy variable 𝐷𝑖 would
indicate higher levels of exports from China as compared to India. β3 is a measure of
exchange rate elasticity of exports, that is, it is an estimate of the impact of bilateral real
exchange rate on exports of India and China. Drawing from earlier studies such as Feenstra
(1989)40, we interpret bilateral real exchange rate as a destination-specific variable cost. β9
captures the differential impact of exchange rate on India and China‘s exports. Therefore, β3
+ β9 is an estimate of impact of exchange rate on China‘s exports alone. Similarly, β2 + β10 is
an estimate of the impact of per capita income of partner country on China‘s exports.
V. Regression Results
Table 6 presents the results from OLS estimation of the gravity model with destination and
time fixed effects. Our results indicate that GDP affects total exports, the coefficient is
positive and significant and close to unity in all specifications. However, per capita income
does not exert any significant impact on total exports of India and China. Interestingly, per
38
This information was downloaded from rtais.wto.org on July 22 nd 2014.
This is the period average exchange rate data from World Development Indicators.
40
Feenstra (1989) provides empirical evidence for symmetry between tariff pass-through and exchange rate
pass-through. This serves as motivation for the interpretation of exchange rate as a trade cost.
39
20
capita income exerts a positive impact on extensive margin and negative impact on intensive
margin. This implies that both India and China diversify their exports to new markets in rich
countries. But, on average, they export lesser volume to high-income countries, as suggested
by the negative income elasticity along the intensive margin.
Regional trade agreements are found to exert a positive influence on exports via the intensive
margin, while the impact on extensive margin is negative. Distance, as expected, exerts a
negative impact on total exports and along the margins. Our results also indicate that exports
to countries sharing common border and language are higher.
As seen in our analysis of export margins, the regression results also show that China exports
significantly more than India. The difference is more evident along the intensive margin,
where China‘s performance is around 4.6 times more than India (see Model 1). Along the
extensive margin, China exports 1.5 times as much as India. Results from Model 1 suggest
that bilateral real exchange rate exerts a significant positive impact on India and China‘s
exports both along the intensive as well as extensive margin. Our results show that a 1%
depreciation of exporting nation‘s currency in real terms leads to 0.11% increase in exports.
For both India and China, the exchange rate elasticity is higher along the intensive margin
than extensive margin. That is to say that, exchange rate depreciation leads to higher exports
primarily along the intensive margin, than extensive margin.
In order to capture the impact of exchange rate on China‘s exports alone, we interact the
exchange rate variable with dummy for China. As expected, our results show that exchange
rate has more impact on China‘s exports as compared to India (see Model 2). For China, the
exchange rate elasticity of exports is 0.15%, while for India it is only 0.07%. Of particular
interest is the fact that, along the extensive margin, exchange rate does not exert any
differential impact for India and China. Exchange rate elasticity along the extensive margin is
0.04% for both India and China. Hence, the higher impact of exchange rate on China‘s
exports comes through the effect on intensive margin41. For both India and China, the price
margins do not respond to exchange rate changes. But China‘s quantity margin responds
positively to exchange rate changes at the rate of 0.09%.
Next, we proceed to analyse if China‘s export growth is due to intensification in high-income
countries. To capture the differential impact of per capita income on India and China‘s
41
For China, exchange rate elasticity along the intensive margin is 0.08%.
21
exports, we interact real per capita GDP of partner country with the dummy variable for
China. The results (see Model 3) indicate that as compared to India, developed country
markets accounts for a significant part of China‘s overall exports. Income elasticity for
China‘s exports is 0.15%, while it is nil of India. We also see that the differential impact of
per capita income arises along the intensive margin, implying that China‘s high growth at the
intensive margin is accounted by its exports to rich countries.
As seen earlier, our results indicate that exchange rate has more impact on China‘s exports
than for India. Also, in the case of both countries, the response of intensive margin surpasses
that of extensive margin. But once China‘s exports to rich countries are taken into account,
exchange rate ceases to have any differential impact along the intensive margin. The
exchange rate elasticity along the intensive margin amounts to 0.06% for both India and
China. Compared to India, China has a larger elasticity coefficient along the extensive
margin.
Our results draws attention to the fact that once we account for China‘s exports to highincome countries, there is no longer any difference between India and China along the
intensive margin. Therefore, it is beyond doubt that China‘s superior performance along the
intensive margin is entirely due to its market share in rich countries.
VI. Concluding Remarks
We compare India‘s export patterns with that of China, in terms of intensive and extensive
margins, and account for the differential performance of the two countries. In doing so, we
identify the factors that enabled China to outdo India. We find that China surpasses India
both in terms of new products and markets and by increasing the volume of its existing
exports. Comparing the relative importance of the two margins, we see that intensive margin
contributes more to China‘s overall export growth. The differential performance between
India and China is more evident along the intensive margin, rather than extensive margin.
That is, compared to diversification, intensification of exports is what has driven China‘s
export success.
We also find that exchange rate exerts a higher impact on China‘s exports than for India.
