Endogenous Trade Policy in a Global Value Chain:
Evidence from Chinese Micro-level Processing Trade
Data∗
Rodney Ludema† , Anna Maria Mayda† , Miaojie Yu‡ , and Zhi Yu§
December 2016

Abstract: This paper exams endogenous trade policy in a global value chain by
exploring the impact of exports of intermediate inputs of a country on its trade barriers
on final products. We use Chinese transaction-level processing trade data and finds that,the more are a country’s intermediate exports to China, the lower is the trade barrier
this country imposes on the imports of the final product from China produced with theseintermediates. The reason is that a low trade barrier on the final product increases thecountry’s demand for Chinese final product, and thus increases Chinese demand for its
intermediate inputs used in the production of that product. This impact exists for boththe permanent tariffs (preferential and MFN tariffs) and temporary trade barriers (TTB,
including anti-dumping and safeguards), is stronger for differentiated products than forcommodities, and is stronger when intermediate export industries are more organized.
For MFN tariffs and safeguards applied to all trade partners, this impact is higher when
the share of a country imports of the final product from China (out of its total imports
from the whole world) is higher, which implies it’s more likely that the MFN tariffs andsafeguards are set mainly against China.
Key Words: Input Export, Output Tariff
JEL Numbers: F10, F13, F14—————————————————————————————————∗

The authors thank Andrew Bernard, Emily Blanchard, Maggie Chen, Teresa Fort, Réka Juhász,
Samuel Kortum, John Romalis, Peter Schott, Heiwai Tang, and participants at numerous seminars andconferences, for helpful comments and suggestions.
†Georgetown University, Department of Economics, 37th and O Streets, Washington, DC 20057,
U.S.A. E-mail Addresses: ludemar@georgetown.edu (Ludema), amm223@georgetown.edu (Mayda).
‡
Peking University, National School of Development, China Center for Economic Research (CCER), Beijing 100871, China. E-mail Address: mjyu@ccer.pku.edu.cn.
§
Shanghai University of Finance and Economics, School of International Business Administration,
777 Guoding Road, Shanghai 200433, China. E-mail Address: yu.zhi@shufe.edu.cn.

11. Introduction
1.1 Overview

This paper exams endogenous trade policy in a global value chain by

exploring the impact of exports of intermediate inputs of a country on its trade barriers
on final products. Our idea was to look at Chinese processing trade. Think, for example,of an iPad. We know that 90% of the value-added of an iPad consists of imported
intermediates that enter the Chinese processing zone duty free. 10% is Chinese laborcost. The final good (the finished iPad) is imported by many countries. Now let’s lookat the tariffs that different countries impose on iPads. The hypothesis that we would liketo test is that the more a country’s intermediate inputs to go into an iPad, the lower
will be its tariff on iPads. The reason is that a low tariff increases a country’s importsof iPads and thus promotes exports of the intermediate inputs that go into the iPads. If
this export-promotion effect is large, there is a strong reason for the country to lower its
tariff. A country that does not produce any of the intermediates that go into the iPadshould have no export-promotion incentive.
We use Chinese transaction-level trade data in the period of 2000-2006 to test thishypothesis. For each export or import transaction, the data record the firm, product (atHS8 level), country (destination of exports or source of imports), time (year and month),type of transaction (processing or ordinary trade), value, quantity, etc. We restrict ouranalysis to processing trade, more specifically, processing with imports, where Chinese
firms import intermediates from foreign firms (processing imports), use them to produce
final products, and then sell the final products to foreign firms (processing exports) typically different from the foreign firms exporting the intermediates to them. We link
the processing exports and processing imports by firm, which gives us a very disaggregate
and direct input-output table for our empirical analysis. Then we analyze how processing
imports of intermediate inputs from different countries impact their trade barriers on the
final products that use these intermediate inputs.
We find that, the more are a country’s intermediate exports to China, the lower isthe trade barrier this country imposes on the imports of the final product from China
produced with these intermediates, which shows that input export promotion could serve2

as an incentive for lowering trade barriers on output.
An endogeneity problem might arise due to reverse causality: the negative relationship between intermediate exports of a foreign country and the trade barrier this country
imposes on the final product might be due to the fact that the low trade barrier of thecountry on the final product indeed increases its imports of the final product from China
and hence promotes its exports of the intermediate inputs to China (which is the premiseof our whole story), instead of the other way around. To take care of this concern, we
instrument the intermediate exports of a country to China with the country-productyear level transport cost between China and this country, which are calculated from theshipping rates in the U.S. Merchandise Import data and adjusted by distance. The IV
estimation results confirm our hypothesis.
In addition, we find that the impact of input export on output trade barrier not
only exists for the permanent tariffs (preferential and MFN tariffs), but also exists for
temporary trade barriers (TTBs, including anti-dumping and safeguards). For temporary
trade barriers, the more are a country’s intermediate exports to China, the lower is theprobability that this country files a TTB against China on the final product producedwith these intermediates.
Since MFN tariffs and safeguards of foreign countries are applied not only to theirimports from China, but also to imports from their other trade partners, people might
wonder whether promotion of intermediate exports to China is indeed an important incentive for them to lower their MFN tariffs or revoke their safeguards towards all countries
on final products, since they might not want their other trade partners to free ride on
their tariff reduction. To take care of this concern, we control for the share of a country’s imports of a final product from China (out of its total imports of the product fromthe world). We find that, the higher is this share (which implies the less severe is thefree-riding problem, and it’s more likely that the MFN tariffs and safeguards are mainly
targeted against China), the higher is the export-promotion incentive when the country
sets its MFN tariffs or safeguards on final products. This shows the export-promotion
incentive indeed has its rational.

3

Moreover, we find that the impact of input exports on output trade barriers isstronger for differentiated final products than for commodities. This is because it is more
likely that differentiated products being traded are specific to an importer-exporter pair.
This gives the importer greater monopsony power, and so its trade barrier will have agreater effect on the exporting firm (since the exporting firm cannot easily substitute toa different importer), which in turn means the trade barrier will have a greater effect on
the exporting firm’s purchases of intermediates. Therefore, the influence of intermediate
exports on final product trade barriers should be higher for differentiated goods than forhomogeneous goods.
Finally, we find that the impact of input exports on output trade barriers is stronger
when intermediate export sectors are more politically organized, which means they havemore influence on lobbying the government for low trade barriers on final products. Thisfurther confirms the mechanism of our hypothesis.
1.2 Literature

The current paper joins a growing literature on political economyof trade policy, influence of international trade and investment on trade policy, global
value chains and trade policy, as well as assembling trade.
In the political economy literature, trade policy, such as tariffs, tariffs suspensions
and antidumping, is always regarded as endogenous. There mainly exist two effects, aquid pro quo effect and a pure information effect, through which individual firms influence
government decisions on trade policy. Grossman and Helpman (1994) theoretically showthat firms in organized sectors offer contributions to politicians as a quid pro quo fortariffs. Campaign contributions by political action committees have been interpreted as
the evidence of this effect in some papers (e.g., Snyder, 1990; Goldberg and Maggi, 1999;
Gawande and Bandyopadhyay, 2000). Recently, a growing number of empirical studies
use lobbing expenditures as a proxy variable of firm level contributions (e.g., Gawande etal., 2006; Kee et al., 2007; Ludema and Mayda, 2009; Stoyanov, 2009; Igan et al., 2010).
Some studies also highlight lobbying’s information role (e.g. Grossman and Helpman,
2001; De Figueiredo and Cameron, 2008). Only few papers disentangle the information
effect from the quid pro quo effect (e.g., Austen-Smith and Wright, 1994; Ludema, Mayda

