China and the Manufacturing Exports of Other Developing Countries

Gordon H. Hanson, UCSD and NBER
Raymond Robertson, Macalester College

January 2008

Abstract. In this paper, we examine the impact of China’s growth on developing countries thatspecialize in manufacturing. Over 2000-2005, manufacturing accounted for 32% of China’s
GDP and 89% of its merchandise exports, making it more specialized in the sector than any otherlarge developing economy. Using the gravity model of trade, we decompose bilateral trade into
components associated with demand conditions in importing countries, supply conditions inexporting countries, and bilateral trade costs. We identify 10 developing economies for which
manufacturing represents more than 75% of merchandise exports (Hungary, Malaysia, Mexico,
Pakistan, the Philippines, Poland, Romania, Sri Lanka, Thailand, and Turkey), which are intheory the countries most exposed to the adverse consequences of China’s export growth. Our
results suggest that had China’s export supply capacity been constant over the 1995-2005 period,
demand for exports would have been 0.8% to 1.6% higher in the 10 countries studied. Thus,
even for the developing countries most specialized in export manufacturing, China’s expansion
has represented only a modest negative shock.

We thank Irene Brambilla, Ernesto Lopez Cordoba, Robert Feenstra, David Hummels, Daniel
Lederman, Marcelo Olarreaga, Guillermo Perry, and Christian Volpe for helpful comments.

1. Introduction
The explosive growth of China’s economy has been extraordinary. Between 1990 and
2005, China’s exports increased by 25 times in real terms, compared to an increase of about fourtimes in the 12 largest exporting nations (Table 1). As of 2005, China’s exports accounted for
25% of the total exports of all countries outside of the top 12.1
What has made China’s emergence potentially disruptive is that the country is highly
specialized in manufacturing. Over the period 2000 to 2005, manufacturing accounted for 32%
of China’s GDP and 89% of its merchandise exports, making it more specialized in the sector
than any other large developing economy (Table 2). In consumer goods and other laborintensive manufactures, China has become a major source of supply, pushing down world
product prices. Meanwhile, China has contributed to a boom in demand for commodities,
leading to increases in the prices of metals, minerals, and farm goods.
The impact of China’s emergence on other developing countries is just beginning to beappreciated (Devlin, Estevadeordal, and Rodriguez-Clare, 2005; Eichengreen and Tong, 2005;
Lopez Cordoba, Micco, and Molina, 2005). In the 1980s and 1990s, international trade became
the engine of growth for much of the developing world. Trade liberalization and market-oriented
reform in Asia and Latin America steered the regions toward greater specialization in exports.
There is a popular conception that for non-oil-exporting developing countries expanding export
production has meant specializing in manufacturing. But in actuality there is considerable
heterogeneity in the production structures of these economies, which means there is variation innational exposure to China’s industrial expansion.

1

This share excludes Hong Kong and Singapore, which are entrepot economies and whose exports contain asubstantial share of re-exports.

1

Even excluding oil exporters and very poor countries, there are many countries thatspecialize in primary commodities. In Chile, Cote d’Ivoire, Kenya, and Peru, for instance,
manufacturing accounts for less than 25% of merchandise exports (Table 2). One might expect
this group to have been most helped by China’s growth, with the commodity boom lifting theirterms of trade. Other countries have diversified export production, spanning agriculture, mining,and manufacturing.

In Argentina, Brazil, Colombia, Egypt, Indonesia, and Vietnam,

manufacturing accounts for 30% to 55% of merchandise exports. For this group, China may
represent a mixed blessing, increasing the prices of some of the goods they produce anddecreasing the prices of others.
manufacturing.

A third group of countries is highly specialized in

In Hungary, Mexico, Pakistan, the Philippines, and Turkey, manufacturingaccounts for more than 80% of merchandise exports. This last group includes the countries most
likely to be adversely affected by China, as it has become a rival source of supply in theirprimary destination markets. Between 1993 and 2005, China’s share of total imports rose from
5% to 15% in the United States and from 4% to 12% in the European Union.
In this paper, we examine the impact of China’s growth on developing countries thatspecialize in export manufacturing. Using the gravity model of trade, we decompose bilateral
trade into components associated with demand conditions in importing countries, supply
conditions in exporting countries, and bilateral trade costs. In theory, growth in China’s exportsupply capabilities would allow it to capture market share in the countries to which it exports its
output, possibly reducing demand for imports from other countries that also supply thesemarkets. We calculate the export demand shock that China’s growth has meant for otherdeveloping countries, as implied by gravity model estimation results.

2

To isolate economies that are most exposed to China’s manufacturing exports, we selectdeveloping countries that are also highly specialized in manufacturing. After dropping richcountries, very poor countries, and small countries, we identify 10 medium-to-large developing
economies for which manufacturing represents more than 75% of merchandise exports:
Hungary, Malaysia, Mexico, Pakistan, the Philippines, Poland, Romania, Sri Lanka, Thailand,
and Turkey.2 This group includes a diverse set of countries in terms of geography and stage ofdevelopment, hopefully making our results broadly applicable.

We focus on developing

countries specialized in manufacturing, as for this group the impact of China on their production
activities is largely captured by trade in manufactures. Manufacturing is also a sector for which
the gravity model is well suited theoretically.
In section 2, we use a standard monopolistic-competition model of trade to develop an
estimation framework. The specification is a regression of bilateral sectoral imports on importer
country dummies, exporter country dummies, and factors that affect trade costs (bilateral
distance, sharing a land border, sharing a common language, belonging to a free trade area, andimport tariffs). When these importer and exporter dummies are allowed to vary by sector and by
year, they can be interpreted as functions of structural parameters and country-specific variables
that determine a country’s export supply and import demand.

Changes in import-demandconditions can be decomposed into two parts, one of which captures changes in income levels inimport markets and another of which captures changes in sectoral import price indices for thosemarkets, which are themselves a function of other countries’ export-supply dummies.
In section 3, we report coefficient estimates based on our framework. The data for the
analysis come from the UN COMTRADE database and the TRAINS dataset, which cover the2

In Table 2, it is apparent India would also satisfy our criteria. We exclude India because its recent growth
represents another potentially important global economic shock for other developing countries.