Also, for both India and China, intensive margin responds more to exchange rate changes
22
than the extensive margin. Considering the geographical pattern of China‘s exports brings a
new perspective to our analysis. Having accounted for China‘s exports to high-income
countries, we find that there is no significant differential performance between India and
China, particularly along the intensive margin. This confirms that export orientation in favour
of developed country markets by specialising in products as per true comparative advantage
is what has enabled China to surpass India.
A major misconception among policy makers in India is that the country should necessarily
diversify to new markets in developing countries so as to increase its exports. Based on this
perception, the Indian government recently announced an export incentive scheme providing
explicit financial supports for market diversification42. Our analysis suggests that a country
can reap rich dividends by adopting policies aimed at accelerating export growth at the
intensive margin. This is evident from the fact that the response to variables like exchange
rate is more at the intensive margin, than extensive margin. In spite of this, if exchange rate
effects do not translate into sustained export increases, it is entirely because of the distorted
specialization technique pursued by India. This also results in India losing its market share in
rich countries. An important lesson to learn from China‘s experience is that sustained export
expansion requires a policy framework which places specialization and comparative
advantage at its centre.
42
See the ― Foreign Trade Policy 2009-14‖, Ministry of Commerce and Industry, Department of Commerce,
Government of India, Viewed on 1st November 2011 (http://dgft.gov.in/exim/2000/policy/ftpplcontent0910.pdf)
23
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Tang, H., Zhang, Y., 2012. Exchange Rates and the Margins of Trade: Evidence from
Chinese Exporters. CESifo Economic Studies 58, 671–702.
Van Ark B., Erumban, A.A., Chen, V., Kumar, U., 2008. The Cost Competitiveness of
Manufacturing in China and India: An Industry and Regional Perspective. Indian Council for
Research on International Economic Relations, Working Paper No. 228.
Veeramani, C., 2008. Impact of Exchange Rate Appreciation on India‘s Exports. Economic
and Political Weekly 43(22), 10-14.
Veeramani, C., Saini, K., 2011. India‘s Export Sophistication in a Comparative Perspective.
In: Nachane, D.M., (ed.), India Development Report 2011, New Delhi: Oxford University
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Veeramani, C., 2012. Anatomy of India‘s Merchandise Export Growth, 1993-94 to 2010-11.
Economic and Political Weekly 47(1), 94-104.
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Manufactured Exports in Comparison to China. In: Dev, S. M., (ed.), India Development
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Comparison with China. IGIDR Working Paper.
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Weekly 26(6), 309-314.
Zhang, Zhichao, 2000. Exchange Rate Reform in China: An Experiment in the Real Targets
Approach. The World Economy 23(8), 1057-1081.
26
Tables and Figures
Table 1: Decomposition of Manufactured Exports, India and China (2000-2012)
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.006
0.007
0.007
0.007
0.008
0.009
0.009
0.009
0.011
0.013
0.010
0.015
0.015
7.4
EMit
0.602
0.602
0.605
0.615
0.646
0.654
0.658
0.689
0.704
0.743
0.439
0.758
0.740
1.1
India
IMit
0.010
0.011
0.012
0.012
0.012
0.013
0.014
0.013
0.015
0.018
0.022
0.019
0.020
6.3
Pit
0.817
0.907
0.874
0.905
0.925
0.850
0.822
0.810
0.690
0.706
1.453
0.797
0.693
-0.5
Qit
0.013
0.012
0.013
0.013
0.013
0.016
0.017
0.016
0.022
0.025
0.015
0.024
0.030
6.9
Sit
0.054
0.061
0.071
0.086
0.099
0.118
0.136
0.150
0.160
0.172
0.199
0.208
0.230
13.0
EMit
0.752
0.748
0.767
0.791
0.810
0.830
0.847
0.849
0.846
0.852
0.859
0.870
0.873
1.4
China
IMit
0.072
0.081
0.093
0.109
0.123
0.143
0.160
0.177
0.189
0.202
0.232
0.239
0.263
11.5
Pit
0.513
0.536
0.515
0.485
0.471
0.401
0.395
0.355
0.342
0.407
0.329
0.370
0.349
-3.9
Qit
0.140
0.151
0.181
0.224
0.260
0.355
0.406
0.499
0.553
0.497
0.704
0.647
0.753
16.0
Note: r denotes average annual growth rates computed using semi-logarithmic regressions.
Source: Authors‘ estimation using COMTRADE-WITS database.