4and Mishra, 2016). In particular, Ludema, Mayda and Mishra (2016) use firm-level dataon tariff suspension bills and lobbying expenditure during 1999-2006 and finds that theeffect of verbal opposition is much larger.
Although lots of papers stress the endogeneity of trade policy in the political economy literature, few studies have analyzed the unique role of international trade orinvestment in trade policy determination. Based on cooperative trade agreements models, Bagwell and Staiger (1990) predict that new tariffs will increase with imports anddecrease with the variance of imports. Bown and Crowley (2013) find that US importpolicy during 1997-2006 is consistent with this prediction.
Several actors in the economy impact the tariff set by a country on a given good, bothfor social-welfare considerations and for political-economy reasons. These different actors
are: Consumers of the goods in the country; The firms from that country, which produce
the good in that country; The firms from that country, which produce the good abroad
and import it back and distribute it (see Blanchard and Matschke (2015)); The firms fromthat country, which use the good as an input, in order to produce some other good in the
country (see Ludema, Mayda and Mishra (2016), Gawande and Bandyopadyhay (2000));
The firms from that country, which produce inputs that are used to produce the goodabroad (this paper, as well as Blanchard, Bown and Johnson (2016)).
The latter three actors are related to the input-output relationships across borders,i.e., global value chains. More specifically, Blanchard (2007, 2010) demonstrate how
overseas investment and cross-border ownership can affect countries’ optimal tariff andmultilateral tariff negotiation. Blanchard and Matschke (2013) find that a 10% increasein U.S. foreign affiliate exports to the U.S. is associated with a 4 percentage point increasein the rate of preferential duty-free access. Ludema, Mayda and Mishra (2016) show thatboth verbal opposition and lobbying expenditures of U.S. firms, which use the imported
good as an input (in order to produce some other goods), reduce the likelihood of a tariff
suspension on the imported good. Gawande and Bandyopadyhay (2000) show that, if
sectors which use the imported good as an input (in order to produce some other goods)
spend in PAC campaign contributions, then the tariff on the good is lower. Blanchard,5Bown and Johnson (2016) show that a country will not tax with a tariff the value addedof its own firms, no matter where that value added is produced or used as an input;
meanwhile, a country will not protect the value added of foreign firms. This is theclosest study to our work. Their paper uses aggregate data from the World Input-Output
Database (WIOD) to get the value-added content of final goods production, while ourpaper uses very disaggregated firm-level processing trade data to get that, which is a big
advantage of our paper.
There is a large difference between our work and the literature mentioned above, as
we mainly focus on the influence of value-added overseas assembling, rather than ordinaryinternational trade or investment, on trade policy. However, most studies on overseas
assembling and outsourcing examine how outsourcing decision would change with cost ofa firm’s home country and its competitor. They find that overseas assembling activities
decline when the countries’ costs rise (e.g., Feenstra et al., 2000; Swenson 2004, 2005).
Some papers analyze the characteristics of processing trade in China and stress the low
productivity of processing firms (e.g., Yu, 2015; Dai and Yu, 2013; Kee and Tang, 2016).
Kee and Tang (2016) assess the domestic value-added in Chinese processing exports, whichshare the same interests with us in line of firm’s value-added realization. However, noneof these studies look at how processing trade impacts trade policy, as what we did in thispaper.
The paper is organized as follows. Section 2 describes the trade and tariff data that
we use in the analysis. Section 3 specifies our empirical model. Section 4 reports theempirical results. Section 5 concludes.

2. Data
2.1 Trade Data

The main dataset that we use in the empirical analysis is the Chinesetransaction-level trade data in the period of 2000-2006. This dataset was collected by China’s General Administration of Customs (CGAC). The dataset contains rich information
for all Chinese export and import transactions. For each export or import transaction, thedata records the firm, product (at HS8 level), country (destination of exports or source6

of imports), time (year and month), value, quantity, custom, transportation mode, etc.
More importantly, the data also records the type of each transaction, that is, whether an
export or import transaction is ordinary trade or a certain type of processing trade.
There are various types of processing trade, and the most important two types are:(1) processing with imports (PWI), that is, Chinese firms import intermediate inputs
from foreign firms, use them to produce final products, and then sell the final products
to foreign firms (typically different from the foreign firms that export intermediate inputs
to them); both the import and export prices are set based on the negotiations between
transaction parties. (2) processing with assembly (PWA), that is, Chinese firms get
intermediate inputs directly from foreign firms for free, assemble them to produce final
products, and then return them back to the same foreign firms for sale; foreign firms pay
Chinese firms a certain amount of processing fees. In both types of processing trade, theforeign intermediates are directly used in the production of exported final products, andhence the data contains an excellent disaggregated input-output table, which is perfectfor testing our hypothesis.
We restrict our analysis to processing with imports (PWI). This is because, for processing with assembly (PWA), foreign firms that provide intermediate inputs to Chinese
firms have to “purchase back” (actually not really “purchase back”, but just get backwith paying assembly fees) the final products from them in any case, and hence governments of foreign countries should have no incentive to lower the tariffs on final products
to increase the imports of these products and in turn promote the exports of their intermediate inputs. Only for processing with imports (PWI), in which foreign countries
that export intermediate inputs do not have the obligation to import the final products,
the governments might have an export-promotion incentive to reduce the tariffs on final
products to increase their imports.
Therefore, we only keep the transactions of processing with imports (PWI) for boththe export data and the import data. We link the export data and import data by firm,
so that we can analyze how exports of intermediate inputs of different countries impact
their trade barriers on the final products that use these intermediate inputs.

7

Table 1a contains the summary statistics for our trade data. Panel A of the table
reports Chinese total export value and number of export firms, as well as those for processing with imports (PWI) and their shares, in each year during the period of 2000-2006.
The total export value increases from 249 to 969 billion dollars during the period, while
the total export value of PWI increases from 97 to 415 billion dollars. The share of PWI
exports out of the total exports is pretty stable at the range of 39-44 percent. The numberof export firms increases from 62,771 to 171,205, while the number of export firms withPWI increases from 27,209 to 37,765. The share of export firms with PWI out of allexport firms declined steadily from 43 to 22 percent.
Panel B reports the same numbers for Chinese imports. The total import value
increases from 225 to 788 billion dollars during the period, while the total import valueof PWI increases from 65 to 247 billion dollars. The share of PWI imports out of thetotal imports is pretty stable at the range of 27-31 percent. The number of import firms
increases from 62,793 to 121,835, while the number of import firms with PWI increases
from 28,435 to 39,579. The share of import firms with PWI out of all import firms declined
steadily from 45 to 32 percent.
2.2 Trade Barriers Data

We also need to use trade barriers of foreign countries

against Chinese exports in the empirical analysis. The trade barriers data we use include
both the permanent tariffs (MFN and preferential tariffs) and the temporary trade barriers(TTB, including anti-dumping and safeguards measures).
The permanent tariffs (MFN and preferential tariffs) come from WITS (World Integrated Trade Solution) - TRAINS at the World Bank. The data records the tariffs foreach “exporter-importer-product(HS6)-time(year)” cell. All tariffs are AV (ad-valorem,
99% of all cells) or AVE (ad-valorem equivalence, 1% of all cells). I chose the tariffs offoreign countries (as importers) against China by product (HS6) and time (year).
For each “importer(country)-product-time” cell, the dataset does not directly specify
whether its tariff is MFN/preferential; instead, it gives 4 series of tariffs: MFN, PRF(preferential), AHS (applied), BND (MFN bound, which only has very rare observations).
I compare the first 3 series for each cell, and decide the nature of tariff for the cell by

8

using the following rules: (1) if AHS=MFN, then the tariff for this cell is MFN (this typeaccounts for about 84% of all cells); (2) if AHS=PRF, the tariff for this cell is preferential(this type accounts for about 7% of all cells); (3) if AHS is missing but PRF is present,
then the tariff for this cell is prefential (this type accounts for about 6% of all cells).
These three types account for 97% of all cells. The other 3% includes several different
and weired cases, which we do not use in the analysis. In the empirical analysis below,
we will check whether the impact of intermediate export on final good tariffs exists forboth preferential tariffs and MFN tariffs.
Table 1b reports the summary statistics of the tariff data. Panel A contains thenumber of product(hs6)-country cells and the mean tariff of these cells for all permanent
tariffs, MFN tariffs and preferential tariffs for each year during the period 2001-2007,
one year later than the period of our trade data.1 The number of product(hs6)-country
cells ranges from 136,998 to 234,721 for the full permanent tariff sample. MFN tariffs
account for 90-95% of them (ranging from 129,364 to 220,308), and preferential tariffs
only account for 5-10% of them (ranging from 6,459 to 18,846). The average tariffs range
from 8.74-11.92% for all permanent tariffs, 8.98-12.47% for MFN tariffs, and much lower
at 2.56-5.97% for preferential tariffs.
Panel B reports the 13 countries that offered preferential tariffs to China during the
2001-2007 period. For each country, the table reports the specific years, number of products (hs6), number of product(hs6)-year cells that these preferential tariffs were offered,as well as the source of these preferential tariffs. In terms of number of product(hs6)year cells, the largest ones are from EU, Japan, Canada, New Zealand, and Turkey. The
source of these preferential tariffs include GSP, China’s regional trade agreements including China-ASEAN Free Trade Agreement and APEC, and China’s bilateral trade
agreements with countries such as Chile and Pakistan.
The temporary trade barriers (TTB, including anti-dumping and safeguards) comefrom the World Bank TTB Database, which was collected by Bown (2014). It records the
TTB information of importing countries/regions at the importer(country) -product(HS6)1

We report them in this way since, as we will see in our empirical specification, we will look at the
impacts of exports of intermediates on the final good tariffs in the lagged period.

9

exporter(country)-year level, including whether a TTB is filed and imposed. We use the
TTB information of 13 importing countries/regions against China on all products during
the 2001-2007 period.
Panel C of Table 1b reports these 13 countries that filed TTBs against China. For
each country, the table reports the number of product(hs6)-year cells for which a TTB caseis filed or a TTB measure is imposed for all TTB measures, antidumping, and safeguards.
For all TTB measures and antidumping, the top 5 countries/regions with the largest
number of product(hs6)-year cells for which a TTB case is filed are U.S., EU, India, Peru,
and Colombia. Safeguards were only filed or imposed by 7 countries: U.S., EU, Canada,
Turkey, Indonesia, India, and Japan. Note that the relationship between TTB filing and
TTB imposition is quite different across countries: for most countries (like U.S., EU, andIndia), TTB measures were eventually imposed for more than 80% of the TTB cases thatare filed; while for some other countries (like Peru and Colombia), no TTB measures were
imposed for any of the TTB cases that are filed.