3

period to 1995 to 2005. We estimate country-sector-year import dummies, country-sector-year
export dummies, and sector-year trade cost elasticities using data on a large set of trading
economies that account for much of world trade. We begin by reporting estimated sectoral
exporter dummy variables for the 10 developing-country exporters vis-à-vis China. For 9 of the
10 countries, export supply dummies are strongly positively correlated with China’s, suggestingthat their comparative advantage is relatively similar to that of China. The results also describe
how each country’s export-supply capacities have evolved over time. Relative to each of the 10
countries, the growth in China’s export supply capabilities has been dramatic.
The main results, presented in section 4, suggest that had China’s export-supply capacity
been constant over the 1995 to 2005 period, export demand would have been 0.6% to 1.8%
higher in the 10 countries studied. The impact is somewhat larger when excluding resource
intensive industries or when focusing on industries in which China’s revealed comparativeadvantage appears to be strongest (apparel, footwear, electronics, toys).

For developing

countries highly specialized in manufacturing, it appears China’s expansion has represented onlya modest negative shock.
It is important to note that our results do not represent a general equilibrium analysis of
China’s impact on other developing economies. China’s export growth may have increased thenumber of product varieties available to these countries, thereby improving consumer welfare(Broda and Weinstein, 2005), or had positive effects on the demand for non-manufacturing
output.

Our approach accounts for neither of these effects or for other possible general-equilibrium consequences. Nevertheless, the results give a sense of the extent to which China isin competition with other large developing country exporters for market share abroad.

4

By way of conclusion, in section 5, we discuss what China’s continued growth may mean
for manufacturing-oriented developing countries.

2. Empirical Specification
Consider a standard monopolistic model of international trade, as in Anderson and van
Wincoop (2004) or Feenstra (2004). Let there be J countries and N manufacturing sectors,
where each sector consists of a large number of product varieties. All consumers have identical
Cobb-Douglas preferences over CES sectoral composites of product varieties, where in each sectorn there are In varieties of n produced, with country j producing Inj varieties. There are increasing
returns to scale in the production of each variety. In equilibrium each variety is produced by amonopolistically-competitive firm and In is large, such that the price for each variety is a constantmarkup over marginal cost. Free entry drives profits to zero, equating price with average cost.
Consider the variation in product prices across countries. We allow for iceberg transport
costs in shipping goods between countries and for import tariffs. The cost-including-freight (c.i.f.)
price of variety i in sector n produced by country j and sold in country k is then⎛ σ ⎞Pinjk = ⎜ n ⎟ w nj t nk (d jk ) γ n ,⎝ σn − 1 ⎠(1)where Pinj is the free-on-board (f.o.b.) price of product i in sector n manufactured in country j; σn isthe constant elasticity of substitution between any pair of varieties in sector n; wnj is unit production
cost in sector n for exporter j; tnk is one plus the ad valorem tariff in importer k on imports of n(assumed constant for all exporters that do not share a free trade area with importer k); djk is
distance between exporter j and importer k; and γn is the elasticity of transport costs with respect todistance for goods in sector n.

5Given the elements of the model, the total value of exports of goods in sector n by exporter j
to importer k can be written as,
1− σ

σ −1

X njk = μ n Yk I nj Pnjk n G nkn,(2)

where μn is the expenditure share on sector n and Gnk is the price index for goods in sector n inimporter k. Equation (2) reduces to

X njk =

μ n Yk Inj w njτnjk (d jk ) γ n

(

)

H

1−σn

1−σn

,

(3)

γ
∑ Inh ⎡ w nh τnhk (d hk ) n ⎤
⎣
⎦
h =1

which can be written in log form as
ln X njk = ln μ n + ln

Yk

1−σn

H

γ
∑ Inh ⎡ w nh τnhk (d hk ) n ⎤
⎣
⎦
h =1

(

1−σ

)

+ ln Injw nj n + (1 − σ n ) ln τnjk + γ n (1 − σn ) ln d jk
(3’)

Regrouping terms in (3’), and allowing for measurement error in trade values, we obtainln X njk = θn + m nk + s nj + β1n ln τ jk + β2n ln d jk + ε njk .

(4)In equation (4), we see that there are five sets of factors that affect country j’s exports to country k
in sector n. The first term ( θn = ln μ n ) captures preference shifters specific to sector n; the second
1−σnHterm ( m nk = ln(Yk / ∑ Inh ⎡ w nh τnhk (d hk ) γ n ⎤
h =1

⎣

⎦

) ) captures demand shifters in sector n andimporter k (which are a function of importer k’s income and supply shifters for other countries that1−σalso export to k); the third term ( s nj = ln(I njw nj n ) ) captures supply shifters in sector n forexporter j (which reflect exporter j’s production costs and the number of varieties it produces in the
sector); the fourth and fifth terms (where β1n = 1 − σn and β2n = γ n (1 − σn ) ) capture trade costs

6

specific to exporter j and importer k (which in the empirical analysis we measure using importtariffs, bilateral distance, whether countries share a common language, whether countries share aland border, and whether countries belong to a free trade area); and the final term ( ε njk ) is a
residual. Exporter j’s shipments to importer k would expand if importer k’s income increases,
production costs increase or the number of varieties produced decreases in other countries that
supply importer k, exporter j’s supply capacity expands, or bilateral trade costs decrease.
Our first empirical exercise is to estimate equation (4). Then, we use the coefficientestimates to examine the role of China in contributing to changes in import demand in othercountries. To motivate this approach, consider import-demand conditions in country k, as embodied
in the importer dummy variables in (4). In theory,⎛ H−σn βn ⎞m nk = ln Yk − ln ⎜ ∑ I nh w1nh−σn τ1nhk
d hk ⎟ ,⎜⎟⎝ h =1⎠(5)which captures average expenditure per imported variety by country k in sector n. Import demandconditions in k are a function of income in k, export supply conditions in k’s trading partners(embodied in the number of varieties they produce and their production costs), and k’s bilateral
trade costs. Average expenditure per variety in country k would decrease if the number of varieties
produced globally increases (since a given sectoral expenditure level would be spread over morevarieties) or production costs in other countries increases (which would deflect expenditure awayfrom their varieties). Using (4), we can write (5) as,⎛ H ŝ βˆ 1n βˆ 2n ⎞m nk = ln Yk − ln ⎜ ∑ e nh τnhk d hk ⎟ ,⎜⎟⎝ h =1⎠(6)

