Tsukuba Economics Working Papers
No. 2018-001

Do Exporters Respond to Both Tariffs and Nominal Exchange Rates? Evidence from
Chinese Firm-Product Databy
Zhe Chen and Yoshinori Kurokawa
February 2018
This version: March 2021

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

Do Exporters Respond to Both Tariffs and Nominal Exchange
Rates? Evidence from Chinese Firm-Product Data∗
Zhe Chen†

Yoshinori Kurokawa‡
March 4, 2021

Abstract
Past evidence on exchange rates and exports implies that nominal exchange rates might not
matter for the extensive margin of exports. Using Chinese firm-product data during 2000-2006,
however, this paper finds that the effect of nominal exchange rates on exporter numbers is
significant and even comparable with that of tariffs. The effects are larger for processing trade,
low income destinations, and differentiated products. Financial constraints are a factor thatsignificantly enlarges the effect of nominal exchange rates on exporter numbers.
JEL classification: F12, F14, F31
Keywords: exchange rates, tariffs, extensive margin of exports, financial constraints, China∗

We are very grateful to Bo-Young Choi, Gian Luca Clementi, Loretta Fung, Lu Han, Chang Hong,
Sewon Hur, Isao Kamata, Tim Kehoe, Matthew Kidder, Bingjing Li, Bernabe Lopez-Martin, FerdinandoMonte, Peter Morrow, Hiroshi Mukunoki, Yang Shen, Nyeong Seon Son, Siqiang Yang, Haishan Yuan, andYuan Yuan for their useful comments and suggestions. We also thank the participants at the Fall 2017
Midwest Macroeconomics Meetings, the Fall 2017 Midwest International Trade Conference, the 2018 AMES,
the 77th Annual Meeting of the JSIE, the 40th Annual Conference of the KIEA, and the ETSG 2019. Chen
gratefully acknowledges financial support from the National Natural Science Foundation of China (Grant
Number 71803020), and Kurokawa gratefully acknowledges financial support from the JSPS KAKENHI(Grant Number JP19K01644). This paper previously circulated under the title “Does Yuan Appreciation
Weaken the Increase in Exporters due to Trade Liberalization? Evidence from Chinese Firm-Product Data.”†
School of International Trade and Economics, University of International Business and Economics. 1302
Boxue Building, 10 Huixin Dongjie, Chaoyang District, Beijing 100029, China. E-mail: zhechen@uibe.edu.cn.
‡
Faculty of Humanities and Social Sciences, University of Tsukuba. 1-1-1 Tennodai, Tsukuba, Ibaraki
305-8571, Japan. E-mail: kurokawa.yoshi.fw@u.tsukuba.ac.jp.

11Introduction

One observation about recent China is that more and more Chinese firms have been entering
foreign markets. Another observation is that China has faced significant changes in bothtariffs, charged by trade partners, and nominal exchange rates in recent years (see Figure 1).
These observations suggest that both tariffs and nominal exchange rates might significantly
affect the number of China’s exporters—the extensive margin (EM) of China’s exports.
Theoretically, the answer is yes. The introduction of nominal exchange rates and sticky
wages into the Melitz (2003) model predicts that the EM of exports can respond to not only
trade liberalization but also nominal exchange rates (e.g., Rodrı́guez-López, 2011). Empirically, on the other hand, the answer might not be yes. The empirical literature has generally
found that the effect of nominal exchange rates on exports is limited unlike that of realexchange rates (e.g., Baxter and Stockman, 1989). Moreover, Fitzgerald and Haller (2018)
argue that even real exchange rates have a much less important effect on the EM of exports
than tariffs. Thus nominal exchange rates might not matter for the EM of exports. However, China is the largest developing country with a managed floating rate system, which isa unique case compared with the cases analyzed by the above empirical studies. It is thus
important to investigate whether this argument is valid for China.
Based on this background, we now use Chinese firm-product annual data from 2000 to2006 with 138 trade partners to test whether the number of China’s exporters—the EM
of China’s exports—responds to both changes in tariffs and changes in nominal exchange
rates. The benchmark results support the theoretical prediction. Overall, changes in tariffs
and nominal exchange rates have comparable effects on exporter numbers at the productlevel. Specifically, if the tariffs decrease by 1 standard deviation, the exporter numbers
would increase by 0.071 standard deviation. If the nominal exchange rates depreciate by 1
standard deviation, the exporter numbers would increase by 0.249 standard deviation. We
further construct another measurement of the EM of exports that is based on China’s export
variety numbers of a product. We find that if the tariffs decrease by 1 standard deviation, theexport variety numbers would increase by 0.043 standard deviation. If the nominal exchange
rates depreciate by 1 standard deviation, the export variety numbers would increase by 0.351
standard deviation. In sum, our paper finds strong evidence that the changes in nominal
exchange rates affect the EM of exports in terms of both exporter numbers and export2

variety numbers and that the effect of nominal exchange rates is even larger than that oftariffs in terms of standard deviation. These findings are different from some past evidence(e.g., Fitzgerald and Haller, 2018) suggesting that tariffs are much more important thanexchange rates for the EM of exports.
We further perform several robustness checks. We find that the benchmark results arerobust for main trade partners, quarterly data, and real exchange rates. When we restrict
our sample to positive trade flows, the coefficient on tariffs remains significant while that on
nominal exchange rates becomes insignificant. This finding indicates that nominal exchange
rates have a larger effect on new export product-destination pairs. In addition, we find thatthe effects of tariffs and nominal exchange rates are larger for processing trade, low income
destinations, and differentiated products.
Then we provide a possible explanation for our results. We suggest the financial constraints (FCs) of exporters as a possible factor that might enlarge the impact of nominal
exchange rates on China’s exporters at the product level. As mentioned by Héricourt andPoncet (2015), exchange rate risk creates uncertainty for the variable costs of exporters andtherefore FCs can disable exporters from hedging exchange rate risk, magnifying the negative
effect on exports. As pointed out by Foley and Manova (2015), developing countries usually
suffer from weak financial institutions. Our product-level results show that the interaction
terms between the nominal exchange rates and the FC index are positive and significant,while the interaction terms between the tariffs and the FC index are negative but insignificant. This suggests that the impact of nominal exchange rates on China’s exporters are moresignificant for financially constrained products.
Finally, we discuss implications from our results on trade elasticity. We particularlycompare our product-level elasticities of the EM of exports with elasticities estimated byother studies. The comparisons reveal that our trade cost elasticities are smaller than andour real exchange rate elasticities are similar to others.
The contributions of our paper are fivefold. First, our paper contributes to the literature on tariffs vs. exchange rates. There are studies that have empirically investigated the
responses of firms or exporters to trade liberalization vs. exchange rates (e.g., Baggs et al.,3

2009; Fitzgerald and Haller, 2018). Motivated by Feenstra’s (1989) symmetric hypothesis,1
Baggs et al. (2009) (BBF) use Canadian industry-firm annual data and find that the overall
effect of the real exchange rate changes on Canadian firm survival/exit is comparable tothat of the Canada-U.S. Free Trade Agreement (CUSFTA) tariff changes. Fitzgerald andHaller (2018) (FH) use Irish firm-product annual data and find that tariffs are much more
important than real exchange rates for Irish exporters. While our nominal results are in linewith the real results by BBF and FH in that the ordinary least squares (OLS) coefficient on
exchange rates is smaller than that on tariffs, we also show the results based on standardized
beta coefficients2 to more appropriately estimate the relative importance of exchange rates totariffs. In addition, FH and BBF both provided evidence for developed countries, our paper
for the developing country China. Therefore, our paper is a satisfactory complement to theexisting literature.
Second, our paper contributes to the literature on nominal exchange rates and exports.
Although past aggregate-level evidence has found that nominal exchange rates do not affectexports significantly, some recent studies at the firm-level find the significant effect of nominal
exchange rates. Using Japanese firm-level data, Dekle and Ryoo (2007) find the significant
effect on the export quantities of firms. Corsetti et al. (2019) use Chinese customs data toshow the significant effect on the markups of exporters. Our paper now adds to this literatureby first showing that nominal exchange rates significantly affect the EM of China’s exports
such as exporter numbers and export variety numbers.
Third, our study contributes to the literature on the effect of exchange rates on the EM of
China’s exports. Some past studies (e.g., Tang and Zhang, 2012; Li et al., 2015) also showed
the significant effect of exchange rates on China’s exporters. However, our paper, the different
version from these studies, is necessary. We mainly look at the effect of exchange rates onexporter numbers at the product-destination level, whereas these studies mainly examine theeffect on firms’ entry & exit decisions. In addition, one interest of our paper is to investigate
the heterogeneous effects of exchange rates across different sectors/products, destinations,
1

Feenstra (1989) theoretically argues that the responses of the import price to tariffs and exchange rates
are symmetric, and empirically verifies this argument using U.S. import prices of Japanese cars, trucks, andmotorcycles.
2
The standardized beta coefficient standardizes the OLS coefficient to capture the change in standard
deviation units of the dependent variable in response to a one standard deviation increase in the independent
variable.

4and trade modes. These past studies are interested in the heterogeneous effects across firms.
We think that it would be important to investigate differences in sectors/products as well as
those in firm productivity.3
Fourth, our paper contributes to the growing empirical evidence on FCs and trade. One
set of studies analyzes the effect of FCs on exporters. Manova (2013) introduces financial
frictions into the Melitz (2003) model and applies it to aggregate trade data for a large
panel of countries. She finds that both the EM and intensive margin (IM) are affected bycredit constraints. Using Chinese firm-level data, Li and Yu (2009) find that firms that are
more capable of obtaining external finance have higher levels of exports. Another set ofstudies extends the argument by adding exchange rates. There are studies that empiricallyshow that the effect of exchange rates on trade can be larger for firms/industries that are
more financially constrained. For example, Héricourt and Poncet (2015), Strasser (2013), andDekle and Ryoo (2007) show firm-level evidence for China, Germany, and Japan, respectively.
Chen et al. (2020) show industry-level evidence for China. In this line, our paper now adds
the product-level evidence for China. While Chen et al. (2020) highlight the effect of FCs
on the relationship between exchange rate risk and trade mode choice, our paper highlightsthat on the relationship between nominal exchange rates and the EM of exports.
Finally, our paper makes contributions to the literature on trade elasticity, in particular,
the elasticity of EM with respect to trade costs or exchange rates. Our elasticity of exporter
numbers with respect to trade costs is 1.031; that with respect to nominal exchange rates
0.113; and that with respect to real exchange rates 0.112. Our results also imply that theelasticity of export variety numbers with respect to trade costs is 0.336; that with respect
to nominal exchange rates 0.0873; and that with respect to real exchange rates 0.107. Ourtrade cost elasticities are smaller than past estimates (Bas et al., 2017; Feenstra and Kee,2007), whereas our real exchange rate elasticities are similar to past estimates (Tang andZhang, 2012; Colacelli, 2010). To our knowledge, no past studies have estimated the nominal
exchange rate elasticity of exporter numbers or export variety numbers.
The rest of this paper is organized as follows. Section 2 provides theoretical motivation forour empirical question. Section 3 documents the regression specification and data. Section 4reports the regression results and robustness checks and provides a possible explanation for3

Fan et al. (2019), for example, also point out the heterogeneous effects of China’s trade liberalization
across sectors.

5

our results. Section 5 discusses some implications from our results. Section 6 concludes.