Table 2: Decomposition of Exports across Disaggregated Product Groups in Manufacturing,
India and China (2000-2012)
Chemicals (SITC 5)
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.009
0.009
0.010
0.010
0.010
0.012
0.013
0.013
0.014
0.015
0.009
0.018
0.020
5.4
EMit
0.538
0.572
0.604
0.632
0.657
0.676
0.672
0.669
0.688
0.709
0.415
0.722
0.721
1.0
India
IMit
0.017
0.016
0.016
0.015
0.016
0.018
0.019
0.019
0.020
0.021
0.022
0.025
0.027
4.3
Pit
0.864
0.916
0.896
0.837
0.887
0.907
0.901
0.897
0.772
0.822
1.082
0.961
0.895
0.5
Qit
0.020
0.018
0.018
0.018
0.018
0.019
0.021
0.021
0.026
0.025
0.020
0.026
0.031
3.9
27
Sit
0.028
0.030
0.031
0.033
0.036
0.043
0.047
0.054
0.061
0.057
0.069
0.077
0.077
9.7
EMit
0.620
0.647
0.683
0.700
0.718
0.752
0.761
0.770
0.769
0.792
0.793
0.799
0.788
2.0
China
IMit
0.046
0.047
0.046
0.047
0.050
0.057
0.062
0.070
0.079
0.072
0.087
0.097
0.098
7.5
Pit
0.665
0.678
0.655
0.632
0.667
0.686
0.662
0.664
0.660
0.671
0.660
0.719
0.711
0.5
Qit
0.069
0.069
0.070
0.075
0.075
0.082
0.093
0.105
0.119
0.107
0.132
0.134
0.138
7.0
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.014
0.015
0.016
0.016
0.016
0.018
0.019
0.018
0.020
0.019
0.022
0.024
0.025
4.3
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.002
0.002
0.002
0.002
0.002
0.003
0.003
0.004
0.005
0.007
0.004
0.008
0.008
14.5
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
Sit
0.012
0.012
0.013
0.013
0.014
0.015
0.016
0.015
0.015
Manufactured Materials (SITC 6 less 68 and 667)
India
China
EMit IMit
Pit
Qit
Sit
EMit IMit
0.504 0.029 0.751 0.038 0.064 0.660 0.097
0.518 0.030 0.785 0.038 0.068 0.684 0.100
0.554 0.029 0.727 0.040 0.074 0.697 0.106
0.573 0.028 0.771 0.036 0.089 0.717 0.124
0.596 0.027 0.796 0.034 0.106 0.761 0.139
0.611 0.030 0.813 0.036 0.127 0.789 0.161
0.613 0.030 0.805 0.038 0.150 0.830 0.180
0.608 0.029 0.815 0.036 0.164 0.846 0.194
0.629 0.032 0.715 0.044 0.177 0.829 0.214
0.632 0.031 0.697 0.044 0.174 0.834 0.208
0.580 0.038 1.380 0.027 0.200 0.839 0.238
0.654 0.036 0.772 0.047 0.216 0.840 0.257
0.655 0.039 0.710 0.054 0.239 0.850 0.282
1.9
2.5
0.9
1.5
12.2
2.2
9.8
Machinery and transport equipment (SITC 7)
India
China
EMit IMit
Pit
Qit
Sit
EMit IMit
0.622 0.003 0.993 0.003 0.035 0.772 0.046
0.611 0.003 1.266 0.003 0.044 0.750 0.058
0.593 0.003 1.275 0.003 0.057 0.773 0.074
0.603 0.004 1.327 0.003 0.076 0.806 0.094
0.641 0.004 1.352 0.003 0.092 0.821 0.112
0.638 0.005 0.867 0.006 0.112 0.840 0.133
0.641 0.005 0.801 0.007 0.130 0.855 0.152
0.700 0.005 0.766 0.007 0.145 0.854 0.170
0.715 0.007 0.652 0.011 0.158 0.858 0.184
0.775 0.009 0.671 0.014 0.184 0.864 0.214
0.372 0.011 1.603 0.007 0.208 0.867 0.240
0.786 0.010 0.797 0.013 0.213 0.886 0.240
0.749 0.010 0.617 0.017 0.231 0.892 0.259
0.9
13.6 -4.2 18.6 16.9
1.4
15.3
Miscellaneous manufactured articles (SITC 8)
India
China
EMit IMit
Pit
Qit
Sit
EMit IMit
0.671 0.019 0.735 0.025 0.133 0.877 0.152
0.673 0.018 0.806 0.023 0.138 0.886 0.155
0.693 0.018 0.782 0.023 0.156 0.896 0.174
0.679 0.019 0.851 0.022 0.173 0.906 0.191
0.704 0.020 0.818 0.024 0.187 0.918 0.204
0.728 0.021 0.840 0.025 0.217 0.927 0.234
0.749 0.022 0.804 0.027 0.243 0.932 0.261
0.755 0.020 0.780 0.026 0.267 0.927 0.289
0.762 0.020 0.633 0.032 0.272 0.919 0.296
28
Pit
0.601
0.615
0.565
0.532
0.531
0.513
0.515
0.492
0.501
0.569
0.455
0.501
0.504
-1.6
Qit
0.162
0.162
0.188
0.233
0.262
0.313
0.350
0.395
0.427
0.366
0.523
0.512
0.559
11.5
Pit
0.531
0.648
0.636
0.580
0.569
0.300
0.284
0.232
0.216
0.263
0.213
0.255
0.217
-9.9
Qit
0.086
0.090
0.116
0.162
0.196
0.444
0.533
0.734
0.853
0.811
1.127
0.941
1.190
28.0
Pit
0.364
0.344
0.338
0.316
0.278
0.278
0.276
0.237
0.220
Qit
0.418
0.451
0.517
0.604
0.734
0.840
0.946
1.220
1.343
2009
2010
2011
2012
r
0.024
0.016
0.023
0.024
5.7
0.778
0.546
0.804
0.808
0.9
0.031
0.029
0.029
0.029
4.7
0.661
1.918
0.706
0.603
0.2
0.048
0.015
0.041
0.049
4.5
0.276
0.335
0.355
0.408
9.7
0.907
0.941
0.941
0.942
0.5
0.304
0.356
0.377
0.433
9.1
0.297
0.212
0.237
0.222
-4.0
1.023
1.674
1.593
1.952
13.7
Note: r denotes average annual growth rates computed using semi-logarithmic regressions.