7

where ŝ nh , βˆ 1n , and βˆ 2n are OLS coefficient estimates from (4).3

Over time, import-demandconditions in k will change as its income changes, its bilateral trade costs change, or export-supply
conditions in its trading partners change. As China’s export supply capacity in sector n improves(due either to increases in the number of varieties it produces or decreases in its production costs),
average expenditure per imported variety in country k would fall, leading to a decrease in the
demand for imports from k’s trading partners.
Following this logic, we construct the implied change in demand for imports by country k
associated with changes in China’s export-supply capacity. Actual import demand conditions in
sector n for country k at time t are⎛ H ˆ ˆ 1n βˆ 2n
ˆ
ˆ ⎞
ˆ
1n
m nkt = ln Ykt − ln ⎜ ∑ esnht τβnhkt
d hk + esnct τβnckt
dβck2n ⎟ ,⎜⎟⎝ h ≠c⎠(7)where c indexes China. Suppose China had experienced no growth in its export-supply capacity
between time 0 and time t. The counterfactual import-demand term for country k would then be
⎛ H
ˆ
ˆ
ˆ
ˆ ⎞
ˆ
1n
1n
 nkt = ln Ykt − ln ⎜ ∑ esˆ nht τβnhktmdβhk2n + esnc0 τβnckt
dβck2n ⎟ .
⎜⎟⎝ h ≠c⎠(8)

For each importing country in each sector, we calculate the value,⎡ ⎛ H⎛ H ˆ ˆ 1n βˆ 2n
ˆ
ˆ
ˆ
ˆ ⎞
ˆ
ˆ ⎞⎤
ˆ
ˆ
1n
1n
1n
 nkt − m nkt = − ⎢ln ⎜ ∑ esˆ nht τβnhktmdβhk2n + esnc0 τβnckt
dβck2n ⎟ − ln ⎜ ∑ esnht τβnhkt
d hk + esnct τβnckt
dβck2n ⎟ ⎥ ,
⎜
⎟
⎜
⎟
⎠
⎝ h ≠c⎠ ⎦⎥⎣⎢ ⎝ h ≠c(9)3

One might imagine estimating (4) subject to the constraint in (6). In practice, imposing such nonlinear constraints
would greatly complicate the regression analysis. As a simple check on whether the constraints on the value of mnk
appear to be satisfied in the data, we estimate equation (6) using OLS (after first estimating (4)), the results forwhich are reported in Table 4. In most specifications, the coefficient on log income ranges between 0.5 and 1.0 andthe coefficient on the import price index (constructed from the coefficient estimates) is -0.3 to -0.5. These
coefficient signs and magnitudes are roughly consistent with theory.

8

which shows the amount by which import demand in k would have differed at time t had China’s
export supply capacity remained unchanged between time 0 and time t.
We refer to the quantity in (9) as the counterfactual change in import demand in country k
and sector n. For each of the 10 developing country exporters, we calculate the weighted average of(9) across importers and sectors. The resulting value is the difference in the demand for a country’s
exports implied by growth in China’s export-supply capacity. An exporter will be more exposed toChina’s growth the more its exports are concentrated in goods for which China’s export-supply
capacity has expanded and the more it trades with countries with which China has relatively low
trade costs. Obviously, this counterfactual exercise is not general-equilibrium in nature, and should
be interpreted with caution. Still, it may be useful for gauging which export producers have been
more exposed to export competition from China.
One problem with estimating (4) is that at the sectoral level there is zero trade between
many country pairs.4 Tenreyro and Santos Silva (2005) propose a Poisson pseudo-maximum
likelihood (PML) estimator to deal with zero observations in the gravity model. In our application,
this approach is subject to an incidental-parameters problem (Wooldridge, 2002). While in a
Poisson model it is straightforward to control for the presence of unobserved fixed effects, it isdifficult in this and many other nonlinear settings to obtain consistent estimates of these effects.
Since, at the sectoral level, most exporters trade with no more than a few dozen countries, PML
estimates of exporter and importer country dummies may be inconsistent.
Our approach is to estimate (4) using OLS for a set of medium to large exporters (OECDcountries plus larger developing countries, which together account for approximately 90% of world
manufacturing exports) and medium to large importers (which together account for approximately
90% of world manufacturing imports). For bilateral trade between larger countries, there are
4

Zero bilateral trade values further complicate estimating (4) subject to the constraint in (6).

9

relatively few zero trade values. Since we do not account explicitly for zero bilateral trade in thedata, we are left with unresolved concerns about the consistency of the parameter estimates, which
the trade literature has only recently begun to address.5

3. Gravity Estimation Results

The trade data for the analysis come from the UN COMTRADE database and cover
manufacturing imports over the period 1995 to 2005. We examine bilateral trade at the fourdigit harmonized system (HS) level for the union of the 40 largest manufacturing exportindustries in each of the 10 developing-country exporters.6 The 40 industries account for the
majority of manufacturing exports in the 10 manufacturing exporters, ranging from 71% to 90%
for 7 of the 10 countries (the Philippines, Mexico, Turkey, Malaysia, Romania, Sri Lanka,
Pakistan) and from 48% to 62% in the 3 others (Hungary, Poland, Thailand). The tariff data,
which are based on Robertson (2007), come from the TRAINS database and are the simple
averages of available tariffs at the 10-digit HS level within each four-digit industry. We use thetariffs that are most applicable to each sector-country pair. For some country pairs, these are theimporter’s MFN tariffs, for other pairs (e.g., NAFTA members) it is tariffs governed by aregional trade agreement, and for others (e.g., U.S.-Israel) it is tariffs governed by a bilateral
trade agreement.7
We estimate the gravity equation in (4) on a year-by-year basis, allowing coefficients onexporter country dummies, importer country dummies, and trade costs to vary by sector andyear. The output from the regression exercise is for each sector a panel of exporter and importer
country dummy variables, trade-cost coefficients, intercepts, and residuals. The country-sector
5