2

Theoretical Motivation

We provide a theoretical motivation for our empirical analysis on the relationship between
trade costs/nominal exchange rates and the EM of exports. Rodrı́guez-López (2011) introduces nominal exchange rates and sticky wages to the Melitz (2003) model of heterogeneous
firm trade. First, he introduces the exogenous nominal exchange rates defined as the priceof the foreign currency in terms of the home currency (e.g., China’s yuan per U.S. dollar).
Second, he assumes that nominal wages are fixed in the home currency. Then the modelshows that the number of exporters—the EM of exports—in an industry can respond to achange in nominal exchange rates, like the Melitz (2003) model shows that it can respond to adecrease in trade costs—trade liberalization. The intuitions are straightforward. A decreasein trade costs would make exports more profitable for firms, and thus encourage more firms
entering foreign markets. Similarly, an increase in nominal exchange rates—depreciation—
would decrease the production costs for firms, given the sticky wage, and thus force more
firms entering foreign markets.
As shown by the Rodrı́guez-López (2011) model, nominal exchange rates can affect thefirm-level exports and thereby the number of exporters in an industry. Empirical studies(e.g., Dekle and Ryoo, 2007; Strasser, 2013; Chen et al., 2020), however, show/imply thatthe effect of nominal exchange rates is different across firms/industries. One factor thatcan lead to differences in the effect of exchange rates is FCs.4 Héricourt and Poncet (2015)
mention that exchange rate risk creates uncertainty for the variable costs of exporters. ThenFCs can disable exporters from hedging exchange rate risk, magnifying the negative effecton exports. This argument implies that the effect of nominal exchange rate changes canbe larger for firms/industries that are more financially constrained. In fact, for the firmlevel, Strasser (2013) finds that financially constrained German firms have higher levels of
exchange rate pass-through than those that are not financially constrained and that the effectof exchange rates on the expected export volumes of financially constrained firms is twice aslarge as that of firms that are not. Dekle and Ryoo (2007) find that financially constrained
4
Foley and Manova (2015) have a detailed literature survey about how financial frictions shape multinationals’ export decisions.

6

Japanese firms have higher elasticities of export volumes with respect to exchange rates thanthose that are not. For the industry level, Chen et al. (2020) find that Chinese firms choose
the pure assembly trade mode to mitigate exchange rate fluctuations and the pattern ismore pronounced for firms in liquidity-constrained industries and mitigated by better local
financial development.
In this paper, we investigate the effect of trade costs/nominal exchange rates on exporter
numbers at the product level. Thus, the theoretical arguments described above yield two
hypotheses, which we will explore empirically:(a) An increase in nominal exchange rates—depreciation—causes an increase in exporter
numbers (the EM of exports) at the product level as does a decrease in trade costs.
(b) FCs enlarge the effect of nominal exchange rates on exporters at the product level.

3

Regression Specification and Data

We now present the regression specification to empirically test the two hypotheses in theprevious section using comprehensive Chinese export data at the product-country-year level.

3.1

Regression Specification

3.1.1

Exporter Numbers and Export Value/Quantity per Exporter

First, we investigate the responses of exporter numbers to trade costs and nominal exchange
rates. The regression specification is as follows:ln(Eijt ) = α0 + α1 ln(1 + T arif fijt ) + α2 ln(N ERjt ) + α3 ln(Relative CP Ijt )(3.1)+ P rocessingijt + Sit + Zjt + ρi + θj + ηkt + ijt
Here, Eijt is the number of exporters of product i from China to country j at time t. T arif fijt
is the import tariff of product i charged by country j at time t. We use 1 + T arif fijt toproxy trade costs (this way is the same as that in previous studies, such as Bas et al., 2017;
Fitzgerald and Haller, 2018). N ERjt is the nominal exchange rate, which is defined as China’s
yuan per currency of country j at time t; thus, when N ERjt increases (decreases), China’s
7

yuan depreciates (appreciates). Notably, using nominal exchange rates in our estimation
is not only from the theoretical demand, but also from the methodological consideration.
As also pointed out by Corsetti et al. (2019), using real exchange rates in the estimation
implicitly imposes a one-to-one linear relationship between nominal exchange rates and theConsumer Price Index (CPI) ratio. This restriction, however, may not be true. Thus werelax this restriction and decompose real exchange rates into two parts: nominal exchange
rates and the relative CPI. The relative CPI is defined as the CPI ratio between country j
and China; thus, when the relative CPI increases, the inflation in country j is larger thanthat in China. As explored by previous studies (e.g., Yu, 2015; Dai et al., 2016; Marquez
and Schindler, 2007), the trade mode would affect Chinese exporters’ behaviors; thus, we
use the processing trade dummy,5 P rocessingijt , to capture the heterogeneity between trademodes. The same kind of product could be exported under both trade modes: processing andordinary trade modes. We thus divide the same product into the processing traded productand the ordinary traded product, and calculate exporter numbers for each. Accordingly, thedummy is 1 for the former and 0 for the latter.
Before China’s entry to the WTO in 2001, only eligible firms were allowed to export
directly.6 Thus, some firms have to export through intermediaries (Ahn et al., 2011). As
required by the WTO, China gradually relaxed the export requirements, and all firms wereallowed to export directly after 2004. This policy change would affect the EM of exports from
China. One way to take into account the export right issue is to control the eligible firmshare (the ratio of eligible firms to all firms) at the product-year level.7 We, however, cannot
get the product-year-level share due to the data limitation. Instead we control the directexport share and its square at the product-year level, which are included in Sit . Following
5

Processing trade refers to importing all or part of the raw and auxiliary materials and re-exporting thefinished products after processing.
6
Bai et al. (2017) discuss this export right issue at the firm level in details. Only firms, whose registeredcapital was above the requirement, were allowed to export directly. Ahn et al. (2011) also point out anotherexport restriction: the export licensing system at the product level. By issuing the export licenses of crucial
products, mostly agriculture and steel related products, Chinese government can control the exports of theseproducts. Before China’s entry to the WTO in 2001, there were 245 HS8 codes listed for trading licenseliberalization out of roughly 7000 HS8 codes. Though the export values of these products only accounted for
0.9 percent of total export values in 2000, this relaxation of export restriction might affect the EM of exports
from China. Our main results remain robust even when we exclude these products.
7
Bai et al. (2017) calculate the eligible firm share at the industry-year level. Since we use industry-year
fixed effects to control the time-variant industry heterogeneity, the eligible firm share variable would beabsorbed by the industry-year dummies.

8

Ahn et al. (2011), we identify intermediaries by firm names and calculate the direct export
share defined as the ratio of export values by non-intermediary firms to total export values.
In addition, the macroeconomic situation in country j could affect the demand of Chinese
goods, and thus we control for ln(GDP), ln(GDP per capita), and ln(Trade) in country j attime t, which are included in Zjt , to capture these demand shocks. The trade index is definedas the sum of exports and imports of goods and services measured as a share of GDP.
Finally, we control for the product fixed effect ρi , country fixed effect θj and industrytime fixed effect ηkt . Here, industry k is defined at the HS2 level. We use the industry-time
fixed effect to capture the demand shock of industry k at time t. The independent variables
that we are interested in are tariffs at the product-country-year level and nominal exchange
rates at country-year level. Thus, we cluster the regression at the country-year level. If α1 is
negative and α2 is positive, then the hypothesis (a) is supported. When tariffs decrease andChina’s yuan depreciates, the EM of exports increases.
Using the same regression specification, we also empirically investigate the responses ofexport value/quantity per exporter—the IM of exports—at the product-country-year level.
In that case, Eijt is the export value/quantity per exporter of product i from China to country
j at time t.
3.1.2

Entry and Exit of Exporters

A change in exporter numbers could be caused by entry or/and exit of exporters. We also
investigate the responses of the entry and exit of exporters to tariffs and nominal exchange
rates, respectively. If the export value of a firm is 0 in year t − 1 but positive in year t, thenwe consider this firm as a new exporter in year t. If the export value of a firm is positivein year t − 1 but 0 in year t, then we consider this firm as an exit exporter in year t. We
normalize the number of new (exit) exporters by the total number of exporters of product i
from China to country j. In particular, we normalize the new exporter numbers in year t by
the total number of exporters in year t while we normalize the exit exporter numbers in yeart by the total number of exporters in year t − 1. Thus, the sample sizes are different between

9entry and exit regressions.8 The regression specification is as follows:ln(ENijt ) = β0 + β1 ln(1 + T arif fijt ) + β2 ln(N ERjt ) + β3 ln(Relative CP Ijt )(3.2)+ P rocessingijt + Sit + Zjt + ρi + θj + ηkt + µijt
Here, the regression specification is similar to equation (3.1). The only difference is that ENijt
now is the normalized number of new (exit) exporters of product i from China to country j
at time t. When ENijt is the normalized number of new exporters, if β1 is negative and β2
is positive, then a decrease in tariffs and China’s yuan depreciation cause additional Chinese
firms to enter foreign markets. When ENijt is the normalized number of exit exporters, if β1
is positive and β2 is negative, then a decrease in tariffs and China’s yuan depreciation cause
less Chinese firms to quit foreign markets.
3.1.3

Export Variety Numbers

The growth of exports is not only driven by more exporters but also by more export productvarieties. We construct another measurement of the EM of exports that is based on theexport variety numbers of a product. We define the product category at the HS4 level andthen use the number of HS6 product varieties within HS4 category from China to country jas the measurement of the EM of exports. The regression specification is as follows:ln(EVijt ) = γ0 + γ1 ln(1 + T arif fijt ) + γ2 ln(N ERjt ) + γ3 ln(Relative CP Ijt )(3.3)+ P rocessingijt + Sit + Zjt + ρi + θj + ηkt + νijt
Here, EVijt is the number of HS6 product varieties within each HS4 category i from China
to country j. The independent variables are the same as equation (3.1). If γ1 is negativeand γ2 is positive, then a decrease in tariffs and China’s yuan depreciation increase China’s
export variety numbers of a product.
8

We also use other definitions of new and exit exporters as robustness checks. If a firm never exported
in previous years and year t is the first year in which this firm exports, then we consider this firm as a
new exporter in year t. If a firm quits foreign markets in year t and never returns to foreign markets, thenwe consider this firm as an exit exporter in year t. The results for these new definitions are robust. Note,however, our dataset is from 2000 to 2006 and thus the new definitions are not accurate for an exporter that
actually enters or exits foreign markets beyond this period.

10

3.1.4

Financial Constraints

As implied by the hypothesis (b) in Section 2, the responses of the EM of exports to tariffs
and nominal exchange rates might also be different depending on product-level FCs. FCs
can enlarge the responses of exporter numbers to tariffs and nominal exchange rates at the
product level. The regression specification is as follows:ln(Eijt ) = α0 + α1 ln(1 + T arif fijt ) + α2 ln(N ERjt ) + α3 ln(Relative CP Ijt )+ α4 ln(1 + T arif fijt ) × F Ci + α5 ln(N ERjt ) × F Ci

(3.4)

+ P rocessingijt + Sit + Zjt + ρi + θj + ηkt + νijt
Here, Eijt is the number of exporters of HS6 product i from China to country j at time t.
F Ci is the FC index of product i. Other independent variables are the same as equation(3.1). If α4 is negative and α5 is positive, then the responses of exporter numbers to tariffs
and nominal exchange rates are larger for the more financially constrained products.

3.2

Data

The nominal exchange rate and CPI data are from the International Monetary Fund (IMF)
at the annual level. The macroeconomic data (GDP, GDP per capita, and trade values) arefrom World Bank at the annual level. The tariff data is from the United Nations Conference
on Trade and Development (UNCTAD)—Trade Analysis Information System (TRAINS).
The tariffs of each HS6 level product charged by each country are at the annual level. In thisstudy, we use the effectively applied (AHS) tariff, which is defined as the lowest available
tariff. In most cases, AHS tariffs are the same as most-favored nation (MFN) tariffs. In
rare cases, however, AHS tariffs are lower than MFN tariffs, which indicates that China has
a trade agreement with these products. China’s export data is from their customs agency
at the transaction level. We aggregate these transactions to the firm-product-country-yearlevel. Thus, we have the number of exporters and export value/quantity per exporter for eachHS6 product. The HS code changed in 2002 at the HS6 level. In order to keep the productcategory consistent, we firstly aggregate the product to the HS6 level and then convert all
products to HS1996 by using the concordance between HS2002 and HS1996. The FC data
is from the U.S. Compustat dataset at the firm level. We aggregate them to the 4-digit
11

Standard Industrial Classification (SIC) level. We then map these SIC codes to the HS6level. Thus, the FC data is at the HS6 level. The sample period is from 2000 to 2006.
Table 1 presents the data summary, which merges the tariff, exchange rate, export andmacroeconomic datasets. The first part of Table 1 demonstrates that both the exporter
numbers and the export destination numbers increased over time. From 2000 to 2006, theexporter numbers increased by 197 percent and the destination numbers increased by 35
percent. The exported product numbers were almost constant. The second part of Table 1
shows that the export values share of direct trade increased from 67 percent to 79 percent.
After the export right reform, more exporters began to export directly.
The third part of Table 1 demonstrates that the tariffs, charged by partners, decreased
over time.9 In 2000, the simple average tariffs were 9.47 percent, and in 2006 the tariffs
were 7.96 percent. In addition, we divide trade partners into two groups: high income
and low income destinations. The high income destinations are defined as countries whose
average GDP per capita during 2000-2006 is above 10,000 U.S. dollars. The simple average
tariffs of the high income destinations decreased by 13 percent and those of the low income
destinations decreased by almost 27 percent from 2000 to 2006. Figure 2 shows the average
evolution of tariffs charged by China’s selected trade partners. The selected trade partners
are China’s top 21 export destinations minus Hong Kong and Singapore. The tariffs chargedby Hong Kong and Singapore are almost zero, and thus we exclude these two destinations
from our analysis. Germany, Netherlands, United Kingdom (UK), Italy, France, Spain, andBelgium are members of European Union (EU) during the sample period and charge thesame tariffs, and thus we use EU to represent them. The average log deviation of grosstariff from 2000 was stable for high income destinations.10 However, this index decreased
sharply for low income destinations, in particular India and Thailand. The pattern is similar
to that of Table 1. Liu and Ma (2020) find that the reductions in foreign tariffs on China’s
exports favored processing exports. We confirm it in our data. The weighted AHS tariffs ismuch smaller in processing trade than that in ordinary trade. From 2000 to 2006, the tariff
reduction for processing trade was 34 percent and that for ordinary trade was 31 percent.
9

Table 1 shows the average of tariffs, calculated by us, over the destinations included in our merged
dataset, while Figure 1 shows the average of tariffs, calculated by TRAINS, over all the destinations includedin the original tariff dataset.
10
We use coefficients on year dummies in country-by-country regression of ln(1 + T arif f ) on HS6 fixed
effects and year dummies.