Source: Authors‘ estimation using COMTRADE-WITS database.
Table 3: Decomposition of Manufactured Exports for Factor Intensity Based Product Groups,
India and China (2000-2012)
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.013
0.017
0.016
0.015
0.012
0.014
0.018
0.021
0.026
0.018
0.027
0.029
0.029
6.7
EMit
0.454
0.484
0.502
0.532
0.563
0.550
0.550
0.554
0.565
0.525
0.491
0.596
0.568
1.3
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Sit
0.021
0.021
0.021
0.020
0.021
0.024
0.023
0.022
0.023
0.027
0.021
0.031
0.029
EMit
0.642
0.639
0.651
0.647
0.671
0.698
0.696
0.685
0.685
0.707
0.605
0.714
0.739
Natural Resource Intensive
India
IMit
Pit
Qit
Sit
EMit
0.029 0.971 0.030 0.060 0.660
0.034 1.000 0.034 0.068 0.672
0.032 0.941 0.034 0.078 0.700
0.029 0.831 0.035 0.085 0.720
0.022 0.880 0.025 0.108 0.748
0.026 0.904 0.029 0.122 0.740
0.032 0.847 0.038 0.133 0.758
0.038 0.838 0.045 0.131 0.687
0.046 0.670 0.069 0.136 0.687
0.033 0.648 0.052 0.132 0.672
0.054 1.298 0.042 0.150 0.681
0.048 0.678 0.071 0.168 0.695
0.051 0.603 0.085 0.180 0.720
5.4
-2.6
8.3
8.9
0.1
Unskilled Labour Intensive
India
IMit
Pit
Qit
Sit
EMit
0.033 0.832 0.040 0.177 0.845
0.032 0.843 0.038 0.185 0.846
0.032 0.770 0.042 0.206 0.841
0.032 0.887 0.036 0.239 0.861
0.031 0.873 0.036 0.263 0.884
0.034 0.878 0.039 0.310 0.915
0.033 0.858 0.039 0.358 0.919
0.031 0.839 0.037 0.380 0.905
0.034 0.728 0.047 0.392 0.908
0.039 0.758 0.051 0.426 0.890
0.035 1.645 0.021 0.527 0.934
0.043 0.805 0.054 0.542 0.928
0.039 0.724 0.053 0.613 0.942
29
China
IMit
0.092
0.101
0.111
0.118
0.145
0.164
0.176
0.191
0.198
0.197
0.220
0.242
0.250
8.9
Pit
0.612
0.620
0.624
0.582
0.543
0.521
0.539
0.494
0.479
0.543
0.520
0.574
0.611
-0.9
Qit
0.149
0.163
0.178
0.203
0.266
0.316
0.326
0.387
0.413
0.362
0.423
0.422
0.409
9.9
China
IMit
0.209
0.219
0.245
0.278
0.297
0.339
0.390
0.419
0.432
0.479
0.564
0.584
0.651
Pit
0.666
0.631
0.573
0.538
0.488
0.480
0.480
0.460
0.439
0.586
0.358
0.396
0.370
Qit
0.314
0.346
0.427
0.517
0.610
0.707
0.813
0.911
0.985
0.816
1.576
1.473
1.758
r
2.7
0.8
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.003
0.003
0.004
0.004
0.004
0.005
0.005
0.006
0.007
0.008
0.004
0.010
0.010
9.7
EMit
0.642
0.647
0.667
0.641
0.661
0.684
0.689
0.743
0.747
0.795
0.339
0.813
0.788
0.4
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.005
0.005
0.006
0.006
0.008
0.009
0.009
0.009
0.010
0.016
0.013
0.016
0.019
12.0
EMit
0.528
0.523
0.490
0.574
0.629
0.602
0.605
0.618
0.660
0.696
0.574
0.702
0.681
2.4
1.9
0.6
1.3
11.2
Technology Intensive
0.9
India
IMit
Pit
Qit
Sit
EMit
0.004 0.879 0.005 0.038 0.801
0.005 1.077 0.005 0.046 0.805
0.005 1.083 0.005 0.059 0.818
0.006 1.092 0.006 0.076 0.840
0.006 1.128 0.006 0.092 0.857
0.007 0.900 0.008 0.111 0.875
0.008 0.854 0.009 0.127 0.882
0.008 0.826 0.009 0.146 0.878
0.009 0.702 0.013 0.157 0.882
0.010 0.736 0.014 0.171 0.893
0.013 1.327 0.010 0.197 0.906
0.012 0.864 0.014 0.203 0.909
0.012 0.728 0.017 0.222 0.905
9.3
-2.0 11.5 15.8
1.1
Human Capital Intensive
India
IMit
Pit
Qit
Sit
EMit
0.009 0.690 0.013 0.034 0.645