See Helpman, Melitz, and Rubinstein (2007).
Choosing a subset of industries helps keep the dimension of the estimation manageable.
7
We replace missing tariff data with interpolated values based on non-missing tariff data. See Robertson (2007).
6

10

dummies are the deviation from U.S. sectoral mean trade by year (as the U.S. in the excluded
country in all regressions). For these coefficients to be comparable across time, the conditioning
set for a given sector (i.e., the set of comparison countries) must be constant. For each sector, welimit the sample to bilateral trading partners that have positive trade in every year during thesample period.8

3.1 Summary of Coefficient Estimates

To provide some background on the industries included in the sample, Table 3 shows the
5 largest industries in terms of manufacturing exports for each of the 10 developing-country
exporters. For 9 of the countries (all except Hungary), manufacturing exports are concentrated ina handful of industries, with the top 5 industries accounting for at least 20% of merchandise
exports, and for 5 of the countries, the top 5 industries account for at least 30% of merchandise
exports. For 7 of the countries, at least one of their top 5 export industries is also one that
accounts for at least 2% of China’s manufacturing exports.
The regression results for equation (4) involve a large amount of output. In each year, weestimate over 10,000 country-sector exporter coefficients and country-sector importer
coefficients and over 200 trade-cost coefficients. To summarize exporter and import dummies
compactly, Figures 1a and 1b plot kernel densities for the sector-country exporter and importer
coefficients (where the densities are weighted by sector-country exports or imports). Figure 1ashows that most exporter coefficients are negative, consistent with sectoral exports for mostcountries being below the United States. Over the sample period, the distribution of exporter
coefficients shifts to the right, suggesting other countries are catching up to the United States.

8

This restriction may introduce selection bias into the estimation.

11Vertical lines indicate weighted mean values for China’s exporter coefficients in 1995 (equal to0.44) and 2005 (equal to 1.78), which rise in value over time relative to the overall distribution ofexporter coefficients, suggesting China’s export-supply capacity has improved relative to othercountries over the sample period. Evidence we report later supports this finding. In Figure 1b,
most importer coefficients are also negative, again indicating sectoral trade values for mostcountries are below those for the United States.
To provide further detail on the coefficient estimates, Table 4 gives median values of the
trade cost elasticities by year, weighted by each sector’s share of world trade. The estimates arein line with results in the literature (Anderson and van Wincoop, 2004). The coefficient on log
distance is negative and slightly larger than one in absolute value; adjacency, common language,
and joint membership in a free trade agreement are each associated with higher levels of bilateral
trade; and the implied elasticity of substitution (given by the tariff coefficient) is close to 3.

3.2 Export Supply Capabilities in Developing Countries vis-à-vis China

Of primary interest is how the 10 countries’ export-supply capacities compare to those of
China.

Figures 2a-2c plot sectoral export coefficients for each country against exporter

coefficients for China over the sample period (using sectoral shares of annual manufacturingexports in each country as weights). For each country, there is a positive correlation in its
sectoral export dummies with China, with the correlation being strongest for Turkey (0.63),
Romania (0.59), Hungary (0.48), Thailand (0.48), Malaysia (0.47), Poland (0.45), Sri Lanka(0.45); somewhat smaller for the Philippines (0.33) and Pakistan (0.32); and weakest for Mexico(0.12). The correlation for Mexico appears to be driven by industries related to petroleum, whichbegan the period as major export sectors for the country but have since declined in importance.

12

The positive correlation in sectoral export coefficients with China suggests that most of
the large developing countries that specialize in manufacturing have strong export supply
capabilities in the same sectors in which China is also strong. In other words, the comparativeadvantage of these countries is closely aligned with that of China. To the extent that the major
trading partners of these countries are the same as those of China, they would be exposed toexport-supply shocks in China, meaning that growth in China would potentially reduce demand
for the manufacturing exports that they produce and lower their terms of trade.
To see how export supply capacities have evolved over time, Figures 3a-3c plot the yearon-year change in country-sector export dummies for each of the 10 developing countries against
those for China, weighted by each country’s sectoral trade shares. Immediately apparent is thatthe range of growth in China’s export-supply capacities is large relative to that of any otherdeveloping country. Changes in China’s export dummies take on a wide range of values, while
none of the 10 countries shows nearly as much variation. As a consequence, the correlation
between changes in sectoral export dummies between each country and China is weaker than thecorrelation in levels. The strongest correlations in changes are for Romania (0.50) and Malaysia(0.47); followed by Thailand (0.32), Sri Lanka (0.31), Hungary (0.30), the Philippines (0.30),
Poland (0.22), and Turkey (0.21); and then by Pakistan (0.16) and Mexico (0.14).

4. Counterfactual Exercises

In this section, we compare the change in import demand conditions facing each of the 10
developing-country exporters under two scenarios, one in which import demand evolved as
observed in the data (as implied by the coefficient estimates from the gravity model) and asecond in which we hold constant the change in China’s export-supply capabilities.