3. Empirical Specification
The main purpose of the paper is to test the export promotion of intermediate inputs as
an incentive for low trade barriers on final products. Our baseline empirical specificationis:T Bict =β1 lnEXic(t−1) + β2 CONic(t−1) + F E + εict(1)

In this baseline specification, the dependent variable, T Bict ∈ {Tict , T T Bict }, represents
trade barriers that country c imposes on imports of product i (at HS6 level) from China inperiod t. It could be permanent tariff rates (ad valorem or ad valorem equivalence MFNor preferential tariff rates), Tict , or a dummy variable indicating whether a temporary
trade barrier is filed or imposed, T T Bict .
The key regressor, EXic(t−1) , is country c’s export value of intermediate inputs that
go to China’s production of final product i in period t − 1. We construct this variable in10the following steps. First, we aggregate the transaction-level Chinese processing exports
of PWI (processing with imports) to firm-product(hs6)-year level, Xf it , which is firm f ’s
exports of final product i in year t. Second, we aggregate the transaction-level Chinese
processing imports of PWI to firm-intermediate(hs6)-country-year level, Mf jct , which isfirm f ’s imports of intermediate j from country c in year t. Third, we merge the firmproduct(hs6)-year level Chinese processing export data in the current year, Xf it , and thefirm-intermediate(hs6)-country-year level Chinese processing import data in the lagged
year, Mf jc(t−1) , by firms. The reason we merge the intermediate imports in the lagged
year is that we assume the intermediate imports in a previous year will be used in the
production of the final products that are exported in the current year.
Next, we split a firm’s imports of an intermediate from each country in the lagged
period, Mf jc(t−1) , among its exporting products in the current period, Xf it , according to
the share of its export of each product out of its total exports of all products, to get thefirm’s imports of each intermediate from each country that are used in the production ofeach of the final product that it exports:
Xf it
XMf ijc(t−1) = Mf jc(t−1) × P
i Xf it(2)which is firm f ’s imports of intermediate j from country c in year t − 1 that are used in
the production of final product i exported in year t. This variable essentially gives us afirm-level input-output table.
Finally, we aggregate this variable over firms and intermediates for each product(hs6)country-year cell to get the imports of all intermediate from each country that are usedin Chinese production of a final product:

EXic(t−1) =XX

XMf ijc(t−1)

(3)

j

f

which is just our key regressor, country c’s export of intermediate inputs in period t − 1that go to China’s production of final product i.
The key hypothesis that we would like to test is: β1 < 0. That is, the more are11

a country’s intermediate exports to China, the lower is the (permanent or temporary)trade barrier this country would impose on the imports of the final product from China
produced with these intermediates.
CONic(t−1) in the specification represents product(hs6)-country-year level control
variables, which we will specify later in various regressions. One common control variable
that we would like to include in all regressions is the Herfindahl-Hirschman concentrationindex of countries exporting intermediate inputs that go to China’s production of the final
product, which is computed as

EXHi(t−1) =2
Σc EXic(t−1)(Σc EXic(t−1) )2

× 100

(4)

We multiply the regular expression (with value between 0 and 1) by 100, so that we caninterpret it in percent. The more concentrated are the countries exporting intermediates
to China, the more likely they would cooperate with each other to lowere their tradebarriers on the final products from China produced with these intermediates. Hence we
expect its coefficient to be negative.
F E in the specification stands for various fixed effects. εict is the error term.
Table 2 contains the summary statistics of the main variables used in the baseline
specification in section 3 in the full permanent tariff sample and its two subsamples(preferential tariffs and MFN tariffs), as well as the TTB sample and its two subsamples(antidumping and safeguards). In the full permanent tariff sample, the average tariff on
final products is 9.91 percent,2 the average log of exports of intermediates is 1.67, and theintermediate exporter concentration index is 39.28 percent. The three numbers are 2.51,
4.73, and 41.08 in the preferential tariff sample, and 10.32, 1.50, and 39.18 in the MFN
tariff sample. The average preferential tariff (2.51) is much lower than the average MFN
tariffs (10.32). The TTB filing rate and imposition rate are low at 0.17% and 0.12%,
respectively, for the full TTB sample, 0.11% and 0.06% for antidumping, and 0.06% and0.05% for safeguards.
2

Note that we have dropped the outliers by removing the top 1% of the permanent tariffs in the fullsample (those greater than or equal to 50 percent) from all the three samples.

124. Empirical Results
In this section, we report the results of our empirical analysis. We first report the baseline OLS regression results. Then we address the endogeneity problem and report the IV
regression results. Next, we address the externality issue of MFN tariffs and safeguards.
Finally, we test the mechanism of our hypothesis by exploring how the impact of intermediate exports on trade barriers on final products depends on product differentiation andpolitical organization variables.

4.1. Baseline OLS Results

We first report the baseline OLS regression results.

Table 3a presents the baseline OLS regression results for permanent tariffs. The threepanels, A, B, and C, report the results for the full permanent tariff sample, the preferential tariff sample, and the MFN tariff sample, respectively. The first four columns
contain the results without any control variables but with various fixed effects: the firstcolumn includes product (hs6), country and year fixed effects; the second column uses
the product-year and country fixed effects; the third column includes country-year andproduct fixed effects; the fourth column uses product-year and country-year fixed effects.3
The fifth column and the last column add the intermediate exporter concentration index
to the first column and the third column, since the index itself is at the product-yearlevel and hence the product-year fixed effects in the second and fourth columns can not
be included.
In panel A for the full sample, the results are very consistent with our conjecture in
the sense that the estimates for coefficient of lnEXic(t−1) are indeed negative (ranging from−0.06 to −0.09) and very significant (all at 0.1% level) under various fixed effects, withoutor with the intermediate exporter concentration index. In the 4th column that includes
the most disaggregate fixed effects, the estimate is −0.09. This means that a 10 percent
increase in a country’s exports of intermediate inputs to China is associated with a tariff
3

Note that we do not include product-country fixed effects, because the main variation of both thedependent variable, Tict , and the key regressor, EXic(t−1) , comes at the product-country combination
but not the year, and hence including product-country fixed effects would absorb most of the impacts ofour key regressor.

13

reduction of 0.009 percentage points of this country on the imports of the final productfrom China using these intermediate inputs. The magnitude of the impact is small, giventhat the average tariff in the sample is 9.91 percent, but it is significant. Meanwhile,in the last two columns, the estimates for the coefficient of the intermediate exporter
concentration index are also negative and significant, consistent with our conjecture. The
estimate is −0.001 in the last column, indicating that a 10 percentage point increase in
the intermediate exporter concentration index is associated with a tariff reduction of 0.01
percentage points of a country on the imports of the final product from China using theseintermediate inputs.
We report the regression results for preferential tariffs (country-specific) in panel B
and MFN tariffs (applied equally to all countries with MFN status) in panel C. The results
for the MFN tariff sample are very close to those for the full permanent tariff sample interms of the sign, magnitude, and significance of the coefficients. This is understandable
since around 95% of the observations in the full sample consist of MFN tariffs. For
the preferential tariff sample, the estimates for the coefficient of lnEXic(t−1) are indeed
negative and significant (stable at −0.01), but are much smaller than the corresponding
ones for the MFN tariff sample. This is understandable since the average preferential
tariff (2.51) is much smaller than the average MFN tariff (9.91).
Table 3b reports the baseline OLS regression results for temporary trade barriers
f iled(TTB), with the TTB filing dummy (T T Bict
) as the dependent variable. The threepanels, A, B, and C, report the results for the full TTB sample, the antidumping sample,
and the safeguards sample, respectively. The format of the table is the same as that ofTable 3a. The estimates for our key regressor are negative and significant in all specifications and all the three samples. In the full TTB sample and the antidumping sample, theestimates are stable at −0.0001. This means a 10 percent increase of a country’s exports
of intermediate inputs to China is associated with a decrease of 0.001 percentage pointsof the probability that this country files a TTB against the imports of the final productfrom China using these intermediate inputs.4 The estimates in the safeguards sample are4

Note that average probability of TTB filing, shown in Table 2, is 0.0017, i.e., 0.17 percent.

14

in double size (−0.0002). None of the estimates for the exporter concentration index issignificant.
imposedWe also run the baseline OLS regression with the TTB imposition dummy (T T Bict)as the dependent variable. The results are qualitatively very similar to those in Table 3b
and are omitted here.