12

The fourth part of Table 1 and Figure 1 demonstrate the time trend of China’s nominal
effective exchange rates (NEERs). After China’s WTO accession in 2001, the NEERs decreased until 2005—depreciation. On July 21, 2005, the People’s Bank of China announced
a revaluation of the yuan and a reform of the exchange rate regime. After 2005, the NEERs
began to increase—appreciation—and did so until 2015.11
3.2.1

Tariffs and Exchange Rates for Selected Trade Partners

In this section, we present more detailed data for tariffs and nominal exchange rates. Figure
3 shows the time trends of tariffs charged to China and nominal exchange rates for China’s
selected trade partners from 2000 to 2006. Here, Germany, Netherlands, Italy, France, Spain,
and Belgium are labeled as Euro Area. The tariff data is at the annual level, and the exchange
rate data is at the monthly level. The figures show that both tariffs and nominal exchange
rates significantly changed during this period. In the case of Euro Area, for example, tariffs,
charged by Euro Area, decreased and the yuan depreciated from 2002 to 2004. In the caseof Japan, the tariffs charged by Japan decreased and the yuan first appreciated and thendepreciated during the period 2001 to 2005. Thus the changes in both tariffs and nominal
exchange rates might have contributed to the fast growth of Chinese exporter numbers.
3.2.2

Product-level Financial Constraints

There are two ways to measure the dependence on external finance. Rajan and Zingales(1998) use the share of capital expenditure not financed by cash flow, and Raddatz (2006)
uses the ratio of inventory to sales. As pointed out by Raddatz (2006), the first measurement
is more of a long-term proxy. In this study, we examine the effect of exchange rates onexports, and we think that the exchange rate shocks are better to be considered as short-run
financial shocks. Thus, we use the ratio of inventory to sales as a proxy for product-levelFCs. First, we extract firm-level information from the U.S. Compustat dataset from 2000
to 2006 and calculate the industry median of the inventory to sales ratios at the 4-digit SIC
level. Second, we map these SIC codes to HS6 level. Figure 4 presents the distribution of
11Here, an increase (decrease) in the NEERs means China’s yuan appreciation (depreciation), whereas anincrease (decrease) in the independent variable N ER—defined as China’s yuan per a foreign currency—means
China’s yuan depreciation (appreciation).

13

inventory/sales ratio across products. The mean is 17 percent and the standard deviation is
0.07.

4

Regression Results

We first show our regression results for the relationship between tariffs/nominal exchange
rates and the EM of exports. We next perform their robustness checks. We finally provide a
possible explanation for our results on the basis of FCs.

4.1

Exporter Numbers and Export Value/Quantity per Exporter

Table 2 presents the coefficients of equation (3.1), that is, the coefficients of the EM and IM
of exports on tariffs and nominal exchange rates. Note that for some product-country-year
triplets, the export flows are zero. Thus, we use ln(Eijt + 1) in equation (3.1).12 According
to the hypothesis (a) in Section 2, a reduction in tariffs charged by trade partners andthe depreciation of China’s yuan would increase the number of China’s exporters. The
benchmark results in the column 1 verify the hypothesis. If the tariffs decrease by 1 percent,the exporter numbers would increase by 1.031 percent. If China’s yuan depreciates by 1
percent, the exporter numbers would increase by 0.113 percent. The coefficient of the EM ofexports on tariffs is larger than that on nominal exchange rates.
Table 2 also demonstrates that the reductions in tariffs and the depreciation of China’s
yuan stimulate the IM of exports from China. If the tariffs decrease by 1 percent, the export
value per exporter would increase by 3.456 percent. If China’s yuan depreciates by 1 percent,the export value per exporter would increase by 1.022 percent. The results are similar forthe export quantity per exporter. If the tariffs decrease by 1 percent, the export quantity
per exporter would increase by 2.478 percent. If China’s yuan depreciates by 1 percent, theexport quantity per exporter would increase by 0.699 percent. Again, the coefficient on tariffs
is larger than that on nominal exchange rates.
In addition, Table 2 demonstrates that exports from China would expand when the inflations in export destinations are higher than that in China. Less exporters participate in
12
Since the number of exporters is count data, following Silva and Tenreyro (2006), we also run the Pseudo
Poisson maximum likelihood estimation for exporter numbers. The results are shown in Appendix A.2.

14

the processing trade mode, and their average export value/quantity is also smaller. GDP
in the destination has a minor impact on exports, but GDP per capita has a positive andsignificant effect on exports. If the export destination has a higher trade/GDP ratio, boththe EM and IM of exports are higher. Finally, the relationship between the direct export
share and the EM (IM) of exports is inversely U-shaped. This implies that the EM (IM) of
exports increases with the direct export share firstly, and then decreases with it.
One concern is that the coefficients on tariffs and nominal exchange rates cannot directly
be interpreted as the relative importance of these two factors due to their heterogeneous
variances. Thus, the column 2 reports the standardized beta coefficients based on the specification in the column 1. If the tariffs decrease by 1 standard deviation, the exporter numbers
would increase by 0.071 standard deviation. If the nominal exchange rates depreciate by 1
standard deviation, the exporter numbers would increase by 0.249 standard deviation. For
the IM of exports, we have the similar results. The beta coefficients indicate that the response of the EM of exports to nominal exchange rates is even much larger than that totariffs in terms of standard deviation. Thus, nominal exchange rates have a significant effecton China’s EM of exports. In this sense, our results are similar to BBF, who also point out
the importance of exchange rates and argue that the overall effect of real USD/CAD changes
was comparable to that of CUSFTA although the OLS coefficient of exchange rates is smaller
than that of tariffs. In addition, the beta coefficient of the relative CPI is much smaller thanthat of nominal exchange rates. Thus, the effect of nominal exchange rates would dominatethat of the relative CPI.

4.2

Entry and Exit of Exporters

In this section, we discuss the exporter dynamics in greater details. Exporter numbers dependon the entry of new exporters and the exit of incumbent exporters. Thus, we separately
examine the responses. Again, we use ln(ENijt + 1) in equation (3.2) to solve the zero
trade flows for some product-country-year triplets. The column 1 of Table 3 shows thatthe reductions in tariffs, charged by trade partners, increase not only entry but also exit ofexporters. If the tariffs decrease by 1 percent, the new exporter numbers would increase by
0.080 percent and exit exporter numbers would increase by 0.073 percent. The coefficientof entry is larger than that of exit. Thus, the net response to the tariff reductions is an15

increase in the exporter numbers. At first sight, the results for exit might appear odd. In
the Melitz model, a tariff reduction causes the entry of new exporters but does not cause
the exit of incumbent exporters. As Feng et al. (2017) argue, however, an extended Melitz
model can show that a tariff reduction increases both the entry and exit of exporters. This isbecause an increase in exporters due to the tariff reduction could increase the competition inforeign markets, forcing low productivity exporters to exit. Feng et al. (2017) also empirically
document that the entry and exit of China’s exporters increased following China’s WTO
accession in 2001. On the other hand, the column 1 of Table 3 shows that the changes of
nominal exchange rates only affect entry but not exit of exporters. If China’s yuan depreciatesby 1 percent, new exporter numbers would increase by 0.032 percent and exit exporter
numbers would, though insignificantly, increase by 0.017 percent.
To gauge the relative importance of the two factors, the column 2 reports the standardized
beta coefficients based on the specification in the column 1. If the tariffs decrease by 1
standard deviation, new exporter numbers would increase by 0.022 standard deviation. If
the nominal exchange rates depreciate by 1 standard deviation, the new exporter numbers
would increase by 0.293 standard deviation. Thus, the response of new exporter numbers tonominal exchange rates is even much larger than that to tariffs in terms of standard deviation.
If the tariffs decrease by 1 standard deviation, exit exporter numbers would increase by 0.02
standard deviation.

4.3

Export Variety Numbers

In this section, we discuss the responses to tariffs and exchange rates from the perspectiveof China’s export variety numbers of a product. We use ln(EVijt + 1) in equation (3.3) tosolve the zero trade flows for some product-country-year triplets. The column 1 of Table 4shows that the reductions in tariffs, charged by trade partners, increase the export variety
numbers of a product. If the tariffs decrease by 1 percent, the export variety numbers ofa product would increase by 0.336 percent. If China’s yuan depreciates by 1 percent, theexport variety numbers of a product would increase by 0.087 percent. The column 2 reports
the standardized beta coefficients based on the specification in the column 1. If the tariffs
decrease by 1 standard deviation, the export variety numbers of a product would increase by
0.043 standard deviation. If the nominal exchange rates depreciate by 1 standard deviation,
16

the export variety numbers of a product would increase by 0.351 standard deviation.
In sum, our benchmark results indicate that (1) changes in tariffs and nominal exchange
rates significantly affect the EM of exports in terms of both the exporter numbers and theexport variety numbers; (2) the changes of tariffs significantly affect both entry and exit ofexporters while the changes of nominal exchange rates significantly affect the entry but notexit of exporters; (3) in particular, the response of the EM of exports to nominal exchange
rates is much larger than that to tariffs in terms of standard deviation.