This

13

exercise allows us to examine whether China’s growth in export production has represented anegative shock to the demand for exports from other developing countries.
According to the theory presented in section 2, sectoral import demand in a country isaffected by its GDP and by its sectoral import price index. Its price index, in turn, is affected byexport supply conditions in the countries from which it imports goods, weighted by trade costs withthese countries. From equation (8), this yields the following relationship:⎛ H ŝ βˆ 1n βˆ 2 n ⎞
m nkt = α 0 + α1 ln Ykt + α 2 ln ⎜ ∑ e nht τnhkt d hk ⎟ + ηnkt ,⎜⎟⎝ h =1⎠(10)

ˆ nht , sˆ nht , βˆ 1n , and βˆ 2n are OLS coefficient estimates of the sectoral importer dummy, thewhere msectoral exporter dummy, the tariff elasticity, and the distance elasticity from equation (4). In
theory, it should be the case that α1=1 and α2=-1.
To verify that the relationships posited by theory are found in the data, Table 5 shows
coefficient estimates for equation (10). Departing from equation (10) slightly, we also include
log population as an explanatory variable (to allow demand to be affected by market size andaverage income), though it is imprecisely estimated in most regressions. We show specifications
under alternative weighting schemes and for three sets of industries: all manufacturing industries,
excluding core resource-intensive industries,9 and excluding all resource-intensive industries.10
Demand conditions in resource-intensive industries may differ from other manufacturingindustries due to their reliance on primary commodities as inputs. Coefficients on GDP (α1 inequation (10)) are all positive and precisely estimated, ranging in value from 0.52 to 1.05.
Coefficients on the import price index (α2 in (10)) are all negative and precisely estimated,
9

At the two-digit HS level, these industries are beverages, cereals, animal oils and fats, sugar, meat and seafood
processing, fruit and vegetable processing, tobacco, non-metallic minerals, mineral fuels and oils, and inorganic
chemicals.
10
In addition to those industries mentioned in note 9, this excludes organic chemicals, pharmaceuticals, fertilizers,
plastics, rubber, leather products, and wood products.

14

ranging in value from -0.31 to -0.53. While the coefficient estimates do not exactly match thetheoretically predictions, they are broadly consistent with the model.
The next exercise is to use the coefficient estimates to examine the difference in demandfor exports faced by the 10 developing country exporters that is associated with the growth inChina’s export supply capacity. The first step is to calculate for each importer in each sector thevalue in equation (9), which is,⎡ ⎛ H⎛ H ˆ ˆ 1n βˆ 2n
ˆ
ˆ
ˆ
ˆ ⎞
ˆ
ˆ ⎞⎤
ˆ
ˆ
1n
1n
1n
 nkt − m nkt = − ⎢ln ⎜ ∑ esˆ nht τβnhktmdβhk2n + esnc0 τβnckt
dβck2n ⎟ − ln ⎜ ∑ esnht τβnhkt
d hk + esnct τβnckt
dβck2n ⎟ ⎥ .
⎜⎟⎜⎟⎢⎣ ⎝ h ≠c⎠⎝ h ≠c⎠ ⎥⎦This shows the amount by which average import demand in country k and sector n at time twould have differed had China’s export supply capacity (which reflects the number of productvarieties it produces and its production costs) had remained constant between time 0 and time t.11
 nkt − m nkt for each of the 10
The second step is to calculate the weighted average value of mdeveloping country exporters, using as weights the share of each importer and sector in a country’s
total manufacturing exports (where these shares are averages over the sample period).12
Table 6 shows the results from the counterfactual calculation where year 0 corresponds to1995 and year t corresponds to 2005.13 The first column shows results in which we set α2 fromequation (10) equal to -1, as implied by theory. In 2005, the difference in export demand ranges

11

An alternative to the counterfactual exercise we propose would be to examine the change in China’s exports
implied by the change in tariffs facing China over the sample period. Were China’s economy in steady state, thenthe change in tariffs would be the primary shock affecting the country’s exports. However, over the sample period
China very much appears to be an economy in transition to a new steady state, associated with a sectoral andregional reallocation of resources brought about by the end of central planning. Thus, focusing on tariffs alone
would miss the primary shock to China’s export growth.
12
In taking this weighted average across industries, we are approximating for the percentage change in imports withthe log change. This approximation becomes less precise as the growth in imports becomes larger. In unreported
results, we experimented with using the percentage change. The findings are similar to what we report in Table 6.
13
Because we do not estimate equation (4) subject to the constraint in equation (6), one needs to be careful ininterpreting our results. The counterfactual exercises we report apply to changes in demand conditions rather thanchanges in trade. Absent imposing the equilibrium conditions implied by the model, we cannot interpret thecounterfactual exercises as implying how trade would change.

15

from 3.3% in Romania to -1.1% in Sri Lanka, with the Philippines and Mexico among the mostaffected countries and Pakistan and Turkey also among the least affected. The mean difference
across countries is 1.6%. Thus, in the developing countries we consider, demand for exports on
average would have been 1.6% higher had China’s export-supply capacity remained constant from1995 to 2005. The negative difference for Sri Lanka indicates that China’s export-supply capacities
declined in the country’s primary export industries (which include tea). The second column shows
results in which we set α2 equal to -0.5, which is at the upper end of the coefficient estimates for
Table 5. The mean difference in export demand across countries drops to 0.8%. For no country
does China represent a negative export demand shock of greater than 1.7%.
Columns (3)-(6) repeat the results, excluding resource-intensive industries from the sample.
China’s comparative advantage appears to lie in labor-intensive activities rather than industries thatuse oil, minerals, timber, or foodstuffs intensively. In column (3), the mean difference acrosscountries is 2.7% (compared to 1.6% in column (1)), indicating that China’s impact is indeed larger
for industries that do not use resources intensively. The most affected countries are Pakistan,
Romania, Mexico, Malaysia, and the Philippines. In column (4), in which the value of α2 is set to 0.5, the mean difference across countries is 1.3%.
Finally, columns (7) and (8) show results when we limit the industries to apparel, footwear,
electronics, and toys. These include labor intensive industries (or, in electronics, industries withlabor intensive stages of production), in which one might imagine that China’s comparativeadvantage is strongest. For these industries, China’s impact is indeed larger, at least for somecountries. The counterfactual increase in export demand would be 3.0% across all countries, withvalues over 4.0% occurring in Romania, Poland, Pakistan, and Mexico.