4.2. Endogeneity Problem and IV Estimates

An endogeneity problem mightarise due to reverse causality: The trade barrier of a country imposed on a final productimported from China (T Bict ) might be impacted by its intermediate export to China in
the previous period (EXic(t−1) ), but this intermediate export to China might also be impacted by the trade barrier of the country imposed on the final product imported from
China in an even earlier period (T Bic(t−2) ); The trade barrier of the country imposed on
the product imported from China in different periods might be serially correlated, giventhat trade barriers are typically stable over a certain period of time. Hence we need tosolve this endogeneity problem and instrument for foreign countries’ intermediate export
to China (EXic(t−1) ).
A valid instrument should satisfy two conditions: (1) it should be correlated withthis export (EXic(t−1) ), and (2) it should not be correlated with their trade barrier on
the final product imported from China (T Bict ). The transport cost between China andforeign countries for intermediates used in product i in period t − 1, T Cic(t−1) , should bea valid instrument that satisfies the two conditions mentioned above: (1) it should becorrelated with this export, and (2) it should not be correlated with their trade barrierson the final product imported from China (T Bict ).5
We do not have direct data on transport cost between China and foreign countries,
but we can construct this cost by using the information available from the U.S transport
5

The natural trade partner hypothesis in the political economy of trade policy literature says thatwhether two countries sign a trade agreement, and hence their bilateral tariffs, depend on the distance
between these two countries, and countries that are close to each other have a high probability to sign a
trade agreement. We know distance is closely related to transport cost. Hence, this hypothesis somehow
challenges the exclusion requirement of using the transport cost as a valid IV. However, as we would see
below, our transport cost varies at the product-country-year level, which has much more variation than
product and year invariant and country specific distance. Hence, we believe it is still a valid instrument.

15

cost data. US Imports of Merchandise Dataset from the U.S. Census Bureau has weight,
value, transport charges (freight and insurance in total) by product (hs10)-country-timemode, where “mode” is one of the transport modes - vessel or airplane. We construct
China’s transport cost T Cict with a three-step procedure:First, we use U.S. import transport charges by product j from country c shipped bymode m at time t divided by the corresponding weight and distance (between U.S. andits trade partner c) to get product-country-mode-time specific shipping rates (in dollars
per kilogram-mile), and then aggregate it over countries to the product-mode-time level:6
us
Cjcmt
us
Wjcmt
× DcusP
SRjcmtSRjmt = cNc

SRjcmt =(5)

where SRjcmt and SRjmt stand for shipping rates at the jcmt and jmt level, respectively;ususrepresent U.S. transport charges and weight at the jcmt level, Dcus is
and Wjcmt
Cjcmt

distance between U.S. and country c, and Nc is number of countries in the jmt cell.
Second, we multiply this shipping rate by distance and weight of shipments of product j by mode m from all countries to China at the base period tj0 (the first year that
China imports product j) to get the Chinese equivalent of transport charges at jcmt level.
We then divide by the corresponding value of shipments to get an ad valorem transportcost, again at jcmt level:

T Cjcmt =SRjmt × Dcchn ×

chn
Wjmt
j0
chn
Vjmt
j0

(6)

where T Cjcmt is the ad valorem transport cost at jcmt level; Dcchn is the distance betweenchnchn
China and country c, Wjmt
and Vjmt
are weight and value, respectively, of shipmentsj0
j0

of product j by mode m from all countries to China in the base period tj0 .
Finally, to determine the ad valorem transport costs of inputs from country c in a
final good i, we take a weighted average of the ad-valorem transport cost of input j from6

We aggregate the shipping rate over countries to the product-mode-time level, because the shipping
rate should not depend on country characteristics, and the country variation of transport cost should
only come from distance.

16

country c by mode m used in final good i, where weights are imports of input j from all
countries by mode m as a share of imports of all inputs (used in final product i) from all
countries by all modes in the base period ti0 (the first year that China exports producti):7

T Cict =XXj∈ji mchn
Vjmt
P i0 chn
T Cjcmt × Pj∈jim Vjmti0!
(7)where T Cict is the ad valorem transport costs of inputs from country c used in a finalchngood i in year t; Vjmt
is imports of input j from all countries by mode m in the basei0

period ti0 . T Cic(t−1) is used as the IV for the exports of country-c inputs used in final
good i at time t − 1, EXic(t−1) .8
Table 4a reports the first stage IV estimation results for the three permanent tariff
samples (full, preferential, and MFN tariffs) and the full TTB sample.9 For all thefour samples and in all specifications, the estimates for the coefficient of transport cost(lnT Cic(t−1) ) are negative and significant, indicating that transport cost indeed has anegative impact on foreign countries’ intermediate export to China, as predicted by theGravity model. Moreover, all the coefficients pass the weak IV test, indicating transport
cost is a valid IV. In column 4 of panel A (full permanent tariff sample) that includes themost disaggregate fixed effects, the coefficient is −1.75, which means that a one percent
increase of the transport cost between a foreign country and China is associated with a
1.75 percent decrease of the foreign county’s intermediate exports to China. The estimate
is −1.65 for MFN tariff sample, −3.41 for the preferential tariff sample, and −2.04 forthe full TTB sample.
Table 4b presents the second stage IV regression results for the three permanent tariff
7

Here the weights are constructed based on imports of inputs from all counties, rather than those fromcountry c. This is because firms might change their source countries of each input used in the productionof a specific final product, but the share of each input out of total inputs stands for the production
technology and should not depend on source countries of inputs.
8
We dropped the outliers by removing the top 5% and bottom 5% of all the transport costs, which
are extremely small or large.
9
The observations in the full TTB sample are the same as those in the antidumping sample and thesafeguards sample. The only difference between them is TTB dummy, but not the intermediate exports.
Hence the first stage IV regression of the intermediate exports on transport costs is the same across thefull TTB sample, the antidumping sample, and the safeguards sample.

17

samples. In panel A for the full permanent tariff sample, the estimates for coefficient oflnEXic(t−1) are indeed negative (ranging from −0.62 to −1.06) and very significant (allat 0.1% level) in all specifications. In the 4th column that includes the most disaggregate
fixed effects, the estimate is −0.67, much larger (in absolute value) than the corresponding
OLS estimate (−0.09) in table 3a. This means that a 10 percent increase in a country’s
exports of intermediate inputs to China is associated with a tariff reduction of 0.067
percentage points of this country on the imports of the final product from China using
these intermediate inputs. Similar to what we observed in the OLS estimates in table 3a,
the estimates in panel B for the preferential tariff sample (ranging from −0.11 to −0.27)
are much smaller than the corresponding ones in panel C for the MFN tariff sample(ranging from −0.68 to −1.12), since the average preferential tariff is much lower than
the average MFN tariff. Overall speaking, the IV estimates are qualitatively very similar
to but quantitatively much larger than the OLS estimates.
Table 4c reports the second stage IV regression results for the three TTB samples,f iledwith the TTB filing dummy (T T Bict
) as the dependent variable. The three panels,A, B, and C, report the results for the full TTB sample, the antidumping sample, andthe safeguards sample, respectively. In all specifications in all samples, the estimatesfor coefficient of lnEXic(t−1) are indeed negative (ranging from −0.0002 to −0.0019) andsignificant. In the 4th column (with the most disaggregate fixed effects) of panel A,
the estimate is −0.0019. This means a 10 percent increase of a country’s exports ofintermediate inputs to China is associated with a decrease of 0.019 percentage points10of the probability that this country files a TTB against the imports of the final productfrom China using these intermediate inputs.
imposedWe also run the IV regression with the TTB imposition dummy (T T Bict
) as thedependent variable. However, the estimates for the key regressor are messy and insignificant in most cases. This might be because, as indicated in section 2.2, the relationshipbetween TTB filing and TTB imposition is quite different across countries: in some countries (like Peru and Colombia), no TTB measures were imposed for any of the TTB
10Note this is about 10% of the average probability of TTB filing (0.0017, i.e., 0.17 percent) shown in
Table 2.

18

cases that are filed. In these cases, agreements between the trade partners might havebeen achieved after the initiation of the cases and hence no TTB measures were imposed.
Therefore, we no longer see the impacts of intermediate exports on the TTB decisions.

4.3. Accounting for MFN/Safeguards Externality

Our key hypothesis has beenverified with both preferential tariffs and MFN tariffs, and both antidumping and safeguards initiation. We know that MFN tariffs and safeguards of foreign countries areapplied not only to their imports from China, but also to imports from their other trade
partners (with MFN status in case of MFN tariffs). Hence people might wonder whether
export promotion of the intermediate inputs of these countries to China is indeed an
important incentive for them to lower their MFN tariffs or revoke safeguards initiation
on final products imported from all trade partners: their imports of final products may
mainly come from their other trade partners instead of China; other trade partners might
free ride on their tariff reduction or safeguards revoking on final products, in the sensethat these partners will export more of the final products to them, without importing
many intermediate inputs from them.
To take care of this externality, we control for the share of a foreign country’s imports
of a final product from China out of its total imports of the product from the whole worldin the lagged period. This share is calculated using the COMTRADE data from the WTO
asSIMic(t−1) =china
Mic(t−1)world
Mic(t−1)

× 100

(8)

china
world
where Mic(t−1)
and Mic(t−1)
are country c’s imports of product i from China and from theworld, respectively.11 The higher is this share, the more likely that its MFN tariffs or
safeguards protections are mainly set against China, the less severe should be the freeriding problem of its other trade partners, and hence the higher should be the exportpromotion incentive when this country sets its MFN tariffs or safeguards. We add thisshare, SIMic(t−1) , and its interaction with our key regressor, lnEXic(t−1) × SIMic(t−1) , in
11

The share is multiplied by 100 to make its coefficient easier to be interpreted. Hence a share of 5%
is shown as 5 in the data.