0.010 0.753 0.013 0.035 0.626
0.012 0.728 0.016 0.040 0.675
0.011 0.721 0.015 0.047 0.710
0.012 0.745 0.016 0.057 0.730
0.014 0.755 0.019 0.069 0.755
0.016 0.744 0.021 0.082 0.791
0.015 0.760 0.020 0.091 0.818
0.016 0.643 0.024 0.101 0.803
0.023 0.620 0.037 0.094 0.808
0.023 1.496 0.015 0.108 0.796
0.023 0.713 0.033 0.120 0.826
0.028 0.626 0.044 0.140 0.839
9.4
0.7
8.6
12.9
2.4
10.2
-4.2
15.0
China
IMit
0.047
0.057
0.072
0.091
0.107
0.127
0.145
0.167
0.178
0.191
0.217
0.223
0.245
14.5
Pit
0.494
0.560
0.544
0.514
0.513
0.357
0.341
0.291
0.270
0.316
0.270
0.313
0.280
-6.4
Qit
0.095
0.102
0.133
0.177
0.209
0.354
0.424
0.572
0.659
0.605
0.804
0.714
0.875
22.4
China
IMit
0.052
0.057
0.060
0.066
0.078
0.092
0.103
0.111
0.125
0.116
0.135
0.145
0.166
10.3
Pit
0.373
0.395
0.398
0.375
0.379
0.361
0.366
0.334
0.345
0.387
0.359
0.396
0.402
-0.1
Qit
0.140
0.143
0.150
0.176
0.206
0.255
0.282
0.333
0.363
0.299
0.377
0.367
0.414
10.3
Note: r denotes average annual growth rates computed using semi-logarithmic regressions.
Source: Authors‘ estimation using COMTRADE-WITS database.
30
Table 4: Export Margins across Different Market Destinations: High Income OECD versus
Other Countries
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.005
0.005
0.006
0.006
0.006
0.007
0.008
0.008
0.009
0.009
0.007
0.011
0.011
6.4
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
Sit
0.009
0.010
0.010
0.010
0.011
0.012
0.012
0.012
0.014
0.019
0.013
0.021
0.021
6.8
Manufactured Exports to high income OECD countries
India
China
EMit IMit
Pit
Qit
Sit
EMit
IMit
Pit
0.647 0.008 0.759 0.011 0.046
0.774
0.059
0.507
0.644 0.008 0.803 0.010 0.051
0.766
0.067
0.508
0.637 0.009 0.792 0.011 0.060
0.789
0.075
0.489
0.661 0.009 0.806 0.011 0.071
0.810
0.088
0.467
0.696 0.009 0.851 0.010 0.082
0.825
0.099
0.458
0.702 0.010 0.769 0.013 0.099
0.843
0.117
0.399
0.705 0.011 0.743 0.014 0.111
0.856
0.129
0.393
0.725 0.010 0.743 0.014 0.121
0.859
0.141
0.354
0.751 0.011 0.645 0.018 0.130
0.856
0.152
0.336
0.782 0.012 0.669 0.018 0.139
0.859
0.162
0.393
0.496 0.015 1.236 0.012 0.162
0.862
0.188
0.342
0.794 0.013 0.755 0.018 0.166
0.878
0.189
0.384
0.776 0.014 0.671 0.021 0.177
0.881
0.201
0.349
1.0
5.3
-0.2
5.5
12.3
1.2
11.1
-3.4
Manufactured Exports to other countries
India
China
EMit IMit
Pit
Qit
Sit
EMit
IMit
Pit
0.491 0.018 0.844 0.021 0.076
0.693
0.110
0.514
0.500 0.021 0.956 0.022 0.087
0.696
0.125
0.545
0.530 0.019 0.911 0.021 0.105
0.707
0.148
0.524
0.512 0.020 0.948 0.021 0.128
0.740
0.172
0.491
0.541 0.020 0.955 0.021 0.146
0.771
0.189
0.475
0.556 0.021 0.884 0.024 0.169
0.799
0.211
0.401
0.569 0.022 0.854 0.025 0.196
0.824
0.238
0.395
0.623 0.019 0.838 0.023 0.218
0.826
0.264
0.355
0.624 0.022 0.707 0.032 0.223
0.826
0.270
0.344
0.677 0.029 0.718 0.040 0.242
0.837
0.289
0.412
0.353 0.037 1.536 0.024 0.271
0.854
0.318
0.325
0.705 0.029 0.811 0.036 0.288
0.854
0.338
0.366
0.691 0.031 0.700 0.044 0.324
0.858
0.378
0.349
1.9
4.8
-0.7
5.6
12.6
2.0
10.4
-4.0
Note: r denotes average annual growth rates computed using semi-logarithmic regressions.