16

The counterfactual exercises indicate that had China’s export-supply capacities remained
unchanged demand for exports would have been modestly larger for other developing countries thatspecialize in manufacturing exports. To repeat, across all manufacturing industries, the average
difference in export demand is 0.8% to 1.6%; for non-resource-intensive industries, the average
difference is 1.3% to 2.7%. These are hardly large values, suggesting that even for the countries
that would appear to be most adversely affected by China’s growth it is difficult to find evidence
that the demand for their exports has been significantly reduced by China’s expansion.

5. Discussion

In this paper, we use the gravity model of trade to examine the impact of China’s growthon the demand for exports in developing countries that specialize in manufacturing. China’s
high degree of specialization in manufacturing makes its expansion a potentially significant
shock for other countries that are also manufacturing oriented. Of the 10 developing countries
we examine, 9 have a pattern of comparative advantage that strongly overlaps with China, as
indicated by countries’ estimated export-supply capacities. Yet, despite the observed similarities
in export patterns, we find that China’s growth represents only a small negative shock in demand
for the other developing countries’ exports. While there is anxiety in many national capitals over
China’s continued export surge, our results suggest China’s impact on the export market share ofother manufacturing exporters has been relatively small.
There are several important caveats to our results. Our framework and analysis are
confined to manufacturing industries. There may be important consequences of China fordeveloping-country commodity trade, which we do not capture. The counterfactual exercises wereport do not account for general-equilibrium effects. There could be feedback effects from17

China’s growth on prices, wages, and the number of product varieties produced that cause us tomisstate the consequences of such shocks for other developing countries.

There are also

concerns about the consistency of the coefficient estimates, due to the fact that we do not accountfor why there is zero trade between some countries.

18References

Anderson, James E. and Van Wincoop, Eric. “Trade Costs.” Journal of Economic Literature,
September 2004.
Broda, Christian, and David Weinstein. 2006. “Globalization and the Gains from Variety.”
Quarterly Journal of Economics, 121(2).
Devlin, Robert, Antoni Estevadeordal, and Andrés Rodriguez-Clare. 2005. The Emergence ofChina: Opportunities and Challenges for Latin America and the Caribbean. Washington,
DC: Inter-American Development Bank.
Eichengreen, Barry, and Hui Tong. 2005. “Is China’s FDI Coming at the Expense of OtherCountries?” NBER Working Paper No. 11335.
Feenstra, Robert C. Advanced International Trade: Theory and Evidence. Princeton: Princeton
University Press, 2003.
Feenstra, Robert C., Robert Lipsey, Haiyan Deng, Alyson C. Ma, and Hengyong Mo. 2005.
“World Trade Flows: 1962-2000.” NBER Working Paper No. 11040.
Helpman, Elhanan, Marc J. Melitz, and Yona Rubinstein. 2007. “Trading Partners and Trading
Volumes,” mimeo, Harvard University.
Lopez Cordoba, Ernesto, Alejandro Micco, and Danielken Molina. 2005. “How Sensitive Are
Latin American Exports to Chinese Competition in the U.S. Market?” Mimeo, InterAmerican Development Bank.
Robertson, Raymond (2007) “World Trade and Tariff Data” mimeo, Macalester College
Santos Silva, J.M.C., and Silvana Tenreryo. 2005. “The Log of Gravity.” The Review of
Economics and Statistics, forthcoming.
Wooldridge, Jeffrey M. 2002. Econometric Analysis of Cross Section and Panel Data.
Cambridge, MA: MIT Press.

19

Table 1: Total Exports by Country Group (billions of 2000 USD)
Exporter

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

China
Sample of ten
developing
country exporters
Twelve largest
industrialized
exporters
Other exporters(developing anddeveloped)

35.9

72.0

89.7

168.2

211.5

250.8

269.4

297.5

296.3

324.8

388.1

389.8

464.1

582.3

749.0

897.7

79.6

155.0

180.3

208.7

266.1

330.0

360.4

389.6

393.6

422.6

489.5

464.5

491.4

554.3

644.4

696.6

1127.1 1561.6 1889.9 2068.6 2693.9 3273.8 3300.4 3314.6 3299.1 3322.6 3456.9 3186.7 3251.1 3614.6 4175.2 4359.2
371.6

478.6

563.4

627.7

794.3

937.9 1006.6 1034.2

968.4

994.5 1133.7 1086.7 1148.2 1355.7 1642.6 1878.4

Notes: Sample of 10 developing country exporters is Hungary, Malaysia, Mexico, Pakistan, the Philippines, Poland, Romania, Sri Lanka, Thailand, andTurkey; the twelve largest industrialized exporters (as of 2005) are Canada, France, Germany, Italy, Japan, Korea, the Netherlands, Spain, Switzerland,
Taiwan, the U.S. and the U.K.; other exporting nations excludes Hong Kong and Singapore.

Table 2: Specialization in Manufacturing for Developing Countries
Country
China
Philippines
Pakistan
Hungary
MexicoTurkeyRomania
PolandMalaysia
India
Sri Lanka
ThailandUkraineMoroccoSouth Africa
BrazilIndonesia
VietnamSenegalEgypt, Arab Rep.
GuatemalaColombia
Argentina
Zimbabwe
Kenya
Russian Federation
Kazakhstan
Peru
Cote d'Ivoire
Chile
Venezuela, RB
Saudi Arabia
EcuadorIran, Islamic Rep.
Syrian Arab Republic

Manufacturing (%merchand. exports)
88.21
85.83
84.96
83.09
82.65
80.14
79.85
78.32
78.26
75.30
74.93
74.23
68.89
62.55
56.22
54.18
52.15
46.47
42.64
35.69
34.53
34.25
31.36
28.34
23.43
23.18
22.61
20.44
18.17
16.15
12.70
10.61
9.93
8.93
8.36