4.4

Robustness Checks

In this section, we examine whether trade modes, export destinations (high income vs. low
income destinations; main trade partners), positive trade flows, data frequency, real exchange
rates, and sectoral/product differences would affect the benchmark results.
Trade Modes
In processing trade, exporters import all or part of materials and re-export the assembled
products. It is possible that the effect of an exchange rate appreciation on the export price ofassembled products is canceled by the effect on the import price of the materials for assembly.
Thus, the response to exchange rate fluctuations on processing trade is likely to be different
from that on ordinary trade. We thus divide trade into two groups: ordinary and processing
trade. The columns 3 and 4 of Table 2 show that the coefficients of the EM and IM of exports
on tariffs and nominal exchange rates are larger for the processing trade. Thus, firms doing
the processing trades are more responsive to the tariff and nominal exchange rate shocks.
The columns 3 and 4 of Table 3 show the opposite results. The coefficients of entry and exiton tariffs and nominal exchange rate are higher in ordinary trade. In this study, however, we
are more interested in the net effects on the EM of exports. Combining the results in Tables
2 and 3 reveals that, although the entry and exit of exporters in ordinary firms are moresensitive to tariffs and nominal exchange rates, the net effects are larger in the processing
trade. The columns 3 and 4 of Table 4 show that the change of tariffs mainly affects theexport variety numbers of a product in processing trade while the change of nominal exchange
rates mainly affects the export variety numbers of a product in ordinary trade. In sum, theresponses of exports in two trade modes to the changes of tariffs and nominal exchange rates
17

are mixed.
The difference in the effects of tariffs and nominal exchange rates between trade modes
could be due to the different distributions of products and destinations. Most products inprocessing trade were electrical equipment (30.2 percent of total export values) and mechanical appliances (27.5 percent), and the major export destinations in processing trade werehigh-income countries (USA 35.6 percent and JPN 18.2 percent). On the other hand, theexported products and destinations in ordinary trade were more diverse.
For the different effects of tariffs on exporter numbers in processing and ordinary trade,
there are two more possible explanations. First, similar to our results, Liu and Ma (2020) alsofind that the reductions in foreign tariffs on China’s exports favored processing exports. They
argue that the results are driven by the fact that relative to ordinary producers, processing
producers were more concentrated in sectors that experienced large export tariff reductions.
We confirm it in our table 1. Second, processing firms import intermediate inputs and capital
equipment duty free but are not allowed to sell the resulting output on the domestic market.
On the other hand, ordinary firms are required to pay tariffs on imports but are then free tosell the resulting output on the domestic market. That is, all products of processing firms areexported, whereas not all products of ordinary firms are exported. Ordinary firms have moreroom to absorb tariff shocks while processing firm are more sensitive to tariff changes. Thus
this can be another possible reason why processing firms are more affected by a reduction in
tariffs imposed by foreign countries.
High Income vs. Low Income Destinations
After China became a WTO member in 2001, the tariffs charged by trade partners, especially low income destinations, decreased considerably; for example, the comparison among
India and high income destinations (Figure 2). Last two columns in Tables 2, 3 and 4 showthat the results are mixed. In terms of the EM of exports (exporter numbers, new exporter
numbers, exit exporter numbers, and export variety numbers), the coefficients for low income
destinations are either larger than or similar to those for high income ones. In terms of the
IM of exports (export value/quantity per exporter), the coefficients are larger for high income
destinations.

18

Main Trade Partners
Our data covers about 138 destinations, many of which are small economies. The trade withthese small economies might be significantly affected by unobservable factors other than tariffs and nominal exchange rates, such as political relationship.13 Thus, we restrict our sample
to the top 50 partners for robustness checks. The export value with these partners is about
97.6 percent of China’s total export value from 2000 to 2006. The results in the column 1 ofTable 5 show that our main findings are still robust.
Positive Trade Flows
For some product-country-year triplets, the trade flows are zero. Thus, in our regression specifications, we add a small constant (1) to the value of trade before taking logarithms, whichmight yield inconsistent results. In order to mitigate this concern, we restrict our sample tothe product-country pairs that appear in the whole period (2000-2006). The export value
in the positive trade flows is about 87 percent of that of the full sample. The column 2 ofTable 5 shows the results. For the continuing product-country pairs, the coefficient on tariffs
is smaller than that for the benchmark, and the coefficient on nominal exchange rates is not
significant. This result indicates that the tariffs and nominal exchange rates have a larger
effect on the new product-country pairs than the continuing product-country pairs. The
appreciation/depreciation of exchange rate would significantly affect Chinese firms to build
new product-destination trade relationship, but it would not affect the exporter numbers inexisting product-destination trade relationship.
Quarterly Data
The tariffs are at the annual level. In order to be consistent with tariffs, we also use theannual nominal exchange rates. However, the fluctuations of the annual exchange rate data
might be small. Thus, we perform robustness checks with the quarterly nominal exchange
13

We also discuss other unobservable shocks in Appendix A.3.

19

rate data.14 We conduct a stacked regression as follows:ln(Eijt ) = α0 + α1 ln(1 + T arif fijt ) +3X

α2h ln(N ERjt−h ) + α3 ln(Relative CP Ijt )
(4.1)

h=0

+ P rocessingijt + Sit + Zjt + ρi + θj + ηkt + ijt
Here,

P3

h=0 α2h measures the response to exchange rate fluctuations over the current andlast three quarters. The column 3 of Table 5 demonstrates that the coefficient of nominal
exchange rates remains robust for the EM of exports. However, the coefficients for the IM ofexports are not significant.
Real Exchange Rates
The nominal and real exchange rates present very different movements in the United Statesfrom 2000-2006 (Figure A1 in Appendix A.1). Motivated by this observation, we performrobustness checks with the real exchange rates. Table 6 presents the results with the realexchange rates. Notably, the results are qualitatively similar to those for nominal exchange
rates. As we have pointed out in Section 4.1, the beta coefficients indicate that the impact
of nominal exchange rates dominates that of the relative CPI between China and a trade
partner. Thus the impacts of nominal and real exchange rates are qualitatively similar, andthe relative CPI has a limited effect.
Sectoral/Product Differences
The responses of the EM of exports to tariffs and nominal exchange rates might be different
across sectors/products. In this section, we firstly examine the responses by China’s top 10
export sectors at the HS2 level, which account for 66 percent of China’s exports. These sectors are Electrical equipment (21.9 percent), Mechanical appliances (18.1 percent), Apparel,
not knitted or crocheted (5.4 percent), Apparel, knitted or crocheted (4.5 percent), Optical
instruments (3.1 percent), Furniture (3 percent), Toys and sports requisites (2.8 percent),
Mineral products (2.5 percent), Footwear (2.5 percent), and Iron or steel (2.4 percent). The
numbers in parentheses are ratios of exports in each sector relative to all exports from China
from 2000 to 2006. Table 7 shows that the responses are different across sectors. The coeffi14

Naknoi (2015) investigates the EM of exports using the quarterly U.S. bilateral trade data.

20

cient of exporter numbers on tariffs is significant in 7 sectors and that on exchange rates isalso significant in 7 sectors.
Second, we test whether the responses of the EM of exports to tariffs and nominal exchange rates would be larger or smaller for differentiated products. The results in Table
8 show that the responses are larger for the differentiated products. First, following theclassification from Rauch (1999), we divide products into two groups: homogeneous and differentiated products. Rauch (1999) defines the product at the SITC4 level, and we convert
all products to HS6 using the concordance between HS1996 and SITC4. The columns 1 and
2 of Table 8 present the results. We find that the coefficients on tariffs and nominal exchange
rates are larger for the differentiated products. Second, following Broda and Weinstein (2006)
and Broda et al. (2008), we use the elasticity of product to measure the differentiation. We
get the export elasticity of product at the HS4 level for China from Broda et al. (2008).15
The elasticity is higher, the differentiation level is lower. The columns 3 and 4 of Table 8
present the results. Again, the coefficients on tariffs and nominal exchange rates are larger
for the lower elasticity products, that is, the more differentiated products. These results
are in line with some other empirical findings. Yi and Biesebroeck (2012) show, using data
for China’s imports from 129 countries during the period 2001–2006, that the response totariffs of the EM of exports to China is higher for differentiated goods than for homogeneous
goods. Colacelli (2010) find, using 136 countries’ bilateral export data during the period
1981–1997, that the response to exchange rates, though real, of the EM of exports is higher
for differentiated goods.
Our results are consistent with the Besedeš and Cole (2017) model. They introduce ad valorem tariffs and a government to the Chaney (2008) model, which is the asymmetric-country
version of the Melitz (2003) model. Then their model shows that the effect of a decreasein ad valorem tariffs on exporter numbers is larger in an industry with the lower elasticity
of substitution between varieties, that is, in the more differentiated industry. Analogously,
the model implies that the effect of an increase in nominal exchange rates—depreciation—on
exporter numbers is larger in the more differentiated industry. The reason is the following.
When tariffs decrease, the cutoff productivity for exports decreases and thus low productivity
firms enter the export market. If the elasticity of substitution is lower, each firm is more
15

Broda et al. (2008) use HS6 import data from the COMTRADE database from 1994-2003 to estimate
4-digit import and export elasticities for 15 non-WTO countries including China.

21

sheltered from competition. Thus a tariff reduction enables more low productivity firms toenter the export market, decreasing the cutoff productivity more.16

4.5

An Explanation for Our Results: Financial Constraints

Our product-level results have shown that the number of China’s exporters—the EM of
China’s exports—responded significantly to not only reductions in tariffs but also changes
in nominal exchange rates. One possible factor that might be able to explain the significant
impact of exchange rates on China’s exporters is the nature of changes in Yuan. Ruhl (2008)
argues that firms do not change their exporting status in response to temporary shocks
while some firms do change it in response to permanent shocks. If changes of Yuan are“non-temporary”, then China’s exporters would respond to the nominal exchange rates. The
volatilities of Yuan, USD, Euro and Japanese Yen against currencies of China’s main trade
partners are presented in Figure 5. Before July 2005, Chinese Yuan was fixed to USD. Thus,
the volatility of Yuan was almost the same as that of USD in most years. Figure 5 showsthat the volatility of Yuan against some currencies of high income countries (South Korea,
UK, and Australia) was similar to that of Euro and Yen, while the volatility of Yuan against
some currencies of low income countries (Malaysia, India, and Thailand) was smaller thanthat of Euro and Yen. Thus, Yuan is less volatile than Euro and Yen against currencies of low
income countries. One possible reason for it is that many low income countries used USD as
an anchor currency as China did. According to Ilzetzki et al. (2019), 112 countries use USD
as an anchor in 2006 while only 55 countries use Euro as an anchor. The above observations
are consistent with our result that the effect of nominal exchange rates on the EM of China’s
exports is larger for low income destinations. Thus this low volatility of Yuan relative toEuro and Yen for low income destinations might be a possible factor causing the significant
effect of exchange rates for China. This is, however, a relative argument between Yuan andEuro/Yen, so we may argue that changes of Yuan are “less temporary” than those of Euroand Yen for low income destinations, but may not argue that changes of Yuan are “non16
While the elasticity of substitution does not affect the elasticity of the export cutoff productivity withrespect to iceberg transport costs in the Chaney (2008) model, it does affect the elasticity of the export cutoff
productivity with respect to ad valorem tariffs in the Besedeš and Cole (2017) model. This is because tariff
revenue is completely captured by the domestic government while firms can recoup a portion of their losses
in transport through their monopolistic power.

22

temporary”. Thus Ruhl’s (2008) argument seems not to provide a satisfactory explanationfor our findings.
Therefore, in order to rationalize our findings, motivated by the hypothesis (b) in Section 2
we now suggest the FCs of exporters as a more plausible factor that might enlarge the impact
of exchange rates on China’s exporters at the product level. Since China is a developing
country and has weaker financial institutions than developed countries, the FCs of exporters
is a factor that could not be neglected. Specifically, we examine whether the responses ofChina’s exporters to tariffs and nominal exchange rates are dependent on the product-levelFCs. The regression specification and data have been described in Section 3. After matching
the custom data and FC data, we have one-third firms left which accounted for 53.33 percent
of total export values from 2000 to 2006. Table 9 presents the coefficients of equation (3.4).
Columns 1, 3 and 5 show the results of matched data are similar to the results of full sample
in Table 2. Thus, there is no selection concern in the matched sample. Columns 2, 4 and 6
show that the interaction terms between the nominal exchange rates and the FC index are
positive and significant, while the interaction terms between the tariffs and the FC index are
negative but insignificant. This confirms our hypothesis: FCs enlarge the impact of nominal
exchange rates on exporters at the product level.