19the baseline regression. In this regression, we do not have an a priori conjecture about
the sign of the share itself, but we expect:(1) The coefficient for our key regressor, β1 = 0 or β1 < 0. The reason is asfollows. β1 captures the export-promotion incentive (the impact of EXic(t−1) on T Bict )when country c does not import any final product i from China (SIMic(t−1) = 0). In
this case, the country may not have the export-promotion incentive at all to reduce thetrade barriers against Chinese exports of the final product. But on the other hand, if
country c exports a lot of intermediate inputs to China (high EXic(t−1) ), it is likely it
also exports a lot of these intermediate inputs to other countries (since, for example,
it has a large endowment of these intermediates). Thus the country may still have an
export-promotion incentive to lower the MFN tariff or revoke the safeguards on the final
product - In this case, the purpose of its MFN tariff reduction or safeguards revoking is
to increase its imports of the final product from other countries instead of China, andhence to promote its intermediate exports to these countries instead of China. Thus β1
might still be negative even if country c does not import any final product i from China.
In sum, we will not be surprised if β1 is either insignificant (β1 = 0) or negative (β1 < 0).
(2) The coefficient for the interaction term, β12 < 0. That is, the higher is theshare of a country’s imports of the final product from China, the higher should be the
incentive to lower MFN tariff or revoke safeguards on the final product (since the freeriding problem is less severe) in order to promote its exports of intermediate inputs to
China. The reason has been explained above. This is the main hypothesis that we shalltest in this section.
Meanwhile, we know that this share, SIMic(t−1) , is obviously endogenous, since itwould be impacted by the our dependent variable, the country’s tariff on the product.
Hence, we need to instrument this share. [Note: Here we are considering MFN tariffs
that are applied to all trade partners. It would impact the country’s total imports of the
product from China, but not the SHARE of imports of product from China. Hence it
looks to me we don’t have an endogeneity problem here, and do not need the instrument.
What do you think?] The instrument we use is share of other counties’s imports of this20

product from China out of their total imports of the product from the whole world, whichis computed as

SIMic0 (t−1) =Micchina
0 (t−1)
Micworld
0 (t−1)× 100 =

china
china
Mi(t−1)− Mic(t−1)
world
world
− Mic(t−1)
Mi(t−1)

× 100

(9)

world
where Micchina
0 (t−1) and Mic0 (t−1) are, respectively, other countries’s imports of product i from
world
china
are, respectively, all
and Mi(t−1)
China and from the world in period t − 1. Mi(t−1)

countries’s imports of product i from China and from the world in period t − 1.
Table 5 reports the 2nd stage IV regression results of this specification for MFN
tariffs in panel A and for safeguards filing in panel B. In both panels, the estimates of
the coefficient for foreign countries’ exports of intermediate inputs to China (lnEXic(t−1) )are negative but sometimes insignificant, as what we expected. The estimates of thecoefficient for the interaction term (lnEXic(t−1) × SIMic(t−1) ) are always negative andsignificant (β12 < 0), which is consistent with our conjecture. The estimates range from−0.009 to −0.003 for MFN tariffs and −0.00003 to −0.00002 for safeguards. For MFN
tariffs, the estimate is −0.003 in the 4th column with the most disaggregate fixed effects,
which means that if the share of country c’s imports of the final product from China
increases by 10 percentage points, its tariff on the final product will reduce by 0.003 percentage points MORE in response to a 10 percent increase of its intermediate exports to
China.12 For safeguards, the estimate is −0.00003 in the 4th column, which means that if
the share of country c’s imports of the final product from China increases by 10 percentage
points, the probability of a safeguards initiation on the final product will reduce by 0.003
percentage points MORE in response to a 10 percent increase of its intermediate exports
to China.13 The estimate for the coefficient of the intermediate exporter concentrationindex (EXHi(t−1) ) is negative and significant as before.
12

Note that the average MFN tariff rate, shown in Table 2, is 10.32 percent. Meanwhile, the 4th columnof Panel C in Table 4b shows that a 10 percent increase in a country’s exports of intermediate inputs to
China is associated with a tariff reduction of 0.073 percentage points (0.73%*10) of this country on theimports of the final product from China using these intermediate inputs.
13
Note that average probability of safeguards filing, shown in Table 2, is 0.0006 (0.06 percent). Meanwhile, the 4th column of Panel C in Table 4c tells us that the probability of a safeguards initiation on
the final product will reduce by 0.016 percentage points (0.0016*10) in response to a 10 percent increaseof its intermediate exports to China.

21

4.4. Accounting for Product Differentiation

We now test the mechanism of ourhypothesis by exploring how it depends on product differentiation. Intermediate export
promotion could serve as an incentive for low trade barriers on final products, because low
trade barriers on final products induce low domestic consumer prices and high domestic
demand for foreign final products, and in turn high foreign demand for home intermediates. This impact should be larger for differentiated goods than for commodities. Thisis because it is more likely that differentiated products being traded are specific to an
importer-exporter pair. This gives the importer greater monopsony power, and so its
trade barrier will have a greater effect on the exporting firm (since the exporting firmcannot easily substitute to a different importer), which in turn means the trade barrier
will have a greater effect on the exporting firm’s purchases of intermediates. Therefore,
the influence of intermediate exports on final product trade barriers should be higher fordifferentiated goods than for homogeneous goods.
To explore whether this is true, we add in the regressions an interaction term of our
key regressor and a measure of product differentiation - Rauch classification. Rauch (1999)
classifies products into two categories: those traded in organized markets or with reference
prices are homogeneous goods, and the rest are differentiated products.14 The original
Rauch classification is at the 4-digit SITC (Standard International Trade Classification)level, and we assign a value of 0 to homogeneous goods and a value of 1 to differentiatedproducts.15 Then we use a SITC-HS concordance to transfer the value to a continuous
variable at the 6-digit HS level, Rauchi .16 We interact this measure with our key regressor
and include the interaction term in our baseline regression.
Table 6 reports the 2nd stage IV estimates for this regression.17 In panel A for
14

Rauch index is highly correlated with the inverse-elasticity of export supply, which measures themarket power of importers, in Broda, Limao and Weinstein (2008), and has proved to be a reliablemeasure of market power of importers in other studies, e.g., Ludema and Mayda (2013).
15
The original Rauch classification has two versions: a conservative version and a liberal version. What
we used here is the conservative version. But the results are robust to the liberal version.
16
More specifically, we use a concordance between 5-digit SITC codes and 10-digit HS codes to get aconcordance between 4-digit SITC codes and 10-digit HS codes. Then we use the concordance to transfer
the Rauch classification from 4-digit SITC level to 10-digit HS level, and aggregate it to the 6-digit HSlevel (by taking the average of all 10-digit HS products within the same 6-digit HS product).
17
The first stage results are available upon request.

22

permanent tariffs, the estimates of the coefficient for this interaction term are all negativeand significant, indicating that the impacts of intermediate exports of foreign countries on
their tariffs on final products using these intermediates are higher for differentiated goods
than for homogeneous goods, which is consistent with our expectation. For example, inthe 4th column, the coefficient for this impact is −0.47 for homogeneous goods, but higher(in absolute value) at −0.76 (−0.47 − 0.29) for differentiated goods. This is also the casein panel B for TTBs. In the 4th column, the coefficient for the interaction term is −0.0015
for homogeneous goods, but higher (in absolute value) at −0.0026 (−0.0015 − 0.0011) for
differentiated goods.

4.5. Accounting for Political Organization

We further test the mechanism of

our hypothesis by exploring the channel of the mechanism. Intermediate exports of acountry could serve as an incentive for low trade barriers on final products, because theexporting firms of intermediates in this country might lobby its government to lower tradebarriers on final products and in turn promote their exports of intermediates. However,the domestic import-competing firms of the final products might lobby the government
to raise the trade barriers to protect the home market. The influence of the intermediate
exporting firms and import-competing firms of final products depends on how organized
they are. We expect that the more organized they are, the higher impacts they have on
the trade barriers on final products.
To test whether this is the case, we add measures of political organization of these
two interest groups to the baseline regressions. Our political organization data werefrom WGTA (?). It records the number of political organizations in each of the 304
WGTA-industries in a total of 189 countries in the world. We use a concordance between
WGTA-industries and 4-digit HS codes to get the number of political organizations in each4-digit HS industry in these countries. From this, we get two measures of the politicalorganizations: one is the political organization of the import-competing industry - theindustry in country c that produces final product (output) i, P OOic ; the other one ispolitical organization of industries in country c that export intermediates to China used23

in final product i, P OIic , which is computed as the weighted average of number of politicalorganizations in each industry in the country that exports intermediates to China usedin the final product:

P OIic =Xj∈ji

EXijct0
P OIijct0 × Pj∈ji EXijct0!
(10)

where P OIijct0 is the number of political organizations in industry j in country c thatexports intermediates to China used in final product i in the base year t0 (the first year ofintermediate exports), and EXijct0 is the corresponding export value of the intermediates.
We then normalize the two political organization variables to 0 (if its original number
is 0) or 1 (if its original number is equal to or greater than 1). We add these twovariables and the interaction between our key regressor and input political organization,
EXic(t−1) × P OIic , to our baseline regressions.
Table 7 reports the 2nd stage IV estimates for this regression.18 In panel A forpermanent tariffs, the estimate of coefficient for our key regressor, EXic(t−1) , is negativein all regressions and significant in the 4th column where we have the most disaggregate
fixed effects. This indicates that even if there are no organized political organizations in
the intermediate exporting sectors (P OIic = 0), they still have impacts on final producttariffs. This might be because firms in these industries still lobby the government bythemselves rather than through formal political organizations.
The estimate of the coefficient for the interaction term, EXic(t−1) ×P OIic , is negativeand significant in all regressions. This shows that more politically-organized intermediate
export industries have a higher (in terms of absolute value) impact on final goods tariffs,
which is consistent with our expectation.
The estimate of the coefficient for the political organization of intermediate exporting
sectors, P OIic , is positive and significant in all regressions. This indicates that whenintermediate producing sectors do not export any intermediates to China (EXic(t−1) = 0),
the political organizations in these sectors will lobby the government for high tariffs on
the final products. This is because these sectors only sell the intermediates to domestic
18

The first stage results are available upon request.

24

producers of final products, and hence they lobby the government to protect the final
products industries to promote their own sales of intermediates.
On the other hand, the estimates of the coefficient for the political organization of
final product import-competing industries, P OOic , is insignificant in most regressions,but is positive and significant in the 4th column with the most disaggregate fixed effects.
This shows that a more organized import competing industry will lobby for higher tariffs
on final products. This is also consistent with our expectation.
In panel B for TTB initiation, the regression results are qualitatively the same as
those in panel A for permanent tariffs.

5. Conclusion
This paper exams endogenous trade policy in a global value chain. We use Chinese
micro-level processing trade data and explore the impacts of a country’s intermediate
inputs exports on its trade barriers on final goods. We find that, the more are a country’s intermediate exports to China, the lower is the trade barrier this country imposeson the imports of the final product from China produced with these intermediates, since a low trade barrier increases the country’s demand for Chinese final product, andhence increases Chinese demand for its intermediate inputs used in the production of that
product. This impact of intermediate exports on trade barriers on final goods exists forboth the permanent tariffs (preferential and MFN tariffs) and temporary trade barriers(anti-dumping and safeguards initiation). The impact is stronger for differentiated goods
than for homogeneous goods, and is stronger when the input export industries are moreorganized. For MFN tariffs and safeguards applied to all countries, this impact is higher
when the share of a country imports of the final product from China (out of its total
imports from the whole world) is higher, which means the tariffs or safeguards are more
likely to be set mainly against China. Both OLS and IV estimates confirm these findings.

25

References
Austen-Smith, David and John Wright, 1994. “Counteractive Lobbying.” American Journalof Political Science, 38(1): 25-44.
Bagwell, Kyle and Robert W. Staiger., 1990. “A theory of managed trade.” American Economic
Review, 80(4): 779-795.
Blanchard, Emily J., 2007. “Foreign Direct Investment, Endogenous Tariffs, and Preferential
Trade Agreements.” B.E. Journal of Economic Analysis and Policy - Advances,7(1).
Blanchard, Emily J., 2010. “Reevaluating the Role of Trade Agreements: Does Investment
Globalization Make the WTO Obsolete?” Journal of International Economics, 82(1): 6372.
Blanchard, Emily and Xenia Matschke, 2015. “US Multinationals and Preferential Market
Access.” Review of Economics and Statistics, 97(4), pp. 839-854.
Blanchard, Emily, Chad Bown and Robert Johnson, 2016. “Global Supply Chains and Trade
Policy.” Working Paper.
Bown, Chad P., 2014. “Temporary Trade Barriers Database.” Available at
http://econ.worldbank.org/ttbd/.
Bown, Chad P., and Meredith A. Crowley, 2013. “Self-enforcing trade agreements: evidence
from time-varying trade policy.” American Economic Review, 103(2): 1071-1090.
Brander, James A., 1995. “Strategic Trade Policy.”, Handbook of International Economics,
vol. III.
Broda, Christian, Nuno Limão, and David Weinstein, 2008. “Optimal Tariffs and Market
Power: The Evidence”, American Economic Review, 98(5), 2032-2065.
Dai, Mi and Miaojie Yu, 2013. “Firm R&D, Absorptive Capacity, and Learning by Exporting:Firm-Level Evidence from China.” The World Economy, pp. 1131-1145.
De Figueiredo, John M. and Charles M. Cameron, 2009. “Endogenous Cost Lobbying: Theory
and Evidence.” CELS 2009 4th Annual Conference on Empirical Legal Studies Paper.
Feenstra, Robert C., Hanson Gordon H., and Swenson Deborah L., 2000. “Offshore assemblyfrom the United States: Production characteristics of the 9802 program.” In The impact
of international trade on wages (pp. 85-125). University of Chicago Press.
Gawande, Kishore and Usree Bandyopadhyay, 2000. “Is Protection for Sale? Evidence on
the Grossman-Helpman Theory of Endogenous Protection.” Review of Economics andStatistics, 82(1): 139-152.
Gawande, Kishore, Pravin Krishna and Michael J. Robbins, 2006. “Foreign Lobbies and U.S.
Trade Policy.” Review of Economics and Statistics, 88(3): 563-571.

26

Goldberg, Pinelopi Koujianou and Maggi Giovanni, 1999. “Protection for Sale: An EmpiricalInvestigation.” American Economic Review, 89(5): 1135-1155.
Grossman, Gene M. and Elhanan Helpman, 1994. “Protection for Sale.” American Economic
Review, 84(4): 833-850.
Grossman, Gene M. and Elhanan Helpman, 2001. “Special Interest Politics.” MIT Press.
Igan Deniz, Mishra Prachi, and Tressel Thierry, 2010. “A Fistful of Dollars: Lobbying and the
Financial Crisis.” NBER Macroeconomics Annual, forthcoming.
Kee Hiau Looi, Marcelo Olarreaga and SilvaPeri, 2007. “Market access for sale.” Journal of
Development Economics, 82(1): 79- 94
Kee Hiau Looi and Heiwai Tang, 2016. “Domestic Value Added in Exports: Theory and Firm
Evidence from China.” American Economic Review, 106(6), pp. 1402-1436.
Ludema, Rodney D. and Anna Maria Mayda, 2009. “Do Countries Free Ride on MFN?”
Journal of International Economics, 77(2):137-150.
Ludema, Rodney D., Anna Maria Mayda, and Prachi Mishra, 2016. “Protection for Free:
The Political Economy of US Tariff Suspensions.” Review of Economics and Statistics,
forthcoming.
Rauch, James, 1999. “Networks versus Markets in International Trade”. Journal of International Economics, 48, 735.
Snyder, James M. 1990. “Campaign contributions as investments: The US House of Representatives 1908-1986.” Journal of Political Economy, 61: 195-206.
Stoyanov, Andrey, 2009. “Trade policy of a free trade agreement in the presence of foreign
lobbying.” Journal of International Economics, 77(1): 37-49
Swenson, Deborah L., 2004. “Entry costs and outsourcing decisions: evidence from the US
overseas assembly provision.” The North American Journal of Economics and Finance,
15(3): 267-286.
Swenson, Deborah L., 2005. “Overseas Assembly and Country Sourcing Choices.” Journal ofInternational Economics, 66(1): 107-130.
Yu, Miaojie, 2015. “Processing Trade, Tariff Reductions and Firm Productivity: Evidence
from Chinese Firms.” Economic Journal, 125(585): 911C1189.

27

Tables

Table 1a. Summary Statistics of Trade Data

A. Exports
Total

PWI

Share of PWI

Year

value*

firms

value*

firms

value(%)

firms(%)

2000

249

62,771

97

27,209

39

43

2001

291

68,487115

27,535

40

40

2002

301

78,313

123

28,395

41

36

2003

438

95,688188

30,668

43

32

2004

594

120,590260

33,633

44

28

2005

762

144,031333

36,205

44

25

2006

969

171,205

415

37,7654322

B. Imports
Total

PWI

Share of PWI

Year

value*

firms

value*

firms

value(%)

firms(%)

2000

225

62,79365

28,435

29

45

2001

266

67,58871

29,200

27

43

2002

273

76,37881

30,138

30

39

2003

413

87,934124

33,020

30

38

2004

561

102,242168

36,383

30

36

2005

660

113,456

207

38,874

31

34

2006

788

121,835

247

39,57931

32∗: in billion dollars.