Manufacturing GDP per capita(% GDP)(2000 US$)
32.28
979
22.56
996
15.91
531
23.48
4591
19.96
5682
15.48
2915
24.11
1805
18.66
4356
30.23
3894
15.79
458
16.12
838
32.60
2085
24.99
691
17.05
1240
19.36
3072
-3441
27.62
842
18.47
406
12.44
424
18.54
1456
13.23
1694
15.49
2039
19.91
7488
15.50
586
11.79
420
17.48
1811
15.10
1329
15.99
2078
19.81
621
19.45
4924
18.82
4749
10.20
9086
12.00
1368
12.66
1634
10.30
1128

Population(millions)1260.3
75.8
138.4
10.2
97.6
67.3
22.2
38.4
23.0
1015.2
18.9
61.4
49.2
27.9
43.6
173.9
206.4
78.4
10.4
67.4
11.2
42.1
36.9
12.5
30.7
146.0
15.0
25.9
16.6
15.4
24.3
20.7
12.3
63.6
16.8

Notes: This table shows data for all countries with more than 10 million inhabitants and per
capita GDP greater than $400 and less than $10,000 (in 2000 prices). Figures are averages overthe period 2000-2005.

Table 3: Major Export Industries in 10 Developing Countries

Mfg rank
1
2
3
4
5

Share of Country's
Total Exports
0.035
0.026
0.023
0.022
0.020

Share of China's
Total Exports
0.026
0.024
0.003
0.013
0.001

Crude OilElectric Circuits
Rough Wood
Receivers
Sawn Wood

1
2
3
4
5

0.103
0.087
0.060
0.050
0.038

0.048
0.001
0.001
0.023
0.001

2709
8703
8708
8544
8407

Crude Oil
Motor Vehicles
Motor Vehicle Parts
Wire
Engines

1
2
3
4
5

0.219
0.066
0.054
0.041
0.036

0.048
0.000
0.001
0.003
0.000

Pakistan
Pakistan
Pakistan
Pakistan
Pakistan

5205
5201
5208
6302
4203

Cotton Yarn
CottonCotton Fabrics
House Linens
Leather Apparel

1
2
3
4
5

0.186
0.097
0.063
0.061
0.056

0.002
0.004
0.010
0.010
0.011

Philippines
Philippines
Philippines
Philippines
Philippines

8542
1513
8471
2603
7403

Electric Circuits
Coconut Oil
Data Processing Machines
CopperRefined Copper

1
2
3
4
5

0.124
0.037
0.031
0.029
0.027

0.001
0.000
0.005
0.000
0.000

Country
Hungary
Hungary
Hungary
Hungary
Hungary

HS4
6204
6403
8544
2710
8708

Description
Female Suits
Footwear
Wire
Non-Crude Oil
Motor Vehicle Parts

Malaysia
Malaysia
Malaysia
Malaysia
Malaysia

2709
8542
4403
8527
4407

MexicoMexicoMexicoMexicoMexico22

Table 3: Continued

Rank
1
2
3
4
5

Share of Country's
Total Exports
0.072
0.047
0.030
0.025
0.022

Share of China's
Total Exports
0.008
0.000
0.026
0.003
0.017

Furniture NES
Iron and Steel
Female Suits
Non-Crude Oil
Seats

1
2
3
4
5

0.079
0.076
0.048
0.046
0.045

0.003
0.003
0.026
0.013
0.002

902
6204
6206
7103
6203

Tea
Female Suits
Female Blouses
Precious Stones
Male Suits

1
2
3
4
5

0.079
0.068
0.062
0.050
0.043

0.003
0.026
0.015
0.000
0.017

ThailandThailandThailandThailandThailand

8473
8471
4001
8542
1701

Office Mach Parts
Data Processing Machines
RubberElectric Circuits
Sugar (Solid)

1
2
3
4
5

0.049
0.048
0.039
0.037
0.028

0.005
0.005
0.000
0.001
0.001

TurkeyTurkeyTurkeyTurkeyTurkey

6110
6204
4203
6104
2401

Sweaters
Female Suits
Leather Apparel
Knit Female Suits
Tobacco

1
2
3
4
5

0.049
0.048
0.045
0.042
0.041

0.031
0.026
0.011
0.003
0.001

Country
PolandPolandPolandPolandPoland

HS4
2701
7403
6204
9403
6203

Description
Coal
Refined Copper
Female Suits
Furniture NES
Not Knit Male Suits

Romania
Romania
Romania
Romania
Romania

9403
7208
6204
2710
9401

Sri Lanka
Sri Lanka
Sri Lanka
Sri Lanka
Sri Lanka

Notes: This table shows for each country the five largest manufacturing industries in terms ofexports, the industry’s share in the country’s total merchandise exports, and the industry’s sharein China’s merchandise exports (each averaged for the period 1995-2005).

23

Table 4: Median Estimated Trade Cost Elasticities

Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005

Log
distance-1.169-1.174-1.174-1.174-1.171-1.171-1.176-1.176-1.178-1.180-1.181

CommonFree Tradelanguage Adjacency Agreement
0.732
0.484
0.325
0.725
0.470
0.313
0.732
0.468
0.314
0.761
0.494
0.339
0.766
0.479
0.337
0.739
0.432
0.306
0.744
0.447
0.311
0.748
0.457
0.323
0.740
0.448
0.317
0.733
0.436
0.307
0.715
0.412
0.289

Tariff-3.173-3.122-3.109-3.097-3.074-3.051-3.030-3.059-3.031-2.999-2.964

Notes: Coefficient estimates are expressed as trade-value-weighted median values for
manufacturing industries.