5

Discussion: The Trade Elasticity

In Section 4, we have demonstrated that China’s exports significantly respond to both tariffs
and exchange rates. This is essentially a problem of the so-called “elasticity of trade”.
First, we obtain the product-level elasticity of exports by summing the coefficient ofexporter numbers and that of export value per exporter, at the HS6 level. According to theresults in the column 1 of Table 2, the product-level elasticity of exports with respect totrade costs is 4.49 (1.031+3.456) and that with respect to nominal exchange rates is 1.14
(0.113+1.022). According to the results in the columns 1 and 2 of Table 6, the product-levelelasticity of exports with respect to real exchange rates is 1.26 (0.112+1.145).
It should be, however, noted that our main interest has been particularly in the responseof the EM of exports, not the response of exports overall. Thus, it is important to compare
our results with the responses of the EM of exports to tariffs and exchange rates in the23

literature, although there are not many studies that estimated the elasticity of the EM ofexports with respect to trade costs or exchange rates. We summarize the elasticities in ourpaper and other studies in Table 10.
We obtain the elasticity of the EM of exports by looking at the coefficient of exporter
numbers or export variety numbers. Let us first focus on exporter numbers as a measurementof the EM of exports. According to the results in the column 1 of Table 2, the elasticity
of exporter numbers with respect to trade costs is 1.031, and that with respect to nominal
exchange rates is 0.113. According to the results in the column 1 of Table 6, the elasticity
of exporter numbers with respect to real exchange rates is 0.112. On the other hand, theestimated elasticity of exporter numbers with respect to trade costs by Bas et al. (2017) is
3.83, which is much larger than ours. That with respect to real exchange rates by Tang andZhang (2012) is 0.17, which is similar to ours in that both are less than 1. Note that while
exporter numbers in our paper are at the product-destination-year level; those in Bas et al.
(2017) are at the product-destination level focusing on the year 2000; and those in Tang andZhang (2012) are at the destination-year level.
Let us next consider export variety numbers as a measurement of the EM of exports.
According to the results in the column 1 of Table 4, the elasticity of export variety numberswith respect to trade costs is 0.336 and that with respect to nominal exchange rates is0.0873. According to the results in Table 6, that with respect to real exchange rates is0.107. According to the literature, on the other hand, the coefficient of export variety on
tariffs by Feenstra and Kee (2007) is about 2, which is much larger than ours. That on realexchange rates by Colacelli (2010) is 0.045, which is similar to ours in that both are less than
1. Note that while export variety in our paper is at the product-destination-year level, thatin Feenstra and Kee (2007) and Colacelli (2010) is at the destination-year level. Note alsothat our export variety numbers of a product directly measures the number of exported HS6
products within a HS4 product from China to a destination country while their measurementsbuild on Feenstra’s (1994) product variety.
In sum, our trade cost elasticities are smaller than and our real exchange rate elasticities
are similar to estimates by other studies. To our knowledge, no past studies have estimatedthe elasticity of exporter numbers or export variety numbers with respect to nominal exchange
rates.

24

6

Conclusion

Using China’s firm-product data from 2000 to 2006 with 138 trade partners, we have tested
whether the EM of China’s exports respond to both trade liberalization and nominal exchange
rates. Based on regressions, we have three main empirical findings. First, overall, changes intariffs (charged by trade partners) and nominal exchange rates have comparable effects onexporter numbers—the EM of exports—at the product level. Second, the effects are larger forprocessing trade, low income destinations, and differentiated products. Finally, our results
suggest FCs as a factor that can enlarge the impact of nominal exchange rates on China’s
exporter numbers at the product level.
The results presented in this paper are valuable, particularly for empirical studies on tradeliberalization and the EM of trade. First, Kehoe and Ruhl (2013), for example, focused onincreases in the EM of trade after trade liberalization or structural changes. Our results,
however, indicate that the observed increases in the EM of trade could be caused by not onlychanges in tariffs but also changes in nominal exchange rates. Thus, future studies, in particular, on China’s EM need to place more importance on exchange rates when investigating
the changes in the EM of trade using firm data. Second, our results provide an important
policy implication. China was a centrally-planned economy and adopted a U.S. dollar pegged
exchange rate policy for a long period. Previous studies paid little attention to the effect of
exchange rates on the EM of exports using Chinese data. China, however, allowed a managed
float of its currency in July 2005 and became the largest trader in the world in 2012. It isthus important to examine the effect of exchange rates. In fact, our results indicate that
foreign currency policy could be an effective tool if Chinese government would like to boost
exports.

25

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Yi, Yingting and Johannes Van Biesebroeck, “The extensive margin of differentiatedgoods and trade liberalization: Evidence from China,” Working Paper, 2012.
Yu, Miaojie, “Processing trade, tariff reductions and firm productivity: Evidence from
Chinese firms,” The Economic Journal, 2015, 125 (585), 943–988.

29

Table 1: Data Summary
Variables

2000

2001

2002

Year
2003

2004

2005

2006

Exporter, Product, and Destination
Exporter Numbers
Product Numbers
Destination Numbers

51,5784,526
93

57,472
4,566
117

66,464
4,555
124

81,733
4,593
117

104,161
4,573
111

126,775
4,567
116

153,014
4,596
126

Direct Trade Share

67%

69%

72%

75%

78%

81%

79%

9.47
12.75

9.48
11.73

9.16
11.42

8.56
10.82

8.23
11.45

8.18
10.15

7.96
10.05

5.11
12.96

5.23
11.76

4.96
12

4.81
11.26

4.53
12.66

4.42
10.6

4.43
10.67

14.45
12.75

13.78
11.73

13.23
11.41

12.12
10.82

11.60
11.45

11.31
10.15

10.58
10.05

4.27

4.45

4.01

3.26

3.02

2.83

2.81

Tariffs Charged by Destinations (%)—All Destinations(simple mean)(standard deviation)—High Income Destinations(simple mean)(standard deviation)—Low Income Destinations(simple mean)(standard deviation)
Tariffs Charged by Trade Modes (%)—Processing Trade(weighted mean)—Ordinary Trade(weighted mean)

10.66

9.44

10.63

10.43

7.41

8.37

7.34

China’s Nominal Effective Exchange Rate

93.18

98.35

97.92

91.88

87.68

87.26

89.23

Note: (a) The high income destinations are defined as countries whose average GDP per capita during 2000-2006 is
above 10,000 U.S. dollars. (b) China’s nominal effective exchange rate index for 2010 is 100.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

30

Table 2: Nominal Exchange Rates, Tariffs, and Exportsln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squaredln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squaredln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squared
Product FE
Country FE
Industry-time FE
Cluster by Country-time(1)
Full Sample

(2)
Betas

-1.031***(0.0814)0.113**(0.0547)0.108*(0.0612)-0.877***(0.0147)-0.216(0.156)0.420**(0.169)0.133***(0.0385)
1.117***(0.0537)-1.138***(0.0449)-0.321(0.681)
1,857,667
0.574-0.0708***(0.00559)
0.249**(0.120)0.0109*(0.00616)-0.335***(0.00561)-0.354(0.256)0.517**(0.209)0.0943***(0.0273)0.220***(0.0106)-0.284***(0.0112)0.0109**(0.00542)
1,857,667
0.574-3.456***(0.299)1.022***(0.321)1.806***(0.385)-1.813***(0.0513)-0.483(1.075)2.068*(1.162)
1.659***(0.216)5.711***(0.217)-5.439***(0.173)-11.38***(4.107)
1,857,667
0.283-0.0503***(0.00435)
0.478***(0.150)0.0386***(0.00822)-0.147***(0.00416)-0.168(0.374)0.541*(0.304)0.249***(0.0325)0.238***(0.00904)-0.289***(0.00920)
0.00638(0.00776)
1,857,667
0.283-2.478***(0.227)0.699***(0.246)1.324***(0.287)-1.230***(0.0396)-0.202(0.790)1.287(0.865)
1.115***(0.156)3.870***(0.175)-3.866***(0.138)-7.793***(2.920)1,857,667
0.354
Yes
Yes
Yes
Yes

-0.0456***(0.00417)
0.412***(0.145)0.0357***(0.00773)-0.126***(0.00405)-0.0887(0.347)0.425(0.286)0.212***(0.0295)
0.204***(0.00922)-0.259***(0.00925)
0.419***(0.0168)
1,857,667
0.354
Yes
Yes
Yes
Yes

(3)
(4)
Ordinary
Processingln(Exporter Numbers +1)-0.978***-1.107***(0.0915)(0.0742)0.09400.171***(0.0713)(0.0513)0.04310.139**(0.0830)(0.0552)-0.351*-0.613***(0.185)(0.153)0.556***
0.716***(0.199)(0.165)0.139***
0.0372(0.0495)(0.0324)0.175***
0.437***(0.0464)(0.0539)-0.170***-0.360***(0.0434)(0.0459)
0.0523
1.699***(0.836)(0.638)1,276,262
581,235
0.644
0.565
ln(Export Value per Exporter + 1)
-2.720***-4.167***(0.317)(0.346)0.970***
1.030**(0.311)(0.403)1.764***
1.643***(0.426)(0.413)(5)
High Income(6)
Low Income-0.214(0.130)0.0512(0.0754)0.588***(0.156)-0.922***(0.0204)-0.224(0.202)0.247(0.225)-0.101*(0.0605)
1.174***(0.0726)-1.176***(0.0614)2.255***(0.721)
914,499
0.666-0.333***(0.0640)0.133*(0.0766)
0.133*(0.0757)-0.883***(0.0208)0.0706(0.214)0.154(0.194)0.225***(0.0406)
0.790***(0.0508)-0.872***(0.0407)-1.478(1.128)943,089
0.523-2.279***(0.479)2.251***(0.556)2.674*(1.387)-1.322***(0.0643)
3.523**(1.479)-2.680(1.737)
1.222***(0.429)6.227***(0.257)-5.732***(0.206)-17.43***(4.113)
914,499
0.336-1.484***(0.282)
1.271***(0.386)1.700***(0.438)-2.602***(0.0422)-1.989(1.368)3.157**(1.289)1.718***(0.230)4.222***(0.264)-4.358***(0.214)-0.154(6.678)943,089
0.270-0.414-1.434(1.099)(1.239)1.813
3.397**(1.158)(1.387)1.651***
1.504***(0.233)(0.220)2.036***
5.624***(0.178)(0.300)-1.712***-4.471***(0.164)(0.240)-9.598**-12.84***(4.308)(4.562)
1,276,262
581,235
0.339
0.316
ln(Export Quantity per Exporter + 1)
-1.785***-3.527***-1.876***(0.232)(0.276)(0.441)0.624***
0.782***
1.563***(0.240)(0.300)(0.452)
1.246***
1.291***
2.418**(0.307)(0.318)(0.975)-0.833***(0.0479)-0.108-1.1462.463**(0.787)(0.941)(1.077)1.061
2.485**-2.004(0.844)(1.057)(1.279)1.104***
0.978***
0.744**(0.164)(0.166)(0.323)
1.261***
3.585***
4.396***(0.128)(0.224)(0.211)-1.051***-2.902***-4.184***(0.117)(0.181)(0.167)-6.732**-7.627**-10.26***(2.970)(3.385)(2.951)
1,276,262
581,235
914,499
0.427
0.356
0.403
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

-0.982***(0.205)0.927***(0.288)1.202***(0.317)-1.851***(0.0322)-1.276(0.996)2.039**(0.956)
1.153***(0.162)2.610***(0.200)-2.958***(0.160)0.441(4.699)943,089
0.344
Yes
Yes
Yes
Yes

Note: (a) The column 2 reports standardized beta coefficients from the column 1. (b) The high income destinations aredefined as countries whose average GDP per capita during 2000-2006 is above 10,000 U.S. dollars. (c) *** p < 0.01, **p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

31

Table 3: Nominal Exchange Rates, Tariffs, and Exporter Dynamics(1)
Full Sample

(2)
Betas

(3)
Ordinary(4)
Processing(5)
High Income(6)
Low Incomeln(1+Tariff)-0.0795***-0.0222***-0.0774***-0.0636***-0.0305*-0.0297**ln(NER)(0.0111)0.0321*(0.00310)
0.293*(0.0122)0.0398*(0.0125)
0.00926(0.0160)
0.0525*(0.0124)
0.0719***(0.0190)(0.174)(0.0208)(0.0210)(0.0317)(0.0257)ln(Relative CPI)

0.108***

0.0368***0.109***

0.0877***

0.09150.130***(0.0262)(0.00891)(0.0323)(0.0222)

Processing Trade-0.178***(0.00176)-0.279***(0.00276)ln(Normalized New Exporter Numbers + 1)

ln(GDP)(0.107)(0.0339)-0.167***(0.00209)-0.198***(0.00228)

0.05830.395

0.0737-0.07370.190*-0.0842(0.0659)(0.446)(0.0709)(0.0663)(0.101)(0.0885)ln(GDP per capita)

0.0236(0.0690)0.120(0.353)0.00132(0.0733)

0.172**(0.0743)-0.125(0.117)0.125(0.0803)ln(Trade)

0.0719***0.209***

0.0721***

0.0660***-0.00546

0.0931***(0.0159)(0.0463)(0.0180)(0.0136)(0.0278)(0.0184)

Direct Trade

0.278***(0.00849)

0.220***(0.00671)

0.178***(0.0109)0.250***(0.0155)

0.288***(0.0116)0.237***(0.0120)