28

Table 1b. Summary Statistics of Tariff Data

A. Permanent Tariffs: Summary
All

MFN

Preferential

Number of

Mean

Number of

Mean

Number of

Mean

Year

hs6-country

Tariffhs6-country

Tariffhs6-country

Tariff

2001

136,99811.30

130,539

11.71

6,459

2.92

2002

158,116

11.92

149,160

12.47

8,956

2.77

2003

138,326

9.94

129,364

10.44

8,962

2.86

2004

140,949

10.23

132,105

10.74

8,844

2.56

2005

210,51110.00

200,947

10.35

9,564

2.70

2006

232,6509.34

220,308

9.71

12,342

2.67

2007

234,7218.74

215,875

8.98

18,846

5.97

B. Preferential Tariffs
Country

Years

Number of

Number of

hs6

hs6-year

SourceAustralia

2001-2006

763

3,314

GSP and APEC

Canada2002-2006

2,027

8,715

GSP and APEC

2006

3,011

3,011

China-Chile FTA

EU

2001-2006

3,742

12,521

GSP

Indonesia

2005-2006

92

177

Japan

2001-2006

1,874

9,125

2005

5

5

New Zealand

2002-2006

1,604

6,586

Norway2002-2006

547

913

GSP

Pakistan

2006

399

399

China-Pakistan FTA

Switzerland

2001-2006

565

2,536

GSP

Turkey2005-2006

2,699

5,005

GSP

Vietnam

2005-2006

114

170

Chile

Lao

29

China-ASEAN FTA
GSP and APEC
China-ASEAN FTA
GSP and APEC

China-ASEAN FTA

C. Temporary Trade Barriers
TTB

Antidumping

Safeguards

Country

Filed

ImposedFiled

ImposedFiled

ImposedAnustralia

12

9

12

9

0

0

Brazil

15

14

15

14

0

0

Canada

59

30

36

30

23

0

Colombia

87

0

87

0

0

0

EU

131

124

67

61

64

63

India

108

95

101

92

7

3

Indonesia

21

14

11

11

10

3

Japan

1

0

0

0

1

0

Korea

12

12

12

12

0

0

Peru

94

0

94

0

0

0

Poland

4

0

4

0

0

0

Taiwan

2

0

2

0

0

0

Turkey

81

76

67

65

14

11

U.S.

176

146

70

44

106

102

30

Table 2. Summary Statistics of Key Variables in Regression Samples(Tict and EXHi(t−1) are in percent)A. All Permanent Tariffs

No. of Obs.

Mean

Std.Dev.

Tict

996,819

9.91

10.121lnEXic(t−1)

996,819

1.67

3.90

EXHi(t−1)

853,686

39.28

23.58

No. of Obs.

Mean

Std.Dev.

Tict

52,501

2.51

3.29

lnEXic(t−1)

52,501

4.73

5.56

EXHi(t−1)

43,610

41.08

24.23

No. of Obs.

Mean

Std.Dev.

Tict

944,318

10.32

10.22

lnEXic(t−1)

944,318

1.50

3.71

EXHi(t−1)

810,076

39.18

23.55

No. of Obs.

Mean

Std.Dev.

f iledT T Bict

1,748,744

0.00170.04

imposedT T Bict

1,748,744

0.00120.03

lnEXic(t−1)

1,748,744

1.661

3.89

EXHi(t−1)

1,463,084

39.07

23.49

No. of Obs.

Mean

Std.Dev.

f iledADict

1,748,744

0.0011

0.04

imposedADict

1,748,744

0.00060.03

lnEXic(t−1)

1,748,744

1.661

3.89

EXHi(t−1)

1,463,084

39.07

23.49

F. Safeguards

No. of Obs.

Mean

Std.Dev.

SGfictiled

1,748,744

0.0006

0.04

SGimposed
ict

1,748,744

0.00050.03

lnEXic(t−1)

1,748,744

1.661

3.89

EXHi(t−1)

1,463,084

39.07

23.49

B. Preferential Tariffs

C. MFN Tariffs

D. All TTBs

E. Antidumping

31

Table 3a. Baseline OLS Estimates, Permanent Tariffs
Dependent Variable: Tict
A. All Permanent Tariffs

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-.08***[0.002]-.09***[0.002]-.08***[0.002]-.09***[0.002]-.06***[0.003]-.001*[0.0006]-.06***[0.003]-0.001**[0.0005]

No. of Obs.
R2
B. Preferential Tariffs

996,7520.59(1)995,987.60(2)996,752.61(3)995,987.62(4)853,678.60(5)853,677.62(6)

lnEXic(t−1)-.01***[0.003]-.01***[0.003]-.01***[0.003]-.01***[0.004]-.01***[0.003]-.0003[0.0007]-.01***[0.003]-.0003[0.0007]

No. of Obs.
R2
C. MFN Tariffs

52,163.66(1)45,247.69(2)52,163.66(3)45,247.69(4)43,306.65(5)43,306.66(6)lnEXic(t−1)-.07***[0.003]-.08***[0.003]-.07***[0.003]-.08***[0.003]-.05***[0.003]-.001[0.0006]-.05***[0.003]-0.001*[0.0006]

944,2480.59
i+c+t

943,427.60
it + c944,248.61
ct + i943,427.61
it + ct

810,068.59
i+c+t

810,067.61
ct + i

EXHi(t−1)

EXHi(t−1)

EXHi(t−1)

No. of Obs.
R2
Fixed Effects

Note: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

32

Table 3b. Baseline OLS Estimates, TTBs - Filingf iledDependent Variable: T T Bict

A. All TTBs

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.0001***[0.00001]-0.0001***[0.00001]-0.0001***[0.00001]-0.0001***[0.00001]-0.0001***[0.00001]
0.0000003[0.000003]-0.0001***[0.00001]
0.0000001[0.000003]

No. of Obs.
R2
B. Antidumping

1,748,684
0.04(1)

1,748,327
0.04(2)

1,748,670
0.05(3)

1,748,313
0.05(4)

1,463,084
0.04(5)

1,463,071
0.04(6)lnEXic(t−1)-0.0001***[0.00001]-0.0001***[0.00001]-0.0001***[0.00001]-0.0001***[0.00001]-0.0001***[0.00001]
0.000001[0.000002]-0.0001***[0.00001]
0.000001[0.00002]

No. of Obs.
R2
C. Safeguards

1,748,684
0.03(1)

1,748,327
0.03(2)

1,748,670
0.03(3)

1,748,313
0.04(4)1,463,084
0.03(5)

1,463,071
0.03(6)

lnEXic(t−1)-0.0002***[0.00001]-0.0002***[0.00001]-0.0002***[0.00001]-0.0002***[0.00001]-0.0002***[0.00001]
0.000001[0.000001]-0.0002***[0.00001]-0.000001[0.000001]

1,748,684
0.05
i+c+t

1,748,327
0.06
it + c1,748,670
0.05
ct + i

1,748,313
0.06
it + ct

1,463,084
0.04
i+c+t

1,463,071
0.05
ct + i

EXHi(t−1)

EXHi(t−1)

EXHi(t−1)

No. of Obs.
R2
Fixed Effects

Note: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

33

Table 4a. Baseline IV Estimates (1st stage), Permanent Tariffs and TTBs
Dependent Variable: lnEXic(t−1)
Instrument for lnEXic(t−1) : lnT Cic(t−1)
A. All Permanent Tariffs

(1)

(2)

(3)

(4)

(5)

(6)

lnT Cic(t−1)-0.36***[0.02]-1.74***[0.05]-0.36***[0.02]-1.75***[0.05]-0.35***[0.02]-.01***[0.0003]-0.35***[0.02]-0.01***[0.0003]

No. of Obs.
Adjusted R2
B. Preferential Tariffs

587,6440.53(1)587,0550.54(2)587,644.53(3)587,0550.54(4)

550,4210.56(5)550,421.56(6)

lnT Cic(t−1)-0.52***[0.11]-3.29***[0.40]-0.54***[0.11]-3.41***[0.41]-0.54***[0.12]-.02***[0.002]-0.56***[0.12]-.02***[0.002]

No. of Obs.
Adjusted R2
C. MFN Tariffs

30,2560.63(1)23,566.57(2)30,256.64(3)23,5660.58(4)27,723.65(5)27,723.66(6)lnT Cic(t−1)-0.33***[0.02]-1.63***[0.05]-0.33***[0.02]-1.65***[0.05]-0.32***[0.02]-.01***[0.0003]-0.33***[0.02]-.01***[0.0003]

No. of Obs.
Adjusted R2
D. All TTBs

557,1020.52(1)556,4510.53(2)557,102.52(3)

556,4510.53(4)522,350.55(5)522,350.55(6)lnT Cic(t−1)-0.37***[0.02]-2.03***[0.04]-0.37***[0.02]-2.04***[0.04]-0.36***[0.02]-0.01***[0.0003]-0.36***[0.02]-.01***[0.0003]

920,5140.51
i+c+t

920,227
0.52
it + c920,5140.01
ct + i

920,227
0.52
it + ct

862,8630.54
i+c+t

862,8630.54
ct + i

EXHi(t−1)

EXHi(t−1)

EXHi(t−1)

EXHi(t−1)