Source: Authors‘ estimation using COMTRADE-WITS database.
31
Qit
0.117
0.131
0.154
0.189
0.217
0.293
0.329
0.397
0.454
0.413
0.549
0.493
0.576
15.0
Qit
0.214
0.230
0.283
0.352
0.398
0.526
0.602
0.744
0.784
0.702
0.978
0.923
1.082
15.0
Table 5: Export Margins of India and China in Different Market Destinations: High Income
OECD versus Other Countries, Factor Intensity Based Classification
Year
India
(others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
0.028
0.030
0.030
0.031
0.033
0.036
0.036
0.033
0.032
0.058
0.050
0.049
0.057
5.9
Year
India
(others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
0.059
0.058
0.056
0.056
0.055
0.048
0.044
0.042
0.053
0.059
0.053
0.071
0.059
0.4
Year
India
(others)
2000
0.008
Human Capital Intensive
Intensive Margin
Extensive Margin
India (high
China
China (high
India
India (high
China
China (high
income
(others)
income
(others)
income
(others)
income
OECD)
OECD)
OECD)
OECD)
0.006
0.120
0.038
0.375
0.572
0.546
0.671
0.006
0.115
0.043
0.394
0.560
0.566
0.642
0.007
0.120
0.047
0.426
0.508
0.584
0.698
0.006
0.127
0.052
0.462
0.608
0.627
0.732
0.007
0.136
0.063
0.497
0.672
0.666
0.748
0.008
0.148
0.075
0.511
0.634
0.722
0.765
0.009
0.164
0.083
0.510
0.641
0.785
0.793
0.009
0.180
0.086
0.497
0.667
0.821
0.817
0.010
0.184
0.099
0.532
0.721
0.815
0.798
0.011
0.175
0.091
0.566
0.758
0.813
0.805
0.012
0.200
0.105
0.469
0.629
0.818
0.787
0.011
0.233
0.105
0.615
0.751
0.818
0.829
0.013
0.275
0.112
0.600
0.730
0.827
0.845
7.2
7.0
9.7
3.4
2.6
3.9
2.0
Unskilled Labour Intensive
Intensive Margin
Extensive Margin
India (high
China
China (high
India
India (high
China
China (high
income
(others)
income
(others)
income
(others)
income
OECD)
OECD)
OECD)
OECD)
0.028
0.342
0.174
0.378
0.750
0.707
0.892
0.027
0.353
0.183
0.399
0.736
0.702
0.894
0.027
0.436
0.198
0.396
0.756
0.652
0.903
0.026
0.531
0.215
0.395
0.751
0.700
0.913
0.026
0.555
0.227
0.414
0.782
0.758
0.926
0.031
0.554
0.276
0.434
0.813
0.810
0.950
0.031
0.648
0.309
0.467
0.803
0.827
0.951
0.028
0.682
0.332
0.472
0.790
0.815
0.940
0.028
0.631
0.355
0.464
0.800
0.853
0.930
0.032
0.677
0.402
0.516
0.806
0.827
0.917
0.030
0.825
0.463
0.403
0.717
0.865
0.964
0.033
0.828
0.482
0.521
0.825
0.853
0.964
0.030
0.953
0.517
0.563
0.848
0.897
0.964
1.5
8.1
10.2
2.7
0.7
2.4
0.6
Technology Intensive
Intensive Margin
Extensive Margin
India (high
China
China (high
India
India (high
China
China (high
income
(others)
income
(others)
income
(others)
income
OECD)
OECD)
OECD)
OECD)
0.003
0.067
0.039
0.564
0.679
0.754
0.820
32
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
0.010
0.009
0.010
0.011
0.012
0.012
0.011
0.012
0.015
0.020
0.015
0.016
5.8
Year
India
(others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
r
0.044
0.066
0.057
0.055
0.038
0.047
0.065
0.072
0.086
0.054
0.096
0.087
0.088
5.4
0.003
0.004
0.004
0.004
0.005
0.006
0.006
0.007
0.007
0.008
0.009
0.010
11.0
0.087
0.046
0.570
0.684
0.760
0.824
0.110
0.057
0.605
0.699
0.777
0.835
0.130
0.075
0.560
0.685
0.807
0.854
0.150
0.089
0.588
0.703
0.830
0.868
0.180
0.103
0.602
0.732
0.846
0.888
0.206
0.116
0.618
0.735
0.856
0.895
0.241
0.132
0.710
0.764
0.847
0.894
0.254
0.141
0.704
0.775
0.847
0.901
0.274
0.149
0.766
0.815
0.873
0.904
0.295
0.174
0.299
0.369
0.893
0.913
0.309
0.175
0.789
0.832
0.897
0.916
0.341
0.189
0.768
0.805
0.894