Direct Trade2-0.264***-0.268***-0.163***-0.201***-0.268***-0.235***(0.00689)(0.00701)(0.0103)(0.0120)(0.00960)(0.00967)

Constant-0.824***(0.275)-0.00555(0.00982)-0.789***(0.299)-0.730***(0.250)-0.854***(0.300)0.0368(0.463)Observations

1,636,514

1,636,514

1,129,507

506,836

791,611844,812R-squared

0.182

0.182

0.179

0.105

0.202

0.188

ln(Normalized Exit Exporter Numbers + 1)ln(1+Tariff)-0.0730***-0.0202***-0.0736***-0.0453***-0.0239*-0.0235*ln(NER)(0.0110)0.0169(0.00304)
0.156(0.0124)0.0264(0.0131)-0.00689(0.0132)
0.0475(0.0121)0.0576*(0.0346)(0.0224)(0.208)(0.0227)(0.0283)(0.0291)ln(Relative CPI)

0.006410.002020.00936-0.0187

0.03350.0107(0.00859)-0.271***(0.0279)(0.0326)

Processing Trade(0.0273)-0.168***(0.0898)-0.159***(0.0368)-0.187***(0.00224)(0.00278)(0.00190)(0.00306)

ln(GDP)

0.00960

0.06590.0294-0.0991

0.150-0.143ln(GDP per capita)(0.0650)
0.0316(0.447)0.164(0.0674)
0.00804(0.0758)
0.148(0.101)-0.0946(0.0909)
0.127(0.0751)(0.389)(0.0754)(0.0928)(0.115)(0.0846)

0.01140.03390.008800.01400.009220.00709(0.0137)(0.0408)(0.0150)(0.0148)(0.0234)(0.0168)

Direct Trade

0.190***(0.00950)

0.153***(0.00764)

0.0703***(0.0123)-0.0211(0.0163)

0.182***(0.0116)0.174***(0.0146)

Direct Trade2-0.197***-0.205***-0.0733***

0.0103-0.186***-0.190***(0.00756)(0.00784)(0.0112)(0.0129)(0.00937)(0.0114)

Constant-0.103(0.251)0.00220(0.0136)-0.102(0.260)0.0324(0.270)-0.723**(0.331)

0.978**(0.495)Observations

1,407,021

1,407,021

947,082459,827

738,673

668,238

R-squared

0.188

0.188

0.182

0.116

0.208

0.191

Product FE

Yes

Yes

Yes

Yes

Yes

Yes

Country FE

Yes

Yes

Yes

Yes

Yes

Yes

Industry-time FE
Cluster by Country-time

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

ln(Trade)

Note: (a) The normalized number of new (exit) exporters is defined as the ratio of new (exit) exporter numbers to totalexporter numbers. (b) The column 2 reports standardized beta coefficients from the column 1. (c) The high income
destinations are defined as countries whose average GDP per capita during 2000-2006 is above 10,000 U.S. dollars. (d)*** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

32

Table 4: Nominal Exchange Rates, Tariffs, and Export Variety Numbers
(1)

(2)

(3)

(4)

(5)

(6)

Full Sample

Betas

Ordinary

Processing

High Income

Low Income-0.336***-0.0429***-0.235***-0.432***-0.177***(0.0322)(0.00411)(0.0353)(0.0378)(0.0483)(0.0314)ln(NER)

0.0873***0.351***

0.0917***0.0688**0.168***

0.163***(0.0328)(0.132)(0.0351)(0.0332)(0.0553)(0.0377)ln(Relative CPI)

0.192***

0.0378***0.197***

0.149***

0.293*

0.192***(0.0370)(0.00726)(0.0410)(0.0350)

Processing Trade-0.432***-0.309***(0.00559)(0.00400)

0.0749

0.230

0.120

-0.145(0.108)(0.331)(0.118)(0.106)(0.137)(0.129)ln(GDP per capita)

0.09700.224

0.04030.330***-0.409**0.319**(0.116)(0.268)(0.125)(0.116)(0.163)(0.124)ln(Trade)

0.176***

0.228***

0.179***

0.156***

0.136***

0.174***(0.0233)(0.0303)(0.0267)(0.0189)(0.0448)(0.0253)

Direct Trade

0.608***

0.197***-0.0456

0.245***

0.700***

0.412***(0.0305)(0.00987)(0.0278)(0.0403)(0.0313)(0.0398)

Direct Trade2-0.602***-0.257***0.0573**-0.211***-0.651***-0.474***(0.0231)(0.00989)(0.0247)(0.0312)(0.0243)(0.0303)

Constant-1.459***0.00273-1.424***-1.141***-2.423***0.497(0.418)(0.00717)(0.459)(0.399)(0.425)(0.584)

Observations

712,835

712,835

462,519

250,293

317,974

394,854

R-squared

0.568

0.568

0.638

0.546

0.642

0.539

Product FE

Yes

Yes

Yes

Yes

Yes

Yes

Country FE

Yes

Yes

Yes

Yes

Yes

Yes

Industry-time FE

Yes

Yes

Yes

Yes

Yes

Yes

Cluster by Country-time

Yes

Yes

Yes

Yes

Yes

Yes

ln(Export Variety Numbers+ 1)ln(1+Tariff)ln(GDP)-0.191***(0.154)(0.0409)-0.353***-0.513***(0.00582)(0.00628)

0.535***-0.230*Note: (a) The column 2 reports standardized beta coefficients from the column 1. (b) The high income
destinations are defined as countries whose average GDP per capita during 2000-2006 is above 10,000 U.S.
dollars. (c) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

33

Table 5: Nominal Exchange Rates, Tariffs, and Exports: Robustness Checks(1)
Main Trade Partners

ln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squaredln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squaredln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squared
Product FE
Country FE
Industry-time FE
Cluster by Country-time-1.078***(0.121)0.183**(0.0926)0.221**(0.0945)-0.976***(0.0162)0.213(0.299)-0.148(0.349)0.0247(0.0581)1.131***(0.0587)-1.150***(0.0490)0.176(1.110)1,310,859
0.618-3.803***(0.425)2.187***(0.530)2.369***(0.511)-1.674***(0.0596)
2.042(1.658)-1.844(1.919)1.166***(0.272)
6.362***(0.222)-5.885***(0.177)-5.331(5.720)1,310,859
0.283-2.659***(0.324)1.644***(0.404)
1.845***(0.406)-1.118***(0.0459)
2.135*(1.204)-2.106(1.401)0.834***(0.200)4.363***(0.181)-4.193***(0.143)-5.082(4.166)
1,310,859
0.366
Yes
Yes
Yes
Yes

(2)
Only Positive Trade Flowsln(Exporter Numbers + 1)
-0.828***(0.112)0.00108(0.0912)-0.00989(0.0977)-0.946***(0.0185)0.727**(0.306)-0.399(0.345)0.0955(0.0630)
1.373***(0.0924)-1.383***(0.0716)-3.505***(1.253)543,649
0.629
ln(Export Value per Exporter + 1)
-0.318***(0.113)0.106(0.113)0.0852(0.134)0.765***(0.0291)-0.0701(0.288)0.355(0.317)0.262***(0.0744)-2.114***(0.180)1.834***(0.131)
9.183***(1.378)543,649
0.473
ln(Export Quantity per Exporter + 1)
-0.263**(0.131)0.222(0.146)
0.347**(0.171)0.760***(0.0265)0.549(0.342)-0.346(0.371)0.371***(0.0968)-1.311***(0.174)0.938***(0.128)4.067**(1.691)
543,649
0.666
Yes
Yes
Yes
Yes

(3)
Quarterly
stacked-0.735***(0.0738)
0.1318***(8.05)
0.1578***(8.89)-0.553***(0.0126)-0.203(0.144)0.225(0.151)0.00316(0.0343)0.675***(0.0620)-0.740***(0.0497)
2.123***(0.633)3,584,574
0.525-0.411***(0.0890)0.0338(0.55)
0.0498(0.58)
0.478***(0.0225)-0.0917(0.234)0.288(0.233)0.163***(0.0493)-1.484***(0.126)
1.222***(0.0928)
9.631***(1.007)
3,584,574
0.331-0.279***(0.102)0.0629(1.63)
0.0712(1.06)
0.473***(0.0203)-0.0185(0.255)0.110(0.249)0.103(0.0672)-0.899***(0.118)0.502***(0.0877)
7.182***(1.189)3,584,574
0.596
Yes
Yes
Yes
Yes

Note: (a) The export value of the top 50 partners is about 97.6 percent of China’s total export value
from 2000 to 2006. (b) The stacked regression in the column 3 measures the responses to exchange rate
fluctuations over the current and last three quarters. (c) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

34

Table 6: Real Exchange Rates, Tariffs, and Exports(1)ln(Exporter Numbers + 1)

(2)
ln(Export Value per Exporter + 1)

(3)
ln(Export Quantity per Exporter + 1)

-1.031***-3.490***-2.505***(0.0814)(0.298)(0.226)ln(RER)

0.112**(0.0541)

1.145***(0.339)0.797***(0.261)

Processing Trade-0.877***-1.813***-1.230***(0.0147)(0.0513)(0.0396)-0.309-13.31***-9.328***Observations(0.697)
1,857,667(4.471)
1,857,667(3.177)
1,857,667
0.353

ln(1+Tariff)

Constant

R-squared

0.574

0.283

Product FE

Yes

Yes

Yes

Country FE

Yes

Yes

Yes

Industry-time FE

Yes

Yes

Yes

Cluster by Country-time

Yes

Yes

Yes

ln(1+Tariff)

(1)

(2)

ln(Normalized New Exporter Numbers + 1)
-0.0821***ln(Normalized Exit Exporter Numbers + 1)
-0.0726***(0.0110)(0.0224)ln(RER)

0.0378*0.0169Processing Trade(0.0208)-0.178***(0.0224)-0.168***(0.00176)(0.00190)

Constant-1.064***-0.0820

Observations(0.320)1,636,514(0.256)
1,407,021

R-squared

0.182

0.188

Product FE

Yes

Yes

Country FE

Yes

Yes

Industry-time FE

Yes

Yes

Cluster by Country-time

Yes

Yes

ln(Export Variety Numbers+ 1)ln(1+Tariff)-0.340***(0.0320)ln(RER)

0.107***

Processing Trade-0.432***

Constant(0.00559)-1.585***Observations

712,835

R-squared

0.567(0.0358)(0.455)

Product FE

YesCountry FE
Industry-time FE

Yes
Yes

Cluster by Country-time

Yes

Note: (a) The normalized number of new (exit) exporters is defined as the ratio of new (exit) exporter numbers to total exporter
numbers. (b) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

35

Table 7: Nominal Exchange Rates, Tariffs, and Exports by Sectors

Electrical equipment(1)
Numbers-0.896***(0.0779)
0.114*

TariffNER

Mechanical appliances

Apparel, not knitted or crocheted

Electrical equipment(0.0633)(0.488)(0.356)-0.618***(0.0787)-2.367***(0.426)-0.874***(0.267)NER

0.123*(0.0675)

1.498***(0.448)0.825***(0.244)

Tariff0.648***

2.100**

1.453**

NER(0.159)0.478***(0.155)(0.937)0.669(0.667)(0.662)0.649(0.501)-0.227(0.193)0.398**(0.188)-0.560(0.816)0.958(0.734)-0.306(0.625)0.901(0.602)-0.481***(0.131)0.519***(0.106)-1.925***(0.631)2.352***(0.570)-1.487***(0.431)1.646***(0.386)

0.0439(0.134)0.274***(0.0930)0.453(0.636)1.262**(0.569)0.382(0.516)0.916**(0.447)-0.123(0.197)0.225**-1.322*(0.722)0.749-1.213**(0.561)
0.449

TariffTariff
NER

Furniture

TariffNER

Toys and sports requisites

TariffNER

Mineral products

Footwear

Iron or steel(3)
Quantity-2.529***(0.291)0.500

Tariff

NER
Optical instruments

(2)
Value
-3.290***(0.393)0.759(0.107)(0.644)(0.485)

Tariff-0.713***(0.107)-1.738***(0.539)-1.301***(0.455)NER

0.114(0.0748)0.902**(0.454)0.583(0.365)