No. of Obs.
Adjusted R2
Fixed Effects

Notes: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

34

Table 4b. Baseline IV Estimates (2nd stage), Permanent Tariffs
Dependent Variable: Tict
Instrument for lnEXic(t−1) : lnT Cic(t−1)
A. All Permanent Tariffs

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.95***[0.14]-0.62***[0.07]-0.98***[0.14]-0.67***[0.07]-1.05***[0.16]-0.02***[0.002]-1.06***[0.15]-0.02***[0.002]

No. of Obs.
Centered R2
B. Preferential Tariffs

587,6440.54(1)587,055.59(2)587,644.56(3)587,0550.61(4)

550,421.53(5)

550,421.55(6)lnEXic(t−1)-0.27*[0.15]-0.14*[0.07]-0.24*[0.14]-0.11*[0.06]-0.23*[0.12]-0.004*[0.002]-0.21*[0.11]-0.004*[0.002]

No. of Obs.
Centered R2
C. MFN Tariffs

30,2560.60(1)23,5660.66(2)30,256.62(3)23,5660.67 0.62(4)27,723.63(5)27,723lnEXic(t−1)-0.99***[0.16]-.68***[0.08]-1.03***[0.16]-0.73***[0.08]-1.11***[0.18]-.02***[0.003]-1.12***[0.17]-.02***[0.003]557,1020.54
i+c+t

556,451
0.58
it + c557,1020.55
ct + i

556,451.60
it + ct

522,350.52
i+c+t

522,350.54
ct + i

EXHi(t−1)

EXHi(t−1)

EXHi(t−1)

No. of Obs.
Centered R2
Fixed Effects(6)

Notes: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

35

Table 4c. Baseline IV Estimates (2nd stage), TTBs - Filingf iledDependent Variable: T T Bict
Instrument for lnEXic(t−1) : lnT Cic(t−1)

A. All TTBs

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.0012*[0.0007]-0.0019***[0.0003]-0.0011*[0.0007]-0.0019***[0.00003]-0.0015**[0.0007]-0.00002*[0.00001]-0.0013*[0.0007]-0.00002*[0.00001]

No. of Obs.
Centered R2
B. Antidumping

920,5140.05(1)920,2270.05(2)920,5140.06(3)920,2270.06(4)862,8630.04(5)862,8630.05(6)lnEXic(t−1)-0.0002***[0.0001]-0.0003***[0.0001]-0.0002***[0.0001]-0.0003***[0.0001]-0.0003***[0.0001]-0.00004***[0.00001]-0.0003***[0.0001]-.00003***[0.00001]

No. of Obs.
Centered R2
C. Safeguards

920,5140.003(1)920,227-0.02(2)920,5140.01(3)920,227-0.02(4)862,863-0.01(5)862,8630.003(6)lnEXic(t−1)-0.0010***[0.0004]-0.0016***[0.0002]-0.0009***[0.0004]-0.0016***[0.0002]-0.0012***[0.0004]-0.00002***[0.00001]-0.0010***[0.0004]-0.00002***[0.00001]

920,5140.08
i+c+t

920,227
0.07
it + c920,5140.08
ct + i

920,227
0.07
it + ct

862,8630.06
i+c+t

862,8630.06
ct + i

EXHi(t−1)

EXHi(t−1)

EXHi(t−1)

No. of Obs.
Centered R2
Fixed Effects

Notes: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

36

Table 5. Accounting for MFN/Safeguards Externality, IV Estimates
Dependent Variable: T Bict
Instrument for lnEXic(t−1) : lnT Cic(t−1)
Instrument for SIMic(t−1) : SIMic0 (t−1)
A. MFN Tariffs: Tict

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.02[0.06]-1.36***[0.41]-0.007*[0.004]-0.01[0.01]-0.71***[0.18]-0.004*[0.002]-0.03[0.06]-1.51***[0.41]-0.009**[0.004]-0.02**[0.01]-0.92***[0.18]-.003*[0.002]-0.10[0.07]-1.60***[0.45]-0.007*[0.004]-0.02***[0.006]-0.11[0.08]-1.69***[0.46]-0.009*[0.004]-.02***[0.006]

No. of Obs.
R2

554,443
0.49

553,8280.58

554,443
0.49

553,8280.58

519,884
0.42

519,884
0.41

f iledB. Safeguards: T T Bict

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.002***[0.001]0.00004[0.0002]-0.00002*[0.00001]-0.003***[0.001]0.0001***[0.00003]-0.00003***[0.00001]-0.002***[0.001]0.00003[0.0002]-0.00002*[0.00001]-0.003***[0.001]0.0001***[0.00003]-0.00003***[0.00001]-0.002***[0.001]0.0001[0.0002]-0.00002*[0.00001]-0.00003*[0.00002]-0.003***[0.001]0.0001[0.0002]-0.00002*[0.00001]-0.00003*[0.00002]

615,7500.07
i+c+t

615,336
0.02
it + c615,7500.07
ct + i615,336
0.02
it + ct

575,1140.04
i+c+t

575,1140.04
ct + i

SIMic(t−1)
lnEXic(t−1) × SIMic(t−1)
EXHi(t−1)

SIMic(t−1)
lnEXic(t−1) × SIMic(t−1)
EXHi(t−1)

No. of Obs.
R2
Fixed Effects

Notes: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

37

Table 6. Accounting for Product Differentiation, IV Estimates
Dependent Variable: T Bict
Instrument for lnEXic(t−1) : lnT Cic(t−1)
A. Permanent Tariffs: Tict

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.73***[0.33]-0.36***[0.15]-0.43***[0.18]-0.27***[0.11]-0.76***[0.34]-0.38***[0.17]-0.47***[0.18]-0.29***[0.12]-0.70***[0.32]-0.34***[0.14]-0.02***[0.004]-0.72***[0.32]-0.35***[0.14]-0.02***[0.004]

No. of Obs.
Centered R2

533,3090.44

532,834
0.56

533,309.44

532,834
0.57

499,205.44499,205.45

f iledB. TTBs: T T Bict

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.0005[0.0016]-0.0022**[0.0009]-0.0014*[0.0007]-0.0011**[0.0004]-0.0009[0.0016]-0.0024***[0.0009]-0.0015*[0.0008]-0.0011**[0.0004]-0.0004[0.0015]-0.0017**[0.0007]-0.00002[0.00002]-0.0001[0.0015]-0.0019**[0.0008]-0.00002[0.00002]

836,8120.04
i+c+t

836,591
0.03
it + c836,8120.05
ct + i

836,591
0.04
it + ct

784,092
0.03
i+c+t

784,092
0.04
ct + ilnEXic(t−1) × Rauchi
EXHi(t−1)

lnEXic(t−1) × Rauchi
EXHi(t−1)

No. of Obs.
Centered R2
Fixed Effects

Notes: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

38

Table 7. Accounting for Political Organization, IV Estimates
Dependent Variable: T Bict
Instrument for lnEXic(t−1) : lnT Cic(t−1)
A. Permanent Tariffs: Tict

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.45[0.35]
7.41***[1.68]-0.71***[0.15]-0.20[0.22]-1.25***[0.29]
9.23***[1.36]-0.77***[0.14]
0.24[0.18]-0.52[0.34]
6.94***[1.63]-0.64***[0.14]-0.18[0.23]-1.39***[0.29]
8.97***[1.36]-0.72***[0.14]
0.30*[0.18]-0.44[0.34]
7.42***[1.68]-0.71***[0.15]-0.20[0.22]-0.02**[0.01]-0.51[0.34]
6.96***[1.64]-0.64***[0.15]-0.18[0.23]-0.02**[0.01]

No. of Obs.
Centered R2

125,051
0.58

122,9990.45

125,036.60

122,9840.45

125,0510.59

125,036.60

f iledB. TTBs: T T Bict

(1)

(2)

(3)

(4)

(5)

(6)

lnEXic(t−1)-0.0015[0.0017]0.0142*[0.0083]-0.0010[0.0008]
0.0036***[0.0009]-0.0028**[0.0011]0.0315***[0.0062]-0.0027***[0.0007]0.0049***[0.0007]-0.0019[0.0018]0.0120[0.0085]-0.0007[0.0008]
0.0036***[0.0010]-0.0032**[0.0012]0.0301***[0.0063]-0.0025***[0.0007]0.0050***[0.0007]-0.0015[0.0017]0.0142*[0.0083]-0.0011[0.0008]
0.0036***[0.0009]-0.00004[0.00005]-0.0020[0.0018]0.0121[0.0085]-0.0008[0.0008]
0.0036***[0.0010]-0.00005[0.00005]

188,2450.15
i+c+t

186,636
0.11
it + c188,2180.15
ct + i186,609
0.11
it + ct

188,2450.15
i+c+t

188,2180.15
ct + iP OIiclnEXic(t−1) × P OIicP OOicEXHi(t−1)

P OIiclnEXic(t−1) × P OIicP OOicEXHi(t−1)

No. of Obs.
Centered R2
Fixed Effects

Notes: Standard errors are robust and clustered at the industry (hs4) and country level; *, **,and *** denote the 5, 1, and 0.1 percent of significance level, respectively.

39