0.912
13.9
14.0
1.2
0.1
1.5
1.0
Natural Resource Intensive
Intensive Margin
Extensive Margin
India (high
China
China (high
India
India (high
China
China (high
income
(others)
income
(others)
income
(others)
income
OECD)
OECD)
OECD)
OECD)
0.025
0.125
0.082
0.333
0.505
0.557
0.697
0.025
0.140
0.089
0.358
0.543
0.532
0.728
0.023
0.153
0.098
0.411
0.543
0.596
0.740
0.019
0.175
0.101
0.474
0.558
0.588
0.769
0.016
0.219
0.122
0.475
0.602
0.635
0.790
0.019
0.248
0.137
0.463
0.590
0.645
0.776
0.020
0.256
0.147
0.452
0.600
0.666
0.796
0.024
0.255
0.167
0.475
0.592
0.641
0.707
0.030
0.268
0.169
0.487
0.605
0.646
0.705
0.024
0.283
0.158
0.451
0.570
0.603
0.708
0.032
0.291
0.185
0.430
0.530
0.612
0.720
0.027
0.319
0.207
0.555
0.621
0.615
0.737
0.029
0.373
0.190
0.518
0.602
0.630
0.773
2.7
8.7
8.0
2.8
0.9
0.9
0.0
Note: r denotes average annual growth rates computed using semi-logarithmic regressions.
Source: Authors‘ estimation using COMTRADE-WITS database.
33
Figure 1: Evolution of Margins in Different Market Destinations: High Income OECD versus
Other Countries, Factor Intensity Based Classification
Intensive Margin, Human Capital Intensive
0.3
0.25
India (others)
0.2
India (high income
OECD)
0.15
0.1
China (others)
0.05
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
China (high income
OECD)
Extensive Margin, Human Capital Intensive
0.9
0.8
India (others)
0.7
India (high income
OECD)
0.6
0.5
China (others)
0.4
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0.3
China (high income
OECD)
Intensive Margin, Unskilled Labour
Intensive
1
India (others)
0.8
0.6
0.4
India (high income
OECD)
0.2
China (others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
34
China (high income
OECD)
Extensive Margin, Unskilled Labour
Intensive
1
India (others)
0.8
India (high
income OECD)
0.6
0.4
China (others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0.2
China (high
income OECD)
Intensive Margin, Technology Intensive
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
India (others)
India (high
income OECD)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
China (others)
China (high
income OECD)
Extensive Margin, Technology Intensive
1
India (others)
0.8
0.6
India (high
income OECD)
0.4
China (others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0.2
35
China (high
income OECD)
Intensive Margin, Natural Resource
Intensive
0.4
India (others)
0.3
India (high
income OECD)
0.2
0.1
China (others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0
China (high
income OECD)
Extensive Margin, Natural Resource
Intensive
0.8
India (others)
0.7
0.6
0.5
India (high
income OECD)
0.4
China (others)
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
0.3
China (high
income OECD)
Table 6: Regression Results
Model 1
lnXijt
lnEMijt
lnGDPjt
1.071***
(0.189)
0.070
(0.211)
0.112***
(0.022)
0.144*
(0.076)
-1.576***
(0.067)
1.583
(0.962)
1.253***
(0.111)
0.404***
(0.050)
-0.123
(0.130)
0.258*
(0.136)
0.284**
(0.139)
0.038***
(0.011)
-0.130***
(0.048)
-0.604***
(0.044)
1.964***
(0.654)
0.460***
(0.059)
-0.112***
(0.037)
0.748***
(0.081)
lnPGDPjt
lnRERijt
RTAijt
lnDistij
Landlockedj
Borderij
Languageij
Colonial tiesij
36
lnIMijt
lnPijt
lnQijt
-0.236*
-0.002
-0.234
(0.143)
(0.098)
(0.187)
-0.527***
0.023
-0.549**
(0.167)
(0.124)
(0.221)
0.063***
0.002
0.061**
(0.016)
(0.018)
(0.025)