Tariff-0.370***-1.298**-0.385NER(0.0991)
0.0276(0.0525)(0.577)0.107(0.435)(0.386)-0.113(0.274)-1.770***(0.178)0.0633(0.0774)-5.128***(0.811)
1.108**(0.498)-4.382***(0.652)
0.697*(0.393)Tariff
NER

Note: (a) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

36

Table 8: Exchange Rates, Tariffs, and Differentiated Products
(1)

(2)
(3)
(4)
Broda et al.
ln(Exporter Numbers + 1)
-0.114-1.045***-1.051***(0.0839)(0.0813)(0.0811)0.07830.114**
0.114**(0.0554)(0.0549)(0.0550)-1.090***(0.0999)0.0473***(0.00366)
0.00335**(0.00139)-0.000211***(4.47e-05)-0.249-0.218-0.221(0.684)(0.686)(0.686)
1,742,118
1,814,785
1,814,785
0.577
0.575
0.575
ln(Export Value per Exporter + 1)
-1.430***-3.491***-3.520***(0.354)(0.297)(0.294)1.040***
1.041***
0.860***(0.321)(0.322)(0.325)-2.322***(0.387)0.213***(0.0121)0.0171**(0.00726)-0.00131***(0.000209)-10.64**-10.81***-10.82***(4.101)(4.101)(4.125)
1,742,118
1,814,785
1,814,785
0.285
0.283
0.283
ln(Export Quantity per Exporter + 1)
-0.971***-2.507***-2.529***(0.301)(0.225)(0.222)0.714***
0.715***
0.560**(0.249)(0.247)(0.247)-1.665***(0.322)0.197***(0.0105)
0.0128*(0.00672)-0.00151***(0.000201)-6.299**-7.334**-7.337**(2.920)(2.919)(2.935)
1,742,118
1,814,785
1,814,785
0.357
0.354
0.355
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Rauch
ln(1+Tariff)ln(NER)-1.098***(0.0834)
0.117**(0.0557)ln(1+Tariff)× Diff Dummyln(NER)×Diff Dummyln(1+Tariff)×Elasln(NER)×Elas
Constant
Observations
R-squaredln(1+Tariff)ln(NER)-0.101(0.691)
1,742,118
0.574-3.678***(0.304)1.038***(0.324)ln(1+Tariff)× Diff Dummyln(NER)×Diff Dummyln(1+Tariff)×Elasln(NER)×Elas
Constant
Observations
R-squaredln(1+Tariff)ln(NER)-10.27**(4.157)
1,742,118
0.283-2.627***(0.227)0.727***(0.249)ln(1+Tariff)× Diff Dummyln(NER)×Diff Dummyln(1+Tariff)×Elasln(NER)×Elas
Constant
Observations
R-squared
Product FE
Industry-time FE
Country FE
Cluster by Country-time-6.775**(2.953)
1,742,118
0.356
Yes
Yes
Yes
Yes

Note: (a) Diff Dummy is 1 if the product is a differentiated product according to the classification from Rauch (1999); otherwise, it is 0. (b) The elasticity is calculated by Broda et al.
(2008), which is at the HS4 level. (c) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

37

Table 9: Exchange Rates, Tariffs, and Financial Constraints
(1)

(2)

(3)

-0.754***(0.103)0.0636(0.0538)-0.508(0.422)0.0765***(0.0111)
0.0984*(0.0544)-0.644***(0.0150)-0.301**(0.144)0.550***(0.157)0.181***(0.0350)
1.439***(0.0440)-1.408***(0.0374)-0.870(0.640)719,928
0.557
Yes
Yes
Yes
Yes

-2.998***(0.269)0.872**(0.352)

Numbersln(1+Tariff)ln(NER)-0.840***(0.0628)
0.0766(0.0536)ln(1+Tariff) × FC
ln(NER) × FC
ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squared
Product FE
Industry-time FE
Country FE
Cluster by Country-time

0.0981*(0.0544)-0.644***(0.0150)-0.302**(0.143)0.552***(0.157)0.180***(0.0350)
1.438***(0.0440)-1.406***(0.0374)-0.863(0.640)719,928
0.557
Yes
Yes
Yes
Yes

(4)

(5)

-2.468***(0.450)0.805**(0.353)-3.137(1.921)0.394***(0.0581)1.743***(0.448)-1.080***(0.0539)-0.436(1.106)2.386**(1.207)1.896***(0.230)6.986***(0.207)-6.506***(0.171)-16.24***(4.329)719,928
0.284
Yes
Yes
Yes
Yes

-2.042***(0.206)0.580**(0.262)Value

1.742***(0.448)-1.080***(0.0539)-0.444(1.106)2.395**(1.207)1.894***(0.231)6.980***(0.207)-6.501***(0.170)-16.21***(4.328)719,928
0.283
Yes
Yes
Yes
Yes

(6)
Quantity

1.252***(0.317)-0.562***(0.0416)-0.550(0.792)
1.839**(0.879)1.259***(0.158)4.550***(0.160)-4.493***(0.132)-9.748***(2.975)
719,928
0.369
Yes
Yes
Yes
Yes

-1.736***(0.342)0.514**(0.262)-1.848(1.497)0.383***(0.0439)
1.253***(0.317)-0.562***(0.0416)-0.543(0.793)
1.832**(0.879)1.261***(0.158)4.556***(0.161)-4.499***(0.132)-9.778***(2.976)
719,928
0.369
Yes
Yes
Yes
Yes

Note: (a) FC is defined as the inventory to sales ratio. (b) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF, TRAINS and U.S. Compustat dataset.

Table 10: Elasticities in Selected Studies
Paper

Country

Trade cost elasticity

Exchange rate elasticity
NER

RER

0.113

0.112

Exporter numbers
Ours
Bas et al. (2017)

China
China & France

1.031
3.83

Tang and Zhang (2012)

China

Export variety
Ours

China

0.336

Mexico136 countries

2.049

Feenstra and Kee (2007)
Colacelli (2010)

0.17

0.0873

0.107
0.045

Note: (a) For exporter numbers, our paper is at the product-destination-year level; Bas et al. (2017) are at the
product-destination level focusing on the year 2000; and Tang and Zhang (2012) are at the destination-yearlevel. (b) For export variety, our paper is at the product-destination-year level, and Feenstra and Kee (2007)
and Colacelli (2010) are at the destination-year level.

38

6

90

8

100

Tariff
10

110
NEER

12

120

14

130

Figure 1: Average Tariffs Charged by Trade Partners and China’s Nominal Effective Exchange Rates

2000

2005

2010

2015

Year
Tariff

NEER

Note: An increase (decrease) in nominal effective exchange rates means China’s yuan appreciation(depreciation).
Source: IMF and TRAINS.

2000

2002

2004

-.2

-.06

Avg Log Deviation of Gross Tariff From 2000
-.15
-.1
-.05

Avg Log Deviation of Gross Tariff From 2000
-.04
-.02
0

0

.02

Figure 2: Average Evolution of Tariffs Charged by Selected Trade Partners

2006

Year
USA
JPN
TWN
AUS

2000

2002

2004

2006

Year

EU
KOR
CAN
MEX

IND
THA
MYS

(a) High Income

BRA
IDN

(b) Low Income

Note: (a) This figure shows coefficients on year dummies in country-by-country regression of ln(1+Tariff)on HS6 fixed effects and year dummies. (b) The high income destinations are defined as countries whose
average GDP per capita during 2000-2006 is above 10,000 U.S. dollars.
Source: TRAINS.

39

2004

2005

.08
.07
Yuan/Yen

Tariff

.065

8

2006

2000

2001

2002

2003

Year

2004

2005

NER

2000

.27
.25
.26
Yuan/TWD

5

13

.24
4.5

2006

2000

2001

2002

2003

2004

2005

2006

2000

2001

2002

2003

Year
NER

NER

tariff

2005

2006

NER

8.4

6.5

2004

2005

2000

2001

2002

2003

2.2
2.18
Yuan/MYR

8.2

2.16
2.14
2000

2001

2002

2003

2005

2005

2006

NER

2000

2001

2002

2003

2004

2005

2006

.2
Yuan/THB

.21

14

.19
.18

6.7
6.6

2006

Tariff12

6.9
Tariff
6.8

.0011

.22

16

(i) Malaysia

.0009
.001
Yuan/IDR.185.18
Yuan/INR
.175
.17
2004

Year

2000

2001

2002

2003

Year
NER

tarifftariff2006(l) Thailand

17
14
13

2000

2001

2002

2003

2004

2005

2006

2000

Year
tariff

2

12

.75

3

Yuan/BRL

4

16
Tariff
15

.9
.85
.8
Yuan/MXN

16
Tariff
14

2005

NER

5

(k) Indonesia

18(j) India

2004

Year
NER

.7

tariff2004

tariff

7

.19

35
30
Tariff
25

2003

2006

NER

(h) Australia

20

2002

2005

Year

tariff

15

2001

2004

Year
NER

(g) Canada

2000

7.8

4.5

2006

Year
tariff

10

2003

8

2002

.0008

2001

.0007

2000

4

3.5

5

2.6

5.5

4

2.8

8

Tariff5
5.5
Yuan/AUD

Tariff
4.5

Tariff3

6
6.5
Yuan/CAD

5

3.2

6

7

2.22

(f) Taiwan

5.5(e) UK
7.5(d) South Korea

2004

Year

tariff

3.4

2006

12

4.2
2005

2005

NER

6.5

16

4.8
4.4

Tariff
4.6

.007
.0075
Yuan/Won
.0065
2004

Year
tariff

2004(c) Japan

.006
2003

2003

tariff

5

.0085
.008

11.5
11
Tariff
10.5
10

2002

2002

NER

(b) Euro Area

9.5

2001

2001

Year

tariff(a) USA

2000

.06

1.4

2006

Year

tariff6

2003

Tariff
5.5

2002

15

2001

14
Yuan/Pound

2000

7

7.8

3.5

4.2

7.9

3.6

4.4

1.6

Tariff
4.6

9
Yuan/Euro

1.8

10

.075

2

11

5
4.8

8.2
8
8.1
Yuan/USD

3.7

Tariff

3.8

3.9

4

8.3

Figure 3: Tariffs Charged to China and Nominal Exchange Rates for Selected Trade Partners

2001

2002

2003

2004

2005

2006

Year
NER

tariff(m) Mexico

NER(n) Brazil

Note: A decrease (increase) in nominal exchange rates means China’s yuan appreciation (depreciation).
Source: IMF and TRAINS.

40

0

5

Density

10

15

Figure 4: The Distribution of Inventory/Sales Ratio

0

.1

.2
.3
Inventory/Sales Ratio

Source: U.S. Compustat dataset.

41

.4

.5

1998

2000

2002

2004

2006

2008

.04
Standard Deviation
.02
.03
.01
0

0

0

.01

Standard Deviation.01.02

Standard Deviation
.02
.03

.03

.04

Figure 5: Volatilities of Nominal Exchange Rates

1998

2000

2002

Year
Yuan

2004

2006

2008

1998

Euro

Yen

Yuan

Euro

Yen

Yuan

(b) Euro Area

2004

2006

2008

Euro

.04
Standard Deviation
.02
.03

Yen

2000

2002

Yuan

2004

2006

2008

1998

2000

2002

Euro

Yen

Yuan

USD

2006
Euro

2008
Yen

(f) Taiwan

1998

2000

2002

2004

2006

2008

0

.01

.01

.015

.02

Standard Deviation.01.02

Standard Deviation
.03
.04

.03

.05

.035

(e) UK

.03

2004
Year

USD

(d) South Korea

Standard Deviation
.02
.025

2008
Yen

0

1998

Year

USD

2006
Euro

.01

Standard Deviation
.02
.025
.015
.01

2002
Year

Yuan

2004

USD

(c) Japan.03.05Standard Deviation
.03
.04
.02
.01

2000

2002
Year

USD

.035

(a) USA

1998

2000

Year

USD

1998

2000

2002

Year
Yuan

2004

2006

2008

1998

Euro

Yen

Yuan

Euro

Yen

Yuan

2006

2008

Standard Deviation
.02
.03

1998

2000

2002

2004

2006

2008

1998

2000

2002

Year
Euro

Yen

Yuan

Euro

Yen

Yuan

(k) Indonesia

USD.1Standard Deviation
.06
.08
.02

2002

2004

2006

2008

1998

2000

2002

Year
Yuan

USD

2004

2006

2008

Year
Euro

Yen

Yuan

(m) Mexico

USD

Euro

(n) Brazil

Source: IMF

42

2006
Euro

(l) Thailand.04

Standard Deviation
.02
.03
.01

2000

2004

2008

Year

USD

.04

(j) India

1998

2008
Yen

.01

.02

2004

USD

2006
Euro

.04

.1
Standard Deviation
.04
.06
.08

Standard Deviation
.02
.03
.01

2002
Year

Yuan

2004

USD

(i) Malaysia

0

2000

2002
Year

USD

(h) Australia.04(g) Canada

1998

2000

Year

USD

Yen

Yen

A

Appendix

A.1

Nominal and Real Exchange Rates

Figure A1 demonstrates the relationship between nominal and real exchange rates for China’s
selected trade partners from 2000/01 to 2006/12. Notably, due to data constraints, to calculate the real exchange rates for the euro area we use producer prices for the euro area’s
CPI.