24

Table 5: Correlates of Country Sector Import Dummies

Samplelog GDPWithout Trade Weights
With Trade Weights
All
Exclude core Exclude all
Exclude core Exclude allAllresource
manufact.
resource
resource
resource
manufact.
intensive
intensive
intensive
intensive
0.939
0.983
1.045
0.529
0.520
0.664
(0.03)(0.01)(0.02)(0.07)(0.05)(0.03)

log population-0.127(0.05)-0.125(0.05)-0.228(0.02)

0.041(0.08)

0.062(0.03)-0.032(0.03)

log importprice index-0.358(0.05)-0.386(0.08)-0.307(0.01)-0.531(0.09)-0.477(0.12)-0.303(0.03)R Squared

0.376

0.378

0.499

0.278

0.184

0.520

12894210809784724

12894210809784724

N

Notes: This table shows regression of country-sector import dummies on log GDP, log
population, and the log import price index. Standard errors (clustered by industry and year) arein parentheses. The sample spans 1995-2005 for one of three groups of industries (allmanufacturing, excluding core resource intensive industries, excluding all resource intensive
industries). All regressions include sector-year dummy variables. Weighted regressions use theshare of a sector in a country’s manufacturing exports as weights.

25

Table 6: Counterfactual Difference in Export Demand

All manufacturing
Excluding coreExcluding all
Apparel, footwear,
industries
resource industries resource industries electronics, toys
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
α=
-1
-0.5
-1
-0.5
-1
-0.5
-1
-0.5
Hungary
0.025
0.013
0.028
0.014
0.029
0.015
0.018
0.009
Malaysia

0.019

0.010

0.032

0.016

0.034

0.017

0.020

0.010

Mexico

0.002

0.001

0.032

0.016

0.034

0.017

0.042

0.021

Pakistan

0.014

0.007

0.015

0.007

0.041

0.021

0.049

0.025

Philippines

0.028

0.014

0.028

0.014

0.032

0.016

0.015

0.008

Poland

0.018

0.009

0.018

0.009

0.022

0.011

0.052

0.026

Romania

0.033

0.017

0.034

0.017

0.040

0.020

0.055

0.028

Sri Lanka-0.011-0.006-0.016-0.008-0.007-0.004-0.006-0.003Thailand

0.017

0.009

0.019

0.010

0.023

0.012

0.023

0.012

Turkey

0.018

0.009

0.021

0.011

0.021

0.011

0.033

0.017

Mean

0.016

0.008

0.021

0.011

0.027

0.013

0.030

0.015

Notes: This table shows how manufacturing export demand would have differed in 2005 for agiven country had China’s export-supply capacities remained unchanged between 1995 and2005, based on the methodology outlined in the text.

26

1996
2005

2000

.3

Density

.2

.1

0
-15

-10

-5
Exporter Coefficients

05

Figure 1a: Estimated Sector-Country Exporter Coefficients, Selected Years

27

1996
2005

2000

Density

.4

.2

0
-10

0
Importer Coefficients

10

Figure 1b: Estimated Sector-Country Importer Coefficients, Selected Years

28

Figure 2a: Sectoral Export Coefficients for Individual Developing Countries and China(deviations from US industry means)(deviations from US industry means)
5

Thailand Exporter Coefficient

Mexico Exporter Coefficient

5

0

-5

-10

-15

0

-5

-10

-15
-10

-5

0
China Exporter Coefficient-55(deviations from US industry means)

55Philippines Exporter Coefficient

5

Malaysia Exporter Coefficient0
China Exporter Coefficient(deviations from US industry means)

0

-5

-10

0

-5

-10

-15
-15
-10

-5

0
China Exporter Coefficient

5

-5

10

0
China Exporter Coefficient5

Figure 2b: Sectoral Export Coefficients for Individual Developing Countries and China(deviations from US industry means)(deviations from US industry means)

5

Poland Exporter Coefficient

Turkey Exporter Coefficient

5

0

-5

-10

-15

0

-5

-10

-15
-10

-5

0
China Exporter Coefficient

5

-5

10

(deviations from US industry means)0
China Exporter Coefficient

5

10

5

10

(deviations from US industry means)

5

Romania Exporter Coefficient

Hungary Exporter Coefficient

5

0

-5

-10

-15

0

-5

-10

-15
-10

-5

0
China Exporter Coefficient

5

-5

0
China Exporter Coefficient29

Figure 2c: Sectoral Export Coefficients for Individual Developing Countries and China(deviations from US industry means)(deviations from US industry means)
5

Sri Lanka Exporter Coefficient

Pakistan Exporter Coefficient

5

0

-5

-10

0

-5

-10

-15
-15
-10

-5

0
China Exporter Coefficient

-5

5

0
China Exporter Coefficient5

Figure 3a: Changes in Sectoral Export Coefficients, Individual Countries and China(changes in deviations from US means)(changes in deviations from US means)Thailand Exporter Coefficient

Mexico Exporter Coefficient

5

5

0

-5

0

-5

-10

-10
-4

-2

0
China Exporter Coefficient

2

-4

4

-2

0
China Exporter Coefficient2(changes in deviations from US means)

Philippines Exporter Coefficient

Malaysia Exporter Coefficient(changes in deviations from US means)

5

0

-5

5

0

-5

-10
-10
-5

0
China Exporter Coefficient

5

-4

-2

0
China Exporter Coefficient230

Figure 3b: Changes in Sectoral Export Coefficients, Individual Countries and China(changes in deviations from US means)(changes in deviations from US means)

5

Poland Exporter Coefficient

Turkey Exporter Coefficient

5

0

-5

0

-5

-10

-10
-5

0
China Exporter Coefficient-55(changes in deviations from US means)555Romania Exporter Coefficient

Hungary Exporter Coefficient0
China Exporter Coefficient(changes in deviations from US means)

0

-5

-10

0

-5

-10
-2

0
2
China Exporter Coefficient

-5

4

0
China Exporter Coefficient5

Figure 3c: Changes in Sectoral Export Coefficients, Individual Countries and China(changes in deviations from US means)(changes in deviations from US means)

5
Sri Lanka Exporter Coefficient

Pakistan Exporter Coefficient

5

0

-5

-10

0

-5

-10
-2

-1

0
China Exporter Coefficient

1

2

-4

-2

0
China Exporter Coefficient231