0.366*** -0.100*** 0.466***
(0.065)
(0.026)
(0.075)
-1.008*** 0.156*** -1.164***
(0.058)
(0.031)
(0.067)
-1.106
0.645
-1.751*
(0.753)
(0.562)
(1.005)
1.037*** 0.135*** 0.902***
(0.112)
(0.033)
(0.117)
0.489***
0.002
0.488***
(0.042)
(0.027)
(0.051)
-0.770*** -0.066 -0.704***
(0.126)
(0.078)
(0.169)
Di
2.565*** 0.927*** 1.716*** -0.754*** 2.470***
(0.049)
(0.027)
(0.039)
(0.034)
(0.054)
Constant
-3.85
-6.08*** 15.37***
-2.15*
17.51***
(3.08)
(2.24)
(2.26)
(1.24)
(2.70)
Partner dummies
Yes
Yes
Yes
Yes
Yes
Time dummies
Yes
Yes
Yes
Yes
Yes
Observations
4,297
4,297
4,297
4,297
4,297
R2
0.941
0.775
0.783
0.613
0.794
Model 2
lnGDPjt
lnXijt
1.071***
(0.193)
lnPGDPjt
0.067
(0.214)
lnRERijt
0.071***
(0.022)
RTAijt
0.153**
(0.076)
lnDistij
-1.574***
(0.066)
Landlockedj
1.556
(0.980)
Borderij
1.330***
(0.109)
Languageij
0.468***
(0.049)
Colonial tiesij
0.119
(0.130)
Di
2.588***
(0.048)
Di ×lnRERijt
0.083***
(0.007)
Constant
-3.82
(3.08)
Partner dummies
Yes
Time dummies
Yes
Observations
4,297
R2
0.943
lnEMijt
lnIMijt
lnPijt
lnQijt
0.258*
(0.136)
0.284**
(0.139)
0.040***
(0.011)
-0.131***
(0.048)
-0.604***
(0.044)
1.965***
(0.653)
0.456***
(0.060)
-0.115***
(0.036)
0.735***
(0.082)
0.925***
(0.027)
-0.004
(0.005)
-6.08***
(2.24)
Yes
Yes
4,297
0.775
-0.236
(0.144)
-0.529***
(0.168)
0.021
(0.017)
0.375***
(0.066)
-1.006***
(0.060)
-1.133
(0.765)
1.115***
(0.109)
0.554***
(0.041)
-0.525***
(0.126)
1.739***
(0.039)
0.084***
(0.007)
15.39***
(2.25)
Yes
Yes
4,297
0.790
-0.002
(0.099)
0.023
(0.124)
0.003
(0.018)
-0.100***
(0.026)
0.156***
(0.031)
0.645
(0.561)
0.134***
(0.034)
0.001
(0.028)
-0.070
(0.079)
-0.755***
(0.034)
-0.001
(0.004)
-2.15*
(1.24)
Yes
Yes
4,297
0.613
-0.234
(0.187)
-0.552**
(0.221)
0.018
(0.027)
0.475***
(0.076)
-1.162***
(0.069)
-1.778*
(1.010)
0.981***
(0.114)
0.554***
(0.051)
-0.455***
(0.169)
2.494***
(0.056)
0.085***
(0.008)
17.54***
(2.66)
Yes
Yes
4,297
0.799
37
Model 3
lnXijt
lnEMijt
lnGDPjt
lnIMijt
lnPijt
1.070*** 0.258*
-0.237
-0.002
(0.203)
(0.134)
(0.157)
(0.099)
lnPGDPjt
-0.002
0.315** -0.623*** 0.0182
(0.222)
(0.138)
(0.179)
(0.124)
lnRERijt
0.096*** 0.029** 0.055***
0.004
(0.022)
(0.012)
(0.017)
(0.018)
RTAijt
0.142* -0.126*** 0.360*** -0.101***
(0.076)
(0.048)
(0.065)
(0.026)
lnDistij
-1.396*** -0.684*** -0.764*** 0.167***
(0.068)
(0.044)
(0.059)
(0.032)
Landlockedj
1.715* 1.894***
-0.917
0.656
(1.025)
(0.645)
(0.823)
(0.562)
Borderij
1.359*** 0.443*** 1.154*** 0.136***
(0.111)
(0.059)
(0.111)
(0.034)
Languageij
0.360*** -0.067* 0.408***
-0.006
(0.051)
(0.036)
(0.041)
(0.028)
Colonial tiesij
0.333** 0.639*** -0.232*
-0.056
(0.134)
(0.082)
(0.127)
(0.080)
Di
1.312*** 1.496***
0.001
-0.838***
(0.158)
(0.108)
(0.143)
(0.068)
Di ×lnRERijt
0.031*** 0.019***
0.013
-0.005
(0.009)
(0.007)
(0.009)
(0.004)
Di ×lnPGDPjt
0.146*** -0.066*** 0.200***
0.010
(0.017)
(0.012)
(0.016)
(0.006)
Constant
-4.94
-5.58** 13.87***
-2.22*
(3.18)
(2.23)
(2.35)
(1.25)
Partner dummies
Yes
Yes
Yes
Yes
Time dummies
Yes
Yes
Yes
Yes
Observations
4,297
4,297
4,297
4,297
R2
0.944
0.776
0.798
0.613
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
38
lnQijt
-0.235
(0.196)
-0.641***
(0.227)
0.050*
(0.026)
0.461***
(0.074)
-0.931***
(0.070)
-1.573
(1.051)
1.019***
(0.115)
0.415***
(0.052)
-0.177
(0.171)
0.839***
(0.165)
0.018*
(0.010)
0.190***
(0.018)
16.09***
(2.75)
Yes
Yes
4,297
0.804