A.2

Poisson Estimation for Exporter Numbers

As the number of exporters is count data, following Silva and Tenreyro (2006), we also run
the Pseudo Poisson maximum likelihood (PPML) estimation. As shown in Table A1, theresults of PPML estimation indicate that tariffs have a significant negative effect on exporter
numbers except for high income destinations, while nominal exchange rates have a significant
positive effect only for low income destinations. Surprisingly, the effects of tariffs and nominal
exchange rates for high income destinations seem to contradict the implications from thetheory. We guess that one possible reason is product quality. Past studies (e.g., Manova andZhang, 2012) have shown that exporters from China export high quality products to highincome destinations. As the product quality improved over the years, more Chinese exporters
could break into high income destinations although the changes of tariffs and exchange rates
were bad for them. Manova and Zhang (2012) use the export price to proxy the productquality. We cannot observe the export price when the trade flow is zero. Thus, this method
does not work for us. We have to admit that without controlling for the product quality, ourresults for high income destinations may be biased.

A.3

Other Unobservable Shocks

In our regression specification, we use the country, product, and industry-year fixed effects tocontrol for many unobservable shocks. There, however, might be some unobservable shocks
at the destination-year level. For example, unobservable political shocks may influence tariffand nominal exchange rate variation simultaneously, which leads to reversal causality in theestimation. Unfortunately, we cannot use the destination-year fixed effects since the nom-43

inal exchange rates are also at the destination-year level. Controlling for destination-year
fixed effects would absorb the main variable—exchange rates. Due to China’s special situation, however, we think that this concern might not be a serious problem. First, China is a
latecomer in international trade. China became a WTO member in 2001 while most tariffs
charged by its trade partners were decided before China’s entry. The first and only free
trade agreement between China and its trade partners during 2000-2006 was implemented in
2004, which covers ten countries in Association of Southeast Asian Nations (ASEAN). Thus,
we think that the political shocks had a limited effect on the tariffs. Second, Chinese yuan
was fixed with U.S. dollar from 2000 to June 2005. Since July 2005, Chinese yuan began toappreciate against U.S. dollar. Thus, we think that the nominal exchange rates were also notaffected by the political shocks. As a robustness check, we drop observations from ASEAN
countries from 2004 to 2006, and the countries using U.S. dollar as currencies from 2005 to
2006. The results for the subsample remain robust, which are presented in Table A2.

44

Table A1: Poisson Estimation for Exporter Numbers
(1)

(2)

(3)

(4)

(5)

Full Sample

Ordinary

Processing

High Income

Low Income-1.106***-1.055***-1.638***0.259(0.190)(0.200)(0.282)(0.194)(0.169)0.02110.0277-0.0295-0.245*0.356**(0.107)(0.119)(0.159)(0.135)(0.149)0.100

0.03840.175

0.997***

0.242*(0.118)(0.136)(0.176)(0.239)(0.135)-1.388***-1.323***-1.707***(0.0290)(0.0309)(0.0306)

Exporter Numbersln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)-0.291*

0.625

0.514

-1.289***-0.3400.421(0.383)(0.396)(0.487)(0.450)(0.592)-0.0932-0.209-0.134

1.697***

0.591(0.410)(0.423)(0.554)(0.520)(0.543)ln(Trade)-0.0415-0.0595-0.103-0.546***0.460***(0.0858)(0.0931)(0.0902)(0.105)(0.0734)

Direct Trade

2.076***

0.428***

1.384***

2.228***

0.943***(0.207)(0.134)(0.140)(0.234)(0.115)

Direct Trade2-2.039***-0.502***-1.080***-2.085***-1.262***(0.151)(0.120)(0.107)(0.169)(0.0875)-2.719-1.4823.931**

4.636***-2.740(1.745)(1.813)(1.911)(1.450)(3.136)

Observations

1,857,623

1,276,221

581,191

914,418

942,879

Product FE

Yes

Yes

Yes

Yes

Yes

Country FE

Yes

Yes

Yes

Yes

Yes

Industry-time FE

Yes

Yes

Yes

Yes

Yes

Cluster by Country-time

Yes

Yes

Yes

Yes

Yes

Constant

Note: (a) The high income destinations are defined as countries whose average GDP per
capita during 2000-2006 is above 10,000 U.S. dollars. (b) *** p < 0.01, ** p < 0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

45

Table A2: Nominal Exchange Rates, Tariffs, and Exports(1)
Full Sample
ln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squaredln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squaredln(1+Tariff)ln(NER)ln(Relative CPI)
Processing Tradeln(GDP)ln(GDP per capita)ln(Trade)
Direct Trade
Direct Trade2
Constant
Observations
R-squared
Product FE
Country FE
Industry-time FE
Cluster by Country-time-0.979***(0.0804)
0.114*(0.0602)0.140**(0.0639)-0.852***(0.0144)-0.235(0.156)0.425**(0.172)0.0781*(0.0398)
1.048***(0.0547)-1.082***(0.0459)
0.0331(0.660)1,748,760
0.571-3.186***(0.297)0.787**(0.359)1.730***(0.435)-1.803***(0.0522)-0.775(1.069)2.405**(1.147)1.312***(0.237)5.618***(0.222)-5.396***(0.178)-9.855**(4.258)
1,748,760
0.283-2.287***(0.227)0.551**(0.279)1.275***(0.329)-1.218***(0.0402)-0.362(0.789)1.469*(0.859)0.882***(0.175)3.810***(0.179)-3.845***(0.142)-6.794**(3.054)
1,748,760
0.352
Yes
Yes
Yes
Yes

(2)
(3)
(4)
Ordinary
Processing
High Incomeln(Exporter Numbers +1)-0.934***-1.078***-0.220(0.0910)(0.0751)(0.134)0.103
0.178***
0.0290(0.0775)(0.0580)(0.0811)0.08220.176***
0.424**(0.0865)(0.0601)(0.168)-0.914***(0.0196)-0.380**-0.628***-0.231(0.185)(0.154)(0.206)0.562***
0.730***
0.293(0.202)(0.169)(0.232)0.07320.0146-0.129**(0.0507)(0.0356)(0.0622)0.192***
0.442***
1.142***(0.0466)(0.0549)(0.0718)-0.186***-0.363***-1.150***(0.0437)(0.0466)(0.0608)
0.529
1.762***
1.993***(0.821)(0.615)(0.702)
1,203,922
544,669
896,962
0.640
0.565
0.662
ln(Export Value per Exporter + 1)
-2.420***-3.891***-2.199***(0.308)(0.359)(0.481)0.689**
0.930**
2.196***(0.346)(0.447)(0.598)1.625***
1.712***
3.461**(0.471)(0.476)(1.555)-1.345***(0.0641)-0.734-1.6643.494**(1.088)(1.244)(1.494)2.176*
3.655***-2.793(1.137)(1.395)(1.776)
1.239***
1.334***
1.293***(0.249)(0.246)(0.463)
1.898***
5.633***
6.212***(0.183)(0.317)(0.260)-1.584***-4.469***-5.719***(0.169)(0.252)(0.209)-7.732*-12.15**-16.03***(4.385)(4.775)(3.956)
1,203,922
544,669
896,962
0.339
0.319
0.333
ln(Export Quantity per Exporter + 1)
-1.591***-3.341***-1.838***(0.230)(0.285)(0.447)0.454*
0.694**
1.598***(0.272)(0.333)(0.484)1.160***
1.316***
2.954***(0.347)(0.367)(1.094)-0.848***(0.0479)-0.283-1.2802.540**(0.783)(0.945)(1.092)1.252
2.651**-2.192*(0.835)(1.063)(1.311)0.837***
0.837***
0.828**(0.182)(0.186)(0.347)
1.192***
3.596***
4.392***(0.132)(0.236)(0.213)-0.994***-2.902***-4.182***(0.121)(0.189)(0.169)-5.500*-7.256**-9.634***(3.069)(3.540)(2.893)
1,203,922
544,669
896,962
0.424
0.357
0.401
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

(5))
Low Income-0.262***(0.0626)
0.165*(0.0852)
0.179**(0.0867)-0.829***(0.0193)0.0341(0.227)0.149(0.202)0.184***(0.0465)
0.666***(0.0479)-0.777***(0.0389)-0.892(1.204)
851,7140.506-1.117***(0.280)0.985**(0.427)1.339***(0.495)-2.601***(0.0455)-2.139(1.400)3.318***(1.271)1.130***(0.251)
4.192***(0.281)-4.409***(0.227)1.734(7.134)
851,7140.266-0.681***(0.203)0.706**(0.322)0.904**(0.359)-1.844***(0.0345)-1.279(1.026)2.049**(0.949)0.725***(0.177)2.582***(0.212)-3.002***(0.170)1.500(5.081)
851,714
0.337
Yes
Yes
Yes
Yes

Note: (a) The high income destinations are defined as countries whose average GDP
per capita during 2000-2006 is above 10,000 U.S. dollars. (b) *** p < 0.01, ** p <0.05, * p < 0.1.
Source: Chinese Customs Export and Import Database, IMF and TRAINS.

46

.1
.09
Yuan/Yen
.08
.07
.06

8
7

7.8

8

Yuan/Euro
9
10

Yuan/USD
8.2
8.4

11

8.6

12

8.8

Figure A1: Nominal and Real Exchange Rates for Selected Trade Partners

2000

2001

2002

2003

2004

2005

2006

2000

2001

2002

2003

Year

2004

2005

2006

2000

RER

RER

2005

2006

Yuan/TWD
.28
.26
.24
2000

2001

2002

2003

2004

2005

2006

2000

2001

2002

2003

Year
RER

RER

NER

2005

2006

(e) UK

RER(f) Taiwan

6.5

2.3

(d) South Korea

2004

Year

NER

Yuan/MYR
2.2

5

2.1

4

4.5

2.15

6

Yuan/CAD

Yuan/AUD
5
5.5

7

2.25

6

8

2006

.3

15
Yuan/Pound
14
13
12
2004

Year
NER

2005

RER

.32

16

.0085

2003

2004

(c) Japan.008
Yuan/Won.007 .0075
.0065

2002

2003

NER

(b) Euro Area

.006

2001

2002

Year

NER

(a) USA

2000

2001

Year

NER

2000

2001

2002

2003

2004

2005

2006

2000

2001

2002

2003

Year

2004

2005

2006

2000

RER

RER

2005

2006

2006

.21
Yuan/THB
.19
.2
.18
.17
2000

2001

2002

Year

2003

2004

2005

2006

2000

2001

2002

Year

NER

2005

RER

.22

.0012
Yuan/IDR
.0008
.0006
2004

.0004

2003

2004

(i) Malaysia

.001

.2
.18
Yuan/INR
.16
.14

2002

2003

NER

(h) Australia

.12

2001

2002

Year

NER

(g) Canada

2000

2001

Year

NER

RER

RER

2004

NER

2005

2006

(k) Indonesia

RER(l) Thailand

1

.65

.7

2

Yuan/BRL
3

Yuan/MXN
.75
.8

4

.85

.9

5

(j) India

2003

Year

NER

2000

2001

2002

2003

2004

2005

2006

2000

Year
NER

2001

2002

2003

2004

2005

2006

Year
RER

NER

(m) Mexico

RER(n) Brazil

Note: A decrease (increase) in nominal/real exchange rates means China’s appreciation (depreciation).
Source: IMF.

47